sem_Helsinki2012.doc

Course Syllabus Amiram D. Vinokurfn: sem_Helsinki2012.doc University of Helsinki June 11-13, 2012 Building and Testing Structural Equation Models (SEM)In the Social SciencesII. Required, Highly Recommended Useful Readings 1 Required reading 2 Highly Recommended readings, guidebooks or textbooks3 Very useful recommended readings or textbooks # available on the Ctools course website).A. Basic Textbooks and Manuals1Kline, R. B. (2011). Principles and practice of structural equation modeling. (3nd Edition). New York: The Guilford Press. ISBN for the paperback is: 9781606238769. This is the main textbook for this course. Every student should have a copy. The local book stores will carry copies or else try A. 2Byrne, B. M. (2006, 2nd Edition). Structural equation modeling with EQS and EQS/Windows: Basic concepts, applications, and programming. Thousand Oaks, CA: Sage Publications. Second most highly recommended textbook, but optional, for only if you intend to continue using EQS. Ideally, every student should have a copy or at least an access to the book in the libraryNearly all the content of the two manuals below is also embedded in the HELP section of the EQS software:2Bentler, P. M. (2006). EQS 6: Structural Equation Program Manual. Encino, CA: Multivariate software software Content of this manual is also included with the EQS software under the Help menu 2Bentler, P.M., & Wu, E.J.C. (2002). EQS 6 for Windows Users Manual.Encino, CA: Multivariate software Content of this manual is also included with the EQS software under the Help menuB. Other useful textbooksBollen, K. A. (1989). Structural equations with latent variables. New York: Wiley. 3Loehlin, J.C. (2004). Latent variable models: An introduction to factor, path, and structural equation analysis. (4th edition). NJ. Hillsdale: Lawrence Erlbaum Associates. Textbook.3Schumacker, R.E., & Lomax, R.G. (2010). A beginners guide to structural equation modeling. (3nd edition) N.J. Mahwah: Lawrence Erlbaum Associates. Textbook. In particular note Chapter 5 “Goodness of fit criteria” pp. 73-120.C. General reviews of SEM2 #Hoyle, R.H. & Smith, G.T. (1994). Formulating clinical research hypotheses as structural equation models: A Conceptual Overview. Journal of Consulting and Clinical Psychology, 62, 429-440. Among the best overview introductory articles to SEM.3, #Klem, L. (2000). Structural equation modeling. In L. G. Grimm, & P. R. Yarnold (Eds.), Reading and understanding more multivariate statistics . Washington, D.C.: American Psychological Association.1, #Russell, D. W., Kahn, J. H., Altmaier, E.M., & Spoth, R. (1998). Analyzing data from experimental studies: A latent variable structural equation modeling approach. Journal of Consulting Psychology, 5(1), 18-29. 2, # Vinokur, A.D. (2005). Structural Equation Modeling (SEM). In S. J. Best & B. Radcliff (Eds.), Polling America: An Encyclopedia of Public Opinion (pp. 800-805). Westport, Connecticut: Greenwood Press.D. Other Available Resources Dedicated to SEMJournals: Structural Equation Modeling: A multidisciplinary Journal Published by Lawrence Erlbaum Associates, Publishers.Websites:1. The website for Klines Textbook: www.G/Kline2. /sem/sem.html3. /kenny.htm (David Kenneys website).4 . SEMNET discussion group, see/mkteer/semnet.html To subscribe, see instructions on the above webpage4. To obtain data for your papers: See below:On a rich archive with thousands of documented datasets available to students and faculty from a consortium of about 400 universities around the world. Datasets can be used to prepare master theses, dissertations and other research publications.E. How to report SEM work in your research paper1,#Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7(3), 461-482.3, #McDonald, R.P., & Ho, M.R. (2002). Principles and practice in reporting structural equationanalyses. Psychological Methods, 7 (1), 64-82.2, # Raykov, T., Tomer, A., & Nesselroade, J. R. (1991). Reporting structural equation modeling results in Psychology and Aging: some proposed guidelines. Psychology and Aging, 6(4), 499503. Important article that set standards for how and what should be reported in articles that include structural analyses but somewhat dated, Boomsma (2000) article is more up to-date and comprehensiveExample:3,#Vinokur, A.D., & Schul, Y. (2002). The web of coping resources and pathways to reemployment following a job loss. Journal of Occupational Health Psychology, 7, 68-83. F. Misc. Specific Topics1. Issues relating to indicators, parcels and factorsOn Parceling:Bandalos, D.L. (2002). The effects of item parceling on Goodness-of-Fit and parameter estimate bias in structural equation modeling. Structural Equation Modeling, 9 (1), 78-102.Bandalos, D.L., & Finney, S.J. (2001). Item parceling issues in structural equation modeling. In. G.A. Marcoulides and R.E. Schumacker (Eds.) New developments and techniques in structural equation modeling. London: Lawrence Erlbaum Associates.Liang, J., Lawrence, R. H., Bennett, J. M., & Whitelaw, N. A. (1990). Appropriateness of composites in structural equation models. Journal of Gerontology: SOCIAL SCIENCES, 45(2), 553559. The practical implications of this article for using one observed indicator (e.g., index) for one latent variable are demonstrated in the article by Frone et al below.Frone, M. R., Russell, M. and Cooper, M. L. (1992). Antecedents and outcomes of work-family conflict:testing a model of the work-family interface, Journal of Applied Psychology, 77(1), 65-78. Or, see Vinokur, Pierce and Buck (1999); Article placed in Ctools.Sass, D.A., & Smith, P.L. (2006). The effects of parceling unidimensional scales on structural parameter estimates in structural equation modeling. Structural Equation Modeling, 13 (4), 566-586.Little, T.D., Lindenberger, U., & Nesselroade, J.R. (1999). On selecting indicators for multvariate measurement and modeling with latent variables: When “good” indicators are bad and “bad” indicators are good. Psychological Methods, 4 (2), 192-211.3,#Marsh, H.W., Hau, K., Balla, J.R., & Grayson, D. (1998). Is more ever too much? The number of indicators per factor in confirmatory factor analysis. Multivariate Behavioral Research, 33(2), 181-220.Chen, F.F., S.G. West, and K.H. Sousa (2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41(2): 189-225.Hall, R.J., A.F. Snell, and M.S. Foust (1999). Item parceling strategies in SEM: Investigating the subtle effects of unmodeled secondary constructs. Organizational Research Methods, 2(3): 233-256.On Reflective and Formative Factors:Bollen, K.A., & Ting, K. (2000). A tetrad test for causal indicators. Psychological Methods, 5 (1), 3-22.3.#Edwards, J.R., & Bagozzi, R.P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155-174.Edwards, J.R. (2011). The fallacy of formative measurement. Organizational Research Methods, 2011. 14(2): 370-388.Howell, R.D., E. Breivik, and J.B. Wilcox (2007). Reconsidering formative measurement. Psychological Methods, 12(2): 205-218.MacKenzie, S.B., Podaskoff, P.M., & Jarvis, C.B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90 (4), 710-730.MacCallum, R.C., & Browne, M.W. (1993). Use of causal indicators in covariance structure models: Some practical issues. Psychological Bulletin. 114 (3), 533-541.Treiblmaier, H., P.M. Bentler, and P. Mair (2011). Formative constructs implemented via common factors. Structural Equation Modeling, 18(1): 1-17. Williams, L.J. and E. OBoyle Jr. (2008). Measurement models for linking latent variables and indicators: A review of human resource management research using parcels. Human Resource Management Review, 18: 233-242.2. Adjustment for reliability1, #Bedeian, A. G., Day, D. V., & Kelloway, E. K. (1997). Correcting for measurement error attenuation in structural equation models: Some important reminders. Educational and psychological Measurement, 57 (5), 785-799.3. Model identification 3, #Rigdon, E. E. (1995). A necessary and sufficient identification for structural models estimated in practice. Multivariate Behavioral Research, 30 (3), 359-383.4. Measures of goodness of fit and misfit Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6 (4), 1-55. Browne, M. W., & Cudek, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & L. S. Long (Eds.), Testing structural equation models. Newbury Park, CA: Sage. Costner, H.L. (1965). Criteria for measures of association. American Sociological Review, 30 (3), 341-353. OBoyle Jr, E.H. and L.J. Williams, (2011). Decomposing model fit: Measurement vs. theory in organizational research using latent variables. J. of Applied Psy., 96(1): 1. Williams, L.J. and E. OBoyle Jr. (2011). The myth of global fit indices and alternatives for assessing latent variable relations. Organizational Research Methods, 14(2): 350-369.5. Model modification: capitalizing on chance MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111(3), 490 504. 6. Equivalent models 3,#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(1), 184199. 7. Power analysis in SEM 3,#MacCallum, R. C., Browne, M. W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1 (2), 130 149. 8. Modeling one or more common method effectsBagozzi, R. P. (1993). Assessing construct validity in personality research: Applications to measures of selfesteem. Journal of Research in Personality, 27, 4987. Lindell, M.K., & Whitney, D.J. (2001). Accounting for common method variance in cross -sectional research designs. Journal of Applied Psychology, 86 (1), 114-121. Demonstrating ways of dealing with negative affectivity and other common method effects also see below: Williams and AndersonPodsakoff, P.M., Mackanzie, S.B., Lee, J., & Podsakoff, N.P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879-903.Vinokur-Kaplan, D. (1995). Treatment teams that work (and those that dont): An application of Hackmans group effectiveness model to interdisciplinary teams in psychiatric hospitals. Journal of Applied Behavioral Science, 31, 303-327.3,#Tomas J.M., & Oliver, A. (1999). Rosenbergs self esteem scale: Two factor or method effects. Structural Equation Modeling, 6(1), 84-98.Williams, L. J., & Anderson, S. E. (1994). An alternative approach to method effects by using latentvariable models: Applications in organizational behavior research. Journal of Applied Psychology, 79(3), 323331. Demonstrating ways of dealing with negative affectivity and other common method effects also see Lindell, M.K., & Whitney9. Modeling data from different sources 3,#Vinokur, A.D., Price, R.H., & Caplan, R.D. (1996). Hard times and hurtful partners: How financial strain affects depression and relationship satisfaction of unemployed persons and their spouses. Journal of Personality and Social Psychology, 71 (1), 166-179. The Couples Stress and Coping Model: model E. Demonstrating analyses based on two sources of data as well as issues relating to longitudinal analyses, and comparison of alternative models, and models for different subgroups 10. Modeling Interactions (using categorical variables and continuous ones) Using categorical variables: Group comparisons 1,#Benbenishty, R., Astor, R.A., Zeira, A, & Vinokur, A.D. (2002). Perception of violence and fear of school attendance among junior high school students in Israel. Social Work Research, 26(2) 71-87. Article with comparisons across groups Using continuous variables: Marsh, H.W., Wen, Z., & Hau, K. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator. Psychological Methods, 9 (3), 275-300. Jackman, M.G.-A., W.L. Leite, and D.J. Cochrane (2011). Estimating latent variable interactions with the unconstrained approach: A comparison of methods to form product indicators for large, unequal numbers of items. Structural Equation Modeling, 18: 274-288. Wolchik, S. A., West, S. G., Westover, S., Sandler, I.N., Martin, A., Lustig, J., Tein, J., & Fisher, J. (1993). The children of divorce parenting intervention: Outcome evaluation of an empirically based program. Amer. J of Community Psychology, 21 (3), 293-331. Aiken, L.S., & West, S.G. (1991), Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA: Sage Publications.11. Using Meta-analysis with SEMHome, P. W., Caranikas-Walker, F., Prussia, G. E., & Griffieth, R. W. (1992). A meta-analytic structural equation analysis of a model of employee turnover. Journal of Applied Psychology, 77 (6), 890-909. Applying SEM to model the results of a Meta-Analysis 12. Modeling changeKessler, R.C., & Greenberg (1981). Linear panel analysis. New York: Academic Press. Chapter 6, pp. 77-80.Maassen, G.H., & Bakker, A.B. (2001). Suppressor variables in path models. Sociological Methods & Research, 30 (2), 241-270. Regarding modeling change: see pp 256- 264.#Price, R. H., Choi, J., & Vinokur, A.D. (2002). Links in the chain of adversity following job loss: How economic hardship and loss of personal control lead to depression, impaired functioning and poor health. Journal Occupational Health Psychology, 7(4), 302-312. #Russell, D. W., Kahn, J. H., Altmaier, E.M., & Spoth, R. (1998). Analyzing data from experimental studies: A latent variable structural equation modeling approach. Journal of Consulting Psychology, 5(1), 18-29. Addition to the article: SEE Slide PP16ee13. On causal inferences using SEMBollen, K. A. (1989). Structural equations with latent variables. New York: Wiley. Chapter 3, pp. 40-79. Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge: Cambridge University Press.14. Mediational analysesBaron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.Holmbeck G. N. (1997). Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: Examples from the child-clinical and pediatric psychology literatures. Journal of Consulting and Clinical Psychology, vol 65(4).Judd, C. M., & Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 5, 602-619. MacKinon, D.P., Krull, J.L., & Lockwood, C.M. (2000). Equivalence of the mediation, confounding and suppression effect. Prevention Science, 1(4), 173-182.Kenny, D. A., Kashy, D. A., & Bolger, N. (1998). Data analysis in social psychology. In D. Gilbert, S. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (Vol. 1, 4th ed., pp. 233-265). Boston, MA: McGraw-Hill.Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422-445.Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhardt (Ed.), Sociological Methodology 1982 (pp. 290-312). Washington DC: American Sociological Association.Preacher, K.J. and A.F. Hayes (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior research methods, 40(3): 879-891.Kaplan Ed, R.M., Health Psychology, 2008. 27(2S): S99-S184. Special ISSUE on mediation.MacKinnon, D.P. (2008). Introduction to statistical mediation analysis. Erlbaum Psych Press.Also, more on Sobel test see: /sobel/sobel.htm and for a test based on Bootstrap see: /medmc/medmc.htmThe following websites include abundance of information on MEDIATION andalso on several topics of SEM:David Kennys website (the first below) is most highly recommended./dakenny/kenny.htm/mkteer/semfaq.html/dakenny/mediate.htm/davidpm/ripl/mediate.htm15. Data preparation