Equal Precision Measurement Structural Models

References

• Arbuckle, J. L. (2014). Amos (Version 23.0) [Computer Program]. IBM SPSS.
   Google Scholar
• Bauer, D. J. (2005). The role of nonlinear factor-to-indicator relationships in tests of measurement equivalence. Psychological Methods, 10, 305–316. https://doi.org/10.1037/1082-989X.10.3.305
   PubMed Web of Science ®Google Scholar
• Bollen, K. A. (2007). On the origins of latent curve models. In R. Cudeck & R. C. MacCallum (Eds.), Factor analysis at 100: Historical developments and future directions (pp. 79–97). Erlbaum.
   Google Scholar
• Charles, E. P. (2005). The correction for attenuation due to measurement error: Clarifying concepts and creating confidence sets. Psychological Methods, 10, 206–226. https://doi.org/10.1037/1082-989X.10.2.206
   PubMed Web of Science ®Google Scholar
• Duncan, O. D., Haller, A. O., & Portes, A. (1968). Peer influences on aspirations: A reinterpretation. American Journal of Sociology, 74, 119–137. https://doi.org/10.1086/224615
   PubMed Web of Science ®Google Scholar
• Ferrer, E., Hamagami, F., & McArdle, J. J. (2004). Modeling latent growth curves with incomplete data using different types of structural equation modeling and multilevel software. Structural Equation Modeling: A Multidisciplinary Journal, 11, 452–483. https://doi.org/10.1207/s15328007sem1103_8
   Web of Science ®Google Scholar
• Ghisletta, P., & McArdle, J. J. (2012). Latent curve models and latent change score models estimated in R. Structural Equation Modeling: A Multidisciplinary Journal, 19, 651–682. https://doi.org/10.1080/10705511.2012.713275.
   PubMed Web of Science ®Google Scholar
• Hancock, G. R., & Choi, J. (2006). A vernacular for linear latent growth models. Structural Equation Modeling, 13, 352–377. https://doi.org/10.1207/s15328007sem1303_2
   Web of Science ®Google Scholar
• Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44, 486–507. https://doi.org/10.1177/0049124114543236
   Web of Science ®Google Scholar
• Little, T. D., Slegers, D. W., & Card, N. A. (2006). A non-arbitrary method of identifying and scaling latent variables in SEM and MACS models. Structural Equation Modeling: A Multidisciplinary Journal, 13, 59–72. https://doi.org/10.1207/s15328007sem1301_3
   Web of Science ®Google Scholar
• Loehlin, J., & Beaujean, A. (2017). Latent variable models: An introduction to factor, path, and structural equation analysis. Routledge. https://doi.org/10.4324/9781410609823.
   Google Scholar
• McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110–133. https://doi.org/10.2307/1130295
   PubMed Web of Science ®Google Scholar
• McDonald, R. P. (1999). Test theory: A unified treatment. Erlbaum.
   Google Scholar
• Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107–122. https://doi.org/10.1007/BF02294746
   Web of Science ®Google Scholar
• Mulaik, S. A. (2010). Foundations of factor analysis (2nd Ed.). CRC Press.
   Google Scholar
• Muthén, L. K., & Muthén, B. O. (1998–2017). Mplus user’s guide (8th ed.). Muthén & Muthén.
   Google Scholar
• Osborne, R. T., & Lindsey, J. M. (1967). A longitudinal investigation of change in the factorial composition of intelligence with age in young school children. The Journal of Genetic Psychology, 110, 49–58. https://doi.org/10.1080/00221325.1967.10533716
   PubMed Web of Science ®Google Scholar
• Osborne, R. T., & Suddick, D. E. (1972). A longitudinal investigation of the intellectual differentiation hypothesis. Journal of Genetic Psychology, 121, 82–89. https://doi.org/10.1080/00221325.1972.10533131.
   Web of Science ®Google Scholar
• Pastore, M., & Altoè, G. (2018). Influence.SEM: Case influence in structural equation models. R package version 2.2.
   Google Scholar
• Pek, J., & MacCallum, R. C. (2011). Sensitivity analysis in structural equation models: Cases and their influence. Multivariate Behavioral Research, 46, 202–228. https://doi.org/10.1080/00273171.2011.561068
   PubMed Web of Science ®Google Scholar
• Raftery, A. E. (1995). Bayesian model selection in social research (with discussion). In P. V. Marsden (Ed.), Sociological methodology (Vol. 25, pp. 111–196). Blackwell. https://doi.org/10.2307/271063
   Google Scholar
• Rindskopf, D. (1983). Parameterizing inequality constraints on unique variances in linear structural models. Psychometrika, 48, 73–83. https://doi.org/10.1007/BF02314677
   Web of Science ®Google Scholar
• Rindskopf, D. (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models. Psychometrika, 49, 37–47. https://doi.org/10.1007/BF02294204
   Web of Science ®Google Scholar
• Schmidt, F. L., & Hunter, J. E. (1996). Measurement error in psychological research: Lessons from 26 research scenarios. Psychological Methods, 1, 199–223. https://doi.org/10.1037/1082-989X.1.2.199
   Web of Science ®Google Scholar
• Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74, 107–120. https://doi.org/10.1007/s11336-008-9101-0
   PubMed Web of Science ®Google Scholar
• Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72–101. https://doi.org/10.2307/1412159
   Web of Science ®Google Scholar
• von Oertzen, T., Brandmaier, A. M., & Tsang, S. (2021). Ωnyx software. Retrieved from https://onyx-sem.com/
   Google Scholar
• Wood, P. K. (2021). Approaches to understanding structural equation models: Relationships between individuals, variables, and occasions. Kindle. https://www.amazon.com/dp/168621068X
   Google Scholar
• Wood, P. K., Steinley, D., & Jackson, K. M. (2015). Right-sizing statistical models for longitudinal data. Psychological Methods, 20, 470–488. https://doi.org/10.1037/met0000037
   PubMed Web of Science ®Google Scholar
• Wright, S. (1960). Path coefficients and path regressions: Alternative or complementary concepts? Biometrics, 16, 189–202. https://doi.org/10.2307/2527551
   Web of Science ®Google Scholar