Textbooks

Hoyle, R. H. (1995). Structural equation modeling: Concepts, issues, and applications. Thousand Oaks, CA: Sage Publications. (buy online)

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed). New York: Guilford Press. (buy online)

Reading Assignments

*Note: Click on the next to a reference to download a pdf of the document. Because of server space limitations, pdfs will be available for download only during the week prior to when they are to be read.

 

August 30: introduction and overview

Optional background readings:

          Chapter 1 in Tucker, L. R., & MacCallum, R. C. (1997). Exploratory factor analysis.

          Comrey, A. L. (1988). Factor-analytic methods of scale development in personality and clinical psychology. Journal of Consulting and Clinical Psychology, 56, 754-761.

          Darlington, R. B. (1968). Multiple regression in psychological research and practice. Psychological Bulletin, 69, 161-182.

          http://www2.chass.ncsu.edu/garson/pa765/regress.htm -- description of multiple regression analysis from the Quantitative Research in Public Administration Web site at NC State University

          http://www.statsoft.com/textbook/stmulreg.html -- basic description of multiple regression analysis from the Statistica Web site

          http://elsa.berkeley.edu/sst/regression.html -- a more technical description of multiple regression analysis from the Statistical Software Tools Web site at Berkeley

September 6: "nuts and bolts"

          Kline: Chapter 1

          Kline: Chapter 2

          Kline: Chapter 4

          Hoyle: Chapter 1

          Hoyle: Chapter 2, pp. 16-27

          Bollen, K. A. (1989). Model notation, covariances, and path analysis. Chapter 2 in Structural equations with latent variables. New York: Wiley.

          *pp. 10-20 on notation; pp. 32-34 on path diagrams; pp. 36-39 on decomposing effects

Optional:

          Kline: Chapter 3

September 13: simple measurement models

          Hoyle, R. H. (2007). Applications of structural equation modeling in personality research. In R. Robins, C. Fraley, & R. Krueger (Eds.), Handbook of research methods in personality psychology (pp. 444-467). New York: Guilford Publications.

          DeShon, R. P. (1998). A cautionary note on measurement error corrections in structural equation models. Psychological Methods, 4, 412-423.

          Steiger, J. H. (2002). When constraints interact: A caution about reference variables, identification constraints, and scale dependencies in structural equation modeling. Psychological Methods, 7, 210-227.

          Bollen, K. A., & Lennox, R. D. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110, 305-314.

          Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5, 155-174.

Optional:

          Little, T. D., Lindenberger, U., & Nesselroade, J. R. (1999). On selecting indicators for multivariate measurement and modeling with latent variables: When "good" indicators are bad and "bad" indicators are good. Psychological Methods, 4, 192-211.

          MacCallum, R. C., & Browne, M. W. (1993). The use of causal indicators in covariance structure models: Some practical issues. Psychological Bulletin, 114, 533-541.

September 20: complex measurement models

          Kline: Chapter 7, section 7.8

          Hoyle: chapter 10, pp. 177-187

          Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9, 151-173.

          Zautra, A. J., Marbach, J. J., Raphael, K. G., Dohrenwend, B. P., Lennon, M. C., & Kenny, D. A. (1995). The examination of myofascial face pain and its relationship to psychological distress among women. Health Psychology, 14, 223-231.

          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.

Optional:

          Lance, C. E., Noble, C. L., & Scullen, S. E. (2002). A critique of the correlated trait-correlated method and correlated uniqueness models for multitrait-multimethod data. Psychological Methods, 7, 228-244.

          Hoyle, R. H., & Duvall, J. L. (2004). Determining the number of factors in exploratory and confirmatory factor analysis. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 301-315). Thousand Oaks, CA: Sage Publications.

          Kenny, D. A., & Zautra, A. (1995). The trait-state-error model for multiwave data. Journal of Consulting and Clinical Psychology, 63, 52-59.

          Cole, D. A., Martin, N. C., & Steiger, J. H. (2005). Empirical and conceptual problems with longitudinal trait-state models: Introducing a trait-state-occasion model. Psychological Methods, 10, 3-20.

September 27: specification and estimation

          Hoyle: Chapter 2, re-read pp. 16-27

          Kline: Chapter 5, sections 5.2-5.4, and Chapter 7, sections 7.1-7.2

          Hoyle: Chapters 3 (pp. 37-46) and 4

          Kline: Chapter 5, sections 5.6-5.7

          Hoyle, R. H. (2000). Confirmatory factor analysis. In H. E. A. Tinsely & S. D. Brown (Eds.), Handbook of applied multivariate statistics and mathematical modeling (pp. 465-497). New York: Academic Press.

October 4: omnibus fit indices; model modification

          Kline: Chapter 6, section 6.2

          MacCallum, R. C. (2003). Working with imperfect models. Multivariate Behavioral Research, 38, 113-139.

          Widaman, K. F., & Thompson, J. S. (2003). On specifying the null model for incremental fit indices in structural equation modeling. Psychological Methods, 8, 16-37.

          Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.

          Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.

          Hoyle: Chapter 2, pp. 31-35

          Jöreskog, K. G. (1993). Testing structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 294-316). Thousand Oaks, CA: Sage Publications.

          MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111, 490-504.

Optional:

          Browne, M. W., MacCallum, R. C., Kim, C.-T., Andersen, B. L., & Glaser, R. (2002). When fit indices and residuals are incompatible. Psychological Methods, 7, 403-421.

          Hu, L.-T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424-453.

October 11: tests of parameters, non-normal & non-continuous data, statistical power

          Gonzalez, R., & Griffin, D. (2001). Testing parameters in structural equation modeling: Every "one" matters. Psychological Methods, 6, 258-269.

          Finney, S. J., & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 269-314). Greenwich, CT: Information Age Publishing.

          Hoyle: Chapter 6

          Hancock, G. R. (2006). Power analysis in covariance structure modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 69-115). Greenwich, CT: Information Age Publishing.

          MacCallum, R. C, Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.

          Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9, 599-620.

October 18: cross-sectional structural models

          Hoyle, R. H., & Kenny, D. A. (1999). Sample size, reliability, and tests of mediation. In R. H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 195-222). Thousand Oaks, CA: Sage Publications.

          McClelland, G. H., and Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114, 376-390.

          Jaccard, J., & Wan, C. K. (1995). Measurement error in the analysis of interaction effects between continuous predictors using multiple regression: Multiple indicator and structural equation approaches. Psychological Bulletin, 117, 348-357.

          Marsh, H. W., Wen, Z., & Hau, K.-T. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9, 275-300.

October 25: autoregressive longitudinal models; latent growth curve models

          Farrell, A. D. (1994). Structural equation modeling with longitudinal data: Strategies for examining group differences and reciprocal relationships. Journal of Consulting and Clinical Psychology, 62, 477-487.

          Hays, R. D., Marshall, G. N., Wang, E. Y. I., & Sherbourne, C. D. (1994). Four-year cross-lagged associations between physical and mental health in the Medical Outcomes Study. Journal of Consulting and Clinical Psychology, 62, 441-449.

          Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363-381.

Optional:

          Newcomb, M. D. (1994). Drug use and intimate relationships among women and men: Separating specific from general effects in prospective data using structural equation models. Journal of Consulting and Clinical Psychology, 62, 463-476.

November 1: latent growth curve models (cont.); other structured means models

          Llabre, M. M., Spitzer, S. B., Saab, P. G., & Schneiderman, N. (2001). Piecewise latent growth curve modeling of systolic blood pressure reactivity and recovery from the cold pressor test. Psychophysiology, 38, 951-960.

          Kline, Chapter 10, sections 10.1-10.4

          Thompson, M. S. & Green, S. B. (2006). Evaluating between-group differences in latent variable means. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 119-169). Greenwich, CT: Information Age Publishing.

Optional:

          Curran, P. J. (2003). Have multilevel models been structural equation models all along? Multivariate Behavioral Research, 38, 529-569.

          Curran, P. J., Bauer, D. J., & Willoughby, M. T. (2004). Testing main effects and interactions in latent curve analysis. Psychological Methods, 9, 220-237.

November 8: missing data methods

          Enders, C. K. (2006). Analyzing structural equation models with missing data. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 313-342). Greenwich, CT: Information Age Publishing.

          Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.

          Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8, 206-213.

Optional:

          Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6, 330-351.

          Sinharay, S., Stern, H. S., & Russell, D. (2001). The use of multiple imputation for the analysis of missing data. Psychological Methods, 6, 317-329.

November 15: presentations

Optional readings on reporting SEM results:

          Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7, 461-483.

          Hoyle: Chapter 9

          McDonald, R., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64-82.

          Raykov, T., Tomer, A. & Nesselroade, J. R. (1991). Reporting structural equation modeling results in psychology and aging: Some proposed guidelines. Psychology and Aging, 6, 499-503.

November 29: limitations and criticisms

          Hoyle: Chapter 7

          Kline: Chapter 12

          Breckler, S. J. (1990). Applications of covariance structure modeling in psychology: Cause for concern? Psychological Bulletin, 107, 260-273.

          MacCallum, R. C., Wegener, D. T., Uchino, B. N., & Fabrigar, L. R. (1993). The problem of equivalent models in applications of covariance structure analysis. Psychological Bulletin, 114, 185-199.

Optional:

          Meehl, P. E., & Waller, N. G. (2002). The path analysis controversy: A new statistical approach to strong appraisal of verisimilitude. Psychological Methods, 7, 283-300.

          Responses to Meehl & Waller (2002) in Psychological Methods, Vol. 7, No. 3.