Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-02T18:56:45.710Z Has data issue: false hasContentIssue false

Part IV - Statistical Approaches

Published online by Cambridge University Press:  25 May 2023

Edited by
Austin Lee Nichols  and
John Edlund
Austin Lee Nichols
Affiliation:
Central European University, Vienna
John Edlund
Affiliation:
Rochester Institute of Technology, New York
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
Volume 1: Building a Program of Research
 , pp. 441 - 626
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Atkinson, I. (2012). Accuracy of data transfer: Double data entry and estimating levels of error. Journal of Clinical Nursing, 21, 2730–2735.Google Scholar
Barchard, K. A. & Pace, L. A. (2011). Preventing human error: The impact of data entry methods on data accuracy and statistical results. Computers in Human Behavior, 27(5), 1834–1839.Google Scholar
Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys (CSUR), 41(3), 1–52.Google Scholar
Batini, C. S. M. & Scannapieca, M. (2006). Data Quality: Concepts, Methodologies and Techniques. Springer.Google Scholar
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216.Google Scholar
Brislin, R. W. & Freimanis, C. (2001). Back-translation. In Pollard, D. E. (ed.), An Encyclopaedia of Translation: Chinese–English, English–Chinese (pp. 22–41). Chinese University Press.Google Scholar
Cope, M. R., Slack, T., Blanchard, T. C., Lee, M. R., & Jackson, J. E. (2020). The Louisiana community oil spill survey (COSS) dataset. Data in Brief, 30, 105390.CrossRefGoogle ScholarPubMed
Cummings, J. & Masten, J. (1994). Customized dual data entry for computerized data analysis. Quality Assurance (San Diego, California), 3(3), 300–303.Google ScholarPubMed
Dasu, T. & Johnson, T. (2003). Exploratory Data Mining and Data Cleaning, Volume 479. John Wiley & Sons.Google Scholar
Database Error Rate (2008). Database error rate. In Kirch, W. (ed.), Encyclopedia of Public Health (pp. 196–196). Springer Netherlands. https://doi.org/10.1007/978-1-4020-5614-7_667Google Scholar
Day, S., Fayers, P., & Harvey, D. (1998). Double data entry: What value, what price? Controlled Clinical Trials, 19(1), 15–24.Google Scholar
Dean, A., Arner, T., Sunki, G., et al. (2011). Epi Info™, a database and statistics program for public health professionals. CDC, Atlanta, GA.Google Scholar
Harris, P. A., Taylor, R., Minor, B. L., et al. (2019). The REDCap consortium: Building an international community of software platform partners. Journal of Biomedical Informatics, 95, 103208.CrossRefGoogle ScholarPubMed
Harris, P. A., Taylor, R., Thielke, R., et al. (2009). Research electronic data capture (REDCap): A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377–381.Google Scholar
INDEPTH Network (2002). Population and Health in Developing Countries: Volume 1; Population, Health, and Survival at INDEPTH Sites. IDRC.Google Scholar
Kaur, A. & Datta, A. (2019). Detecting and ranking outliers in high-dimensional data. International Journal of Advances in Engineering Sciences and Applied Mathematics, 11(1), 75–87.CrossRefGoogle Scholar
Kawado, M., Hinotsu, S., Matsuyama, Y., et al. (2003). A comparison of error detection rates between the reading aloud method and the double data entry method. Controlled Clinical Trials, 24(5), 560–569.Google Scholar
King, D. W. & Lashley, R. (2000). A quantifiable alternative to double data entry. Controlled Clinical Trials, 21(2), 94–102.Google Scholar
Koepsell, T. D. & Weiss, N. S. (2014). Epidemiologic Methods: Studying the Occurrence of Illness. Oxford University Press.Google Scholar
McKnight, P. E., McKnight, K. M., Sidani, S., & Figueredo, A. J. (2007). Missing Data: A Gentle Introduction. Guilford Press.Google Scholar
Muir, J. A., Braudt, D. B., Swindle, J., Flaherty, J., & Brown, R. B. (2018). Cultural antecedents to community: An evaluation of community experience in the United States, Thailand, and Vietnam. City & Community, 17(2), 485–503.Google Scholar
Muir, J. A., Cope, M. R., Angeningsih, L. R., Jackson, J. E., & Brown, R. B. (2019). Migration and mental health in the aftermath of disaster: Evidence from Mt. Merapi, Indonesia. International Journal of Environmental Research and Public Health, 16(15), 2726.Google Scholar
Muir, J. A., Cope, M. R., Angeningsih, L. R., & Brown, R. B. (2020a). Community recovery after a natural disaster: Core data from a survey of communities affected by the 2010 Mt. Merapi eruptions in Central Java, Indonesia. Data in Brief, 32, 106040.Google Scholar
Muir, J. A., Cope, M. R., Angeningsih, L. R., & Jackson, J. E. (2020b). To move home or move on? Investigating the impact of recovery aid on migration status as a potential tool for disaster risk reduction in the aftermath of volcanic eruptions in Merapi, Indonesia. International Journal of Disaster Risk Reduction, 46, 101478.CrossRefGoogle Scholar
Oni, S., Chen, Z., Hoban, S., & Jademi, O. (2019). A comparative study of data cleaning tools. International Journal of Data Warehousing and Mining (IJDWM), 15(4), 48–65.Google Scholar
Osborne, J. W. (2013). Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data. SAGE Publications.Google Scholar
R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Available at: www.r-project.org.Google Scholar
Redman, T. C. (2001). Data Quality: The Field Guide. Digital Press.Google Scholar
Reynolds-Haertle, R. A. & McBride, R. (1992). Single vs. double data entry in CAST. Controlled Clinical Trials, 13(6), 487–494.Google Scholar
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.Google Scholar
Sadiq, S., Yeganeh, N. K., & Indulska, M. (2011). 20 years of data quality research: Themes, trends and synergies. Proceedings of the Twenty-Second Australasian Database Conference, Perth, January 17–20, Volume 115,Google Scholar
StataCorp (2021). Stata statistical software: Release 17. StataCorp LLC.Google Scholar
Van den Broeck, J., Argeseanu Cunningham, S., Eeckels, R., & Herbst, K. (2005). Data cleaning: detecting, diagnosing, and editing data abnormalities. PLoS Medicine, 2(10), e267.Google Scholar

References

Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. http://dx.doi.org/10.1037/0033-2909.112.1.155CrossRefGoogle ScholarPubMed
Garner, D. M., Olmstead, M. P., & Polivy, J. (1983). Development and validation of a multidimensional eating disorder inventory for anorexia nervosa and bulimia. International Journal of Eating Disorders, 2, 15–34.Google Scholar
Montoya, A. & Cannon, B. (2019). Practicing quantitative psychology (from an aerial circus trapeze!?) with Amanda Montoya, PhD. Eye on Psi Chi, 23(3), 22–25. https://doi.org/10.24839/2164-9812.Eye23.3.22Google Scholar
Smith, N. N. & Ransford, C. (1999). The relationship between eating disorders and conformity in female college students. Psi Chi Journal of Undergraduate Research, 4, 9–11. Available at: www.psichi.org/resource/resmgr/journal_1999/Spring99_Smith.pdfGoogle Scholar
Smith, R. A. & Davis, S. F. (2010). Using statistics to answer questions. In The Psychologist as Detective: An Introduction to Conducting Research in Psychology, 5th ed. (pp. 171–202). Prentice Hall.Google Scholar
Wright, R. R., Broadbent, C., Graves, A., & Gibson, J. (2016). Health behavior change promotion among Latter-Day Saint college students. Psi Chi Journal of Psychological Research, 21, 200–215. https://doi.org/10.24839/2164-8204.JN21.3.200Google Scholar

References

Baguley, T. S. (2012). Serious Stats: A Guide to Advanced Statistics for the Behavioral Sciences. Palgrave Macmillan.Google Scholar
Benjamin, D. J., Berger, J. O., Johannesson, M., et al. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 6–10. https://doi.org/10.1038/s41562-017-0189-zGoogle Scholar
Chambers, C. D. (2019). What’s next for registered reports? Nature, 573(7773), 187–189.Google Scholar
Cortex (2021). Guidelines for users. Available at: http://cdn.elsevier.com/promis_misc/PROMIS%20pub_idt_CORTEX%20Guidelines_RR_29_04_2013.pdf.Google Scholar
Cowles, M. & Davis, C. (1982). On the origins of the .05 level of statistical significance. American Psychologist, 37, 553–558.CrossRefGoogle Scholar
Cuddy, A. C., Wilmuth, C. A., Yap, A. J., & Carney, D. R. (2015). Preparatory power posing affects nonverbal presence and job interview performance. Journal of Applied Psychology, 100, 1286–1295.CrossRefGoogle ScholarPubMed
DeGroot, M. H. (1986). Probability and Statistics, 2nd ed. Addison-Wesley.Google Scholar
Devezer, B., Navarro, D. J., Vandekerckhove, J., & Buzbas, E. O. (2020). The case for formal methodology in scientific reform. bioRxiv. https://doi.org/10.1101/2020.04.26.048306CrossRefGoogle Scholar
Dienes, Z. (2008). Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference. Palgrave Macmillan.Google Scholar
Dienes, Z. (2016). How Bayes factors change scientific practice. Journal of Mathematical Psychology, 72, 78–89.Google Scholar
Dienes, Z. (2019). How do I know what my theory predicts? Advances in Methods and Practices in Psychological Science, 2, 364–377.Google Scholar
Dienes, Z. (2021a). How to use and report Bayesian hypothesis tests. Psychology of Consciousness: Theory, Research, and Practice, 8, 9–26Google Scholar
Dienes, Z. (2021b). The inner workings of registered reports [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/yhp2aGoogle Scholar
Dienes, Z. & McLatchie, N. (2018). Four reasons to prefer Bayesian over significance testing. Psychonomic Bulletin & Review, 25, 207–218.Google Scholar
Edlund, J., Cuccolo, K., Irgens, M. S., Wagge, J. R., & Zlokovich, M. S. (2021). Saving science through replication studies. Perspectives on Psychological Science, March 8. https://doi.org/10.1177/1745691620984385Google Scholar
Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver & Boyd.Google Scholar
Greenland, S. (2017). The need for cognitive science in methodology. American Journal of Epidemiology, 186, 639–645.Google Scholar
Hendriksen, A., de Heide, R., & Grünwald., P. (2020). Optional stopping with Bayes factors. Available at https://arxiv.org/pdf/1807.09077.pdf.Google Scholar
Jeffreys, H. (1939). The Theory of Probability. Oxford University Press.Google Scholar
Klaschinski, L., Schnabel, K., & Schröder-Abé, M. (2017) Benefits of power posing: Effects on dominance and social sensitivity, Comprehensive Results in Social Psychology, 2, 55–67.Google Scholar
Kruschke, J. K. (2011). Bayesian assessment of null values via parameter estimation and model comparison. Perspectives on Psychological Science, 6, 299–312.Google Scholar
Kruschke, J. K. (2013a). Posterior predictive checks can and should be Bayesian. British Journal of Mathematical and Statistical Psychology, 66, 45–56.Google Scholar
Kruschke, J. K. (2013b). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142, 573–603Google Scholar
Lindley, D. V. (1957). A statistical paradox. Biometrika, 44, 187–192.Google Scholar
Lindley, D. V. (2014). Understanding Uncertainty, revised edition. John Wiley & Sons.Google Scholar
MacCoun, R. & Perlmutter, S. (2015). Hide results to seek the truth. Nature, 526, 187–189.Google Scholar
Mayo, D. G. (2018). Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars. Cambridge University Press.Google Scholar
Meehl, P. E. (1967). Theory testing in psychology and physics: A methodological paradox. Philosophy of Science, 34, 103–115.Google Scholar
McIntosh, R. D. (2017). Exploratory reports: A new article type for Cortex. Cortetx, 96, A1–A4.Google Scholar
McPhetres, J., Albayrak-Aydemir, N., Barbosa Mendes, A., et al. (2021). A decade of theory as reflected in psychological science (2009–2019). PLOS One, March 5. https://doi.org/10.1371/journal.pone.0247986Google Scholar
Miller, D. (1999). Critical Rationalism: A Restatement and Defence. Open Court.Google Scholar
Morey, R. (2018). Redefining statistical significance: The statistical arguments. Available at: https://medium.com/@richarddmorey/redefining-statistical-significance-the-statistical-arguments-ae9007bc1f91.Google Scholar
Morey, R. D., Romeijn, J. W., & Rouder, J. N. (2013). The humble Bayesian: Model checking from a fully Bayesian perspective. British Journal of Mathematical and Statistical Psychology. 66, 68–75Google Scholar
Morey, R. D., Romeijn, J.-W., & Rouder, J. N. (2016). The philosophy of Bayes factors and the quantification of statistical evidence. Journal of Mathematical Psychology, 72, 6–18.Google Scholar
Notturno, M. A. (1999). Science and the Open Society. Central European University Press.Google Scholar
Palfi, B. & Dienes, Z. (2019). When and how to calculate the Bayes factor with an interval null hypothesis. PsyArXiv. https://doi.org/10.31234/osf.io/9chmwGoogle Scholar
Palfi, B., Moga, G., Lush, P., Scott, R. B., & Dienes, Z. (2020). Can hypnotic suggestibility be measured online? Psychological Research, 84, 1460–1471. https://doi.org/10.1007/s00426-019-01162-wGoogle Scholar
Pericchia, L. & Pereira, C. (2016). Adaptative significance levels using optimal decision rules. Brazilian Journal of Probability and Statistics, 30, 70–90.Google Scholar
Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson.Google Scholar
Popper, K. R. (1963). Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge.Google Scholar
Popper, K. R. (1972). Objective Knowledge: An Evolutionary Approach. Oxford University Press.Google Scholar
Rouder, J. & Haaf, J. M. (2020). Optional stopping and the interpretation of the Bayes factor. https://doi.org/10.31234/osf.io/m6dhwRGoogle Scholar
Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225–237.Google Scholar
Skora, L., Livermore, J. J. A., Dienes, Z., Seth, A., & Scott, R. B. (2020). Feasibility of unconscious instrumental conditioning: A registered replication. PsyArXiv. https://doi.org/10.31234/osf.io/p9dgnGoogle Scholar
Stigler, S. M. (1999). Statistics on the Table: The History of Statistical Concepts and Methods. Harvard University Press.Google Scholar
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662Google Scholar
Szollosi, A., Kellen, D., Navarro, D. J., et al. (2020). Is preregistration worthwhile? Trends in Cognitive Sciences, 24, 94–95.Google Scholar
Tendeiro, J. N. & Kiers, H. A. L. (2019). A review of issues about null hypothesis Bayesian testing. Psychological Methods, 24(6), 774–795.Google Scholar
van Dongen, N. N. N., Wagenmakers, E., & Sprenger, J. (2020). A Bayesian perspective on severity: Risky predictions and specific hypotheses. PsyArXiv. https://doi.org/10.31234/osf.io/4et65Google Scholar
Vanpaemel, W. (2020). Strong theory testing using the prior predictive and the data prior. Psychological Review, 127, 136–145, http://dx.doi.org/10.1037/rev0000167Google Scholar
Wagenmakers, E. (2017). How to test interval-null hypotheses in JASP. Available at: https://jasp-stats.org/2017/10/25/test-interval-null-hypotheses-jasp/.Google Scholar
Wagenmakers, E. (2019). A breakdown of “preregistration is redundant, at best”. Available at: www.bayesianspectacles.org/a-breakdown-of-preregistration-is-redundant-at-best.Google Scholar
Wagenmakers, E., Gronau, Q. F., & Vandekerckhove, J. (2019). Five Bayesian intuitions for the stopping rule principle. PsyArXiv. https://doi.org/10.31234/osf.io/5ntkdGoogle Scholar
Westfall, P. H., Johnson, W. O., & Utts, J. M. (1997). A Bayesian perspective on the Bonferroni adjustment. Biometrika, 84, 419–427.Google Scholar
Wiseman, R. & Greening, E. (2002) The mind machine: A mass participation experiment into the possible existence of extrasensory perception. British Journal of Psychology, 93, 487–99.Google Scholar

References

Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317–332.Google Scholar
Arendasy, M. (2009) BFSI: Big-Five Struktur-Inventar (Test & Manual). Mödling, Schuhfried GmbH.Google Scholar
Asparouhov, T. & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397–438.Google Scholar
Auerswald, M. & Moshagen, M. (2019). How to determine the number of factors to retain in exploratory factor analysis: A comparison of extraction methods under realistic conditions. Psychological Methods, 24(4), 468–491. https://doi.org/10.1037/met0000200Google Scholar
Bandalos, D. L. (2008). Is parceling really necessary? A comparison of results from item parceling and categorical variable methodology. Structural Equation Modeling, 15(2), 211–240.Google Scholar
Barendse, M. T., Oort, F. J., & Timmerman, M. E. (2015). Using exploratory factor analysis to determine the dimensionality of discrete responses. Structural Equation Modeling, 22(1), 87–101.Google Scholar
Beauducel, A. (2001). On the generalizability of factors: The influence of changing contexts of variables on different methods of factor extraction. Methods of Psychological Research Online, 6(1), 69–96.Google Scholar
Beauducel, A. & Herzberg, P. Y. (2006). On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Structural Equation Modeling, 13(2), 186–203.Google Scholar
Ben-Porath, Y. S. & Tellegen, A. (2008). Minnesota Multiphasic Personality Inventory-2 Restructured Form: Manual for Administration, Scoring and Interpretation. University of Minnesota Press.Google Scholar
Berger, J. L. & Karabenick, S. A. (2016). Construct validity of self-reported metacognitive learning strategies. Educational Assessment, 21(1), 19–33. https://doi.org/10.1080/10627197.2015.1127751Google Scholar
Braeken, J. & van Assen, M. A. (2017). An empirical Kaiser criterion. Psychological Methods, 22(3), 450–466. https://doi.org/10.1037/met0000074Google Scholar
Beavers, A. S., Lounsbury, J. W., Richards, J. K., et al. (2013). Practical considerations for using exploratory factor analysis in educational research. Practical Assessment, Research, and Evaluation, 18(1). https://doi.org/10.7275/qv2q-rk76Google Scholar
Brown, T. A. (2014). Confirmatory Factor Analysis for Applied Research. Guilford Press.Google Scholar
Browne, M. W. (1977). Generalized least-squares estimators in the analysis of covariance structures. In Aigner, D. J. & Goldberger, A. S. (eds.), Latent Variables in Socio-Economic Models (pp. 205–226). North-Holland.Google Scholar
Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36(1), 111–150. https://doi.org/10.1207/S15327906MBR3601_05.Google Scholar
Cattell, R. B. (1943). The description of personality: Basic traits resolved into clusters. Journal of Abnormal and Social Psychology, 38, 476–506.Google Scholar
Cattell, R. B. (1945). The description of personality: Principles and findings in a factor analysis. The American Journal of Psychology, 58(1), 69–90.Google Scholar
Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. https://doi.org/10.1207/s15327906mbr0102_10Google Scholar
Cattell, R. B. & Tsujioka, B. (1964). The importance of factor-trueness and validity, versus homogeneity and orthogonality, in test scales. Educational and Psychological Measurement, 24(1), 3–30.Google Scholar
Clarkson, D. B. & Jennrich, R. I. (1988). Quartic rotation criteria and algorithms. Psychometrika, 53, 251–259.Google Scholar
Conway, J. M. & Huffcutt, A. I. (2003). A review and evaluation of exploratory factor analysis practices in organizational research. Organizational Research Methods, 6(2), 147–168.Google Scholar
Crawford, C. B. & Ferguson, G. A. (1970). A general rotation criterion and its use inorthogonal rotation. Psychometrika, 35, 321–332.Google Scholar
De Winter, J. C. & Dodou, D. (2012). Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size. Journal of Applied Statistics, 39(4), 695–710.Google Scholar
Detrick, P., Ben-Porath, Y. S., & Sellbom, M. (2016). Associations between MMPI-2-RF (restructured form) and Inwald Personality Inventory (IPI) scale scores in a law enforcement pre-employment screening sample. Journal of Police and Criminal Psychology, 31, 81–95.Google Scholar
Dinno, A. (2009). Exploring the sensitivity of Horn’s parallel analysis to the distributional form of random data. Multivariate Behavioral Research, 44(3), 362–388. https://doi.org/10.1080/00273170902938969Google Scholar
DiStefano, C., & Morgan, G. B. (2014). A comparison of diagonal weighted least squares robust estimation techniques for ordinal data. Structural Equation Modeling, 21(3), 425–438.Google Scholar
Everitt, B. & Hothorn, T. (2011) Exploratory factor analysis. In An Introduction to Applied Multivariate Analysis with R (pp. 135–161). Springer. https://doi.org/10.1007/978-1-4419-9650-3_5Google Scholar
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272.Google Scholar
Floyd, F. J. & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7(3), 286–299.Google Scholar
Goretzko, D. & Bühner, M. (2020). One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis. Psychological Methods, 25(6), 776–786. https://doi.org/10.1037/met0000262Google Scholar
Goretzko, D., Pham, T. T. H., & Bühner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology, 40(1), 3510–3521. https://doi.org/10.1007/s12144-019-00300-2Google Scholar
Gorsuch, R. L. (1983). Factor Analysis. Erlbaum.Google Scholar
Graham, J. R., Ben-Porath, Y. S., & McNulty, J. L. (1999). MMPI-2 Correlates for Outpatient Mental Health. University of Minnesota Press.Google Scholar
Harman, H. H. (1976). Modern Factor Analysis, 3rd ed. University of Chicago Press.Google Scholar
Harman, H. H. & Jones, W. H. (1966). Factor analysis by minimizing residuals (minres). Psychometrika, 31, 351–368. https://doi.org/10.1007/BF02289468Google Scholar
Hendrickson, A. E. & White, P. O. (1964). Promax: A quick method for rotation to oblique simple structure. British Journal of Statistical Psychology, 17(1), 65–70. https://doi.org/10.1111/j.2044-8317.1964.tb00244.xGoogle Scholar
Henson, R. K. & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393–416.Google Scholar
Holzinger, K. J. (1946). A comparison of the principal-axis and centroid factor. Journal of Educational Psychology, 37(8), 449–472. https://doi.org/10.1037/h0056539Google Scholar
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185. https://doi.org/10.1007/BF02289447Google Scholar
Howard, M. C. (2016). A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve?. International Journal of Human-Computer Interaction, 32(1), 51–62.Google Scholar
Jennrich, R. I. (1979). Admissible values of γ in direct oblimin rotation. Psychometrika, 44, 173–177.Google Scholar
Jokiniemi, K., Pietilä, A. M., & Mikkonen, S. (2021). Construct validity of clinical nurse specialist core competency scale: An exploratory factor analysis. Journal of Clinical Nursing, 30(13–14), 1863–1873.Google Scholar
Jöreskog, K. G., Olsson, U. H., Wallentin, F. Y. (2016) Exploratory factor analysis (EFA). In Multivariate Analysis with LISREL. Springer Series in Statistics (pp. 257–282). Springer. https://doi.org/10.1007/978-3-319-33153-9_6Google Scholar
Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187–200.Google Scholar
Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151. https://doi.org/10.1177/001316446002000116CrossRefGoogle Scholar
Kaiser, H. F. (1974). A note on the equamax criterion. Multivariate Behavioral Research, 9(4), 501–503.Google Scholar
Kaiser, H. F. & Rice, J. (1974). Little jiffy, mark IV. Educational and Psychological Measurement, 34(1), 111–117.Google Scholar
Kirkegaard, E. O. (2016). Some new methods for exploratory factor analysis of socioeconomic data. Open Quantitative Sociology & Political Science, 1(1), November 7. https://doi.org/10.26775/OQSPS.2016.11.07Google Scholar
Krueger, R. F. (2013). Personality disorders are the vanguard of the post-DSM-5.0 era. Personality Disorders: Theory, Research, and Treatment, 4(4), 355–362. https://doi.org/10.1037/per0000028Google Scholar
Li, C. H. (2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48(3), 936–949.Google Scholar
Lim, S. & Jahng, S. (2019). Determining the number of factors using parallel analysis and its recent variants. Psychological Methods, 24(4), 452–467. https://doi.org/10.1037/met0000230Google Scholar
Little, T. D., Rhemtulla, M., Gibson, K., & Schoemann, A. M. (2013). Why the items versus parcels controversy needn’t be one. Psychological Methods, 18(3), 285–300. https://doi.org/10.1037/a0033266Google Scholar
Lorenzo-Seva, U., Timmerman, M. E., & Kiers, H. A. L. (2011). The hull method for selecting the number of common factors. Multivariate Behavioral Research, 46(2), 340–364. https://doi.org/10.1080/00273171.2011.564527Google Scholar
Marsh, H. W., Lüdtke, O., Nagengast, B., Morin, A. J., & Von Davier, M. (2013). Why item parcels are (almost) never appropriate: Two wrongs do not make a right – Camouflaging misspecification with item parcels in CFA models. Psychological Methods, 18(3), 257–284.Google Scholar
Montoya, A. K. & Edwards, M. C. (2021). The poor fit of model fit for selecting number of factors in exploratory factor analysis for scale evaluation. Educational and Psychological Measurement, 81(3), 413–440.Google Scholar
Mulaik, S. A. (2010). Foundations of Factor Analysis. CRC Press.Google Scholar
Park, H. S., Dailey, R., & Lemus, D. (2002). The use of exploratory factor analysis and principal components analysis in communication research. Human Communication Research, 28(4), 562–577.Google Scholar
Pearson, K. (1909). Determination of the coefficient of correlation. Science, 30(757), 23–25.Google Scholar
Preacher, K. J., Zhang, G., Kim, C., & Mels, G. (2013). Choosing the optimal number of factors in exploratory factor analysis: A model selection perspective. Multivariate Behavioral Research, 48(1), 28–56. doi:10.1080/00273171.2012.710386Google Scholar
Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315.Google Scholar
Ruscio, J. & Roche, B. (2012). Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure. Psychological Assessment, 24(2), 282–292. https://doi.org/10.1037/a0025697Google Scholar
Sass, D. A. & Schmitt, T. A. (2010). A comparative investigation of rotation criteria within exploratory factor analysis. Multivariate Behavioral Research, 45(1), 73–103. https://doi.org/10.1080/00273170903504810.Google Scholar
Schmitt, T. A. (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29(4), 304–321.Google Scholar
Schmitt, T. A., Sass, D. A., Chappelle, W., & Thompson, W. (2018). Selecting the “best” factor structure and moving measurement validation forward: An illustration. Journal of Personality Assessment, 100(4), 345–362.Google Scholar
Schoedel, R., Au, J. Q., Völkel, S. T., et al. (2018) Digital footprints of sensation seeking. Zeitschrift Für Psychologie, 226(4), 232–245.Google Scholar
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.Google Scholar
Sellbom, M. & Tellegen, A. (2019). Factor analysis in psychological assessment research: Common pitfalls and recommendations. Psychological Assessment, 31(12), 1428–1441. https://doi.org/10.1037/pas0000623Google Scholar
Spearman, C. (1904). “General intelligence,” objectively determined and measured. American Journal of Psychology, 15, 201–293.Google Scholar
Thurstone, L. L. (1938). Primary Mental Abilities. University of Chicago Press.Google Scholar
Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.Google Scholar
Thurstone, L. L. & Thurstone, T. G. (1941). Factorial studies of intelligence. Psychometric Monographs, 2, 94.Google Scholar
Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321–327. https://doi.org/10.1007/BF02293557Google Scholar
Wedel, M. & Kamakura, W. A. (2001). Factor analysis with (mixed) observed and latent variables in the exponential family. Psychometrika, 66(4), 515–530.Google Scholar
Widaman, K.F. (1993). Common factor analysis versus principal component analysis: Differential bias in representing model parameters? Multivariate Behavioral Research, 28, 263–311.Google Scholar
Yates, A. (1987). Multivariate Exploratory Data Analysis: A Perspective on Exploratory Factor Analysis. State University of New York Press.Google Scholar
Yuan, K. H. & Bentler, P. M. (1998). Normal theory based test statistics in structural equation modelling. British Journal of Mathematical and Statistical Psychology, 51, 289–309.Google Scholar

References

Appelbaum, M., Cooper, H., Kline, R. B., et al. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board Task Force report. American Psychologist, 73(1), 3–25. https://doi.org/10.1037/amp0000191Google Scholar
Arbuckle, J. L. (2021). IBM SPSS Amos 28 User’s Guide. Amos Development Corporation.Google Scholar
Astrachan, C. B., Patel, V. K., & Wanzenried, G. (2014). A comparative study of CB-SEM and PLS-SEM for theory development in family firm research. Journal of Family Business Strategy, 5(1), 116–128. https://doi.org/10.1016/j.jfbs.2013.12.002Google Scholar
Bagozzi, R. P. & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1) 8–34. https://doi.org/10.1007/s11747-011-0278-xGoogle Scholar
Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42(5), 815–824. https://doi.org/10.1016/j.paid.2006.09.018Google Scholar
Bentler, P. M. & Wu, E. J. C. (2020). EQS 6.4 for Windows [Computer software]. Available at: https://mvsoft.com/.Google Scholar
Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research, 2nd ed. Guilford Press.Google Scholar
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd ed. Erlbaum.Google Scholar
Deng, L., Yang, M., & Marcoulides, K. M. (2018). Structural equation modeling with many variables: A systematic review of issues and developments. Frontiers in Psychology, 9, Article 580. https://doi.org/10.3389/fpsyg.2018.00580Google Scholar
Fan, Y., Chen, J., Shirkey, G., et al. (2016). Applications of structural equation modeling (SEM) in ecological studies: An updated review. Ecological Processes, 5(1), Article 19. https://doi.org/10.1186/s13717-016-0063-3Google Scholar
Filippetti, V. A. & Krumm, G. (2020). A hierarchical model of cognitive flexibility in children: Extending the relationship between flexibility, creativity and academic achievement. Child Neuropsychology, 26(6), 770–800. https://doi.org/10.1080/09297049.2019.1711034Google Scholar
Hayduk, L. A. (2014). Shame for disrespecting evidence: The personal consequences of insufficient respect for structural equation model testing. Medical Research Methodology, 14(1), Article 124. https://doi.org/10.1186/1471-2288-14-124Google Scholar
Henley, A. B., Shook, C. L., & Peterson, M. (2006). The presence of equivalent models in strategic management research using structural equation modeling: Assessing and addressing the problem. Organizational Research Methods, 9(4), 516–535. https://doi.org/10.1177/1094428106290195Google Scholar
Hoyle, R. H. & Isherwood, J. C. (2013). Reporting results from structural equation modeling analyses in Archives of Scientific Psychology. Archives of Scientific Psychology, 1, 14–22. https://doi.org/10.1037/arc0000004Google Scholar
JASP Team (2022). JASP (Version 0.16.1) [Computer software]. Available at: https://jasp-stats.org/Google Scholar
Jöreskog, K. G. (1993). Testing structural equation models. In Bollen, K. A. & Lang, J. S. (eds.), Testing Structural Equation Models (pp. 294–316). SAGE Publications.Google Scholar
Jöreskog, K. G. & Sörbom, D. (1976). LISREL III: Estimation of Linear Structural Equation Systems by Maximum Likelihood Methods. National Educational Resources.Google Scholar
Jöreskog, K. G. & Sörbom, D. (2021). LISREL 11 for Windows [Computer software]. Available at: https://ssicentral.com/.Google Scholar
Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2022). semTools: Useful tools for structural equation modeling (R package 0.5-6). Available at: https://CRAN.R-project.org/package=semTools.Google Scholar
Kale, P., Singh, H., & Perlmutter, H. (2000). Learning and protection of proprietary assets in strategic alliances: Building relational capital. Strategic Management Journal, 21(3), 217–237. https://doi.org/10.1002/(SICI)1097-0266(200003)21:3<217::AID-SMJ95>3.0.CO;2-YGoogle Scholar
Kenny, D. A. & Milan, S. (2012). Identification: A nontechnical discussion of a technical issue. In Hoyle, R. H. (ed.), Handbook of structural equation modeling (pp. 145–163). Guilford Press.Google Scholar
Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling, 5th ed. Guilford Press.Google Scholar
Kühnel, S. (2001). The didactical power of structural equation modeling. In Cudeck, R., Toit, S. du, & Sörbom, D. (eds.), Structural Equation Modeling: Present and Future. A Festschrift in Honor of Karl Jöreskog (pp. 79–96). Scientific Software International.Google Scholar
Lang, K. M. & Little, T. D. (2018). Principled missing data treatments. Prevention Science, 19(3), 284–294. https://doi.org/10.1007/s11121-016-0644-5Google Scholar
Little, T. D. (2013). Longitudinal Structural Equation Modeling. Guilford Press.Google Scholar
McDonald, R. P. & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64–82. https://doi.org/10.1037/1082-989X.7.1.64Google Scholar
Mulaik, S. A. (2009). Linear Causal Modeling with Structural Equations. CRC Press.Google Scholar
Müthen, L. K. & Müthen, B. O. (1998–2017). Mplus User’s Guide, 8th ed. Muthén & Muthén.Google Scholar
Boker, S., Nerale, M., Maes, H., et al. (2023). OpenMx: The OpenMx statistical modeling package. (R package 2.20.7). Available at. https://CRAN.R-project.org/package=OpenMx.Google Scholar
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellora, D. T. (2018). The preregistration revolution. PNAS, 115(11), 2600–2606. https://doi.org/10.1073/pnas.1708274114Google Scholar
Pearl, J. (2009). Causality: Models, Reasoning, and Inference, 2nd ed. Cambridge University Press. https://doi.org/10.1017/CBO9780511803161Google Scholar
Pek, J. & Hoyle, R. H. (2016). On the (in)validity of tests of simple mediation: Threats and solutions. Social and Personality Psychology Compass, 10(3), 150–163. https://doi.org/10.1111/spc3.12237Google Scholar
Raykov, T. & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling, 2nd ed. Erlbaum.Google Scholar
Recio, L. A., Martín, P., Carvajal, F., Ruiz, M., & Serrano, J. M. (2013). A holistic analysis of relationships between executive function and memory in Parkinson’s disease. Journal of Clinical and Experimental Neuropsychology, 35(2), 147–159. http://dx.doi.org/10.1080/13803395.2012.758240Google Scholar
Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(5–6), 341–358. https://doi.org/10.1016/j.lrp.2012.09.010Google Scholar
Rosseel, Y., Jorgensen, T. D., & Rockwood, N. (2022). lavaan: Latent variable analysis (R package 0.6-11). Available at: https://CRAN.R-project.org/package=lavaan.Google Scholar
SAS Institute Inc. (2021). SAS/STAT 15.2 User’s Guide. SAS Institute Inc.Google Scholar
Sauvé, G., Kline, R. B., Shah, J. L., et al. (2019). Cognitive capacity similarly predicts insight into symptoms in first- and multiple-episode psychosis. Schizophrenia Research, 206, 236–243. https://doi.org/10.1016/j.schres.2018.11.013Google Scholar
Shah, R. & Goldstein, S. M. (2006). Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management, 24(2), 148–169. https://doi.org/10.1016/j.jom.2005.05.001Google Scholar
StataCorp LLC (1985–2021). Stata Structural Equation Modeling: Release 17. Stata Press.Google Scholar
Steiger, J. H. (2001). Driving fast in reverse: The relationship between software development, theory, and education in structural equation modeling. Journal of the American Statistical Association, 96(453), 331–338. https://doi.org/10.1198/016214501750332893Google Scholar
Tabachnick, B. G. & Fidell, L. S. (2013). Using Multivariate Statistics, 6th ed. Pearson.Google Scholar
Tarka, P. (2018). An overview of structural equation modeling: Its beginnings, historical development, usefulness and controversies in the social sciences. Quality & Quantity, 51(1), 313–354. https://doi.org/10.1007/s11135-017-0469-8Google Scholar
Teo, T. (2010). A case for using structural equation modelling (SEM) in educational technology research. British Journal of Educational Technology, 41(5), 89–91. https://doi.org/10.1111/j.1467-8535.2009.00999.xGoogle Scholar
Textor, J., van der Zander, B., & Ankan, A. (2020). dagitty: Graphical analysis of structural causal models (R package 0.3-0.). Available at: https://CRAN.R-project.org/package=dagitty.Google Scholar
Thelwall, M. & Wilson, P. (2016). Does research with statistics have more impact? The citation rank advantage of structural equation modeling. Journal of the Association for Information Science and Technology, 67, 1233–1244. https://doi.org/10.1002/asi.23474Google Scholar
Tomarken, A. J. & Waller, N. G. (2003). Potential problems with “well-fitting” models. Journal of Abnormal Psychology, 112(4), 578–598. https://doi.org/10.1037/0021-843X.112.4.578Google Scholar
Westfall, P. H., Henning, K. S. S., & Howell, R. D. (2012). The effect of error correlation on interfactor correlation in psychometric measurement. Structural Equation Modeling, 19(1), 99–117. http://dx.doi.org/10.1080/10705511.2012.634726Google Scholar
Williams, L. J. (2012). Equivalent models: Concepts, problems, alternatives. In Hoyle, R. H. (ed.), Handbook of Structural Equation Modeling (pp. 247–260). Guilford Press.Google Scholar
Williams, T. C., Bach, C. C., Matthiesen, N. B., Henriksen, T. B., & Gagliardi, L. (2018). Directed acyclic graphs: A tool for causal studies in paediatrics. Pediatric Research, 84(4), 487–493. https://doi.org/10.1038/s41390-018-0071-3Google Scholar
Wold, H. (1982). Soft modeling: The basic design and some extensions. In Jöreskog, K. G. & Wold, H., (eds.), Systems Under Indirect Observations: Part II (pp. 1–54). North-Holland.Google Scholar
Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 73(6), 913–934. https://doi.org/10.1177/0013164413495237Google Scholar
Wright, S. (1920). The relative importance of heredity and environment in determining the piebald pattern of guinea-pigs. Proceedings of the National Academy of Sciences, 6(6), 320–332. https://doi.org/10.1073/pnas.6.6.320Google Scholar
von Oertzen, T., Brandmaier, A. M., & Tsang, S. (2015). Structural equation modeling with Ωnyx. Structural Equation Modeling, 22(1), 148–161. https://doi.org/10.1080/10705511.2014.935842Google Scholar
Xia, Y. & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Methods, 51(1),409–428. https://doi.org/10.3758/s13428-018-1055-2Google Scholar
Yamaga, E., Sato, Y., & Minakuchi, S. (2013). A structural equation model relating oral condition, denture quality, chewing ability, satisfaction, and oral health-related quality of life in complete denture wearers. Journal of Dentistry, 41(8), 710–717. https://doi.org/10.1016/j.jdent.2013.05.015Google Scholar
Zhang, M. F., Dawson, J., & Kline, R. B. (2021). Evaluating the use of covariance-based structural equation modelling with reflective measurement in organisational and management research: A review and recommendations for best practice. British Journal of Management, 32(2), 257–272. https://doi.org/10.1111/1467-8551.12415Google Scholar

References

Aiken, L. S. & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions. SAGE Publications.Google Scholar
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Petrov, B. N. & Csaki, B. F. (eds.), Second International Symposium on Information Theory (pp. 267–281). Academiai Kiado.Google Scholar
Berkhof, J. & Snijders, T. A. B. (2001). Variance component testing in multilevel models. Journal of Educational and Behavioral Statistics, 26(2), 133–152. https://doi.org/10.3102/10769986026002133Google Scholar
Box, G. E. P. & Draper, N. R. (1987), Empirical Model-Building and Response Surfaces. John Wiley & Sons.Google Scholar
Burnham, K. P. & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644Google Scholar
Burnham, K. P., Anderson, D. R., & Huyvaert, K. P. (2011). AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behavioral Ecological Sociobiology, 65, 23–35. https://doi.org/10.1007/s00265-010-1029-6Google Scholar
Dominicus, A., Skrondal, A., Gjessing, H. K., Pedersen, N. L., & Palmgren, J. (2006). Likelihood ratio tests in behavioral genetics: Problems and solutions. Behavior Genetics, 36(2), 331–340. https://doi.org/10.1007/s10519-005-9034-7Google Scholar
Enders, C. K. & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121–138. http://dx.doi.org/10.1037/1082-989X.12.2.121Google Scholar
Forster, M. R. (2000). Key concepts in model selection: Performance and generalizability. Journal of Mathematical Psychology, 44(1), 205–231. https://doi.org/10.1006/jmps.1999.1284Google Scholar
Goldstein, H. (2011). Multilevel Statistical Models (Kendall’s Library of Statistics 3), 4th ed. Edward Arnold.Google Scholar
Gully, S. M. & Phillips, J. M. (2019). On finding your level. In Humphrey, S. E. & LeBreton, J. M. (eds.), The Handbook of Multilevel Theory, Measurement, and Analysis (pp. 11–38). American Psychological Association. https://doi.org/10.1037/0000115-002Google Scholar
Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications, 2nd ed. Routledge.Google Scholar
Hox, J. J., Moerbeek, M., & van de Schoot, R. (2017). Multilevel Analysis: Techniques and Applications, 3rd ed. Routledge.CrossRefGoogle Scholar
Kelloway, E. K. (1995). Structural equation modeling in perspective. Journal of Organizational Behavior, 16, 215–224.Google Scholar
LaHuis, D. M. & Ferguson, M. W. (2009). The accuracy of significance tests for slope variance components in multilevel random coefficient models. Organizational Research Methods, 12(3), 418–435. https://doi.org/10.1177%2F1094428107308984Google Scholar
McCoach, D. B. (2019). Multilevel modeling. In Hancock, G.R., Stapleton, L. M., & Mueller, R. O. (eds.) The Reviewers Guide to Quantitative Methods in the Social Sciences (pp. 292-312), 2nd ed. Routledge.Google Scholar
McCoach, D. B. & Cintron, D. W. (2022). An Introduction to Modern Modeling Methods. SAGE Publications.Google Scholar
McCoach, D. B., Rifenbark, G. G., Newton, S. D., et al. (2018). Does the package matter? A comparison of five common multilevel modeling software packages. Journal of Educational and Behavioral Statistics, 43(5), 594–627. https://doi.org/10.3102/1076998618776348Google Scholar
McCoach, D. B., Newton, S., & Gambino, A. J. (2022). Evaluating the fit and adequacy of multilevel models. In O’Connell, A. A., McCoach, D. B., & Bell, B. A. (eds.), Multilevel Modeling Methods with Introductory and Advanced Applications. Information Age Publishing.Google Scholar
O’Connell, A. A., McCoach, D. B., & Bell, B. A. (eds.), Multilevel Modeling Methods with Introductory and Advanced Applications. Information Age Publishing.Google Scholar
Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd ed. SAGE Publications.Google Scholar
Raudenbush, S., Bryk, A., Cheong, Y. & Congdon, R. (2000). HLM Manual. SSI International.Google Scholar
Rights, J. D. & Sterba, S. K. (2019a). New recommendations on the use of R-squared differences in multilevel model comparisons. Multivariate Behavioral Research, 55(4), 568–599. https://doi.org/10.1080/00273171.2019.1660605Google Scholar
Rights, J. D. & Sterba, S. K. (2019b). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods, 24(3), 309–338. https://doi.org/10.1037/met000018Google Scholar
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://www.jstor.org/stable/2958889Google Scholar
Skrondal, A. & Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Chapman & Hall/CRC Press.Google Scholar
Snijders, T. A. B. & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, 2nd ed. SAGE Publications.Google Scholar
Spybrook, J., Raudenbush, S. W., Liu, X. F., Congdon, R., & Martínez, A. (2006). Optimal design for longitudinal and multilevel research: Documentation for the “Optimal Design” software. Survey Research Center of the Institute of Social Research at University of Michigan.Google Scholar
Stoel, R. D., Garre, F. G., Dolan, C., & van den Wittenboer, G. (2006). On the likelihood ratio test in structural equation modeling when parameters are subject to boundary constraints. Psychological Methods, 11(4), 439–455. https://doi.org/10.1037/1082-989X.11.4.439Google Scholar
West, B. T., Welch, K. B., & Galecki, A. T. (2015). Linear Mixed Models: A Practical Guide Using Statistical Software, 2nd ed. Routledge.Google Scholar

References

Adams, D. C., Gurevitch, J., & Rosenberg, M. S. (1997). Resampling tests for meta-analysis of ecological data. Ecology, 78(4), 1277–1283. https://doi.org/10.1890/0012-9658(1997)078[1277:RTFMAO]2.0.CO;2CrossRefGoogle Scholar
Boedeker, P. & Henson, R. K. (2020). Evaluation of heterogeneity and heterogeneity interval estimators in random-effects meta-analysis of the standardized mean difference in education and psychology. Psychological Methods, 25(3), 346–364. https://doi.org/10.1037/met0000241Google Scholar
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. John Wiley & Sons.Google Scholar
Campbell, J. M., Klugar, M., Ding, S., Carmody, D. P., Hakonsen, S. J., Jadotte, Y. T., White, S., & Munn, Z. (2015). Diagnostic test accuracy: methods for systematic review and meta-analysis. International Journal of Evidence-Based Healthcare, 13(3), 154–162. https://doi.org/10.1097/xeb.0000000000000061 Google Scholar
Card, N. A. (2016). Applied Meta-Analysis for Social Science Research (paperback edition). Guilford Press.Google Scholar
Cassey, P., Ewen, J. G., Blackburn, T. M., & Møller, A. P. (2004). A survey of publication bias within evolutionary ecology. Proceedings of the Royal Society of London. Series B: Biological Sciences, 271 (suppl 6), S451–S454.Google Scholar
Cinar, O., Umbanhowar, J., Hoeksema, J. D., & Viechtbauer, W. (2021). Using information‐theoretic approaches for model selection in meta‐analysis. Research Synthesis Methods, 12(4), 537–556. https://doi.org/10.1002/jrsm.1489Google Scholar
Dickersin, K. (1990). The existence of publication bias and risk factors for its occurrence. JAMA, 263(10), 1385–1389.Google Scholar
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634.Google Scholar
Farooq, F., Mogayzel, P. J., Lanzkron, S., Haywood, C., & Strouse, J. J. (2020). Comparison of US federal and foundation funding of research for sickle cell disease and cystic fibrosis and factors associated with research productivity. JAMA Network Open, 3(3), e201737. https://doi.org/10.1001/jamanetworkopen.2020.1737Google Scholar
Forstmeier, W., Wagenmakers, E., & Parker, T. H. (2017). Detecting and avoiding likely false‐positive findings–a practical guide. Biological Reviews, 92(4), 1941–1968.Google Scholar
Gurevitch, J. & Hedges, L. V. (1999). Statistical issues in ecological meta-analyses. Ecology, 80(4), 1142–1149. https://doi.org/10.1890/0012-9658(1999)080[1142:SIIEMA]2.0.CO;2Google Scholar
Gurevitch, J., Koricheva, J., Nakagawa, S., & Stewart, G. (2018). Meta-analysis and the science of research synthesis. Nature, 555(7695), 175–182. https://doi.org/10.1038/nature25753Google Scholar
Hamberg, K. (2008). Gender bias in medicine. Women’s Health, 4(3), 237–243.Google Scholar
Hedges, L. V. & Olkin, I. (1985). Statistical Methods for Meta-Analysis. Elsevier Science. http://qut.eblib.com.au/patron/FullRecord.aspx?p=1901162Google Scholar
Hedges, L. V. & Vevea, J. L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 3(4), 486–504.Google Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 61–83. https://doi.org/10.1017/S0140525X0999152XGoogle Scholar
Higgins, J. P. T. & Thompson, S. G. (2002). Quantifying heterogeneity in a meta‐analysis. Statistics in Medicine, 21(11), 1539–1558.Google Scholar
Hobbs, N. T. & Hilborn, R. (2006). Alternatives to statistical hypothesis testing in ecology: A guide to self teaching. Ecological Applications, 16(1), 5–19. https://doi.org/10.1890/04-0645Google Scholar
Ioannidis, J. P. A. (2018). The proposal to lower p value thresholds to .005. JAMA, 319(14), 1429–1430. https://doi.org/10.1001/jama.2018.1536Google Scholar
Kambach, S., Bruelheide, H., Gerstner, K., et al. (2020). Consequences of multiple imputation of missing standard deviations and sample sizes in meta-analysis. Ecology and Evolution, 10(20), 11699–11712. https://doi.org/10.1002/ece3.6806Google Scholar
Kinlock, N. L. (2019). A meta-analysis of plant interaction networks reveals competitive hierarchies as well as facilitation and intransitivity. American Naturalist, 194(5), 640–653. https://doi.org/10.1086/705293Google Scholar
Lajeunesse, M. J. (2016). Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution, 7(3), 323–330. https://doi.org/10.1111/2041-210X.12472Google Scholar
Lane, A., Luminet, O., Nave, G., & Mikolajczak, M. (2016). Is there a publication bias in behavioural intranasal oxytocin research on humans? Opening the file drawer of one laboratory. Journal of Neuroendocrinology, 28(4). https://doi.org/10.1111/jne.12384.Google Scholar
Lockwood, C., Munn, Z., & Porritt, K. (2015). Qualitative research synthesis: Methodological guidance for systematic reviewers utilizing meta-aggregation. International Journal of Evidence-Based Healthcare, 13(3), 179–187.Google Scholar
Lowry, E., Rollinson, E. J., Laybourn, A. J., et al. (2013). Biological invasions: A field synopsis, systematic review, and database of the literature. Ecology and Evolution, 3(1), 182–196. https://doi.org/10.1002/ece3.431Google Scholar
Marshall, I. J. & Wallace, B. C. (2019). Toward systematic review automation: A practical guide to using machine learning tools in research synthesis. Systematic Reviews, 8(1), 1–10.Google Scholar
Munn, Z., Aromataris, E., Tufanaru, C., et al. (2019). The development of software to support multiple systematic review types: The Joanna Briggs Institute System for the Unified Management, Assessment and Review of Information (JBI SUMARI). International Journal of Evidence-Based Healthcare, 17(1), 36–43.Google Scholar
Nosek, B. A., Alter, G., Banks, G. C., et al. (2015). Promoting an open research culture. Science, 348(6242), 1422–1425. https://doi.org/10.1126/science.aab2374Google Scholar
O’Dea, R. E., Lagisz, M., Jennions, M. D., et al. (2021). Preferred reporting items for systematic reviews and meta-analyses in ecology and evolutionary biology: A PRISMA extension. Biological Reviews, 96(5), 1695–1722. https://doi.org/10.1111/brv.12721Google Scholar
Orwin, R. G. (1983). A fail-safe N for effect size in meta-analysis. Journal of Educational Statistics, 8(2), 157–159.Google Scholar
Peters, M. D., Godfrey, C. M., Khalil, H., et al. (2015). Guidance for conducting systematic scoping reviews. International Journal of Evidence-Based Healthcare, 13(3), 141–146. https://doi.org/10.1097/xeb.0000000000000050 Google Scholar
Rosenberg, M. S. (2005). The file‐drawer problem revisited: A general weighted method for calculating fail‐safe numbers in meta‐analysis. Evolution, 59(2), 464–468.Google Scholar
Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638.Google Scholar
Salgado, J. F. & Moscoso, S. (2019). Meta-analysis of the validity of general mental ability for five performance criteria: Hunter and Hunter (1984) revisited. Frontiers in Psychology, October 17. https://doi.org/10.3389/fpsyg.2019.02227Google Scholar
Schmid, C., Stewart, G., Rothstein, H., & Lajeunesse, M. (2013). Software for statistical meta-analysis. In Gurevitch, J. & Mengersen, K. (eds.), Handbook of Meta-Analysis in Ecology and Evolution (pp. 174–192). Princeton University Press.Google Scholar
Snilstveit, B., Vojtkova, M., Bhavsar, A., Stevenson, J., & Gaarder, M. (2016). Evidence & gap maps: A tool for promoting evidence informed policy and strategic research agendas. Journal of Clinical Epidemiology, 79, 120–129.Google Scholar

References

Ahmadvand, M. (2011). Critical discourse analysis an introduction to major approaches. Dinamika Bahasa Dan Budaya, 5(1), 82–90 https://doi.org/10.1007/978-3-319-12616-6_4Google Scholar
Andrews, M., Squire, S., & Tamboukou, M. (2013). Doing Narrative Research. SAGE Publications.Google Scholar
Atawneh, A. M. (2008). The discourse of war in the Middle East: Analysis of media reporting. Journal of Pragmatics, 41(2),263–278 https://doi.org/10.1016/j.pragma.2008.05.013Google Scholar
Babbie, E. (1992). The Practice of Social Research. Macmillan.Google Scholar
Bartlett, F. C. (1932). Remembering. Cambridge University Press.Google Scholar
Beloff, H. (1997). Making and un-making identities: A psychologist looks at art-work. In Hayes, N. (ed.), Doing Qualitative Analysis in Psychology (pp. 55–68). Psychology Press.Google Scholar
Bieneck, S. (2009). How adequate is the vignette technique as a research tool for psycho-legal research? In Oswald, M. E., Bieneck, S., & Hupfeld-Heinemann, J. (eds.), Social Psychology of Punishment of Crime. John Wiley & Sons.Google Scholar
Boyatzis, R. E. (1998). Transforming Qualitative Information: Thematic Analysis and Code Development. SAGE Publications.Google Scholar
Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77-101. https://doi.org/10.1191/1478088706qp063oaGoogle Scholar
Brown, G. & Harris, T. (1978). The Social Origins of Depression: A Study of Psychiatric Disorder in Women. Routledge.Google Scholar
Bryant, A. (2019). The Varieties of Grounded Theory. SAGE Publications.Google Scholar
Bryant, A. & Charmaz, K. (2010). The SAGE Handbook of Grounded Theory. SAGE Publications.Google Scholar
Charmaz, K. (2014). Constructing Grounded Theory, 2nd ed. Sage Publications.Google Scholar
Cho, J. Y. & Lee, E-H. (2014). Reducing confusion about grounded theory and qualitative content analysis: Similarities and differences. The Qualitative Report, 19, 1–20. https://doi.org/10.46743/2160-3715/2014.1028Google Scholar
Creswell, J. W. (2003). Research Design: Qualitative, Quantitative and Mixed Methods Approaches. SAGE Publications.Google Scholar
Daghigh, A. J & Rahim, H. A. (2020). Representation of Muslim minorities in politicians’ discourse: Jacinda Ardern vs. Donald Trump. Journal of Muslim Minority Affairs, 40(2), 179–195. https://doi.org/10.1080/13602004.2020.1773099Google Scholar
Downe-Wamboldt, B. (1992). Content analysis: Method, applications, and issues. Health Care for Women International, 13, 313–321. https://doi.org/10.1080/07399339209516006Google Scholar
Edwards, D. (1997). Discourse and Cognition. SAGE Publications.Google Scholar
Fairclough, N. (1995). Critical Discourse Analysis. Longman.Google Scholar
Georgakopoulou, A. (2007). Small Stories, Interaction and Identities. John Benjamins.Google Scholar
Gill, R. (1996). Discourse analysis: Practical implementation. In Richardson, J. T. E. (ed.), Handbook of Qualitative Research Methods. BPS Books.Google Scholar
Glaser, B. G. (1998). Doing Grounded Theory: Issues and Discussions. Sociology Press.Google Scholar
Glaser, B. G. & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine.Google Scholar
Graneheim, U. H. & Lundman, B. (2004). Qualitative content analysis in nursing research: Concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24(2),105–112. https://doi.org/10.1016/j.nedt.2003.10.001Google Scholar
Grounded Theory Institute (2008). What is grounded theory? Available at: www.groundedtheory.com/what-is-gt.aspx.Google Scholar
Hayes, N. (2021). Doing Psychological Research, 2nd ed. Open University Press.Google Scholar
Hayes, N. (ed.) (1997a). Doing Qualitative Analysis in Psychology. Psychology Press.Google Scholar
Hayes, N. (1997b). Theory-led thematic analysis. In Hayes, N. (ed.), Doing Qualitative Analysis in Psychology (pp. 93–114). Psychology Press.Google Scholar
Hesse-Biber, S. N. & Leavy, P. (2011). The Practice of Qualitative Research, 2nd ed. SAGE Publications.Google Scholar
Hseih, H-F. & Shannon, S. E. (2005). Three approaches to qualitative content analysis Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687Google Scholar
Jahoda, M., Lazarsfeld, P. F., & Zeisel, H. (1932). Marienthal: The Sociography of an Unemployed Community. Aldine. (English ed. 1971, Routledge).Google Scholar
Kiernan, M. D. and Hill, M. (2018). Framework analysis: a whole paradigm approach.Qualitative Research Journal, 18(3), 248–261. https://doi.org/10.1108/QRJ-D-17-00008Google Scholar
Krauss, S. E. (2005). Research paradigms and meaning making: A primer. The Qualitative Report. 10(4), 758–770. https://doi.org/10.46743/2160-3715/2005.1831Google Scholar
Masroor, F., Khan, Q. N., Aib, I., & Ali, Z. (2019). Polarization and ideological weaving in Twitter discourse of politicians. Social Media and Society. October–December, 1–14. https://doi.org/10.1177/2056305119891220.Google Scholar
Mead, M. (1928). Coming of Age in Samoa. William Morrow.Google Scholar
Miles, M. B. (1990). New methods for qualitative data collection: Vignettes and pre-structured cases. International Journal of Qualitative Studies in Education, 3(1), 37–51. https://doi.org/10.1080/0951839900030104Google Scholar
Miller, T., Velleman, R., Rigby, K., et al. (1997). The use of vignettes in the analysis of interview data: Relatives of people with drug problems. In Hayes, N. (ed.), Doing Qualitative Analysis in Psychology (pp. 201–226). Psychology Press.Google Scholar
Nortio, E., Varjonen, S. Mähönen, T. A., & Jasinskaja-Lahti, I. (2016). Interpretive repertoires of multiculturalism: Supporting and challenging hierarchical intergroup relations. Journal of Social and Political Psychology, 4(2), 2195–3325. https://doi.org/10.5964/jspp.v4i2.639Google Scholar
Parker, I. (1994). Discourse analysis. In Banister, P., Burman, E., Parker, I., Taylor, M., & Tindall, C. (eds.), Qualitative Methods in Psychology: A Research Guide. Open University Press.Google Scholar
Patton, M. Q. (2002). Qualitative Research and Evaluation Methods. SAGE Publications.Google Scholar
Pidgeon, N. & Henwood, K. (1997). Using grounded theory in psychological research. In Hayes, N. (ed.), Doing Qualitative Analysis in Psychology (pp. 245–274). Psychology Press.Google Scholar
Pidgeon, N. & Henwood, K. (2004). Grounded theory. In Hardy, M. A. & Bryman, A. (eds.), Handbook of Data Analysis. SAGE Publications.Google Scholar
Potter, J. (1996). Discourse analysis and constructionist approaches: Theoretical background. In: Richardson, J. T. E. (ed.), Handbook of Qualitative Research Methods. BPS Books.Google Scholar
Potter, W. J. & Levine-Donnerstein, D. (1999). Rethinking validity and reliability in content analysis. Journal of Applied Communication Research, 27, 258–284. https://doi.org/10.1080/00909889909365539Google Scholar
Ritchie, J. & Spencer, L. (1994). Qualitative data analysis for applied policy research. In Bryman, A. and Burgess, R. G. (eds.), Analyzing Qualitative Data. Routledge.Google Scholar
Ritchie, J. and Spencer, L. (2002). Qualitative data analysis for applied policy research. In Huberman, M. A. & Miles, M. B. (eds.), The Qualitative Research Companion. SAGE Publications.Google Scholar
Ritchie, J., Lewis, J., McNaughton-Nicholls, C. & Ormston, R. (2013). Qualitative Research Practice: A Guide for Social Science Students and Researchers. SAGE Publications.Google Scholar
Ryan, G. W. & Bernard, H. R. (2000). Data management and analysis methods. In Denzin, N. K. & Lincoln, Y. S. (eds.), Handbook of Qualitative Research, 2nd ed. (pp. 769–802. SAGE Publications.Google Scholar
Ryan, G. W. & Bernard, H. R. (2003). Techniques to identify themes. Field Methods, 15(1), 85–109. https://doi.org/10.1177/1525822X02239569Google Scholar
Sherrard, C. (1997). Repertoires in discourse: Social identification and aesthetic taste. In Hayes, N. (ed.), Doing Qualitative Analysis in Psychology (pp. 69–92). Psychology Press.Google Scholar
Singh, S. & Estefan, A. (2018). Selecting a grounded theory approach for nursing research. Global Qualitative Nursing Research, 5(2). https://doi.org/333393618799571Google Scholar
Smith, T. (2020). The Root and Uses of Marketing Knowledge. deGruyter.Google Scholar
Squire, C. (2013). From experience-centred to socially-oriented approaches to narrative. In Andrews, M., Squire, S., & Tamboukou, M. (eds.), Doing Narrative Research. SAGE Publications.Google Scholar
Srivastava, A. & Thomson, S. B. (2009). Framework analysis: A qualitative methodology for applied policy research. Journal of Administration and Governance, 4(2), 72–79.Google Scholar
Stenner, P. (1993). Discoursing jealousy. In E. Burnam & A. Parker (eds.), Discourse Analytic Research: Repertoires and Readings of Texts in Action. Routledge.Google Scholar
Strauss, A. & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. SAGE Publications.Google Scholar
Suddaby, R. (2006). What grounded theory is not. Academy of Management Journal, 49, 633–642. https://doi.org/10.5465/amj.2006.22083020Google Scholar
Ten Have, P. (2007). Doing Conversation Analysis: A Practical Guide, 2nd ed. SAGE Publications.Google Scholar
Thornberg, R. (2012). Informed grounded theory. Scandinavian Journal of Educational Research, 56(3), 243–259. https://doi.org/10.1080/00313831.2011.581686Google Scholar
Thornberg, R. Perhamus, L. M., & Charmaz, K. (2015). Grounded theory. In Saracho, O. (ed.), Handbook of Research Methods in Early Childhood Education. Volume 1 (pp. 405–439). Information Age Publishing.Google Scholar
Urquhart, C., Lehmann, H., & Myers, M.D. (2010). Putting the ‘theory’ back into grounded theory: Guidelines for grounded theory studies in information systems. Information Systems Journal, 20, 357–381. https://doi.org/10.1111/j.1365-2575.2009.00328.xGoogle Scholar
Ussher, J. M. & Mooney-Somers, J. (2000). Negotiating desire and sexual subjectivity: Narratives of young Lesbian Avengers. Sexualities. 3, 183–200. https://doi.org/10.1177/136346000003002005Google Scholar
Vaismoradi, M., Jones, J., Turunen, H., & Snelgrove, S. (2016). Theme development in qualitative content analysis and thematic analysis. Journal of Nursing Education and Practice, 6(5), 100–110. https://doi.org/10.5430/jnep.v6n5p100Google Scholar
Van Dijk, T. (2006). Discourse and manipulation. Discourse and Society, 17, 359–383. https://doi.org/10.1177/0957926506060250Google Scholar
Weber, R. P. (1990) Basic Content Analysis. SAGE Publications. https://dx.doi.org/10.4135/9781412983488Google Scholar
Wodak, R. (2001). What CDA is about: A summary of its history, important concepts, and its development. In Wodak, R. and Meyers, M. (eds.), Methods of Critical Discourse Analysis. SAGE Publications. https://dx.doi.org/10.1057/9780230288423_3Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Statistical Approaches
  • 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
  • Online publication: 25 May 2023
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Statistical Approaches
  • 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
  • Online publication: 25 May 2023
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Statistical Approaches
  • 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
  • Online publication: 25 May 2023
Available formats
×