Kaynaklar

Allen, M. J., ve Yen, W. M. (1979). Introduction to Measurement Theory. California: Brooks/Cole Publishing Company.

Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (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/amp0000191

Bagozzi, R. P., Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. J. of the Acad. Mark. Sci. 40, 8–34. https://doi.org/10.1007/s11747-011-0278-x

Bornstein, M. H., Hahn, C. S., & Haynes, O. M. (2010). Social competence, externalizing, and internalizing behavioral adjustment from early childhood through early adolescence: developmental cascades. Development and psychopathology, 22(4), 717–735. https://doi.org/10.1017/S0954579410000416

Borsboom, D. (2006). When does measurement invariance matter? Medical Care, 44(11 Suppl 3), s.176–181.

Boyle, G. J., Saklofske, D. H., & Matthews, G. (2015). Criteria for selection and evaluation of scales and measures. In G. J. Boyle, D. H. Saklofske, & G. Matthews (Eds.), Measures of personality and social psychological constructs (pp. 3–15). Elsevier Academic Press. https://doi.org/10.1016/B978-0-12-386915-9.00001-2

Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). New York, NY: The Guilford Press.

Byrne, B. M., and Stewart, S. M. (2006). The MACS Approach to Testint for Multigroup Invariance of a Second-Order Structure: A Walk Through the Process. Structural Equation Modeling, 13(2), 204–228.

Byrne, B. M., and Watkins, D. (2003). The Issue Of Measurement Invariance Revisited. Journal of Cross-Cultural Psychology, 34(2), 155–175.

Cao, C., & Liang, X. (2023). The Impact of Ignoring Cross-loadings on the Sensitivity of Fit Measures in Measurement Invariance Testing. Structural Equation Modeling: A Multidisciplinary Journal, 31(1), 64–80. https://doi.org/10.1080/10705511.2023.2223360

Cattell, R.B. (1978) The Scientific Use of Factor Analysis in Behavioral and Life Sciences. Plenum, New York.

Cheung, G. W., and Rensvold, R. B. (2002). Evaluating Goodness-of- Fit Indexes for Testing Measurement Invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255.

Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Lawrence Erlbaum Associates, Inc.

Cox, E. P. (1980). The optimal number of response alternatives for a scale: a review. Journal of Marketing Research, 17, 407±422.

Çelik, H. E., ve Yılmaz, V. (2013). Lisrel 9.1 ile Yapısal Eşitlik Modellemesi Temek Kavramlar-Uygulamalar-Programlama. Ankara: Anı Yayıncılık.

Çokluk, Ö., Şekercioğlu, G., ve Büyüköztürk, Ş. (2014). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.

Dimitrov, D. M. (2010). Testing for factorial invariance in the context of construct validation. Measurement and Evaluation in Counseling and Development, 43(2), 121–149. https://doi.org/10.1177/0748175610373459

Epskamp, S. (2022). semPlot: Path Diagrams and Visual Analysis of Various SEM Packages’ Output. R package version 1.1.6, https://CRAN.R-project.org/package=semPlot

Finch, W. H., and French, B. F. (2015). Latent Variable Modeling with R. New York: Routledge.

Finney, S. J., and DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock and R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 269- 314). Greenwich, CT: Information Age.

French, B. F., and Finch, W. H. (2008). Multigroup confirmatory factor analysis: Locating the invariant referent sets. Structural Equation Modeling, 15(1), 96-113.

Gorsuch, R. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

Gregorich, S. E. (2006). Do Self-Report Instruments Allow Meaningful Comparisons Across Diverse Population Groups? Testing Measurement Invariance Using the Confirmatory Factor Analysis Framework. Medical Care, 44(11 Suppl 3), S78.

Hair Jr, J. F., Black, C. W., Babin, B. J., and Anderson, R. E. (2014). Multivariate Data Analysis (7th ed.). Pearson.

Hambleton, R. K. (2005). Issues, Designs, and Technical Guidelines for Adapting Tests Into Multiple Languages and Cultures. In R. K. Hambleton, P. F. Merenda, and C. D. Spielberger (Eds.), Adapting Educational and Psychological Tests for Cross-Cultural Assessment. New Jersey, London: Lawrence Erlbaum Associates, Publishers.

Hofstede Insights. (2023). Country comparison tool: Australia, South Korea, Turkey. https://www.hofstede-insights.com/country-comparison-tool?countries=australia%2Csouth+korea%2Cturkey

Horn, J. L., and McArdle, J. J. (2007). Understanding human intelligence since Spearman. In R. Cudeck and R. C. MacCallum (Eds.), Factor analysis at 100: Historical developments and future directions (pp. 205 - 247). Mahwah, NJ: Erlbaum.

Hu, L., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

Jaccard, J., and Wan, C. K. (1996). LISREL Approaches to interaction effects in multiple regression (no. 114). Sage.

Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2022). semTools: Useful tools for structural equation modeling. R package version 0.5-6. Retrieved from https://CRAN.R-project.org/package=semTools

Jöreskog, K. G. (1971). Simultaneous Factor Analysis in Several Population. Psychometrika, 36(4), 409–426. Jöreskog, K. G., and Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. IL: Scientific Software International, Inc.

Kline, P. (1994). An Easy Guide to Factor Analysis (1st ed.). Routledge. https://doi.org/10.4324/9781315788135

Kline, R. B. (2015). Principles and practices of structural equation modelling (4th ed.). Guilford.

Korkmaz, S., Goksuluk, D., Zararsiz, G. (2014). “MVN: An R Package for Assessing Multivariate Normality.” The R Journal, 6(2), 151–162.

Leitgöb, H., Seddig, D., Asparouhov, T., Behr, D., Davidov, E., De Roover, K., … & van de Schoot, R. (2023). Measurement invariance in the social sciences: Historical development, methodological challenges, state of the art, and future perspectives. Social Science Research, 110, 102805.

Leung, S.-O. (2011). A comparison of psychometric properties and normality in 4-, 5-, 6-, and 11-point Likert scales. Journal of Social Service Research, 37, 412–421. doi:10.1080/01488376.2011.580697

Lord, F. M., Novick, M. R., and Birnbaum, A. (1968). Statistical theories of mental test scores. Addison & Wesley.

McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates Publishers.

Mehrens, W. A., and Lehmann, I. J. (1991). Measurement and Evaluation in Education and Psychology (4th ed.). Belmont, CA: Holt, Rinehart and Winston.

Meredith, W., andMillsap, R. E. (1992). On the misuse of manifest variables in the detection of measurement bias. Psychometrika, 57(2), 289–311.

Milfont, T. L., & Fischer, R. (2010). Testing measurement invariance across groups: Applications in crosscultural research. International Journal of Psychological Research, 3(1),111-121. doi: 10.21500/20112084.857

Millsap, R. E. (2010). Testing measurement invariance using item response theory in longitudinal data: An introduction. Child Development Perspectives, 4(1), 5–9.

Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge.

Morin, A. J., Arens, A. K., & Marsh, H. W. (2016). A bifactor exploratory structural equation modeling framework for the identification of distinct sources of construct-relevant psychometric multidimensionality. Structural Equation Modeling: A Multidisciplinary Journal, 23(1), 116-139.

Muthén, L. K., and Muthén, B. O. (2010). Mplus User’s Guide (6th Ed.). Los Angeles, CA: Muthén & Muthén.

OECD (2019), PISA 2018 Assessment and Analytical Framework, PISA, OECD Publishing, Paris, https://doi.org/10.1787/b25efab8-en

OECD (2020), PISA 2018 Results (Volume VI): Are Students Ready to Thrive in an Interconnected World?, PISA, OECD Publishing, Paris, https://doi.org/10.1787/d5f68679-en

Preston, C. C., & Colman, A. M. (2000). Optimal number of response categories in rating scales: Reliability, validity, discriminating power, and respondent preferences. Acta Psychologica, 104(1), 1–15. https://doi.org/10.1016/S0001-6918(99)00050-5

Putnick, D. L., & Bornstein, M. H. (2016). Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research. Developmental review : DR, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004

R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

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

Robinson, J. P., Shaver, P. R. and Wrightsman, L. S. (1991) Criteria for Scale Selection and Evaluation. In: Robinson, J.P., Shaver, P.R. and Wrightsman, L.S., Eds., Measures of Personality and Social Psychological Attitudes, Academic Press, San Diego, 1-15. https://doi.org/10.1016/B978-0-12-590241-0.50005-8

Robitzsch, A. (2020). Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods. Front. Educ. 5:589965. doi: 10.3389/feduc.2020.589965

Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02

Satorra, A., and Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507-514.

Sireci, S.G., Patsula, L., & Hambleton, R.K. (2005). Statistical methods for identifying flaws in the test adaptation process. R.K. Hambleton, P.F.Merenda & C.D. Spielberger (Ed.) Adapting educational and psychological tests for crosscultural assessment (pp. 93-135). Lowrence Erlbaum Associates, Publishers Mahwah:Mahwah.

Somer, O., Korkmaz, M., Dural, S., ve Can, S. (2009). Ölçme Eşdeğerliğinin Yapısal Eşitlik Modellemesi ve Madde Cevap Kuramı Kapsamında İncelenmesi. Türk Psikoloji Dergisi, 24(64), 61–75.

Steinmetz, H., Schmidt, P., Tina-Booh, A., Wieczorek, S., and Schwartz, S. H. (2009). Testing measurement invariance using multigroup CFA: Differences between educational groups in human values measurement. Quality and Quantity, 43(4), 599– 616.

Tabachnick, B. G., and Fidell, L. S. (2013). Using Multivariate Statistics (6th Ed.). Boston: Pearson.

Van de Schoot, R., Lugtig, P., and Hox, J. (2012). A Checklist for Testing Measurement İnvariance. European Journal of Developmental Psychology, 9(4), 486–492.

Van de Vijver, F. J. R., & Leung, K. (1997). Methods and data analysis for cross-cultural research. Sage Publications, Inc.

Van de Vijver, F., & Tanzer, N. K. (2004). Bias and equivalence in cross-cultural assessment: An overview. European Review of Applied Psychology / Revue Européenne de Psychologie Appliquée, 54(2), 119–135. https://doi.org/10.1016/j.erap.2003.12.004

Vandenberg, R. J., and Lance, C. E. (2000). A Review and Synthesis of the Measurement Invariance Literature : Suggestions , Practices , and Recommendations for Organizational Research. Organizational Research Methods, 3(1), 4–70.

Wang, J., and Wang, X. (2012). Structural Equation Modeling: Applications Using Mplus. John Wiley & Sons.

Widaman, K. F., and Reise, S. P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281–324).

Wu, A. D., Li, Z., and Zumbo, B. D. (2007). Decoding the Meaning of Factorial Invariance and Updating the Practice of Multi-group Confirmatory Factor Analysis: A Demonstration With TIMSS Data. Practical Assessment Research & Evaluation, 12(3), 1–26.

Xu, H., & Tracey, T. J. G. (2017). Use of multi-group confirmatory factor analysis in examining measurement invariance in counseling psychology research. The European Journal of Counselling Psychology, 6(1), 75–82. https://doi.org/10.5964/ejcop.v6i1.120