Startseite Use of structural equation modeling in human development research
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Use of structural equation modeling in human development research

  • Daniel T.L. Shek EMAIL logo und Lu Yu
Veröffentlicht/Copyright: 16. April 2014

Abstract

In social sciences, it is common to hypothesize that latent factors (e.g., psychological well-being) underlie the observed variables (e.g., depression and risk behavior). Hence, it is important to examine the nature of latent variables, the inter-relationships among such variables, and their associations with other predictors and outcome variables. These latent variable-related issues can be well addressed by adopting the approach of structural equation modeling. Apart from describing the use of structural equation modeling in research on human development, this paper also presents the assumptions underlying structural equation modeling, steps of model construction and model assessment, and both the strengths and limitations of this method in human development research. Finally, some examples using structural equation modeling in the Chinese contexts are also illustrated.


Corresponding author: Professor Daniel T.L. Shek, PhD, FHKPS, BBS, SBS, JP, Associate Vice President (Undergraduate Programme) and Chair Professor of Applied Social Sciences, Faculty of Health and Social Sciences, Department of Applied Social Sciences, The Hong Kong Polytechnic University, Room HJ407, Core H, Hunghom, Hong Kong, P.R. China, e-mail:

Acknowledgments

The preparation of this paper and the Project P.A.T.H.S. were financially supported by The Hong Kong Jockey Club Charities Trust. The authorship is carried equally between the first and second authors.

References

1. Bartholomew DJ, Knott M. Latent variable models and factor analysis. London: Edward Arnold, 1999.Suche in Google Scholar

2. Cureton E. Factor analysis: an applied approach. Hillsdale, NJ: Erlbaum, 1983.Suche in Google Scholar

3. Fabrigar L, Wegener D, MacCallum R, Strahan, E. Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods 1999;4:272–99.10.1037/1082-989X.4.3.272Suche in Google Scholar

4. Hox JJ, Bechger TM. An introduction to structural equation modeling. Fam Sci Rev 1998;11:354–73.Suche in Google Scholar

5. Bentler PM, Chou C. Practical issues in structural modeling. Sociol Method Res 1987;16:78–117.10.1177/0049124187016001004Suche in Google Scholar

6. Loehlin JC. Latent variable analysis. Hillsdale, NJ: Lawrence Erlbaum, 1992.Suche in Google Scholar

7. Byrne BM. Structural equation modeling with AMOS: basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum, 2000.Suche in Google Scholar

8. Hu LT, Bentler PM. Evaluating model fit. In: Hoyle RH, editor. Structural equation modeling: concepts, issues, and applications. Thousand Oaks, CA: Sage, 1995:76–99.Suche in Google Scholar

9. Jöreskog KG, Sörbom D. LISREL 7: a guide to the program and applications. Chicago: SPSS Inc, 1998.Suche in Google Scholar

10. Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull, 1980;88:588–606.10.1037/0033-2909.88.3.588Suche in Google Scholar

11. Bentler PM. Comparative fit indexes in structural models. Psychol Bull 1990;107:238–46.10.1037/0033-2909.107.2.238Suche in Google Scholar

12. Browne MW, Cudeck R. Alternative ways of assessing model fit. In Bollen KA, Long JS, editors. Testing structural equation models. Newbury Park, CA: Sage, 1993:445–55.Suche in Google Scholar

13. MacCallum RC, Browne MW, Sugawara HM. Power analysis and determination of sample size for covariance structure modeling. Psychol Methods 1996;1:130–49.10.1037/1082-989X.1.2.130Suche in Google Scholar

14. Bandalos DL. Factors influencing the cross-validation of confirmatory factor analysis models. Multivar Behav Res 1993;28:351–74.10.1207/s15327906mbr2803_3Suche in Google Scholar

15. Shek DT. The factorial structure of the Chinese version of the State–Trait Anxiety Inventory: a confirmatory factor analysis. Educ Psychol Meas 1991;51:985–97.10.1177/001316449105100418Suche in Google Scholar

16. Shek DT. Factor structure of the Chinese version of the General Health Questionnaire (GHQ–30): a confirmatory factor analysis. J Clin Psychol 1993;49:678–84.10.1002/1097-4679(199309)49:5<678::AID-JCLP2270490510>3.0.CO;2-HSuche in Google Scholar

17. Shek DT, Cheung CK. Dimensionality of the Chinese dyadic adjustment scale based on confirmatory factor analyses. Soc Indic Res 2008;86:201–12.10.1007/s11205-007-9108-4Suche in Google Scholar

18. Shek DT, Ma CM. Dimensionality of the Chinese Perceived Causes of Poverty Scale: findings based on confirmatory factor analyses. Soc Indic Res 2009;90:155–164.10.1007/s11205-008-9266-zSuche in Google Scholar

19. Shek DT, Ma CM. The Chinese Family Assessment Instrument (C–FAI): hierarchical confirmatory factor analyses and factorial invariance. Res Social Work Prac 2010;20:112–23.10.1177/1049731509355145Suche in Google Scholar

20. Shek DT, Ma CM. Dimensionality of the Chinese positive youth development scale: confirmatory factor analyses. Soc Indic Res 2010;98:41–59.10.1007/s11205-009-9515-9Suche in Google Scholar

21. Sun RC, Shek DT. Life satisfaction, positive youth development and problem behaviour among Chinese adolescents in Hong Kong. Soc Indic Res 2010;95:455–74.10.1007/s11205-009-9531-9Suche in Google Scholar

22. Sun RC, Shek DT. Positive youth development, life satisfaction and problem behavior among Chinese adolescents in Hong Kong: a replication. Soc Indic Res 2012;105:541–59.10.1007/s11205-011-9786-9Suche in Google Scholar

23. McArdle JJ, Epstein D. Latent growth curves within developmental structural equation models. Child Dev 1987;58:110–33.10.2307/1130295Suche in Google Scholar

24. Bollen KA. Structural equation modeling with latent variables, 2nd ed. New York: Wiley, 1989.10.1002/9781118619179Suche in Google Scholar

25. Muthén BO, Curran PJ. General longitudinal modeling of individual differences in experimental designs: a latent variable framework for analysis and power estimation. Psychol Methods 1997;2(4):371–402.10.1037/1082-989X.2.4.371Suche in Google Scholar

26. Rovine MJ, Molenaar PC. The covariance between level and shape in the latent growth curve model with estimated basis vector coefficients. MPR-Online 1998;3:95–107.Suche in Google Scholar

27. Curran PJ, Hussong AM. The use of latent trajectory models in psychopathology research. J Abnorm Psychol 2003;112:526–44.10.1037/0021-843X.112.4.526Suche in Google Scholar

28. Chou C, Bentler PM. Model modification in structural equation modeling by imposing constraints. Comput Stat Data An 2002;41:271–87.10.1016/S0167-9473(02)00097-XSuche in Google Scholar

29. Hox JJ. Multilevel analysis: techniques and Applications. Mahwah, NJ: Lawrence Erlbaum, 2002.10.4324/9781410604118Suche in Google Scholar

30. Algina J, Moulder BC. A note on estimating the Jöreskog-Yang Model for latent lariable interaction using LISREL 8.3. Struc Equ Modeling 2001;8:40–52.10.1207/S15328007SEM0801_3Suche in Google Scholar

Received: 2013-1-1
Accepted: 2013-2-2
Published Online: 2014-4-16
Published in Print: 2014-5-1

©2014 by Walter de Gruyter Berlin/Boston

Heruntergeladen am 21.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/ijdhd-2014-0302/html
Button zum nach oben scrollen