Traditional methods of assessing construct stability are reviewed and longitudinal mean and covariance structures (LMACS) analysis, a modern approach, is didactically illustrated using psychological entitlement data. Measurement invariance and latent variable stability results are interpreted, emphasizing substantive implications for educators and the utility of LMACS analysis in longitudinal research.
American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text revision). Washington, DC: American Psychiatric Association.
2.
BarnetteJ. J. (2000). Effects of stem and Likert response option reversals on survey internal consistency: If you feel the need, there is a better alternative to using those negatively worded stems. Educational and Psychological Measurement, 60, 361–370.
3.
BontempoD. E.HoferS. M. (2006). Assessing factorial invariance in cross-sectional and longitudinal studies. In OngA. D.van DulmenM. (Eds.), Handbook of methods in positive psychology (pp. 153–175). New York, NY: Oxford University Press.
4.
BrowneM. W.CudeckR. (1993). Alternative ways of assessing model fit. In BollenK. A.LongJ. S. (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.
5.
ByrneB. M.StewartS. M. (2006). The MACS approach to testing for multigroup invariance of a second-order factor structure: A walk through the process. Structural Equation Modeling: A Multidisciplinary Journal, 13, 287–321.
6.
CampbellW. K.BonacciA. M.SheltonJ.ExlineJ. J.BushmanB. J. (2004). Psychological entitlement: Interpersonal consequences and validation of a self-report measure. Journal of Personality Assessment, 83, 29–45.
7.
ChanD. (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.
8.
CheungG. W.RensvoldR. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9, 233–255.
9.
DimitrovD. M. (2006). Comparing groups on latent variables: A structural equation modeling approach. WORK: A Journal of Prevention, Assessment & Rehabilitation, 26, 429–436.
10.
DimitrovD. M. (2012). Statistical methods for validation of assessment scale data in counseling and related fields. Alexandria, VA: American Counseling Association.
11.
DiStefanoC.MotlR. W. (2006). Further investigating method effects associated with negatively worded items on self-report surveys. Structural Equation Modeling: A Multidisciplinary Journal, 13, 440–464.
FanX.SivoS. A. (2007). Sensitivity of fit indices to model misspecification and model types. Multivariate Behavioral Research, 42, 509–529.
14.
FinneyS. J.DiStefanoC. (2006). Non-normal and categorical data in structural equation modeling. In HancockG. R.MuellerR. O. (Eds.), Structural equation modeling: A second course (pp. 269–314). Greenwich, CT: Information Age.
15.
FrenchB. F.FinchW. H. (2006). Confirmatory factor analytic procedures for the determination of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 13, 378–402.
16.
GolembiewskiR. T.BillingsleyK.YeagerS. (1976). Measuring change and persistence in human affairs: Types of change generated by OD designs. Journal of Applied Behavioral Science, 12, 133–157.
17.
HancockG. R. (1997). Structural equation modeling methods of hypothesis testing of latent variable means. Measurement and Evaluation in Counseling and Development, 30,91–105.
18.
HancockG. R. (2003). Fortune cookies, measurement error, and experimental design. Journal of Modern and Applied Statistical Methods, 2, 293–305.
19.
HarveyP.HarrisJ. K. (2010). Frustration-based outcomes of entitlement and the influence of supervisor communication. Human Relations, 63, 1639–1660.
20.
HuL.BentlerP. M. (1995). Evaluating model fit. In HoyleR. H. (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76–99). Thousand Oaks, CA: Sage.
21.
HuL.BentlerP. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453.
22.
HuL.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55.
KlineR. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.
25.
LittleT. D.CardN. A.SlegersD. W.LedfordE. (2007). Representing contextual effects in multiple-group MACS models. InLittleT. D.BovairdJ. A.CardN. A. (Eds.), Modeling ecological and contextual effects in longitudinal analyses of human development (pp. 121–147). Mahwah, NJ: Erlbaum.
26.
LloydJ. (2010). Construct commensurability and the analysis of change. Educational and Psychological Measurement, 70, 252–266.
27.
MarshH. W. (1994). Confirmatory factor analysis models of factorial invariance: A multifaceted approach. Structural Equation Modeling: A Multidisciplinary Journal, 1, 5–34.
28.
MarshH. W.GraysonD. (1994). Longitudinal stability of latent means and individual differences: A unified approach. Structural Equation Modeling: A Multidisciplinary Journal, 1, 317–359.
29.
MarshH. W.HauK. T.WenZ. (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: A Multidisciplinary Journal, 11, 320–341.
30.
MarshH. W.ScalasL. F.NagengastB. (2010). Longitudinal tests of competing factor structures for the Rosenberg Self-Esteem Scale: Traits, ephemeral artifacts, and stable response styles. Psychological Assessment, 22, 366–381.
31.
McCraeR. R.TerraccianoA.KhouryB. (2007). Dolce far niente: The positive psychology of personality stability and invariance. In Ong, &A.van DulmenM. (Eds.), Handbook of methods in positive psychology (pp. 176–188). New York, NY: Oxford University Press.
32.
MillsapR. E.ChamH. (2012). Investigating factorial invariance in longitudinal data. In LaursenB.LittleT. D.CardN. A. (Eds.), Handbook of developmental research methods (pp. 109–126). New York, NY: Guilford Press.
33.
NyeC. D.RobertsB. W.SaucierG.ZhouX. (2008). Testing the measurement equivalence of personality adjective items across cultures. Journal of Research in Personality, 42, 1524–1536.
34.
RiordanC. M.RichardsonH. A.SchafferB. S.VandenbergR. J. (2001). Alpha, beta, and gamma change: A review of past research with recommendations for new directions. In SchriesheimC. A.NeiderL. L. (Eds.), Research management (pp. 51–98). Greenwich, CT: Information Age.
35.
SassD. A. (2011). Testing measurement invariance and comparing latent factor means within a confirmatory factor analysis framework. Journal of Psychoeducational Assessment, 29, 347–363.
36.
SatorraA.BentlerP. M. (1994). Corrections to test statistics and standard errors on covariance structure analysis. In von EyeA.CloggC. C. (Eds.), Latent variable analysis (pp. 299–419). Thousand Oaks, CA: Sage.
37.
SchmittN.KuljaninG. (2008). Measurement invariance: Review of practice and limitations. Human Resource Management Review, 18, 210–222.
38.
SteenkampJ. E. M.BaumgartnerH. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25, 78–90.
39.
ThompsonB. (1997). The importance of structure coefficients in structural equation modeling confirmatory factor analysis. Educational and Psychological Measurement, 57, 5–19.
40.
ThompsonM. S.GreenS. B. (2006). Evaluating between-group differences in latent variable means. In HancockG. R.MuellerR. O. (Eds.), A second course in structural equation modeling (pp. 119–169). Greenwich, CT: Information Age.
41.
U.S. Department of Education. (2006). A test of leadership: Charting the future of American higher education (Report of the Commission Appointed by Secretary of Education Margaret Spellings). Washington, DC: Author.
42.
VandenbergR. J.LanceC. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4–69.
43.
WestS. G.FinchJ. F.CurranP. J. (1995). Structural equation models with non-normal variables: Problems and remedies. InHoyleR. H. (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56–75). Thousand Oaks, CA: Sage.
44.
WichertsJ. M.DolanC. V. (2010). Measurement invariance in confirmatory factor analysis: An illustration using IQ test performance of minorities. Educational Measurement: Issues and Practice, 29, 39–47.
45.
WidamanK. F.FerrerE.CongerR. D. (2010). Factorial invariance within longitudinal structural equation models: Measuring the same construct across time. Child Development Perspectives, 4(1), 10–18.
46.
WidamanK. F.ReiseS. P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In BryantK. J.WindleM.WestS. G. (Eds.), The science of prevention (pp. 281–324). Washington, DC: American Psychological Association.