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Part III - Data Collection

Published online by Cambridge University Press:  25 May 2023

Austin Lee Nichols
Affiliation:
Central European University, Vienna
John Edlund
Affiliation:
Rochester Institute of Technology, New York
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Print publication year: 2023

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References

Further Reading

The following are sources that describe various aspects of cross-sectional studies.

Axelson, O., Fredriksson, M., & Ekberg, K. (1994). Use of the prevalence ratio v the prevalence odds ratio as a measure of risk in cross sectional studies. Occupational and Environmental Medicine, 51(8), 574. https://doi.org/10.1136/oem.51.8.574Google Scholar
Barros, A. J. & Hirakata, V. N. (2003). Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Medical Research Methodology, 3, 21. https://doi.org/10.1186/1471-2288-3-21Google Scholar
Brumback, B. & Berg, A. (2008). On effect-measure modification: Relationships among changes in the relative risk, odds ratio, and risk difference. Statistics in Medicine, 27(18), 34533465. https://doi.org/10.1002/sim.3246CrossRefGoogle ScholarPubMed
Campbell, D. & Fiske, D. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81105.Google Scholar
Colditz, G. A. (2010). Overview of the epidemiology methods and applications: strengths and limitations of observational study designs. Critical Reviews in Food Science and Nutrition, 50 Suppl 1, 1012. https://doi.org/10.1080/10408398.2010.526838Google Scholar
Freemantle, N., Marston, L., Walters, K., et al. (2013). Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research. BMJ, 347, f6409. https://doi.org/10.1136/bmj.f6409Google Scholar
Garger, J. (2020). A definition of single source bias in social science research. Available at: www.johngarger.com/blog/a-definition-of-single-source-bias-in-social-science-research.Google Scholar
Hennekens, C. H. & Buring, J. E. (1987). Epidemiology in Medicine. Lippincott Williams & Wilkins.Google Scholar
Hughes, K. (1995). Odds ratios in cross-sectional studies. International Journal of Epidemiology, 24(2), 463464, 468. https://doi.org/10.1093/ije/24.2.463CrossRefGoogle ScholarPubMed
Hulley, S, B., Cummings, S. R. Browner, W. S., et al. (2001). Designing Clinical Research, 2nd ed. Lippincot Williams & Wilkins.Google Scholar
Jewell, N. (2004). Statistics for Epidemiology. Chapman and Hall/CRC.Google Scholar
Kleinbaum, D., Kupper, L., & Morgenstern, H. (1982). Epidemiologic Research. John Wiley & Sons.Google Scholar
Lee, J. (1994). Odds ratio or relative risk for cross-sectional data? International Journal of Epidemiology, 23(1), 201203. https://doi.org/10.1093/ije/23.1.201CrossRefGoogle ScholarPubMed
Martinez, B. A. F., Leotti, V. B., Silva, G. S. E., et al. (2017). Odds ratio or prevalence ratio? An overview of reported statistical methods and appropriateness of interpretations in cross-sectional studies with dichotomous outcomes in veterinary medicine. Frontiers in Veterinary Science, 4, 193. https://doi.org/10.3389/fvets.2017.00193Google Scholar
Mellis, C. M. (2020). How to choose your study design. Journal of Paediatrics and Child Health, 56(7), 10181022. https://doi.org/10.1111/jpc.14929Google Scholar
Mitchell, T. (1985). An evaluation of the validity of correlational research conducted in organizations. Academy of Management Review, 10(2), 192205.Google Scholar
Moore, D., & McCabe, G. (2002). Introduction to the Practice of Statistics, 4th ed. WH Freeman and Company.Google Scholar
Mukaka, M. M. (2012). Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Medical Journal, 24(3), 6971. https://www.ncbi.nlm.nih.gov/pubmed/23638278Google Scholar
Pandis, N. (2014a). Cross-sectional studies. American Journal of Orthodontics and Dentofacial Orthopedics, 146(1), 127129. https://doi.org/10.1016/j.ajodo.2014.05.005Google Scholar
Pandis, N. (2014b). Introduction to observational studies: part 2. American Journal of Orthodontics and Dentofacial Orthopedics, 145(2), 268269. https://doi.org/10.1016/j.ajodo.2013.11.002Google Scholar
Pearce, N. (2004). Effect measures in prevalence studies. Environmental Health Perspectives, 112(10), 10471050. https://doi.org/10.1289/ehp.6927Google Scholar
Polychronopoulou, A., & Pandis, N. (2014). Interpretation of observational studies. American Journal of Orthodontics and Dentofacial Orthopedics, 146(6), 815817. https://doi.org/10.1016/j.ajodo.2014.10.004Google Scholar
Reichenheim, M. E. & Coutinho, E. S. (2010). Measures and models for causal inference in cross-sectional studies: arguments for the appropriateness of the prevalence odds ratio and related logistic regression. BMC Medical Research Methodology, 10, 66. https://doi.org/10.1186/1471-2288-10-66Google Scholar
Rothman, K. J. Greenland, S., & Lash, T. L. (2008). Modern Epidemiology, 3rd ed. Lippincott Williams & Wilkins.Google Scholar
Santos, C. A., Fiaccone, R. L., Oliveira, N. F., et al. (2008). Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data. BMC Medical Research Methodology, 8, 80. https://doi.org/10.1186/1471-2288-8-80Google Scholar
Sedgwick, P. (2014). Spearman’s rank correlation coefficient. BMJ, 349, g7327. https://doi.org/10.1136/bmj.g7327Google Scholar
Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian Journal of Dermatology, 61(3), 261264. https://doi.org/10.4103/0019-5154.182410Google Scholar
Sedgewick, P. (2018). Spearman’s rank correlation coefficient, (correction). BMJ, 362, k4131. https://doi.org/10.1136/bmj.k4131Google Scholar
Stromberg, U. (1994). Prevalence odds ratio v prevalence ratio. Occupational and Environmental Medicine, 51(2), 143144. https://doi.org/10.1136/oem.51.2.143CrossRefGoogle ScholarPubMed
Tamhane, A. R., Westfall, A. O., Burkholder, G. A., & Cutter, G. R. (2017). Prevalence odds ratio versus prevalence ratio: choice comes with consequences. Statistics in Medicine, 36(23), 3760. https://doi.org/10.1002/sim.7375Google Scholar
Thiese, M. S. (2014). Observational and interventional study design types; an overview. Biochemia Medica, 24(2), 199210. https://doi.org/10.11613/BM.2014.022Google Scholar
Thompson, M. L., Myers, J. E., & Kriebel, D. (1998). Prevalence odds ratio or prevalence ratio in the analysis of cross sectional data: what is to be done? Occupational and Environmental Medicine, 55(4), 272277. https://doi.org/10.1136/oem.55.4.272Google Scholar
Thorndike, E. (1920). A constant error in psychological ratings. Journal of Applied Psychology, 4(1), 2529.Google Scholar
Traissac, P., Martin-Prevel, Y., Delpeuch, F., & Maire, B. (1999). Regression logistique vs autres modeles lineaires generalises pour l’estimation de rapports de prevalences.[Logistic regression vs other generalized linear models to estimate prevalence rate ratios.] La Revue d’épidémiologie et de santé publique 47(6), 593604. https://www.ncbi.nlm.nih.gov/pubmed/10673593Google Scholar
Twisk, J. W. R.(2013). Applied Longitudinal Data Analysis for Epidemiology, 2nd ed. Cambridge University Press.Google Scholar
Zocchetti, C., Consonni, D., & Bertazzi, P. A. (1995). Estimation of prevalence rate ratios from cross-sectional data. International Journal of Epidemiology, 24(5), 10641067. https://doi.org/10.1093/ije/24.5.1064CrossRefGoogle ScholarPubMed

References

Alexander, L., Lopes, B., Ricchetti-Masterson, K., & Yeatts, K. (2014–15). Cross-sectional Studies. UNC CH Department of Epidemiology. Available at: https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC8.pdf.Google Scholar
Favero, N. & Bullock, J. (2015). How (not) to solve the problem: An evaluation of scholarly responses to common source bias. Journal of Public Administration Research and Theory, 25(1), 285308.Google Scholar
George, B. & Pandey, S. K. (2017). We know the yin – but where is the yang? Toward a balanced approach on common source bias in public administration scholarship. Review of Public Personnel Administration, 37(2), 245270. https://doi.org/10.1177/0734371X17698189Google Scholar
Gullon, P., Bilal, U., & Franco, M. (2014). Physical activity environment measurement and same source bias. Gaceta Sanitaria, 28(4), 344345. https://doi.org/10.1016/j.gaceta.2013.12.011Google Scholar
Lee, J. & Chia, K. S. (1994). Use of the prevalence ratio v the prevalence odds ratio as a measure of risk in cross sectional studies. Occupational and Environmental Medicine, 51(12), 841. https://doi.org/10.1136/oem.51.12.841CrossRefGoogle ScholarPubMed
Lee, J., Tan, C. S., & Chia, K. S. (2009). A practical guide for multivariate analysis of dichotomous outcomes. Annals of the Academy of Medicine, Singapore, 38(8), 714719. https://www.ncbi.nlm.nih.gov/pubmed/19736577Google Scholar
Rodriguez-Romo, G., Garrido-Munoz, M., Lucia, A., Mayorga, J. I., & Ruiz, J. R. (2013). Asociacion entre las caracteristicas del entorno de residencia y la actividad fisica.[Association between the characteristics of the neighborhood environment and physical activity.] Gaceta Sanitaria, 27(6), 487493. https://doi.org/10.1016/j.gaceta.2013.01.006Google Scholar
Swinscow, T. (1997). Study design and choosing a statistical test. BMJ Publishing Group. Available at: www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/13-study-design-and-choosing-statisti.Google Scholar
Szklo, M. & Javier Nieto, F. (2004). Epidemiology: Beyond the Basics. Jones and Bartlett Learning.Google Scholar

References

Aiken, L. S., West, S. G., Schwalm, D. E., Carroll, J. L., & Hsiung, S. (1998). Comparison of a randomized and two quasi-experimental designs in a single outcome evaluation: Efficacy of a university-level remedial writing program. Evaluation Review, 22(2), 207244.Google Scholar
Angrist, J. D. & Pischke, J-S. (2015). Mastering ‘Metrics: The Path from Cause to Effect. Princeton University Press.Google Scholar
Arum, R. & Roksa, J. (2010). Academically Adrift: Limited Learning on College Campuses. University of Chicago Press.Google Scholar
Berk, R., Barnes, G., Ahlman, L., & Kurtz, E. (2010). When second best is good enough: A comparison between a true experiment and a regression discontinuity quasi-experiment. Journal of Experimental Criminology, 6(2), 191208.CrossRefGoogle Scholar
Bloom, H. S. (2003). Using “short” interrupted time-series analysis to measure the impacts of whole-school reforms: With application to a study of accelerated schools. Evaluation Review, 27(1), 349.Google Scholar
Braden, J. P. & Bryant, T. J. (1990). Regression discontinuity designs: Applications for school psychologists. School Psychology Review, 19(2), 232239.Google Scholar
Cook, T. D. (2008). “Waiting for life to arrive”: A history of the regression–discontinuity designs in psychology, statistics and economics. Journal of Econometrics, 142(2), 636654.CrossRefGoogle Scholar
Cook, T. D., Shadish, W. R., & Wong, V. C. (2008). Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons. Journal of Policy Analysis and Management, 27(4), 724750.CrossRefGoogle Scholar
Cook, T. D., Steiner, P. M., & Pohl, S. (2009). Assessing how bias reduction is influenced by covariate choice, unreliability and data analysis mode: An analysis of different kinds of within-study comparisons in different substantive domains. Multivariate Behavioral Research, 44(6), 828847.Google Scholar
Eckert, W. A. (2000). Situational enhancement of design validity: The case of training evaluation at the World Bank Institute. American Journal of Evaluation, 21(2) 185193.Google Scholar
Eysenck, H. J. (1952). The effects of psychotherapy: An evaluation. Journal of Consulting Psychology, 16(5), 319324.Google Scholar
Goldberger, A. S. (2008). Selection bias in evaluation treatment effects: Some formal illustrations. In Fomby, T., Hill, R. C., Millimet, D. L., Smith, J. A., & Vytlacil, E. J. (eds.), Modeling and Evaluating Treatment Effects in Economics (pp. 131). JAI Press.Google Scholar
Goplan, M., Rosinger, K., & Ahn, J. B. (2020). Use of quasi-experimental research designs in education research: Growth, promise, and challenges. Review of Research in Education, 44(1), 218243.Google Scholar
Heinsman, D. T. & Shadish, W. R. (1996). Assignment methods in experimentation: When do nonrandomized experiments approximate answers from randomized experiments? Psychological Methods, 1(2), 154169.Google Scholar
Henry, G. T., Fortner, C. K., & Thompson, C. L. (2010). Targeted funding for educationally disadvantaged students: A regression discontinuity estimate of the impact on high school student achievement. Educational Evaluation and Policy Analysis, 32(2), 183204.Google Scholar
Henry, G. T. & Harbatkin, E. (2020). The next generation of state reforms to improve their lowest performing schools: An evaluation of North Carolina’s school transformation intervention. Journal of Research on Educational Effectiveness, 13(4), 702730.Google Scholar
Hudson, J., Fielding, S., & Ramsay, C.R. (2019). Methodology and reporting characteristics of studies using interrupted time series design in healthcare. BMC Medical Research Methodology, 19(1), 137.CrossRefGoogle ScholarPubMed
Imbens, G. W. & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice, Journal of Econometrics, 142(2), 615635.Google Scholar
Jacob, R., Zhu, P., Somers, M-A., & Bloom, H. (2012). A Practical Guide to Regression Discontinuity. Manpower Demonstration Research Corporation.Google Scholar
Kazden, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings, 2nd ed. Oxford University Press.Google Scholar
Lee, D. S. & Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), 281355.Google Scholar
Lehman, D. R., Lempert, R. O., & Nisbett, R. E. (1988). The effects of graduate training on reasoning: Formal discipline and thinking about everyday-life events. American Psychologist, 43(6), 431442.CrossRefGoogle Scholar
Lipsey, M.W., Cordray, D.S., & Berger, D.E. (1981). Evaluation of a juvenile diversion program: Using multiple lines of evidence. Evaluation Review, 5(3), 283306.Google Scholar
Mark, M. M. & Mellor, S. (1991). The effect of self-relevance of an event on hindsight bias: The foreseeability of a layoff. Journal of Applied Psychology, 76(4), 569577.CrossRefGoogle Scholar
Matthews, M. S., Peters, S. J., & Housand, A. M. (2012). Regression discontinuity design in gifted and talented education research. Gifted Child Quarterly, 56(2), 105112.Google Scholar
McCleary, R. & McDowall, D. (2012). Time-series designs. In Cooper, H., Camic, P. M., Long, D. L., et al. (eds.), APA Handbook of Research Methods in Psychology, Volume 2. Research Designs: Quantitative, Qualitative, Neuropsychological, and Biological (pp. 613627). American Psychological Association.Google Scholar
McCrary, J. (2008). Manipulation of the running variable in the regression discontinuity design: A density test. Journal of Econometrics, 142(2), 698714.Google Scholar
Nugent, W. R. (2010). Analyzing Single System Design Data. Oxford University Press.Google Scholar
Palmgreen, P. (2009) Interrupted time-series designs for evaluating health communication campaigns. Communication Methods and Measures, 3(1–2), 2946.Google Scholar
Paluck, E. L. & Green, D. P. (2009). Prejudice reduction: What works? A review and assessment of research practice. Annual Review of Psychology, 60, 339367.Google Scholar
Reichardt, C. S. (2019). Quasi-Experimentation: A Guide to Design and Analysis. Guilford Press.Google Scholar
Reynolds, K. D. & West, S. G. (1987). A multiplist strategy for strengthening nonequivalent control group designs. Evaluation Review, 11(6), 691714.Google Scholar
Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100(469), 322331.Google Scholar
Sagarin, B. J., West, S. G., Ratnikov, A., Homan, W. K., & Ritchie, T. D. (2014). Treatment noncompliance in randomized experiments: Statistical approaches and design issues. Psychological Methods, 19(3), 317333.Google Scholar
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton-Mifflin.Google Scholar
Somers, M.-A., Zhu, P., Jacob, R., & Bloom, H. (2013). The Validity and Precision of the Comparative Interrupted Time Series Design and the Difference-in-Difference Design in Educational Evaluation. Manpower Demonstration Research Corporation.Google Scholar
St. Pierre, R. G., Ricciuti, A., & Creps, C. (1999). Synthesis of Local and State Even Start Evaluations. Abt Associates.Google Scholar
Thistlewaite, D. L. & Campbell, D. T. (1960). Regression–discontinuity analysis: An alternative to the ex-post-facto experiment. Journal of Educational Psychology, 51(2), 309317.Google Scholar
Trochim, W. M. K. (1984). Research Designs for Program Evaluation: The Regression–Discontinuity Approach. SAGE Publications.Google Scholar

Further Reading

Barber, T. X. (1973). Pitfalls in research: Nine investigator and experimenter effects. In Travers, R. (ed.). Second Handbook of Research on Teaching. Rand McNally.Google Scholar
Cook, D. L. (1967). The Impact of the Hawthorne Effect in Experimental Design in Educational Research, Cooperative Research Project, 1967, No. 1757. US Office of Education.Google Scholar
Gephart, W. J. & Antonoplos, D. P. (1969). The effects of expectancy and other research-biasing factors. The Phi Delta Kappan, 50(10) 579583. https://www.jstor.org/stable/20372478.Google Scholar
Lee, J. (2021). Situation, background, assessment, and recommendation stepwise education program: A quasi-experimental study. Nurse Education Today, 100, 104847. https://doi.org/10.1016/j.nedt.2021.104847Google Scholar
Noh, G. O. & Kim, M. (2021). Effectiveness of assertiveness training, SBAR, and combined SBAR and assertiveness training for nursing students undergoing clinical training: A quasi-experimental study. Nurse Education Today, 103, 104958. https://doi.org/10.1016/j.nedt.2021.104958Google Scholar
Osman, K. & Lee, T. (2014). Impact of interactive multimedia module with pedagogical agents on students’ understanding and motivation in the Learning of electrochemistry. International Journal of Science & Mathematics Education, 12(2), 395421. https://doi.org/10.1007/s10763-013-9407Google Scholar
Rosenthal, R. (1963). On the social psychology of the psychological experiment: The experimenter’s hypothesis as unintended determinant of experimental results. American Science, 51, 268283.Google Scholar
Whitley, E. & Ball, J. (2002). Statistics review 4: Sample size calculations. Critical Care, 6(4), 335341. https://doi.org/10.1186/cc1521CrossRefGoogle ScholarPubMed
Yu, F.-Y. & Chen, C.-Y. (2021). Student- versus teacher-generated explanations for answers to online multiple-choice questions: What are the differences? Computers & Education, 173, 104273. https://doi.org/10.1016/j.compedu.2021.104273Google Scholar

References

Asher, H. B. (1983). Casual Modelling. SAGE Publications.Google Scholar
Barnett, V. & Lewis, T. (1994). Outliers in Statistical Data, 3rd ed. John Wiley & Sons.Google Scholar
Barrett, A. C. & White, D. A. (1991). How John Henry effects confound the measurement of self-esteem in primary prevention programs for drug abuse in middle schools. Journal of Alcohol and Drug Education, 36(3), 87102.Google Scholar
Bell, J. (1993). Doing Your Own Research Project. Open University Press.Google Scholar
Benjamin, L. (1988). A History of Psychology. McGraw-Hill.Google Scholar
Bonate, P. (2000). Analysis of Pretest–Posttest Designs. Chapman & Hall.CrossRefGoogle Scholar
Bordens, K. & Abbott, B. (2007). Research Design and Methods: A Process Approach. McGrath Hill.Google Scholar
Braaten, L. J. (1989). The effects of person-centred group therapy. Person Centred Review, 4(2), 18.Google Scholar
Brace, N., Kemp, R., & Snelgar, R. (2003). SPSS for Psychologists. A Guide to Data Analysis using SPSS for Windows, 2nd ed. Palgrave.Google Scholar
Campbell, D. T. & Stanley, J. C. (1966). Experimental and Quasi-Experimental Designs for Research. Rand McNally.Google Scholar
Chiva-Bartoll, O., Montero, P. J. R, Capella-Peris, C., & Salvador-García, C. (2020). Effects of service learning on physical education teacher education students’ subjective happiness, prosocial behavior, and professional learning. Frontiers in Psychology. 11, 331.Google Scholar
Cleveland, W. S. (1993). Visualising Data. Hobart Press.Google Scholar
Cohen, J. (1962). The statistical power of abnormal social psychological research. Journal of Abnormal and Social Psychology, 65(3), 145153.Google Scholar
Cohen, J. (1969). Statistical Power Analysis for the Behavioral Sciences. Academic Press.Google Scholar
Cohen, J. (1973). Eta-squared and partial eta-squared statistics in fixed factor ANOVA designs. Educational and Psychological Measurement, 33, 107112.Google Scholar
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Lawrence Erlbaum Associates.Google Scholar
Cook, T. D. & Campbell, D. T. (1979). Quasi-Experimentation: Design and Analysis for Field Settings. Rand McNally.Google Scholar
Crotty, M. (2006). The Foundations of Social Research: Meaning and Perspectives in the Research Process, 2nd ed. SAGE Publications.Google Scholar
Dawes, M., Davies, P., Gray, A., et al. (2005). Evidence Based Practice: A Primer for Health Care Professionals, 2nd ed. Elsevier Churchill Livingstone.Google Scholar
Denny, M., Denieffe, S. & Pajnkihar, M. (2017). Using a Non-equivalent Control Group Design in Educational Research. Research Methods Cases Part 2. SAGE Publications.Google Scholar
Dickinson, K. P., Johnson, T. R., &. West, R. W. (1987). An analysis of the sensitivity of quasi experimental net impact estimates of CETA programmes. Evaluation Review, 11, 452472.Google Scholar
Fisher, R. A. (1971). The Design of Experiments, 8th ed. Oxford University Press.Google Scholar
Frank, M. G. & Gilovich, T. (1988). The dark side of self- and social perception: Black uniforms and aggression in professional sports. Journal of Personality and Social Psychology, 54(1), 7485. https://doi.org/10.1037/0022-3514.54.1.74Google Scholar
Goodwin, J. C. (1995). Research in Psychology: Methods and Design. John Wiley & Sons.Google Scholar
Gravetter, F. J. & Wallnau, L. B. (2000). Statistics for the Behavioural Sciences. Wadsworth/Thomson Learning.Google Scholar
Hains, A. A. & Szyjakowski, M. (1990). A cognitive stress-reduction intervention program for adolescents. Journal of Counseling Psychology, 37(1), 80.Google Scholar
Heppner, P. P. (1999). Extending the tradition of the counseling psychologist by building on strengths. The Counseling Psychologist, 27(1), 5972. https://doi.org/10.1177/0011000099271005Google Scholar
Heppner, P. P., Kivlighan, D. M., & Wampold, B. E. (1992). Research Design in Counseling. Brooks/Cole Publishing Company.Google Scholar
Heppner, P. P., Kivlighan, D. M., & Wampold, B. E (2004). Research Design in Counseling, 2nd ed. Brooks/Cole Publishing Company.Google Scholar
Hershberger, S. L. (2005). History of multivariate analysis of variance. In Everitt, B. & Howell, D. C. (Eds.), Encyclopedia of Statistics in Behavioral Science (vol. 2, pp. 864869). John Wiley & Sons.Google Scholar
Hopkins, W. G. (2016). A new view of statistics. Available at: www.sportsci.org/resource/stats/.Google Scholar
Hoinville, J. & Jowell, R. (1978). Survey Research Practice. Heinemann.Google Scholar
Houle, T. T., Penzien, D. B., & Houle, C. K. (2005). Statistical power and sample size estimation for headache research: An overview and power calculation tools. Headache: The Journal of Head and Face Pain, 45(5), 414418.Google Scholar
Huck, S. W. & Cormier, W. H. (1996). Principles of research design. In Jennison, C. (ed.), Reading Statistics and Research (pp. 578622). 2nd ed. Harper Collins.Google Scholar
Huck, S. W., Cormier, W. H., & Bounds, W. F. (1974). Reading Statistics and Research. Harper Collins.Google Scholar
Hunsley, J. & Lee, C.M. (2006). Introduction to Clinical Psychology. John Wiley & Sons.Google Scholar
Kazdin, E. & Bass, D. (1989). Power to detect differences between alternative treatments in comparative psychotherapy outcomes research. Journal of Consulting and Clinical Psychology, 57(1), 138147.CrossRefGoogle Scholar
Kerlinger, F. N. (1986). Foundations of Behavioural Research. Holt, Reinhart & Winston.Google Scholar
Kim, Y. & Steiner, P. M. (2019). Gain scores revisited: A graphical models perspective. Sociological Methods & Research, 50(3). https://doi.org/10.1177/0049124119826155Google Scholar
Kirk, R. E. (2005). Handbook of Research in Experimental Psychology. Blackwell Publishing.Google Scholar
Kush, K. & Cochran, L. (1993). Enhancing a sense of agency through career planning. Journal of Counseling Psychology, 40(4), 434439.Google Scholar
Lee, S. & Lee, E. (2020). Effects of cognitive behavioral group program for mental health promotion of university students. International Journal of Environmental Research and Public Health, 17(10), 3500.Google Scholar
Lipsey, M. W. & Wilson, D.B. (1993). The efficacy of psychological, educational and behavioural treatment: Conformation from meta-analysis. American Psychologist, 48, 11811209.Google Scholar
Loftin, L. & Madison, S. (1991). The extreme dangers of covariance corrections. In Thompson, B. (ed.), Advances in Educational Research: Substantive Findings, Methodological Developments. JAI Press.Google Scholar
Miles, J. (2003). A framework for power analysis using a structural equation modelling procedure. BMC Medical Research Methodology, 3, 27.CrossRefGoogle ScholarPubMed
McMillan, J. H. (2000). Educational Research: Fundamentals for the Consumer. Addison Wesley Longman.Google Scholar
Murray, T. R. (2003). Blending Qualitative and Quantitative Methods in Theses and Dissertations. Corwin Press Inc.Google Scholar
Noh, G. O. & Kim, D. H. (2019). Effectiveness of a self-directed learning program using blended coaching among nursing students in clinical practice: A quasi-experimental research design. BMC Med Education, 19(1), 225.Google Scholar
Parsons, H. M. (1974). What happened at Hawthorn? Science, 183, 93.Google Scholar
Patton, P. Q. (1990). Qualitative Evaluation and Research Methods, 2nd ed. SAGE Publications.Google Scholar
Pallant, J. (2006). SPSS Survival Manual, 2nd ed. McGrath Hill.Google Scholar
Pastor, D. A. & Kaliski, P. K. (2007). Examining college students’ gains in general education. Research and Practice in Assessment, 1(2), 120.Google Scholar
Polit, D. F. (2005). Essentials of Nursing Research: Methods, Appraisal and Utilization, 6th ed. Lippincott Williams & Wilkins.Google Scholar
Polit, D. F. & Beck, C. T. (2004). Nursing Research: Principles and Methods, 7th ed. Lippincott Williams & Wilkins.Google Scholar
Polit, D. F., Beck, C. T., & Hungler, B. P. (2006). Nursing Research: Methods, Appraisals, and Utilization, 6th ed. Lippincott Williams & Wilkins.Google Scholar
Robson, C. (2002). Real World Research: A Resource for Social Scientists and Practitioner-Researchers, 2nd ed. Blackwell.Google Scholar
Rosenthal, R. (1984). Meta-analytic Procedures for Social Research. SAGE Publications.Google Scholar
Rosenthal, R. & Jacobson, L. (1968). Pygmalion in the Classroom: Teacher Expectation and Pupils’ Intellectual Development. Rinehart and Winston.Google Scholar
Rosenthal, R. & Rosnow, R. L. (2008). Essentials of Behavioural Research: Method and Data Analysis, 3rd ed. McGrath Hill.Google Scholar
Rosnow, R. L. & Rosenthal, R. (1996). Computing contrasts, effect sizes and counter nulls on other people’s published data: General procedures for research consumers. Psychological Methods, 1, 331340.Google Scholar
Rubin, D. B. (1979) Using multivariate matched sampling and regression adjustment to control bias in observational studies. Journal of the American Statistical Association, 74, 318328.Google Scholar
Riley, M. W. (1967). Sociological Research; A Case Approach. Harcourt Brace and JovanovichGoogle Scholar
Saks, M. & Allsop, J. (2007). Health Research Sampling Methods. SAGE Publications.Google Scholar
Serlin, R. C. & Lapsley, D. K. (1985). Rationality in psychological research: The good- enough principle. American Psychologist, 40, 7383.Google Scholar
Sokal, R. R. & Rohif, F. J. (1981). Biometry: The principles and practices of Statistics in Biological Research. W. H. Freeman and Company.Google Scholar
Stevens, J. (2002). Applied Multivariate Statistics for the Social Sciences, 4th ed. Erlbaum.Google Scholar
Tabachnick, B. G. and Fidell, L. S. (2001). Using Multivariate Statistics, 4th ed. Harper Collins.Google Scholar
Thistlethwaite, D. & Campbell, D. (1960). Regression–discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology, 51 309317.Google Scholar
Thorndike, E. L. (1920). A constant error on psychological rating. Journal of Applied Psychology, 4, 2529.Google Scholar
White, H. & Sabarwal, S. (2014). Quasi-experimental Design and Methods, Methodological Briefs: Impact Evaluation 8. UNICEF.Google Scholar
Zimmerman, D. W. & Williams, R. H. (1982). Gain scores in research can be highly reliable. Journal of Educational Measurement, 19(2), 149154.Google Scholar

References

Anderson, C. A., Lindsay, J. J., & Bushman, B. J. (1999). Research in the psychological laboratory: Truth or triviality? Current Directions in Psychological Science, 8, 39.Google Scholar
Anderson, C. A., Berkowitz, L., & Donnerstein, E. (2003). The influence of media violence on youth. Psychological Science in the Public Interest, 4, 81110. https://doi.org/10.1111/j.1529-1006.2003.pspi_1433.xGoogle Scholar
Anderson, C. A., Shibuya, A., Ihori, N., et al. (2010). Violent video game effects on aggression, empathy, and prosocial behavior in eastern and western countries: A meta-analytic review. Psychological Bulletin, 136(2), 151173.Google Scholar
Banaji, M. R. & Crowder, R. G. (1989). The bankruptcy of everyday memory. American Psychologist, 44, 11851193.Google Scholar
Bender, J., Rothmund, T., & Gollwitzer, M. (2013). Biased estimation of violent video game effects on aggression: Contributing factors and boundary conditions. Societies, 3, 383398. https://doi.org/10.3390/soc3040383CrossRefGoogle Scholar
Berkowitz, L. & Donnerstein, E. (1982). External validity is more than skin deep: Some answers to criticisms of laboratory experiments. American Psychologist, 37(3), 245257. https://doi.org/10.1037/0003-066X.37.3.245Google Scholar
Bernstein, M. H. & Wood, M. D. (2017). Effect of anticipatory stress on placebo alcohol consumption in a bar laboratory. The American Journal of Drug and Alcohol Abuse, 43(1), 95102.CrossRefGoogle Scholar
Blake, K. R., Brooks, R., Arthur, L. C., & Denson, T. F. (2020). In the context of romantic attraction, beautification can increase assertiveness in women. PloS One, 15(3), e0229162.Google Scholar
Burke, B. L., Martens, A., & Faucher, E. H. (2010). Two decades of terror management theory: A meta-analysis of mortality salience research. Personality and Social Psychology Review, 14(2), 155195.Google Scholar
Carnagey, N. L. & Anderson, C. A. (2007). Changes in attitudes towards war and violence after September 11, 2001, Aggressive Behavior, 33, 118129.Google Scholar
Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review. Journal of Abnormal and Social Psychology, 65(3), 145153.Google Scholar
Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Academic Press.Google Scholar
Denson, T. F., Creswell, J. D., Terides, M. D., & Blundell, K. (2014). Cognitive reappraisal increases neuroendocrine reactivity to acute social stress and physical pain. Psychoneuroendocrinology, 49, 6978.Google Scholar
Edlund, J. E., Cuccolo, K., Irgens, M. S., Wagge, J. R., & Zlokovich, M. S. (2022). Saving science through replication studies. Perspectives on Psychological Science, 17(1), 216225.Google Scholar
Gable, P. A., Poole, B. D., & Harmon-Jones, E. (2015). Anger perceptually and conceptually narrows cognitive scope. Journal of Personality and Social Psychology, 109(1), 163174.Google Scholar
Gentile, D. A., Bender, P. K., & Anderson, C. A. (2017). Violent video game effects on salivary cortisol, arousal, and aggressive thoughts in children. Computers in Human Behavior, 70, 3943. http://dx.doi.org/10.1016/j.chb.2016.12.045Google Scholar
Gilbert, D. T., King, G., Pettigrew, S. & Wilson, T. D. (2016). Comment on “Estimating the reproducibility of psychological science.Science, 351(6277), 1037. http://dx.doi.org/10.1126/science.aad7243.Google Scholar
Greitemeyer, T. & Mügge, D. O. (2014). Video games do affect social outcomes: A meta-analytic review of the effects of violent and prosocial video game play. Personality and Social Psychology Bulletin, 40(5), 578589.Google Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). Most people are not WEIRD. Nature, 466(7302), 29.Google Scholar
Kalokerinos, E. K., Greenaway, K. H., & Denson, T. F. (2015). Reappraisal but not suppression downregulates the experience of positive and negative emotion. Emotion, 15(3), 271275.CrossRefGoogle Scholar
Krahé, B. & Busching, R. (2015). Breaking the vicious cycle of media violence use and aggression: A test of intervention effects over 30 months. Psychology of Violence, 5(2), 217226.Google Scholar
Lieberman, J. D., Solomon, S., Greenberg, J., & McGregor, H. A. (1999). A hot new way to measure aggression: Hot sauce allocation. Aggressive Behavior: Official Journal of the International Society for Research on Aggression, 25(5), 331348.Google Scholar
McDermott, R. (2011). Internal and external validity. In J. N. Druckman, D. P. Greene, J. H. Kuklinski, & A. Lupia (eds.), Cambridge Handbook of Experimental Political Science (pp. 27–40). Cambridge University Press.Google Scholar
Moons, W. G. & Mackie, D. M. (2007). Thinking straight while seeing red: The influence of anger on information processing. Personality and Social Psychology Bulletin, 33(5), 706720.Google Scholar
Mook, D. G. (1983). In defense of external invalidity. American Psychologist, 38, 379387.Google Scholar
Nichols, A. L. & Edlund, J. E. (2015). Practicing what we preach (and sometimes study): Methodological issues in experimental laboratory research. Review of General Psychology, 19(2), 191202.Google Scholar
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349, 943. https://doi.org/10.1126/science.aac4716Google Scholar
Orne, M. T. (1962). On the social psychology of the psychological experiment: With particular reference to demand characteristics and their implications. American Psychologist, 17(11), 776783.Google Scholar
Parrott, D. J. & Lisco, C. G. (2015). Effects of alcohol and sexual prejudice on aggression toward sexual minorities. Psychology of Violence, 5(3), 256265.CrossRefGoogle ScholarPubMed
Pettigrew, T. F. (2021). Contextual Social Psychology: Reanalyzing Prejudice, Voting, and Intergroup Contact. American Psychological Association.Google Scholar
Prentice, D. A. & Miller, D. T. (1992). When small effects are impressive. Psychological Bulletin, 112(1), 160164. https://doi.org/10.1037/0033-2909.112.1.160CrossRefGoogle Scholar
Prot, S. & Anderson, C. A. (2013). Research methods, design, and statistics in media psychology. In Dill, K. (ed.), The Oxford Handbook of Media Psychology (pp. 109136). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195398809.013.0007Google Scholar
Richard, F. D., Bond, C. F., Jr., & Stokes-Zoota, J. (2003). One hundred years of social psychology quantitatively described. Review of General Psychology, 7(4), 331363. http://dx.doi.org/10.1037/1089-2680.7.4.331Google Scholar
Riva, P., Romero Lauro, L. J., DeWall, C. N., Chester, D. S., & Bushman, B. J. (2015). Reducing aggressive responses to social exclusion using transcranial direct current stimulation. Social Cognitive and Affective Neuroscience, 10(3), 352356.Google Scholar
Ronquillo, J., Denson, T. F., Lickel, B., et al. (2007). The effects of skin tone on race-related amygdala activity: An fMRI investigation. Social Cognitive and Affective Neuroscience, 2(1), 3944.Google Scholar
Rosenthal, R. (1990). How are we doing in soft psychology? American Psychologist, 45(6), 775777. https://doi.org/10.1037/0003-066X.45.6.775Google Scholar
Weigold, A. & Weigold, I. K. (2021). Traditional and modern convenience samples: An investigation of college student, Mechanical Turk, and Mechanical Turk college student samples. Social Science Computer Review. https://doi.org/10.1177/08944393211006847Google Scholar
Yuan, R., Xu, Q. H., Xia, C. C., et al. (2020). Psychological status of parents of hospitalized children during the COVID-19 epidemic in China. Psychiatry Research, 288, 112953.Google Scholar

References

Andreß, H-J. (2017). The need for and use of panel data. IZA World of Labor, 352. https://www.doi.org/10.15185/izawol.352Google Scholar
Blossfeld, H. P. & Rohwer, G. (2013). Techniques of Event History Modeling. New Approaches to Causal Analysis. Routledge.Google Scholar
Brücker, H., Kroh, M., Bartsch, S., Goebel, J., et al. (2014). The New IAB–SOEP Migration Sample: An Introduction into the Methodology and the Contents. SOEP Survey Papers 216: Series C. German Institute for Economic Research (DIW)/German Socio-Economic Panel (SOEP).Google Scholar
Brüderl, J., Castiglioni, L., Volker, L., Pforr, K., & Schmiedeberg, C. (2017). Collecting event history data with a panel survey: Combining an electronic event history calendar and dependent interviewing. Methods, Data, Analyses, 11(1), 4566.Google Scholar
Buck, N. & McFall, S. (2012). Understanding society: design overview. Longitudinal and Life Course Studies, 3(1), 517.Google Scholar
Buck, N., Ermisch, J., & Jenkins, S. (1995). Choosing a longitudinal survey design: the issues. Paper ESRC Research Centre on Micro-Social Change, University of Essex. Available at: www.iser.essex.ac.uk/files/occasional_papers/pdf/op96-1.pdf.Google Scholar
Busse, B. & Backeberg, L. (2015). Longitudinal research on children and young people in Europe and beyond. In Pollock, G., Ozan, J., Goswami, H., Rees, G., & Stasulane, A. (eds.), Measuring Youth Well-Being. How a Pan-European Longitudinal Survey Can Improve Policy (pp. 7189). Springer.Google Scholar
Caruana, E. J., Roman, M., Hernández-Sánchez, J., & Solli, P. (2015). Longitudinal studies. Journal of Thoracic Disease, 7(11), 537540.Google Scholar
Coe, R. D., Duncan, G. J., & Hill, M. S. (1982). Dynamic aspects of poverty and welfare use in the United States. Paper presented at the Conference on Problems of Poverty, Clark University, Worcester, MA, August.Google Scholar
Connelly, R. & Platt, L. (2014). Cohort profile: UK Millennium Cohort Study (MCS). International Journal of Epidemiology, 43(6), 17191725.Google Scholar
Dale, A. & Davies, R. B. (eds.) (1994). Analysing Social and Political Change. A Casebook of Methods. SAGE Publications.CrossRefGoogle Scholar
Davies, R. B. (1994). From cross-sectional to longitudinal analysis. In Dale, A. & Davies, R. B. (eds.), Analysing Social and Political Change. A Casebook of Methods (pp. 2040). SAGE Publications.Google Scholar
Devaney, C. & Rooney, C. (2018). The Feasibility of Conducting a Longitudinal Study on Children in Care or Children Leaving Care within the Irish Context. UNESCO Child and Family Research Centre, National University of Ireland.Google Scholar
Dex, S. (1995). The reliability of recall data: a literature review. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 49(1), 5889. https://www.doi.org/10.1177/075910639504900105Google Scholar
Doll, R. (2001). Cohort studies: History of the method II. Retrospective cohort studies. Sozial und Präventivmedizin, 46, 152160.Google Scholar
Duncan, G. J. (2000). Using panel studies to understand household behavior and well-being. In Rose, D. (ed.), Researching Social and Economic Change. The Uses of Household Panel Studies (pp. 5475). Routledge.Google Scholar
Duncan, G. J., Coe, R. D., Corcoran, M. E., et al. (1984). Years of Poverty, Years of Plenty: The Changing Economic Fortunes of American Workers and Families. Institute for Social Research, University of Michigan.Google Scholar
Dziadkowiec, O., Durbin, J., Jayaraman Muralidharan, V., Novak, M., & Cornett, B. (2020). Improving the quality and design of retrospective clinical outcome studies that utilize electronic health records. HCA Healthcare Journal of Medicine, 1(3), article 4.Google Scholar
Elder, G. H., Jr. (1985) Perspectives on the life-course. In Elder, G. H (ed.) Lifecourse Dynamics. Trajectories and Transitions, 1968–1980 (pp. 2349). Cornell University Press.Google Scholar
Elder, G. H., Jr. & Giele, J. Z. (2009). The Craft of Life Course Research. The Guilford Press.Google Scholar
Farrall, S., Hunter, B., Sharpe, G., & Calverley, A. (2016). What ‘works’ when retracting sample members in a qualitative longitudinal study? International Journal of Social Research Methodology, 19(3), 287300.Google Scholar
Farrington, D. P., Loeber, R., & Welsh, B. C. (2009). Longitudinal-experimental studies. In Piquero, A. R, & Weisburd, D. (eds.), Handbook of Quantitative Criminology (pp. 503518). Springer.Google Scholar
Finkel, S. E. (1995). Causal Analysis with Panel Data. SAGE Publications.Google Scholar
Firebaugh, G. (1997). Analyzing Repeated Surveys. SAGE Publications.Google Scholar
Freed Taylor, M. (2000). Dissemination issues for panel studies: Metadata and documentation. In Rose, D. (ed.), Researching Social and Economic Change. The Uses of Household Panel Studies (pp. 146162). Routledge.Google Scholar
Fuller, W. A. (1987). Measurement Error Models. John Wiley & Sons.CrossRefGoogle Scholar
Fumagalli, L., Laurie, H., & Lynn, P. (2012) Experiments with methods to reduce attrition in longitudinal surveys. Journal of the Royal Statistical Society. Series A, 176(2), 499519.CrossRefGoogle Scholar
Ghellini, G. & Trivellato, U. (1996) Indagini panel sul comportamento socio-economico di individui e famiglie: una selezionata rassegna di problemi ed esperienze. In Quintano, C. (ed.) Scritti di Statistica Economica 2. Rocco Curto Editore.Google Scholar
Giroux, É. (2011). The origins of the prospective cohort study: American cardiovascular epidemiology and the Framingham Heart Study. Revue d’histoire des sciences, 2(2), 297318.Google Scholar
Goebel, J., Grabka, M., Liebig, S., et al. (2019). The German Socio-Economic Panel (SOEP). Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), 239(2), 345360.Google Scholar
Hagenaars, J. A. (1990). Categorical Longitudinal Data; Log-Linear Panel, Trend, and Cohort Analysis. SAGE Publications.Google Scholar
Hakim, C. (1987). Research Design. Strategies and Choices in the Design of Social Research. Allen and Unwin.Google Scholar
Henderson, S. J., Holland, J., McGrellis, S., Sharpe, S., & Thomson, R. (2006). Inventing Adulthoods: A Biographical Approach to Youth Transitions. SAGE Publications.Google Scholar
Hermanowicz, J. C. (2013). The longitudinal qualitative interview. Qualitative Sociology, 36, 189208.Google Scholar
Janson, C-G. (1990). Retrospective data, undesirable behavior, and the longitudinal perspective. In Magnusson, D. & Bergman, L.R. (eds.), Data Quality in Longitudinal Research (pp. 100121). Cambridge University Press.Google Scholar
Kaelber, D. C., Liu, W., Ross, M., et al. (2016). Diagnosis and medication treatment of pediatric hypertension: A retrospective cohort study. Pediatrics, 138(6), e20162195. https://www.doi.org/10.1542/peds.2016-2195Google Scholar
Kühne, S., Kroh, M., Liebig, S., & Zinn, S. (2020). The need for household panel surveys in times of crisis: The case of SOEP-CoV. Survey Research Methods, 14(2),195203.Google Scholar
Laurie, H. (2003). From PAPI to CAPI: Consequences for data quality on the British Household Panel Survey. Working Papers of the Institute for Social and Economic Research, paper 2003–14.Google Scholar
Laurie, H. (2013). Panel studies. Oxford Bibliographies in Sociology. https://www.doi.org/10.1093/obo/9780199756384-0108Google Scholar
Lavrakas, P. J. (2008). Encyclopedia of Survey Research Methods. SAGE Publications.CrossRefGoogle Scholar
Leisering, L. & Walker, R. (1998). Preface. In Leisering, L. & Walker, R. (eds.), The Dynamics of Modern Society (pp. xxvii). The Policy Press.Google Scholar
Lepkowski, J. M. & Couper, M. P. (2002). Nonresponse in the second wave of longitudinal household surveys. In Groves, R. M., Dillman, D. A., Eltinge, J. L., & Little, R. J. A. (eds.). Survey Nonresponse (pp. 259272). John Wiley & Sons.Google Scholar
Linton, M. (1982). Transformations of memory in everyday life. In Neisser, U (ed.), Memory Observed. Remembering in Natural Contexts. W. H. Freeman.Google Scholar
Lynn, P. (ed.) (2009). Methodology of Longitudinal Surveys. John Wiley & Sons,CrossRefGoogle Scholar
Longhi, S. & Nandi, A. (2015). A Practical Guide to Using Panel Data. SAGE Publications.CrossRefGoogle Scholar
Magnusson, D. & Bergman, L. R. (eds.) (1990). Data Quality in Longitudinal Research. Cambridge University Press.Google Scholar
Magnusson, D., Bergman, L. R., Rudinger, G., & Torestad, B. (eds.) (1991). Problems and Methods in Longitudinal Research: Stability and Change. Cambridge University Press.Google Scholar
Mayer, K. U. (2015). The German life history study: An introduction. European Sociological Review, 31(2), 137143.Google Scholar
McManus, S. (2020). Using repeated cross-sectional surveys to measure trends in rates of self-harm. Sage Research Methods Cases: Medicine and Health. https://www.doi.org/10.4135/9781529733679Google Scholar
Menard, S. (2002). Longitudinal Research, 2nd ed. SAGE Publications.Google Scholar
Morrow, V. & Crivello, G. (2015). What is the value of qualitative longitudinal research with children and young people for international development? International Journal of Social Research Methodology, 18(3), 267280.Google Scholar
Neale, B. (2019). What Is Qualitative Longitudinal Research? Bloomsbury Academic.Google Scholar
Nesselroade, J. R., & Baltes, P. B. (1979). Longitudinal Research in the Study of Behavior and Development. Academic Press.Google Scholar
Pfeffer, F. T., Fomby, P., & Insolera, N. (2020). The longitudinal revolution: Sociological research at the 50-year milestone of the Panel Study of Income Dynamics. Annual Review of Sociology, 46(1), 83108.Google Scholar
Platt, L., Knies, G., Luthra, R., Nandi, A., & Benzeval, M. (2020). Understanding society at 10 years. European Sociological Review, 36(6), 976988.CrossRefGoogle Scholar
Ployhart, R. E. & Ward, A.-K. (2011). The “quick start guide” for conducting and publishing longitudinal research. Journal of Business and Psychology, 26(4), 413422.Google Scholar
Power, C. & Elliott, J (2006). Cohort profile: 1958 British birth cohort (National Child Development Study). International Journal of Epidemiology, 35(1), 3441.Google Scholar
Rafferty, A., Walthery, P., & King-Heşe, S. (2015). Analysing Change Over Time: Repeated Cross-Sectional and Longitudinal Survey Data. UK Data Service, University of Essex and University of Manchester.Google Scholar
Rajulton, F. (2001). The fundamentals of longitudinal research: An overview special issue on longitudinal methodology. Canadian Studies in Population, 28(2), 169185.Google Scholar
Rajulton, F. & Ravanera, Z. R. (2000). Theoretical and analytical aspects of longitudinal research. PSC Discussion Papers Series, 14(5), article 1. https://ir.lib.uwo.ca/pscpapers/vol14/iss5/1.Google Scholar
Rose, D. (ed.). (2000). Researching Social and Economic Change: The Uses of Household Panel Studies. Routledge.Google Scholar
Ruspini, E. (2002). Introduction to Longitudinal Research. Routledge.Google Scholar
Ruspini, E. (2008). Longitudinal research. An emergent method in the social sciences. In Hesse-Biber, S. N. & Leavy, P. (eds.), Handbook of Emergent Methods (pp. 437460). The Guilford Press.Google Scholar
Ryder, N. B. (1965). The cohort as a concept in the study of social change. American Sociological Review, 30(6), 843861.Google Scholar
Samet, J. M. & Muñoz, A. (1998). Evolution of the cohort study. Epidemiologic Reviews, 20(1), 114.CrossRefGoogle ScholarPubMed
Sarigiani, P. A. & Spierling, T. (2011). Sleeper effect of divorce. In Goldstein, S. & Naglieri, J. A. (eds.), Encyclopedia of Child Behavior and Development. Springer. https://doi.org/10.1007/978-0-387-79061-9_2666Google Scholar
Schonlau, M., Watson, N., & Kroh, M. (2011). Household survey panels: How much do following rules affect sample size? Survey Research Methods, 5(2), 5361.Google Scholar
Schröder, M. (2011). Retrospective Data Collection in the Survey of Health, Ageing and Retirement in Europe. SHARELIFE Methodology. Mannheim Research Institute for the Economics of Aging (MEA).Google Scholar
Smeeding, T. M. (2018). The PSID in research and policy. Annals of the American Academy of Political and Social Science, 680(1), 2947. https://doi.org/10.1177/0002716218798802Google Scholar
Sontag, L. (1971). The history of longitudinal research: Implications for the future. Child Development, 42(4), 9871002.Google Scholar
Sudman, S. & Bradburn, N.A. (1982). Asking Questions. Jossey-Bass Publishers.Google Scholar
Taris, T. W. (2000). A Primer in Longitudinal Data Analysis. SAGE Publications.Google Scholar
Terman, L. M. et al. (1925). Genetic Studies of Genius. Volume I. Mental and Physical Traits of a Thousand Gifted Children. Stanford University Press.Google Scholar
Terman, L. M. et al. (1929). Genetic Studies of Genius. Volume II. [Authored by C. M. Cox] The Early Mental Traits of Three Hundred Geniuses. Stanford University Press.Google Scholar
Terman, L. M. et al. (1930). Genetic Studies of Genius. Volume III. [Authored by B. S. Burks, D. W. Jensen, & L. M. Terman] The Promise of Youth: Follow-Up Studies of a Thousand Gifted Children. Stanford University Press.Google Scholar
Thomson, R. & Holland, J. (2003). Hindsight, foresight and insight: The challenges of longitudinal qualitative research. International Journal of Social Research Methodology, 6(2), 233244.Google Scholar
Thomson, R. & McLeod, J. (2015). New frontiers in qualitative longitudinal research: An agenda for research. International Journal of Social Research Methodology, 18(3), 243250.Google Scholar
Tsao, C. W. & Vasan, R. S. (2015). Cohort profile: The Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiology. International Journal of Epidemiology, 44(6), 18001813.Google Scholar
van der Kamp, L. J. T. & Bijleveld, C. C. J. H. (1998). Methodological issues in longitudinal research. In Bijleveld, C. C. J. H. & van der Kamp, L. J. Th (eds.), Longitudinal Data Analysis. Designs, Models and Methods (pp. 145). SAGE Publications.Google Scholar
Venkatesh, A. & Vitalari, N. (1991). Longitudinal surveys in information systems research: An examination of issues, methods, and applications. In Kraemer, K. L. (ed.), The Information Systems Research Challenge: Survey Research Methods (pp. 115144). Harvard Business School Press.Google Scholar
Voelkle, M. C. & Adolf, J. (2015). History of longitudinal statistical analyses. In Pachana, N. (ed.), Encyclopedia of Geropsychology. Springer. https://doi.org/10.1007/978-981-287-080-3_135-1Google Scholar
Vogl, S., Zartler, U., Schmidt, E-M., & Rieder, I. (2018). Developing an analytical framework for multiple perspective. Qualitative longitudinal interviews (MPQLI). International Journal of Social Research Methodology, 21(2), 177190.Google Scholar
Wall, W. D. & Williams, H. L. (1970). Longitudinal Studies and the Social Sciences. Heinemann.Google Scholar
Wallerstein, J. S., Lewis, J. M., & Blakeslee, S. (2000). The Unexpected Legacy of Divorce: A 25 Year Landmark Study. Hyperion.Google Scholar
Winiarska, A. (2017). Qualitative longitudinal research: Application, potentials and challenges in the context of migration research, Centre of Migration Research (CMR) Working Paper 103/161, Warsaw: University of Warsaw. Available at: www.econstor.eu/bitstream/10419/180968/1/1018535470.pdf.Google Scholar
World Health Organization (1999). European longitudinal study of pregnancy and childhood (ELSPAC): Report on a WHO meeting, Bristol, 13–18 September 1999. WHO Regional Office for Europe. Available at: https://apps.who.int/iris/handle/10665/108279.Google Scholar
Ziniel, S. (2008). Telescoping. In Lavrakas, P. J. (ed.), Encyclopedia of Survey Research Methods,: SAGE Publications. Available at: https://methods.sagepub.com/reference/encyclopedia-of-survey-research-methods/n579.xml?fromsearch=true.Google Scholar

References

Antoun, C., Couper, M. P., & Conrad, F. G. (2017). Effects of mobile versus PC web on survey response quality: A crossover experiment in a probability web panel. Public Opinion Quarterly, 81(S1), 280306. https://doi.org/10.1093/poq/nfw088Google Scholar
Anwyl-Irvine, A. L., Massonnié, J., Flitton, A., Kirkham, N., & Evershed, J. K. (2020). Gorilla in our midst: An online behavioral experiment builder. Behavior Research Methods, 52(1), 388407. https://doi.org/10.3758/s13428-019-01237-xGoogle Scholar
Aristeidou, M., Scanlon, E., & Sharples, M. (2017). Profiles of engagement in online communities of citizen science participation. Computers in Human Behavior, 74, 246256. https://doi.org/10.1016/j.chb.2017.04.044Google Scholar
Armstrong, B., Reynolds, C., Bridge, G., et al. (2020). How does citizen science compare to online survey panels? A comparison of food knowledge and perceptions between the Zooniverse, Prolific and Qualtrics UK panels. Frontiers in Sustainable Food Systems, 4, 306. https://doi.org/10.3389/fsufs.2020.575021Google Scholar
Babbie, E. R. (2020). The Practice of Social Research. Cengage Learning.Google Scholar
Barnhoorn, J. S., Haasnoot, E., Bocanegra, B. R., & van Steenbergen, H. (2015). QRTEngine: An easy solution for running online reaction time experiments using Qualtrics. Behavior Research Methods, 47(4), 918929. https://doi.org/10.3758/s13428-014-0530-7Google Scholar
Bell, D. (2006). An Introduction to Cybercultures. Routledge.Google Scholar
Beymer, M. R., Holloway, I. W., & Grov, C. (2018). Comparing self-reported demographic and sexual behavioral factors among men who have sex with men recruited through Mechanical Turk, Qualtrics, and a HIV/STI clinic-based sample: Implications for researchers and providers. Archives of sexual behavior, 47(1), 133142. https://doi.org/10.1007/s10508-016-0932-yGoogle Scholar
Bortree, D. S. (2005). Presentation of self on the Web: An ethnographic study of teenage girls’ weblogs. Education, Communication & Information, 5(1), 2539. https://doi.org/10.1080/14636310500061102Google Scholar
Bronner, F. & Kuijlen, T. (2007). The live or digital interviewer: A comparison between CASI, CAPI and CATI with respect to differences in response behaviour. International Journal of Market Research, 49(2), 167190. https://doi.org/10.1177/147078530704900204CrossRefGoogle Scholar
Callegaro, M. (2010). Do you know which device your respondent has used to take your online survey. Survey Practice, 3(6), 112.Google Scholar
Casler, K., Bickel, L., & Hackett, E. (2013). Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing. Computers in Human Behavior, 29(6), 21562160. https://doi.org/10.1016/j.chb.2013.05.009Google Scholar
Chew, C. & Eysenbach, G. (2010). Pandemics in the age of Twitter: Content analysis of Tweets during the 2009 H1N1 outbreak. PloS One, 5(11), e14118. https://doi.org/10.1371/journal.pone.0014118Google Scholar
Christenson, D. P. & Glick, D. M. (2013). Crowdsourcing panel studies and real-time experiments in MTurk. The Political Methodologist, 20(2), 2732.Google Scholar
Clifford, S., Jewell, R. M., & Waggoner, P. D. (2015). Are samples drawn from Mechanical Turk valid for research on political ideology? Research & Politics, 2(4). https://doi.org/10.1177/2053168015622072Google Scholar
Cook, C., Heath, F., & Thompson, R. L. (2000). A meta-analysis of response rates in web-or Internet-based surveys. Educational and Psychological Measurement, 60(6), 821836. https://doi.org/10.1177/00131640021970934Google Scholar
Coste, J., Quinquis, L., Audureau, E., & Pouchot, J. (2013). Non response, incomplete and inconsistent responses to self-administered health-related quality of life measures in the general population: Patterns, determinants and impact on the validity of estimates – a population-based study in France using the MOS SF-36. Health and Quality of Life Outcomes, 11(1), 115. https://doi.org/10.1186/1477-7525-11-44Google Scholar
Crump, M. J., McDonnell, J. V., & Gureckis, T. M. (2013). Evaluating Amazon’s Mechanical Turk as a tool for experimental behavioral research. PloS One, 8(3), e57410. https://doi.org/10.1371/journal.pone.0057410Google Scholar
Dandurand, F., Shultz, T. R., & Onishi, K. H. (2008). Comparing online and lab methods in a problem-solving experiment. Behavior Research Methods, 40(2), 428434. https://doi.org/10.3758/BRM.40.2.428Google Scholar
De Wever, B., Schellens, T., Valcke, M., & Van Keer, H. (2006). Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review. Computers & Education, 46(1), 628. https://doi.org/10.1016/j.compedu.2005.04.005Google Scholar
De Leeuw, J. R. (2015). jsPsych: A JavaScript library for creating behavioral experiments in a web browser. Behavior Research Methods, 47(1), 112. https://doi.org/10.3758/s13428-014-0458-yGoogle Scholar
Denissen, J. J., Neumann, L., & Van Zalk, M. (2010). How the Internet is changing the implementation of traditional research methods, people’s daily lives, and the way in which developmental scientists conduct research. International Journal of Behavioral Development, 34(6), 564575. https://doi.org/10.1177/0165025410383746Google Scholar
Denscombe, M. (2009). Item non‐response rates: A comparison of online and paper questionnaires. International Journal of Social Research Methodology, 12(4), 281291. https://doi.org/10.1080/13645570802054706Google Scholar
Dietrich, S. & Winters, M. S. (2015). Foreign aid and government legitimacy. Journal of Experimental Political Science, 2(2), 164171. https://doi.org/10.1017/XPS.2014.31Google Scholar
Dietz, P., Striegel, H., Franke, A. G., et al. (2013). Randomized response estimates for the 12‐month prevalence of cognitive‐enhancing drug use in university students. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, 33(1), 4450. https://doi.org/10.1002/phar.1166Google Scholar
Dillman, D. A. (2000). Procedures for conducting government-sponsored establishment surveys: Comparisons of the total design method (TDM), a traditional cost-compensation model, and tailored design. In Proceedings of American Statistical Association, Second International Conference on Establishment Surveys, Buffalo, New York, June 17–21 (pp. 343–352).Google Scholar
Edlund, J. E., Lange, K. M., Sevene, A. M., et al. (2017). Participant crosstalk: Issues when using the Mechanical Turk. Tutorials in Quantitative Methods for Psychology, 13(3), 174182. https://doi.org/10.20982/tqmp.13.3.p174Google Scholar
Eysenbach, G. & Till, J. E. (2001). Ethical issues in qualitative research on internet communities. BMJ, 323(7321), 11031105. https://doi.org/10.1136/bmj.323.7321.1103Google Scholar
Eysenbach, G. & Wyatt, J. (2002). Using the Internet for surveys and health research. Journal of Medical Internet Research, 4(2), e13. https://doi.org/10.2196/jmir.4.2.e13Google Scholar
Fan, W. & Yan, Z. (2010). Factors affecting response rates of the web survey: A systematic review. Computers in Human Behavior, 26(2), 132139. https://doi.org/10.1016/j.chb.2009.10.015Google Scholar
Fox, F. E., Morris, M., & Rumsey, N. (2007). Doing synchronous online focus groups with young people: Methodological reflections. Qualitative Health Research, 17(4), 539547. https://doi.org/10.1177/1049732306298754Google Scholar
Galesic, M. & Bosnjak, M. (2009). Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opinion Quarterly, 73(2), 349360. https://doi.org/10.1093/poq/nfp031Google Scholar
Gandomi, A. & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007Google Scholar
Greenacre, Z. A. (2016). The importance of selection bias in Internet surveys. Open Journal of Statistics, 6(03), 397. https://doi.org/10.4236/ojs.2016.63035Google Scholar
Greenlaw, C. & Brown-Welty, S. (2009). A comparison of web-based and paper-based survey methods: Testing assumptions of survey mode and response cost. Evaluation Review, 33(5), 464480. https://doi.org/10.1177/0193841X09340214CrossRefGoogle ScholarPubMed
Grootswagers, T. (2020). A primer on running human behavioural experiments online. Behavior Research Methods, 1(4), 22832286. https://doi.org/10.3758/s13428-020-01395-3Google Scholar
Gureckis, T. M., Martin, J., McDonnell, J., (2016). psiTurk: An open-source framework for conducting replicable behavioral experiments online. Behavior Research Methods, 48(3), 829842. https://doi.org/10.3758/s13428-015-0642-8Google Scholar
Hall, M. G., Grummon, A. H., Lazard, A. J., Maynard, O. M., & Taillie, L. S. (2020). Reactions to graphic and text health warnings for cigarettes, sugar-sweetened beverages, and alcohol: An online randomized experiment of US adults. Preventive Medicine, 137, 106120. https://doi.org/10.1016/j.ypmed.2020.106120Google Scholar
Hallett, R. E. & Barber, K. (2014). Ethnographic research in a cyber era. Journal of Contemporary Ethnography, 43(3), 306330. https://doi.org/10.1177/0891241613497749Google Scholar
Hamby, T. & Taylor, W. (2016). Survey satisficing inflates reliability and validity measures: An experimental comparison of college and Amazon Mechanical Turk samples. Educational and Psychological Measurement, 76(6), 912932. https://doi.org/10.1177/0013164415627349CrossRefGoogle ScholarPubMed
Heerwegh, D. & Loosveldt, G. (2008). Face-to-face versus web surveying in a high-internet-coverage population: Differences in response quality. Public Opinion Quarterly, 72(5), 836846. https://doi.org/10.1093/poq/nfn045Google Scholar
Hilbig, B. E. (2016). Reaction time effects in lab-versus web-based research: Experimental evidence. Behavior Research Methods, 48(4), 17181724. https://doi.org/10.3758/s13428-015-0678-9CrossRefGoogle ScholarPubMed
Hine, C. (2000). Virtual Ethnography. SAGE Publications.Google Scholar
Hoare, K. J., Buetow, S., Mills, J., & Francis, K. (2013). Using an emic and etic ethnographic technique in a grounded theory study of information use by practice nurses in New Zealand. Journal of Research in Nursing, 18(8), 720731. https://doi.org/10.1177/1744987111434190Google Scholar
Hohwü, L., Lyshol, H., Gissler, M., et al. (2013). Web-based versus traditional paper questionnaires: A mixed-mode survey with a Nordic perspective. Journal of Medical Internet Research, 15(8), e173. https://doi.org/10.2196/jmir.2595CrossRefGoogle ScholarPubMed
Holsti, O. R. (1969). Content Analysis for the Social Sciences and Humanities. Addison-Wesley.Google Scholar
Ibarra, J. L., Agas, J. M., Lee, M., Pan, J. L., & Buttenheim, A. M. (2018). Comparison of online survey recruitment platforms for hard-to-reach pregnant smoking populations: Feasibility study. JMIR Research Protocols, 7(4), e8071. https://doi.org/10.2196/resprot.8071Google Scholar
Johnson, N. F. & Humphry, N. (2012). The Teenage Expertise Network (TEN): An online ethnographic approach. International Journal of Qualitative Studies in Education, 25(6), 723739. https://doi.org/10.1080/09518398.2011.590160Google Scholar
Joinson, A. N., Woodley, A., & Reips, U. D. (2007). Personalization, authentication and self-disclosure in self-administered Internet surveys. Computers in Human Behavior, 23(1), 275285. https://doi.org/10.1016/j.chb.2004.10.012Google Scholar
Kaplowitz, M. D., Hadlock, T. D., & Levine, R. (2004). A comparison of web and mail survey response rates. Public Opinion Quarterly, 68(1), 94101. https://doi.org/10.1093/poq/nfh006Google Scholar
Kenny, A. J. (2005). Interaction in cyberspace: An online focus group. Journal of Advanced Nursing, 49(4), 414422. https://doi.org/10.1111/j.1365-2648.2004.03305.xGoogle Scholar
King, D. B., O’Rourke, N., & DeLongis, A. (2014). Social media recruitment and online data collection: A beginner’s guide and best practices for accessing low-prevalence and hard-to-reach populations. Canadian Psychology/Psychologie Canadienne, 55(4), 240. https://doi.org/10.1037/a0038087Google Scholar
Kongsved, S. M., Basnov, M., Holm-Christensen, K., & Hjollund, N. H. (2007). Response rate and completeness of questionnaires: A randomized study of Internet versus paper-and-pencil versions. Journal of Medical Internet Research, 9(3), e25. https://doi.org/10.2196/jmir.9.3.e25Google Scholar
Kozinets, R. V. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39, 6172. https://doi.org/10.1509/jmkr.39.1.61.18935Google Scholar
Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. SAGE Publications.Google Scholar
Kramer, J., Rubin, A., Coster, W., et al. (2014). Strategies to address participant misrepresentation for eligibility in Web‐based research. International Journal of Methods in Psychiatric Research, 23(1), 120129. https://doi.org/10.1002/mpr.1415Google Scholar
Kraut, R., Olson, J., Banaji, M., et al. (2004). Psychological research online: Report of Board of Scientific Affairs’ Advisory Group on the Conduct of Research on the Internet. American Psychologist, 59(2), 105. https://doi.org/10.1037/0003-066X.59.2.105Google Scholar
Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology. SAGE Publications.Google Scholar
Lallukka, T., Pietiläinen, O., Jäppinen, S., et al. (2020). Factors associated with health survey response among young employees: A register-based study using online, mailed and telephone interview data collection methods. BMC Public Health, 20(1), 184. https://doi.org/10.1186/s12889-020-8241-8Google Scholar
Larsen, M. C. (2008). Understanding social networking: On young people’s construction and co-construction of identity online. Online Networking: Connecting People. Icfai University Press.Google Scholar
Lazer, D., Pentland, A., Adamic, L., et al. (2009). Social science. Computational social science. Science, 323(5915), 721723. https://doi.org/10.1126/science.1167742CrossRefGoogle ScholarPubMed
Leach, M. J., Hofmeyer, A., & Bobridge, A. (2016). The impact of research education on student nurse attitude, skill and uptake of evidence‐based practice: A descriptive longitudinal survey. Journal of Clinical Nursing, 25(1–2), 194203. https://doi.org/10.1111/jocn.13103CrossRefGoogle ScholarPubMed
Lee, H., Wright, K. B., O’Connor, M., & Wombacher, K. (2014). Framing medical tourism: An analysis of persuasive appeals, risks and benefits, and new media features of medical tourism broker websites. Health Communication, 29(7), 637645. https://doi.org/10.1080/10410236.2013.794412CrossRefGoogle ScholarPubMed
Lefever, S., Dal, M. & Matthiasdottir, A. (2007). Online data collection in academic research: Advantages and limitations. British Journal of Educational Technology, 38(4), 574582. https://doi.org/10.1111/j.1467-8535.2006.00638.xGoogle Scholar
Levay, K. E., Freese, J., & Druckman, J. N. (2016). The demographic and political composition of Mechanical Turk samples. Sage Open, 6(1), 2158244016636433. doi: https://doi.org/10.1177/2158244016636433Google Scholar
Lieberman, D. Z. (2008). Evaluation of the stability and validity of participant samples recruited over the Internet. Cyberpsychology & Behavior, 11(6), 743745. https://doi.org/10.1089/cpb.2007.0254Google Scholar
Lindhjem, H. & Navrud, S. (2011). Are Internet surveys an alternative to face-to-face interviews in contingent valuation? Ecological Economics, 70(9), 16281637. https://doi.org/10.1016/j.ecolecon.2011.04.002Google Scholar
Link, M. W. & Mokdad, A. H. (2005). Effects of survey mode on self-reports of adult alcohol consumption: A comparison of mail, web and telephone approaches. Journal of Studies on Alcohol, 66(2), 239245. https://doi.org/10.15288/jsa.2005.66.239CrossRefGoogle ScholarPubMed
Manninen, V. J. (2017). Sourcing practices in online journalism: an ethnographic study of the formation of trust in and the use of journalistic sources. Journal of Media Practice, 18(2–3), 212228. https://doi.org/10.1080/14682753.2017.1375252Google Scholar
Manovich, L. (2012). How to compare one million images? In D. M. Berry (ed.), Understanding Digital Humanities (pp. 249278). Palgrave Macmillan.Google Scholar
Manyika, J., Chui, M., Brown, B., et al. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.Google Scholar
Markham, A. N. (2005). The methods, politics, and ethics of representation in online ethnography. In The SAGE Handbook of Qualitative Research. SAGE Publications.Google Scholar
Mason, W. & Watts, D. J. (2012). Collaborative learning in networks. Proceedings of the National Academy of Sciences, 109(3), 764769. https://doi.org/10.1073/pnas.1110069108Google Scholar
McInroy, L. B. (2016). Pitfalls, potentials, and ethics of online survey research: LGBTQ and other marginalized and hard-to-access youths. Social Work Research, 40(2), 8394. https://doi.org/10.1093/swr/svw005Google Scholar
McMillan, S. J. (2000). The microscope and the moving target: The challenge of applying content analysis to the World Wide Web. Journalism & Mass Communication Quarterly, 77(1), 8098. https://doi.org/10.1177/107769900007700107Google Scholar
Mullinix, K. J., Leeper, T. J., Druckman, J. N., & Freese, J. (2015). The generalizability of survey experiments. Journal of Experimental Political Science, 2(2), 109138. https://doi.org/10.1017/XPS.2015.19CrossRefGoogle Scholar
Murray, E., Khadjesari, Z., White, I., et al. (2009). Methodological challenges in online trials. Journal of Medical Internet Research, 11(2), e9. https://doi.org/10.2196/jmir.1052Google Scholar
Murthy, D. (2011). Emergent digital ethnographic methods for social research. In Hesse-Biber, S. N. (ed.), Handbook of Emergent Technologies in Social Research (pp. 158–179). Oxford University Press.Google Scholar
Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton University Press.Google Scholar
Nayak, M. S. D. P. & Narayan, K. A. (2019). Strengths and weakness of online surveys. IOSR Journal of Humanities and Social Science, 24(5), 3138. doi: 10.9790/0837-2405053138Google Scholar
Nimrod, G. (2018). Technophobia among older Internet users. Educational Gerontology, 44(2–3), 148162. https://doi.org/10.1080/03601277.2018.1428145Google Scholar
Parigi, P., Santana, J. J., & Cook, K. S. (2017). Online field experiments: Studying social interactions in context. Social Psychology Quarterly, 80(1), 119. https://doi.org/10.1177/0190272516680842Google Scholar
Pechey, R. & Marteau, T. M. (2018). Availability of healthier vs. less healthy food and food choice: An online experiment. BMC Public Health, 18(1), 111. https://doi.org/10.1186/s12889-018-6112-3CrossRefGoogle ScholarPubMed
Peirce, J., Gray, J. R., Simpson, S., et al. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods, 51(1), 195203. https://doi.org/10.3758/s13428-018-01193-yGoogle Scholar
Pfeil, U. & Zaphiris, P. (2009). Investigating social network patterns within an empathic online community for older people. Computers in Human Behavior, 25(5), 11391155. https://doi.org/10.1016/j.chb.2009.05.001Google Scholar
Pullmann, H., Allik, J., & Realo, A. (2009). Global self-esteem across the life span: A cross-sectional comparison between representative and self-selected Internet samples. Experimental Aging Research, 35(1), 2044. https://doi.org/10.1080/03610730802544708Google Scholar
Radford, J., Pilny, A., Reichelmann, A., et al. (2016). Volunteer science: An online laboratory for experiments in social psychology. Social Psychology Quarterly, 79(4), 376396. https://doi.org/10.1177/0190272516675866Google Scholar
Ramo, D. E. & Prochaska, J. J. (2012). Broad reach and targeted recruitment using Facebook for an online survey of young adult substance use. Journal of Medical Internet Research, 14(1), e28. https://doi.org/10.2196/jmir.1878Google Scholar
Rains, S. A., Peterson, E. B., & Wright, K. B. (2015). Communicating social support in computer-mediated contexts: A meta-analytic review of content analyses examining support messages shared online among individuals coping with illness. Communication Monographs, 82(4), 403430. https://doi.org/10.1080/03637751.2015.1019530CrossRefGoogle Scholar
Reimers, S. & Stewart, N. (2015). Presentation and response timing accuracy in Adobe Flash and HTML5/JavaScript Web experiments. Behavior Research Methods, 47(2), 309327. https://doi.org/10.3758/s13428-014-0471-1CrossRefGoogle ScholarPubMed
Riffe, D., Lacy, S., Fico, F., & Watson, B. (2019). Analyzing Media Messages: Using Quantitative Content Analysis in Research. Routledge.Google Scholar
Russomanno, J., Patterson, J. G., & Tree, J. M. J. (2019). Social media recruitment of marginalized, hard-to-reach populations: Development of recruitment and monitoring guidelines. JMIR Public Health and Surveillance, 5(4), e14886. https://doi.org/10.2196/14886Google Scholar
Salmons, J. (2014). Qualitative Online Interviews: Strategies, Design, and Skills. SAGE Publications.CrossRefGoogle Scholar
Schneider, S. M. & Foot, K. A. (2004). The Web as an object of study. New Media & Society, 6(1), 114122. https://doi.org/10.1177/1461444804039912Google Scholar
Shen, G. C. C., Chiou, J. S., Hsiao, C. H., Wang, C. H., & Li, H. N. (2016). Effective marketing communication via social networking site: The moderating role of the social tie. Journal of Business Research, 69(6), 22652270. https://doi.org/10.1016/j.jbusres.2015.12.040Google Scholar
Simmons, A. D. & Bobo, L. D. (2015). Can non-full-probability internet surveys yield useful data? A comparison with full-probability face-to-face surveys in the domain of race and social inequality attitudes. Sociological Methodology, 45(1), 357387. https://doi.org/10.1177/0081175015570096Google Scholar
Skitka, L. J. & Sargis, E. G. (2006). The Internet as psychological laboratory. Annual Review of Psychology, 57, 529555. https://doi.org/10.1146/annurev.psych.57.102904.190048Google Scholar
Stern, M. J., Bilgen, I., & Dillman, D. A. (2014). The state of survey methodology: Challenges, dilemmas, and new frontiers in the era of the tailored design. Field Methods, 26(3), 284301. doi: https://doi.org/10.1177/1525822X13519561Google Scholar
Takahashi, Y., Uchida, C., Miyaki, K., et al. (2009). Potential benefits and harms of a peer support social network service on the Internet for people with depressive tendencies: Qualitative content analysis and social network analysis. Journal of Medical Internet Research, 11(3), e29. https://doi.org/10.2196/jmir.1142Google Scholar
Valkenburg, P. M. & Peter, J. (2007). Online communication and adolescent well-being: Testing the stimulation versus the displacement hypothesis. Journal of Computer-Mediated Communication, 12(4), 11691182. https://doi.org/10.1111/j.1083-6101.2007.00368.xCrossRefGoogle Scholar
Wagg, A. J., Callanan, M. M., & Hassett, A. (2019). Online social support group use by breastfeeding mothers: A content analysis. Heliyon, 5(3), e01245. https://doi.org/10.1016/j.heliyon.2019.e01245CrossRefGoogle ScholarPubMed
Walther, J. B. (2007). Selective self-presentation in computer-mediated communication: Hyperpersonal dimensions of technology, language, and cognition. Computers in Human Behavior, 23(5), 25382557. https://doi.org/10.1016/j.chb.2006.05.002Google Scholar
Walther, J. B. & Burgoon, J. K. (1992). Relational communication in computer‐mediated interaction. Human Communication Research, 19(1), 5088. https://doi.org/10.1111/j.1468-2958.1992.tb00295.xCrossRefGoogle Scholar
Wang, Y. & Sandner, J. (2019). Like a “frog in a well”? An ethnographic study of Chinese rural women’s social media practices through the WeChat platform. Chinese Journal of Communication, 12(3), 324339. https://doi.org/10.1080/17544750.2019.1583677Google Scholar
Wang, Y. C., Kraut, R. E., & Levine, J. M. (2015). Eliciting and receiving online support: Using computer-aided content analysis to examine the dynamics of online social support. Journal of Medical Internet Research, 17(4), e99. https://doi.org/10.2196/jmir.3558Google Scholar
Weigold, A., Weigold, I. K., & Russell, E. J. (2013). Examination of the equivalence of self-report survey-based paper-and-pencil and internet data collection methods. Psychological Methods, 18(1), 53. https://doi.org/10.1037/a0031607Google Scholar
Weinberg, J. D., Freese, J., & McElhattan, D. (2014). Comparing data characteristics and results of an online factorial survey between a population-based and a crowdsource-recruited sample. Sociological Science, 1, 292310. https://doi.org/10.15195/v1.a19Google Scholar
Wenz, A. (2021). Do distractions during web survey completion affect data quality? Findings from a laboratory experiment. Social Science Computer Review, 39(1), 148161. https://doi.org/10.1177/0894439319851503Google Scholar
Williams, T. A. & Shepherd, D. A. (2017). Mixed method social network analysis: Combining inductive concept development, content analysis, and secondary data for quantitative analysis. Organizational Research Methods, 20(2), 268298. https://doi.org/10.1177/1094428115610807Google Scholar
Woo, S. E., Keith, M., & Thornton, M. A. (2015). Amazon Mechanical Turk for industrial and organizational psychology: Advantages, challenges, and practical recommendations. Industrial and Organizational Psychology, 8(2), 171. https://doi.org/10.1017/iop.2015.21CrossRefGoogle Scholar
Wright, K. B. (2005). Researching Internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of Computer-Mediated Communication, 10(3), JCMC1034. https://doi.org/10.1111/j.1083-6101.2005.tb00259.xGoogle Scholar
Wright, K. B. (2016). Communication in health-related online social support groups/communities: A review of research on predictors of participation, applications of social support theory, and health outcomes. Review of Communication Research, 4, 6587. https://doi.org/10.12840/issn.2255-4165.2016.04.01.010Google Scholar
Wright, K. B. (2017). Web-based survey methodology. In Liamputtong, P. (ed.), Handbook of Research Methods in Health Social Sciences (pp. 114). Springer. https://doi.org/10.1007/978-981-10-2779-6_18-1Google Scholar
Wright, K., Fisher, C., Rising, C., Burke-Garcia, A., Afanaseva, D., & Cai, X. (2019). Partnering with mommy bloggers to disseminate breast cancer risk information: Social media intervention. Journal of Medical Internet Research, 21(3), e12441. https://doi.org/10.2196/12441CrossRefGoogle ScholarPubMed
Zhang, J., Calabrese, C., Ding, J., Liu, M., & Zhang, B. (2018). Advantages and challenges in using mobile apps for field experiments: A systematic review and a case study. Mobile Media & Communication, 6(2), 179196. https://doi.org/10.1177/2050157917725550Google Scholar
Zhou, H. & Fishbach, A. (2016). The pitfall of experimenting on the Web: How unattended selective attrition leads to surprising (yet false) research conclusions. Journal of Personality and Social Psychology, 111(4), 493. https://doi.org/10.1037/pspa0000056Google Scholar

References

Aiken, L. S. & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions. SAGE Publications.Google Scholar
Ali, A., Klasa, S., & Yeung, E. (2008). The limitations of industry concentration measures constructed with Compustat data: Implications for finance research. Review of Financial Studies, 22(10), 38393871.Google Scholar
Almon, S. (1965). The distributed lag between capital appropriations and expenditures. Econometrica, 33(1), 178196.Google Scholar
Angrist, J. D. & Pischke, J. S. (2010). The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of Economic Perspectives, 24(2), 330.Google Scholar
Asparouhov, T. & Muthén, B. (2014). Multiple-group factor analysis alignment. Structural Equation Modeling: A Multidisciplinary Journal, 21(4), 495508.Google Scholar
Asparouhov, T. & Muthén, B. (2020). Comparison of models for the analysis of intensive longitudinal data. Structural Equation Modeling: A Multidisciplinary Journal, 27(2), 275297.Google Scholar
Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359388.Google Scholar
Astbury, B. & Leeuw, F. L. (2010). Unpacking black boxes: Mechanisms and theory building in evaluation. American Journal of Evaluation, 31(3), 363381.Google Scholar
Bai, X. (2018). Forecasting short term trucking rates. Unpublished Master’s Thesis, Massachusetts Institute of Technology. Available at: https://dspace.mit.edu/handle/1721.1/117796.Google Scholar
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, 131(4), 15931636.Google Scholar
Basu, S. (2019). Are price-cost markups rising in the United States? A discussion of the evidence. Journal of Economic Perspectives, 33(3), 322.Google Scholar
Bauer, D. J. & Hussong, A. M. (2009). Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models. Psychological Methods, 14(2), 101125.Google Scholar
Blinder, A. S. & Watson, M. W. (2016). Presidents and the US economy: An econometric exploration. American Economic Review, 106(4), 1015–45.CrossRefGoogle Scholar
Bloom, N., Sadun, R., & Van Reenen, J. (2012). Americans do IT better: US multinationals and the productivity miracle. American Economic Review, 102(1), 167201.Google Scholar
Bollen, K. A. (1989). Structural Equations with Latent Variables. John Wiley & Sons.Google Scholar
Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53(1), 605634.Google Scholar
Braguinsky, S., Ohyama, A., Okazaki, T., & Syverson, C. (2015). Acquisitions, productivity, and profitability: Evidence from the Japanese cotton spinning industry. American Economic Review, 105(7), 20862119.Google Scholar
Brave, S. A., Butters, R. A., & Fogarty, M. (2021). The perils of working with big data and a SMALL checklist you can use to recognize them. Business Horizons, 65(4), 481492. https://doi.org/10.1016/j.bushor.2021.06.004Google Scholar
Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36(1), 111150.Google Scholar
Browne, M. W., MacCallum, R. C., Kim, C. T., Andersen, B. L., & Glaser, R. (2002). When fit indices and residuals are incompatible. Psychological Methods, 7(4), 403421.CrossRefGoogle ScholarPubMed
Bunge, M. (2004). How does it work? The search for explanatory mechanisms. Philosophy of the Social Sciences, 34(2), 182210.Google Scholar
Bureau of Economic Analysis (2021). Personal income. Available at: https://fred.stlouisfed.org/series/PI.Google Scholar
Bureau of Labor Statistics (2021a). Handbook of methods. Available at: www.bls.gov/opub/hom/home.htm.Google Scholar
Bureau of Labor Statistics (2021b). Producer price indexes. Available at: www.bls.gov/pPI/.Google Scholar
Bureau of Labor Statistics (2021c). Producer price index by industry: General freight trucking, long-distance truckload (PCU484121484121). Available at: https://fred.stlouisfed.org/series/PCU484121484121.Google Scholar
Casciaro, T. & Piskorski, M. J. (2005). Power imbalance, mutual dependence, and constraint absorption: A closer look at resource dependence theory. Administrative Science Quarterly, 50(2), 167199.Google Scholar
Census Bureau (2014). American community survey design and methodology (January 2014). Available at: www2.census.gov/programs-surveys/acs/methodology/design_and_methodology/acs_design_methodology_report_2014.pdf.Google Scholar
Census Bureau (2021a). Economic census, technical documentation, methodology, nonsampling error. Available at: www.census.gov/programs-surveys/economic-census/technical-documentation/methodology.html#nonsampling-error.Google Scholar
Census Bureau (2021b). Monthly state retail sales technical documentation. Available at: www.census.gov/retail/mrts/www/statedata/msrs_technical_documentation.pdf.Google Scholar
Cizek, G. J. (2012). Defining and distinguishing validity: Interpretations of score meaning and justifications of test use. Psychological Methods, 17(1), 3143.Google Scholar
Cook, D. A. & Beckman, T. J. (2006). Current concepts in validity and reliability for psychometric instruments: Theory and application. American Journal of Medicine, 119(2), 166.e7–166.e16.Google Scholar
Cudeck, R. (1985). A structural comparison of conventional and adaptive versions of the ASVAB. Multivariate Behavioral Research, 20(3), 305322.Google Scholar
Cudeck, R. & Henly, S. J. (1991). Model selection in covariance structures analysis and the” problem” of sample size: A clarification. Psychological Bulletin, 109(3), 512519.Google Scholar
DAT Freight & Analytics (2021). National van rates. Available at: www.dat.com/industry-trends/trendlines/van/national-rates.Google Scholar
Downing, S. M. (2003). Validity: On the meaningful interpretation of assessment data. Medical Education, 37(9), 830837.Google Scholar
Emerson, R. M. (1962). Power-dependence relations. American Sociological Review, 27(1), 3141.Google Scholar
Enders, W. (2015). Applied Econometric Time Series, 4th ed. John Wiley & Sons.Google Scholar
Espeland, W. N. & Sauder, M. (2007). Rankings and reactivity: How public measures recreate social worlds. American Journal of Sociology, 113(1), 140.CrossRefGoogle Scholar
Falleti, T. G. & Lynch, J. F. (2009). Context and causal mechanisms in political analysis. Comparative Political Studies, 42(9), 11431166.Google Scholar
Forbes, S. J., Lederman, M., & Tombe, T. (2015). Quality disclosure programs and internal organizational practices: Evidence from airline flight delays. American Economic Journal: Microeconomics, 7(2), 126. https://www.aeaweb.org/articles?id=10.1257/mic.20130164.Google Scholar
Foster, S. L. & Cone, J. D. (1995). Validity issues in clinical assessment. Psychological Assessment, 7(3), 248260.Google Scholar
Frazier, G. L. (1983). On the measurement of interfirm power in channels of distribution. Journal of Marketing Research, 20(2), 158166.Google Scholar
General Social Survey (2021). About the GSS. Available at: https://gss.norc.org/About-The-GSS.Google Scholar
Gentzkow, M. & Shapiro, J. M. (2010). What drives media slant? Evidence from US daily newspapers. Econometrica, 78(1), 3571.Google Scholar
Goldsby, T. J., Michael Knemeyer, A., Miller, J. W., & Wallenburg, C. M. (2013). Measurement and moderation: Finding the boundary conditions in logistics and supply chain research. Journal of Business Logistics, 34(2), 109116.CrossRefGoogle Scholar
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 13601380.Google Scholar
Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Multivariate Behavioral Research, 53(6), 820841.Google Scholar
Hedström, P. & Ylikoski, P. (2010). Causal mechanisms in the social sciences. Annual Review of Sociology, 36, 4967.CrossRefGoogle Scholar
Heide, J. B. & John, G. (1988). The role of dependence balancing in safeguarding transaction-specific assets in conventional channels. Journal of Marketing, 52(1), 2035.Google Scholar
Horowitz, K. J. & Planting, M. A. (2009). Concepts and methods of the US input–output accounts. Available at: www.bea.gov/sites/default/files/methodologies/IOmanual_092906.pdf.Google Scholar
Ibanez, M. R. & Toffel, M. W. (2020). How scheduling can bias quality assessment: Evidence from food-safety inspections. Management Science, 66(6), 23962416.Google Scholar
Jin, G. Z. & Leslie, P. (2003). The effect of information on product quality: Evidence from restaurant hygiene grade cards. Quarterly Journal of Economics, 118(2), 409451.Google Scholar
Kane, M. T. (1992). An argument-based approach to validity. Psychological Bulletin, 112(3), 527535.CrossRefGoogle Scholar
Kane, M. T. (2001). Current concerns in validity theory. Journal of Educational Measurement, 38(4), 319342.Google Scholar
Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50(1), 173.Google Scholar
Kane, T. J. & Staiger, D. O. (2002). The promise and pitfalls of using imprecise school accountability measures. Journal of Economic Perspectives, 16(4), 91114.Google Scholar
Ketchen, D. J., Ireland, R. D., & Baker, L. T. (2013). The use of archival proxies in strategic management studies: Castles made of sand? Organizational Research Methods, 16(1), 3242.Google Scholar
Lipton, P. (2004). Inference to the Best Explanation, 2nd ed. RoutledgeGoogle Scholar
Little, T. D. (2013). Longitudinal Structural Equation Modeling. Guilford Press.Google Scholar
Macher, J. T., Mayo, J. W., & Nickerson, J. A. (2011). Regulator heterogeneity and endogenous efforts to close the information asymmetry gap. Journal of Law and Economics, 54(1), 2554.Google Scholar
Mahoney, J. (2001). Beyond correlational analysis: Recent innovations in theory and method. Sociological Forum 16(3), 575593.Google Scholar
McKendall, M. A. & Wagner, J. A., III (1997). Motive, opportunity, choice, and corporate illegality. Organization Science, 8(6), 624647.Google Scholar
McKone, K. E. & Weiss, E. N. (1998). TPM: Planned and autonomous maintenance – bridging the gap between practice and research. Production and Operations Management, 7(4), 335351.CrossRefGoogle Scholar
Meehl, P. E. (1990). Appraising and amending theories: The strategy of Lakatosian defense and two principles that warrant it. Psychological Inquiry, 1(2), 108141.Google Scholar
Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741749.Google Scholar
Miller, J. & Parast, M. M. (2019). Learning by applying: The case of the Malcolm Baldrige National Quality Award. IEEE Transactions on Engineering Management, 66(3), 337353.CrossRefGoogle Scholar
Miller, J. W. & Saldanha, J. P. (2016). A new look at the longitudinal relationship between motor carrier financial performance and safety. Journal of Business Logistics, 37(3), 284306.CrossRefGoogle Scholar
Miller, J. & Saldanha, J. P. (2018). An exploratory investigation of new entrant motor carriers’ longitudinal safety performance. Transportation Journal, 57(2), 163192.Google Scholar
Miller, J. W., Golicic, S. L., & Fugate, B. S. (2018). Reconciling alternative theories for the safety of owner–operators. Journal of Business Logistics, 39(2), 101122.Google Scholar
Miller, J. W., Muir, W. A., Bolumole, Y., & Griffis, S. E. (2020). The effect of truckload driver turnover on truckload freight pricing. Journal of Business Logistics, 41(4), 294309.Google Scholar
Miller, J. W., Bolumole, Y., & Muir, W. A. (2021a). Exploring longitudinal industry‐level large truckload driver turnover. Journal of Business Logistics, 42(4), 428450. https://doi.org/10.1111/jbl.12235Google Scholar
Miller, J. W., Scott, A., & Williams, B. D. (2021b). Pricing dynamics in the truckload sector: The moderating role of the electronic logging device mandate. Journal of Business Logistics, 42(4), 388405. https://doi.org/10.1111/jbl.12256CrossRefGoogle Scholar
Miller, J., Davis‐Sramek, B., Fugate, B. S., Pagell, M., & Flynn, B. B. (2021c). Editorial commentary: Addressing confusion in the diffusion of archival data research. Journal of Supply Chain Management, 57(3), 130146. https://doi.org/10.1111/jscm.12236Google Scholar
Miller, J., Skowronski, K., & Saldanha, J. (2022) Asset ownership & incentives to undertake non‐contractible actions: The case of trucking. Journal of Supply Chain Management, 58, 6591. https://doi.org/10.1111/jscm.12263Google Scholar
Muir, W. A., Miller, J. W., Griffis, S. E., Bolumole, Y. A., & Schwieterman, M. A. (2019). Strategic purity and efficiency in the motor carrier industry: A multiyear panel investigation. Journal of Business Logistics, 40(3), 204228.CrossRefGoogle Scholar
Muthén, B. & Asparouhov, T. (2014). IRT studies of many groups: The alignment method. Frontiers in Psychology, 5, 978.Google Scholar
Muthén, B. & Asparouhov, T. (2018). Recent methods for the study of measurement invariance with many groups: Alignment and random effects. Sociological Methods & Research, 47(4), 637664.CrossRefGoogle Scholar
Nyrup, R. (2015). How explanatory reasoning justifies pursuit: A Peircean view of IBE. Philosophy of Science, 82(5), 749760.Google Scholar
Pawson, R. & Manzano-Santaella, A. (2012). A realist diagnostic workshop. Evaluation, 18(2), 176191.Google Scholar
Peltzman, S. (2000). Prices rise faster than they fall. Journal of Political Economy, 108(3), 466502.Google Scholar
Sauder, M. & Espeland, W. N. (2009). The discipline of rankings: Tight coupling and organizational change. American Sociological Review, 74(1), 6382.CrossRefGoogle Scholar
Schwieterman, M. A., Miller, J., Knemeyer, A. M., & Croxton, K. L. (2020). Do supply chain exemplars have more or less dependent suppliers? Journal of Business Logistics, 41(2), 149173.Google Scholar
Scott, A. (2015). The value of information sharing for truckload shippers. Transportation Research Part E: Logistics and Transportation Review, 81, 203214.Google Scholar
Scott, A. (2018). Carrier bidding behavior in truckload spot auctions. Journal of Business Logistics, 39(4), 267281.Google Scholar
Scott, A. (2019). Concurrent business and buyer–supplier behavior in B2B auctions: Evidence from truckload transportation. Production and Operations Management, 28(10), 26092628.Google Scholar
Scott, A. & Nyaga, G. N. (2019). The effect of firm size, asset ownership, and market prices on regulatory violations. Journal of Operations Management, 65(7), 685709.Google Scholar
Scott, A., Balthrop, A., & Miller, J. W. (2021). Unintended responses to IT‐enabled monitoring: The case of the electronic logging device mandate. Journal of Operations Management, 67(2), 152181.Google Scholar
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.Google Scholar
Singer, J. D. and Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press.Google Scholar
Skowronski, K. & BentonJr, W. C. (2018). The influence of intellectual property rights on poaching in manufacturing outsourcing. Production and Operations Management, 27(3), 531552.Google Scholar
Steel, D. (2004). Social mechanisms and causal inference. Philosophy of the Social Sciences, 34(1), 5578.Google Scholar
Sutton, R. I. & Staw, B. M. (1995). What theory is not. Administrative Science Quarterly, 40(3), 371384.Google Scholar
Syverson, C. (2004). Market structure and productivity: A concrete example. Journal of Political Economy, 112(6), 11811222.Google Scholar
Vegter, A., Taylor, J. K., & Haider-Markel, D. P. (2020). Old and new data sources and methods for interest group research. Interest Groups & Advocacy, 9(3), 436450.Google Scholar
Williamson, O. E. (2005). The economics of governance. American Economic Review, 95(2), 118.CrossRefGoogle Scholar
Winter, S. G., Szulanski, G., Ringov, D., & Jensen, R. J. (2012). Reproducing knowledge: Inaccurate replication and failure in franchise organizations. Organization Science, 23(3), 672685.Google Scholar
Wirth, R. J. & Edwards, M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12(1), 5879.Google Scholar
Zhang, G., Browne, M. W., Ong, A. D., & Chow, S. M. (2014). Analytic standard errors for exploratory process factor analysis. Psychometrika, 79(3), 444469.Google Scholar

References

Aurini, J.D., Heath, M., & Howells, S. (2016). The How to of Qualitative Research. SAGE Publications.Google Scholar
Banks, S., Armstrong, A., Carter, K., et al. (2013). Everyday ethics in community-based participatory research. Contemporary Social Science, 8(3), 263277.Google Scholar
Blumer, H. (1969). Symbolic Interactionism: Perspective and Method. Prentice Hall.Google Scholar
Byskov, M. F. (2020). What makes epistemic injustice an ‘injustice’? Journal of Social Philosophy, 52(1), 114131.Google Scholar
Calarco, J. M. (2018). Negotiating Opportunities: How the Middle Class Secures Advantages in School. Oxford University Press.Google Scholar
Carpenter, L. (2005). Virginity Lost: An Intimate Portrait of First Sexual Experiences. New York University Press.Google Scholar
Charmaz, K. (2014). Constructing Grounded Theory, 2nd ed. SAGE Publications.Google Scholar
Collins, P. H. (1989). The social construction of Black feminist thought. Signs, 14, 745773.CrossRefGoogle Scholar
Collins, P. H. (2000). Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment, 2nd ed. Routledge.Google Scholar
Coughlin, S. S., Smith, S. A., & Fernandez, M. E. (2017). Overview of community-based participatory research. In Coughlin, S. S., Smith, S. A., & Fernandez, M. E. (eds.), Handbook of Community-Based Participatory Research (pp. 1–10). Oxford University Press.Google Scholar
Crenshaw, K. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43(6), 12411299.Google Scholar
Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing Ethnographic Fieldnotes, 2nd ed. University of Chicago Press.CrossRefGoogle Scholar
Esterberg, K. G. (2002). Qualitative Methods in Social Research. McGraw Hill.Google Scholar
Fricker, M. (2007). Epistemic Injustice: Power and the Ethics of Knowing. Oxford University Press.CrossRefGoogle Scholar
Gerson, K. & Damaske, S. (2021). The Science and Art of Interviewing. Oxford University Press.Google Scholar
Glaser, B. G. & Strauss, A. L. (1967). The Discovery of Grounded Theory. Aldine de Gruyter.Google Scholar
Hays, S. (1996). The Cultural Contradictions of Motherhood. Yale University Press.Google Scholar
Hochschild, A. R. (1979). Emotion work, feeling rules, and social structure. American Journal of Sociology, 85(3), 551575.CrossRefGoogle Scholar
Holstein, A. & Gubrium, A. F. (2002). Active interviewing. In Weinberg, D. (ed.), Qualitative Research Methods (pp. 112126). Blackwell.Google Scholar
Jones, N. (2010). Between Good and Ghetto: African American Girls and Inner-City Violence. Rutgers University Press.Google Scholar
Lara-Millán, A. (2021). Redistributing the Poor: Jails, Hospitals, and the Crisis of Law and Fiscal Austerity. Oxford University Press.Google Scholar
Luker, K. (2008). Salsa Dancing into the Social Sciences: Research in an Age of Info Glut. Harvard University Press.Google Scholar
Manzo, L. C. & Brightbill, N. (2007). Toward a participatory ethics. In Participatory Action Research Approaches and Methods (pp. 5966). Routledge.Google Scholar
Morris, A. (2015). The Scholar Denied: W. E. B. Du Bois and the Birth of Modern Sociology. University of California Press.Google Scholar
Oakley, A. (1981). Interviewing women: A contradiction in terms. In Roberts, H. (ed.), Doing Feminist Research (pp. 3061). Routledge and Kegan Paul.Google Scholar
Pascoe, C. J. (2011). Dude, You’re a Fag: Masculinity and Sexuality in High School. University of California Press.Google Scholar
Ray, R. (2017). The Making of a Teenage Service Class: Poverty and Mobility in an American City. University of California Press.Google Scholar
Ray, R. (2021). Ethnographers’ circle. Presentation at the Annual Meeting of the Pacific Sociological Association, March 19.Google Scholar
Small, M. L (2009). “How many cases do I need?”: On science and the logic of case selection in field-based research. Ethnography, 10(1): 538.CrossRefGoogle Scholar
Small, M. L (2015). De-exoticizing ghetto poverty: On the ethics of representation in urban ethnography. City & Community, 14(4), 352358.Google Scholar
Sprague, J. (1997). Holy men and big guns: The can(n)on in social theory. Gender & Society, 11(1), 88107.CrossRefGoogle Scholar
Stacey, J. (1988). Can there be a feminist ethnography? Women’s Studies International Forum, 11(1), 2127.CrossRefGoogle Scholar
Strauss, A. (1987). Qualitative Analysis for Social Scientists. Cambridge University Press. doi:10.1017/CBO9780511557842Google Scholar
Sweet, P. L. (2020). Who knows? Reflexivity in feminist standpoint theory and Bourdieu. Gender & Society, 34(6), 922950.Google Scholar
Todd, Z. (2016). An Indigenous feminist’s take on the ontological turn: ‘Ontology’ is just another word for colonialism. Journal of Historical Sociology, 29(1), 422.CrossRefGoogle Scholar
Tuck, E., McKenzie, M., & McCoy, K. (2014). Land education: Indigenous, post-colonial, and decolonizing perspectives on place and environmental education research. Environmental Education Research, 20(1), 120. https://doi.org/10.1080/13504622.2013.877708Google Scholar
Qin, D. (2016). Positionality. In The Wiley Blackwell Encyclopedia of Gender and Sexuality Studies. https://doi.org/10.1002/9781118663219.wbegss619Google Scholar
Varpio, L., Ajjawi, R., Monrouxe, L. V., O’Brien, B. C., & Rees, C. E. (2017). Shedding the cobra effect: Problematising thematic emergence, triangulation, saturation and member checking. Medical education, 51(1), 4050.Google Scholar
Weiss, R. S. (1994). Learning from Strangers: The Art and Method of Qualitative Interview Studies. The Free Press.Google Scholar
Williams, C. L. (1991). Case studies and the sociology of gender. In Feagin, J., Orum, A., & Sjoberg, G. (eds.), A Case for the Case Study (pp. 224243). University of North Carolina Press.Google Scholar

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  • Edited by Austin Lee Nichols, Central European University, Vienna, John Edlund, Rochester Institute of Technology, New York
  • Book: The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
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  • Online publication: 25 May 2023
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