Longitudinal Multi-Trait-State-Method Model Using Ordinal Data

REFERENCES

• Andrews, F. (1984). Construct validity and error components of survey measures. Public Opinion Quarterly, 48, 409–442.
   Web of Science ®Google Scholar
• Bagozzi, R.P., & Bagozzi Yi, Y. (1991). Multitrait-multimethod matrices in consumer research. Journal of Consumer Psychology, 17, 426–439.
   Web of Science ®Google Scholar
• Bollen, K.A. (1989). Structural equations with latent variables. New York, NY: Wiley.
   Google Scholar
• Browne, M.W., & Nesselroade, J.R. (2005). Representing psychological processes with dynamic factor models: Some promising uses and extensions of autoregressive moving average time series models. In A. Maydeu-Olivares & J.J. McArdle (Eds), Contemporary psychometrics: A Festschrift for Roderick P. McDonald (pp. 415–452). Mahwah, NJ: Erlbaum.
   Google Scholar
• Burns, G.L., Walsh, J.A. & Gomez, R. (2003). Convergent and discriminant validity of trait and source effects in ADHD-inattention and hyperactivity/impulsivity measures across a 3-month interval. Journal of Abnormal Child Psychology, 15, 529–541.
   Web of Science ®Google Scholar
• Cai, L., Du Toit, S.H. C. & Thissen, D. (2011). IRTPRO: Flexible, multidimensional, multiple categorical IRT modeling (1st ed.) Chicago, IL: Scientific Software International.
   Google Scholar
• Campbell, D.T., & Fiske, D.W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–104.
   PubMed Web of Science ®Google Scholar
• Chen, W.H., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavorial Statistics, 22(3), 265–289.
   Web of Science ®Google Scholar
• Chou, B.P.C.P., & Satorra, A. (1991). Scaled test statistics and robust standard errors for non-normal data in covariance structure analysis: A Monte Carlo study. British Journal of Mathematical and Statistical Psychology, 44(2), 347–357.
   PubMed Web of Science ®Google Scholar
• Cole, D., Martin, J., Powers, B. & Trugilo, R. (1996). Modeling causal relations between academic and social competence and depression: A multitrait-multimethod longitudinal study of children. Journal of Abnormal Psychology, 105(2), 258–270.
   PubMed Web of Science ®Google Scholar
• Conley, J. (1985). Longitudinal stability of personality traits: A multitrait-multimethod-multioccasion analysis. Journal of Personality and Social Psychology, 49(5), 1266–1282.
   PubMed Web of Science ®Google Scholar
• Cudeck, R. (1985). Multiplicative models and MTMM matrices. Journal of Educational Statistics, 13, 131–147.
   Google Scholar
• Curran, W.S. P. J., & Finch, J. (2006). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29.
   Web of Science ®Google Scholar
• Du Toit, S.H. C., & Browne, M.W. (2001). The covariance structure of a vector ARMA time series. In R. Kudeck, S. H. C. Du Toit, & D. Sörbom (Eds.), Structural equation modeling: Present and future (pp. 279–314). Chicago, IL: Scientific Software International.
   Google Scholar
• Du Toit, S.H. C., & Browne, M.W. (2007). Structural equation modeling of multivariate time series. Multivariate Behavioral Research, 42, 67–101.
   PubMed Web of Science ®Google Scholar
• Eid, M. (2000). A multitrait-multimethod model with minimal assumptions. Psychometrika, 65, 241–261.
   Web of Science ®Google Scholar
• Emotions and Dynamic Systems Laboratory. (2010). The Affective Dynamics and Individual Differences (ADID) study: Developing non-stationary and network-based methods for modeling the perception and physiology of emotions. . Unpublished manual, University of North Carolina at Chapel Hill.
   Google Scholar
• Feldman Barrett, L.A., & Russell, J.A. (1998). Independence and bipolarity in the structure of current affect. Journal of Personality and Social Psychology, 74, 967–984.
   Web of Science ®Google Scholar
• Geiser, C., Eid, M., Nussbeck, F., Courvoisier, D. & Cole, D. (2010). Analyzing true change in longitudinal multitrait-multimethod studies: Application of a multimethod change model to depression and anxiety in children. Developmental Psychology, 46(1), 29–45.
   PubMed Web of Science ®Google Scholar
• Grimm, K., Pianta, R. & Konold, T. (2009). Longitudinal multitrait-multimethod models for developmental research. Multivariate Behavorial Research, 44, 233–258.
   PubMed Web of Science ®Google Scholar
• Hamilton, J.D. (1994). Time series analysis. Princeton, NJ: Princeton University Press.
   Google Scholar
• Jöreskog, K.G. (1974). Analyzing psychological data by structural analysis of covariance matrices. In R. Atkinson, D. Krantz, R. Luce & P. Suppes (Eds.), . Contemporary developments in mathematical psychology (Vol. 2) (pp. 1–56). San Francisco, CA: Freeman.
   Google Scholar
• Kenny, D.A. (1976). An empirical application of confirmatory factor analysis to the multitrait-multimethod matrix. Journal of Experimental and Social Psychology, 12, 247–252.
   Web of Science ®Google Scholar
• Kenny, D.A., & Zautra, A. (1995). The trait-state-error model for multiwave data. Journal of Consulting and Clinical Psychology, 63, 52–59.
   PubMed Web of Science ®Google Scholar
• Kenny, D.A., & Zautra, A. (2001). Trait-state models for longitudinal data. In L.M. Collins & A.G. Sayer (Eds.), . New methods for the analysis of change: Decade of behavior (pp. 243–263). Washington, DC: American Psychological Association.
   Google Scholar
• Lang, P.J., Bradley, M.M. & Cuthbert, B.N. (2005). International affective picture system (IAPS): Instruction manual and affective ratings . (Tech. Rep. No. A–6). University of Florida, The Center for Research in Psychophysiology.
   Google Scholar
• Luzzo, D. (1993). A multi-trait, multi-method analysis of three career development measures. The Career Development Quarterly, 41(4), 367–375.
   Web of Science ®Google Scholar
• Marsh, H.W. (1989). Confirmatory factor analyses of multitrait-multimethod data: Many problems and a few solutions. Applied Psychological Measurement, 13, 335–361.
   Web of Science ®Google Scholar
• Marsh, H.W., & Bailey, M. (1991). Confirmatory factor analyses of multitrait-multimethod data: A comparison of alternative models. Applied Psychological Measurement, 15, 47–70.
   Web of Science ®Google Scholar
• Marsh, H.W., & Grayson, D.A. (1995). . Latent variable models of multitrait-multimethod data. In R.H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 177–198). Thousands Oaks, CA: Sage.
   Google Scholar
• McArdle, J.J., & Hamagami, F. (2001). Latent difference score structural models for linear dynamic analysis with incomplete longitudinal data. In L. Collins & A. Sayer (Eds.), New methods for the analysis of change (pp. 139–175). Washington, DC: American Psychological Association.
   Google Scholar
• Molenberghs, G., & Verbeke, G. (2007). Likelihood ratio, score, and Wald Tests in a Constrained Parameter Space. The American Statistician, 61(1), 22–27. http://dx.doi.org/10.1198/000313007X171322 doi: 10.1198/000313007X171322
   Web of Science ®Google Scholar
• Muthén, L.K., & Muthén, B.O. (1998–2007). Mplus user's guide(5th ed.). Los Angeles, CA: Author.
   Google Scholar
• Nesselroade, J.R., & Bartsch, T.W. (1977). Multivariate perspectives on the construct validity of the trait-state distinction. In R.B. Cattell & R.M. Dreger (Eds), Handbook of modern personality theory (pp. 221–238). Baton Rouge, LA: Hemisphere.
   Google Scholar
• Rogosa, D. (1980). A critique of cross-lagged correlation. Psychological Bulletin, 88(2), 245–295.
   Web of Science ®Google Scholar
• Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores (psychometric monograph no. 17).
   Google Scholar
• Satorra, A., & Bentler, P.M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507–514.
   Web of Science ®Google Scholar
• Satorra, A., & Bentler, P. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75, 243–248.
   PubMed Web of Science ®Google Scholar
• Scherpenzeel, A., & Saris, W. (2007). . Multitrait-multimethod models for longitudinal research. In K. van Montfort, J. Oud, & A. Satorra (Eds.), Longitundinal models in the behavorial and related sciences (pp. 381–402). Mahwah, NJ: Erlbaum.
   Google Scholar
• Schmitt, N., & Stults, D. (1986). Methodolgical review: Analysis of MTMM matricies. Applied Psychological Measurement, 10, 1–22.
   Web of Science ®Google Scholar
• Takane, Y., & de Leeuw, J. (1987). On the relationship between item response theory and factor analysis of discretized variables. Psychometrika, 52, 393–408.
   Web of Science ®Google Scholar
• Waksman, S. (1978). An application of a multitrait-multimethod test to validity data of a social learning treatment for aggressive children. Journal of Abnormal Child Psychology, 6(1) 1–10.
   PubMed Web of Science ®Google Scholar
• Watson, D., & Clark, L.A. (1997). The measurement and mismeasurement of mood: Recurrent and emergent issues. Journal of Personality Assessment, 86, 267–296.
   Web of Science ®Google Scholar
• Watson, D., Clark, L.A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scale. Journal of Personality and Social Psychology, 54(6), 1063–1070.
   PubMed Web of Science ®Google Scholar
• Werts, C., Jöreskog, K., & Linn, R. (1972). A multitrait-multimethod model for studying growth. Educational and Psychological Measurement, 32, 655–678.
   Web of Science ®Google Scholar
• Widaman, K.F. (1985). Hierarchically nested covariance structure models for multitrait-multimethod data. Applied Psychological Measurement, 9(1), 1–26.
   Web of Science ®Google Scholar
• Winne, P., & Marx, R. (1977). A multitrait-multimethod study of three self-concept inventories. Child Development, 48(3), 893–901.
   Web of Science ®Google Scholar
• Wirth, R., & Edwards, M. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12(1), 58–79.
   PubMed Web of Science ®Google Scholar
• Zevon, M.A., & Tellegen, A. (1982). The structure of mood change: An idiographic/nomothetic analysis. Journal of Personality and Social Psychology, 43, 111–122.
   Web of Science ®Google Scholar
• Zhang, G., & Browne, M. (2010a). Bootstrap standard error estimates in dynamic factor analysis. Multivariate Behavioral Research, 45, 453–482.
   PubMed Web of Science ®Google Scholar
• Zhang, G., & Browne, M. (2010b). Dynamic factor analysis with ordinal variables. In S.- M. Chow, E. Ferrer & F. Hsieh (Eds.), Notre Dame series on quantitative methodology: Vol. 4. Statistical Methods for Modeling Human Dynamics: An Interdisciplinary Dialogue (pp. 241–263). New York, NY: Psychology Press.
   Google Scholar