the meaning of a research findings

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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

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How to Write the Dissertation Findings or Results – Steps & Tips

Published by Grace Graffin at August 11th, 2021 , Revised On October 9, 2023

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

It is vitally important to focus only on clear and precise observations. The findings chapter of the  dissertation  is theoretically the easiest to write.

It includes  statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

• Always report the findings of your research in the past tense.
• The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
• Avoid reporting results that are not relevant to your research questions or research hypothesis.

Does your Dissertation Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of the Dissertation strong.

1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Paired samples correlation, paired samples test.

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

How do I report quantitative findings?

The best way to present your quantitative findings is to structure them around the  research hypothesis  or  research questions  you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the  qualitative research  results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your  research question  or  hypothesis  should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the  discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

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Writing a dissertation can be tough if this is the first time you are doing it. You need to look into relevant literature, analyze past researches, conduct surveys, interviews etc.

Your dissertation introduction chapter provides detailed information on the research problem, significance of research, and research aim & objectives.

Appendices or Appendixes are used to provide additional date related to your dissertation research project. Here we explain what is appendix in dissertation

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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE :   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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In This Article Expand or collapse the "in this article" section Reporting Research Findings

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• Linguistic Analyses of Written Research Results
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Reporting Research Findings by James T. Austin LAST REVIEWED: 27 May 2020 LAST MODIFIED: 24 June 2020 DOI: 10.1093/obo/9780199846740-0032

Not all research culminates in publication. This updated article surveys themes in reporting research findings for scholars and students. As context, consider that investigations of organizational phenomena require a series of choices that are cast here as craft. Choices span primary, secondary, and synthesis designs across qualitative and quantitative traditions. Primary research is the traditional design, measurement, and analysis of collected data, while secondary research involves reanalysis of existing data sets (obtained from peers or repositories), and research synthesis involves narrative or quantitative aggregation of studies. This distinction also holds for the qualitative mode. Reporting research findings is important for dissemination and for synthesis and evidence-based management (EBM). Primarily, the importance lies in dissemination across conferences, journals, books, and increasingly digital media. Understanding and replication by outside scholars depend on complete and accurate reporting; this centrality to the research craft commands a learning-development focus. Within a communications paradigm, individuals or teams create or send a persuasive message and the reader or listener receives (or may choose not to receive) the message. Persuasion is targeted via rhetoric across writing and graphics. Although oral and written forms of dissemination dominate, data repositories are emerging. Two additional reasons for importance pertain to the accumulation of knowledge. One is research synthesis. Structuring knowledge through synthesis uses the results of individual studies as data, and the audience is scientists. Narrative and quantitative reviews depend on the completeness and accuracy of reported findings. A related source of importance pertains to evidence-based management at the interface of research and practice—translation of research findings into practices and bundles of practices that can be used by managers. Given that practicing managers appear to rely on obsolete knowledge (aka “fads, fashions, and folderol” as used by Dunnette), proponents of evidence-based management advocate that firms consider the adoption of evidence-based medicine (EBM). Communicating clearly and establishing a context of implementation to assist practitioners are essential for EBM (in parallel to research synthesis, for an audience of practitioners). This article organizes a range of resources on writing and reviewing articles across the taxonomy above. For completeness, this article includes citations for scientific graphics (tables, charts, figures, etc.) organized around conceptualizations of graphics and related guidance, research on perception of scientific graphics, and recent developments in computing technology. Especially relevant are software routines for interactive graphics based on “grammars.” While this article draws on work in management studies (organizational behavior and human resources), it necessarily searches beyond traditional boundaries for relevant insights.

There are sporadic specialized sources on reporting of research findings. On scholarly writing, Cummings and Frost 1995 is an influential analysis of the publishing system in the organizational sciences. Abelson 1995 defines rhetoric as styles of writing up results in psychology. Research synthesis writing is addressed comprehensively in Cooper, et al. 2009 (cited under Guidance on Reporting Quantitative Reports, Syntheses, and Meta-analyses ). There are two major standards available for research synthesis: Meta-Analysis Reporting Standards (MARS) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ).For graphics and quantitative studies, Tufte 2001 and Tukey 1977 are classics for guidance and perspective; others, including Cleveland 1985 , Kosslyn 2006 , Wainer 2000 (cited under History and Trends ), and Wilkinson 2005 , provide unique value. The work on maps in Börner 2015 is aptly named Atlas of Knowledge , while Grant 2019 provides a concise introduction to data visualization with a section on interactive graphics (a related instance is the class of data explorers used for large data sets as the Programme for International Student Assessment [PISA] and the National Assessment of Educational Progress [NAEP]—both large-scale testing programs). Sternberg and Sternberg 2010 is typical guidance offered to students and is not the only such resource. Many of these texts can be mined for dimensions to code the content and results of published organizational behavior and human resources research to facilitate critique A trio of books by Katy Börner ( Börner 2010 , Börner 2015 ) and colleagues ( Börner and Polley 2014 ) represents the newest in knowledge mapping. In addition, a rapidly emerging topic across science is the reproducibility and replicability of results—the consensus review published in 2019 by a committee of the National Academies of Science, Medicine, and Engineering provides an excellent overview.

Abelson, Robert P. Statistics as Principled Argument . Mahwah, NJ: Lawrence Erlbaum, 1995.

Describes magnitude-articulation-generality-interestingness-credibility (MAGIC) criteria to organize rhetoric in presenting research findings. Accepting statistics as an organizer of arguments using quantitative evidence allows identification of styles. Brash and stuffy are end points on a liberal-conservative style dimension. Management students and scholars could learn MAGIC for reporting quantitative findings; qualitative researchers might consider translation.

Börner, Katy. Atlas of Science: Visualizing What We Know . Cambridge, MA: Massachusetts Institute of Technology Press, 2010.

Books by Katy Börner show the potential and the practice of science and knowledge mapping. Atlas of Science (2010) presents three themes: power of maps (switching from geographic cartography to research-collaboration mapping), reference systems, and forecasts, as well as numerous examples.

Börner, Katy. Atlas of Knowledge: Anyone Can Map . Cambridge, MA: Massachusetts Institute of Technology Press, 2015.

Börner deftly gives readers principles for visualizing knowledge with more than forty large-scale and over a hundred small-scale color maps. Drives home the point that data literacy is as important as language literacy. She introduces a theoretical framework meant to guide readers through user and task analysis; data preparation, analysis, and visualization; visualization deployment; and the interpretation of science maps. Together with Börner 2010 and Börner and Polley 2014 , this trio provides levels of analysis from frameworks to workflow that support improved visualizations of science, knowledge, and interdisciplinary collaboration.

Börner, Katy, and David E. Polley. Visual Insights: A Practical Guide to Making Sense of Data . Cambridge, MA: Massachusetts Institute of Technology Press, 2014.

Along with Börner 2010 and Börner 2015 , a practical book by Börner and Polley based on the Information Visualization MOOC includes seven chapters—from a visualization framework through “when, where, what, and with whom” and dynamic visualizations—and concludes with chapters on case studies and discussion/outlook.

Cleveland, William S. The Elements of Graphing Data . Monterey, CA: Wadsworth Advanced Books and Software, 1985.

Cognitive science and statistical principles help dissect and improve graphics (a predecessor book from 1983 and articles that searched prestigious journals for common graphic errors are also useful). Based on extensive experience with AT&T data, the author distills and emphasizes procedural knowledge for constructing graphic displays.

Cummings, Larry L., and Peter J. Frost, eds. Publishing in the Organizational Sciences . 2d ed. Foundations of Organizational Science. Thousand Oaks, CA: SAGE, 1995.

This classic covers most aspects of publishing in organizational behavior and human resources (absent are emergent digital-technological issues). Organized into sections on perspectives on and realities of publishing, which are insightful for scholar and student alike. Benjamin Schneider’s ten propositions on “getting research published” end with practicing the skill of writing. This edition inaugurated the Foundations of Organizational Science series, and the 1985 edition is also useful.

Few, Stephen. Now You See It: Simple Visualization Techniques for Quantitative Analysis . Oakland, CA: Analytics, 2009.

Suggests that in a data-dense world, the human brain—and hence, visualization—is key to avoiding overload. Three sections, namely “Building Core Skills for Visual Analysis” and “Honing Skills,” each with six chapters plus a “Further Thoughts and Hopes” with eight promising trends, cover much ground. Based on quantitative preferences, the most substantive portion is contained in Part 2. The book ends with an excerpt from the poetry of T. S. Eliot.

Grant, Robert. Data Visualization: Charts, Maps and Interactive Graphics . Boca Raton, FL: CRC Press, 2019.

This author provides a vast range of examples of data visualization, mostly open source and with code available on a website . It provides a good mix of detail with sharing of tacit knowledge.

Kosslyn, Stephen M. Graph Design for the Eye and Mind . New York: Oxford University Press, 2006.

DOI: 10.1093/acprof:oso/9780195311846.001.0001

Based on sound cognitive science and ample research by the author, presents and elaborates eight principles of effective graph construction (summarized in pp. 5–20). Analyzes prominent guidance on graphics, Edward R. Tufte for example, and suggests flaws. that could lead to productive research.

Sternberg, Robert J., and Karin Sternberg The Psychologist’s Companion: A Guide to Writing Scientific Papers for Students and Researchers . 5th ed. Cambridge, UK: Cambridge University Press, 2010.

DOI: 10.1017/CBO9780511762024

Aligned to American Psychological Association (APA) style as a prototype of good practice in publishing; the author is a productive researcher and APA journal editor; thus tacit knowledge in this edition is well grounded and expressed. Represents a class of books on research communication. Some translation required to organizational behavior and human resources context. Comparable to Cooper 2010 (cited under Writing Review Articles ). Next edition will need to conform to the seventh edition of the Publication Manual of the American Psychological Association .

Tufte, Edward R. The Visual Display of Quantitative Information . 2d ed. Cheshire, CT: Graphics Press, 2001.

Revises a classic 1983 text in analytic design (Tufte’s preferred term); presents and expands on five core principles and coins numerous terms (“chartjunk” as well as “sparkline” and “data-ink ratios” are personal favorites). Critiqued for its advice, however, by other researchers on graphics ( Kosslyn 2006 ).

Tukey, John W. Exploratory Data Analysis . Reading, MA: Addison-Wesley, 1977.

A classic presenting Tukey’s data detective work rooted in his 1962 “The Future of Data Analysis” exposition ( Annals of Mathematical Statistics 33.1: 1–67). Premise is that exploratory data analysis (EDA) deserves status with confirmatory. Loaded with philosophy of EDA and tools—the stem leaf, box plot, and “five-number summary.” Graphic display and analysis are covered in the service of learning about data. A part of research craft to be honed post-schooling.

Wilkinson, Leland L. The Grammar of Graphics . 2d ed. New York: Springer-Verlag, 2005.

Cited by many, this conceptualization rooted in the work of Jacques Bertin extends work done with the Task Force on Statistical Reporting in 1999. Within an object-oriented design approach, the grammar consists of the rules and elements of graphics, for example, geoms, scales, and coordinates. Framework has been useful for deriving tools, such as Wilkinson’s GPL, Wickham’s ggplot2, and others.

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Reporting the findings

While the writing process for a systematic review is generally like writing any other kind of review ,  there are several aspects to note.  

In writing the systematic review you should provide an  answer to the research question .

Careful  documentation of the methodology  is important as  it should  outline the search process and the selection process . A reader should understand why sources were chosen, how they were assessed, and how conclusions were reached.

The  structure  of the systematic review will differ from the traditional (or narrative) literature review as it should  reflect the stages outlined in the pro tocol .  Refer to the  27 item PRISMA checklist  to see what should be addressed in the protocol.

The value of a systematic review is the  critical reflection and interpretation of the findings .

Reporting the findings of the systematic review will differ slightly if it is to be presented as part of a thesis, or as a manuscript for publication.

The following examples are available from the RMIT Research Repository.

Dissertation / Thesis

• Ear-acupressure for allergic rhinitis
• Ear-acupressure for allergic rhinitis: A systematic review ​
• Acupressure for respiratory allergic diseases: A systematic review of  randomised controlled trials

To see how a systematic review is written check out examples of published papers and/or completed theses.

• Find a systematic review by  searching a database and  examine how the review has been written.  For example, s earch  the  PubMed   database   on  your topic and filter results by  ‘ article type ’ selecting   ‘ systematic reviews. ’
• Find a systematic review paper by searching with the words  ‘ systematic review ’  in the  RMIT Research Repository .  Consider adding an additional topic word.
• HDR candidates may like to ask their supervisor if they can recommend a completed thesis that includes a systematic review . 

The following image shows how to search the Research Repository for a thesis with a systematic r eview  if an a uthor’s name is unknown.  In the search box include a topic along with the words  ‘ systematic review ’  a nd s elect ‘Dissertations & Theses’ from the drop-down menu.

Research and Writing Skills for Academic and Graduate Researchers Copyright © 2022 by RMIT University is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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What is Research? Definition, Types, Methods and Process

By Nick Jain

Published on: July 25, 2023

Table of Contents

What is Research?

Types of research methods, research process: how to conduct research, top 10 best practices for conducting research in 2023.

Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base. This dedication to systematic inquiry serves as the bedrock of progress, steering advancements across sciences, technology, social sciences, and diverse disciplines. Through the dissemination of meticulously gathered insights, scholars not only inspire collaboration and innovation but also catalyze positive societal change.

In the pursuit of knowledge, researchers embark on a journey of discovery, seeking to unravel the complexities of the world around us. By formulating clear research questions, researchers set the course for their investigations, carefully crafting methodologies to gather relevant data. Whether employing quantitative surveys or qualitative interviews, data collection lies at the heart of every research endeavor. Once the data is collected, researchers meticulously analyze it, employing statistical tools or thematic analysis to identify patterns and draw meaningful insights. These insights, often supported by empirical evidence, contribute to the collective pool of knowledge, enriching our understanding of various phenomena and guiding decision-making processes across diverse fields. Through research, we continually refine our understanding of the universe, laying the foundation for innovation and progress that shape the future.

Research embodies the spirit of curiosity and the pursuit of truth. Here are the key characteristics of research:

• Systematic Approach: Research follows a well-structured and organized approach, with clearly defined steps and methodologies. It is conducted in a systematic manner to ensure that data is collected, analyzed, and interpreted in a logical and coherent way.
• Objective and Unbiased: Research is objective and strives to be free from bias or personal opinions. Researchers aim to gather data and draw conclusions based on evidence rather than preconceived notions or beliefs.
• Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other data collection methods. This evidence serves as the foundation for drawing conclusions and making informed decisions.
• Clear Research Question or Problem: Every research study begins with a specific research question or problem that the researcher aims to address. This question provides focus and direction to the entire research process.
• Replicability: Good research should be replicable, meaning that other researchers should be able to conduct a similar study and obtain similar results when following the same methods.
• Transparency and Ethics: Research should be conducted with transparency, and researchers should adhere to ethical guidelines and principles. This includes obtaining informed consent from participants, ensuring confidentiality, and avoiding any harm to participants or the environment.
• Generalizability: Researchers often aim for their findings to be generalizable to a broader population or context. This means that the results of the study can be applied beyond the specific sample or situation studied.
• Logical and Critical Thinking: Research involves critical thinking to analyze and interpret data, identify patterns, and draw meaningful conclusions. Logical reasoning is essential in formulating hypotheses and designing the study.
• Contribution to Knowledge: The primary purpose of research is to contribute to the existing body of knowledge in a particular field. Researchers aim to expand understanding, challenge existing theories, or propose new ideas.
• Peer Review and Publication: Research findings are typically subject to peer review by experts in the field before being published in academic journals or presented at conferences. This process ensures the quality and validity of the research.
• Iterative Process: Research is often an iterative process, with findings from one study leading to new questions and further research. It is a continuous cycle of discovery and refinement.
• Practical Application: While some research is theoretical in nature, much of it aims to have practical applications and real-world implications. It can inform policy decisions, improve practices, or address societal challenges.

These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress, knowledge, and innovation in various disciplines.

Research methods refer to the specific approaches and techniques used to collect and analyze data in a research study. There are various types of research methods, and researchers often choose the most appropriate method based on their research question, the nature of the data they want to collect, and the resources available to them. Some common types of research methods include:

1. Quantitative Research: Quantitative research methods focus on collecting and analyzing quantifiable data to draw conclusions. The key methods for conducting quantitative research are:

Surveys- Conducting structured questionnaires or interviews with a large number of participants to gather numerical data.

Experiments-Manipulating variables in a controlled environment to establish cause-and-effect relationships.

Observational Studies- Systematically observing and recording behaviors or phenomena without intervention.

Secondary Data Analysis- Analyzing existing datasets and records to draw new insights or conclusions.

2. Qualitative Research: Qualitative research employs a range of information-gathering methods that are non-numerical, and are instead intellectual in order to provide in-depth insights into the research topic. The key methods are:

Interviews- Conducting in-depth, semi-structured, or unstructured interviews to gain a deeper understanding of participants’ perspectives.

Focus Groups- Group discussions with selected participants to explore their attitudes, beliefs, and experiences on a specific topic.

Ethnography- Immersing in a particular culture or community to observe and understand their behaviors, customs, and beliefs.

Case Studies- In-depth examination of a single individual, group, organization, or event to gain comprehensive insights.

3. Mixed-Methods Research: Combining both quantitative and qualitative research methods in a single study to provide a more comprehensive understanding of the research question.

4. Cross-Sectional Studies: Gathering data from a sample of a population at a specific point in time to understand relationships or differences between variables.

5. Longitudinal Studies: Following a group of participants over an extended period to examine changes and developments over time.

6. Action Research: Collaboratively working with stakeholders to identify and implement solutions to practical problems in real-world settings.

7. Case-Control Studies: Comparing individuals with a particular outcome (cases) to those without the outcome (controls) to identify potential causes or risk factors.

8. Descriptive Research: Describing and summarizing characteristics, behaviors, or patterns without manipulating variables.

9. Correlational Research: Examining the relationship between two or more variables without inferring causation.

10. Grounded Theory: An approach to developing theory based on systematically gathering and analyzing data, allowing the theory to emerge from the data.

11. Surveys and Questionnaires: Administering structured sets of questions to a sample population to gather specific information.

12. Meta-Analysis: A statistical technique that combines the results of multiple studies on the same topic to draw more robust conclusions.

Researchers often choose a research method or a combination of methods that best aligns with their research objectives, resources, and the nature of the data they aim to collect. Each research method has its strengths and limitations, and the choice of method can significantly impact the findings and conclusions of a study.

Learn more: What is Research Design?

Conducting research involves a systematic and organized process that follows specific steps to ensure the collection of reliable and meaningful data. The research process typically consists of the following steps:

Step 1. Identify the Research Topic

Choose a research topic that interests you and aligns with your expertise and resources. Develop clear and focused research questions that you want to answer through your study.

Step 2. Review Existing Research

Conduct a thorough literature review to identify what research has already been done on your chosen topic. This will help you understand the current state of knowledge, identify gaps in the literature, and refine your research questions.

Step 3. Design the Research Methodology

Determine the appropriate research methodology that suits your research questions. Decide whether your study will be qualitative , quantitative , or a mix of both (mixed methods). Also, choose the data collection methods, such as surveys, interviews, experiments, observations, etc.

Step 4. Select the Sample and Participants

If your study involves human participants, decide on the sample size and selection criteria. Obtain ethical approval, if required, and ensure that participants’ rights and privacy are protected throughout the research process.

Step 5. Information Collection

Collect information and data based on your chosen research methodology. Qualitative research has more intellectual information, while quantitative research results are more data-oriented. Ensure that your data collection process is standardized and consistent to maintain the validity of the results.

Step 6. Data Analysis

Analyze the data you have collected using appropriate statistical or qualitative research methods . The type of analysis will depend on the nature of your data and research questions.

Step 7. Interpretation of Results

Interpret the findings of your data analysis. Relate the results to your research questions and consider how they contribute to the existing knowledge in the field.

Step 8. Draw Conclusions

Based on your interpretation of the results, draw meaningful conclusions that answer your research questions. Discuss the implications of your findings and how they align with the existing literature.

Step 9. Discuss Limitations

Acknowledge and discuss any limitations of your study. Addressing limitations demonstrates the validity and reliability of your research.

Step 10. Make Recommendations

If applicable, provide recommendations based on your research findings. These recommendations can be for future research, policy changes, or practical applications.

Step 11. Write the Research Report

Prepare a comprehensive research report detailing all aspects of your study, including the introduction, methodology, results, discussion, conclusion, and references.

Step 12. Peer Review and Revision

If you intend to publish your research, submit your report to peer-reviewed journals. Revise your research report based on the feedback received from reviewers.

Make sure to share your research findings with the broader community through conferences, seminars, or other appropriate channels, this will help contribute to the collective knowledge in your field of study.

Remember that conducting research is a dynamic process, and you may need to revisit and refine various steps as you progress. Good research requires attention to detail, critical thinking, and adherence to ethical principles to ensure the quality and validity of the study.

Learn more: What is Primary Market Research?

Best practices for conducting research remain rooted in the principles of rigor, transparency, and ethical considerations. Here are the essential best practices to follow when conducting research in 2023:

1. Research Design and Methodology

• Carefully select and justify the research design and methodology that aligns with your research questions and objectives.
• Ensure that the chosen methods are appropriate for the data you intend to collect and the type of analysis you plan to perform.
• Clearly document the research design and methodology to enhance the reproducibility and transparency of your study.

2. Ethical Considerations

• Obtain approval from relevant research ethics committees or institutional review boards, especially when involving human participants or sensitive data.
• Prioritize the protection of participants’ rights, privacy, and confidentiality throughout the research process.
• Provide informed consent to participants, ensuring they understand the study’s purpose, risks, and benefits.

3. Data Collection

• Ensure the reliability and validity of data collection instruments, such as surveys or interview protocols.
• Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures.

4. Data Management and Analysis

• Implement robust data management practices to maintain the integrity and security of research data.
• Transparently document data analysis procedures, including software and statistical methods used.
• Use appropriate statistical techniques to analyze the data and avoid data manipulation or cherry-picking results.

5. Transparency and Open Science

• Embrace open science practices, such as pre-registration of research protocols and sharing data and code openly whenever possible.
• Clearly report all aspects of your research, including methods, results, and limitations, to enhance the reproducibility of your study.

6. Bias and Confounders

• Be aware of potential biases in the research process and take steps to minimize them.
• Consider and address potential confounding variables that could affect the validity of your results.

7. Peer Review

• Seek peer review from experts in your field before publishing or presenting your research findings.
• Be receptive to feedback and address any concerns raised by reviewers to improve the quality of your study.

8. Replicability and Generalizability

• Strive to make your research findings replicable, allowing other researchers to validate your results independently.
• Clearly state the limitations of your study and the extent to which the findings can be generalized to other populations or contexts.

9. Acknowledging Funding and Conflicts of Interest

• Disclose any funding sources and potential conflicts of interest that may influence your research or its outcomes.

10. Dissemination and Communication

• Effectively communicate your research findings to both academic and non-academic audiences using clear and accessible language.
• Share your research through reputable and open-access platforms to maximize its impact and reach.

By adhering to these best practices, researchers can ensure the integrity and value of their work, contributing to the advancement of knowledge and promoting trust in the research community.

Learn more: What is Consumer Research?

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What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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How To Write the Findings Section of a Research Paper

Posted by Rene Tetzner | Sep 2, 2021 | Paper Writing Advice | 0 |

How To Write the Findings Section of a Research Paper Each research project is unique, so it is natural for one researcher to make use of somewhat different strategies than another when it comes to designing and writing the section of a research paper dedicated to findings. The academic or scientific discipline of the research, the field of specialisation, the particular author or authors, the targeted journal or other publisher and the editor making the decisions about publication can all have a significant impact. The practical steps outlined below can be effectively applied to writing about the findings of most advanced research, however, and will prove especially helpful for early-career scholars who are preparing a research paper for a first publication.

Step 1 : Consult the guidelines or instructions that the targeted journal (or other publisher) provides for authors and read research papers it has already published, particularly ones similar in topic, methods or results to your own. The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches. Watch particularly for length limitations and restrictions on content. Interpretation, for instance, is usually reserved for a later discussion section, though not always – qualitative research papers often combine findings and interpretation. Background information and descriptions of methods, on the other hand, almost always appear in earlier sections of a research paper. In most cases it is appropriate in a findings section to offer basic comparisons between the results of your study and those of other studies, but knowing exactly what the journal wants in the report of research findings is essential. Learning as much as you can about the journal’s aims and scope as well as the interests of its readers is invaluable as well.

Step 2 : Reflect at some length on your research results in relation to the journal’s requirements while planning the findings section of your paper. Choose for particular focus experimental results and other research discoveries that are particularly relevant to your research questions and objectives, and include them even if they are unexpected or do not support your ideas and hypotheses. Streamline and clarify your report, especially if it is long and complex, by using subheadings that will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Consider appendices for raw data that might interest specialists but prove too long or distracting for other readers. The opening paragraph of a findings section often restates research questions or aims to refocus the reader’s attention, and it is always wise to summarise key findings at the end of the section, providing a smooth intellectual transition to the interpretation and discussion that follows in most research papers. There are many effective ways in which to organise research findings. The structure of your findings section might be determined by your research questions and hypotheses or match the arrangement of your methods section. A chronological order or hierarchy of importance or meaningful grouping of main themes or categories might prove effective. It may be best to present all the relevant findings and then explain them and your analysis of them, or explaining the results of each trial or test immediately after reporting it may render the material clearer and more comprehensible for your readers. Keep your audience, your most important evidence and your research goals in mind.

Step 3 : Design effective visual presentations of your research results to enhance the textual report of your findings. Tables of various styles and figures of all kinds such as graphs, maps and photos are used in reporting research findings, but do check the journal guidelines for instructions on the number of visual aids allowed, any required design elements and the preferred formats for numbering, labelling and placement in the manuscript. As a general rule, tables and figures should be numbered according to first mention in the main text of the paper, and each one should be clearly introduced and explained at least briefly in that text so that readers know what is presented and what they are expected to see in a particular visual element. Tables and figures should also be self-explanatory, however, so their design should include all definitions and other information necessary for a reader to understand the findings you intend to show without returning to your text. If you construct your tables and figures before drafting your findings section, they can serve as focal points to help you tell a clear and informative story about your findings and avoid unnecessary repetition. Some authors will even work on tables and figures before organising the findings section (Step 2), which can be an extremely effective approach, but it is important to remember that the textual report of findings remains primary. Visual aids can clarify and enrich the text, but they cannot take its place.

Step 4 : Write your findings section in a factual and objective manner. The goal is to communicate information – in some cases a great deal of complex information – as clearly, accurately and precisely as possible, so well-constructed sentences that maintain a simple structure will be far more effective than convoluted phrasing and expressions. The active voice is often recommended by publishers and the authors of writing manuals, and the past tense is appropriate because the research has already been done. Make sure your grammar, spelling and punctuation are correct and effective so that you are conveying the meaning you intend. Statements that are vague, imprecise or ambiguous will often confuse and mislead readers, and a verbose style will add little more than padding while wasting valuable words that might be put to far better use in clear and logical explanations. Some specialised terminology may be required when reporting findings, but anything potentially unclear or confusing that has not already been defined earlier in the paper should be clarified for readers, and the same principle applies to unusual or nonstandard abbreviations. Your readers will want to understand what you are reporting about your results, not waste time looking up terms simply to understand what you are saying. A logical approach to organising your findings section (Step 2) will help you tell a logical story about your research results as you explain, highlight, offer analysis and summarise the information necessary for readers to understand the discussion section that follows.

Step 5 : Review the draft of your findings section and edit and revise until it reports your key findings exactly as you would have them presented to your readers. Check for accuracy and consistency in data across the section as a whole and all its visual elements. Read your prose aloud to catch language errors, awkward phrases and abrupt transitions. Ensure that the order in which you have presented results is the best order for focussing readers on your research objectives and preparing them for the interpretations, speculations, recommendations and other elements of the discussion that you are planning. This will involve looking back over the paper’s introductory and background material as well as anticipating the discussion and conclusion sections, and this is precisely the right point in the process for reviewing and reflecting. Your research results have taken considerable time to obtain and analyse, so a little more time to stand back and take in the wider view from the research door you have opened is a wise investment. The opinions of any additional readers you can recruit, whether they are professional mentors and colleagues or family and friends, will often prove invaluable as well.

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How To Write the Findings Section of a Research Paper These five steps will help you write a clear & interesting findings section for a research paper

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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What is your plagiarism score?

Doing Research: A New Researcher’s Guide pp 1–15 Cite as

What Is Research, and Why Do People Do It?

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  
• Open Access
• First Online: 03 December 2022

15k Accesses

Part of the book series: Research in Mathematics Education ((RME))

Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

You have full access to this open access chapter,  Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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Research Guide

Chapter 7 presenting your findings.

Now that you have worked so hard in your project, how to ensure that you can communicate your findings in an effective and efficient way? In this section, I will introduce a few tips that could help you prepare your slides and preparing for your final presentation.

7.1 Sections of the Presentation

When preparing your slides, you need to ensure that you have a clear roadmap. You have a limited time to explain the context of your study, your results, and the main takeaways. Thus, you need to be organized and efficient when deciding what material will be included in the slides.

You need to ensure that your presentation contains the following sections:

• Motivation : Why did you choose your topic? What is the bigger question?
• Research question : Needs to be clear and concise. Include secondary questions, if available, but be clear about what is your research question.
• Literature Review : How does your paper fit in the overall literature? What are your contributions?
• Context : Give an overview of the issue and the population/countries that you analyzed
• Study Characteristics : This section is key, as it needs to include your model, identification strategy, and introduce your data (sources, summary statistics, etc.).
• Results : In this section, you need to answer your research question(s). Include tables that are readable.
• Additional analysis : Here, include any additional information that your public needs to know. For instance, did you try different specifications? did you find an obstacle (i.e. your data is very noisy, the sample is very small, something else) that may bias your results or create some issues in your analysis? Tell your audience! No research project is perfect, but you need to be clear about the imperfections of your project.
• Conclusion : Be repetitive! What was your research question? How did you answer it? What did you find? What is next in this topic?

7.2 How to prepare your slides

When preparing your slides, remember that humans have a limited capacity to pay attention. If you want to convey your convey your message in an effective way, you need to ensure that the message is simple and that you keep your audience attention. Here are some strategies that you may want to follow:

• Have a clear roadmap at the beginning of the presentation. Tell your audience what to expect.
• Number your slides. This will help you and your audience to know where you are in your analysis.
• Ensure that each slide has a purpose
• Ensure that each slide is connected to your key point.
• Make just one argument per slide
• State the objective of each slide in the headline
• Use bullet points. Do not include more than one sentence per bullet point.
• Choose a simple background.
• If you want to direct your audience attention to a specific point, make it more attractive (using a different font color)
• Each slide needs to have a similar structure (going from the general to the particular detauls).
• Use images/graphs when possible. Ensure that the axes for the graphs are clear.
• Use a large font for your tables. Keep them as simple as possible.
• If you can say it with an image, choose it over a table.
• Have an Appendix with slides that address potential questions.

7.3 How to prepare your presentation

One of the main constraints of having simple presentations is that you cannot rely on them and read them. Instead, you need to have extra notes and memorize them to explain things beyond what is on your slides. The following are some suggestions on how to ensure you communicate effectively during your presentation.

• Practice, practice, practice!
• Keep the right volume (practice will help you with that)
• Be journalistic about your presentation. Indicate what you want to say, then say it.
• Ensure that your audience knows where you are going
• Avoid passive voice.
• Be consistent with the terms you are using. You do not want to confuse your audience, even if using synonyms.
• Face your audience and keep an eye contact.
• Do not try reading your slides
• Ensure that your audience is focused on what you are presenting and there are no other distractions that you can control.
• Do not rush your presentation. Speak calmly and controlled.
• Be comprehensive when answering questions. Avoid yes/no answers. Instead, rephrase question (to ensure you are answering the right question), then give a short answer, then develop.
• If you lose track, do not panick. Go back a little bit or ask your audience for assistance.
• Again, practice is the secret.

You have worked so hard in your final project, and the presentation is your opportunity to share that work with the rest of the world. Use this opportunity to shine and enjoy it.

Since this is the first iteration of the Guide, I expect that there are going to be multiple typos and structure issues. Please feel free to let me know, and I will correct accordingly. ↩︎

Note that you would still need to refine some of the good questions even more. ↩︎

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Definition of research

 (Entry 1 of 2)

Definition of research  (Entry 2 of 2)

transitive verb

intransitive verb

• disquisition
• examination
• exploration
• inquisition
• investigation
• delve (into)
• inquire (into)
• investigate
• look (into)

Examples of research in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'research.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle French recerche , from recercher to go about seeking, from Old French recerchier , from re- + cerchier, sercher to search — more at search

1577, in the meaning defined at sense 3

1588, in the meaning defined at transitive sense 1

Phrases Containing research

• market research
• operations research
• oppo research
• translational research
• research park

research and development

• marketing research

Dictionary Entries Near research

Cite this entry.

“Research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/research. Accessed 18 Apr. 2024.

Kids Definition

Kids definition of research.

Kids Definition of research  (Entry 2 of 2)

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Home » Research Process – Steps, Examples and Tips

Research Process – Steps, Examples and Tips

Table of Contents

Research Process

Definition:

Research Process is a systematic and structured approach that involves the collection, analysis, and interpretation of data or information to answer a specific research question or solve a particular problem.

Research Process Steps

Research Process Steps are as follows:

Identify the Research Question or Problem

This is the first step in the research process. It involves identifying a problem or question that needs to be addressed. The research question should be specific, relevant, and focused on a particular area of interest.

Conduct a Literature Review

Once the research question has been identified, the next step is to conduct a literature review. This involves reviewing existing research and literature on the topic to identify any gaps in knowledge or areas where further research is needed. A literature review helps to provide a theoretical framework for the research and also ensures that the research is not duplicating previous work.

Formulate a Hypothesis or Research Objectives

Based on the research question and literature review, the researcher can formulate a hypothesis or research objectives. A hypothesis is a statement that can be tested to determine its validity, while research objectives are specific goals that the researcher aims to achieve through the research.

Design a Research Plan and Methodology

This step involves designing a research plan and methodology that will enable the researcher to collect and analyze data to test the hypothesis or achieve the research objectives. The research plan should include details on the sample size, data collection methods, and data analysis techniques that will be used.

Collect and Analyze Data

This step involves collecting and analyzing data according to the research plan and methodology. Data can be collected through various methods, including surveys, interviews, observations, or experiments. The data analysis process involves cleaning and organizing the data, applying statistical and analytical techniques to the data, and interpreting the results.

Interpret the Findings and Draw Conclusions

After analyzing the data, the researcher must interpret the findings and draw conclusions. This involves assessing the validity and reliability of the results and determining whether the hypothesis was supported or not. The researcher must also consider any limitations of the research and discuss the implications of the findings.

Communicate the Results

Finally, the researcher must communicate the results of the research through a research report, presentation, or publication. The research report should provide a detailed account of the research process, including the research question, literature review, research methodology, data analysis, findings, and conclusions. The report should also include recommendations for further research in the area.

Review and Revise

The research process is an iterative one, and it is important to review and revise the research plan and methodology as necessary. Researchers should assess the quality of their data and methods, reflect on their findings, and consider areas for improvement.

Ethical Considerations

Throughout the research process, ethical considerations must be taken into account. This includes ensuring that the research design protects the welfare of research participants, obtaining informed consent, maintaining confidentiality and privacy, and avoiding any potential harm to participants or their communities.

Dissemination and Application

The final step in the research process is to disseminate the findings and apply the research to real-world settings. Researchers can share their findings through academic publications, presentations at conferences, or media coverage. The research can be used to inform policy decisions, develop interventions, or improve practice in the relevant field.

Research Process Example

Following is a Research Process Example:

Research Question : What are the effects of a plant-based diet on athletic performance in high school athletes?

Step 1: Background Research Conduct a literature review to gain a better understanding of the existing research on the topic. Read academic articles and research studies related to plant-based diets, athletic performance, and high school athletes.

Step 2: Develop a Hypothesis Based on the literature review, develop a hypothesis that a plant-based diet positively affects athletic performance in high school athletes.

Step 3: Design the Study Design a study to test the hypothesis. Decide on the study population, sample size, and research methods. For this study, you could use a survey to collect data on dietary habits and athletic performance from a sample of high school athletes who follow a plant-based diet and a sample of high school athletes who do not follow a plant-based diet.

Step 4: Collect Data Distribute the survey to the selected sample and collect data on dietary habits and athletic performance.

Step 5: Analyze Data Use statistical analysis to compare the data from the two samples and determine if there is a significant difference in athletic performance between those who follow a plant-based diet and those who do not.

Step 6 : Interpret Results Interpret the results of the analysis in the context of the research question and hypothesis. Discuss any limitations or potential biases in the study design.

Step 7: Draw Conclusions Based on the results, draw conclusions about whether a plant-based diet has a significant effect on athletic performance in high school athletes. If the hypothesis is supported by the data, discuss potential implications and future research directions.

Step 8: Communicate Findings Communicate the findings of the study in a clear and concise manner. Use appropriate language, visuals, and formats to ensure that the findings are understood and valued.

Applications of Research Process

The research process has numerous applications across a wide range of fields and industries. Some examples of applications of the research process include:

• Scientific research: The research process is widely used in scientific research to investigate phenomena in the natural world and develop new theories or technologies. This includes fields such as biology, chemistry, physics, and environmental science.
• Social sciences : The research process is commonly used in social sciences to study human behavior, social structures, and institutions. This includes fields such as sociology, psychology, anthropology, and economics.
• Education: The research process is used in education to study learning processes, curriculum design, and teaching methodologies. This includes research on student achievement, teacher effectiveness, and educational policy.
• Healthcare: The research process is used in healthcare to investigate medical conditions, develop new treatments, and evaluate healthcare interventions. This includes fields such as medicine, nursing, and public health.
• Business and industry : The research process is used in business and industry to study consumer behavior, market trends, and develop new products or services. This includes market research, product development, and customer satisfaction research.
• Government and policy : The research process is used in government and policy to evaluate the effectiveness of policies and programs, and to inform policy decisions. This includes research on social welfare, crime prevention, and environmental policy.

Purpose of Research Process

The purpose of the research process is to systematically and scientifically investigate a problem or question in order to generate new knowledge or solve a problem. The research process enables researchers to:

• Identify gaps in existing knowledge: By conducting a thorough literature review, researchers can identify gaps in existing knowledge and develop research questions that address these gaps.
• Collect and analyze data : The research process provides a structured approach to collecting and analyzing data. Researchers can use a variety of research methods, including surveys, experiments, and interviews, to collect data that is valid and reliable.
• Test hypotheses : The research process allows researchers to test hypotheses and make evidence-based conclusions. Through the systematic analysis of data, researchers can draw conclusions about the relationships between variables and develop new theories or models.
• Solve problems: The research process can be used to solve practical problems and improve real-world outcomes. For example, researchers can develop interventions to address health or social problems, evaluate the effectiveness of policies or programs, and improve organizational processes.
• Generate new knowledge : The research process is a key way to generate new knowledge and advance understanding in a given field. By conducting rigorous and well-designed research, researchers can make significant contributions to their field and help to shape future research.

Tips for Research Process

Here are some tips for the research process:

• Start with a clear research question : A well-defined research question is the foundation of a successful research project. It should be specific, relevant, and achievable within the given time frame and resources.
• Conduct a thorough literature review: A comprehensive literature review will help you to identify gaps in existing knowledge, build on previous research, and avoid duplication. It will also provide a theoretical framework for your research.
• Choose appropriate research methods: Select research methods that are appropriate for your research question, objectives, and sample size. Ensure that your methods are valid, reliable, and ethical.
• Be organized and systematic: Keep detailed notes throughout the research process, including your research plan, methodology, data collection, and analysis. This will help you to stay organized and ensure that you don’t miss any important details.
• Analyze data rigorously: Use appropriate statistical and analytical techniques to analyze your data. Ensure that your analysis is valid, reliable, and transparent.
• I nterpret results carefully : Interpret your results in the context of your research question and objectives. Consider any limitations or potential biases in your research design, and be cautious in drawing conclusions.
• Communicate effectively: Communicate your research findings clearly and effectively to your target audience. Use appropriate language, visuals, and formats to ensure that your findings are understood and valued.
• Collaborate and seek feedback : Collaborate with other researchers, experts, or stakeholders in your field. Seek feedback on your research design, methods, and findings to ensure that they are relevant, meaningful, and impactful.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What Is The Significance Of The Study?

In the vast landscape of academia, every research study serves a purpose beyond just adding to the pile of existing knowledge. It’s about unraveling mysteries, solving problems, and making the world a little better. But before diving into any research, one crucial question needs answering: What is the significance of the study? Let’s embark on a journey to understand the importance of this question and how it shapes the landscape of research.

What Is The Importance Of Studying?

Table of Contents

Studying is a fundamental aspect of human learning and development, playing a crucial role in various aspects of life. Its importance spans across personal, academic, professional, and societal domains. Here’s a breakdown of why studying is essential:

• Academic Achievement: Studying is essential for academic success. It helps students grasp concepts, retain information, and demonstrate their understanding through assessments. Whether it’s preparing for exams, completing assignments, or engaging in class discussions, studying forms the backbone of academic achievement.
• Skill Development: Studying isn’t just about memorizing facts; it’s also about developing critical skills such as problem-solving, analytical thinking, and communication. Through studying, individuals hone these skills, which are invaluable in both academic and real-world settings.
• Personal Growth: Studying expands one’s horizons and fosters personal growth. It exposes individuals to new ideas, perspectives, and experiences, challenging them to think critically and question assumptions. Additionally, studying encourages self-discipline, time management, and perseverance, all of which are essential qualities for personal success.
• Career Advancement: In today’s competitive job market, continuous learning is essential for career advancement. Studying allows individuals to acquire new knowledge, skills, and qualifications, making them more competitive and marketable to employers. Whether it’s pursuing higher education, attending professional development courses, or staying updated on industry trends, studying is crucial for career growth.
• Intellectual Stimulation: Studying stimulates the mind and fosters intellectual curiosity. It allows individuals to delve into topics of interest, explore complex ideas, and engage in meaningful intellectual discourse. This intellectual stimulation not only enriches one’s understanding of the world but also enhances cognitive abilities and overall mental well-being.
• Societal Contribution: Studying plays a vital role in advancing society as a whole. Through research, innovation, and knowledge dissemination, studying drives progress in various fields, from science and technology to arts and humanities. Additionally, educated individuals are better equipped to contribute positively to their communities, advocate for social change, and address pressing global challenges.

The significance of a study lies in its ability to address a specific problem or question, contribute to existing knowledge, and have practical applications or implications for various stakeholders. Let’s delve into each aspect with relevant examples:

Addressing a Specific Problem or Question

• Example: A study on the impact of social media usage on mental health among teenagers addresses the pressing concern of rising mental health issues in young people attributed to excessive screen time and online interactions.

Contributing to Existing Knowledge

• Example: A research project investigating the effects of climate change on biodiversity builds upon previous studies by providing new insights into how changing environmental conditions affect different species and ecosystems. By adding to the body of knowledge on this topic, the study contributes to our understanding of the complex interactions between climate and biodiversity.

Practical Applications or Implications

• Example: A study on the effectiveness of mindfulness-based interventions in reducing workplace stress offers practical implications for employers and employees alike. By demonstrating the positive impact of mindfulness practices on employee well-being and productivity, the study informs organizational policies and practices aimed at promoting a healthier work environment.

Informing Policy Decisions

• Example: Research on the economic impact of renewable energy adoption provides policymakers with valuable insights into the potential benefits of transitioning to sustainable energy sources. By quantifying the economic advantages and environmental benefits of renewable energy investments, the study informs policy decisions related to energy planning and resource allocation.

Addressing Social or Health Issues

• Example: Research into how well vaccination campaigns work to lower the spread of diseases is important for public health. This kind of study looks at how good vaccination plans are at stopping diseases from spreading. It also figures out what stops people from getting vaccinated. With this information, health programs can do better at preventing outbreaks and keeping communities safe from diseases.

Fostering Innovation and Progress

• Example: Research on the development of artificial intelligence algorithms for medical diagnosis advances technological innovation in healthcare. By harnessing the power of machine learning and data analytics, the study enables more accurate and efficient diagnosis of medical conditions, leading to improved patient outcomes and advancements in medical practice.

What Is The Significance Of The Study And Statement Of The Problem?

The significance of the study and the statement of the problem are two critical components of any research endeavor, as they lay the foundation for the entire study. Let’s explore their significance individually:

Significance of the Study

• The significance of the study articulates why the research is important and why it matters. It provides justification for conducting the study and highlights its relevance in the broader context of academia, society, or a specific field.
• Significance is about identifying the value and impact of the research in terms of its potential contributions to knowledge, practical applications, policy implications, or societal relevance.
• Without a clear understanding of the significance of the study, researchers may struggle to convey the importance of their work to stakeholders, peers, and the broader community.
• A well-defined significance statement serves as a guiding principle throughout the research process, helping researchers stay focused on the overarching goals and objectives of their study.

Statement of the Problem

• The statement of the problem defines the specific issue or question that the research seeks to address. It clarifies the scope and boundaries of the study by identifying the key variables, concepts, or phenomena under investigation.
• The problem statement highlights the gap or deficiency in existing knowledge that the research aims to fill. It identifies the research gap by demonstrating what is currently unknown, unresolved, or underexplored in the literature.
• A well-crafted problem statement provides a clear and concise description of the research problem, making it easier for readers to understand the purpose and rationale behind the study.
• By defining the problem upfront, researchers can effectively plan their research design, methodology, and data collection strategies to address the identified research gap.
• The statement of the problem serves as a roadmap for the research, guiding the selection of research questions, hypotheses, and analytical approaches to be used in the study.

How Do You Write The Significance Of Research?

Writing the significance of research involves clearly articulating why the study is important, relevant, and worthy of attention. Here’s a step-by-step guide on how to write the significance of research:

• Identify the Problem or Issue

Begin by clearly defining the problem, question, or issue that the research seeks to address. This sets the stage for explaining why the research is necessary.

• Review Existing Literature

Conduct a thorough review of existing literature in the field to understand what has already been studied and what gaps or limitations exist in current knowledge.

• Highlight the Gap in Knowledge

Identify the specific gap or deficiency in existing research that the study aims to fill. This could be a lack of research on a particular topic, conflicting findings in the literature, or unanswered questions that need further exploration.

• Explain the Relevance and Importance

Clearly articulate why the research is important and relevant in the broader context. Consider the potential implications of the research for theory development, practical applications, policy decisions, or societal impact.

• Demonstrate Potential Contributions

Explain how the research will contribute to advancing knowledge in the field. This could involve providing new insights, validating existing theories, developing innovative methodologies, or addressing practical problems.

• Consider Stakeholder Perspectives

Identify the stakeholders or audiences who will benefit from the research findings. Consider their perspectives and interests when explaining the significance of the research.

• Emphasize Practical Applications

Highlight any practical applications or real-world implications of the research. This could include informing policy decisions, improving practices, addressing societal challenges, or benefiting specific industries or communities.

• Provide Justification for Conducting the Study

Offer a compelling rationale for why the research is worth undertaking. This could involve emphasizing the urgency of the problem, the potential benefits of finding a solution, or the intellectual merit of exploring a novel research question.

• Be Concise and Clear

Write the significance of research in a clear, concise, and compelling manner. Avoid jargon or technical language that may obscure the message and focus on communicating the importance of the research in accessible terms.

• Revise and Refine

Review and revise the significance of research to ensure clarity, coherence, and persuasiveness. Solicit feedback from peers, mentors, or colleagues to refine your argument and strengthen your rationale.

In the ever-evolving world of research, the significance of each study lies in its ability to push the boundaries of knowledge, address pressing issues, and make a meaningful impact on the world.

By understanding why a study matters, researchers can ensure that their work contributes meaningfully to the collective pursuit of knowledge and progress. 

So the next time you embark on a research journey, remember to ask yourself: What is the significance of the study? The answer could shape the course of history.

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AI Index: State of AI in 13 Charts

In the new report, foundation models dominate, benchmarks fall, prices skyrocket, and on the global stage, the U.S. overshadows.

This year’s AI Index — a 500-page report tracking 2023’s worldwide trends in AI — is out.

The index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. This year’s report covers the rise of multimodal foundation models, major cash investments into generative AI, new performance benchmarks, shifting global opinions, and new major regulations.

Don’t have an afternoon to pore through the findings? Check out the high level here.

A Move Toward Open-Sourced

This past year, organizations released 149 foundation models, more than double the number released in 2022. Of these newly released models, 65.7% were open-source (meaning they can be freely used and modified by anyone), compared with only 44.4% in 2022 and 33.3% in 2021.

But At a Cost of Performance?

Closed-source models still outperform their open-sourced counterparts. On 10 selected benchmarks, closed models achieved a median performance advantage of 24.2%, with differences ranging from as little as 4.0% on mathematical tasks like GSM8K to as much as 317.7% on agentic tasks like AgentBench.

Biggest Players

Industry dominates AI, especially in building and releasing foundation models. This past year Google edged out other industry players in releasing the most models, including Gemini and RT-2. In fact, since 2019, Google has led in releasing the most foundation models, with a total of 40, followed by OpenAI with 20. Academia trails industry: This past year, UC Berkeley released three models and Stanford two.

Industry Dwarfs All

If you needed more striking evidence that corporate AI is the only player in the room right now, this should do it. In 2023, industry accounted for 72% of all new foundation models.

Prices Skyrocket

One of the reasons academia and government have been edged out of the AI race: the exponential increase in cost of training these giant models. Google’s Gemini Ultra cost an estimated $191 million worth of compute to train, while OpenAI’s GPT-4 cost an estimated $78 million. In comparison, in 2017, the original Transformer model, which introduced the architecture that underpins virtually every modern LLM, cost around $900.

What AI Race?

At least in terms of notable machine learning models, the United States vastly outpaced other countries in 2023, developing a total of 61 models in 2023. Since 2019, the U.S. has consistently led in originating the majority of notable models, followed by China and the UK.

Move Over, Human

As of 2023, AI has hit human-level performance on many significant AI benchmarks, from those testing reading comprehension to visual reasoning. Still, it falls just short on some benchmarks like competition-level math. Because AI has been blasting past so many standard benchmarks, AI scholars have had to create new and more difficult challenges. This year’s index also tracked several of these new benchmarks, including those for tasks in coding, advanced reasoning, and agentic behavior.

Private Investment Drops (But We See You, GenAI)

While AI private investment has steadily dropped since 2021, generative AI is gaining steam. In 2023, the sector attracted $25.2 billion, nearly ninefold the investment of 2022 and about 30 times the amount from 2019 (call it the ChatGPT effect). Generative AI accounted for over a quarter of all AI-related private investments in 2023.

U.S. Wins $$ Race

And again, in 2023 the United States dominates in AI private investment. In 2023, the $67.2 billion invested in the U.S. was roughly 8.7 times greater than the amount invested in the next highest country, China, and 17.8 times the amount invested in the United Kingdom. That lineup looks the same when zooming out: Cumulatively since 2013, the United States leads investments at $335.2 billion, followed by China with $103.7 billion, and the United Kingdom at $22.3 billion.

Where is Corporate Adoption?

More companies are implementing AI in some part of their business: In surveys, 55% of organizations said they were using AI in 2023, up from 50% in 2022 and 20% in 2017. Businesses report using AI to automate contact centers, personalize content, and acquire new customers. 

Younger and Wealthier People Worry About Jobs

Globally, most people expect AI to change their jobs, and more than a third expect AI to replace them. Younger generations — Gen Z and millennials — anticipate more substantial effects from AI compared with older generations like Gen X and baby boomers. Specifically, 66% of Gen Z compared with 46% of boomer respondents believe AI will significantly affect their current jobs. Meanwhile, individuals with higher incomes, more education, and decision-making roles foresee AI having a great impact on their employment.

While the Commonwealth Worries About AI Products

When asked in a survey about whether AI products and services make you nervous, 69% of Aussies and 65% of Brits said yes. Japan is the least worried about their AI products at 23%.  

Regulation Rallies

More American regulatory agencies are passing regulations to protect citizens and govern the use of AI tools and data. For example, the Copyright Office and the Library of Congress passed copyright registration guidance concerning works that contained material generated by AI, while the Securities and Exchange Commission developed a cybersecurity risk management strategy, governance, and incident disclosure plan. The agencies to pass the most regulation were the Executive Office of the President and the Commerce Department. 

The AI Index was first created to track AI development. The index collaborates with such organizations as LinkedIn, Quid, McKinsey, Studyportals, the Schwartz Reisman Institute, and the International Federation of Robotics to gather the most current research and feature important insights on the AI ecosystem. 

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How Inclusive Brands Fuel Growth

• Omar Rodríguez-Vilá,
• Dionne Nickerson,
• Sundar Bharadwaj

Years before the Barbie movie phenomenon, leaders at Mattel became concerned that consumer perceptions of the famous doll were out of sync with demographic trends. The company conducted in-depth research to understand how customers felt about Barbie and to determine whether more-inclusive versions presented a strong market opportunity. The findings led to a new inclusion strategy that affected all areas of the brand—product design, distribution, and commercial activities—and coincided with a period of significant growth. Barbie revenues increased 63% from 2015 to 2022—before the boost from the film.

Research shows that in most industries the perception of inclusion can materially change customers’ likelihood to purchase and willingness to recommend products and services.

This article presents a framework for increasing marketplace inclusion in three areas: seeing the market, which is about market definition, market intelligence, and strategies for growth; serving the market, which involves developing products, packaging, and other commercial practices; and being in the market, which looks at advocacy and the customer experience.

They unlock new sources of value by meeting the needs of underrecognized customers.

Idea in Brief

The opportunity.

Research shows that the perception of inclusion can materially change customers’ likelihood to purchase and willingness to recommend products and services.

The Problem

Despite the many business and societal benefits of marketplace inclusion, there is a systematic lack of it across industries.

The Approach

Greta Gerwig’s Barbie grossed more than $1 billion at the box office in about two weeks. Only 53 films have ever hit that mark (adjusted for inflation). The 2023 movie, which features themes of women’s empowerment, multiculturalism, and inclusiveness, was a divergence from the narrow social and demographic representation of the original tall, thin, white doll that Mattel introduced in 1959.

• OR Omar Rodríguez-Vilá is a professor of marketing practice at the Goizueta Business School at Emory University and the academic director of education at its Business & Society Institute.
• DN Dionne Nickerson is an assistant professor of marketing at the Goizueta Business School.
• SB Sundar Bharadwaj is the Coca-Cola Company Chair of Marketing at the University of Georgia’s Terry College of Business. LinkedIn: Sundar Bharadwaj

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What the trans care recommendations from the NHS England report mean

The report calls for more research on puberty blockers and hormone therapies.

A new report commissioned by the National Health Service England advocates for further research on gender-affirming care for transgender youth and young adults.

Dr. Hillary Cass, a former president of the Royal College of Paediatrics and Child Health, was appointed by NHS England and NHS Improvement to chair the Independent Review of Gender Identity Services in 2020 amid a rise in referrals to NHS' gender services. Upon review, she advises "extreme caution" for the use of hormone therapies.

"It is absolutely right that children and young people, who may be dealing with a complex range of issues around their gender identity, get the best possible support and expertise throughout their care," Cass states in the report.

Around 2022, about 5,000 adolescents and children were referred to the NHS' gender services. The report estimated that roughly 20% of children and young people seen by the Gender Identity Development Service (GIDS) enter a hormone pathway -- roughly 1,000 people under 18 in England.

Following four years of data analysis, Cass concluded that "while a considerable amount of research has been published in this field, systematic evidence reviews demonstrated the poor quality of the published studies, meaning there is not a reliable evidence base upon which to make clinical decisions, or for children and their families to make informed choices."

Cass continued: "The strengths and weaknesses of the evidence base on the care of children and young people are often misrepresented and overstated, both in scientific publications and social debate," read the report.

Among her recommendations, she urged the NHS to increase the available workforce in this field, to work on setting up more regional outlets for care, increase investment in research on this care, and improve the quality of care to meet international guidelines.

Cass' review comes as the NHS continues to expand its children and young people's gender identity services across the country. The NHS has recently opened new children and young people's gender services based in London and the Northwest.

NHS England, the country's universal healthcare system, said the report is expected to guide and shape its use of gender affirming care in children and potentially impact youth patients in England accessing gender-affirming care.

MORE: Lawsuit filed by families against Ohio trans care ban legislation

The debate over transgender youth care.

In an interview with The Guardian , Cass stated that her findings are not intended to undermine the validity of trans identities or challenge young people's right to transition but to improve the care they are receiving.

"We've let them down because the research isn't good enough and we haven't got good data," Cass told the news outlet. "The toxicity of the debate is perpetuated by adults, and that itself is unfair to the children who are caught in the middle of it. The children are being used as a football and this is a group that we should be showing more compassion to."

In the report, Cass argued that the knowledge and expertise of "experienced clinicians who have reached different conclusions about the best approach to care" has been "dismissed and invalidated" amid arguments concerning transgender care in youth.

Cass did not immediately respond to ABC News' request for comment.

Recommendations for trans youth care

Cass is calling for more thorough research that looks at the "characteristics, interventions and outcomes" of NHS gender service patients concerning puberty blockers and hormone therapy, particularly among children and adolescents.

The report's recommendations also urge caregivers to take an approach to care that considers young patients "holistically and not solely in terms of their gender-related distress."

The report notes that identity exploration is "a completely natural process during childhood and adolescence."

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Cass recommends that pre-pubertal children and their families have early discussions about how parents can best support their child "in a balanced and non-judgemental way," which may include "psychological and psychopharmacological treatments" to manage distress associated with gender incongruence and co-occurring conditions.

In past interviews, U.S. physicians told ABC News , that patients, their physicians and their families often engage in a lengthy process of building a customized and individualized approach to care, meaning not every patient will receive any or every type of gender-affirming medical care option.

Cass' report states that evidence particularly for puberty blockers in children and adolescents is "weak" regarding the impact on "gender dysphoria, mental or psychosocial health. The effect on cognitive and psychosexual development remains unknown."

The NHS has said it will halt routine use of puberty blockers as it prepares for a study into the practice later this year.

MORE: Amid anti-LGBTQ efforts, transgender community finds joy in 'chosen families'

According to the Endocrine Society puberty blockers, as opposed to hormone therapy, temporarily pause puberty so patients have more time to explore their gender identity.

The report also recommends "extreme caution" for transgender youth from age 16 who take more permanent hormone therapies.

"There should be a clear clinical rationale for providing hormones at this stage rather than waiting until an individual reaches 18," the report's recommendations state.

Hormone therapy, according to the Endocrine Society , triggers physical changes like hair growth, muscle development, body fat and more, that can help better align the body with a person's gender identity. It's not unusual for patients to stop hormone therapy and decide that they have transitioned as far as they wish, physicians have told ABC News.

Cass' report asserts that there are many unknowns about the use of both puberty blockers and hormones for minors, "despite their longstanding use in the adult transgender population."

"The lack of long-term follow-up data on those commencing treatment at an earlier age means we have inadequate information about the range of outcomes for this group," the report states.

Cass recommends that NHS England facilities have procedures in place to follow up with 17 to 25-year-old patients "to ensure continuity of care and support at a potentially vulnerable stage in their journey," as well as allow for further data and research on transgender minors through the years.

Several British medical organizations, including British Psychological Society and the Royal College of Paediatrics and Child Health, commended the report's recommendations to expand the workforce and invest in further research to allow young people to make better informed decisions.

“Dr Cass and her team have produced a thought-provoking, detailed and wide-ranging list of recommendations, which will have implications for all professionals working with gender-questioning children and young people," said Dr Roman Raczka, of the British Psychological Society. "It will take time to carefully review and respond to the whole report, but I am sure that psychology, as a profession, will reflect and learn lessons from the review, its findings and recommendations."

Some groups expressed fears that the report will be misused by anti-transgender groups.

"All children have the right to access specialist effective care on time and must be afforded the privacy to make decisions that are appropriate for them in consultation with a specialist," said human rights group Amnesty International. "This review is being weaponised by people who revel in spreading disinformation and myths about healthcare for trans young people."

Transgender care for people under 18 has been a source of contention in both the United States and the United Kingdom. Legislation is being pushed across the U.S. by many Republican legislators focused on banning all medical care options like puberty blockers and hormone therapies for minors. Some argue that gender-affirming care is unsafe for youth, or that they should wait until they're older.

Gender-affirming medical does come with risks, according to the Endocrine Society , including impacts to bone mineral density, cholesterol levels, and blood clot risks. However, physicians have told ABC News that all medications, surgeries or vaccines come with some kind of risk.

Major national medical associations in the U.S., including the American Academy of Pediatrics, the American Medical Association, the American Academy of Child and Adolescent Psychiatry, and more than 20 others have argued that gender-affirming care is safe, effective, beneficial, and medically necessary.

The first-of-its-kind gender care clinic at Johns Hopkins Hospital in Maryland opened in the 1960s, using similar procedures still used today.

Some studies have shown that some gender-affirming options can have positive impacts on the mental health of transgender patients, who may experience gender-related stress.

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Today’s nyt ‘connections’ hints and answers for thursday, april 18.

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Find the links between the words to win today's game of Connections.

Looking for Wednesday’s Connections hints and answers? You can find them here:

Happy Thursday, folks! I hope you’re having a spectacular week. Just the very best one possible.

Today’s NYT Connections hints and answers are coming right up.

How To Play Connections

In Connections , you’re presented with a grid of 16 words. Your task is to arrange them into four groups of four by figuring out the links between them. The groups could be things like horror movie franchises, a type of verb or rappers.

There’s only one solution for each puzzle, and you’ll need to be careful when it comes to words that might fit into more than one category. You can shuffle the words to perhaps help you see links between them.

Each group is color coded. The yellow group is usually the easiest to figure out, blue and green fall in the middle, and the purple group is typically the hardest one to deduce. The purple group often involves wordplay, so bear that in mind.

Soundgarden Hits No 1 For The First Time On A Billboard Chart With A 30 Year Old Song

Goldman sachs issues stark bitcoin halving price warning, an update on wednesday season 2 with a fantastic casting decision.

Select four words you think go together and press Submit. If you make a guess and you’re incorrect, you’ll lose a life. If you’re close to having a correct group, you might see a message telling you that you’re one word away from getting it right, but you’ll still need to figure out which one to swap.

If you make four mistakes, it’s game over. Let’s make sure that doesn’t happen with the help of some hints, and, if you’re really struggling, today’s Connections answers.

What Are Today’s Connections Hints?

Scroll slowly! Just after the hints for each of today’s Connections groups, I’ll reveal what the groups are without immediately telling you which words go into them.

Today’s 16 words are:

And the hints for today’s groups are:

• Yellow group — seen out on the street
• Green group — envisage
• Blue group — measurements, for short
• Purple group — linked by a precious metal on the elemental table

What Are Today’s Connections Groups?

Need some extra help?

Be warned: we’re starting to get into spoiler territory.

Today’s groups are...

• Yellow group — sidewalk sights
• Green group — have in mind
• Blue group — unit abbreviations
• Purple group — golden ____

What Are Today’s Connections Answers?

Spoiler alert! Don’t scroll any further down the page until you’re ready to find out today’s Connections answers.

This is your final warning!

Today’s Connections answers are...

• Yellow group — sidewalk sights (CURB, GRATE, GUTTER, MANHOLE)
• Green group — have in mind (AIM, INTEND, MEAN, PLAN)
• Blue group — unit abbreviations (CAL, GAL, IN, OZ)
• Purple group — golden ____ (FLEECE, GIRLS, PARACHUTE, RULE)

Back-to-back perfect games means I now have nine straight wins overall.

I thought at first there might be a group of HBO shows thanks to GIRLS, OZ and CURB, but I couldn't see a fourth word to go with those. I then turned my attention to MANHOLE and GUTTER, which gave me the yellows. After that, INTEND was my gateway into the greens.

I spent a few minutes considering my next move before disregarding the short words and realizing the purple connection. That left the blues for the win.

That’s all there is to it for today’s Connections clues and answers. Be sure to check my blog for hints and the solution for Friday’s game if you need them.

P.S. It’s day 2 of a mini column I’m currently calling British bands Kris listened to a lot while growing up (there must be a catchier name for this). Oddly enough, Biffy Clyro, the band I featured yesterday, has a song called “That Golden Rule.”

I really enjoyed the first Hell is for Heroes album, especially this song. The opening harmonics draw me in every time and that chorus hits like thunder.

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• Indian J Anaesth
• v.60(9); 2016 Sep

Interpretation and display of research results

Dilip kumar kulkarni.

Department of Anaesthesiology and Intensive Care, Nizam's Institute of Medical Sciences, Hyderabad, Telangana, India

It important to properly collect, code, clean and edit the data before interpreting and displaying the research results. Computers play a major role in different phases of research starting from conceptual, design and planning, data collection, data analysis and research publication phases. The main objective of data display is to summarize the characteristics of a data and to make the data more comprehensible and meaningful. Usually data is presented depending upon the type of data in different tables and graphs. This will enable not only to understand the data behaviour, but also useful in choosing the different statistical tests to be applied.

INTRODUCTION

Collection of data and display of results is very important in any study. The data of an experimental study, observational study or a survey are required to be collected in properly designed format for documentation, taking into consideration the design of study and different end points of the study. Usually data are collected in the proforma of the study. The data recorded and documented should be stored carefully in documents and in electronic form for example, excel sheets or data bases.

The data are usually classified into qualitative and quantitative [ Table 1 ]. Qualitative data is further divided into two categories, unordered qualitative data, such as blood groups (A, B, O, AB); and ordered qualitative data, such as severity of pain (mild, moderate, severe). Quantitative data are numerical and fall into two categories: discrete quantitative data, such as the internal diameter of endotracheal tube; and continuous quantitative data, such as blood pressure.[ 1 ]

Examples of types of data and display of data

Data Coding is needed to allow the data recorded in categories to be used easily in statistical analysis with a computer. Coding assigns a unique number to each possible response. A few statistical packages analyse categorical data directly. If a number is assigned to categorical data, it becomes easier to analyse. This means that when the data are analysed and reported, the appropriate label needs to be assigned back to the numerical value to make it meaningful. The codes such as 1/0 for yes/no has the added advantage that the variable's 1/0 values can be easily analysed. The record of the codes modified is to be stored for later reference. Such coding can also be done for categorical ordinal data to convert in to numerical ordinal data, for example the severity of pain mild, moderate and severe into 1, 2 and 3 respectively.

PROCESS OF DATA CHECKING, CLEANING AND EDITING

In clinical research, errors occur despite designing the study properly, entering data carefully and preventing errors. Data cleaning and editing are carried out to identify and correct these errors, so that the study results will be accurate.[ 2 ]

Data entry errors in case of sex, dates, double entries and unexpected results are to be corrected unquestionably. Data editing can be done in three phases namely screening, diagnosing and editing [ Figure 1 ].

Process of data checking, cleaning and editing in three phases

Screening phase

During screening of data, it is possible to distinguish the odd data, excess of data, double entries, outliers, and unexpected results. Screening methods are checking of questionnaires, data validation, browsing the excel sheets, data tables and graphical methods to observe data distribution.

Diagnostic phase

The nature of the data can be assessed in this phase. The data entries can be true normal, true errors, outliers, unexpected results.

Treatment phase

Once the data nature is identified the editing can be done by correcting, deleting or leaving the data sets unchanged.

The abnormal data points usually have to be corrected or to be deleted.[ 2 ] However some authors advocate these data points to be included in analysis.[ 3 ] If these extreme data points are deleted, they should be reported as “excluded from analysis”.[ 4 ]

ROLE OF COMPUTERS IN RESEARCH

The role of computers in scientific research is very high; the computers have the ability to perform the analytic tasks with high speed, accuracy and consistency. The Computers role in research process can be explained in different phases.[ 5 ]

Role of computer in conceptual phase

The conceptual phase consists of formulation of research problem, literature survey, theoretical frame work and developing the hypothesis. Computers are useful in searching the literatures. The references can be stored in the electronic database.

Role of computers in design and planning phase

This phase consists of research design preparation and determining sample design, population size, research variables, sampling plan, reviewing research plan and pilot study. The role of computers in these process is almost indispensable.

Role of computers in data collection phase

The data obtained from the subjects stored in computers are word files or excel spread sheets or statistical software data files or from data centers of hospital information management systems (data warehouse). If the data are stored in electronic format checking the data becomes easier. Thus, computers help in data entry, data editing, and data management including follow up actions. Examples of editors are Word Pad, SPSS data editor, word processors.

Role of computers in data analysis

This phase mainly consist of statistical analysis of the data and interpretation of results. Software like Minitab (Minitab Inc. USA.), SPSS (IBM Crop. New York), NCSS (LLC. Kaysville, Utah, USA) and spreadsheets are widely used.

Role of computer in research publication

Research article, research paper, research thesis or research dissertation is typed in word processing software in computers and stored. Which can be easily published in different electronic formats.[ 5 ]

DATA DISPLAY AND DESCRIPTION OF RESEARCH DATA

Data display and description is an important part of any research project which helps in knowing the distribution of data, detecting errors, missing values and outliers. Ultimately the data should be more comprehensible and meaningful.

Tables are commonly used for describing both qualitative and quantitative data. The graphs are useful for visualising the data and understanding the variations and trends of the data. Qualitative data are usually described by using bar or pie charts. Histograms, polygons or box plots are used to represent quantitative data.[ 1 ]

Qualitative data

Tabulation of qualitative data.

The qualitative observations are categorised in to different categories. The category frequency is nothing but the number of observations with in that category. The category relative frequency can be calculated by dividing the number of observations in the category by total number of observations. The Percentage for a category is more commonly used to describe qualitative data. It can be computed by multiplying relative frequency with hundred.[ 6 , 7 ]

The classification of 30 Patients of a group by severity of postoperative pain presented in Table 2 . The frequency table for this data computed by using the software NCSS[ 8 ] is shown in Table 3 .

The classification of post-operative pain in patients

The frequency table for the variable pain

Graphical display of qualitative data

The qualitative data are commonly displayed by bar graphs and pie charts.[ 9 ]

Bar graphs displays information of the frequency, relative frequency or percentage of each category on vertical axis or horizontal axis of the graph. [ Figure 2 ] Pie charts depicts the same information in divided slices in a complete circle. The area for the circle is equal to the frequency, relative frequency or percentage of that category [ Figure 3 ].

The bar graph generated by computer using NCSS software for the variable pain

The Pie graph generated by computer using NCSS software for the variable pain

Quantitative data

Tabulation of quantitative data.

The quantitative data are usually presented as frequency distribution or relative frequency rather than percentage. The data are divided into different classes. The upper and lower limits or the width of classes will depend up on the size of the data and can easily be adjusted.

The frequency distribution and relative frequency distribution table can be constructed in the following manner:

• The quantitative data are divided into number of classes. The lower limit and upper limit of the classes have to be defined.
• The range or width of the class intervals can be calculated by dividing the difference in the upper limit and lower limit by total number of classes.
• The class frequency is the number of observations that fall in that class.
• The relative class frequency can be calculated by dividing class frequency by total number of observations.

Example of frequency table for the data of Systolic blood pressure of 60 patients undergoing craniotomy is shown in Table 4 . The number of classes were 20, the lower limit and the upper limit were 86 mm of Hg and 186 mm of Hg respectively.

Frequency tabulation of systolic blood pressure in sixty patients (unit is mm Hg)

Graphical description of quantitative data

The frequency distribution is usually depicted in histograms. The count or frequency is plotted along the vertical axis and the horizontal axis represents data values. The normality of distribution can be assessed visually by histograms. A frequency histogram is constructed for the dataset of systolic blood pressure, from the frequency Table 4 [ Figure 4 ].

The frequency histogram for the data set of systolic blood pressure (BP), for which the frequency table is constructed in Table 4

Box plot gives the information of spread of observations in a single group around a centre value. The distribution pattern and extreme values can be easily viewed by box plot. A boxplot is constructed for the dataset of systolic blood pressure, from the frequency Table 4 [ Figure 5 ].

Box plot is constructed from data of Table 4

Polygon construction is similar to histogram. However it is a line graph connecting the data points at mid points of class intervals. The polygon is simpler and outline the data pattern clearly[ 8 ] [ Figure 6 ].

A frequency polygon constructed from data of Table 4 in NCSS software

It is often necessary to further summarise quantitative data, for example, for hypothesis testing. The most important elements of a data are its location, which is measured by mean, median and mode. The other parameters are variability (range, interquartile range, standard deviation and variance) and shape of the distribution (normal, skewness, and kurtosis). The details of which will be discussed in the next chapter.

The proper designing of research methodology is an important step from the conceptual phase to the conclusion phase and the computers play an invaluable role from the beginning to the end of a study. The data collection, data storage and data management are vital for any study. The data display and interpretation will help in understating the behaviour of the data and also to know the assumptions for statistical analysis.