Search results
Results: 7
Number of items: 7
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Kolbe, L., Molenaar, D., Jak, S., & Jorgensen, T. D. (2024). Assessing measurement invariance with moderated nonlinear factor analysis using the R package OpenMx. Psychological Methods, 29(2), 388–406. https://doi.org/10.1037/met0000501 -
Kolbe, L., Oort, F., & Jak, S. (2021). Bivariate distributions underlying responses to ordinal variables. Psych, 3(4), 562-578. https://doi.org/10.3390/psych3040037
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Kolbe, L., Jorgensen, T. D., & Molenaar, D. (2021). The Impact of Unmodeled Heteroskedasticity on Assessing Measurement Invariance in Single-group Models. Structural Equation Modeling, 28(1), 82-98. https://doi.org/10.1080/10705511.2020.1766357
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Jak, S., Li, H., Kolbe, L., de Jonge, H., & Cheung, MW.-L. (2021). Meta‐analytic structural equation modeling made easy: A tutorial and web application for one‐stage MASEM. Research synthesis methods, 12(5), 590-606. https://doi.org/10.1002/jrsm.1498
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Kolbe, L., & Jorgensen, T. D. (2019). Using restricted factor analysis to select anchor items and detect differential item functioning. Behavior Research Methods, 51(1), 138-151. https://doi.org/10.3758/s13428-018-1151-3
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Kolbe, L., & Jorgensen, T. D. (2018). Using product indicators in restricted factor analysis models to detect nonuniform measurement bias. In M. Wiberg, S. Culpepper, R. Janssen, J. González, & D. Molenaar (Eds.), Quantitative Psychology: The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017 (pp. 235-245). (Springer Proceedings in Mathematics & Statistics; Vol. 233). Springer. https://doi.org/10.1007/978-3-319-77249-3_20
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