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1. INTRODUCTION

2. background, 5. discussion, 6. conclusions, author contributions, competing interests, funding information, data availability, how common are explicit research questions in journal articles.

Handling Editor: Ludo Waltman

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Mike Thelwall , Amalia Mas-Bleda; How common are explicit research questions in journal articles?. Quantitative Science Studies 2020; 1 (2): 730–748. doi: https://doi.org/10.1162/qss_a_00041

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Although explicitly labeled research questions seem to be central to some fields, others do not need them. This may confuse authors, editors, readers, and reviewers of multidisciplinary research. This article assesses the extent to which research questions are explicitly mentioned in 17 out of 22 areas of scholarship from 2000 to 2018 by searching over a million full-text open access journal articles. Research questions were almost never explicitly mentioned (under 2%) by articles in engineering and physical, life, and medical sciences, and were the exception (always under 20%) for the broad fields in which they were least rare: computing, philosophy, theology, and social sciences. Nevertheless, research questions were increasingly mentioned explicitly in all fields investigated, despite a rate of 1.8% overall (1.1% after correcting for irrelevant matches). Other terminology for an article’s purpose may be more widely used instead, including aims, objectives, goals, hypotheses, and purposes, although no terminology occurs in a majority of articles in any broad field tested. Authors, editors, readers, and reviewers should therefore be aware that the use of explicitly labeled research questions or other explicit research purpose terminology is nonstandard in most or all broad fields, although it is becoming less rare.

Academic research is increasingly multidisciplinary, partly due to team research addressing practical problems. There are also now large multidisciplinary journals, such as PLOS ONE and Nature Scientific Reports , with editorial teams that manage papers written by people from diverse disciplinary backgrounds. There is therefore an increasing need for researchers to understand disciplinary norms in writing styles and paradigms. The authors of a research paper need to know how to frame its central contribution so that it is understood by multidisciplinary audiences. One strategy for this is to base an article around a set of explicitly named research questions that address gaps in prior research. Employing the standard phrase “research question” gives an unambiguous signpost for the purpose of an article and may therefore aid clarity. Other strategies include stating hypotheses, goals, or aims, or describing an objective without calling it an objective (e.g., “this paper investigates X”). Similarly, structured abstracts are believed to help readers understand a paper ( Hartley, 2004 ), perhaps partly by having an explicit aim, objective, or goal section. A paper that does not recognize or value the way in which the central contribution is conveyed may be rejected by a reviewer or editor if they are unfamiliar with the norms of the submitting field. It would therefore be helpful for authors, reviewers, and editors to know which research fields employ explicitly labeled research questions or alternative standard terminology.

Purpose statements and research questions or hypotheses are interrelated elements of the research process. Research questions are interrogative statements that reflect the problem to be addressed, usually shaped by the goal or objectives of the study ( Onwuegbuzie & Leech, 2006 ). For example, a healthcare article argued that “a good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper” and “the key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community” ( Perneger & Hudelson, 2004 ).

The choice of terminology to describe an article’s purpose seems to be conceptually arbitrary, with the final decision based on community norms, journal guidelines, and author style. For example, a research paper investigating issue X could phrase its purpose in the following ways: “research question 1: is X true?,” “this paper aims to investigate X,” “the aim/objective/purpose/goal is to investigate X,” or “X?” (as in the current paper). Implicit purpose statements might include “this paper investigates X” or just “X,” where the context makes clear that this is the purpose. Alternatively, the reader might deduce the purpose of a paper after reading it, with all these options achieving the same result with different linguistic strategies. Some research purposes might not be easily expressible as a research question, however. For example, a humanities paper might primarily discuss an issue (e.g., “Aspects of the monastery and monastic life in Adomnán’s Life of Columba ”) but even these could perhaps be expressed as research questions, if necessary (e.g., “Which are the most noteworthy aspects of the monastery and monastic life in Adomnán’s Life of Columba ?”).

In which fields are explicitly named research questions commonly used?

Has the use of explicitly named research questions increased over time?

Are research purposes addressed using alternative language in different fields?

Do large journals guide authors to use explicitly named research questions or other terminology for purpose statements in different fields?

2.1. Advice for Authors

There are some influential guidelines for reporting academic research. In the social sciences, Swales’ (1990 , 2004) Create A Research Space (CARS) model structures research article introductions in three moves (establishing a territory, establishing a niche, and occupying a niche), which are subdivided into steps. Within the 1990 model, move 3 includes the steps “outlining purposes” and “announcing present research,” but research questions are not explicitly included, being similar the “question raising” step in move 2. In the updated 2004 model, move 3 includes an obligatory step named “announcing present research descriptively and/or purposively” (that joins the steps “outlining purposes” and “announcing present research” from the 1990 model), whereas “listing research questions or hypotheses” is a new optional step.

In medicine, the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative is a checklist of items that should be included to improve reporting quality. One of these is a statement of objectives that “may be formulated as specific hypotheses or as questions that the study was designed to address” or may be less precise in early studies ( Vandenbroucke, von Elm, et al., 2014 ). This description therefore includes stating research questions as one of a range of ways of specifying objectives. An informal advice article in medicine instead starts by arguing that the paper’s aim should be clearly defined ( McIntyrei, Nisbet, et al., 2007 ).

Researchers may also be guided about the language to use in papers by any ethical or other procedures that they need to follow before conducting their work. For example, clinical trials often need to be registered and declared in a standard format, which may include explicit descriptions of objectives (e.g., see “E.2.1: Main objective of the trial” at: https://www.clinicaltrialsregister.eu/ctr-search/trial/2015-002555-10/GB ).

2.2. Empirical Evidence

Journal article research questions and other purpose statements, such as aims, objectives, goals, and hypotheses ( Shehzad, 2011 ), are usually included within Introduction sections or introductory phases, sometimes appearing as separate sections ( Kwan, 2017 ; Yang & Allison, 2004 ). Some studies have analyzed research article introductions in different disciplines and languages based on the Swales’ (1990 , 2004) CARS model. Although these studies analyze small sets of articles, they seem to agree that the research article introduction structure varies across disciplines (e.g., Joseph, Lim & Nor, 2014 ) and subdisciplines within a discipline, including for engineering ( Kanoksilapatham, 2012 ; Maswana, Kanamaru, & Tajino, 2015 ), applied linguistics ( Jalilifar, 2010 ; Ozturk, 2007 ) and environmental sciences ( Samraj, 2002 ). Introductions in English seem to follow this pattern more closely than introductions in other languages ( Ahamad & Yusof, 2012 ; Hirano, 2009 ; Loi & Evans, 2010 ; Rahimi & Farnia, 2017 ; Sheldon, 2011 ), reflecting cultural differences. Research questions and other purpose terminology, such as aims, objectives, goals, or hypotheses, might also reappear within the Results or Discussion sections ( Amunai & Wannaruk, 2013 ; Brett, 1994 ; Hopkins & Dudley-Evans, 1988 ; Kanoksilapatham, 2005 ).

Previous research has shown that research questions and hypotheses are more common among English-language papers than non-English papers ( Loi & Evans, 2010 ; Mur Dueñas, 2010 ; Omidi & Farnia, 2016 ; Rahimi & Farnia, 2017 ; Sheldon, 2011 ), especially those written by English native speakers ( Sheldon, 2011 ). However, a study analyzing 119 English research article introductions from Iranian and international journals in three subdisciplines within applied linguistics found that “announcing present research” was more used in international journals whereas research questions were proclaimed explicitly more often in local journals ( Jalilifar, 2010 ).

In some fields the verbs examine , determine , evaluate , assess , and investigate are associated with the research purpose ( Cortés, 2013 ; Jalali & Moini, 2014 ; Kanoksilapatham, 2005 ) and the verbs expect , anticipate , and estimate are associated with hypotheses ( Williams, 1999 ). Some computer scientists seem to prefer to write the details of the method(s) used rather than stating the purpose or describing the nature of their research and use assumptions or research questions rather than hypotheses ( Shehzad, 2011 ). Moreover, scholars might state the hypotheses in other ways, such as “it was hypothesized that” ( Jalali & Moini, 2014 ).

A study analyzing lexical bundles (usually phrases) in medical research article introductions showed that the most frequent four-word phrases are related to the research objective, such as “the aim of the,” “aim of the present,” and “study was to evaluate” ( Jalali & Moini, 2014 ). Another study examined lexical bundles in a million-word corpus of research article introductions from several disciplines, showing that the main bundle used to announce the research descriptively and/or purposefully included the terms aim , objective , and purpose (e.g., “the aim of this paper,” “the objective of this study,” “the purpose of this paper”), but no bundles related to research questions or hypotheses were identified ( Cortés, 2013 ).

These findings are in line with other previous studies investigating the structure of research articles, especially the introduction section, which report a much higher percentage of journal papers specifying the research purpose than the research questions or hypotheses across disciplines, regardless of the language in which they are published, with the exception of law articles (see Table 1 ). These studies also show that research questions and hypotheses are much more frequent among social sciences articles (see Table 1 ), which has also been found in other genres, such as PhD theses and Master’s theses (see Table 2 ).

Reference to a wide research purposes, without specifying if they are objectives or RQs/hypotheses.

Restating RQs in the result section.

Note: Studies that have based their analysis on the Swales’s (1990) CARS model ( Anthony, 1999 ; Posteguillo, 1999 ; Mahzari & Maftoon, 2007 ) report the percentage related to “outlining purposes” and “announcing present research.” For these studies, the column “Present the research purpose” reports the higher value. Moreover, for these studies, the value reported in the RQs/hypotheses column refers to the “Question raising” information.

A few studies have focused exclusively on research purposes, research questions, and hypotheses. Some have discussed the development of research questions in qualitative ( Agee, 2009 ) or mixed method ( Onwuegbuzie & Leech, 2006 ) studies, whereas others have examined the ways of constructing research questions or hypotheses within some fields, such as organization studies ( Sandberg & Alvesson, 2011 ) or applied linguistics doctoral dissertations ( Lim, 2014 ; Lim, Loi, & Hashim, 2014 ). Shehzad (2011) examined the strategies and styles employed by computer scientists outlining purposes and listing research questions. She found an increase in the use of research nature or purpose statements and suggested that the “listing research questions or hypotheses” step of Swales’s model was obligatory in computing. No study seems to have examined how often journal guidelines give authors explicit advice about research questions or other purpose statements, however.

The PMC (Pub Med Central) Open Access subset ( www.ncbi.nlm.nih.gov/pmc/tools/openftlist/ ) was downloaded in XML format in November 2018. This is a collection of documents from open access journals or open access articles within hybrid journals. The collection has a biomedical focus, but includes at least a few articles from all broad disciplinary areas. Although a biased subset is not ideal, this is apparently the largest open access collection. Only documents declared in their XML to be of type “research article” were retained for analysis. This excludes many short contributions, such as editorials, that would not need research goals.

The XML of the body section of each article was searched for the test strings “research question,” “RESEARCH QUESTION,” “Research Question,” or “Research question,” recording whether each article contained at least one. This would miss papers exclusively using abbreviations, such as RQ1.

Full body text searches are problematic because terms could be mentioned in other contexts, depending on the part of an article. For example, the phrase “research question” in a literature review section may refer to an article reviewed. For a science-wide analysis it is not possible to be prescriptive about the sections in which a term must occur, however, because there is little uniformity in section names or orders ( Thelwall, 2019 ). Making simplifying assumptions about the position in a text in which a term should appear, such as that a research question should be stated in the first part of an article, would also not be defensible. This is because the structure of articles varies widely between journals and fields. For example, methods can appear at the end rather than the middle, and some papers start with results, with little introduction. There are also international cultural differences in the order in which sections are presented in some fields ( Teufel, 1999 ). The current paper therefore uses full-text searches without any heuristics to restrict the results for transparency and to give an almost certain upper bound to the prevalence of terms, given the lack of a high-quality alternative.

Articles were separated into broad fields using the Science-Metrics public journal classification scheme ( Archambault, Beauchesne, & Caruso, 2011 ), which allocates each journal into exactly one category. This seems to be more precise than the Scopus or Web of Science schemes ( Klavans & Boyack, 2017 ). The Science-Metrics classification was extended by adding the largest 100 journals in the PMC collection that had not been included in the original Science-Metrics classification scheme. These were classified into a Science-Metrics category by first author based on their similarity to other journals in the Science-Metrics scheme.

Five of the broad fields had too little data to be useful (Economics & Business; Visual & Performing Arts; Communication & Text Studies; General Arts, Humanities & Social Sciences; Built Environment & Design) and were removed. Years before 2000 were not included because of their age and small amount of data. Individual field/year combinations were also removed when there were fewer than 30 articles, since they might give a misleading percentage. Each of the 17 remaining categories contained at least 630 articles ( Table 3 ), with exact numbers for each field and year available in the online supplementary material (columns AE to AW: https://doi.org/10.6084/m9.figshare.10274012 ). For all broad fields, most articles have been published in the last 5 years (2014–2018), with the exception of Historical Studies, Chemistry, and Enabling & Strategic Technology.

For the third research question, alternative terms for research goals were searched for in the full text of articles. These terms might all be used in different contexts, so a match is not necessarily related to the main goal of the paper (e.g., the term “question” could be part of a discussion of a questionnaire), but the rank order between disciplines may be informative and the results serve as an upper bound for valid uses. The terms searched for were “research questions,” “questions,” “hypotheses,” “aims,” “objectives,” “goals,” and “purposes” in both singular and plural forms. These have been identified above as performing similar functions in research. For this exploration, the term “question” is used in addition to “research question” to capture more general uses.

Any of the queried terms could be included in an article out of context. For example, “research question” could be mentioned in a literature review rather than to describe the purpose of the new article. To check the context in which each term was used, a random sample of 100 articles (using a random number generator) matching each term (200 for each concept, counting both singular and plural, totaling 1,400 checks) was manually examined to ascertain whether any use of the term in the article stated the purpose of the paper directly (e.g., “Our research questions were…”) or indirectly (e.g., “This answered our research questions”), unless mentioned peripherally as information to others (e.g., “The study research questions were explained to interviewees”). There did not seem to be stock phrases that could be used to eliminate a substantial proportion of the irrelevant matches (e.g., “objective function” or “microscope objective”). There also was not a set of standard phrases that collectively could unambiguously identify the vast majority of research questions (e.g., “Our research questions were” or “This article’s research question is”).

Journal guidelines given to authors were manually analyzed to check whether they give advice about research questions and other purpose statements. Three journals with the most articles in each of the 17 academic fields were selected for this (see online supplement doi.org/10.6084/m9.figshare.10274012 ). This information is useful background context to help interpret the results.

4.1. RQ1 and RQ2: Articles Mentioning Research Questions

Altogether, 23,282 out of 1,314,412 articles explicitly mentioned the phrases “research question” or “research questions” (1.8%), although no field included them in more than a fifth of articles in recent years and there are substantial differences between broad fields ( Figure 1 ). When the terms are used in an article they usually (63%, from the 1,400 manual checks) refer to the article’s main research question(s). Other uses of these terms include referring to questions raised by the findings, and a discussion of other articles’ research questions in literature review sections or as part of the selection criteria of meta-analyses. Thus, overall, only 1.1% of PMC full-text research articles mention their research questions explicitly using the singular or plural form. There has been a general trend for the increasing use of these terms, however ( Figure 2 ).

The percentage of full-text research articles containing the phrases “research question” or “research questions” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 63% of occurrences of these terms described the hosting article’s research question(s) ( n = 801,895 research articles).

As for Figure 1 but covering 2000–2018 ( n = 1,314,412 research articles). (All fields can be identified in the Excel versions of the graph within the online supplement 10.6084/m9.figshare.10274012).

If the terms “question” or “questions” are searched for instead, there are many more matches, although for a minority of articles in most fields ( Figures 3 and 4 ). When these terms are mentioned, they rarely (17%) refer to the hosting article’s research questions (excluding matches with the exact phrases “research question” or “research questions” to avoid overlaps with the previous figure). Common other contexts for these terms include questions in questionnaires and questions raised by the findings. Sometimes the term “question” occurred within an idiomatic phrase or issue rather than a query (e.g., “considerable temperature gradients occur within the materials in question” and “these effects may vary for different medications. Future studies are needed to address this important question”). In Philosophy & Theology, the matches could be for discussions of various questions within an article, rather than a research question that is an article’s focus. Similarly for Social Sciences and Public Health & Health Services, the question mentioned might be in questionnaires rather than being a research question. After correcting for the global irrelevant matches, which is a rough approximation, in all broad fields fewer than 14% of research articles use these terms to refer to research questions. Nevertheless, this implies that the terms “question” or “questions” are used much more often than the phrases “research question” or “research questions” (1.8%) to refer to an article’s research purposes.

The percentage of full-text research articles containing the terms “question” or “questions” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 17% of occurrences of these terms described the hosting article’s main research question(s) without using the exact phrases “research question” or “research questions,” not overlapping with Figure 1(a) ( n = 801,895 research articles).

As for Figure 3 , but covering 2000–2018 ( n = 1,314,412 research articles).

4.2. RQ3: Other Article Purpose Terms

The terms “hypothesis” and “hypotheses” are common in Psychology and Cognitive Science as well as in Biology ( Figure 5 ). They are used in a minority of articles in all other fields, but, by 2018 were used in at least 15% of all (or 4% after correcting for irrelevant matches). The terms can be used to discuss statistical results from other papers and in philosophy and mathematics they can be used to frame arguments, so not all matches relate to an article’s main purpose, and only 28% of the random sample checked used the terms to refer to the articles’ main hypothesis or hypotheses.

The percentage of full-text research articles containing the terms “hypothesis” or “hypotheses” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 28% of occurrences of these terms described the hosting article’s main hypothesis or hypotheses. A corresponding time series graph showing little change is in the online supplement ( n = 801,895 research articles).

The use of the terms “aim” and “aims” is increasing overall, possibly in all academic fields ( Figures 6 and 7 ). Fields frequently using the term include Philosophy & Theology, Information & Communication Technologies (ICTs) and Public Health & Health Services, whereas it is used in only about 20% of Chemistry and Biomedical Research papers. Articles using the terms mostly use them (especially the singular “aim”) to describe their main aim (70%), so these are the terms most commonly used to describe the purpose of a PMC full-text article. The terms are also sometimes used to refer to wider project aims or relevant aims outside of the project (e.g., “The EU’s biodiversity protection strategy aims to preserve…”).

The percentage of full-text research articles containing the terms “aim” or “aims” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 70% of occurrences of these terms described the hosting article’s main aim(s) ( n = 801,895 research articles).

As for Figure 6 , but covering 2000–2018 ( n = 1,314,412 research articles).

The terms “objective” and “objectives” are reasonably common in most academic fields ( Figure 8 ) and are used half of the time (52%) for the hosting article’s objectives. Other common uses include lenses and as an antonym of subjective (e.g., “high-frequency ultrasound allows an objective assessment…”). It is again popular within ICTs, Philosophy & Theology, and Public Health & Health Services, whereas it is used in only about 12% of Physics & Astronomy articles.

The percentage of full-text research articles containing the terms “objective” or “objectives” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 52% of occurrences of these terms described the hosting article’s objective(s). A corresponding time series graph showing little change is in the online supplement ( n = 801,895 research articles).

The terms “goal” and “goals” follow a similar pattern to “aim” and “objective” ( Figure 9 ), but refer to the hosting paper’s goals in only 28% of cases. Common other uses include methods goals (“the overall goal of this protocol is…”) and field-wide goals (e.g., “over the last decades, attempts to integrate ecological and evolutionary dynamics have been the goal of many studies”).

The percentage of full-text research articles containing the terms “goal” or “goals” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 28% of occurrences of these terms described the hosting article’s research question(s). A corresponding time series graph showing little change is in the online supplement ( n = 801,895 research articles).

Some articles may also use the terms “purpose” or “purposes” rather than the arguably more specific terms investigated above, and there are disciplinary differences in the extent to which they are used ( Figure 10 ). These terms may also be employed to explain or justify aspects of an article’s methods. When used, they referred to main purposes in fewer than a third of articles (29%), and were often instead used to discuss methods details (e.g., “it was decided a priori that physical examination measures would not be collected for the purpose of this audit”), background information (e.g., “species are harvested through fishing or hunting, mainly for alimentary purposes”) or ethics (e.g., “Animal care was carried out in compliance with Korean regulations regarding the protection of animals used for experimental and other scientific purposes.”).

The percentage of full-text research articles containing the terms “purpose” or “purposes” in the body of the text, 2014–2018, for articles in the PMC Open Access collection from 17 out of 22 Science-Metrics broad fields; 29% of occurrences of these terms described the hosting article’s purpose(s). A corresponding time series graph showing little change is in the online supplement ( n = 801,895 research articles).

4.3. RQ4: Journal Guidelines

“The motivation or purpose of your research should appear in the Introduction, where you state the questions you sought to answer” ( zookeys.pensoft.net/about )

“Define the purpose of the work and its significance, including specific hypotheses being tested” ( www.mdpi.com/journal/nutrients/instructions )

“The introduction briefly justifies the research and specifies the hypotheses to be tested” ( www.ajas.info/authors/authors.php )

“A brief outline of the question the study attempts to address” ( onlinelibrary.wiley.com/page/journal/20457758/homepage/registeredreports.html )

“Acquaint the reader with the findings of others in the field and with the problem or question that the investigation addresses.” ( www.oncotarget.com )

“State the research objective of the study, or hypothesis tested” ( www.springer.com/biomed/human+physiology/journal/11517 )

In the first quote above, for example, “state the questions” could be addressed literally by listing (research) questions or less literally by stating the research objectives. Thus, journal guidelines seem to leave authors the flexibility to choose how to state their research purpose, even if suggesting that research questions or hypotheses are used. This also applies to the influential American Psychological Society guidelines, such as, “In empirical studies, [explaining your approach to solving the problem] usually involves stating your hypotheses or specific question” ( APA, 2009 , p. 28).

An important limitation of the methods is that the sample contains a small and biased subset of all open access research articles. For example, the open access publishers BMC, Hindawi, and MDPI have large journals in the data set. The small fields ( Table 3 ) can have unstable lines in the graphs because of a lack of data. Sharp changes between years for the same field are likely due to either small amounts of data or changes in the journals submitted to PubMed in those years, rather than changes in field norms. It is possible that the proportions discovered would be different for other collections. Another limitation is that although articles were searched with the text string “research question,” this may not always have signified research questions in the articles processed (e.g., if mentioned in a literature review or in a phrase such as “this research questions whether”). Although the corrections reported address this, they provide global correction figures rather than field-specific corrections. Conversely, a research question may just be described as a question (e.g., “the query of this research”) or phrased as a question without describing it as such (e.g., “To discover whether PGA implants are immunologically inert…”). Thus, the field-level results are only indicative.

RQ1: Only 23,282 (1.8%, 1.1% after correcting for irrelevant matches) out of 1,314,412 articles assessed in the current paper explicitly mentioned “research question(s),” with significant differences between fields. Although there has been a general trend for the increasing use of explicitly named research questions, they were employed in fewer than a quarter of articles in all fields. Research questions were mostly used by articles in Social Sciences, Philosophy & Theology, and ICTs, whereas they have been mentioned by under 2% of articles in engineering, physical, life, and medical sciences. Previous studies have shown that 73.3% of English articles in Physical Education ( Omidi & Farnia, 2016 ), 33% of Applied Linguistics articles ( Sheldon, 2011 ) and 32% of Computer Science articles ( Shehzad, 2011 ) included research questions or hypotheses. Studies focused on doctoral dissertations show that 97% of U.S. Applied Linguistics ( Lim, 2014 ), 90% of English Language Teaching ( Geçíklí, 2013 ), 70% of Education Management ( Cheung, 2012 ), and 50% of computing doctoral dissertations ( Soler-Monreal, Carbonell-Olivares, & Gil-Salom, 2011 ) listed research questions, a large difference.

The results also show that about 13% of Public Health and Health Services articles and 12% of Psychology and Cognitive Science articles use the term “research questions.” However, a study focused on Educational Psychology found that 35% of English-language papers listed research questions and 75% listed hypotheses ( Loi & Evans, 2010 ). Thus, the current results reveal a substantially lower overall prevalence than suggested by previous research.

RQ2: There has been a substantial increase in the use of the term “research questions” in some subjects, including ICTs, Social Sciences, and Public Health and Health Services ( Figure 2 ), as well as a general trend for increasing use of this term, but with most fields still rarely using it. This suggests that some disciplines are standardizing their terminology, either through author guidelines in journals (RQ4), formal training aided by frameworks such as Swales’ CARS model, or informal training or imitation. For example, the analysis of the “instructions for authors” given by 51 journals (online supplement doi.org/10.6084/m9.figshare.10274012 ) showed that the three biology journals, the three psychology journals, and two biomedical journals included in the analysis referred to both research questions and hypotheses in their author guidelines.

RQ3: Terminology for the purpose of an article seems to be quite widely used, including aims, objectives, and goals ( Figures 5 – 9 ). This is in line with a study examining the lexical bundles identified in research article introductions from several disciplines, which reported the terms “aim,” “objective,” and “purpose” as the main terms used to announce the research descriptively and/or purposefully, although no phrase related to research questions or hypotheses was identified ( Cortés, 2013 ), and with another study reporting similar terminology in medical articles ( Jalali & Moini, 2014 ). Related to this (RQ4), the analysis of the “instructions for authors” given by 51 journals (online supplement 10.6084/m9.figshare.10274012) showed that “purpose” is the term mostly mentioned in the Abstract guidelines and “aims” is the term mainly used in the body of the text (Introduction or Background) guidelines. The term “objective” also appears in some article body guidelines, whereas the term “goal” is not mentioned in them. After correcting for irrelevant matches (e.g., articles using the term “hypothesis” but not for their main research hypotheses) using the percentages reported with the figures above, no terminology was found in a majority of articles in any field. Thus, at least from the perspective of PMC Open Access publications, there is no standardization of research terminology in any broad field.

There are substantial disciplinary differences in the terminology used. Whereas the term “research question” is relevant in Social Sciences, Philosophy & Theology, and ICTs, the term “hypothesis” is important in Psychology and Cognitive Science, used in over 60% of articles. This is in line with a study focused on Educational Psychology, which found that the 75% out of 20 English papers introduced the hypotheses, whereas 35% of them introduced the research questions ( Loi & Evans, 2010 ). The three psychology journals with the highest frequency in the data set used for this study referred to hypotheses in their author guidelines (see online supplement 10.6084/m9.figshare.10274012).

The terms “aim,” “objective,” and “goal” are mainly used in Philosophy, Theology, ICTs, and Health. The term “aim” is also quite often used in health, mathematics, and psychological articles, whereas the term “objective” is also used in engineering and mathematics articles. The term “goal” is also used in psychology and biomedical articles. Although most articles in all fields include a term that could be used to specify the purpose of an article (question or questions, hypothesis, aim, objective, goal), they are relatively scarce in Chemistry and Physics & Astronomy. The use of purpose-related terms has also increased over time in most academic fields. This agrees with a study about Computer Science research articles that found an increasing use of outlining purpose or stating the nature of the research ( Shehzad, 2011 ).

An example article from Chemistry illustrates how a research purpose can be implicit. The paper, “Fluid catalytic cracking in a rotating fluidized bed in a static geometry: a CFD analysis accounting for the distribution of the catalyst coke content” has a purpose that is clear from its title but that is not described explicitly in the text. Its abstract starts by describing what the paper offers, but not why, “Computational Fluid Dynamics is used to evaluate the use of a rotating fluidized bed in a static geometry for the catalytic cracking of gas oil.” The first sentence of the last paragraph of the introduction performs a similar role, “The current paper presents CFD simulations of FCC in a RFB-SG using a model that accounts for a possible nonuniform temperature and catalyst coke content distribution in the reactor.” Both sentences could easily be rephrased to start with, “The purpose of this paper is to,” but it is apparently a stylistic feature of chemical research not to do this. Presumably purposes are clear enough in typical chemistry research that they do not need to be flagged linguistically, but this is untrue for much social science and health research, for example, partly due to nonstandard goals (i.e., task uncertainty: Whitley, 2000 ).

5.1. Possible Origins of the Differences Found

Broad epistemological: Fields work with knowledge in different ways and naturally use different terminology as a result. Arts and humanities research may have the goal to critique or analyze, or may be practice-based research rather than having a more specific knowledge purpose. For this, research questions would be inappropriate. Thus, terminology variation may partly reflect the extent to which a broad field typically attempts to create knowledge.

Narrow epistemological: Narrow fields that address similar problems may feel that they do not need to use research problem terminology to describe their work because the purpose of a paper is usually transparent from the description of the methods or outcome. For example, it would be unnecessary to formulate, “This paper investigates whether treatment x reduces death rates from disease y” as a named research question or even explain that it is the goal of a paper. This may also be relevant for fields that write short papers. It may be most relevant for papers that use statistical methods and have high standards of evidence requirement (e.g., medicine) and clearly defined problems. In contrast, many social sciences research projects are not intrinsically clearly demarcated and need an explanation to define the problem (as for the current article). Thus, describing what the problem is can be an important and nontrivial part of the research. This relates to “task uncertainty,” which varies substantially between fields ( Whitley, 2000 ) and affects scholarly communication ( Fry, 2006 ).

Field or audience homogeneity: Fields with homogeneous levels and types of expertise may avoid terminology that field members would be able to deduce from the context. For example, a mixed audience paper might need to specify statistical hypotheses, whereas a narrow audience paper might only need to specify the result, because the audience would understand the implicit null and alternative hypotheses.

Field cultures for term choice: Academic publishing relies to some extent on imitation and reaching a consensus about the ways in which research is presented (e.g., Becher & Trowler, 2001 ). It might therefore become a field norm to use one term in preference to a range of synonyms, such as “aims” instead of “objectives.”

Field cultures for term meaning: Following from the above, a field culture may evolve an informal convention that two synonyms have different specific uses. For example, “aims” could be used for wider goals and “objectives” for the narrower goals of a paper.

Guidelines: Fields or their core journals may adopt guidelines that specify terminology, presumably because they believe that this standardization will improve overall communication clarity.

The results suggest that the explicit use of research questions, in the sense that they are named as such, is almost completely absent in some research fields, and they are at best a substantial minority (under 20%) in most others (ignoring the fields that did not meet the inclusion threshold). Although the word search approach does not give conclusive findings, the results suggest that alternative terminologies for describing the purpose of a paper are more widespread in some fields, but no single terminology is used to describe research purposes in a majority of articles in any of the broad fields examined.

The lack of standardization for purpose terminology in most or all fields may cause problems for reviewers and readers expecting to see explicit statements. It is not clear whether guidelines to standardize terminology for journals or fields would be practical or helpful, however, but this should be explored in the future. Presumably any guidelines should allow exceptions for articles that make nonstandard contributions, although there are already successful journals with prescriptive guidelines, and the advantage of standardization through structured abstracts seems to be accepted ( Hartley, 2004 ).

The disciplinary differences found may cause problems for referees, authors, editors, and readers of interdisciplinary research or research from outside of their natural field if they fail to find an article’s purpose expressed in the terminology that they expect. This issue could not reasonably be resolved by standardizing across science because of the differing nature of research. Instead, evidence in the current article of the existence of valid disciplinary differences in style may help reviewers and editors of large interdisciplinary journals to accept stylistic differences in research problem formulations.

Mike Thelwall: Conceptualization, Investigation, Software, Writing—original draft. Amalia Mas-Bleda: Investigation, Writing—original draft.

The authors have no competing interests to declare.

This research received no funding.

The data behind the results are available at FigShare ( https://doi.org/10.6084/m9.figshare.10274012 ).

Author notes

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How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | March 2024

For many students, crafting a strong research paper from scratch can feel like a daunting task – and rightly so! In this post, we’ll unpack what a research paper is, what it needs to do , and how to write one – in three easy steps. 🙂 

Overview: Writing A Research Paper

What (exactly) is a research paper.

• How to write a research paper
• Stage 1 : Topic & literature search
• Stage 2 : Structure & outline
• Stage 3 : Iterative writing
• Key takeaways

Let’s start by asking the most important question, “ What is a research paper? ”.

Simply put, a research paper is a scholarly written work where the writer (that’s you!) answers a specific question (this is called a research question ) through evidence-based arguments . Evidence-based is the keyword here. In other words, a research paper is different from an essay or other writing assignments that draw from the writer’s personal opinions or experiences. With a research paper, it’s all about building your arguments based on evidence (we’ll talk more about that evidence a little later).

Now, it’s worth noting that there are many different types of research papers , including analytical papers (the type I just described), argumentative papers, and interpretative papers. Here, we’ll focus on analytical papers , as these are some of the most common – but if you’re keen to learn about other types of research papers, be sure to check out the rest of the blog .

With that basic foundation laid, let’s get down to business and look at how to write a research paper .

Overview: The 3-Stage Process

While there are, of course, many potential approaches you can take to write a research paper, there are typically three stages to the writing process. So, in this tutorial, we’ll present a straightforward three-step process that we use when working with students at Grad Coach.

These three steps are:

• Finding a research topic and reviewing the existing literature
• Developing a provisional structure and outline for your paper, and
• Writing up your initial draft and then refining it iteratively

Let’s dig into each of these.

Need a helping hand?

Step 1: Find a topic and review the literature

As we mentioned earlier, in a research paper, you, as the researcher, will try to answer a question . More specifically, that’s called a research question , and it sets the direction of your entire paper. What’s important to understand though is that you’ll need to answer that research question with the help of high-quality sources – for example, journal articles, government reports, case studies, and so on. We’ll circle back to this in a minute.

The first stage of the research process is deciding on what your research question will be and then reviewing the existing literature (in other words, past studies and papers) to see what they say about that specific research question. In some cases, your professor may provide you with a predetermined research question (or set of questions). However, in many cases, you’ll need to find your own research question within a certain topic area.

Finding a strong research question hinges on identifying a meaningful research gap – in other words, an area that’s lacking in existing research. There’s a lot to unpack here, so if you wanna learn more, check out the plain-language explainer video below.

Once you’ve figured out which question (or questions) you’ll attempt to answer in your research paper, you’ll need to do a deep dive into the existing literature – this is called a “ literature search ”. Again, there are many ways to go about this, but your most likely starting point will be Google Scholar .

If you’re new to Google Scholar, think of it as Google for the academic world. You can start by simply entering a few different keywords that are relevant to your research question and it will then present a host of articles for you to review. What you want to pay close attention to here is the number of citations for each paper – the more citations a paper has, the more credible it is (generally speaking – there are some exceptions, of course).

Ideally, what you’re looking for are well-cited papers that are highly relevant to your topic. That said, keep in mind that citations are a cumulative metric , so older papers will often have more citations than newer papers – just because they’ve been around for longer. So, don’t fixate on this metric in isolation – relevance and recency are also very important.

Beyond Google Scholar, you’ll also definitely want to check out academic databases and aggregators such as Science Direct, PubMed, JStor and so on. These will often overlap with the results that you find in Google Scholar, but they can also reveal some hidden gems – so, be sure to check them out.

Once you’ve worked your way through all the literature, you’ll want to catalogue all this information in some sort of spreadsheet so that you can easily recall who said what, when and within what context. If you’d like, we’ve got a free literature spreadsheet that helps you do exactly that.

Step 2: Develop a structure and outline

With your research question pinned down and your literature digested and catalogued, it’s time to move on to planning your actual research paper .

It might sound obvious, but it’s really important to have some sort of rough outline in place before you start writing your paper. So often, we see students eagerly rushing into the writing phase, only to land up with a disjointed research paper that rambles on in multiple

Now, the secret here is to not get caught up in the fine details . Realistically, all you need at this stage is a bullet-point list that describes (in broad strokes) what you’ll discuss and in what order. It’s also useful to remember that you’re not glued to this outline – in all likelihood, you’ll chop and change some sections once you start writing, and that’s perfectly okay. What’s important is that you have some sort of roadmap in place from the start.

At this stage you might be wondering, “ But how should I structure my research paper? ”. Well, there’s no one-size-fits-all solution here, but in general, a research paper will consist of a few relatively standardised components:

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

First up is the introduction section . As the name suggests, the purpose of the introduction is to set the scene for your research paper. There are usually (at least) four ingredients that go into this section – these are the background to the topic, the research problem and resultant research question , and the justification or rationale. If you’re interested, the video below unpacks the introduction section in more detail. 

The next section of your research paper will typically be your literature review . Remember all that literature you worked through earlier? Well, this is where you’ll present your interpretation of all that content . You’ll do this by writing about recent trends, developments, and arguments within the literature – but more specifically, those that are relevant to your research question . The literature review can oftentimes seem a little daunting, even to seasoned researchers, so be sure to check out our extensive collection of literature review content here .

With the introduction and lit review out of the way, the next section of your paper is the research methodology . In a nutshell, the methodology section should describe to your reader what you did (beyond just reviewing the existing literature) to answer your research question. For example, what data did you collect, how did you collect that data, how did you analyse that data and so on? For each choice, you’ll also need to justify why you chose to do it that way, and what the strengths and weaknesses of your approach were.

Now, it’s worth mentioning that for some research papers, this aspect of the project may be a lot simpler . For example, you may only need to draw on secondary sources (in other words, existing data sets). In some cases, you may just be asked to draw your conclusions from the literature search itself (in other words, there may be no data analysis at all). But, if you are required to collect and analyse data, you’ll need to pay a lot of attention to the methodology section. The video below provides an example of what the methodology section might look like.

By this stage of your paper, you will have explained what your research question is, what the existing literature has to say about that question, and how you analysed additional data to try to answer your question. So, the natural next step is to present your analysis of that data . This section is usually called the “results” or “analysis” section and this is where you’ll showcase your findings.

Depending on your school’s requirements, you may need to present and interpret the data in one section – or you might split the presentation and the interpretation into two sections. In the latter case, your “results” section will just describe the data, and the “discussion” is where you’ll interpret that data and explicitly link your analysis back to your research question. If you’re not sure which approach to take, check in with your professor or take a look at past papers to see what the norms are for your programme.

Alright – once you’ve presented and discussed your results, it’s time to wrap it up . This usually takes the form of the “ conclusion ” section. In the conclusion, you’ll need to highlight the key takeaways from your study and close the loop by explicitly answering your research question. Again, the exact requirements here will vary depending on your programme (and you may not even need a conclusion section at all) – so be sure to check with your professor if you’re unsure.

Step 3: Write and refine

Finally, it’s time to get writing. All too often though, students hit a brick wall right about here… So, how do you avoid this happening to you?

Well, there’s a lot to be said when it comes to writing a research paper (or any sort of academic piece), but we’ll share three practical tips to help you get started.

First and foremost , it’s essential to approach your writing as an iterative process. In other words, you need to start with a really messy first draft and then polish it over multiple rounds of editing. Don’t waste your time trying to write a perfect research paper in one go. Instead, take the pressure off yourself by adopting an iterative approach.

Secondly , it’s important to always lean towards critical writing , rather than descriptive writing. What does this mean? Well, at the simplest level, descriptive writing focuses on the “ what ”, while critical writing digs into the “ so what ” – in other words, the implications . If you’re not familiar with these two types of writing, don’t worry! You can find a plain-language explanation here.

Last but not least, you’ll need to get your referencing right. Specifically, you’ll need to provide credible, correctly formatted citations for the statements you make. We see students making referencing mistakes all the time and it costs them dearly. The good news is that you can easily avoid this by using a simple reference manager . If you don’t have one, check out our video about Mendeley, an easy (and free) reference management tool that you can start using today.

Recap: Key Takeaways

We’ve covered a lot of ground here. To recap, the three steps to writing a high-quality research paper are:

• To choose a research question and review the literature
• To plan your paper structure and draft an outline
• To take an iterative approach to writing, focusing on critical writing and strong referencing

Remember, this is just a b ig-picture overview of the research paper development process and there’s a lot more nuance to unpack. So, be sure to grab a copy of our free research paper template to learn more about how to write a research paper.

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Home » Research Questions – Types, Examples and Writing Guide

Research Questions – Types, Examples and Writing Guide

Table of Contents

Research Questions

Definition:

Research questions are the specific questions that guide a research study or inquiry. These questions help to define the scope of the research and provide a clear focus for the study. Research questions are usually developed at the beginning of a research project and are designed to address a particular research problem or objective.

Types of Research Questions

Types of Research Questions are as follows:

Descriptive Research Questions

These aim to describe a particular phenomenon, group, or situation. For example:

• What are the characteristics of the target population?
• What is the prevalence of a particular disease in a specific region?

Exploratory Research Questions

These aim to explore a new area of research or generate new ideas or hypotheses. For example:

• What are the potential causes of a particular phenomenon?
• What are the possible outcomes of a specific intervention?

Explanatory Research Questions

These aim to understand the relationship between two or more variables or to explain why a particular phenomenon occurs. For example:

• What is the effect of a specific drug on the symptoms of a particular disease?
• What are the factors that contribute to employee turnover in a particular industry?

Predictive Research Questions

These aim to predict a future outcome or trend based on existing data or trends. For example :

• What will be the future demand for a particular product or service?
• What will be the future prevalence of a particular disease?

Evaluative Research Questions

These aim to evaluate the effectiveness of a particular intervention or program. For example:

• What is the impact of a specific educational program on student learning outcomes?
• What is the effectiveness of a particular policy or program in achieving its intended goals?

How to Choose Research Questions

Choosing research questions is an essential part of the research process and involves careful consideration of the research problem, objectives, and design. Here are some steps to consider when choosing research questions:

• Identify the research problem: Start by identifying the problem or issue that you want to study. This could be a gap in the literature, a social or economic issue, or a practical problem that needs to be addressed.
• Conduct a literature review: Conducting a literature review can help you identify existing research in your area of interest and can help you formulate research questions that address gaps or limitations in the existing literature.
• Define the research objectives : Clearly define the objectives of your research. What do you want to achieve with your study? What specific questions do you want to answer?
• Consider the research design : Consider the research design that you plan to use. This will help you determine the appropriate types of research questions to ask. For example, if you plan to use a qualitative approach, you may want to focus on exploratory or descriptive research questions.
• Ensure that the research questions are clear and answerable: Your research questions should be clear and specific, and should be answerable with the data that you plan to collect. Avoid asking questions that are too broad or vague.
• Get feedback : Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, feasible, and meaningful.

How to Write Research Questions

Guide for Writing Research Questions:

• Start with a clear statement of the research problem: Begin by stating the problem or issue that your research aims to address. This will help you to formulate focused research questions.
• Use clear language : Write your research questions in clear and concise language that is easy to understand. Avoid using jargon or technical terms that may be unfamiliar to your readers.
• Be specific: Your research questions should be specific and focused. Avoid broad questions that are difficult to answer. For example, instead of asking “What is the impact of climate change on the environment?” ask “What are the effects of rising sea levels on coastal ecosystems?”
• Use appropriate question types: Choose the appropriate question types based on the research design and objectives. For example, if you are conducting a qualitative study, you may want to use open-ended questions that allow participants to provide detailed responses.
• Consider the feasibility of your questions : Ensure that your research questions are feasible and can be answered with the resources available. Consider the data sources and methods of data collection when writing your questions.
• Seek feedback: Get feedback from your supervisor, colleagues, or peers to ensure that your research questions are relevant, appropriate, and meaningful.

Examples of Research Questions

Some Examples of Research Questions with Research Titles:

Research Title: The Impact of Social Media on Mental Health

• Research Question : What is the relationship between social media use and mental health, and how does this impact individuals’ well-being?

Research Title: Factors Influencing Academic Success in High School

• Research Question: What are the primary factors that influence academic success in high school, and how do they contribute to student achievement?

Research Title: The Effects of Exercise on Physical and Mental Health

• Research Question: What is the relationship between exercise and physical and mental health, and how can exercise be used as a tool to improve overall well-being?

Research Title: Understanding the Factors that Influence Consumer Purchasing Decisions

• Research Question : What are the key factors that influence consumer purchasing decisions, and how do these factors vary across different demographics and products?

Research Title: The Impact of Technology on Communication

• Research Question : How has technology impacted communication patterns, and what are the effects of these changes on interpersonal relationships and society as a whole?

Research Title: Investigating the Relationship between Parenting Styles and Child Development

• Research Question: What is the relationship between different parenting styles and child development outcomes, and how do these outcomes vary across different ages and developmental stages?

Research Title: The Effectiveness of Cognitive-Behavioral Therapy in Treating Anxiety Disorders

• Research Question: How effective is cognitive-behavioral therapy in treating anxiety disorders, and what factors contribute to its success or failure in different patients?

Research Title: The Impact of Climate Change on Biodiversity

• Research Question : How is climate change affecting global biodiversity, and what can be done to mitigate the negative effects on natural ecosystems?

Research Title: Exploring the Relationship between Cultural Diversity and Workplace Productivity

• Research Question : How does cultural diversity impact workplace productivity, and what strategies can be employed to maximize the benefits of a diverse workforce?

Research Title: The Role of Artificial Intelligence in Healthcare

• Research Question: How can artificial intelligence be leveraged to improve healthcare outcomes, and what are the potential risks and ethical concerns associated with its use?

Applications of Research Questions

Here are some of the key applications of research questions:

• Defining the scope of the study : Research questions help researchers to narrow down the scope of their study and identify the specific issues they want to investigate.
• Developing hypotheses: Research questions often lead to the development of hypotheses, which are testable predictions about the relationship between variables. Hypotheses provide a clear and focused direction for the study.
• Designing the study : Research questions guide the design of the study, including the selection of participants, the collection of data, and the analysis of results.
• Collecting data : Research questions inform the selection of appropriate methods for collecting data, such as surveys, interviews, or experiments.
• Analyzing data : Research questions guide the analysis of data, including the selection of appropriate statistical tests and the interpretation of results.
• Communicating results : Research questions help researchers to communicate the results of their study in a clear and concise manner. The research questions provide a framework for discussing the findings and drawing conclusions.

Characteristics of Research Questions

Characteristics of Research Questions are as follows:

• Clear and Specific : A good research question should be clear and specific. It should clearly state what the research is trying to investigate and what kind of data is required.
• Relevant : The research question should be relevant to the study and should address a current issue or problem in the field of research.
• Testable : The research question should be testable through empirical evidence. It should be possible to collect data to answer the research question.
• Concise : The research question should be concise and focused. It should not be too broad or too narrow.
• Feasible : The research question should be feasible to answer within the constraints of the research design, time frame, and available resources.
• Original : The research question should be original and should contribute to the existing knowledge in the field of research.
• Significant : The research question should have significance and importance to the field of research. It should have the potential to provide new insights and knowledge to the field.
• Ethical : The research question should be ethical and should not cause harm to any individuals or groups involved in the study.

Purpose of Research Questions

Research questions are the foundation of any research study as they guide the research process and provide a clear direction to the researcher. The purpose of research questions is to identify the scope and boundaries of the study, and to establish the goals and objectives of the research.

The main purpose of research questions is to help the researcher to focus on the specific area or problem that needs to be investigated. They enable the researcher to develop a research design, select the appropriate methods and tools for data collection and analysis, and to organize the results in a meaningful way.

Research questions also help to establish the relevance and significance of the study. They define the research problem, and determine the research methodology that will be used to address the problem. Research questions also help to determine the type of data that will be collected, and how it will be analyzed and interpreted.

Finally, research questions provide a framework for evaluating the results of the research. They help to establish the validity and reliability of the data, and provide a basis for drawing conclusions and making recommendations based on the findings of the study.

Advantages of Research Questions

There are several advantages of research questions in the research process, including:

• Focus : Research questions help to focus the research by providing a clear direction for the study. They define the specific area of investigation and provide a framework for the research design.
• Clarity : Research questions help to clarify the purpose and objectives of the study, which can make it easier for the researcher to communicate the research aims to others.
• Relevance : Research questions help to ensure that the study is relevant and meaningful. By asking relevant and important questions, the researcher can ensure that the study will contribute to the existing body of knowledge and address important issues.
• Consistency : Research questions help to ensure consistency in the research process by providing a framework for the development of the research design, data collection, and analysis.
• Measurability : Research questions help to ensure that the study is measurable by defining the specific variables and outcomes that will be measured.
• Replication : Research questions help to ensure that the study can be replicated by providing a clear and detailed description of the research aims, methods, and outcomes. This makes it easier for other researchers to replicate the study and verify the results.

Limitations of Research Questions

Limitations of Research Questions are as follows:

• Subjectivity : Research questions are often subjective and can be influenced by personal biases and perspectives of the researcher. This can lead to a limited understanding of the research problem and may affect the validity and reliability of the study.
• Inadequate scope : Research questions that are too narrow in scope may limit the breadth of the study, while questions that are too broad may make it difficult to focus on specific research objectives.
• Unanswerable questions : Some research questions may not be answerable due to the lack of available data or limitations in research methods. In such cases, the research question may need to be rephrased or modified to make it more answerable.
• Lack of clarity : Research questions that are poorly worded or ambiguous can lead to confusion and misinterpretation. This can result in incomplete or inaccurate data, which may compromise the validity of the study.
• Difficulty in measuring variables : Some research questions may involve variables that are difficult to measure or quantify, making it challenging to draw meaningful conclusions from the data.
• Lack of generalizability: Research questions that are too specific or limited in scope may not be generalizable to other contexts or populations. This can limit the applicability of the study’s findings and restrict its broader implications.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• How it works

How to Write the Research Questions – Tips & Examples

Published by Owen Ingram at August 13th, 2021 , Revised On October 3, 2023

Conducting research and writing an academic paper requires a clear direction and focus.

A good research question provides purpose to your research and clarifies the direction. It further helps your readers to understand what issue your research aims to explore and address.

If you are unsure about how to write research questions, here is a list of the attributes of a good research question;

• The research question should contain only a single problem
• You should be able to find the answer to it using  primary and secondary data sources
• You should be able to address it within the time limit and other constraints
• Can attain in-depth and detailed results
• Relevant and applicable
• Should relate to your chosen field of research

Whenever you want to discover something new about a  topic , you will ask a question about it. Therefore, the research question is important in the overall research process  and provides the author with the reading and writing guidelines.

In a research paper or an essay, you will need to create a single research question that highlights just one problem or issue. The thesis statement should include the specific problem you aim to investigate to establish your argument’s central position or claim.

A larger project such as a  dissertation or thesis , on the other hand, can have multiple research questions, but every question should focus on your main  research problem .  Different types of research will help you answer different research questions, but they should all be relevant to the research scope.

How to Write a Research Question

Steps to develop your research question.

• Choose a topic  with a wide range of published literature
• Read and skim relevant articles to find out different problems and issues
• Specify a theoretical or practical  research problem  that your research question will address
• Narrow down the focus of your selected core niche

Example Research Question (s)

Here are examples of research problems and research questions to help you understand how to create a research question for a given research problem.

Types of Research Questions

There are two main types of research;  quantitative and qualitative research . Both types of research require research questions. What research question you will answer is dependent on the type of research you wish to employ.

The first part of  designing research  is to find a gap and create a fully focused research question.

The following table shows common research questions for a dissertation project. However, it is important to note that these examples of dissertation research questions are straightforward, and the actual research questions may be more complicated than these examples.

What data collection method best suits your research?

• Find out by hiring an expert from ResearchProspect today!
• Despite how challenging the subject may be, we are here to help you.

Steps to Write Research Questions

The research question provides you with a path and focuses on the real problem and the research gap you aim to fill. These are steps you need to take if you are unsure about how to write a research question:

Choose an Interesting Topic

Choose a topic  of research according to your interest. The selected topic should be neither too broad nor too narrow.

Do Preliminary Research on the Topic

Find articles, books, journals, and theses relevant to your chosen topic. Understand what research problem each scholar addressed as part of their research project.

Consider your Audience

It is necessary to know your audience to develop focused research questions for your essay or dissertation. You can find aspects of your topic that could be interesting to your audience when narrowing your topic.

Start Asking Questions

What, why, when, how, and other open-ended questions will provide in-depth knowledge about the topic.

Evaluate your Question

After formulating a research question, evaluate to check its effectiveness and how it can serve the purpose. Revise and refine the dissertation research question.

• Do you have a clear research question? 

It would help if you formed the research question after finding a research gap. This approach will enable the research to solve part of the problem.

• Do you have a focused research question?

It is necessary that the research question is specific and relating to the central aim of your research.

• Do you have a complex research question? 

The research question cannot be answered by yes or no but requires in-depth analysis. It often begins with “How” or “Why.”

Begin your Research

After you have prepared dissertation research questions, you should research the existing literature on similar topics to find various perspectives.

Also See: Formulation of Research Question

If you have been struggling to devise research questions for your dissertation or are unsure about which topic would be suitable for your needs, then you might be interested in taking advantage of our dissertation topic and outline service, which includes several topic ideas in your preferred area of study and a 500/1000 words plan on your chosen topic. Our topic and outline service will help you jump-start your dissertation project.

Find out How Our Topics & Outline Service Can Help You!

Tips on How to Write a Strong Research Question

A research question is the foundation of the entire research. Therefore, you should spend as much time as required to refine the research question.

If you have good research questions for the dissertation, research paper , or essay, you can perform the research and analyse your results more effectively. You can evaluate the strength of the research question with the help of the following criteria. Your research question should be;

Intensive and Researchable

• It should cover a single issue
• The question shouldn’t include a subjective judgment
• It can be answerable with the data analysis or research=

Practical and Specific

• It should not include a course of action, policy, or solution
• It should be well-defined
• Answerable within research limits

Complicated and Arguable

• It should not be simple to answer
• Need in-depth knowledge to find facts
• Provides scope for debate and deliberation

Unique and Relevant

• It should lie in your field of study
• Its results should be contributable
• It should be unique

Conclusion – How to Write Research Questions

A research question provides a clear direction for research work. A bigger project, such as a dissertation, may have more than one research question, but every question should focus on one issue only.

Your research questions should be researchable, feasible to answer, specific to find results, complex (for Masters and PhD projects), and relevant to your field of study. Dissertation research questions depend upon the research type you are basing your paper on.

Start creating a research question by choosing an interesting topic, do some preliminary research, consider your audience, start asking questions, evaluating your question, and begin your research.

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Frequently Asked Questions

How are research questions written.

Research questions are written by:

• Identifying your topic.
• Considering what you want to explore.
• Making questions clear and concise.
• Ensuring they’re researchable.
• Avoiding bias or leading language.
• Focusing on one main idea per question.

What are examples of research questions?

• Does regular exercise improve mental well-being in adults over 50?
• How do online courses impact student engagement compared to traditional classes?
• What are the economic effects of prolonged pandemic lockdowns?
• How does early childhood nutrition influence academic performance in later life?
• Does urban green space reduce stress levels?

How to write a research question?

• Identify a specific topic or issue of interest.
• Conduct preliminary research to understand existing knowledge.
• Narrow the focus to address gaps or unresolved issues.
• Phrase the question to be clear, concise, and researchable.
• Ensure it is specific enough for systematic investigation.

How to formulate my research questions for my geography dissertation?

• Identify a geographical topic or phenomenon of interest.
• Review existing literature to find gaps.
• Consider spatial, temporal, environmental, or societal aspects.
• Ensure questions are specific, feasible, and significant.
• Frame questions to guide methodology: quantitative, qualitative, or mixed.
• Seek feedback from peers/advisors.

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How to Write a Research Question: Types and Examples 

The first step in any research project is framing the research question. It can be considered the core of any systematic investigation as the research outcomes are tied to asking the right questions. Thus, this primary interrogation point sets the pace for your research as it helps collect relevant and insightful information that ultimately influences your work.   

Typically, the research question guides the stages of inquiry, analysis, and reporting. Depending on the use of quantifiable or quantitative data, research questions are broadly categorized into quantitative or qualitative research questions. Both types of research questions can be used independently or together, considering the overall focus and objectives of your research.  

What is a research question?

A research question is a clear, focused, concise, and arguable question on which your research and writing are centered. 1 It states various aspects of the study, including the population and variables to be studied and the problem the study addresses. These questions also set the boundaries of the study, ensuring cohesion. 

Designing the research question is a dynamic process where the researcher can change or refine the research question as they review related literature and develop a framework for the study. Depending on the scale of your research, the study can include single or multiple research questions. 

A good research question has the following features: 

• It is relevant to the chosen field of study. 
• The question posed is arguable and open for debate, requiring synthesizing and analysis of ideas. 
• It is focused and concisely framed. 
• A feasible solution is possible within the given practical constraint and timeframe. 

A poorly formulated research question poses several risks. 1   

• Researchers can adopt an erroneous design. 
• It can create confusion and hinder the thought process, including developing a clear protocol.  
• It can jeopardize publication efforts.  
• It causes difficulty in determining the relevance of the study findings.  
• It causes difficulty in whether the study fulfils the inclusion criteria for systematic review and meta-analysis. This creates challenges in determining whether additional studies or data collection is needed to answer the question.  
• Readers may fail to understand the objective of the study. This reduces the likelihood of the study being cited by others. 

Now that you know “What is a research question?”, let’s look at the different types of research questions. 

Types of research questions

Depending on the type of research to be done, research questions can be classified broadly into quantitative, qualitative, or mixed-methods studies. Knowing the type of research helps determine the best type of research question that reflects the direction and epistemological underpinnings of your research. 

The structure and wording of quantitative 2 and qualitative research 3 questions differ significantly. The quantitative study looks at causal relationships, whereas the qualitative study aims at exploring a phenomenon. 

• Quantitative research questions:  
• Seeks to investigate social, familial, or educational experiences or processes in a particular context and/or location.  
• Answers ‘how,’ ‘what,’ or ‘why’ questions. 
• Investigates connections, relations, or comparisons between independent and dependent variables. 

Quantitative research questions can be further categorized into descriptive, comparative, and relationship, as explained in the Table below. 

• Qualitative research questions  

Qualitative research questions are adaptable, non-directional, and more flexible. It concerns broad areas of research or more specific areas of study to discover, explain, or explore a phenomenon. These are further classified as follows: 

• Mixed-methods studies  

Mixed-methods studies use both quantitative and qualitative research questions to answer your research question. Mixed methods provide a complete picture than standalone quantitative or qualitative research, as it integrates the benefits of both methods. Mixed methods research is often used in multidisciplinary settings and complex situational or societal research, especially in the behavioral, health, and social science fields. 

What makes a good research question

A good research question should be clear and focused to guide your research. It should synthesize multiple sources to present your unique argument, and should ideally be something that you are interested in. But avoid questions that can be answered in a few factual statements. The following are the main attributes of a good research question. 

• Specific: The research question should not be a fishing expedition performed in the hopes that some new information will be found that will benefit the researcher. The central research question should work with your research problem to keep your work focused. If using multiple questions, they should all tie back to the central aim. 
• Measurable: The research question must be answerable using quantitative and/or qualitative data or from scholarly sources to develop your research question. If such data is impossible to access, it is better to rethink your question. 
• Attainable: Ensure you have enough time and resources to do all research required to answer your question. If it seems you will not be able to gain access to the data you need, consider narrowing down your question to be more specific. 
• You have the expertise 
• You have the equipment and resources 
• Realistic: Developing your research question should be based on initial reading about your topic. It should focus on addressing a problem or gap in the existing knowledge in your field or discipline. 
• Based on some sort of rational physics 
• Can be done in a reasonable time frame 
• Timely: The research question should contribute to an existing and current debate in your field or in society at large. It should produce knowledge that future researchers or practitioners can later build on. 
• Novel 
• Based on current technologies. 
• Important to answer current problems or concerns. 
• Lead to new directions. 
• Important: Your question should have some aspect of originality. Incremental research is as important as exploring disruptive technologies. For example, you can focus on a specific location or explore a new angle. 
• Meaningful whether the answer is “Yes” or “No.” Closed-ended, yes/no questions are too simple to work as good research questions. Such questions do not provide enough scope for robust investigation and discussion. A good research question requires original data, synthesis of multiple sources, and original interpretation and argumentation before providing an answer. 

Steps for developing a good research question

The importance of research questions cannot be understated. When drafting a research question, use the following frameworks to guide the components of your question to ease the process. 4  

• Determine the requirements: Before constructing a good research question, set your research requirements. What is the purpose? Is it descriptive, comparative, or explorative research? Determining the research aim will help you choose the most appropriate topic and word your question appropriately. 
• Select a broad research topic: Identify a broader subject area of interest that requires investigation. Techniques such as brainstorming or concept mapping can help identify relevant connections and themes within a broad research topic. For example, how to learn and help students learn. 
• Perform preliminary investigation: Preliminary research is needed to obtain up-to-date and relevant knowledge on your topic. It also helps identify issues currently being discussed from which information gaps can be identified. 
• Narrow your focus: Narrow the scope and focus of your research to a specific niche. This involves focusing on gaps in existing knowledge or recent literature or extending or complementing the findings of existing literature. Another approach involves constructing strong research questions that challenge your views or knowledge of the area of study (Example: Is learning consistent with the existing learning theory and research). 
• Identify the research problem: Once the research question has been framed, one should evaluate it. This is to realize the importance of the research questions and if there is a need for more revising (Example: How do your beliefs on learning theory and research impact your instructional practices). 

How to write a research question

Those struggling to understand how to write a research question, these simple steps can help you simplify the process of writing a research question. 

Sample Research Questions

The following are some bad and good research question examples 

• Example 1 
• Example 2 

References:  

• Thabane, L., Thomas, T., Ye, C., & Paul, J. (2009). Posing the research question: not so simple.  Canadian Journal of Anesthesia/Journal canadien d’anesthésie ,  56 (1), 71-79. 
• Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research.  Nephrology Nursing Journal ,  45 (2), 209-213. 
• Kyngäs, H. (2020). Qualitative research and content analysis.  The application of content analysis in nursing science research , 3-11. 
• Mattick, K., Johnston, J., & de la Croix, A. (2018). How to… write a good research question.  The clinical teacher ,  15 (2), 104-108. 
• Fandino, W. (2019). Formulating a good research question: Pearls and pitfalls.  Indian Journal of Anaesthesia ,  63 (8), 611. 
• Richardson, W. S., Wilson, M. C., Nishikawa, J., & Hayward, R. S. (1995). The well-built clinical question: a key to evidence-based decisions.  ACP journal club ,  123 (3), A12-A13 

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How to Write a Research Paper

Use the links below to jump directly to any section of this guide:

Research Paper Fundamentals

How to choose a topic or question, how to create a working hypothesis or thesis, common research paper methodologies, how to gather and organize evidence , how to write an outline for your research paper, how to write a rough draft, how to revise your draft, how to produce a final draft, resources for teachers .

It is not fair to say that no one writes anymore. Just about everyone writes text messages, brief emails, or social media posts every single day. Yet, most people don't have a lot of practice with the formal, organized writing required for a good academic research paper. This guide contains links to a variety of resources that can help demystify the process. Some of these resources are intended for teachers; they contain exercises, activities, and teaching strategies. Other resources are intended for direct use by students who are struggling to write papers, or are looking for tips to make the process go more smoothly.

The resources in this section are designed to help students understand the different types of research papers, the general research process, and how to manage their time. Below, you'll find links from university writing centers, the trusted Purdue Online Writing Lab, and more.

What is an Academic Research Paper?

"Genre and the Research Paper" (Purdue OWL)

There are different types of research papers. Different types of scholarly questions will lend themselves to one format or another. This is a brief introduction to the two main genres of research paper: analytic and argumentative. 

"7 Most Popular Types of Research Papers" (Personal-writer.com)

This resource discusses formats that high school students commonly encounter, such as the compare and contrast essay and the definitional essay. Please note that the inclusion of this link is not an endorsement of this company's paid service.

How to Prepare and Plan Out Writing a Research Paper

Teachers can give their students a step-by-step guide like these to help them understand the different steps of the research paper process. These guides can be combined with the time management tools in the next subsection to help students come up with customized calendars for completing their papers.

"Ten Steps for Writing Research Papers" (American University)  

This resource from American University is a comprehensive guide to the research paper writing process, and includes examples of proper research questions and thesis topics.

"Steps in Writing a Research Paper" (SUNY Empire State College)

This guide breaks the research paper process into 11 steps. Each "step" links to a separate page, which describes the work entailed in completing it.

How to Manage Time Effectively

The links below will help students determine how much time is necessary to complete a paper. If your sources are not available online or at your local library, you'll need to leave extra time for the Interlibrary Loan process. Remember that, even if you do not need to consult secondary sources, you'll still need to leave yourself ample time to organize your thoughts.

"Research Paper Planner: Timeline" (Baylor University)

This interactive resource from Baylor University creates a suggested writing schedule based on how much time a student has to work on the assignment.

"Research Paper Planner" (UCLA)

UCLA's library offers this step-by-step guide to the research paper writing process, which also includes a suggested planning calendar.

There's a reason teachers spend a long time talking about choosing a good topic. Without a good topic and a well-formulated research question, it is almost impossible to write a clear and organized paper. The resources below will help you generate ideas and formulate precise questions.

"How to Select a Research Topic" (Univ. of Michigan-Flint)

This resource is designed for college students who are struggling to come up with an appropriate topic. A student who uses this resource and still feels unsure about his or her topic should consult the course instructor for further personalized assistance.

"25 Interesting Research Paper Topics to Get You Started" (Kibin)

This resource, which is probably most appropriate for high school students, provides a list of specific topics to help get students started. It is broken into subsections, such as "paper topics on local issues."

"Writing a Good Research Question" (Grand Canyon University)

This introduction to research questions includes some embedded videos, as well as links to scholarly articles on research questions. This resource would be most appropriate for teachers who are planning lessons on research paper fundamentals.

"How to Write a Research Question the Right Way" (Kibin)

This student-focused resource provides more detail on writing research questions. The language is accessible, and there are embedded videos and examples of good and bad questions.

It is important to have a rough hypothesis or thesis in mind at the beginning of the research process. People who have a sense of what they want to say will have an easier time sorting through scholarly sources and other information. The key, of course, is not to become too wedded to the draft hypothesis or thesis. Just about every working thesis gets changed during the research process.

CrashCourse Video: "Sociology Research Methods" (YouTube)

Although this video is tailored to sociology students, it is applicable to students in a variety of social science disciplines. This video does a good job demonstrating the connection between the brainstorming that goes into selecting a research question and the formulation of a working hypothesis.

"How to Write a Thesis Statement for an Analytical Essay" (YouTube)

Students writing analytical essays will not develop the same type of working hypothesis as students who are writing research papers in other disciplines. For these students, developing the working thesis may happen as a part of the rough draft (see the relevant section below). 

"Research Hypothesis" (Oakland Univ.)

This resource provides some examples of hypotheses in social science disciplines like Political Science and Criminal Justice. These sample hypotheses may also be useful for students in other soft social sciences and humanities disciplines like History.

When grading a research paper, instructors look for a consistent methodology. This section will help you understand different methodological approaches used in research papers. Students will get the most out of these resources if they use them to help prepare for conversations with teachers or discussions in class.

"Types of Research Designs" (USC)

A "research design," used for complex papers, is related to the paper's method. This resource contains introductions to a variety of popular research designs in the social sciences. Although it is not the most intuitive site to read, the information here is very valuable. 

"Major Research Methods" (YouTube)

Although this video is a bit on the dry side, it provides a comprehensive overview of the major research methodologies in a format that might be more accessible to students who have struggled with textbooks or other written resources.

"Humanities Research Strategies" (USC)

This is a portal where students can learn about four methodological approaches for humanities papers: Historical Methodologies, Textual Criticism, Conceptual Analysis, and the Synoptic method.

"Selected Major Social Science Research Methods: Overview" (National Academies Press)

This appendix from the book  Using Science as Evidence in Public Policy , printed by National Academies Press, introduces some methods used in social science papers.

"Organizing Your Social Sciences Research Paper: 6. The Methodology" (USC)

This resource from the University of Southern California's library contains tips for writing a methodology section in a research paper.

How to Determine the Best Methodology for You

Anyone who is new to writing research papers should be sure to select a method in consultation with their instructor. These resources can be used to help prepare for that discussion. They may also be used on their own by more advanced students.

"Choosing Appropriate Research Methodologies" (Palgrave Study Skills)

This friendly and approachable resource from Palgrave Macmillan can be used by students who are just starting to think about appropriate methodologies.

"How to Choose Your Research Methods" (NFER (UK))

This is another approachable resource students can use to help narrow down the most appropriate methods for their research projects.

The resources in this section introduce the process of gathering scholarly sources and collecting evidence. You'll find a range of material here, from introductory guides to advanced explications best suited to college students. Please consult the LitCharts  How to Do Academic Research guide for a more comprehensive list of resources devoted to finding scholarly literature.

Google Scholar

Students who have access to library websites with detailed research guides should start there, but people who do not have access to those resources can begin their search for secondary literature here.

"Gathering Appropriate Information" (Texas Gateway)

This resource from the Texas Gateway for online resources introduces students to the research process, and contains interactive exercises. The level of complexity is suitable for middle school, high school, and introductory college classrooms.

"An Overview of Quantitative and Qualitative Data Collection Methods" (NSF)

This PDF from the National Science Foundation goes into detail about best practices and pitfalls in data collection across multiple types of methodologies.

"Social Science Methods for Data Collection and Analysis" (Swiss FIT)

This resource is appropriate for advanced undergraduates or teachers looking to create lessons on research design and data collection. It covers techniques for gathering data via interviews, observations, and other methods.

"Collecting Data by In-depth Interviewing" (Leeds Univ.)

This resource contains enough information about conducting interviews to make it useful for teachers who want to create a lesson plan, but is also accessible enough for college juniors or seniors to make use of it on their own.

There is no "one size fits all" outlining technique. Some students might devote all their energy and attention to the outline in order to avoid the paper. Other students may benefit from being made to sit down and organize their thoughts into a lengthy sentence outline. The resources in this section include strategies and templates for multiple types of outlines. 

"Topic vs. Sentence Outlines" (UC Berkeley)

This resource introduces two basic approaches to outlining: the shorter topic-based approach, and the longer, more detailed sentence-based approach. This resource also contains videos on how to develop paper paragraphs from the sentence-based outline.

"Types of Outlines and Samples" (Purdue OWL)

The Purdue Online Writing Lab's guide is a slightly less detailed discussion of different types of outlines. It contains several sample outlines.

"Writing An Outline" (Austin C.C.)

This resource from a community college contains sample outlines from an American history class that students can use as models.

"How to Structure an Outline for a College Paper" (YouTube)

This brief (sub-2 minute) video from the ExpertVillage YouTube channel provides a model of outline writing for students who are struggling with the idea.

"Outlining" (Harvard)

This is a good resource to consult after completing a draft outline. It offers suggestions for making sure your outline avoids things like unnecessary repetition.

As with outlines, rough drafts can take on many different forms. These resources introduce teachers and students to the various approaches to writing a rough draft. This section also includes resources that will help you cite your sources appropriately according to the MLA, Chicago, and APA style manuals.

"Creating a Rough Draft for a Research Paper" (Univ. of Minnesota)

This resource is useful for teachers in particular, as it provides some suggested exercises to help students with writing a basic rough draft. 

Rough Draft Assignment (Duke of Definition)

This sample assignment, with a brief list of tips, was developed by a high school teacher who runs a very successful and well-reviewed page of educational resources.

"Creating the First Draft of Your Research Paper" (Concordia Univ.)

This resource will be helpful for perfectionists or procrastinators, as it opens by discussing the problem of avoiding writing. It also provides a short list of suggestions meant to get students writing.

Using Proper Citations

There is no such thing as a rough draft of a scholarly citation. These links to the three major citation guides will ensure that your citations follow the correct format. Please consult the LitCharts How to Cite Your Sources guide for more resources.

Chicago Manual of Style Citation Guide

Some call  The Chicago Manual of Style , which was first published in 1906, "the editors' Bible." The manual is now in its 17th edition, and is popular in the social sciences, historical journals, and some other fields in the humanities.

APA Citation Guide

According to the American Psychological Association, this guide was developed to aid reading comprehension, clarity of communication, and to reduce bias in language in the social and behavioral sciences. Its first full edition was published in 1952, and it is now in its sixth edition.

MLA Citation Guide

The Modern Language Association style is used most commonly within the liberal arts and humanities. The  MLA Style Manual and Guide to Scholarly Publishing  was first published in 1985 and (as of 2008) is in its third edition.

Any professional scholar will tell you that the best research papers are made in the revision stage. No matter how strong your research question or working thesis, it is not possible to write a truly outstanding paper without devoting energy to revision. These resources provide examples of revision exercises for the classroom, as well as tips for students working independently.

"The Art of Revision" (Univ. of Arizona)

This resource provides a wealth of information and suggestions for both students and teachers. There is a list of suggested exercises that teachers might use in class, along with a revision checklist that is useful for teachers and students alike.

"Script for Workshop on Revision" (Vanderbilt University)

Vanderbilt's guide for leading a 50-minute revision workshop can serve as a model for teachers who wish to guide students through the revision process during classtime. 

"Revising Your Paper" (Univ. of Washington)

This detailed handout was designed for students who are beginning the revision process. It discusses different approaches and methods for revision, and also includes a detailed list of things students should look for while they revise.

"Revising Drafts" (UNC Writing Center)

This resource is designed for students and suggests things to look for during the revision process. It provides steps for the process and has a FAQ for students who have questions about why it is important to revise.

Conferencing with Writing Tutors and Instructors

No writer is so good that he or she can't benefit from meeting with instructors or peer tutors. These resources from university writing, learning, and communication centers provide suggestions for how to get the most out of these one-on-one meetings.

"Getting Feedback" (UNC Writing Center)

This very helpful resource talks about how to ask for feedback during the entire writing process. It contains possible questions that students might ask when developing an outline, during the revision process, and after the final draft has been graded.

"Prepare for Your Tutoring Session" (Otis College of Art and Design)

This guide from a university's student learning center contains a lot of helpful tips for getting the most out of working with a writing tutor.

"The Importance of Asking Your Professor" (Univ. of Waterloo)

This article from the university's Writing and Communication Centre's blog contains some suggestions for how and when to get help from professors and Teaching Assistants.

Once you've revised your first draft, you're well on your way to handing in a polished paper. These resources—each of them produced by writing professionals at colleges and universities—outline the steps required in order to produce a final draft. You'll find proofreading tips and checklists in text and video form.

"Developing a Final Draft of a Research Paper" (Univ. of Minnesota)

While this resource contains suggestions for revision, it also features a couple of helpful checklists for the last stages of completing a final draft.

Basic Final Draft Tips and Checklist (Univ. of Maryland-University College)

This short and accessible resource, part of UMUC's very thorough online guide to writing and research, contains a very basic checklist for students who are getting ready to turn in their final drafts.

Final Draft Checklist (Everett C.C.)

This is another accessible final draft checklist, appropriate for both high school and college students. It suggests reading your essay aloud at least once.

"How to Proofread Your Final Draft" (YouTube)

This video (approximately 5 minutes), produced by Eastern Washington University, gives students tips on proofreading final drafts.

"Proofreading Tips" (Georgia Southern-Armstrong)

This guide will help students learn how to spot common errors in their papers. It suggests focusing on content and editing for grammar and mechanics.

This final set of resources is intended specifically for high school and college instructors. It provides links to unit plans and classroom exercises that can help improve students' research and writing skills. You'll find resources that give an overview of the process, along with activities that focus on how to begin and how to carry out research. 

"Research Paper Complete Resources Pack" (Teachers Pay Teachers)

This packet of assignments, rubrics, and other resources is designed for high school students. The resources in this packet are aligned to Common Core standards.

"Research Paper—Complete Unit" (Teachers Pay Teachers)

This packet of assignments, notes, PowerPoints, and other resources has a 4/4 rating with over 700 ratings. It is designed for high school teachers, but might also be useful to college instructors who work with freshmen.

"Teaching Students to Write Good Papers" (Yale)

This resource from Yale's Center for Teaching and Learning is designed for college instructors, and it includes links to appropriate activities and exercises.

"Research Paper Writing: An Overview" (CUNY Brooklyn)

CUNY Brooklyn offers this complete lesson plan for introducing students to research papers. It includes an accompanying set of PowerPoint slides.

"Lesson Plan: How to Begin Writing a Research Paper" (San Jose State Univ.)

This lesson plan is designed for students in the health sciences, so teachers will have to modify it for their own needs. It includes a breakdown of the brainstorming, topic selection, and research question process. 

"Quantitative Techniques for Social Science Research" (Univ. of Pittsburgh)

This is a set of PowerPoint slides that can be used to introduce students to a variety of quantitative methods used in the social sciences.

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Where to Put the Research Question in a Paper

Silke Haidekker has a PhD in Pharmacology from the University of Hannover. She is a Clinical Research Associate in multiple pharmaceutical companies in Germany and the USA. She now works as a full-time medical translator and writer in a small town in Georgia.

Of Rats and Panic Attacks: A Doctoral Student’s Tale

You would probably agree that the time spent writing your PhD dissertation or thesis is not only a time of taking pride or even joy in what you do, but also a time riddled with panic attacks of different varieties and lengths. When I worked on my PhD thesis in pharmacology in Germany many years back, I had  my  first panic attack as I first learned how to kill rats for my experiments with a very ugly tool called a guillotine! After that part of the procedure, I was to remove and mash their livers, spike them with Ciclosporin A (an immunosuppressive agent), and then present the metabolites by high-pressure liquid chromatography.

Many rats later, I had another serious panic attack. It occurred at the moment my doctoral adviser told me to write my first research paper on the Ciclosporin A metabolites I had detected in hundreds of slimy mashes of rat liver. Sadly, this second panic attack led to a third one that was caused by living in the pre-internet era, when it was not as easy to access information about  how to write research papers .

How I got over writing my first research paper is now ancient history. But it was only years later, living in the USA and finally being immersed in the language of most scientific research papers, that my interest in the art of writing “good” research papers was sparked during conferences held by the  American Medical Writers Association , as well as by getting involved in different writing programs and academic self-study courses.

How to State the Research Question in the Introduction Section

Good writing begins with clearly stating your research question (or hypothesis) in the Introduction section —the focal point on which your entire paper builds and unfolds in the subsequent Methods, Results, and Discussion sections . This research question or hypothesis that goes into the first section of your research manuscript, the Introduction, explains at least three major elements:

a) What is  known  or believed about the research topic?

B) what is still  unknown  (or problematic), c) what is the  question or hypothesis  of your investigation.

Some medical writers refer to this organizational structure of the Introduction as a “funnel shape” because it starts broadly, with the bigger picture, and then follows one scientifically logical step after the other until finally narrowing down the story to the focal point of your research at the end of the funnel.

Let’s now look in greater detail at a research question example and how you can logically embed it into the Introduction to make it a powerful focal point and ignite the reader’s interest about the importance of your research:

a) The Known

You should start by giving your reader a brief overview of knowledge or previous studies already performed in the context of your research topic.

The topic of one of my research papers was “investigating the value of diabetes as an independent predictor of death in people with end-stage renal disease (ESRD).” So in the Introduction, I first presented the basic knowledge that diabetes is the leading cause of end-stage renal disease (ESRD) and thus made the reader better understand our interest in this specific study population. I then presented previous studies already showing that diabetes indeed seems to represent an independent risk factor for death in the general population. However, very few studies had been performed in the ESRD population and those only yielded controversial results.

Example :  “It seems well established that there is a link between diabetic nephropathy and hypertensive nephropathy and end-stage renal disease (ESRD) in Western countries. In 2014, 73% of patients in US hospitals had comorbid ESRD and type 2 diabetes (1, 2, 3)…”

b) The Unknown

In our example, this “controversy” flags the “unknown” or “problematic” and therefore provides strong reasons for why further research is justified. The unknown should be clearly stated or implied by using phrases such as “were controversial” (as in our example), “…has not been determined,” or “…is unclear.” By clearly stating what is “unknown,” you indicate that your research is new. This creates a smooth transition into your research question.

Example :  “However, previous studies have failed to isolate diabetes as an independent factor, and thus much remains unknown about specific risk factors associated with both diabetes and ESRD .”

c) The Research Question (Hypothesis)

Your research question is the question that inevitably evolves from the deficits or problems revealed in the “Unknown” and clearly states the goal of your research. It is important to describe your research question in just one or two short sentences, but very precisely and including all variables studied, if applicable. A transition should be used to mark the transition from the unknown to the research question using one word such as “therefore” or “accordingly,” or short phrases like “for this reason” or “considering this lack of crucial information.”

In our example, we stated the research question as follows:

Example :  “Therefore, the primary goal of our study was to perform a Kaplan-Meier survival study and to investigate, by means of the Cox proportional hazard model, the value of diabetes as an independent predictor of death in diabetic patients with ESRD.”

Note that the research question may include the  experimental approach  of the study used to answer the research question.

Another powerful way to introduce the research question is to  state the research question as a hypothesis  so that the reader can more easily anticipate the answer. In our case, the question could be put as follows:

Example :  “To test the hypothesis that diabetes is an independent predictor of death in people with ESRD, we performed a Kaplan-Survival study and investigated the value of diabetes by means of the Cox proportional hazard model.”

Note that this sentence leads with an introductory clause that indicates the hypothesis itself, transitioning well into a synopsis of the approach in the second half of the sentence.

The generic framework of the Introduction can be modified to include, for example,  two  research questions instead of just one. In such a case, both questions must follow inevitably from the previous statements, meaning that the background information leading to the second question cannot be omitted. Otherwise, the Introduction will get confusing, with the reader not knowing where that question comes from.

Begin with your research purpose in mind

To conclude, here is my simple but most important advice for you as a researcher preparing to write a scientific paper (or just the Introduction of a research paper) for the first time: Think your research question through precisely before trying to write it down; have in mind the reasons for exactly why you wanted to do this specific research, what exactly you wanted to find out, and how (by which methods) you did your investigation. If you have the answers to these questions in mind (or even better, create a comprehensive outline ) before starting the paper, the actual writing process will be a piece of cake and you will finish it “like a rat up a drainpipe”! And hopefully with no panic attacks.

Wordvice Resources

Before submitting your master’s thesis or PhD dissertation to academic journals for publication, be sure to receive proofreading services (including research paper editing , manuscript editing , thesis editing , and dissertation editing ) to ensure that your research writing is error-free. Impress your journal editor and get into the academic journal of your choice.    

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Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

What's Next?

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These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

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• Published: 25 May 2024

Neither right nor wrong? Ethics of collaboration in transformative research for sustainable futures

• Julia M. Wittmayer   ORCID: orcid.org/0000-0002-4738-6276 1 , 2 ,
• Ying-Syuan (Elaine) Huang 3 ,
• Kristina Bogner   ORCID: orcid.org/0000-0002-1871-9828 4 ,
• Evan Boyle 5 ,
• Katharina Hölscher 6 ,
• Timo von Wirth 2 , 7 ,
• Tessa Boumans 2 ,
• Jilde Garst 8 ,
• Yogi Hale Hendlin 9 ,
• Mariangela Lavanga   ORCID: orcid.org/0000-0001-5925-9509 10 ,
• Derk Loorbach 1 ,
• Neha Mungekar   ORCID: orcid.org/0000-0002-4663-0716 1 , 2 ,
• Mapula Tshangela 11 ,
• Pieter Vandekerckhove 12 &
• Ana Vasques 13  

Humanities and Social Sciences Communications volume  11 , Article number:  677 ( 2024 ) Cite this article

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• Environmental studies
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Transformative research is a broad and loosely connected family of research disciplines and approaches, with the explicit normative ambition to fundamentally question the status quo, change the dominant structures, and support just sustainability transitions by working collaboratively with society. When engaging in such science-practice collaborations for transformative change in society, researchers experience ethical dilemmas. Amongst others, they must decide, what is worthwhile to be researched, whose reality is privileged, and whose knowledge is included. Yet, current institutionalised ethical standards, which largely follow the tradition of medical ethics, are insufficient to guide transformative researchers in navigating such dilemmas. In addressing this vacuum, the research community has started to develop peer guidance on what constitutes morally good behaviour. These formal and informal guidelines offer a repertoire to explain and justify positions and decisions. However, they are only helpful when they have become a part of researchers’ practical knowledge ‘in situ’. By focusing on situated research practices, the article addresses the need to develop an attitude of leaning into the uncertainty around what morally good behaviour constitutes. It also highlights the significance of combining this attitude with a critical reflexive practice both individually and collaboratively for answering questions around ‘how to’ as well as ‘what is the right thing to do’. Using a collaborative autoethnographic approach, the authors of this paper share their own ethical dilemmas in doing transformative research, discuss those, and relate them to a practical heuristic encompassing axiological, ontological, and epistemological considerations. The aim is to support building practical wisdom for the broader research community about how to navigate ethical questions arising in transformative research practice.

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Introduction.

There is a growing recognition that current research has failed to adequately address persistent societal challenges, which are complex, uncertain, and evaluative in nature (Ferraro et al., 2015 ; Loorbach et al., 2017 ; Saltelli et al., 2016 ). Along with this recognition come calls for science to help address these increasingly urgent and complex challenges faced at a global and local level, such as biodiversity loss, climate change, or social inequalities (Future Earth, 2014 ; Parks et al., 2019 ; WBGU, 2011 ). This call is echoed from within academia (Bradbury et al., 2019 ; Fazey et al., 2018 ; Norström et al., 2020 ) and has also translated into corresponding research funding (Arnott et al., 2020 ; Gerber et al., 2020 ; Vermeer et al., 2020 ). The fundamental premise is that addressing complex societal challenges requires more than disciplinary knowledge alone and extends beyond the confines of academia (Gibbons et al., 1994 ; Hirsch Hadorn et al., 2008 ; Lang et al., 2012 ). That is, addressing them necessitates interactive knowledge co-production and social learning with societal actors to produce actionable and contextually embedded knowledge for societal transformations (Chambers et al., 2021 ; Hessels et al., 2009 ; Schäpke et al., 2018 ). This trend has prompted a (re)surge of socially engaged approaches to research, including transdisciplinary research, phronetic social sciences, participatory research, action- and impact-oriented research, and transformative research. These approaches involve collaboration between academics and various societal stakeholders, such as policymakers, communities, enterprises, and civil society organisations.

However, often, such socially engaged research approaches are at odds with the institutional traditions designed for monodisciplinary knowledge production. Transformative research, for instance, does not claim an objective observer position; instead, it explicitly embraces a normative orientation. Its goal, as many have argued, is to facilitate transformative societal change towards justice and sustainability by recognising and addressing the deep and persistent socio-ecological challenges inherent in our current society (Mertens, 2007 ; Wittmayer et al., 2021 ). This motive to transform existing systems through collaborative research, in our view, obliges researchers to be more critical and vigilant in their decisions (Fazey et al., 2018 ). As we will present later in this paper, many of these decisions constitute ethical dilemmas, such as who decides what ‘good’ research is, whose knowledge to prioritise, or who should engage and under which circumstances. These ethical dilemmas are only poorly addressed by the ethical review processes in place at most universities, which remain dominated by linear and positivist framings of knowledge production and research design (Wood and Kahts-Kramer, 2023 ). Consequently, transformative researchers are often left struggling to choose “ between doing good (being ethically responsive to the people being researched) and doing good research (maintaining pre-approved protocols) ” (Macleod et al., 2018 , p. 10). The translation of the values and principles of transformative research into formal and informal ethical guidelines is only starting (Caniglia et al., 2023 ; Fazey et al., 2018 ; West and Schill, 2022 ).

Confronting these ethical dilemmas calls for greater reflexivity and dialogue with ourselves, among researchers, between researchers and their collaborators (including funders and professionals), and between researchers and the institutions within which they operate (Finlay, 2002 ; Horcea-Milcu et al., 2022 ; Pearce et al., 2022 ). Attesting to this call, the authors of this paper engaged in a ‘collaborative autoethnography’ (Lapadat, 2017 ; Miyahara & Fukao, 2022 ; Phillips et al., 2022 ) to explore the following research question: Which ethical dilemmas do researchers face in research collaborations that seek to catalyse transformations? And how do they navigate these in their collaborative practice? Thus, as an interdisciplinary group of researchers affiliated with academic research institutes, we shared, compared, and discussed our experiences concerning ethical dilemmas in our transformative research endeavours. In these discussions, we considered our interactions, engagements, and relationships with collaborators along with how institutional rules and norms influence or constrain our practices and relations.

This paper begins with an overview of transformative research and the challenges that arise when working collaboratively. It also testifies to the formal and informal attempts to support researchers in navigating those challenges (“Ethics in transformative research”). From there, we develop the argument that formal or informal guidelines are most meaningful when they have become a part of the practical wisdom of researchers. When they are, they support researchers in leaning into the uncertainty of what constitutes morally good behaviour and in navigating collaboration ‘in situ’. Inspired by Mertens ( 2017 ), we relate our own dilemmas to the three philosophical commitments that comprise a research paradigm: axiology, ontology, and epistemology (“Transformative research practice investigated through collaborative autoethnography”, also for an elaboration of the terms). We share concrete dilemmas while embedding and relating them to a broader body of knowledge around similar dilemmas and questions (“Collaboration in transformative research practice”). We close the paper by pointing to the importance of bottom-up ethics and the need to embed those into revalued and redesigned ethical standards, processes, and assessments that can provide external guidance and accountability (“Concluding thoughts”).

Ethics in transformative research

In this section, we first introduce transformative research (TR) in terms of its underlying values and its ontological and epistemological premises (Mertens, 2007 , 2017 ) (“Introducing transformative research”). We then connect it to its institutional context, where ethical standards and procedures fit the linear production of knowledge, leading to tensions with TR practices (“Institutional context: Formal ethical standards and processes”). Finally, we outline how the research community tries to address this misfit and the felt need for understanding what constitutes morally ‘right’ behaviour by providing peer guidance on the ethical conduct of TR (“Peer context: Informal heuristics for transformative research”).

Introducing transformative research

TR refers to a broad and loosely connected family of research disciplines and approaches, with the explicit normative ambition to fundamentally question the status quo, change the dominant structures, and support just sustainability transitions (Hölscher et al., 2021 ; Jaeger-Erben et al., 2018 ; Mertens, 2021 ; Schneidewind et al., 2016 ; Wittmayer et al., 2021 ). Transformative researchers thus start from the basic premise that “ all researchers are essentially interveners ” (Fazey et al., 2018 , p. 63). Consequently, they are explicit about the kind of normative orientation of their interventions to further a social justice and environmental sustainability agenda. There is no denying the fact that such research approaches can also be used with a different normative mindset and value orientation, which will have other ethical consequences.

TR builds on methodological and theoretical pluralism that knits together kindred, or even conflicting, perspectives to complement disciplinary specialism (Hoffmann et al., 2017 ; Horcea-Milcu et al., 2022 ; Midgley, 2011 ). As such, it also comes as a diverse phenomenon, and where such diversity is “ not haphazard […] we must be cautious about developing all-embracing standards to differentiate the ‘good’ from the ‘bad ’” (Cassell and Johnson, 2006 , p. 783). Such an ontological stance involves letting go of the idea of absolute truth and the need to tightly control the research process and outcomes (van Breda and Swilling, 2019 ). Instead, TR encourages continuous societal learning to generate actionable knowledge and transformative action that manifests in real-world changes in behaviours, values, institutions, etc. (Bartels and Wittmayer, 2018 ; Hölscher et al., 2021 ). In doing so, TR is often based upon pragmatist assumptions about the ways knowledge and action inform one another, generating contingent knowledge in a process of action and experimentation (Harney et al., 2016 ; Popa et al., 2015 ). The research process serves as a means to assess ideas in practical application, blending a critical realist stance on socially constructed reality with acknowledging subjectivism and the existence of multiple realities (Cassell and Johnson, 2006 ).

TR also represents an epistemological shift from the notion of the distanced, presumably unbiased, and all-knowing researcher and recognises individuals as sense-makers, agency holders, and change agents (Horcea-Milcu et al., 2022 ; Hurtado, 2022 ). Collaboration enables the elicitation of different kinds of knowledge, including scientific knowledge across disciplines as well as phronetic and tacit knowledge from practice. It aims at capturing the plurality of knowing and doing that is relevant to specific contexts and actors (Frantzeskaki and Kabisch, 2016 ; Nugroho et al., 2018 ; Pohl, 2008 ). This sort of mutual social learning supports joint sense-making and experimental processes. These then invite us to rethink existing situations, (re)define desired futures, and (re)position short-term action (Fazey et al., 2018 ; Lotz-Sisitka et al., 2016 ; Schneider et al., 2019 ). The co-creation of knowledge and action can increase ownership, legitimacy, and accountability and can help facilitate trust-building among diverse societal groups (Hessels et al., 2009 ; Lang et al., 2012 ). The latter is an essential ingredient for tackling complex societal problems during times of discrediting science and the rise of populist, antidemocratic movements (Saltelli et al., 2016 ).

Institutional context: formal ethical standards and processes

The institutional environment is challenging for researchers engaging in TR for multiple reasons; one challenge is the formal ethical standards and processes. Current approaches to ethical assessment in social science emerged from several international conventions in the field of medical ethics (BMJ, 1996 ; General Assembly of the World Medical Association, 2014 ; National Commission for the Protection of Human Subjects of Biomedical, & Behavioural Research, 1979 ). Most formal research ethics reviews adopt the four principles of Beauchamp and Childress ( 2001 ), which include: (1) non-maleficence by attempting to not harm others; (2) respect for autonomy by attempting to provide information about the research that allows decisions to be taken; (3) beneficence by attempting to achieve useful outcomes outweighing the risks of participation; and (4) justice by attempting fairness in participation and distribution of benefits. These principles have found their way into formal ethical reviews, often practicing value-neutral and utilitarian ethics. This approach is debatable for TR approaches (Detardo-Bora, 2004 ) and seems more effective at protecting research institutions (foregrounding bureaucratically controllable compliance) than research participants (Christians, 2005 ). Indeed, many engaged in TR have raised concerns that neither these principles nor their formal translation account for the particularity, situatedness, epistemic responsibilities, and relationality that are key to the conduct and ethics of TR (Cockburn and Cundill, 2018 ; Lincoln, 2001 ; Parsell et al., 2014 ; Wijsman and Feagan, 2019 ). In the following paragraphs, we highlight several tensions between the understanding of research, as it informs many ethical standards in place, and an understanding of TR.

First, a pre-defined versus an emerging research design. Due to its real-world orientation, TR needs to be able to deal flexibly with changing contexts and windows of opportunity that might arise (Hurtado, 2022 ). Due to the relationality of TR, it requires ongoing interaction and negotiation between researchers and their collaborators (Bartels and Wittmayer, 2018 ; Bournot-Trites and Belanger, 2005 ; Williamson and Prosser, 2002 ). One-off general consent at the start (e.g., through informed consent forms), as is common for ethical review processes, is thus at odds with the emergent design of TR and is also argued to be insufficient in maintaining participants’ autonomy (Smith, 2008 ). As an alternative, Locke et al. ( 2013 ) posit that informed consent should be seen as a collective, negotiated, continuous process, especially in collaborative action research.

Second, assumed neutrality versus dynamic aspects of researchers’ positionalities. Ethical review protocols are geared towards upholding the objective position of researchers as outsiders in the investigated context, ensuring that they will not influence this research context in any way. However, TR explicates its ambition to influence real-world problems through engagement, acknowledging that research needs to confront existing hegemonic orders and emancipate those involved through a democratic process (Cassell and Johnson, 2006 ). Furthermore, researchers co-design, facilitate, and participate in the process of knowledge co-production, making them also participants and subjects of their own research (Janes, 2016 ). To enhance the validity and integrity of the research, Wood, and Kahts-Kramer ( 2023 ), among others, suggest that transformative researchers explicitly state their positionality. This involves reflecting on their assumptions, values, and worldviews.

Third, the primacy of knowledge generation versus the importance of action. Ethical review protocols, given their historical roots in medical practice, assume that the act of falsifying, generating, or improving theories alone would benefit participants, collaborators, and the public at large. Yet, researchers engaged in TR take a step further, seeking to develop both scientific and actionable knowledge in a way that addresses persistent societal problems and stimulates social change (Bartels and Wittmayer, 2018 ; Caniglia et al., 2021 ; Greenwood and Levin, 2007 ). As put by Wood and Kahts-Kramer ( 2023 , p. 7), “ the ethical imperative of participatory research is to bring about positive change and generate theory from reflection on the purposeful action ”. This approach strengthens the responsiveness of research to societal and political needs (Stilgoe et al., 2013 ).

Transformative researchers thus perceive a lack of utility and guidance from ethical standards and processes in place that have institutionalised a certain understanding of research and related sets of principles. Following Clouser and Gert ( 1990 ), one might question whether such institutionalisation of a moral consciousness is possible in the first place. They argue that so-called ‘principlism,’ “ the practice of using ‘principles’ to replace both moral theory and particular moral rules and ideals in dealing with the moral problems that arise in medical practice ” (Clouser and Gert, 1990 , p. 219), has reduced the much-needed debates on morality vis-à-vis research and results in inconsistent and ambiguous directives for morally ‘right’ action in practice. In response to the vacuum left by institutionalised ethics standards and processes and the perceived necessity of defining morally ‘right’ behaviour, the research community is turning inward to develop peer guidance on ethical conduct in TR. The subsequent section highlights several contributions to this endeavour.

Peer context: Informal heuristics for transformative research

Transformative researchers have started offering general principles or frameworks as informal heuristics for what constitutes ‘ethical’ TR. Caniglia et al. ( 2023 ), for example, argue that practical wisdom can serve as a moral compass in complex knowledge co-production contexts, and propose four central ‘wills’ for researchers to follow: committing to justice, embracing care, fostering humility, and developing courage. Under the framing of post-normal or Mode-2 science (Funtowicz and Ravetz, 1994 ; Gibbons et al., 1994 ; Nowotny et al., 2003 ), Fazey et al. ( 2018 ) present ten ‘essentials’ of action-oriented research on transforming energy systems and climate change research Footnote 1 . One of these essentials highlights that, as researchers, we intervene, and that failing to acknowledge and engage with this reality opens the doors to sustaining unjust power relations or positioning science as apolitical. To address this, they echo Lacey et al.’s ( 2015 , p. 201) assertion that such acknowledgment means “ be[ing] transparent and accountable about the choices made about what science is undertaken, and how it is funded and communicated ”.

Looking beyond sustainability scholarship, other researchers have also developed practical actions or strategies for enhancing their ethical behaviours in the research collaboration. Taking the unique attributes of community-based participatory research, Kwan and Walsh ( 2018 , p. 382) emphasise a “ focus on equity rather than equality ” and on practicing a constructive or generative use of power “ rather than adopting a power neutral or averse position ”. Others provide guiding questions to think about the forms and quality of relationships between researchers and participants (Rowan, 2000 ) and to support the navigation of the relationship between action research and other participants (Williamson and Prosser, 2002 ). Such questions should cover not only process-focused questions but also the risks and benefits of the intended outcomes, as well as questions around purpose, motivation, and directionalities (Stilgoe et al., 2013 ). Others also propose broader guidelines in which they pay attention to non-Western and non-human-centred virtue ethics, such as ‘Ubuntu’ (I am because we are) (Chilisa, 2020 ). In forwarding climate change as a product of colonisation, Gram-Hanssen et al. ( 2022 ) join Donald’s ( 2012 ) call for an ethical relationality and reiterate the need to ground all transformation efforts on a continuous process of embodying ‘right relations’ (see also Chilisa, 2020 ; Wilson, 2020 ).

Yet, as argued before, ethics in collaboration cannot be approached through developing principles and strategies alone. Not only might they not be at hand or on top of one’s mind when being immersed in a collaborative practice, which often requires a certain reaction on the spot. They also cannot or should not replace the quest for what morality means within that collaboration (cf. Clouser and Gert, 1990 ). Further questions have been prompted about the necessary skillsets for realising ethical principles in practice (Jaeger-Erben et al., 2018 ; Pearce et al., 2022 ; West and Schill, 2022 ). Caniglia et al. ( 2023 ), for example, propose that researchers need skills such as dealing with plural values with agility and traversing principles and situations with discernment. Others focus on competency building among research participants (Menon and Hartz-Karp, 2023 ). The subsequent section turns to the point of supporting researchers in navigating collaboration ‘in situ’ and in leaning into the uncertainty around what morally good behaviour constitutes—in concrete TR contexts that are plural and uncertain.

Transformative research practice investigated through collaborative autoethnography

Transformative research as a situated practice.

The aforementioned institutionalised ethical standards and procedures, as well as the informal peer heuristics, are two vantage points for guidance on what constitutes morally good behaviour for transformative researchers. These existing vantage points are either developed based on theoretical and philosophical framings or based on researchers’ actual experiences of doing TR. They do offer a repertoire to explain and justify positions and decisions in ethical dilemmas during research collaborations. However, it is not until such heuristics or principles have become part of the practical knowledge of researchers that they are useful for actual TR in situ.

Considering research more as a practice situates it as a social activity in a ‘real-world context’. In such a practice, researchers often make decisions on the spot. Moreover, due to the constraints posed by available time and resources, researchers often engage in what Greenwood and Levin ( 2007 , p. 130) term “ skilful improvisation ” or “ pragmatic concessions ” (Greenwood and Levin, 2007 , p. 85). This “ improvisational quality ” (Yanow, 2006 , p. 70) of the research process does not mean it is not carried out systematically. Such systematicity is based on “ action repertoires ” (Yanow, 2006 , p. 71) that researchers creatively use and remake (Malkki, 2007 ). This improvisation is thus neither spontaneous nor random; rather, it builds on and is based on the practical knowledge of researchers (formed through their experiences and their situatedness) guiding their behaviours in normatively complex situations. Using ‘organic design’ (Haapala et al., 2016 ), the researchers blend real-world settings into formal spaces, fostering bricolage and driving sustainable institutional evolution over time. Such practical knowledge includes “ both ‘know how’ knowledge (techne), […] and ethical and political-practical knowledge (phronesis)” (Fazey et al., 2018 , p. 61). Research can thus be considered a craft (Wittmayer, 2016 ): the skilful mastery of which develops over time through learning based on experience and reflection (Kolb, 1984 ).

Such experiential learning should go beyond reflecting on what lies in view to include seeing how attributes of the viewer shape what is being viewed (cf. Stirling, 2006 ). Engaging in TR includes being one’s own research instrument, which puts a researcher’s positionality, i.e., their social, cultural, and political locations, centre stage. It reminds us that researchers are “ located within networks of power and participate in the (re)configuration of power relations ” (Wijsman and Feagan, 2019 , p. 74). This positionality, the sum of what makes a person and how this informs their actions (Haraway, 1988 ; Kwan and Walsh, 2018 ; Marguin et al., 2021 ), is increasingly being acknowledged in academia. It has a long history in feminist theories, participatory action research, and the critical pedagogy of decolonisation. Positionality refers to the “ researcher’s self-understanding and social vision ” (Coghlan and Shani, 2005 , p. 539) as well as their motivation to ‘better society’ (Boyle et al., 2023 ; Kump et al., 2023 ) and how these affect how researchers interpret ethical guidelines, conduct research, interpret data, and present findings. Consequently, one’s positionality can make certain research choices seem unethical. Mertens ( 2021 , p. 2), for example, considers “ continuing to do research in a business-as-usual manner” unethical as it makes the researcher “ complicit in sustaining oppression ”.

Acknowledging one’s positionality and normative role is part of a broader reflexive practice of critically questioning, reflecting on, and being transparent about values, as well as taking responsibility and accountability for research processes and outcomes (Fazey et al., 2018 ; Pearce et al., 2022 ; Wijsman and Feagan, 2019 ). Such a reflexive practice can support individual researchers to act ethically, but more so, to improve our collective ways of being and doing (i.e., an ethically informed research community) by constantly connecting what should be (i.e., the guidelines) and how it has been done (i.e., the practices) through critical reflexive practices. This improvement at the collective level includes a re-valuation and redesign of existing processes and guidelines for morally good research.

A collaborative autoethnography

Responding to this need for critical reflexivity, we engaged with our storied experience in navigating concrete and immediate ethical dilemmas that we have encountered when collaborating with others for TR in practice. We did so through collaborative autoethnography, a multivocal approach in which two or more researchers work together to share personal stories and interpret the pooled autoethnographic data (Chang et al., 2016 ; Lapadat, 2017 ; Miyahara and Fukao, 2022 ). Collaborative autoethnography is appropriate for our inquiry as it broadens the gaze from the dilemmas of the self to locate them within categories of experience shared by many. Interrogating our personal narratives and understanding the shared experiences through multiple lenses not only facilitates a more rigorous, polyvocal analysis but also reveals possibilities for practical action or intervention (Lapadat, 2017 ). Collaborative auto-ethnography can thus be considered an approach that moves “ beyond the clichés and usual explanations to the point where the written memories come as close as they can make them to ‘an embodied sense of what happened’ ” (Davies and Gannon, 2006 , p. 3). It also supports developing researcher reflexivity (Miyahara and Fukao, 2022 ).

Overall, we engaged in two types of collaborative activities over the course of a period of 18 months: writing and discussing. In hindsight, this period can be divided into three phases: starting up, exploring, and co-working. The first phase was kicked off by an online dialogue session with about 30 participants convened by the Design Impact Transition Platform of the Erasmus University Rotterdam in April 2022. The session was meant to explore and share experiences with a wide range of ethical dilemmas arising from TR collaboration in practice. Following this session, some participants continued deliberating on the questions and dilemmas raised in differing constellations and developed the idea of codifying and sharing our experiences and insights via a publication. In a second phase, we started writing down individual ethical dilemmas, both those we had discussed during the seminar and additional ones. These writings were brought together in an online shared file, where we continued our discussions. This was accompanied by meetings in differing constellations and of differing intensity for the researchers involved.

A third phase of intense co-work was framed by two broader online sessions. During a session in May 2023, we shared and discussed a first attempt at an analysis and sense-making of our individual dilemmas. During this session, we discerned the heuristic by Mertens et al. (2017) and discussed how it could be helpful in structuring our different experiences. Inspired by Mertens et al. (2017), we re-engaged with the three critical dimensions of any research paradigm to scrutinise our philosophical commitments to doing TR. A re-engagement with issues of axiology (the nature of ethics and values), ontology (the nature of reality), and epistemology (the nature of knowledge), as illustrated in Table 1 , allowed us to reconcile our ethical dilemmas and opened a space for a more nuanced understanding and bottom-up approach to the ethics of collaboration in TR. In moving forward, the heuristic also helped to guide the elicitation of additional dilemmas. This session kicked off a period of focused co-writing leading up to a second session in December 2023, where we discussed writing progress and specifically made sense of and related the ethical dilemmas to existing literature and insights.

Especially in this last phase, as we interacted dialogically to analyse and interpret the collection of storied experiences of ethical dilemmas, our thinking about the ethics of collaboration has evolved. It went beyond considering the inadequacy of institutional rules and how we navigated those, towards acknowledging their interplay with individual positionality and a researcher’s situated practice. Closer attention to the contexts within which the ethical dilemmas have arisen has led us to return to our philosophical commitments as transformative researchers and reflect on our assumptions about collaboration and research from a transformative standpoint.

The author team thus comprises a high proportion of those participating in the initial session, as well as others who joined the ensuing collective interpretation and analysis resulting in this paper. An important characteristic of the authors is that we are all affiliated with academic research institutions and that all but one of these institutions are based in high-income countries. It is in this context that we have shared our experiences, which is also limited by it. As such, this paper will mainly speak to other researchers affiliated with academic institutions in comparable settings. Acknowledging these limitations, we are from different (inter)disciplinary backgrounds Footnote 2 , nationalities, and work in different national settings and urban and rural locations. This diversity of contexts impacts the constellation of ethical dilemmas that we were faced with. We thus synthesise lessons from disparate yet still limited contexts, whilst remaining cognisant of the ungeneralisable nature of such a study.

Collaboration in transformative research practice

At the heart of our collaborative autoethnographic experience was the sharing and sensemaking of ethical dilemmas. In this section, we share those dilemmas (see Tables 2 – 4 ) clustered along the three philosophical commitments that served to deepen the analysis and interpretation of our storied experience. We embed our dilemmas with the broader body of knowledge around similar issues to discuss ways forward for practical knowledge around ‘what is good’ TR practice and ‘how to’ navigate ethical dilemmas.

Axiological dimension

Axiology is the study of value, which concerns what is considered ‘good’, what is valued, and most importantly, what ‘ought to be’. The axiological standpoint of TR is to address persistent societal problems and to contribute to transitions towards more just and sustainable societies. The commitment to knowledge development and transformative actions is also shaped by different personal judgements, disciplinary traditions, and institutional contexts. Together, these raise ethical concerns around the shape and form of research collaborations, the research lines being pursued, and where and for whom the benefits of the research accrue. Table 2 provides the details of the ethical dilemmas (described as encounters) that we discuss in the following.

Taking up a transformative stance goes hand in hand with individual researchers holding different roles at the same time (Hoffmann et al., 2022 ; Horlings et al., 2020 ; Jhagroe, 2018 ; Schut et al., 2014 ). Often resulting from this, they also perceive a wide range of responsibilities towards diverse groups (stakeholders, peers, the academic community, etc.). This is why transformative researchers face questions of who is responsible for what and whom in front of whom, and these questions influence and are influenced by what they consider the ‘right’ thing to do in relation to others in a collaborative setting. As a result, their axiological position is constructed intersubjectively in and through interactions unfolding in the communities of important others. It is thus relational and may differ depending on ‘the other’ in the research collaboration (Arrona & Larrea, 2018 ; Bartels and Wittmayer, 2018 ). Encounter 1 illustrates this through a constellation of the research collaboration that holds the potential to become a conflict of interest.

Such conflicts of interest can also occur in the very choice of which ‘community’ is being considered as the main beneficiary of the collaboration. The emphasis on action in TR, especially with regards to the principles of beneficence and justice that we mentioned in “Ethics in transformative research”, can increase this dilemma. Researchers are to continuously evaluate their (perceived) obligations. This includes, for example, obligations towards the scientific community (contributions to the academic discourse via publications) vs. obligations towards stakeholders (being a provider of free practical advice or consultant) vs. scientific requirements (academic rigour and independence) vs. stakeholder requests (answering practical questions). Researchers have to position themselves in this contested field of what ‘good research’ and ‘useful outcomes’ mean and sometimes question or challenge their peers or the academic system at large (see also Kump et al., 2023 ). This is the very question raised by Encounter 2 , where researchers are forced to decide which stakeholders’ values and needs should be prioritised in transforming clinical practice and improving the lives of patients.

Moreover, a similar prioritisation between the interests of different groups needs to be made between whether to create knowledge according to traditional scientific standards of systematicity and rigour or supporting collaborators in developing usable knowledge. This is surely a dilemma that arises from being embedded in an institutional context that judges according to different standards, but it also arises from the double commitment of TR to knowledge development and transformative action (Bartels et al., 2020 ). Huang et al. ( 2024 ) for example show how axiological assumptions serve as the base from which different notions of research excellence (e.g., scientific rigour, ‘impactful’ scholarship) are operationalised and supported institutionally. Encounter 3 reflects a similar dilemma as the lecturer juggles conflicting priorities that are inherent to the axiological concerns of TR. That is, can the goals of knowledge development in the traditional academic sense and transformative action be achieved simultaneously? The answer provided by Encounter 3 seems to suggest a redefinition of what ‘good’ scientific knowledge is, for immediate action to be possible.

Yet, perceived responsibilities—towards human and non-human actors, but also towards the own university, the institutional arrangements in which we partake, and what we understand as ethical behaviours—exist in a close, interdependent relationship with our inner ethical standards. Creed et al. ( 2022 , p. 358) capture this “ collection of sedimented evaluations of experiences, attachments, and commitments ” as an ‘embodied world of concern’. This can illustrate the complexity of how an individual researcher’s values, emotions, or sentiments tend to intertwine, and can sometimes clash, with the concerns of their communities and the social-political situation where they operate. Given that one’s embodied world of concern is not fixed but characterised by emerging pluralism, as Encounter 4 illustrates, the consequence of an ethical decision tends to fall more heavily on those with less axiological privilege, such as early career researchers or those located in regions where the opportunity for scientific publishing is limited (Kruijf et al., 2022 ).

As transformative researchers seek systemic change, their values cannot help but influence their research collaboration, including the choice of whom they work with and which methods to use. However, the intention of strengthening the responsiveness of research to societal and political needs through TR collaborations risks being co-opted by the interests of those funding research activities (Bauwens et al., 2023 ; Strydom et al., 2010 ). As illustrated in Encounter 5 , this might cause dilemmas when being approached by stakeholders (e.g., oil and gas companies) to do research, which may not sit well with the subjective judgements of the researcher or with an overall need for transformative change. Researchers can be caught in an odd position and left to wonder whether a compromise of values is worth the risks and end gain, depending on whether a positive contribution can still be achieved. Negotiating our axiological stances with collaborators thus allows researchers to be seen as social beings embedded in patterns of social interdependence, who are not only “ capable and can flourish ” but also “ vulnerable and susceptible to various kinds of loss or harm [and] can suffer ” (Sayer, 2011 , p. 1).

Ontological dimension

Ontology is the philosophical study of being, which concerns the nature of reality and what really exists. TR can start from diverse ontological stances, including critical realist, pragmatist, or subjectivist perspectives. This includes a strong acknowledgement that “ there are multiple versions of what is believed to be real ” (Mertens, 2017 , p. 21). Yet, such a pluralist stance remains a theoretical exercise up until the point that researchers ought to define what are ‘the things’ that need to be transformed and into what. In this situation, at least two debates arise: Do ‘the things’ exist based on a specific ontological commitment, such as the divide between measurable constructs and socially constructed understandings of risks and inequities. And is the existence of ‘the things’ universal or merely a construct of a specific time, space, or social group? As the researcher illustrated in Encounter 6 (see Table 3 for the detailed encounters), if maths anxiety and eco-anxiety are recognised as ‘real’ because of growing clinical research, why can’t the research team accept the construct of ‘science anxiety’ that their teacher collaborators have perceived in their classrooms? Collaboration thus remains especially challenging when researchers strive for academic rigour from an empiricist standpoint while having to cross paths or work with individuals from different ontological positions (Midgley, 2011 ).

Commitments to working collaboratively with members of ‘marginalised’ and ‘vulnerable’ communities add to this dilemma, as researchers are bound to encounter the ethical dilemmas of whose reality is privileged, whose reality can or should be legitimised and considered ‘true’ in a TR process (Kwan and Walsh, 2018 ). In Encounter 7 , for instance, research participants do not recognise themselves as ‘climate displaced persons’ or ‘climate migrants’ because they have a long history of migration for a plethora of reasons. Now, should researchers continue using this term with a view to gain political attention to the issues of climate change, or should they abstain from doing so? How does this relate to their commitment to transformative action, including shaping political agendas? The intention to target system-level change in TR (Burns, 2014 ; Kemmis, 2008 ) also means that researchers ought to interrogate the mechanisms that inflict certain perceived realities on the powerless in the name of good causes (Edelman, 2018 ; Feltham-King et al., 2018 ), the ways in which these narratives are deployed by powerful stakeholders (Thomas and Warner, 2019 ) and how these are translated into (research) action.

Moreover, research and action on ‘scientific’ problems can deflect attention from other problems that local communities most care about or lead to unexpected, even negative, implications for some stakeholders. With increasing pressure on the societal impact of research and funding tied to certain policy goals, the issues of labelling and appropriation might only perpetuate a deficit perspective on specific groups (Eriksen et al., 2021 ; Escobar, 2011 ; van Steenbergen, 2020 ). Encounter 8 highlights that, without caution, well-intended efforts risk perpetuating harm and injustice —upholding a certain deficit perspective of the community in question. Communities accustomed to ‘helicopter’ research, where academics ‘fly-in, fly-out’ to further their careers at the expense of the communities, may be reluctant to collaborate. This necessitates transparency, active listening, deliberative involvement, and trust building (Adame, 2021 ; Haelewaters et al., 2021 ). It also reminds us of the ‘seagull syndrome’,’ which attests to the frustration felt by community members towards outsider ‘experts’ making generalisations and false diagnoses based on what is usually a superficial or snapshot understanding of local community dynamics (Porter, 2016 ). In some incidents, transformative researchers may need to redesign collaboration processes in TR that centre on the realities of people in the study (Hickey et al., 2018 ).

Epistemological dimension

Epistemology is the philosophical study of knowledge, and its primary concern is the relationship between the knower and what can be known. Transformative researchers usually work at the interface of disciplines, each with their own ideas on what constitutes ‘scientifically sound’ but also ‘socially robust’ or ‘actionable’ knowledge (Mach et al., 2020 ; Nowotny et al., 2003 ). Many thus hold the epistemological assumption that knowledge is created through multiple ways of knowing, and the processes of knowledge generation need to recognise how power inequities may shape the normative definition of legitimate knowledge. This stance raises ethical concerns about whose knowledge systems and ways of knowing are included, privileged, and/or legitimised in TR practice. Moreover, it raises concerns about ways of ensuring a plurality of knowledge spaces (Savransky, 2017 ).

Using an epistemological lens to interrogate collaborative practice in TR can illuminate a wide range of ethical dilemmas associated with longstanding critiques of Western norms and ‘scientific superiority’ (Dotson, 2011 ; Dutta et al., 2022 ; Wijsman and Feagan, 2019 ). It also brings to the fore the power dynamics inherent within collaborative processes of TR for sustainability (de Geus et al., 2023 ; Frantzeskaki and Rok, 2018 ; Kanemasu and Molnar, 2020 ; Kok et al., 2021 ; Strumińska-Kutra and Scholl, 2022 ). A particular ethical challenge is related to the fact that it is typically researchers from the Global North who design and lead research collaborations, even when these take place in the Global South. This immediately creates “ an inequality that is not conducive to effective co-production ” and requires “ dedicated commitment to identify and confront the embodied power relations [and] hegemonic knowledge systems among the participants in the process ” (Vincent, 2022 , p. 890). See Table 4 for details on the ethical dilemmas that we discuss in the following.

Concerns about epistemic justice (Ackerly et al., 2020 ; Harvey et al., 2022 ; Temper and Del Bene, 2016 ) and interpretation of voices (Komulainen, 2007 ) are largely rooted in the deficit narratives about the capacity of certain groups for producing knowledge or for being knowers. Encounter 9 shows how easily certain voices can be muted as not being considered to speak from a position of knowledge. Research processes can usefully be expanded to include disinterested or disengaged citizens (Boyle et al., 2022 ), or those opposing a project or initiative so as to lay bare the associated tensions of knowledge integration and co-production (Cockburn, 2022 ). Encounter 10 illustrates that such silencing also relates to the question of who holds legitimate knowledge. This research has three parties that may hold legitimate knowledge: the researcher, the corporation, and the local community. However, the extent to which the researchers’ knowledge is heard remains unclear since the corporation does not consider it in its actions. It also illustrates common insecurities about what one can attain using certain research methods. The reliance of political institutions and citizens on expert advice, particularly when dealing with acute crises (e.g., Covid-19 pandemic), also tends to exacerbate the depoliticisation of decisions (Rovelli, 2021 ).

Moreover, TR practice nearly inevitably results in privileging certain ways of knowing and knowledges. Researchers make space for shared action or dialogue around a certain issue, inviting certain groups but not others, and choosing certain methods and not others. Encounter 11 illustrates the issue of favouritism in research collaboration. It elaborates on how thoughtful facilitation can intervene to level the playing field and provide a way out of the dilemma going beyond the question of whose benefit it serves. This facilitation enables meaningful collaboration among all parties involved. Particularly in policy sectors dominated by political and economic considerations, which exhibit strong vested interests, there is a need to foster meaningful and safe participation (Nastar et al., 2018 ). Skilled facilitation is crucial for uniting marginalised groups, preparing them to deal with the intricacies of scientific jargon and technological hegemony (Djenontin and Meadow, 2018 ; Reed and Abernethy, 2018 ). The contextual dimensions of collaborators, their associated worldviews, and the social networks in which they are situated are important epistemological foundations. Yet, these are not static and can shift over time throughout collaborative partnerships.

As explicated in “Introducing transformative research”, TR represents an epistemological shift to recognise researchers as sense-makers, agency holders, and change agents. This philosophical commitment can create dilemmas for ‘embedded researchers’ seeking to strengthen the science-policy interface. Encounter 12 illustrates how occupying a dual role — to dive into action and to publish scientifically — can be at odds. This encounter alludes to the fact that transformative researchers often navigate different roles, which come with different, at times conflicting, epistemological priorities and ways of knowing (e.g., roles as a change agent and a reflective scientist, the approach of ‘Two-Eyed Seeing’ by Indigenous scholars) (Bulten et al., 2021 ; Temper et al., 2019 ; Wittmayer and Schäpke, 2014 ). Importantly, such roles change over time in a TR practice and over the course of a researcher’s career (McGowan et al., 2014 ; Pohl et al., 2017 ).

Involving diverse stakeholders in knowledge co-production also inevitably leads to ethical questions concerning how to integrate diverse knowledge systems, especially those using multi-method research designs or models to aid decision-making (Hoffmann et al., 2017 ). Models can be useful in providing scenarios, however, they are constructed by people based on certain assumptions. These assumptions serve as the fundamental lenses through which complex real-world systems are simplified, analysed, and interpreted within the model framework. Despite the well-intention of researchers, the practice of establishing a shared understanding and reaching consensus about key constructs in a model is often unattainable. As Encounter 13 illustrates, participatory model building requires the capacity and willingness of all involved to knit together kindred, or even conflicting, perspectives to complement disciplinary specialism.

We explored the dilemmas of researchers pertaining to knowing ‘how to’ act in a certain situation and considering ‘what is doing good’ in that situation. Transformative researchers (re)build their practical knowledge of what doing research means through cultivating a reflexive practice that puts experiences in context and allows to learn from them. From a meta-perspective, doing TR is a form of experiential learning (Kolb, 1984 ) and doing TR involves traversing an action research cycle: experiencing and observing one’s action research practice, abstracting from it, building knowledge, and experimenting with it again to cultivate what has been referred to as first person inquiry (Reason and Torbert, 2001 ).

Concluding thoughts

In this article, we set out to explore which ethical dilemmas researchers face in TR and how they navigate those in practice. We highlighted that researchers engaging in TR face a context of uncertainty and plurality around what counts as ethically acceptable collaboration. With TR emphasising collaboration, it becomes important to discern the notion of ‘right relations’ with others (Gram-Hanssen et al., 2022 ), to attend to the positionality of the researcher, and to reconfigure power relations. Importantly, with TR emphasising the need for structural and systematic changes, researchers need to be aware of how research itself is characterised by structural injustices.

Using a collaborative autoethnography, we shared ethical dilemmas to uncover the messiness of collaborative TR practice. We established how guidance from institutionalised reference systems (i.e., ethical review boards and procedures) currently falls short in recognising the particularities of TR. We described how the research community generates informal principles, or heuristics to address this gap. However, we also appreciated that in actual collaboration, researchers are often ‘put on the spot’ to react ‘ethically’ in situ, with limited time and space to withdraw and consult guidelines on ‘how to behave’. Such informal heuristics are thus but a start and a helpful direction for developing the practical knowledge of researchers on how to navigate a plural and uncertain context.

This practical knowledge is based on an awareness of the uncertainty around what constitutes morally good behaviour and builds through experience and a critical reflexive practice. Our aim is not to share another set of principles, but rather to highlight the situatedness of TR and the craftsmanship necessary to navigate it and, in doing so, build practical knowledge through experiential learning and insight discovery (Kolb, 1984 ; Pearce et al., 2022 ). Such a bottom-up approach to research ethics builds on the experiences of researchers engaging in TR as a situated practice vis-à-vis their personal motivations and normative ambitions and the institutional contexts they are embedded in. This approach nurtures the critical reflexivity of researchers about how they relate to ethical principles and how they translate this into their normative assumptions, practical hypotheses, and methodological strategy.

Next to continuous learning, this critical reflexivity on TR as craftmanship can enhance practical wisdom not only for the individual but also for the broader community of researchers. We envision such wisdom not as a set of closed-ended guidelines or principles, but rather as a growing collection of ethical questions enabling the TR community to continuously deepen the interrogation of their axiological, ontological, and epistemological commitments (see Table 5 ). Only through this ongoing process of reacting, reflecting, and questioning—or as referred to by Pearce et al. ( 2022 , p. 4) as “an insight discovery process”—can we collectively learn from the past to improve our future actions.

However, such a bottom-up approach to ethics can only form one part of the answer, set in times of an evolving research ethics landscape. Researchers engaging in transformative academic work cannot and should not be left alone. Additionally, researchers’ ethical judgements cannot be left to their goodwill and virtuous values alone. Therefore, another important part of the answer is the carving out of appropriate institutions that can provide external guidance and accountability. This will require nothing less than structural and cultural changes in established universities and research environments. Rather than having researchers decide between doing good and doing ‘good’ research, such environments should help to align those goals.

From this work, questions arise on how institutional environments can be reformed or transformed to be more conducive to the particularities of TR, and to help nurture critical reflexivity. We highlight the critical role that ethic review boards can play in starting to rethink their roles, structures, and underlying values. Practical ideas include employing mentors for transformative research ethics, having ethical review as a process rather than as a one-off at the start of the project, or continuously investing in moral education. Thus, we underscore the importance of individual reflexivity and learning. However, we would like to set this in the broader context of organisational learning, and even unlearning, among academic institutions to overhaul our academic systems in response to the urgent imperative of tackling socio-ecological challenges globally. In this transformative endeavour, careful consideration of how the ethics of research and collaboration shape academics’ socially engaged work is indispensable.

The full set of essentials is the following: (1) Focus on transformations to low-carbon, resilient living; (2) Focus on solution processes; (3) Focus on ‘how to’ practical knowledge; (4) Approach research as occurring from within the system being intervened; (5) Work with normative aspects; (6) Seek to transcend current thinking; (7) Take a multi-faceted approach to understand and shape change; (8) Acknowledge the value of alternative roles of researchers; (9) Encourage second-order experimentation; and (10) Be reflexive. Joint application of the essentials would create highly adaptive, reflexive, collaborative, and impact-oriented research able to enhance capacity to respond to the climate challenge.

Disciplines include amongst others anthropology, business administration, climate change adaptation, cultural economics, economics, economic geography, education, health sciences, human geography, international development studies, philosophy, political science, sociology, urban planning.

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Julia M. Wittmayer and Ying-Syuan Huang drafted the work for important intellectual content, substantially contributed to the concept and design of the work, and contributed to the analysis and interpretation of data for the work. Kristina Bogner, Evan Boyle, Katharina Hölscher, and Timo von Wirth substantially contributed to the concept or design of the work and contributed to the analysis or interpretation of data for the work. Tessa Boumans, Jilde Garst, Yogi Hendlin, Mariangela Lavanga, Derk Loorbach, Neha Mungekar, Mapula Tshangela, Pieter Vandekerckhove, and Ana Vasues contributed to the analysis or interpretation of data for the work.

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Wittmayer, J.M., Huang, YS.(., Bogner, K. et al. Neither right nor wrong? Ethics of collaboration in transformative research for sustainable futures. Humanit Soc Sci Commun 11 , 677 (2024). https://doi.org/10.1057/s41599-024-03178-z

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Diving Deeper into Postsecondary Value, IHEP Research Series Explores Equity and Economic Outcomes

Washington, DC (May 29, 2024) –   Higher education has long been recognized as a key driver of economic opportunity, but new research, spearheaded by the Institute for Higher Education Policy (IHEP), is diving deeper into questions of postsecondary value and equity. The “Elevating Equitable Value: Investigating Economic Outcomes of Postsecondary Education” series, informed by data from the Equitable Value Explorer tool along with state-level data, surveys, and additional sources, is exploring pressing questions about postsecondary value. Research released today by Trellis Strategies, the American Association of State Colleges and Universities (AASCU), Wayne State University, and the Research Institute at Dallas College completes the seven-paper series.  

“Earning a college degree can change the trajectory of students’ lives, their families, and their communities, for generations to come, but the benefits are not evenly distributed,” said IHEP President Mamie Voight . “By unpacking the nuances of value delivery across different contexts, this research strengthens the evidence-base showing that college is worth the investment. It also can inform policymakers and institutions about targeted strategies to improve the returns on postsecondary education for all students.”  

Field-based researchers across the country built on the work of the Postsecondary Value Commission by exploring critical questions on post-college outcomes in their own specific context:  

How Can Policy and Practice Shape Equitable Value?

Several papers in the series explored how institutional policy and practice can ensure all students benefit from a college degree. Research by Trellis Strategies underscores the connection between student financial well-being during college and future economic returns. A ten-percentage-point decrease in the number of students experiencing financial instability during college was correlated with higher economic returns to students, especially at four-year public schools. The authors highlight the importance of programs that expand access to emergency aid, promote financial literacy, and enhance transparency around college costs and financial aid options.  

A study by the American Association of State Colleges and Universities (AASCU) found that all 33 institutions participating in their Student Success Equity Intensive program had earnings that exceeded the Postsecondary Value Commission’s minimum economic return threshold, but equity gaps persist. Institutions serving a larger proportion of Black, Latinx, and Indigenous students or Pell Grant recipients saw a smaller economic return – the amount by which earnings exceed those of a typical high school graduate, plus the cost of their education  – demonstrating the need for continued efforts to promote student success and the financial value of college degrees.  

Another key finding shows a strong link between faculty composition and student outcomes. Research by Frederick Tucker of the City University of New York reveals that institutions with more tenured or tenure-track faculty, alongside a smaller proportion of full-time adjuncts, see stronger economic returns for students. This is particularly true at Minority-Serving Institutions (MSIs) and colleges serving a large share of Pell Grant recipients.  

How Does Postsecondary Value Differ By and Within States?

The research in this series adds to our growing understanding of how certain states are delivering value. For example, a project by Wayne State University found that while postsecondary education generally leads to higher earnings in Michigan, disparities exist. Public, four-year universities in the state provided the highest economic return, exceeding the minimum threshold by over $22,000, while most other types of institutions in the state offered a smaller but still positive return. Michigan ranked high nationally in terms of the amount that students’ earnings typically exceed the minimum economic return threshold,  despite having a moderate ranking in median post-college earnings.  

The Research Institute at Dallas College’s analysis paired data from the Equitable Value Explorer with state longitudinal data to measure the economic return at more than 500 Hispanic-Serving Institutions (HSIs) nationwide, in addition to outcomes for all Hispanic students in Texas. Overall, economic returns were positive for Hispanic students, but disparities persisted even within Hispanic communities, particularly for immigrants, those from low-income backgrounds and women.   

What is the Role of Geography in the Postsecondary Value Equation?

Location plays a significant role in education options and economic returns. Two papers in the series examined the geographic dimension of value. The American Institutes for Research’s study found that community college students generally earn more after college than those with only a high school diploma in their region. However, community colleges serving a higher percentage of underrepresented students tend to deliver a lower economic value, underscoring the need for additional support to ensure these institutions can effectively serve their students.  

Boston College researchers’ analysis of rural-serving institutions (RSIs) found that while median post-college earnings may be slightly lower compared to urban and suburban institutions, RSIs are typically more affordable. The majority of RSIs still provide a positive economic return for students after factoring in these relatively low education costs, highlighting their vital role in expanding access to higher education in underserved areas.  

IHEP is expanding and strengthening the community of researchers, practitioners and advocates exploring postsecondary value through an equity lens, by providing the tools necessary for researchers, associations, and institutions to tackle these critical questions. For more information about the Equitable Value Explorer, please visit: https://equity.postsecondaryvalue.org/     

Supporting the Whole Student Through Holistic Advising: Reflections on ED’s Raise The Bar Summit

Investing in Student Success: IHEP’s Federal Funding Priorities for FY25

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ALEKS Frequently Asked Questions

• Mathematics
• Scholarships
• Placement Assessment
• Information
• Understanding Placement Levels

Below is a quick reference for students who have some of the more common questions surrounding the Math Placement Assessment.

Should I take the Math Placement Assessment?

Incoming new students may be assigned a Math Level or Math Transfer Level (see Understanding Math Levels) and do not, by default, have to take the Assessment; however, if any student is entering the university without a designated math level, they are required to take the Math Placement Assessment before registering for a math course at SOU.

What placement can I get from SAT, ACT, or SBAC scores?

Please see the Understanding Math Placement Levels page.

I have Advanced Placement, International Baccalaureate, or CLEP Credit... How will that transfer?

For information regarding Credit-by-Examination courses, please review the following page: sou.edu/admissions/apply/equivalencies

Can I take the Assessment, even if I have a Math Level?

Students are encouraged to take the Assessment, especially if they feel their Math Level is incorrect. This is a great way to assure you are going to start in an appropriate math course. Students who elect to take the Assessment should be aware that if they are placed into a lower math level than what they were initially assigned, they will be designated the new math level. For example, if a student has a Math Level of 111 and they use the placement Assessment and are assigned a Math Level of 100, they will have to re-test to place back into Math Level 111.

Can I test out of a course?

Students who are designated a Math Level, say from an SAT or ACT test, which can only go to MTH 111 (College Algebra) but they feel they have skills beyond this level, they may take the Math Placement Assessment in order to place into courses all the way to MTH 251 (Calculus I). This is a great option for students who scored well on the ACT or SAT test, or have taken advanced coursework in high school, but didn’t receive college credit.

Please know, students who place into a higher level of math will not receive credit for any class which come before it, but simply be allowed to start at a higher level.

If I don’t need to take it, should I consider taking it?

We do not want you to waste your time or money taking a class for which you are under or over qualified. Placement scores are used to determine the most appropriate math courses for you as you move forward with your college coursework. After taking a Placement Assessment, you should meet with their advisor who will advise you on the best math class according to your placement score and your major.

This is a “Placement Assessment,” not a test. The difference is that a Placement Assessment is designed to determine what you know and what you need to work on. At the end of the ALEKS PPL Assessment, you will have a much better sense of your strengths and weaknesses in math. You will then have a chance to brush on topics that you may have forgotten.

Be honest. It is important that you take the Placement Assessment seriously and give it your honest effort so your score truly reflects your current level of knowledge and math preparedness. There is no benefit to cheating on the Placement Assessment – the only result will be that you will enroll in a class that is too difficult, or not challenging enough, potentially costing extra time and money. While taking the Placement Assessment, do not consult any outside sources for help (friends/family, internet searches, textbooks, notes etc…). We want an accurate measure of your current mathematical knowledge state.

Please note: Once you activate your ALEKS account, your access to the system will expire after one year, and you will be required to purchase an additional set of ALEKS tests.

Does the Math Placement Test cost?

Students who are taking the assessment for the first time will not have to pay for it, as the cost is included in the matriculation fee to register for courses. Students who are taking the assessment more than 5 times in their first year will incur a small $15 fee for an additional 5 assessments. Currently a student can take the assessment up to 5 times.

Can I take the Math Placement Test multiple times?

Yes! Students can take the test up to 5 times, without having to pay any extra fees.

You can retake the placement assessment to improve your score, however, please keep in mind that the most recent score will be used for math placement.

Students must wait 72 hours between Placement Assessments and are required to work in the Prep and Learning Modules for a specific amount of time prior to each Placement Assessment to increase success. There is generally no benefit to re-taking the Placement Assessment immediately after completing a prior attempt. You cannot improve your results by simply re-taking the Placement Assessment without spending time in the Prep and Learning Module to refresh material that you may have forgotten.

• Second attempt—required to complete 3 hours of work in the Prep and Learning Modules before attempting assessment.
• Third attempt—required to complete 5 additional hours of work in the Prep and Learning Modules before attempting assessment.
• Fourth attempt—required to complete 8 additional hours of work in the Prep and Learning Modules before attempting assessment.
• Fifth attempt—required to complete 10 additional hours of work in the Prep and Learning Modules before attempting assessment.

What should I bring with me?

Testing Format ALEKS will begin with a brief tutorial to make sure you are comfortable with the math palette tools before your Placement Assessment begins. The tutorial shows you how to enter different types of answers, how to use the ALEKS calculator, and how to graph. If you aren’t sure how to input an answer, or need help while you are taking the ALEKS Placement Assessment, select the Help button below the answer pallet tools. Going to the tutorial during your Placement Assessment will NOT impact your Placement Assessment results.

ALEKS is not a multiple-choice Placement Assessment. It is open-response and requires you to work out solutions with a paper and pencil, then enter them into ALEKS. Though the test has a time limit of 2.5 hours, many students finish within less time.

It is likely that you will be asked questions on material you have not yet learned. On such questions it is appropriate to answer, “I don’t know”. On any question that you have familiarity with, however, it is important to do your best. “I don’t know” is interpreted by ALEKS to mean that you do not know how to solve the topic, and this will be reflected in the Placement Assessment results. There is no penalty for incorrectly answering a question on the Placement Assessment, it only helps ALEKS understand what you know and don’t know.

What is on the Math Placement Assessment?

ALEKS PPL is an online, adaptive system that covers a broad spectrum of mathematics topics. The length of the Placement Assessment will vary, but can be up to thirty questions. You will see some, but not all, of the math you have learned in high school. It is a Placement Assessment, not a preview of math courses at SOU. It is designed to identify if you are prepared for a particular course. After you take your first Placement Assessment, you will have the opportunity to review and master additional topics to reassess and improve your placement.

Topics covered:

• Real numbers (including fractions, integers, and percentages)
• Equations and inequalities (including linear equations, linear inequalities, systems of linear equations, and quadratic equations)
• Linear and quadratic functions (including graphs and functions, linear functions, and parabolas), exponents and polynomials (including integer exponents, polynomial arithmetic, factoring, and polynomial equations), rational expressions (including rational equations and rational functions
• Radical expressions (including higher roots and rational exponents)
• Exponentials and logarithms (including function compositions and inverse functions, properties of logarithms, and logarithmic equations)
• Geometry and trigonometry (including perimeter, area, and volume, coordinate geometry, trigonometric functions, and identities and equations)

How can I prepare?

Students should take the time to review material for the assessment, but only the material which they have had a previous exposure to. For example, students who have never taken a course on Precalculus should not try to test at a level above the course. There are many different resources to use to review for the assessment, depending on the student’s academic goal.

Are you interested in getting tutored? SOU students have access to the excellent peer tutors in our Math Tutoring Center located in the SOU Library. Are you away from campus? That’s okay! Our Math Tutoring Center is proud to offer distance-based tutoring through Zoom calls.

If students are aiming to place into introductory collegiate math courses, and are looking to study independently, they should study some of the following resources:

• SAT Math Review
• ACT Math Review

For students who have a desire to place into more advanced courses, they can consider reviewing the following material:

• College Algebra and Precalculus Review – Written Examples and Notes
• Khan Academy: Algebra , Trigonometry and Precalculus

How do I interpret my results?

Your placement result (overall score) is a number between 0 and 100. It represents the percentage of topics ALEKS has identified you have mastered.

Course placement is determined as follows:

Where can I see my score?

• You will receive your score immediately upon completion of your Placement Assessment.
• Your ALEKS score can be viewed by re-entering ALEKS using the same access link for which you took the Placement Assessment.
• Your scores are automatically added to your student profile, which is accessible by your advisor.

Need more information on understanding your score-tier? Check our Understanding Math Placement page.

How long is the placement result valid?

Your placement result is valid for two years. However, we strongly encourage students to begin on their math requirements during their first year.

What if I didn’t get the placement I wanted?

This is a perfect opportunity to take advantage of the Prep and Learning Modules offered within ALEKS PPL. An individualized study plan will be created based on your performance on the Initial Placement Assessment. ALEKS will identify what you know and what you are ready to learn next so you can brush up on lost knowledge.

Which math course is required for my Major?

Students should always contact their advisor(s) for the most accurate information regarding math requirements based on professional or academic interests. As a quick reference, you may check Math requirements for your major here .

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This paper is in the following e-collection/theme issue:

Published on 30.5.2024 in Vol 26 (2024)

An Infrastructure Framework for Remote Patient Monitoring Interventions and Research

Authors of this article:

• Jennifer Claggett 1, 2 , PhD   ; 
• Stacie Petter 1 , PhD   ; 
• Amol Joshi 1, 2 , PhD   ; 
• Todd Ponzio 3 , PhD   ; 
• Eric Kirkendall 2 , MD  

1 School of Business, Wake Forest University, Winston-Salem, NC, United States

2 Center for Healthcare Innovation, School of Medicine, Wake Forest University, Winston-Salem, NC, United States

3 Health Science Center, University of Tennessee, Memphis, TN, United States

Corresponding Author:

Jennifer Claggett, PhD

School of Business

Wake Forest University

1834 Wake Forest Rd

Winston-Salem, NC, 27109-6000

United States

Phone: 1 3363027991

Email: [email protected]

Remote patient monitoring (RPM) enables clinicians to maintain and adjust their patients’ plan of care by using remotely gathered data, such as vital signs, to proactively make medical decisions about a patient’s care. RPM interventions have been touted as a means to improve patient care and well-being while reducing costs and resource needs within the health care ecosystem. However, multiple interworking components must be successfully implemented for an RPM intervention to yield the desired outcomes, and the design and key driver of each component can vary depending on the medical context. This viewpoint and perspective paper presents a 4-component RPM infrastructure framework based on a synthesis of existing literature and practice related to RPM. Specifically, these components are identified and considered: (1) data collection, (2) data transmission and storage, (3) data analysis, and (4) information presentation. Interaction points to consider between components include transmission, interoperability, accessibility, workflow integration, and transparency. Within each of the 4 components, questions affecting research and practice emerge that can affect the outcomes of RPM interventions. This framework provides a holistic perspective of the technologies involved in RPM interventions and how these core elements interact to provide an appropriate infrastructure for deploying RPM in health systems. Further, it provides a common vocabulary to compare and contrast RPM solutions across health contexts and may stimulate new research and intervention opportunities.

Introduction

Remote patient monitoring (RPM; sometimes referred to as eHealth, telehealth, telemonitoring, or telemedicine) involves the capture of patient data through sensors or devices outside of a clinical setting, such as at the patient’s home or work while the patient is engaging in everyday activities. Ideally, the data captured through RPM devices are analyzed and used to inform clinicians’ decisions on patient care. For example, typical decisions include adjusting the recommended dosage or timing of a patient’s medication based on observed changes in the patient’s vital signs or patterns of activity.

RPM interventions have increased exponentially in the United States of America since 2020 [ 1 ]. The COVID-19 pandemic exacerbated the need for remote patient care solutions when there were severe resource shortages of clinicians, equipment, and capacity within health care systems [ 2 - 4 ] and patients were required to socially distance themselves to mitigate the spread of COVID-19. As the United States of America eased regulations and made changes to encourage reimbursements for RPM interventions, health care providers sought to reap RPM’s potential benefits along three main dimensions: (1) enhancing quality by offering more personalized care; (2) achieving scale by growing their customer (patient) base; and (3) securing new reimbursement opportunities by evolving in response to shifts in payment policies [ 1 , 3 ].

The excitement and promise of the benefits of RPM to improve patient care while also expanding a health system’s market are well-documented in meta-analyses that find evidence of RPM reducing hospital admissions and length of stay for certain conditions, such as cardiovascular disease or chronic obstructive pulmonary disease [ 5 , 6 ]. Decreased travel time, cost savings, and increased access to services are commonly ascribed as benefits for patients, and most eHealth interventions are described as successes [ 7 ]. However, other scholars counter that RPM interventions may not live up to the hype. One study finds that RPM interventions do not impact patient health factors, such as weight, body fat percentage, and blood pressure [ 8 ], and other related studies raise concerns about the limited evidence that RPM interventions can indeed adequately scale to meaningfully improve patient outcomes and demonstrably reduce health care costs [ 3 ].

These mixed results regarding the impact of RPM interventions showcase the current challenge of understanding how to design and effectively implement the infrastructure to support successful RPM programs. Successful RPM programs should meet at least one, but ideally both, of the following standards: (1) improved management of symptoms (evaluated using population-normalized values or patient feedback) and (2) reduced financial costs (evaluated in terms of the health system, payers, and patient out-of-pocket expenses). Previous work reporting on RPM interventions tends to report details on isolated projects and is focused, understandably, on a specific medical condition without offering generalizable advice to a broader audience or a catalog of best practices. Although RPM has been implemented in many different types of contexts, we contend that the key infrastructure points are consistent across interventions. Therefore, we present a framework consisting of 4 core infrastructure components necessary for any RPM intervention and identify common questions across contexts that should influence the RPM intervention design and results. This RPM infrastructure framework is useful to scholars and clinicians implementing RPM projects in that it (1) presents a shared vocabulary and reference point, (2) serves as a resource to guide some of the major decisions associated with an RPM implementation, and (3) provides a logical scaffolding to categorize and disseminate lessons learned within RPM projects to leverage them in other contexts. While the set of considerations nested within the four infrastructure components is not exhaustive, these considerations serve as a useful starting point as RPM research and interventions are planned and developed in the future.

RPM Infrastructure Framework

As an information technology (IT), RPM relies on a combined and layered infrastructure of hardware, software, and networks to support the collection, storage, processing, and management of data. By considering emergent patterns and themes from the literature, cases, and reports, discussing this topic in various panels and workshops, and reflecting on our experiences designing and assessing RPM projects, we propose a four-component infrastructure framework that is necessary in any RPM infrastructure project: (1) data collection, (2) data transmission and storage, (3) algorithmic data analysis, and (4) information presentation. The first RPM infrastructure component, data collection, collects a patient’s vital signs and other biometric data remotely through a measurement device such as a wearable sensor. Data transmission and storage, the second infrastructure component, leverages software interface services, networking, and hardware to transfer the data from the patient’s device to a centralized data architecture [ 9 , 10 ]. Third, software-based algorithms analyze the stored remote patient data to identify patterns and outliers for a single patient or for a patient population. The final RPM infrastructure component is to present information obtained from the analysis to support clinicians’ decision-making processes [ 11 , 12 ]. Figure 1 depicts the RPM infrastructure framework, and each of the following sections describes key considerations for each component.

Component 1: Remote Patient Data Collection

Patients interact with an RPM device to enable the collection of data outside of clinical settings. Some devices are worn continuously throughout a person’s day, while other devices are used at specific times to capture health indicators periodically based on the patient’s medical condition and the provider’s care protocol. Patients may use a specialized RPM device that registers a single form of biometric data (eg, a continuous glucose monitor capturing blood glucose levels) or a device that captures multiple data types (eg, a blood pressure cuff that measures blood pressure, pulse rate, and oxygen saturation). Given the growing number of technologies capable of collecting patient health data along with the need for patients to interact with a device for data collection, several questions must be carefully addressed when considering how to best collect data for an RPM intervention.

How Should Patients be Selected for RPM?

While RPM has the potential to improve patients’ quality of care and reduce clinic costs, successful implementation relies on the effective use of the device and the fidelity of the collected data. The existing literature highlights several key considerations and components for identifying patients who are a good match for remote monitoring. Of paramount importance is suitability—is the patient’s medical condition one that is likely to actually benefit from the collection and analysis of more data? Patients with chronic diseases such as diabetes, heart failure, hypertension, or chronic obstructive pulmonary disease are often more likely to benefit from RPM, as it can help them better manage their health status and condition over the long-term [ 13 ]. Comorbidities also play a significant role in patient selection, as those with multiple chronic conditions or complex health situations might require more comprehensive monitoring [ 14 ]. RPM can provide a more holistic view of their health, making it a potentially valuable tool for these patients; however, the complexity of their medical conditions may limit their ability to adhere to the monitoring program and necessitate more immediate and direct medical interventions.

Patients who are noncompliant or have a history of difficulty adhering to their treatment plans might benefit from RPM, as it can help improve compliance and provide additional support [ 15 ]. RPM solutions may make a patient feel more engaged, empowered, and informed through messaging systems that interact with patients on a routinely structured basis [ 16 , 17 ]. Patient motivation and engagement are key factors, as patients who are motivated and engaged in managing their health are more likely to actively participate in and adhere to the RPM program [ 18 ].

Other patient-specific factors—commonly referred to as the social determinants of health—such as socioeconomic status, age, and social support should be considered when designing RPM interventions [ 19 ]. For instance, patients with lower socioeconomic status might benefit from RPM the most, as it can help reduce health care disparities and provide better access to care [ 20 - 22 ]. A disproportionally large number of people affected by chronic conditions are from socioeconomically disadvantaged groups [ 23 ]. Communities of color, immigrants, and women are particularly likely to be in distress from undiagnosed chronic diseases, and even when diagnosed, these populations are more likely than their counterparts to face structural and logistical obstacles to obtaining the appropriate level of intermittent care. So long as they have reliable connectivity to the internet, patients who live in remote or rural areas or have limited access to transportation might benefit from RPM, as it can help overcome geographical barriers to care [ 18 , 24 ]. Age can also play a role in identifying suitable patients for RPM, in that elderly patients or those with age-related conditions may benefit from RPM. The patient’s living situation is another important factor. A strong support system, such as family or caregivers, can facilitate device use, data collection, and overall engagement, making these patients more suitable for RPM [ 25 ].

Finally, technological competence plays a crucial role in a patient’s ability to engage with an RPM device. Patients with some level of technology literacy (eg, “digital natives”) are more likely to engage with and effectively use RPM devices and systems [ 26 ]. However, patients with lower socioeconomic status or those who are elderly may have lower levels of technological competence or may have other barriers that could limit the effectiveness of an RPM program [ 27 - 29 ]. There is a natural continuum of sophistication and familiarity with devices and the inevitable troubleshooting they often require, and a more “set and forget” approach may be advisable for certain populations.

Which Device and Which Types of Data?

A fundamental characteristic of RPM is the acquisition of data outside of conventional clinical environments. Consequently, patient data must be collected remotely using sensors and equipment such as wearable devices, mobile phones, or portable devices installed at a patient’s residence or other environments [ 30 ]. One strategy involves using data from off-the-shelf, general-purpose smart health consumer electronics purchased by the patient, while another option is to rely on data from specialized devices or software prescribed or supplied by the health care provider. Technological advancements enable the collection, through devices within the RPM infrastructure, of various types of data, such as electrocardiograms, electroencephalograms, heartbeats and respiration rates, oxygen saturation in the blood or pulse oximetry, nervous system signals, blood pressure, body or skin temperature, blood glucose levels, patient weight, and sleep patterns, among others [ 31 ].

A crucial consideration is the optimal combination of metrics to be collected for a specific patient. The US Centers for Disease Control report that 51.8% of US adults have at least one chronic condition, and 27.2% have multiple chronic conditions such as obesity, diabetes, and cardiovascular disease [ 32 ]. Emerging evidence indicates that RPM initiatives are more likely to succeed when multiple metrics are evaluated concurrently [ 33 ]. For instance, compiling data from various physiological sensors measuring heart rate, blood oxygen saturation, and blood glucose levels simultaneously can offer a more comprehensive overview of a patient’s health, which is particularly significant for patients with comorbidities and additional complications. This suggests that the diagnostic value of data can be enhanced by carefully considering what health indicators are needed to manage a patient’s care.

How Frequently are Data Collected?

Determining the optimal frequency of data collection in RPM scenarios is a critical consideration, as it can significantly impact the effectiveness of patient care and the efficient use of health care resources. The appropriate frequency for data collection depends on various factors, including the severity and type of the patient’s condition, the objectives of monitoring, and the required patient involvement in data collection [ 34 ]. For instance, some conditions may necessitate multiple data readings per day, while others may only require weekly monitoring [ 35 ]. Passive data collection methods, such as continuous monitoring of vital signs using wearable sensors, can be advantageous for patients requiring frequent monitoring, whereas active data collection methods, which involve patient involvement and interaction, may be suitable for other conditions [ 16 , 36 ]. Passive methods are usually less likely to cause patient burnout and abandonment [ 37 ].

Health care providers should consider adopting several best practices to ensure that patients remain engaged and compliant with RPM protocols. These typically include providing personalized and clear instructions, offering training and support to ensure device functionality, improving patients’ understanding and comfort with the technology, and fostering regular remote communication between patients and health care providers [ 38 ]. Furthermore, involving patients in the decision-making process regarding their monitoring plans and adjusting the frequency and type of data collection based on their individual needs and preferences can lead to increased patient engagement and satisfaction [ 13 , 39 ].

Component 2: Remote Patient Data Transmission and Storage

Once remote patient data are collected by one or more devices, the data must be transmitted and shared with clinicians, and stored in a data architecture. The manner in which the data are transmitted from an RPM device is dependent on the device and the network access of the patient. RPM data transmission may occur through a network using a wired link, or high-speed wireless link with or without human intervention. In some cases, patients or caregivers may be asked to record readings or values from devices into an app available on their smartphone or computer that will transmit data to the medical provider. Another option could be that a patient must physically visit a clinician’s office with the device to upload the data to the patient’s electronic medical record. The storage of remote patient data may be in a system that is managed by the device manufacturer and accessed through a web portal, and the data may or may not be integrated within the patient’s electronic medical record.

Is There Sufficient Connectivity?

Connectivity plays a vital role in the successful implementation of RPM, as it enables the transmission of patient data from monitoring devices to health care providers and fosters timely interventions and informed decision-making. Addressing the digital divide is crucial to ensuring equitable access to RPM services, as patients with limited internet access or low digital literacy may face barriers to fully benefiting from RPM [ 40 ]. This disparity is particularly concerning for patients from socioeconomically disadvantaged backgrounds, who may experience greater difficulties in accessing health care services and could benefit the most from RPM [ 40 , 41 ]. Some patients may have access to home internet solutions through local internet service providers that include Wi-Fi networks at home, while others may be limited to cellular network access through mobile devices. Often, the latter is subject to slower connections and data caps that place constraints on the patient’s connectivity.

Strategies for addressing connectivity for RPM interventions should consider alternatives, such as the constant connectivity approach or using batch or episodic data uploads when data connections are available [ 42 ]. Constant connectivity can facilitate real-time monitoring and immediate interventions, which may be especially beneficial for patients with critical or rapidly changing health conditions [ 43 ]. However, this approach may not be feasible for patients living in areas with limited or unreliable internet access or for those who cannot afford consistent connectivity. In these cases, episodic data uploads when a connection is possible may provide a more accessible and cost-effective solution, allowing health care providers to track patient progress and identify potential issues while accommodating the patients’ connectivity limitations [ 44 ]. Additionally, some RPM hardware solutions may include direct cellular network connectivity, where the device sends the data through a connection provided by the wearable device to the provider, bypassing the need for a patient home network. These solutions will incur additional costs related to data transmission and may not naturally provide a patient dashboard or a way for patients to easily view data that may traditionally be housed in a patient application.

Is the Transmission Secure?

The sensitive nature of medical data necessitates robust protection measures to maintain patient privacy and prevent unauthorized access. Data breaches and cyberattacks can have severe consequences for patients and their health care providers, including identity theft, financial loss, and reputational damage [ 45 ]. The increasing connectivity of medical devices and the use of cloud-based data storage have created new opportunities for cybercriminals, leading to the emergence of threats such as medjacking [ 46 ]. Medjacking, a term coined from “medical device hijacking,” refers to the unauthorized access and manipulation of medical devices, such as pacemakers or insulin pumps, to cause harm to patients or extract sensitive data [ 47 ]. As RPM technologies rely on a variety of connected devices for data collection across multiple networks, they can be vulnerable to medjacking and other cybersecurity risks. Furthermore, the rapid expansion of the internet of things in health care has amplified these risks, as a larger number of interconnected devices create more potential entry points for attackers [ 48 , 49 ].

Health care providers and technology developers should prioritize the implementation of robust security measures to mitigate the risks associated with medjacking and other security threats in RPM. These may include strong encryption protocols for data transmission (“in flight”) and storage (“at rest”), regular security updates, and the development of secure communication channels between devices and health care providers [ 45 , 48 ]. Additionally, health care organizations should adopt a proactive approach to security by conducting regular risk assessments, promoting cybersecurity awareness and training among staff, and fostering a culture of security-mindedness [ 50 ].

Can Data Move Across Health Systems Software?

Interoperability is a crucial aspect of RPM projects, as it enables seamless communication and data sharing among different health information systems, devices, and providers. This encompasses not only the technical aspects of data exchange but also the semantic understanding and interpretation of shared data, ensuring that the information can be effectively used by health care providers, patients, and other stakeholders. Effective interoperability contributes to improved patient care by ensuring that clinicians have access to comprehensive and up-to-date medical information, allowing for better decision-making and coordination of care [ 51 ]. However, achieving interoperability in RPM poses several challenges, including the need to balance data accessibility with patient privacy and maintain control over personal health information.

One of the primary challenges in achieving interoperability in RPM is the heterogeneity of health information systems and devices used by health care providers. These systems often rely on different (often proprietary) data formats, communication protocols, and standards, which can create barriers to effective information exchange. To address this issue, several major standards have been developed to facilitate interoperability in health IT (eg, [ 52 , 53 ]). For example, the US Department of Health and Human Services Office of the National Coordinator for Health IT released the third version (V3) of the US Core Data for Interoperability in 2022 [ 54 ].

Another challenge in achieving interoperability is protecting patient privacy while sharing data freely among authorized health care providers [ 44 ]. Using privacy-preserving techniques, such as pseudonymization, which replaces personally identifiable information with unique identifiers to maintain patient anonymity, may reinforce privacy during the transmission of data between systems. However, these approaches must be rigorously tested to systematically mitigate privacy risks [ 55 ]. One-way hashing of sensitive identifiers is another technique that can reduce the risk of leakage of personal health identifiers. Additionally, the implementation of access control mechanisms can help ensure that only authorized users can access and share patient data, further safeguarding privacy [ 56 ].

A related issue to moving data across health systems is determining the appropriate granularity to share between stakeholders and systems. For example, in a remote blood pressure monitoring project, should each reading be recorded, transmitted, and made available, including any relevant metadata about time, place, and cuff placement, or should only summary data about daily or weekly averages be shared between systems? Like any sensor-based technology, the amount of raw data generated by RPM initiatives may be overwhelming [ 57 ]; however, providing only summarized data limits the transparency and future uses of the data.

Component 3: Algorithmic Analysis of Remote Patient Data

Remote patient data that are stored within an information system and are not analyzed provide no value to the patient or the clinician. After transmitting and storing RPM data, they should be processed and analyzed to identify and summarize patterns and trends in individual patients and patient populations [ 58 ]. The process of analyzing raw data to deliver actionable insights could also form the basis for financial reimbursement, which is fundamental to any sustainable RPM program.

What Analysis Techniques are Appropriate?

Data analysis involves the use of algorithms, or a series of steps, to process the data in a meaningful way. Algorithms may use static rule logic, which can be used to draw attention to results over a certain threshold, or they may leverage machine learning techniques to dynamically adapt and learn from large sets of patient data, such as adjusting the threshold based on similar patients with similar conditions recorded in the data [ 59 ]. The distinction between static and dynamic rules has implications that need to be explored.

Static rules can be based on established medical guidelines, such as thresholds for vital signs or other clinical parameters, which can help health care providers identify potential health issues and take appropriate actions [ 60 ]. While this method can be effective in some cases, it may not account for the unique characteristics and complexities of individual patients, which may limit its ability to provide personalized care [ 61 ].

Alternatively, machine learning techniques offer more advanced and adaptable solutions for analyzing RPM data [ 62 ]. These techniques use algorithms that can learn from data patterns and make predictions or decisions without being explicitly programmed [ 63 , 64 ]. Machine learning can be used to identify trends, anomalies, and correlations in patient data, enabling health care providers to make more informed decisions and deliver personalized care [ 65 , 66 ]. Adaptive interpretation techniques take RPM data analysis a step further by dynamically adjusting their approach based on real-time patient data. These methods, which often rely on artificial intelligence and machine learning algorithms, can continuously refine their analysis and predictions to better understand the evolving health status of individual patients [ 63 ]. This adaptive approach can help health care providers identify subtle changes in patients’ conditions that may not be evident through traditional analysis techniques, leading to more proactive and personalized care [ 67 ].

Which Comorbidities Should be Included in the Analysis?

This question centers around the appropriate complexity level of analyses of RPM solutions. Incorporating comorbidities into the analysis of RPM data can help health care providers better understand the complex interactions between various conditions and their impact on patients’ health. This, in turn, can lead to more accurate and personalized treatment recommendations. Static rules that solely focus on a single condition, such as high blood pressure, may not adequately account for the impact of comorbidities on patients’ overall health status. For instance, a patient with both diabetes and hypertension may require a different treatment approach than a patient with hypertension alone, which is why any given individual should be managed holistically with a consolidated approach, rather than divided by symptoms and specialty [ 68 ].

This comprehensive monitoring can provide a more accurate representation of the patient’s health status, allowing health care providers to make more informed decisions regarding treatment and care management [ 69 , 70 ]. However, these solutions may be so patient-specific that cognitive efficiencies and the ability to scale the solution are compromised in the absence of built-in coordination systems with well-defined decision-making heuristics and robust care protocols.

What Biases Exist Within the Analysis and How Should They be Mitigated?

Biases in the analysis of remote patient data can have a significant impact on the accuracy and effectiveness of health care services. Particularly in machine learning-based analysis techniques, biases can arise from various sources, such as data sampling, measurement errors, or algorithmic design, leading to potentially biased predictions or recommendations [ 71 , 72 ]. It is essential to detect and account for biases to ensure that the solutions provided are equitable and reliable for all patients.

One primary source of bias in data analysis is the data itself. If the training data used to develop machine learning models do not accurately represent the diverse patient population, the resulting models may be skewed toward specific subgroups, leading to suboptimal or even harmful recommendations for other groups [ 73 , 74 ]. For instance, if a model is trained predominantly on data from patients of a particular age, gender, or ethnicity, it may not perform well on patients from other demographics. To mitigate such biases, it is crucial to ensure that the training data are representative of the target patient population, considering factors such as age, gender, ethnicity, and socioeconomic status [ 75 ].

Another source of bias can arise from the choice of features or variables used in the analysis. If certain relevant variables are not included, or if irrelevant variables are considered, the resulting predictions or recommendations may be biased or even spurious [ 76 ]. Careful feature selection, based on domain knowledge and a thorough understanding of the underlying data, can help address this issue.

Algorithmic biases can also emerge from the choice of machine learning methods or algorithms, as well as their specific implementations. To address this, it is essential to evaluate and compare multiple algorithms and implementations to identify potential biases and select the most appropriate method for the specific application [ 77 ]. Patients themselves can serve as their own baselines too, particularly for measurements that do not lend themselves as easily to a population approach (eg, mood and gastric motility).

Lastly, ongoing monitoring and evaluation of the performance of data analysis solutions, including machine learning models, is critical to detecting and addressing biases. Regular assessments of model performance, particularly with respect to various subgroups within the patient population, can help identify potential biases and ensure that the solutions remain equitable and effective for all patients [ 78 ].

Component 4: Presentation of RPM Data to a Clinician

Once the data have been analyzed, the results need to be presented as information to support clinicians’ decision-making. Unless the RPM data are used to inform patient care, the RPM intervention will not yield the intended results. Therefore, it is critical that the information is presented in a manner that is likely to inform clinicians as they make decisions that affect specific patients and patient populations.

Is RPM Information Accessible in the Right Electronic Health Record Software?

Physicians and other clinical decision makers often face significant time constraints and high cognitive workloads in their daily practice, making it challenging for them to manage and monitor patient data effectively. A study by Sinsky et al [ 79 ] found that primary care physicians spent nearly half of their workday interacting with EHR systems, leaving them with limited time for direct patient care. The high volume of clinical tasks and responsibilities can lead to cognitive overload, increasing the risk of burnout and negatively impacting the quality of care provided [ 80 ]. Given these constraints, it is critical to ensure that RPM data are easily accessible within the existing EHR systems without requiring clinicians to log into additional platforms or apps. Integrating RPM data into EHRs can help streamline clinical workflows and reduce the cognitive burden on health care providers, enabling them to focus on essential tasks such as patient evaluation, diagnosis, and treatment planning [ 81 ]. This underscores the importance of seamless integration and interoperability between RPM solutions and EHR systems, ultimately supporting more efficient and effective patient care by easing the pathway of the information being used in decision-making.

One of the key benefits of integrating RPM data into EHR systems is the ability to provide a comprehensive and up-to-date view of a patient’s health status. By combining RPM data with other health information such as medical history, laboratory results, and imaging studies, clinicians can gain a more holistic understanding of a patient’s condition, enabling them to make more informed decisions about treatment plans and care management strategies [ 82 ].

Integration of RPM data into EHR systems can also support the development and implementation of clinical decision support (CDS) tools, which can help health care providers make more informed, evidence-based decisions about patient care [ 83 ]. By leveraging RPM data, CDS tools can provide real-time alerts or recommendations to clinicians, assisting them in diagnosing, treating, or managing a patient’s condition more effectively.

How Should the Decision Maker Receive Information?

In the context of RPM solutions, there is a delicate balance between providing exception reporting and summary data reporting. Exception reporting involves the generation of alerts or notifications only when specific events or abnormal values are detected, which require immediate attention from health care providers. This yields the advantage of focusing health care providers’ attention on situations that need prompt intervention, potentially improving the efficiency and timeliness of care and reducing the number of alerts [ 84 ]. However, exception reporting may not always provide sufficient context or information about a patient’s overall health status, making it difficult for clinicians to assess the impact of treatment strategies or identify more subtle changes in condition over time. On the other hand, summary data reporting provides a broader overview of a patient’s progress over time, allowing clinicians to evaluate trends and assess the overall effectiveness of treatment plans. Both approaches have their merits and challenges, making the choice between them a critical consideration in RPM projects.

Alert fatigue is a critical concern in the context of RPM solutions, as it can have significant implications for the effectiveness of the system and the quality of patient care. Alert fatigue occurs when health care providers are exposed to a high volume of alerts, leading to desensitization and potentially reduced responsiveness to these notifications [ 85 - 87 ]. This phenomenon has been observed in various clinical settings, including electronic health record systems and CDS tools, where excessive alerts can contribute to cognitive overload, increased stress, and the risk of overlooking critical information [ 88 ].

In RPM systems, balancing the type and frequency of messaging is essential to minimize alert fatigue. The choice between push and pull messaging strategies can play a significant role in this regard. Push messaging involves automatically sending alerts or notifications to health care providers, whereas pull messaging requires providers to actively request or retrieve the information. Although push messaging can ensure timely delivery of critical information, it may also contribute to alert fatigue if used indiscriminately or too frequently. Solutions to alleviate this tension may involve tailoring alert thresholds based on individual patient needs, incorporating CDS algorithms to filter and prioritize alerts, and using a combination of push and pull messaging to strike the right balance between proactively notifying providers and allowing them to access information on demand.

What is the Right Amount of Information to Provide to Decision Makers?

Balancing transparency and detail in the presentation of RPM data with cognitive ease is crucial for ensuring that health care providers effectively use the information in their decision-making processes. While transparency is essential for building trust and understanding of the underlying data analysis, providing excessive detail can overwhelm clinicians and hinder their ability to quickly assimilate the information [ 89 ]. Consequently, it is vital to strike an optimal balance between presenting comprehensive information and ensuring cognitive ease for end users.

One approach to achieving this balance is to use a tiered or “drill-down” presentation of data, which allows health care providers to access additional layers of detail only if they require it [ 90 ]. This design can present a high-level summary of the patient’s condition and only flag critical alerts, while enabling providers to delve deeper into the data if they desire further context or clarification. This, in turn, helps mitigate information overload and supports more efficient decision-making by prioritizing the most relevant and actionable insights [ 91 ]. Moreover, incorporating the principles of cognitive ergonomics and human-centered design can further enhance the usability of RPM solutions. This may involve the use of visual aids, such as graphs, charts, and color-coding, to facilitate rapid comprehension of complex data and even presenting proposed treatment plans based on the algorithmic analysis of the patient’s full record [ 92 ] and providing reference statistics from the health system’s relevant patient population.

The mixed results with RPM interventions have raised concerns about the scalability and value of this technology. This viewpoint paper highlights some of the key questions and core considerations that affect the various infrastructure components of an RPM intervention. Differences between health conditions, metrics, devices, storage, analysis, and information presentation across RPM implementations result in countless permutations. If scholars fail to document and clearly explain the RPM infrastructure and choices made for an RPM implementation, it will be difficult to create an evidence-based research tradition. Having a shared vocabulary and more consistent documentation of the RPM infrastructure can support future literature reviews and meta-analyses seeking to evaluate the outcomes of RPM interventions. The RPM infrastructure framework presented in this article offers scholars a means to describe the different choices and constraints associated with their RPM interventions.

We also identify how each of the infrastructure components can stimulate new research and intervention opportunities in Table 1 . While not exhaustive, the list offers a sampling of the many research questions that could be studied to further increase the understanding associated with RPM interventions. The RPM framework offers scholars and clinicians a more comprehensive guide to exploring various aspects of RPM implementation. As a result, they can further optimize the design and functionality of RPM solutions for improved patient care and health care provider experiences.

Acknowledgments

This research is supported in part by funding from Wake Forest University through the School of Business and the School of Medicine Center for Healthcare Innovation. These sponsors had no involvement in the research.

Conflicts of Interest

None declared.

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Abbreviations

Edited by T de Azevedo Cardoso; submitted 26.07.23; peer-reviewed by M Baucum, H Ewald, E Vashishtha, R Williams, A Georgiou, R Bidkar; comments to author 24.08.23; revised version received 12.10.23; accepted 09.04.24; published 30.05.24.

©Jennifer Claggett, Stacie Petter, Amol Joshi, Todd Ponzio, Eric Kirkendall. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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• „Semrush AI Summarizer“.
• Santrauka, org

1. Smodin – geriausias apskritai

Taip pat turėtume paminėti, kad Smodin turi galimybę palaikyti kelias kalbas. Tai reiškia, kad vartotojai visame pasaulyje gali pasinaudoti pažangiomis technologijomis.

• Smodin rašytojas – AI rašytojas, galintis greitai sukurti elegantiškos struktūros turinį.
• Kelių kalbų palaikymas, įskaitant daugiau nei 50 kalbų.
• Integruojamas tiesiogiai su tokiomis platformomis kaip Microsoft Word ir Google Docs.
• Siūlomas nemokamas režimas su pagrindinėmis funkcijomis.
• MLA ir APA citatų generavimas.

Argumentai "už"

• Lengvas naudoti įrankis su švaria sąsaja.
• Greitas apdorojimo greitis suvestinėms kurti.
• Prieinama keliuose įrenginiuose ir platformose.
• Nemokama versija turi ribotas funkcijas.
• Kartais sudėtingas turinys pernelyg supaprastinamas.

Sistemos suderinamumas: Windows, macOS, Linux, iOS, Android

2. Jaspis – geriausiai tinka išplėstinėms suvestinės funkcijoms

Vartotojai gali įvesti tekstą, pasirinkti tikslinę auditoriją ir kurti santraukas nenusileisdami prakaito. Tai dar viena platforma, kuri puikiai tinka užimtiems specialistams ir studentams.

• Pritaikomas prekės ženklo balsas nuosekliam tonui.
• Konkrečių suvestinių formatų šablonai.
• Komandinis bendradarbiavimas ir įmonės žinių sistema.
• Sutaupysite laiko sudarant greitas ir tikslias suvestines.
• Pakartotinai pateikia santraukas socialinėje žiniasklaidoje arba el.
• Gali turėti įtakos dokumentų importavimo galimybėms.
• Papildomoms funkcijoms reikalingas „Premium“ planas.

Sistemos suderinamumas: žiniatinklio naršyklė, „Google“ dokumentai, „Chrome“ plėtinys

3. QuillBot – geriausias universalumui

Taip pat verta paminėti, kad šis apibendrinimo įrankis naudoja natūralios kalbos apdorojimą (NLP), kad tiksliai išskirtų pagrindinius taškus, išlaikant pradinį kontekstą.

• Suvestinio režimo pasirinkimas.
• Gramatikos tikrinimo įrankiai.
• Nemokama versija su neribota santrauka.
• Paprastas rašymo procesas.
• Skirtingi pritaikytų suvestinių režimai.
• Nemokama versija neturi pažangių funkcijų.
• Kad darbo eiga būtų sklandi, reikalingas Microsoft Word arba Chrome plėtinys.

Sistemos suderinamumas: žiniatinklio naršyklė, Microsoft Word, Chome plėtinys

4. Frase.io – geriausiai tinka įvairių tipų turiniui tvarkyti

Įrankis idealiai tinka tinklaraštininkams, rinkodaros specialistams ir tyrėjams, kuriems reikia veiksmingų įvairių tipų turinio santraukų. Su juo skaitytojai gali akimirksniu suvokti svarbius dalykus.

• Veiksmingai išsprendžia rašytojo bloką su pasiūlymais.
• Siūlomos glaustos santraukos, pateikiamos prieš raginimus veikti.
• Naudoja BLUF (Bottom Line Up Front) ir apverstos piramidės santraukos metodus.
• Lengva naudoti rašymo proceso produktyvumui.
• Sukuria tikslias santraukas su minimaliu redagavimu.
• Apribota iki 700 žodžių vienoje įvestyje.
• Reikia patobulinti nišines temas.
• Visiškai prieigai prie išplėstinių algoritmų reikalingas aukščiausios kokybės planas.

Sistemos suderinamumas: žiniatinklio naršyklė

5. Mokslas – geriausia apibendrinti ilgos formos dokumentus

Jei esate studentas, mokslininkas ar tyrinėtojas, „Scholarcy“ AI suvestinės generatorius gali būti geras pasirinkimas. Jis sukuria struktūrizuotas santraukas, naudodamas korteles, išryškindamas pagrindines sąvokas ir leisdamas vartotojams nuskaityti arba pasinerti gilyn.

„Scholarcy“ taip pat turi gana smagią sąsają, tačiau daugelis vartotojų nurodo, kad joje trūksta pažangesnių funkcijų, leidžiančių santraukas perkelti į kitą lygį. Tačiau ji vis tiek puikiai suskaido sudėtingą turinį į paprastas santraukas.

• Nurodo pagrindinius akademinių darbų terminus ir išvadas.
• Palaiko PDF, knygų skyrius, internetinius straipsnius ir kt.
• Pateikiamos momentinių nuotraukų santraukos, kad būtų galima greitai perskaityti.
• Nemokama versija pasiekiama iki trijų suvestinių per dieną.
• Tiksliai nustato esminius taškus.
• Išplėstinėms funkcijoms reikalingas Scholarcy Plus planas.
• Neapibendrina tam tikro dydžio dokumentų.

Sistemos suderinamumas: žiniatinklio naršyklė, „Chrome“ plėtinys

6. genei – geriausiai tinka išsamioms santraukoms

genei nėra pats įdomiausias suvestinės įrankis mūsų sąraše, tačiau tai nereiškia, kad į jį reikėtų atsižvelgti. Ji vis dar atlieka gerą darbą suteikdama studentams, specialistams ir tyrėjams galių paspartinti mokslinius tyrimus ir turinio kūrimą. genei apibendrina dokumentus, analizuoja tinklalapius ir ištraukia raktinius žodžius. Taigi, su juo jums nebus sunku greitai rasti svarbios informacijos.

• PDF ir tinklalapių santrauka.
• Kelių dokumentų apibendrinimas ir atsakymas į klausimus.
• Išplėstinė GPT-3 pagrindu sukurta kalbų generacija.
• Siūlo nemokamą bandomąją versiją ir pagrindinį planą.
• Palengvina pažangius tyrimus naudojant tinkinamus aplankus.
• „Chrome“ plėtinys leidžia išsaugoti turinį.
• Aukščiausios kokybės funkcijos pasiekiamos tik „Genei Pro“.
• Su kai kuriomis funkcijomis gali prireikti padirbėti labai konkrečiose tyrimų temose.

7. Notta – geriausiai tinka netekstinio turinio (garso, vaizdo, skambučių ir kt.) apibendrinimui.

„Notta“ AI suvestinės įrankis vos keliais paspaudimais gali paversti garso ir vaizdo turinį turtingomis santraukomis. Tai puikiai tinka susitikimams, interviu ir podcast'ams. „Notta“ automatiškai sukuria veiksmingus skyrius ir svarbiausius dalykus. Tai labai padeda per trumpą laiką gauti daug turinio.

„Notta“ gali būti geras sprendimas, jei esate pedagogas, turinio kūrėjas ar verslo komandos narys.

• Transkribuoja ir apibendrina vaizdo ir garso failus.
• Generuoja skyrius ir pagrindinius veiksmų elementus.
• AI šablonai įvairiems susitikimų tipams.
• Pažangus santraukų kūrimas efektyviam turinio virškinimui.
• Supaprastina apibendrinimą keliose platformose.
• Nemokamas planas siūlo labai ribotą transkripcijos trukmę.
• Išplėstiniams šablonams reikalingas prenumeratos planas.
• Netinka prastos kokybės garsui perrašyti.

Sistemos suderinamumas: žiniatinklio naršyklė, programa mobiliesiems, „Chrome“ plėtinys

8. Glasp – geriausiai tinka vartotojų pastaboms apibendrinti

Visiems, norintiems paryškinti svarbias citatas iš tinklalapių ir PDF failų, „Glasp“ AI suvestinės įrankis yra jūsų atsakymas. Jo AI sukurtos santraukos yra suasmenintos pagal jūsų svarbiausius dalykus ir pastabas. Dėl to tai puiki priemonė besimokantiesiems, nes padeda išlaikyti pagrindines įžvalgas ir dalytis savo išvadomis. Tai taip pat padeda jiems susisiekti su panašiai mąstančiais asmenimis.

• AI santrauka apie tinklalapius, PDF ir „YouTube“ vaizdo įrašus.
• Žinių grafikas mokymosi pažangai vizualizuoti.
• Integruojamas su „Kindle“, „Roam Research“ ir kitomis programomis.
• Siūlo nemokamą versiją su tinkamomis funkcijomis.
• Įgalina prasmingus ryšius per socialinį mokymąsi.
• Visas funkcijų spektras gali būti sudėtingas naujiems vartotojams.
• Norint naudoti visas funkcijas, reikalingas „Chome“ arba „Safari“ plėtinys.
• Kai kuriems vartotojams bendruomenės sklaidos kanalas gali būti didžiulis.

Sistemos suderinamumas: žiniatinklio naršyklė, „Chrome“ plėtinys, „Safari“ plėtinys

9. „Hypotenuse AI“ – geriausia pašalinti nereikalingą informaciją

„Hypotenuse AI“ apibendrinimo įrankis nesiliauja, kai reikia pateikti tikslias pastraipų, straipsnių ir vaizdo įrašų santraukas. Tai supaprastina ilgą tekstą, sutraukdama esminę informaciją į glaustus ženklelius arba pastraipas per pažangų AI. Puikus dalykas yra tai, kad jis puikiai išpjausto pūkus ir aiškiai mato, kas svarbu.

• Siūlo nemokamą bandomąją versiją.
• Apibendrina iki 200,000 50,000 simbolių (XNUMX XNUMX žodžių).
• Išplėstinė AI analizuoja ir generuoja santraukas nuo nulio.
• Optimizuotas pakartotiniam naudojimui socialinės žiniasklaidos įrašuose ar infografikoje.
• Sutrumpina rašymo procesą į paprastus veiksmus.
• Leidžia rašytojams patobulinti turinį, pateikdamas geresnius pagrindinius dalykus.
• Visoms funkcijoms reikalingas aukščiausios kokybės planas.
• Nepriima daugiau nei 50,000 XNUMX žodžių dokumentų.
• Gali reikėti redaguoti dėl techninio tikslumo.

Sistemos suderinamumas: žiniatinklio naršyklė, integruojama su Shopify ir WordPress

10. „Sharly AI“ – geriausia glaustoms santraukoms

„Sharly AI“ yra dar vienas išsamus apibendrinimo įrankis. Jis gali tvarkyti straipsnius, PDF failus ir daugybę dokumentų formatų. Naudodami AI santraukų generatorius galite greitai gauti santrauką, kuri puikiai subalansuoja trumpumą ir detalumą. Apskritai šis įrankis geriausiai tinka profesionalams, kuriems reikia tikslios santraukos.

• Palaiko kelis failų tipus.
• Leidžia atlikti kelių dokumentų analizę, siekiant pagerinti įžvalgas.
• Pritaikomi suvestinių formatai ir ilgiai.
• Sukurkite tikslias santraukas per kelias minutes.
• Sutaupo daug akademikų ir specialistų laiko.
• Išplėstinės funkcijos yra tik dalis „premium“ plano.
• Santraukos kokybė gali skirtis priklausomai nuo šaltinio.
• Ribotas suderinamumas su ne tekstiniais dokumentais.

11. Wordtune – geriausia išmaniesiems vertimams

„Wordtune“ yra dar vienas patikimas pasirinkimas, nors, be abejonės, ne toks populiarus kaip anksčiau minėti. Tai naudinga apibendrindama dokumentus, straipsnius ir „YouTube“ vaizdo įrašus, padėdamas vartotojams maksimaliai padidinti produktyvumą. Naudodama skyrių santraukas ir suasmenintą biblioteką, „Wordtune“ užtikrina supaprastintą darbo eigą tyrėjams, norintiems sutrumpinti analizės laiką.

• Naršyklės plėtinys, skirtas apibendrinti tinklalapį.
• Siūlo pastabas apie tyrimo taškus ir įžvalgas.
• Saugo nuorodas, vaizdo failus ir santraukas asmeninėje bibliotekoje.
• Leidžia naudotojams išplėsti konkrečias skiltis, kad būtų galima išsamiai ištirti.
• Patobulina tyrimo procesą su organizuotomis santraukomis.
• Puikiai integruojamas su naršyklės plėtiniais, kad darbas būtų sklandus.
• Daugiausia dėmesio skirta angliškam turiniui.
• Išplėstinėms funkcijoms reikalinga mokama prenumerata.
• Gali reikėti toliau redaguoti išsamias ataskaitas.

Sistemos suderinamumas: žiniatinklio naršyklė, „Chrome“ plėtinys, „Microsoft Edge“ plėtinys

12. Bet kokia santrauka – geriausia įvairiems įvesties metodams

Pavadinimas tikrai viską pasako. Bet kuri suvestinė gali sukurti išsamias įvairių dokumentų, garso ir vaizdo failų santraukas. Priimdamas kelis formatus ir leisdamas vartotojams pasirinkti tarp ženklelių ar santraukų, šis įrankis supaprastina ilgą turinį. Jis gali puikiai pasitarnauti įvairiems žmonėms, tačiau tyrėjai ir specialistai paprastai išnaudos visas Santraukos galimybes.

• Parengta naudojant „ChatGPT“, kad gautumėte tikslesnių įžvalgų.
• Priima URL ir failus iki 100 MB.
• Palaiko suasmenintas santraukų instrukcijas arba automatines AI santraukas.
• Tvarko daugybę skirtingų failų, kad būtų galima įvairiapusiškai naudoti.
• Pritaikomos santraukos leidžia geriau valdyti.
• Daugeliui funkcijų reikalingas mokamas planas.
• Failo dydžio apribojimai gali būti varginantys, priklausomai nuo formato.

Sistemos suderinamumas: žiniatinklio naršyklė.

13. „Semrush AI Summarizer“ – geriausiai tinka pritaikomam suvestinės formatui

Semrushas išmetė savo kepurę AI arenoje ir nuo to laiko atlieka gana gerą darbą. Jų AI generatorius paverčia straipsnius, ataskaitas ir pastraipas į labai lengvai suprantamas santraukas. Jis gali supaprastinti turinį į pastraipų arba ženklelių formatus – nieko neįtikėtinai novatoriško.

Viena didžiausių jos savybių yra ta, kad ji leidžia vartotojams tinkinti santraukas pagal pageidaujamą ilgį ir stilių.

• Apibendrina bet kokio ilgio turinį be simbolių apribojimo.
• Reguliuojamas santraukos ilgis: trumpas, vidutinis, ilgas.
• Nemokamas naudojimas, jokių apribojimų.
• Teikia santraukas be plagiato.
• Leidžia vartotojams turėti ir laisvai naudoti savo sukurtas santraukas.
• Šiuo metu palaikoma tik anglų kalba.
• Gali reikėti neautomatinio konkrečios pramonės šakos žargono peržiūros.

14. Summarizer.org – geriausias paprastumui

Summarizer.org neturi tiek daug funkcijų, ką pasiūlyti. Tai paprasta, bet tai daro labai gerai. Panašiai kaip QuillBot, jis naudoja natūralios kalbos apdorojimo metodus, kad pateiktų trumpas daugelio tipų turinio santraukas. Įrankis lengvai identifikuoja pagrindinius teksto taškus ir pateikia lengvai įsisavinamus rezultatus. Vartotojai taip pat gali kontroliuoti išvesties ilgį ir struktūrą, kad atitiktų jų poreikius.

• Apibendrina tekstą keliomis kalbomis.
• Generuoja punktų santraukas arba vienos eilutės teiginius.
• Pateikiamas žodžių skaičius prieš ir po apibendrinimo.
• Suteikia daug lankstumo koreguojant suvestinės ilgį.
• Skelbimai svetainėje.
• Ribotas funkcijų kiekis.

15. BooksAI – geriausias nemokamas suvestinė

Ir galiausiai, mes turime BooksAI. Jame pateikiamos dirbtinio intelekto sukurtos knygų santraukos ir rekomendacijos, kurias abu palaiko „ChatGPT“.

Programa puikiai sujungia sudėtingas idėjas ir padeda skaitytojams rasti kitą knygą pagal jų pageidavimus, o tai gali būti labai naudinga akademinėje bendruomenėje. Taip pat galite sukurti asmeninį skaitymo sąrašą ir tyrinėti knygų santraukas įvairiomis kalbomis.

• Siūlykite daugiau nei 40 milijonų knygų santraukas.
• Teikia populiarių knygų santraukas be spoilerių.
• Palaiko santraukas devyniomis kalbomis.
• Išplečia skaitymo pasirinkimą kuruojamais pasiūlymais.
• Palaiko santraukas keliomis kalbomis, kad būtų pasiektas visuotinis pasiekiamumas.
• Norint pasiekti visišką prieigą, reikia įdiegti programą.
• Daugiausia dėmesio skirta populiariems knygų pavadinimams.
• Kai kurioms funkcijoms reikia bendrinti naudotojo duomenis.

Sistemos suderinamumas: iOS, Android

Kaip pasirinkti AI suvestinės įrankį

Suteikėme jums daug dirbtinio intelekto įrankių, iš kurių galite rinktis. Taigi, galite būti šiek tiek sumišę dėl to, kaip pasirinkti vieną teksto suvestinės įrankį. Štai keletas pagrindinių funkcijų, į kurias turėtumėte atkreipti dėmesį:

• Santraukos formato parinktys: AI suvestinė turėtų pateikti daug formatavimo parinkčių, pvz., ženklelių, pastraipų ir santraukų. Šis lankstumas padės patenkinti įvairius projektų poreikius.
• Tikslumas ir aktualumas: Įsitikinkite, kad įrankis generuoja tikslias santraukas, kuriose užfiksuoti pagrindiniai dalykai, nesumaišant pradinės reikšmės.
• Kelių kalbų palaikymas: jei jūsų dokumentai pateikiami keliomis kalbomis, ieškokite suvestinės, siūlančios vertimą. Suvestinės keliomis kalbomis taip pat yra puiki premija.
• Tinkinimas ir ilgio valdymas: pasirinkite AI suvestinę, kuri leidžia koreguoti santraukos ilgį pagal konkrečius poreikius.
• Duomenų privatumas: įrankis turi apsaugoti jūsų duomenis, kad būtų išvengta neteisėtos prieigos ar netinkamo naudojimo.
• Kainodara: apsvarstykite įrankius, kurie siūlo nemokamą versiją arba bent jau bandomąjį laikotarpį, kad išbandytumėte jo funkcijas prieš prisiregistruodami gauti mokamą planą.

Apibendrinkite ir daugiau su Smodin

Tyrimai išsiaiškino, kad 88 % studentų mano, kad užrašų darymas yra raktas į akademinę sėkmę. Tačiau atminkite, kad užsirašinėti yra lengviausia dalis, o sunkiausia yra juos apibendrinti, kad išsiaiškintumėte, kas svarbiausia.

Galite praleisti daugybę valandų apibendrindami savo pastabas ir atlikdami tyrimus arba leisti dirbtiniam intelektui atlikti sunkų darbą už jus. Apsaugokite nuo galvos skausmo – leiskite tokiam įrankiui kaip Smodin visam laikui pakeisti jūsų apibendrinimo būdą!

Smodin yra AI įrankis, kurio daugelis kitų tiesiog negali išmatuoti. Dėl pažangių algoritmų ir universalumo jis yra idealus AI santraukų rašytojas. Siūlydamas pagrindines funkcijas, pvz., tinkinamus santraukų formatus ir citatų generavimą, „Smodin“ yra beveik geriausias akademiko draugas.

Galite naudoti jo suvestinę, norėdami pakoreguoti išsamių ataskaitų ar greitų ženklelių santraukos ilgį ir formatą. Šis lankstumas užtikrina tikslumą, nesvarbu, ar tvarkote akademinius tekstus, verslo ataskaitas ar kūrybinius projektus.

Be to, visapusiškas „Smodin“ kalbos palaikymas ir automatinis plagiato aptikimas daro jį vienu geriausių turimų AI įrankių.