References
- American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. American Educational Research Association.
- Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 397–438. https://doi.org/10.1080/10705510903008204
- Asparouhov, T., & Muthén, B. (2018). SRMR in Mplus. http://www.statmodel.com/download/SRMR2.pdf.
- Bandalos, D. L. (2018). Measurement theory and applications for the social sciences. Guilford Press.
- Banks, J., Bowman, N. D., Lin, J.-H., Pietschmann, D., & Wasserman, J. (2019). The common Player-Avatar Interaction scale (cPAX): Expansion and cross-language validation. International Journal of Human-Computer Studies. https://doi.org/10.1016/j.ijhcs.2019.03.003
- Bauer, G. R., Braimoh, J., Scheim, A. I., Dharma, C., & Dalby, A. R. (2017). Transgender-inclusive measures of sex/gender for population surveys: Mixed-methods evaluation and recommendations. PLoS One, 12(5), https://doi.org/10.1371/journal.pone.0178043
- Bowman, N. D., Wasserman, J., & Banks, J. (2018). Development of the video game Demand scale. In N. D. Bowman (Ed.), Video games: A medium that demands our attention (pp. 208–233). Routledge.
- Brown, J. D. (2009). Choosing the right type of rotation in PCA and EFA. JALT Testing and Evaluation SIG Newsletter, 13(3), 20–25.
- Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press.
- Browne, M. W., & Cudeck, R. (1989). Single sample cross-validation indices for covariance structures. Multivariate Behavioral Research, 24(4), 445–455. https://doi.org/10.1207/s15327906mbr2404_4
- Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). SAGE.
- Byrne, B. M. (2012). Choosing structural equation modeling computer software: Snapshots of LISREL, EQS, Amos, and Mplus. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 307–324). Guilford Press.
- Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105. https://doi.org/10.1037/h0046016
- Cerny, B. A., & Kaiser, H. F. (1977). A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behavioral Research, 12(1), 43–47. https://doi.org/10.1207/s15327906mbr1201_3
- Čertický, M., Čertický, M., Sinčák, R., Magyar, G., Vaščák, J., & Cavallo, F. (2019). Psychophysiological indicators for modeling user experience in interactive digital entertainment. Sensors, 19(5), 989. https://doi.org/10.3390/s19050989
- Costello, A. B., & Osborne, J. W. (2005). Best practice in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), https://pareonline.net/getvn.asp?v=10%26n=7
- Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29. https://doi.org/10.1037/1082-989X.1.1.16
- de Ayala, R. J. (2009). The theory and practice of item response theory. Guilford Press.
- de Winter, J. C. F., & 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. https://doi.org/10.1080/02664763.211.610445
- Erba, J., Ternes, B., Bobkowski, P., Logan, T., & Liu, Y. (2018). Sampling methods and sample populations in quantitative mass communication research studies: A 15-year census of six journals. Communication Research Reports, 35(1), 42–47. https://doi.org/10.1080/08824096.2017.1362632
- 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
- Flora, D. B., & Flake, J. K. (2017). The purpose and practice of exploratory and confirmatory factor analysis in psychological research: Decisions for scale development and validation. Canadian Journal of Behavioural Science / Revue Canadienne des Sciences du Comportement, 49(2), 78–88. https://doi.org/10.1037/cbs0000069
- Fraser, G. (2018). Evaluating inclusive gender identity measures for use in quantitative psychological research. Psychology & Sexuality, 9(4), 343–357. doi: 10.1080/19419899.2018.1497693
- Goodboy, A. K., & Kline, R. B. (2017). Statistical and practical concerns with published communication research featuring structural equation modeling. Communication Research Reports, 34(1), 68–77. https://doi.org/10.1080/08824096.2016.1214121
- Heggestad, E. D., Scheaf, D. J., Banks, G. C., Monroe Hausfeld, M., Tonidandel, S., & Williams, E. B. (2019). Scale adaptation in organizational science research: A review and best-practice recommendations. Journal of Management, 45(6), 2596–2627. https://doi.org/10.1177/0149206319850280
- 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. https://doi.org/10.1177/0013164405282485
- 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/BF02289447
- Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. doi: 10.1080/10705519909540118
- Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151. https://doi.org/10.1177/001316446002000116
- Kline, R. B. (2012). Assumptions in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 111–125). Guilford Press.
- Kline, R. B. (2013). Exploratory and confirmatory factor analysis. In Y. Petscher, & C. Schatsschneider (Eds.), Applied quantitative analysis in the social sciences (pp. 171–207). Routledge.
- Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed). Guilford Press.
- Lei, P., & Wu, Q. (2012). Estimation in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 164–179). Guilford Press.
- Levine, T. R. (2005). Confirmatory factor analysis and scale validation in communication research. Communication Research Reports, 22(4), 335–338. https://doi.org/10.1080/00036810500317730
- MacCallum, R. C., Wideman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Bulletin, 4, 84–99. https://doi.org/10.1037/1082-989X.4.1.84
- Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in over generalizing Hu and Bentler’s (1999) findings. Structural Equation Modeling, 11, 320–341. doi: 10.1207/s15328007sem1103_2
- Matsunaga, M. (2010). How to factor-analyze your data right: Do’s, don’ts, and how-to’s. International Journal of Psychological Research, 3(1), 97–110. https://www.redalyc.org/pdf/2990/299023509007.pdf. https://doi.org/10.21500/20112084.854
- McCroskey, J. C., & Young, T. J. (1979). The use and abuse of factor analysis in communication research. Human Communication Research, 5(4), 375–382. https://doi.org/10.1111/j.1468-2958.1979.tb00651.x
- Morin, A. J. S., Marsh, H. W., & Nagengast, B. (2013). Exploratory structural equation modeling. In G. R. Hancock, & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 267–308). Information Age Publishing.
- Mundfrom, D. J., Shaw, D. G., & Ke, T. L. (2009). Minimum sample size recommendations for conducting factor analysis. International Journal of Testing, 5(2), 159–168. https://doi.org/10.1207/s15327574ijt0502_4
- Oliver, M. B., Bowman, N. D., Woolley, J. K., Rogers, R., Sherrick, B. I., & Chung, M.-Y. (2016). Video games as meaningful entertainment experiences. Psychology of Popular Media Culture, 5(4), 390–405. https://doi.org/10.1037/ppm0000066
- Osborne, J. W. (2014). Best practices in exploratory factor analysis. Self-published.
- Park, H. S., Dailey, R., & Lemus, D. (2002). The use of exploratory factor analysis and principle components analysis in communication research. Human Communication Research, 28(4), 562–577. https://doi.org/10.1111/j.1468-2958.2002.tb00824.x
- Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye, & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.
- Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin Company.
- Spearman, C. (1904). General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. https://doi.org/10.2307/1412107
- Tobias, S., & Carlson, J. E. (1969). Brief report: Bartlett’s test of sphericity and chance findings in factor analysis. Multivariate Behavioral Research, 4(3), 375–377. https://doi.org/10.1207/s15327906mbr0403_8
- Vieira, A. L. (2011). Interactive LISREL in practice: Getting started with a SIMPLIS approach. Springer.
- Watkins, M. (2008). Monte Carlo for PCA parallel analysis (Version 2.3). www.softpedia.com/get/Others/HomeEducation/Monte-Carlo-PCA-for-ParallelAnalysis.shtml.
- Westen, D., & Rosenthal, R. (2003). Quantifying construct validity: Two simple measures. Journal of Personality and Social Psychology, 84(3), 608–618. https://doi.org/10.1037/0022-3514.84.3.608
- Wheaton, B. (1987). Assessment of fit in overidentified models with latent variables. Sociological Methods & Research, 16(1), 118–154. https://doi.org/10.1177/0049124187016001005
- Wheaton, B., Muthén, B., Alwin, D., & Summers, G. (1977). Assessing reliability and stability in panel models. In D. R. Heise (Ed.), Sociological methodology (pp. 84–136). Josey-Bass.
- Yates, A. (1987). Multivariate exploratory data analysis: A perspective on exploratory factor analysis. State University of New York Press.
- Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology, 30, 167–202. https://doi.org/10.1111/0081-1750.00078
- Zwick, W. R., & Velicer, W. F. (1982). Factors influencing four rules for determining the number of components to retain. Multivariate Behavioral Research, 17(2), 253–269. doi: 10.1207/s15327906mbr1702_5