Distinguishing state variability from trait change in longitudinal data: The role of measurement (non)invariance in latent state-trait analyses

Behavior Research Methods

Volumn 47, Issue 1, 2015, Pages 172-203

  Geiser, Christian ^a   Keller, Brian T ^b   Lockhart, Ginger ^a   Eid, Michael ^c   Cole, David A ^d   Koch, Tobias ^c  

^a UTAH STATE UNIVERSITY   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

^c FREIE UNIVERSITÄT BERLIN   (Germany)

^d VANDERBILT UNIVERSITY   (United States)

Author keywords

Latent growth curve models; Latent state trait analysis; Measurement invariance; Model misspecification; State variability versus trait change

Indexed keywords

COMPUTER ANALYSIS; HUMAN; MEDICAL RESEARCH; METHODOLOGY; MULTIVARIATE ANALYSIS; PROCEDURES; REPRODUCIBILITY; STATISTICAL BIAS; STATISTICAL MODEL; TIME;

BIAS (EPIDEMIOLOGY); BIOMEDICAL RESEARCH; COMPUTING METHODOLOGIES; HUMANS; MODELS, STATISTICAL; MULTIVARIATE ANALYSIS; REPRODUCIBILITY OF RESULTS; RESEARCH DESIGN; TIME;

EID: 84896419003     PISSN: 1554351X     EISSN: 15543528     Source Type: Journal    
DOI: 10.3758/s13428-014-0457-z     Document Type: Article

References (64)

1. Alessandri, G., Caprara, G. V., & Tisak, J. (2012). A unified latent curve, latent state-trait analysis of the developmental trajectories and correlates of positive orientation. Multivariate Behavioral Research, 47, 341–368.
2. Anastasi, A. (1983). Traits, states, and situations: A comprehensive view. In H. Wainer & S. Messick (Eds.), Principals of modern psychological measurement (pp. 345–356). Hillsdale, NJ: Erlbaum.
3. Baumgartner, H., & Steenkamp, J.-B. E. M. (2006). An extended paradigm for measurement analysis of marketing constructs applicable to panel data. Journal of Marketing Research, 43, 431–442.
4. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.
5. Bishop, J., Geiser, C., & Cole, D. A. (2013). Modeling growth with multiple indicators: A comparison of three approaches. Manuscript submitted for publication.
6. Boll, T., Michels, T., Ferring, D., & Filipp, S.-H. (2010). Trait and state components of perceived parental differential treatment in middle adulthood: A longitudinal study. Journal of Individual Differences, 31, 158–165.
7. Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation approach. Hoboken, NJ: Wiley.
8. Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71, 425–440.
9. Byrne, B. M., Shavelson, R. J., & Muthén, B. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456–466.
10. Chan, D. (1998). The conceptualization and analysis of change over time: An integrative approach incorporating longitudinal mean and covariance structures analysis (LMACS) and multiple indicator latent growth modeling (MLGM). Organizational Research Methods, 1, 421–483.
11. Chen, F., Curran, P. J., Bollen, K. A., Kirby, J., & Paxton, P. (2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociological Methods & Research, 36, 462–494.
12. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255.
13. Cheung, G. W., & Rensvold, R. B. (1999). Testing factorial invariance across groups: A reconceptualization and proposed new method. Journal of Management, 25, 1–27.
14. Ciesla, J. A., Cole, D. A., & Steiger, J. H. (2007). Extending the trait-state-occasion model: How important is within-wave measurement equivalence? Structural Equation Modeling, 14, 77–97.
15. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
16. Cole, D. A. (2012). Latent trait-state models. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 585–600). New York: Guilford.
17. Collins, L. M., & Sayer, A. G. (2001). New methods for the analysis of change. Washington, D.C.: American Psychological Association.
18. Deinzer, R., Steyer, R., Eid, M., Notz, P., Schwenkmezger, P., Ostendorf, F., & Neubauer, A. (1995). Situational effects in trait assessment: The FPI, NEOFFI and EPI questionnaires. European Journal of Personality, 9, 1–23.
19. Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to latent variable growth curve modeling: Concepts, issues, and applications (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
20. Eid, M. (1996). Longitudinal confirmatory factor analysis for polytomous item responses: Model definition and model selection on the basis of stochastic measurement theory. Methods of Psychological Research - Online, 1, 65–85.
21. Eid, M. (2007). Latent class models for analyzing variability and change. In A. Ong & M. van Dulmen (Eds.), Handbook of methods in positive psychology (pp. 591–607). Oxford: Oxford University Press.
22. Eid, M., Courvoisier, D. S., & Lischetzke, T. (2012). Structural equation modeling of ambulatory assessment data. In M. R. Mehl & T. S. Connor (Eds.), Handbook of research methods for studying daily life (pp. 384–406). New York: Guilford.
23. Eid, M., & Diener, E. (2004). Global judgments of subjective well-being: Situational variability and long-term stability. Social Indicators Research, 65, 245–277.
24. Eid, M., & Hoffmann, L. (1998). Measuring variability and change with an item response model for polytomous variables. Journal of Educational and Behavioral Statistics, 23, 193–215.
25. Eid, M., Schneider, C., & Schwenkmezger, P. (1999). Do you feel better or worse? The validity of perceived deviations of mood states from mood traits. European Journal of Personality, 13, 283–306.
26. Ferrer, E., Balluerka, N., & Widaman, K. F. (2008). Factorial invariance and the specification of second-order growth models. Methodology, 4, 22–36.
27. Geiser, C., & Lockhart, G. (2012). A comparison of four approaches to account for method effects in latent state trait analyses. Psychological Methods, 17, 255–283.
28. Geiser, C., Keller, B. T., & Lockhart, G. (2013). First- versus second-order latent growth curve models: Some insights from latent state-trait theory. Structural Equation Modeling, 20, 479–503.
29. Hermes, M., Hagemann, D., Britz, P., Lieser, S., Bertsch, K., Naumann, E., & Walter, C. (2009). Latent state-trait structure of cerebral blood flow in a resting state. Biological Psychology, 80, 196–202.
30. Hertzog, C., & Nesselroade, J. R. (1987). Beyond autoregressive models: Some implications of the trait-state distinction for the structural modeling of developmental change. Child Development, 58, 93–109.
31. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
32. Jagodzinski, W., Kühnel, S. M., & Schmidt, P. (1987). Is there a ‘Socratic Effect’ in non-experimental panel studies? Consistency of an attitude toward guestworkers. Sociological Methods & Research, 15, 259–302.
33. Kenny, D. A. (2001). Trait-state models for longitudinal data. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 243–263). Washington, D.C.: American Psychological Association.
34. Kertes, D. A., & van Dulmen, M. (2012). Latent state trait modeling of children's cortisol at two points of the diurnal cycle. Psychoneuroendocrinology, 37, 249–255. doi:10.1016/j.psyneuen.2011.06.009
35. Lorber, M. F., & O'Leary, K. D. (2011). Stability, change, and informant variance in newlywed's physical aggression: Individual and dyadic processes. Aggressive Behavior, 37, 1–15.
36. Luhmann, M., Schimmack, U., & Eid, M. (2011). Stability and variability in the relationship between subjective well-being and income. Journal of Research in Personality, 45, 186–197.
37. 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 overgeneralizing Hu and Bentler's (1999) findings. Structural Equation Modeling, 11, 320–341.
38. McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60, 577–605.
39. McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In R. B. Cattell & J. Nesselroade (Eds.), Handbook of multivariate experimental psychology (2nd ed., pp. 561–614). New York: Plenum Press.
40. McArdle, J. J., & Hamagami, F. (2001). Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 139–175). Washington, DC: American Psychological Association.
41. Meredith, W. (1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika, 58, 525–543.
42. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107–122.
43. Millsap, R. E. (2011). Statistical approaches to measurement invariance. New York: Routledge.
44. Millsap, R. E., & Meredith, W. (2007). Factorial invariance: Historical perspectives and new problems. In R. Cudeck & R. MacCallum (Eds.), Factor analysis at 100: Historical developments and future directions (pp. 130–152). Mahwah, NJ: Erlbaum.
45. Muthén, L. K., & Muthén, B. O. (1998–2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
46. Nesselroade, J. R. (1991). Interindividual differences in intraindividual change. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change. Recent advances, unanswered questions, future directions (pp. 92–105). Washington, DC: American Psychological Association.
47. Ploubidis, G. B., & Frangou, S. (2011). Neuroticism and psychological distress: To what extent is their association due to the person-environment correlation? European Psychiatry, 26, 1–5.
48. Raykov, T. (1993). On estimating true change interrelationships with other variables. Quality and Quantity, 27, 353–370.
49. Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Test of significance and descriptive goodness-of-fit measures. Methods of Psychological Research - Online, 8, 23–74.
50. Schermelleh-Engel, K., Keith, N., Moosbrugger, H., & Hodapp, V. (2004). Decomposing person and occasion-specific effects: An extension of latent state-trait theory to hierarchical LST models. Psychological Methods, 9, 198–219.
51. Schmitt, M. J., & Steyer, R. (1993). A latent state-trait model (not only) for social desirability. Personality and Individual Differences, 14, 519–529.
52. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173–180.
53. Steyer, R. (1989). Models of classical psychometric test theory as stochastic measurement models: Representation, uniqueness, meaningfulness, identifiability, and testability. Methodika, 3, 25–60.
54. Steyer, R., Eid, M., & Schwenkmezger, P. (1997). Modeling true intraindividual change: True change as a latent variable. Methods of Psychological Research Online, 2, 21–33.
55. Steyer, R., Ferring, D., & Schmitt, M. J. (1992). States and traits in psychological assessment. European Journal of Psychological Assessment, 8, 79–98.
56. Steyer, R., Geiser, C., & Fiege, C. (2012). Latent state-trait models. In H. Cooper (Ed.), Handbook of research methods in psychology (pp. 291–308). Washington, DC: American Psychological Association.
57. Steyer, R., Krambeer, S., & Hannöver, W. (2004). Modeling latent trait-change. In K. Van Montfort, H. Oud, & A. Satorra (Eds.), Recent developments on structural equation modeling: Theory and applications (pp. 337–357). Amsterdam: Kluwer Academic Press.
58. Steyer, R., Majcen, A.-M., Schwenkmezger, P., & Buchner, A. (1989). A latent state-trait anxiety model and its application to determine consistency and specificity coefficients. Anxiety Research, 1, 281–299.
59. Steyer, R., Schmitt, M., & Eid, M. (1999). Latent state-trait theory and research in personality and individual differences. European Journal of Personality, 13, 389–408.
60. Tisak, J., & Tisak, M. S. (2000). Permanency and ephemerality of psychological measures with application to organizational commitment. Psychological Methods, 5, 175–198.
61. Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4–70.
62. von Oerzen, T., Hertzog, C., Lindenberger, U., & Ghisletta, P. (2010). The effect of multiple indicators on the power to detect inter-individual differences in change. British Journal of Mathematical and Statistical Psychology, 63, 627–646.
63. Widaman, K. F., & Reise, S. P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281–324). Washington, DC: American Psychological Association.
64. Windle, M., & Dumenci, L. (1998). An investigation of maternal and adolescent depressed mood using a latent trait-state model. Journal of Research on Adolescence, 8, 461–484.