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1. | |
A. | Wilkinson |
B. | CR Kothari |
C. | Kerlinger |
D. | Goode and Halt |
Answer» D. Goode and Halt |
2. | |
A. | Marshall |
B. | P.V. Young |
C. | Emory |
D. | Kerlinger |
Answer» C. Emory |
3. | |
A. | Young |
B. | Kerlinger |
C. | Kothari |
D. | Emory |
Answer» A. Young |
4. | |
A. | Experiment |
B. | Observation |
C. | Deduction |
D. | Scientific method |
Answer» D. Scientific method |
5. | |
A. | Deduction |
B. | Scientific method |
C. | Observation |
D. | experience |
Answer» B. Scientific method |
6. | |
A. | Objectivity |
B. | Ethics |
C. | Proposition |
D. | Neutrality |
Answer» A. Objectivity |
7. | |
A. | Induction |
B. | Deduction |
C. | Research |
D. | Experiment |
Answer» A. Induction |
8. | |
A. | Belief |
B. | Value |
C. | Objectivity |
D. | Subjectivity |
Answer» C. Objectivity |
9. | |
A. | Induction |
B. | deduction |
C. | Observation |
D. | experience |
Answer» B. deduction |
10. | |
A. | Caroline |
B. | P.V.Young |
C. | Dewey John |
D. | Emory |
Answer» B. P.V.Young |
11. | |
A. | Facts |
B. | Values |
C. | Theory |
D. | Generalization |
Answer» C. Theory |
12. | |
A. | Jack Gibbs |
B. | PV Young |
C. | Black |
D. | Rose Arnold |
Answer» B. PV Young |
13. | |
A. | Black James and Champion |
B. | P.V. Young |
C. | Emory |
D. | Gibbes |
Answer» A. Black James and Champion |
14. | |
A. | Theory |
B. | Value |
C. | Fact |
D. | Statement |
Answer» C. Fact |
15. | |
A. | Good and Hatt |
B. | Emory |
C. | P.V. Young |
D. | Claver |
Answer» A. Good and Hatt |
16. | |
A. | Concept |
B. | Variable |
C. | Model |
D. | Facts |
Answer» C. Model |
17. | |
A. | Objects |
B. | Human beings |
C. | Living things |
D. | Non living things |
Answer» B. Human beings |
18. | |
A. | Natural and Social |
B. | Natural and Physical |
C. | Physical and Mental |
D. | Social and Physical |
Answer» A. Natural and Social |
19. | |
A. | Causal Connection |
B. | reason |
C. | Interaction |
D. | Objectives |
Answer» A. Causal Connection |
20. | |
A. | Explain |
B. | diagnosis |
C. | Recommend |
D. | Formulate |
Answer» B. diagnosis |
21. | |
A. | Integration |
B. | Social Harmony |
C. | National Integration |
D. | Social Equality |
Answer» A. Integration |
22. | |
A. | Unit |
B. | design |
C. | Random |
D. | Census |
Answer» B. design |
23. | |
A. | Objectivity |
B. | Specificity |
C. | Values |
D. | Facts |
Answer» A. Objectivity |
24. | |
A. | Purpose |
B. | Intent |
C. | Methodology |
D. | Techniques |
Answer» B. Intent |
25. | |
A. | Pure Research |
B. | Action Research |
C. | Pilot study |
D. | Survey |
Answer» A. Pure Research |
26. | |
A. | Pure Research |
B. | Survey |
C. | Action Research |
D. | Long term Research |
Answer» B. Survey |
27. | |
A. | Survey |
B. | Action research |
C. | Analytical research |
D. | Pilot study |
Answer» C. Analytical research |
28. | |
A. | Fundamental Research |
B. | Analytical Research |
C. | Survey |
D. | Action Research |
Answer» D. Action Research |
29. | |
A. | Action Research |
B. | Survey |
C. | Pilot study |
D. | Pure Research |
Answer» D. Pure Research |
30. | |
A. | Quantitative |
B. | Qualitative |
C. | Pure |
D. | applied |
Answer» B. Qualitative |
31. | |
A. | Empirical research |
B. | Conceptual Research |
C. | Quantitative research |
D. | Qualitative research |
Answer» B. Conceptual Research |
32. | |
A. | Clinical or diagnostic |
B. | Causal |
C. | Analytical |
D. | Qualitative |
Answer» A. Clinical or diagnostic |
33. | |
A. | Field study |
B. | Survey |
C. | Laboratory Research |
D. | Empirical Research |
Answer» C. Laboratory Research |
34. | |
A. | Clinical Research |
B. | Experimental Research |
C. | Laboratory Research |
D. | Empirical Research |
Answer» D. Empirical Research |
35. | |
A. | Survey |
B. | Empirical |
C. | Clinical |
D. | Diagnostic |
Answer» A. Survey |
36. | |
A. | Ostle |
B. | Richard |
C. | Karl Pearson |
D. | Kerlinger |
Answer» C. Karl Pearson |
37. | |
A. | Redmen and Mory |
B. | P.V.Young |
C. | Robert C meir |
D. | Harold Dazier |
Answer» A. Redmen and Mory |
38. | |
A. | Technique |
B. | Operations |
C. | Research methodology |
D. | Research Process |
Answer» C. Research methodology |
39. | |
A. | Slow |
B. | Fast |
C. | Narrow |
D. | Systematic |
Answer» D. Systematic |
40. | |
A. | Logical |
B. | Non logical |
C. | Narrow |
D. | Systematic |
Answer» A. Logical |
41. | |
A. | Delta Kappan |
B. | James Harold Fox |
C. | P.V.Young |
D. | Karl Popper |
Answer» B. James Harold Fox |
42. | |
A. | Problem |
B. | Experiment |
C. | Research Techniques |
D. | Research methodology |
Answer» D. Research methodology |
43. | |
A. | Field Study |
B. | diagnosis tic study |
C. | Action study |
D. | Pilot study |
Answer» B. diagnosis tic study |
44. | |
A. | Social Science Research |
B. | Experience Survey |
C. | Problem formulation |
D. | diagnostic study |
Answer» A. Social Science Research |
45. | |
A. | P.V. Young |
B. | Kerlinger |
C. | Emory |
D. | Clover Vernon |
Answer» B. Kerlinger |
46. | |
A. | Black James and Champions |
B. | P.V. Young |
C. | Mortan Kaplan |
D. | William Emory |
Answer» A. Black James and Champions |
47. | |
A. | Best John |
B. | Emory |
C. | Clover |
D. | P.V. Young |
Answer» D. P.V. Young |
48. | |
A. | Belief |
B. | Value |
C. | Confidence |
D. | Overconfidence |
Answer» D. Overconfidence |
49. | |
A. | Velocity |
B. | Momentum |
C. | Frequency |
D. | gravity |
Answer» C. Frequency |
50. | |
A. | Research degree |
B. | Research Academy |
C. | Research Labs |
D. | Research Problems |
Answer» A. Research degree |
51. | |
A. | Book |
B. | Journal |
C. | News Paper |
D. | Census Report |
Answer» C. News Paper |
52. | |
A. | Lack of sufficient number of Universities |
B. | Lack of sufficient research guides |
C. | Lack of sufficient Fund |
D. | Lack of scientific training in research |
Answer» D. Lack of scientific training in research |
53. | |
A. | Indian Council for Survey and Research |
B. | Indian Council for strategic Research |
C. | Indian Council for Social Science Research |
D. | Inter National Council for Social Science Research |
Answer» C. Indian Council for Social Science Research |
54. | |
A. | University Grants Commission |
B. | Union Government Commission |
C. | University Governance Council |
D. | Union government Council |
Answer» A. University Grants Commission |
55. | |
A. | Junior Research Functions |
B. | Junior Research Fellowship |
C. | Junior Fellowship |
D. | None of the above |
Answer» B. Junior Research Fellowship |
56. | |
A. | Formulation of a problem |
B. | Collection of Data |
C. | Editing and Coding |
D. | Selection of a problem |
Answer» D. Selection of a problem |
57. | |
A. | Fully solved |
B. | Not solved |
C. | Cannot be solved |
D. | half- solved |
Answer» D. half- solved |
58. | |
A. | Schools and Colleges |
B. | Class Room Lectures |
C. | Play grounds |
D. | Infra structures |
Answer» B. Class Room Lectures |
59. | |
A. | Observation |
B. | Problem |
C. | Data |
D. | Experiment |
Answer» B. Problem |
60. | |
A. | Solution |
B. | Examination |
C. | Problem formulation |
D. | Problem Solving |
Answer» C. Problem formulation |
61. | |
A. | Very Common |
B. | Overdone |
C. | Easy one |
D. | rare |
Answer» B. Overdone |
62. | |
A. | Statement of the problem |
B. | Gathering of Data |
C. | Measurement |
D. | Survey |
Answer» A. Statement of the problem |
63. | |
A. | Professor |
B. | Tutor |
C. | HOD |
D. | Guide |
Answer» D. Guide |
64. | |
A. | Statement of the problem |
B. | Understanding the nature of the problem |
C. | Survey |
D. | Discussions |
Answer» B. Understanding the nature of the problem |
65. | |
A. | Statement of the problem |
B. | Understanding the nature of the problem |
C. | Survey the available literature |
D. | Discussion |
Answer» C. Survey the available literature |
66. | |
A. | Survey |
B. | Discussion |
C. | Literature survey |
D. | Re Phrasing the Research problem |
Answer» D. Re Phrasing the Research problem |
67. | |
A. | Title |
B. | Index |
C. | Bibliography |
D. | Concepts |
Answer» A. Title |
68. | |
A. | Questions to be answered |
B. | methods |
C. | Techniques |
D. | methodology |
Answer» A. Questions to be answered |
69. | |
A. | Speed |
B. | Facts |
C. | Values |
D. | Novelty |
Answer» D. Novelty |
70. | |
A. | Originality |
B. | Values |
C. | Coherence |
D. | Facts |
Answer» A. Originality |
71. | |
A. | Academic and Non academic |
B. | Cultivation |
C. | Academic |
D. | Utilitarian |
Answer» B. Cultivation |
72. | |
A. | Information |
B. | firsthand knowledge |
C. | Knowledge and information |
D. | models |
Answer» C. Knowledge and information |
73. | |
A. | Alienation |
B. | Cohesion |
C. | mobility |
D. | Integration |
Answer» B. Cohesion |
74. | |
A. | Scientific temper |
B. | Age |
C. | Money |
D. | time |
Answer» A. Scientific temper |
75. | |
A. | Secular |
B. | Totalitarian |
C. | democratic |
D. | welfare |
Answer» D. welfare |
76. | |
A. | Hypothesis |
B. | Variable |
C. | Concept |
D. | facts |
Answer» C. Concept |
77. | |
A. | Abstract and Coherent |
B. | Concrete and Coherent |
C. | Abstract and concrete |
D. | None of the above |
Answer» C. Abstract and concrete |
78. | |
A. | 4 |
B. | 6 |
C. | 10 |
D. | 2 |
Answer» D. 2 |
79. | |
A. | Observation |
B. | formulation |
C. | Theory |
D. | Postulation |
Answer» D. Postulation |
80. | |
A. | Formulation |
B. | Postulation |
C. | Intuition |
D. | Observation |
Answer» C. Intuition |
81. | |
A. | guide |
B. | tools |
C. | methods |
D. | Variables |
Answer» B. tools |
82. | |
A. | Metaphor |
B. | Simile |
C. | Symbols |
D. | Models |
Answer» C. Symbols |
83. | |
A. | Formulation |
B. | Calculation |
C. | Abstraction |
D. | Specification |
Answer» C. Abstraction |
84. | |
A. | Verbal |
B. | Oral |
C. | Hypothetical |
D. | Operational |
Answer» C. Hypothetical |
85. | |
A. | Kerlinger |
B. | P.V. Young |
C. | Aurthur |
D. | Kaplan |
Answer» B. P.V. Young |
86. | |
A. | Same and different |
B. | Same |
C. | different |
D. | None of the above |
Answer» C. different |
87. | |
A. | Greek |
B. | English |
C. | Latin |
D. | Many languages |
Answer» D. Many languages |
88. | |
A. | Variable |
B. | Hypothesis |
C. | Data |
D. | Concept |
Answer» B. Hypothesis |
89. | |
A. | Data |
B. | Concept |
C. | Research |
D. | Hypothesis |
Answer» D. Hypothesis |
90. | |
A. | Lund berg |
B. | Emory |
C. | Johnson |
D. | Good and Hatt |
Answer» D. Good and Hatt |
91. | |
A. | Good and Hatt |
B. | Lund berg |
C. | Emory |
D. | Orwell |
Answer» B. Lund berg |
92. | |
A. | Descriptive |
B. | Imaginative |
C. | Relational |
D. | Variable |
Answer» A. Descriptive |
93. | |
A. | Null Hypothesis |
B. | Working Hypothesis |
C. | Relational Hypothesis |
D. | Descriptive Hypothesis |
Answer» B. Working Hypothesis |
94. | |
A. | Relational Hypothesis |
B. | Situational Hypothesis |
C. | Null Hypothesis |
D. | Casual Hypothesis |
Answer» C. Null Hypothesis |
95. | |
A. | Abstract |
B. | Dependent |
C. | Independent |
D. | Separate |
Answer» C. Independent |
96. | |
A. | Independent |
B. | Dependent |
C. | Separate |
D. | Abstract |
Answer» B. Dependent |
97. | |
A. | Causal |
B. | Relational |
C. | Descriptive |
D. | Tentative |
Answer» B. Relational |
98. | |
A. | One |
B. | Many |
C. | Zero |
D. | None of these |
Answer» C. Zero |
99. | |
A. | Statistical Hypothesis |
B. | Complex Hypothesis |
C. | Common sense Hypothesis |
D. | Analytical Hypothesis |
Answer» C. Common sense Hypothesis |
100. | |
A. | Null Hypothesis |
B. | Casual Hypothesis |
C. | Barren Hypothesis |
D. | Analytical Hypothesis |
Answer» D. Analytical Hypothesis |
Numbers, Facts and Trends Shaping Your World
Read our research on:
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Table of contents.
The American Trends Panel (ATP), created by Pew Research Center, is a nationally representative panel of randomly selected U.S. adults. Panelists participate via self-administered web surveys. Panelists who do not have internet access at home are provided with a tablet and wireless internet connection. Interviews are conducted in both English and Spanish. The panel is being managed by Ipsos.
Data in this report is drawn from ATP Wave 146, conducted from April 8 to April 14, 2024. It includes oversample s of non-Hispanic Asian adults, non-Hispanic Black adults, Hispanic adults, and adults ages 18 to 29 in order to provide more precise estimates of the opinions and experiences of these smaller demographic subgroups. It also included an oversample of validated 2016 and 2020 “vote switchers,” who either voted for Donald Trump in 2020 but not in 2016, or who voted for Joe Biden in 2020 but not for Hillary Clinton in 2016. These oversampled groups are weighted back to reflect their correct proportions in the population.
A total of 8,709 panelists responded out of 9,527 who were sampled, for a response rate of 91%. The cumulative response rate accounting for nonresponse to the recruitment surveys and attrition is 3%. The break-off rate among panelists who logged on to the survey and completed at least one item is less than 1%. The margin of sampling error for the full sample of 8,709 respondents is plus or minus 1.5 percentage points.
The ATP was created in 2014, with the first cohort of panelists invited to join the panel at the end of a large, national, landline and cellphone random-digit-dial survey that was conducted in both English and Spanish. Two additional recruitments were conducted using the same method in 2015 and 2017, respectively. Across these three surveys, a total of 19,718 adults were invited to join the ATP, of whom 9,942 (50%) agreed to participate.
In August 2018, the ATP switched from telephone to address-based sampling (ABS) recruitment. A study cover letter and a pre-incentive are mailed to a stratified, random sample of households selected from the U.S. Postal Service’s Delivery Sequence File. This Postal Service file has been estimated to cover as much as 98% of the population, although some studies suggest that the coverage could be in the low 90% range. 1 Within each sampled household, the adult with the next birthday is asked to participate. Other details of the ABS recruitment protocol have changed over time but are available upon request. 2
We have recruited a national sample of U.S. adults to the ATP approximately once per year since 2014. In some years, the recruitment has included additional efforts (known as an “oversample”) to boost sample size with underrepresented groups. For example, Hispanic adults, Black adults and Asian adults were oversampled in 2019, 2022 and 2023, respectively.
Across the six address-based recruitments, a total of 23,862 adults were invited to join the ATP, of whom 20,917 agreed to join the panel and completed an initial profile survey. Of the 30,859 individuals who have ever joined the ATP, 11,902 remained active panelists and continued to receive survey invitations at the time this survey was conducted.
The American Trends Panel never uses breakout routers or chains that direct respondents to additional surveys.
The overall target population for this survey was noninstitutionalized persons ages 18 and older living in the U.S., including Alaska and Hawaii. It featured a stratified random sample from the ATP in which the following groups were selected with certainty:
The remaining panelists were sampled at rates designed to ensure that the share of respondents in each stratum is proportional to its share of the U.S. adult population to the greatest extent possible. Respondent weights are adjusted to account for differential probabilities of selection as described in the Weighting section below.
The questionnaire was developed by Pew Research Center in consultation with Ipsos. The web program was rigorously tested on both PC and mobile devices by the Ipsos project management team and Pew Research Center researchers. The Ipsos project management team also populated test data that was analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey.
All respondents were offered a post-paid incentive for their participation. Respondents could choose to receive the post-paid incentive in the form of a check or a gift code to Amazon.com or could choose to decline the incentive. Incentive amounts ranged from $5 to $20 depending on whether the respondent belongs to a part of the population that is harder or easier to reach. Differential incentive amounts were designed to increase panel survey participation among groups that traditionally have low survey response propensities.
The data collection field period for this survey was April 8 to April 14, 2024. Postcard notifications were mailed to a subset of ATP panelists 4 with a known residential address on April 8.
Invitations were sent out in two separate launches: soft launch and full launch. Sixty panelists were included in the soft launch, which began with an initial invitation sent on April 8. The ATP panelists chosen for the initial soft launch were known responders who had completed previous ATP surveys within one day of receiving their invitation. All remaining English- and Spanish-speaking sampled panelists were included in the full launch and were sent an invitation on April 9.
All panelists with an email address received an email invitation and up to two email reminders if they did not respond to the survey. All ATP panelists who consented to SMS messages received an SMS invitation and up to two SMS reminders.
To ensure high-quality data, the Center’s researchers performed data quality checks to identify any respondents showing clear patterns of satisficing. This includes checking for whether respondents left questions blank at very high rates or always selected the first or last answer presented. As a result of this checking, three ATP respondents were removed from the survey dataset prior to weighting and analysis.
The ATP data is weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process.
First, each panelist begins with a base weight that reflects their probability of selection for their initial recruitment survey. These weights are then rescaled and adjusted to account for changes in the design of ATP recruitment surveys from year to year. Finally, the weights are calibrated to align with the population benchmarks in the accompanying table to correct for nonresponse to recruitment surveys and panel attrition. If only a subsample of panelists was invited to participate in the wave, this weight is adjusted to account for any differential probabilities of selection.
Among the panelists who completed the survey, this weight is then calibrated again to align with the population benchmarks identified in the accompanying table and trimmed at the 2nd and 98th percentiles to reduce the loss in precision stemming from variance in the weights. This trimming is performed separately among non-Hispanic Black, non-Hispanic Asian, Hispanic and all other respondents. Sampling errors and tests of statistical significance take into account the effect of weighting.
The following table shows the unweighted sample sizes and the error attributable to sampling that would be expected at the 95% level of confidence for different groups in the survey.
Sample sizes and sampling errors for other subgroups are available upon request. In addition to sampling error, one should bear in mind that question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls.
This report also features questions fielded in a later survey, ATP Wave 148. Further information for those questions can be found here .
© Pew Research Center 2024
Family income data reported in this study is adjusted for household size and cost-of-living differences by geography. Panelists then are assigned to income tiers that are based on the median adjusted family income of all American Trends Panel members. The process uses the following steps:
Two examples of how a given area’s cost-of-living adjustment was calculated are as follows: the Anniston-Oxford metropolitan area in Alabama is a relatively inexpensive area, with a price level that is 16.2% less than the national average. The San Francisco-Oakland-Berkeley metropolitan area in California is one of the most expensive areas, with a price level that is 19.8% higher than the national average. Income in the sample is adjusted to make up for this difference. As a result, a family with an income of $41,900 in the Anniston-Oxford area is as well off financially as a family of the same size with an income of $59,900 in San Francisco.
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ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .
© 2024 Pew Research Center
Table of Contents
Business research methodologies enable organizations to gather meaningful data and derive actionable insights. From qualitative interviews to quantitative analytics, selecting the appropriate research approach is foundational. This article will explore “ DBA research methodologies ” utilizing both qualitative and quantitative techniques to demonstrate how “doctoral studies” rigorously examine topics for optimal “business research methods”.
“ DBA research methodologies ” incorporate diverse academic disciplines to study complex business problems. Common approaches include:
“Qualitative research” methods like ethnography and case study analysis are used to gather in-depth, descriptive data on behaviors, processes, and “why” questions through interviews, focus groups, observations, etc.
“ Quantitative analysis ” uses statistical modeling and large datasets to identify correlations and patterns that answer “what” and “how many” questions.
Not all techniques work for every research scenario. Choosing the proper methodology requires clearly defining the question and desired outcomes upfront.
“DBA research methodologies” selection criteria include:
“ Qualitative research “, like case studies, offers an intimate understanding of management challenges through first-hand experiences and perspectives.
For example, interviewing executives on leadership development initiatives may reveal:
Such vivid insights direct specific improvements. They also inform quantitative follow-up studies predicting retention boosts from particular changes.
While qualitative designs provide depth, “quantitative analysis” delivers breadth by statistically testing hypotheses on large samples. Benefits include:
For example, leadership surveys across 500 managers could model links between specific coaching interactions, engagement gains and productivity metrics.
Combining qualitative and quantitative business research techniques as part of robust “DBA research methodologies” boosts the credibility and practical value of findings. The strengths of each approach offset the other’s limitations.
Sophisticated “DBA research methodologies” necessitate understanding the full toolkit of “business research methods” from ethnographies to experiments. While qualitative designs reveal key psychological and social dynamics, quantitative analytics assess their business impacts more conclusively. Combining these techniques produces superior insights to empower impactful organizational decisions and leadership strategies.
Common qualitative methods include in-depth interviews, focus groups, participant observations, case study analysis, and ethnographic research. These techniques gather non-numerical data on behaviors, emotions, organizational processes, and experiential perspectives.
Quantitative analytics, such as surveys and experiments, that collect numerical data for statistical analysis are preferred for testing hypotheses, predicting outcomes, generalizing results to wider populations, and establishing causal, correlational, or probabilistic relationships between variables.
Combining qualitative and quantitative techniques mitigates the limitations of each, providing richer insights through an initial exploratory phase to uncover themes, behaviors and language for follow-up hypothesis testing using broader samples and correlational statistics.
Yes, clearly defining the research purpose and goals upfront provides criteria to select the most appropriate primary and supporting techniques, whether qualitative, quantitative or both.
Rigorous quality standards include mitigating bias, establishing validity and reliability measures, choosing representative samples, aligning analysis with data collected, accurately reporting limitations, and ethically obtaining informed consent.
Literature reviews critically examine prior theories and findings to position new questions, avoid duplication, select proven measurements, build foundational knowledge, and identify promising methodological directions.
Data mining, machine learning predictive modeling, social network analysis, multivariate statistics, and text mining are increasingly supplementing traditional analytics to uncover insights from today’s complex business datasets.
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The proliferation of microplastics (MPs) represents a burgeoning environmental and health crisis. Measuring less than 5 mm in diameter, MPs have infiltrated atmospheric, freshwater, and terrestrial ecosystems, penetrating commonplace consumables like seafood, sea salt, and bottled beverages. Their size and surface area render them susceptible to chemical interactions with physiological fluids and tissues, raising bioaccumulation and toxicity concerns. Human exposure to MPs occurs through ingestion, inhalation, and dermal contact. To date, there is no direct evidence identifying MPs in penile tissue. The objective of this study was to assess for potential aggregation of MPs in penile tissue. Tissue samples were extracted from six individuals who underwent surgery for a multi-component inflatable penile prosthesis (IPP). Samples were obtained from the corpora using Adson forceps before corporotomy dilation and device implantation and placed into cleaned glassware. A control sample was collected and stored in a McKesson specimen plastic container. The tissue fractions were analyzed using the Agilent 8700 Laser Direct Infrared (LDIR) Chemical Imaging System (Agilent Technologies. Moreover, the morphology of the particles was investigated by a Zeiss Merlin Scanning Electron Microscope (SEM), complementing the detection range of LDIR to below 20 µm. MPs via LDIR were identified in 80% of the samples, ranging in size from 20–500 µm. Smaller particles down to 2 µm were detected via SEM. Seven types of MPs were found in the penile tissue, with polyethylene terephthalate (47.8%) and polypropylene (34.7%) being the most prevalent. The detection of MPs in penile tissue raises inquiries on the ramifications of environmental pollutants on sexual health. Our research adds a key dimension to the discussion on man-made pollutants, focusing on MPs in the male reproductive system.
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All relevant data to the current study that was generated and analyzed is available upon reasonable request from the corresponding author.
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Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
Jason Codrington, Alexandra Aponte Varnum, Joginder Bidhan, Kajal Khodamoradi, Aymara Evans, David Velasquez, Christina C. Yarborough, Ashutosh Agarwal, Edoardo Pozzi, Francesco Mesquita, Francis Petrella, David Miller & Ranjith Ramasamy
Institute of Coastal Environmental Chemistry, Department for Inorganic Environmental Chemistry, Helmholtz-Zentrum Hereon, Max-Planck-Str 1, 21502, Geesthacht, Germany
Lars Hildebrandt & Daniel Pröfrock
Institute of Membrane Research, Helmholtz-Zentrum Hereon, Max-Planck-Str 1, 21502, Geesthacht, Germany
Anke-Lisa Höhme & Martin Held
Dr. J.T. MacDonald Foundation BioNIUM, Miller School of Medicine, University of Miami, Miami, FL, USA
Bahareh Ghane-Motlagh
Department of Biomedical Engineering, University of Miami, Miami, FL, USA
Ashutosh Agarwal
University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
Justin Achua
Vita-Salute San Raffaele University, Milan, Italy
Edoardo Pozzi
IRCCS Ospedale San Raffaele, Urology, Milan, Italy
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Jason Codrington—conceptualization, methodology, investigation, project administration, data curation, visualization, writing—original draft, editing. Alexandra Aponte Varnum—investigation, writing—original draft, editing, data curation, visualization. Lars Hildebrandt—investigation, writing—original draft, validation, resources. Daniel Pröfrock—investigation, editing, validation, resources. Joginder Bidhan—resources, writing—original draft. Kajal Khodamoradi—project administration, resources. Anke-Lisa Höhme—investigation, visualization. Martin Held—writing—original draft, editing. Aymara Evans—writing—original draft. David Velasquez—writing—original draft. Christina C. Yarborough—writing—original draft. Bahareh Ghane-Motlagh—investigation. Ashutosh Agarwal—investigation. Justin Achua—writing—original draft. Edoardo Pozzi—editing. Francesco Mesquita—editing. Francis Petrella—writing—review. David Miller—writing—review. Ranjith Ramasamy—conceptualization, methodology, project administration, resources, supervision, editing, funding acquisition
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Codrington, J., Varnum, A.A., Hildebrandt, L. et al. Detection of microplastics in the human penis. Int J Impot Res (2024). https://doi.org/10.1038/s41443-024-00930-6
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Macrophages play a critical role in the immune system by fighting infections and aiding in tissue repair. Understanding how these cells are activated in different scenarios is important for developing new medical treatments. However, identifying and studying macrophage activation has been challenging due to the complex nature of these cells and their responses.
A research team has identified a protein called "colony stimulating factor 1 receptor" (CSF1R) as a reliable marker for monocytes and dendritic cells in blood and macrophages in tissues—allowing for the clear identification and separation of different sample types. The new method works reliably for people of all ages and sexes.
The study is published in the journal Cell Reports .
Dr. Fernando Martinez Estrada, who led the research project and is Senior Lecturer in Innate Immunology in the School of Biosciences at the University of Surrey, said, "We have developed a method using CSF1R that can identify all types of Mononuclear phagocyte system cells in the body.
"This marker is incredibly useful for studying these cells in both health and disease, and it unlocks exciting new possibilities for cell isolation and quantification for diagnosing and monitoring various conditions with a single cell marker."
The study developed a set of tools to understand and check how these immune cells respond when they are activated. These tools focus on signals in the body, including IL-4 (involved in healing and fibrosis), steroids (deactivation), IFNγ (fights infections), and LPS (a bacterial product that causes inflammation).
The research team also described a novel concept that they call Macrophage Activation Mosaicism. This means that macrophages do not simply switch between the canonical two states previously described; instead, they can exhibit a mix of activation characteristics, reflecting the complexity of real tissue environments.
Dr. Federica Orsenigo, co-author of the study, explains, "This discovery is significant because it changes how we perceive macrophage activation.
"Recognizing that macrophages can have mixed activation status helps us better understand their roles in different diseases and could lead to more targeted and effective treatments."
Emeritus Professor Siamon Gordon, co-author of the study from the University of Oxford, said, "Therapies that seek to re-educate macrophages are widely sought. However, the tools to measure activation are underdeveloped.
"Having a robust multi-gene tool to study macrophage activation can help in drug screening , identify drugs that revert macrophage activation , and eventually help with patient characterization and personalized medicine."
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Methodology
Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.
When you start planning a research project, developing research questions and creating a research design , you will have to make various decisions about the type of research you want to do.
There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:
This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.
Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.
The first thing to consider is what kind of knowledge your research aims to contribute.
Type of research | What’s the difference? | What to consider |
---|---|---|
Basic vs. applied | Basic research aims to , while applied research aims to . | Do you want to expand scientific understanding or solve a practical problem? |
vs. | Exploratory research aims to , while explanatory research aims to . | How much is already known about your research problem? Are you conducting initial research on a newly-identified issue, or seeking precise conclusions about an established issue? |
aims to , while aims to . | Is there already some theory on your research problem that you can use to develop , or do you want to propose new theories based on your findings? |
Professional editors proofread and edit your paper by focusing on:
See an example
The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.
Type of research | What’s the difference? | What to consider |
---|---|---|
Primary research vs secondary research | Primary data is (e.g., through or ), while secondary data (e.g., in government or scientific publications). | How much data is already available on your topic? Do you want to collect original data or analyze existing data (e.g., through a )? |
, while . | Is your research more concerned with measuring something or interpreting something? You can also create a research design that has elements of both. | |
vs | Descriptive research gathers data , while experimental research . | Do you want to identify characteristics, patterns and or test causal relationships between ? |
Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?
Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.
Type of research | What’s the difference? | What to consider |
---|---|---|
allows you to , while allows you to draw conclusions . | Do you want to produce knowledge that applies to many contexts or detailed knowledge about a specific context (e.g. in a )? | |
vs | Cross-sectional studies , while longitudinal studies . | Is your research question focused on understanding the current situation or tracking changes over time? |
Field research vs laboratory research | Field research takes place in , while laboratory research takes place in . | Do you want to find out how something occurs in the real world or draw firm conclusions about cause and effect? Laboratory experiments have higher but lower . |
Fixed design vs flexible design | In a fixed research design the subjects, timescale and location are begins, while in a flexible design these aspects may . | Do you want to test hypotheses and establish generalizable facts, or explore concepts and develop understanding? For measuring, testing and making generalizations, a fixed research design has higher . |
Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.
Read more about creating a research design
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.
Research bias
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Hair shedding scores: more than heat stress.
Jamie Courter State Beef Genetics Extension Specialist
Traditionally, when the topic of hair shedding arises, it is in the context of mitigating summer heat stress among cattle in the southeastern U.S. while grazing toxic fescue. This is not in error. It is estimated that cattle suffering from fescue toxicosis and heat stress alone cost the beef industry over a billion dollars a year. This makes hair shedding an economically relevant trait in cattle.
As research into hair shedding continues, more information about its importance becomes known. Most recently, a relationship between hair shedding in cattle and their ability to sense change in daylight has been discovered. This suggests that cattle who shed their winter coats earlier are more able to adapt to their environment making hair shedding an indicator trait in cattle regardless of location.
This MU Extension guide is meant to 1) provide background information on the economic importance of hair shedding scores, 2) introduce the hair shedding (1-5) scoring system, 3) discuss how to implement hair shedding into a selection program, and 4) promote hair shedding as a management tool.
Anytime a trait can directly be linked to profitability, it is characterized as economically relevant. In the case of hair shedding, cows who shed their winter coat earlier tend to wean heavier calves (Genetic correlation = -0.19). Figure 1 shows the average weaning weight of calves born to dams who began shedding their winter coats between March and July. The average weight of a calf born to a dam that shed in March was 57.2 lbs heavier than those that shed in July.
Most research that investigates the relationship between hair shedding and profitability target weaning weight because it encompasses many different production issues created by heat stress. To start, cows who undergo heat stress in the summer are less likely to get pregnant early in the breeding season. This could be due to low body condition scoring (BCS) because of heat stress while grazing summer pasture before fall breeding, or directly due to heat stress during spring breeding. Regardless, cows bred later in the season also calve later and therefore wean lighter calves.
Secondly, cows who undergo stress after calving may see an impact on milk production, which also impacts weaning weight.
Because increases in temperature happen at the same time hours of daylight are increasing, it is difficult to identify whether animals start shedding due to changes in temperature or daylight. Recent research conducted at the University of Missouri investigated the relationship between the DNA of the animal and temperature 30 days prior to the collection of a hair shedding score. This analysis only identified 17 interactions between DNA and temperature that influenced hair shedding. In a second analysis, researchers instead investigated the interaction between the DNA of the animal and the average length of daylight 30 days before the hair shedding score was observed. This time, there were 1,040 significant DNA-by-daylength associations identified. This supports the idea that cattle shed their winter coats in response to increasing amounts of daylight instead of a drop in temperature. This association is important because it promotes hair shedding as an indicator of an animal’s ability to sense and respond to their environment.
What/How: Hair shedding scores represent a visual appraisal of the extent of hair shedding and are reported on a 1 to 5 scale ( Figure 2 ) in which:
Half scores, such as 3.5, are not reported. In general, cattle tend to shed hair from the front to the back and from their topline to their belly ( Figure 3 ), but there is individual animal variation in this pattern. Typically, animals begin shedding around their neck, followed by their topline. The last spots to shed are an animal’s lower quarter above its hock and its underline.
When: It is only necessary to collect hair shedding scores once in late spring or early summer. The date to evaluate cattle for shedding progress will vary by geographic location and environmental conditions. The goal should be to score cattle when there is the most variation in hair shedding within a herd. In other words, a few animals with a hair shedding score of 1 or 5 with a majority receiving a hair shedding score of 3. Mid-May has been identified as an ideal hair shedding evaluation period for cattle in the Southeastern U.S. As a rule of thumb, the hotter and more humid the climate the earlier in the spring scores should be collected.
Who: If using hair shedding score as a selection method or reporting scores to a breed association, all cows in the herd should be observed. While it is recommended to score all animals in a herd on the same day, it is important to keep in mind that males tend to begin shedding a few weeks prior to females and therefore should likely be scored separately.
Where: Being a subjective observation of the amount of hair an animal has shed, these scores are easy to collect. This can be done as cattle pass through a chute during routine handling timeframes or while out on pasture.
As a moderately heritable trait (h2 = 0.35 to 0.42), producers can expect to create positive genetic change in their herds by simply adding hair shedding scores as a selection criterion when making selection decisions. To do this, producers would need to assess the hair shedding score of the whole herd on the same day, consider culling older animals with higher scores (more hair), and selecting the replacement heifers who shed earlier in the season in addition to other components of interest.
In addition to phenotypic selection, some bulls will also have an expected progeny difference (EPD) for hair shedding available for use. If available, selecting bulls with a lower hair shedding EPD will result in daughters born who shed earlier in the season, on average.
When using hair shedding as a selection criterion, it is important to also consider the age and nutrient requirements of the female. Yearlings and first-calf heifers tend to have higher hair shedding scores compared to older, established cows ( Figure 4 ). This does not mean younger females are necessarily worse shedders than their dams. Younger cows are, by default, the most nutritionally stressed as they are growing and raising a calf while also growing themselves. Therefore, it may be more beneficial to rank and select younger females within their age group instead of comparing them to older herd mates.
When evaluating the effect of age on hair shedding score, the average score for each age group tends to decrease as age increases ( Figure 4 ). This could reflect the impact of late shedding on production. Cows who shed their winter coats later in the summer may have fallen out of the herd due to weaning lighter calves, failure to conceive, or low body condition.
It is anticipated that hair shedding scores could be used in conjunction with body condition scores to assess the current nutritional stress of the herd. Genetic associations were also discovered between hair shedding and functions related to metabolism. Therefore, hair shedding may also pose as an indication of an animal’s overall nutritional plane, thus helping to inform management decisions. It is no coincidence that younger females shed their coats later than their older herd mates. Similarly, older cows who may have had a ‘hard winter’ would shed later in the year. The repeatability of hair shedding is only 45%, which indicates over half (55%) of the variance in hair shedding is due to year-to-year differences in management and environment of the cow. Understanding that later hair shedding (higher scores) indicates increased nutritional demands could be used to identify animals who would benefit from additional supplemental feed heading into spring and summer.
Although hair shedding has traditionally been associated with heat stress and fescue toxicosis, recent research shows this quick and easy phenotypic assessment of cattle could be a trait of even more economic importance. Producers wishing to select females based on hair shedding scores can do so based on a simple 1 to 5 scoring system. With its moderate heritability, combining this score with a hair shedding EPD or score on bulls would result in positive genetic progress over time.
More detailed information on the scoring system and some frequently asked questions can be found in publication G2041, How to Use the Hair Shedding Scale .
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The first question asks for a ready-made solution, and is not focused or researchable. The second question is a clearer comparative question, but note that it may not be practically feasible. For a smaller research project or thesis, it could be narrowed down further to focus on the effectiveness of drunk driving laws in just one or two countries.
Designing the study: Research questions guide the design of the study, including the selection of participants, the collection of data, and the analysis of results. Collecting data: Research questions inform the selection of appropriate methods for collecting data, such as surveys, interviews, or experiments. Analyzing data: Research questions ...
Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect, analyze, and interpret data to answer research questions or solve research problems.
1. Focus on your objectives and research questions. The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions. 2.
A good research question is essential to guide your research paper, dissertation, or thesis. All research questions should be: Focused on a single problem or issue. Researchable using primary and/or secondary sources. Feasible to answer within the timeframe and practical constraints. Specific enough to answer thoroughly.
Central Role of Research Questions: A research question is foundational to the entire research process, guiding the scope, methodology, and analysis of a study. Types of Research Questions: Research questions can be categorized into quantitative, qualitative, and mixed-methods, each requiring different approaches and designs.
What is research methodology? Research methodology simply refers to the practical "how" of a research study. More specifically, it's about how a researcher systematically designs a study to ensure valid and reliable results that address the research aims, objectives and research questions. Specifically, how the researcher went about deciding:
A good research question usually focuses on the research and determines the research design, methodology, and hypothesis. It guides all phases of inquiry, data collection, analysis, and reporting. You should gather valuable information by asking the right questions.
Research Questions and Hypotheses I nvestigators place signposts to carry the reader through a plan for a study. The first signpost is the purpose statement, which establishes the ... question, consistent with the emerging methodology of qualitative research, as a general issue so as to not limit the inquiry. To arrive at this question, ...
Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings.
Answer. Research, research methodology, and publication ethics are all essential components of scientific inquiry. Conducting research using rigorous methodology and adhering to ethical ...
Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach ...
This invaluable guide answers the essential questions that students ask about research methods in a concise and accessible way. 100 Questions (and Answers) about Research Methods summarizes the most important questions that lie in those inbetween spaces that one could ask about research methods while providing an answer as well. This is a short ...
A qualitative research question is a type of systematic inquiry that aims at collecting qualitative data from research subjects. The aim of qualitative research questions is to gather non-statistical information pertaining to the experiences, observations, and perceptions of the research subjects in line with the objectives of the investigation.
Provide the rationality behind your chosen approach. Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome. 3. Explain your mechanism.
A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more. You can think of your research methodology as being a formula. One part will be how you plan on putting your research into ...
Definition, Types, and Examples. Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of ...
Choose a broad topic, such as "learner support" or "social media influence" for your study. Select topics of interest to make research more enjoyable and stay motivated. Preliminary research. The goal is to refine and focus your research question. The following strategies can help: Skim various scholarly articles.
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:
The Research Process: A Quick Glance. Reviewing the Literature. Formulating a Research Problem. Identifying Variables. Constructing Hypotheses. The Research Design. Selecting a Study Design. Selecting a Method of Data Collection. Collecting Data Using Attitudinal Scales.
A research question framework can help you organize the concepts in your foreground question. There are a number of different frameworks. ... Useful for questions of experience or perspectives (questions that may be addressed by qualitative or mixed methods research). PICO Framework Example. P (Population/Problem of Interest) ...
a) Research refers to a series of systematic activity or activities undertaken to find out the solution to a problem. b) It is a systematic, logical and unbiased process wherein verification of hypotheses, data analysis, interpretation and formation of principles can be done. c) It is an intellectual inquiry or quest towards truth,
430+ Research Methodology (RM) Solved MCQs. 119. 50.3k. 19. Take a Test Download as PDF. Hide answers. 1 of 5 Sets. 1.
AAPOR Task Force on Address-based Sampling. 2016. "AAPOR Report: Address-based Sampling." ↩ Email [email protected]. ↩; A validated voter is a citizen who told us that they voted in an election and have a record for voting in that election in a commercial voter file. A voter file is a list of adults that includes information such as which elections they have voted in. Federal ...
Tailoring the Methodology to the Research Question. Qualitative Business Research in Practice; Advantages of Quantitative Analytics; Achieving Research Objectives Through a Mixed Methods Approach. Qualitative stage: Quantitative stage: Conclusion FAQs. 1. What are some examples of qualitative business research methods? 2.
Our research adds a key dimension to the discussion on man-made pollutants, focusing on MPs in the male reproductive system. ... (method blanks) comprising the entire sample processing protocol ...
Dr. Fernando Martinez Estrada, who led the research project and is Senior Lecturer in Innate Immunology in the School of Biosciences at the University of Surrey, said, "We have developed a method ...
Testing the "no test" method. The researchers analyzed the experiences of 585 patients at clinics in Colorado, Illinois, Maryland, Minnesota, Virginia and Washington from May 2021 to March 2023, dividing them into three groups. The first were evaluated for eligibility for medication abortion using telehealth.
Types of Research Designs Compared | Guide & Examples. Published on June 20, 2019 by Shona McCombes.Revised on June 22, 2023. When you start planning a research project, developing research questions and creating a research design, you will have to make various decisions about the type of research you want to do.. There are many ways to categorize different types of research.
Most research that investigates the relationship between hair shedding and profitability target weaning weight because it encompasses many different production issues created by heat stress. ... Methods of Selecting for Hair Shedding As a moderately heritable trait (h2 = 0.35 to 0.42), producers can expect to create positive genetic change in ...