Week 1: CONFIRMATORY FACTOR ANALYSIS for COMPARATIVE RESEARCH
Generally
In the practical exercises, we will focus exclusively on the software Mplus Version 8.4. Additionally, we provide syntaxes for all examples in R-lavaan.
In case you have your own data available, we strongly encourage you to use them and work with them over the course of the summer school. We offer time for individual consultations concerning your data and projects and we incorporate presentations of your own models and findings at the end of the course.
DAY 1
Theory and illustration: Types of models, Causality and Data-Sets
Overview of the whole course. Types of models, comparative data-set (European Social Survey) and the concept of values in the ESS. Causality and empirical research, notation, the generalized latent variable model and different types of models, theory testing, use of the Mplus manual, Mplus 8 additions, Transformation of MPLUS syntax into R-lavaan and vice versa. Discussion of the course material.
Practical exercise: CFA with one construct and four indicators
Mplus and the logic of its use, preparing and reading-in data into Mplus. Selected items and constructs: values and attitudes in the three Benelux countries. Confirmatory factor analysis (CFA) with one theoretical construct (factor): tradition and conformity in the Netherlands with four indicators.
Essential Reading: Brown (2015), chapter 3; Byrne (2012), chapters 1 and 2; Davidov & Schmidt (2007); Muthén & Muthén (2012), chapter 1 and 2; Schwartz (2007, 2012).
Additional reading: Davidov, Schmidt, & Schwartz (2008); Marsh et al. (2009).
DAY 2
Theory and illustration: CFA Model specification, estimation and model fit
Foundation of CFA: linear causal modelling, raw data as input, assumptions, types of constraints including formalization, equality constraints. Maximum likelihood estimation (ML) and robust maximum likelihood estimation (MLR). Global and Detailled Model Fit. Ordinal indicators and weighted least squares (WLSMV) estimation. List of presentations of individual projects on Day 10.
Practical exercise: CFA with two constructs and Model Fit
Modelling the values tradition and conformity in the Netherlands with four indicators, estimation and output interpretation. Estimation of 2-factor models. Comparison of factor loadings, measurement errors. Using global and detailed model fit and model modification statistics with ML and MLR.
Essential Reading: Brown (2006), chapters 4 and 7, 238-265; Byrne (2012), chapters 3, 4 and 5; Schmidt & Hermann (2011).
Additional Reading: Muthén & Muthén (2012), chapters 4 and 5; Davidov, Datler, Schmidt, & Schwartz (2011).
DAY 3
Theory and illustration: Simultaneous Confirmatory Factor Analysis, restrictions and identification, comparison of model fit.
Equality constraints. Restrictions, identification, simultaneous confirmatory factor analysis (SCFA) vs. separate confirmatory factor analysis, cross-loadings and measurement error correlations. Comparison of Model-Fit measures and cut-off Values..
Practical exercise: Test Theory models and simultaneous confirmatory factor analysis with three constructs.
CFA with tau-equivalent, parallel and strictly parallel constraints. Output interpretation and comparison of model fit, coefficients and explained variance. SCFA and its modification: Tradition/conformity, universalism and attitude toward immigration. Examination of detailed and global model fit.
Essential Reading: Brown (2015), chapters 3, 4 and 5; Byrne (2012), chapters 3 and 4; Davidov, Schmidt, & Schwartz (2008).
Additional reading: Davidov & Schmidt (2007); Marsh, Hau, & Wen (2004); Muthén & Muthén (2012), chapters 4 and 5; Saris et al. (2009); Saris & Knoppen (2009).
DAY 4
Theory and illustration: Multi-group confirmatory factor analysis (MGCFA) and measurement invariance(MI)
Multiple group confirmatory factor analysis (MGCFA). Configural, metric and scalar invariance in cross cultural research. Classical and new fit measures for testing measurement invariance. The concept of partial invariance.
Practical exercise: MGCFA and MI in the Benelux countries
MGCFA by hand. Multiple group comparisons with the bottom-up approach (MGCFA) across Benelux countries. Using the Mplus convenience feature for a simultaneous test of measurement invariance. Introduction of partial measurement invariance.
Essential Reading: Brown (2015), chapter 7; Byrne (2012), chapter 7; Ciechuch et al. (2016); Davidov (2008); Davidov et al. (2008); Steinmetz et al. (2009); Davidov et al. (2014); Davidov et al. (2015).
Additional Reading: Asparouhov & Muthen (2014); Chen (2007); Davidov & De Beuckelaer (2010); Muthén & Muthén (2012), chapter 5; Munck et al. (2016), Marsh et al. (2016), Ciecuch et al. (2017), Kotzur et al., (2019).
DAY 5
Theory and illustration: Scalar Invariance and latent means. Higher order and Bifactor Models.
Scalar invariance in cross cultural research as a prerequisite of comparing observed and latent means. Mean comparisons of items, indices and latent variables. Drawbacks of the t-test. Using the new alignment procedure in Mplus as an alternative invariance testing procedure for comparisons of many groups (fixed and random). Higher order CFA and Bifactor Models. General strategy for testing measurement models in comparative research. How to report CFA results in comparative research. Using the new alignment procedure in Mplus as an alternative invariance testing procedure for comparisons of many groups (fixed and random)
Practical exercise: CFA with latent means.
CFA with latent means, output interpretation, consolidation of the contents of the first week. Optional: Alignment optimization.
Essential Reading: Brown (2015), chapters 6, 7 and 8; Byrne (2012), chapters 5, 8 and 10; Muthén & Muthén (2012), chapter 5; Van der Schoot et al. (2013); Ciecuch et al. (2014); Davidov et al. (2015).
Additional reading: Byrne & Stewart (2006); Davidov et al. (2011); Davidov et al. (2014, 2015); Lomazzi & Seddig (2020); Reise et al. 2017; Seddig et al. (2020); Steinmetz et al. (2009); Zercher et al. (2015); Zick et al. (2008); McDonald et al. (2002).
Week 2: STRUCTURAL EQUATION MODELS
DAY 6
Theory and illustration: full SEM and Multiple Indicator Multiple causes (MIMIC) Models
Structural equation models (SEM) with latent variables and multiple indicators: specification, identification and estimation. The eight matrices that represent the model, MIMIC Models , causality and equivalent models, a typology of model testing: “The two step strategy“. Fit measures and model modification revisited. :
Practical exercise: Values and Attitudes toward Immigration
Preparation of a full SEM: demographic (control) variables, values and attitudes toward immigration. Comparison of the results for the three countries for the separate analyses with (un)constrained measurement models.
Essential Reading: Byrne (2012), chapter 6; Davidov et al. (2008b); Schmidt & Hermann (2011).
Additional Reading: Anderson & Gerbing (1988); Kline (2011), chapters 6, 7 and 8; Muthén & Muthén (2012), chapter 5.
DAY 7
Theory and illustration: model modification, interpretation of parameters, mediation
Model testing and model modification, detailed and global fit measures, interpretation of parameters, feedback models, decomposition of effects, bootstrapping for testing indirect and total causal effects, full and partial mediation.
Practical exercise: values and attitudes toward immigration
Full SEM with decomposition of effects into indirect and total effects with the Sobel test and bootstrapping, full versus partial mediation, output interpretation.
Essential Reading: Marsh et al. (2004); Muthén & Muthén (2012), chapter 5; Muthen (2012a).
Additional Reading: Paxton et al (2011); Mc Kinnon (2006); Saris et al. (2009), Bou & Satorra (2010); Muthén, Muthén, & Aspourov (2016).
DAY 8
Theory and illustration: Moderation in SEM
Multiple Group Structural equation Modeling (MGSEM) as screening procedure. Direct test of interaction effects, interaction effects/moderation on the observed, observed/latent and latent level, moderated mediation. Direct estimation of interaction effects with QML in Mplus.
Practical exercise: age as moderator of the effect of values on attitudes toward migration
SEM, moderation and direct estimation of interaction (moderation) effects in Mplus, unstandardized and standardized effects, plotting effects.
Essential Reading: Byrne (2012), chapter 9; Davidov et al. (2008b).
Additional Reading: Muthén & Muthén (2010), chapter 5; Davidov et al. (2011); Davidov et al. (2014); Ganzeboom (2009); Yang-Wallentin et al. (2006).
DAY 9
Theory and illustration:missing Values and how to report SEM results
Handling of missing values, pairwise and listwise deletion, full information maximum likelihood estimation (FIML) and multiple Imputation; how to report CFA and SEM results.
Practical exercise:
Preparation for individual presentations on Friday.
Essential Reading: Byrne (2012), chapter 12; Muthén & Muthén (2010), chapter 6 and 11.
Additional Reading: Kline (2011), 289-292 and 356-366; Schaefer & Graham (2002); Muthen & Asparouhov (2012); Kuiper et al. (2013).
DAY 10 Presentation of results of participants
Presentation of the SEM models of the participants using datasets from their projects. 15 minutes presentation, 5-10 minutes discussion for every presentation.
References
- Methodological References
– Anderson, J. C. & Gerbing, D. W. (1988) Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
– Beierlein, C. & Davidov, E. & Schwartz, S. H. & Schmidt, P. & Rammstedt, B. (2012 – erscheint): Testing the discriminant validity of Schwartz‘ Portrait Value Questionnaire items: a replication and extension of Knoppen and Saris (2009). In Survey Research Methods 6(1), 25-36.
– Bollen, K. A. (2002). Latent Variables in Psychology and the Social Sciences. Annual Review of Psychology, 53, 605-634.
– Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7(3), 461-483.
– Brown, T.A. (2015) Confirmatory factor Analysis for Applied Research. Paperback Second Edition, Guilford.
– Byrne, B. M. & Stewart, S. M. (2006) The MACS Approach to Testing for Multigroup Invariance of Second-Order Structure: A Walk Through the Process, Structural Equation Modeling 13(2), 287-321.
– Byrne, B.M. (2012). Structural Equation Modeling with Mplus. Basic Concepts, Applications and Programming.
– Chen, F. F. (2007) Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance, Structural Equation Modeling 14(3), 464-504.
– Ciecuch, J.& Davidov, E.& Schmidt, P.& Algesheimer,& S. Schwartz, S.(2014) Comparing results of an exact vs. an approximate (Bayesian) measurement invariance test: a cross country illustration with a scale to measure 19 values, Frontiers in Psychology,5, 59-68.
– Davidov, E. (2011) Nationalism and Constructive Patriotism: A Longitudinal Test of Comparability in 22 Countries with the ISSP. International Journal of Public Opinions. 23(1):, 88-103.
– Davidov, E. & De Beuckelaer, A. (2010) How Harmful are Survey Translations? – A Test with Schwartz´s Human Values Instrument, International Journal of Public Opinions Research, in Press
– Davidov, E. & Thörner, S. & Schmidt, P. & Gosen, S. & Wolf, C. (2011) Level and change of group-focused enmity in Germany: unconditional and conditional latent growth curve models with four panel waves. AStA Advances in Statistical Analysis. 95(4), 481-500.
– Davidov, E. & Datler, G. & Schmidt, P & Schwartz S. H. (2010) Testing the invariance of values in the Benelux countries with the European Social Survey: Accounting for ordinality. In: E. Davidov&P. Schmidt&J. Billiet (Eds.): Cross-Cultural Analysis: Methods and Applications (European Association of Methodology). Taylor and Francis, 2010
– Davidov,E. & Meuleman B.& Ciecuch J. & Schmidt P. & Billiet J. Equivalence in cross-national research, Annual Review of Sociology 2014.
– Davidov, E. &Ciecuch J.&Meulemann B.&Schmidt,P.& Algesheimer R.&Hausherr M.(2015) The comparability of Measurements of Attitudes toward Immigration in the European Social Survey, Public Opinion Quarterly, 79, 244-266.
– Hoogland, J. J. & Boomsma, A. (1998). Robustness studies in covariance structure modeling. An overview and a metanalysis. Sociological Methods & Research, 26(3), 329-367.
– Ganzeboom H. B. G. (2009) Multiple Indicators Models for social Background. Paper presented at European Survey Research Association, Warsaw, July 2009
– Kline,R. Structural Equation Modeling(2011), Third edition, Guilford.
– Lomazzi, V., & Seddig, D. (2020). Gender role attitudes in the International Social Survey Programme: Cross-national comparability and relationships to cultural values. Cross-Cultural Research, 54(4), 398–431.
– Marsh, H. W. & Muthén, B. & Asparouhov, T. & Lüdtke, O. & Robitzsch, A. & Morin, J. S. & Trautwein, U. (2009) Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students´ Evaluations of University Teaching. Structural Equation Modeling 16(3) 439 – 476.
– 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) Finings. Structural Equation Modeling 11(3) 320- 341.
– Marsh, H.W.(2016)
– Muller, D. & Judd, C. M. & Yzerbyt, V. Y. (2005) When Moderation Is Mediated and Mediation Is Moderated. Journal of Personality and Social Psychology. 89 (6), 852–863.
– Muthén, L. K. & Muthén, B. O. (2012) MPLUS – Statistical Analysis With Latent Variables User’s Guide. Muthén & Muthén
– Muthén, B. O. (2011) Applications of Causally Defined Direct and Indirect Effects in Mediation Analysis using SEM in Mplus. Muthén & Muthén
– Preacher, K. J. & Kelley, K. (2011) Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods. 16 (2), 93-115.
– Saris, W. E. (2001). Measurement models in sociology and political science. In Structural Equation Modeling: present and future. Robert Cudeck, Stephen Du Toit, Dag Soerbom, editors.
– Saris, W. E. & Satorra, A. & van der Veld, W. M. (2009) Testing Structural Equation Models or detection of Misspecifications? Structural Equation Modeling 16(4) 561- 582.
– Schafer, J. L. & Graham, J. W. (2002): Missing Data: Our View of the State of the Art. Psychological Methods. (7(2)) 147–177.
– Scherpenzeel, A.C. & Saris, W. E. (1997). The validity and reliability of survey questions. In Sociological Methods and Research, 25, 347-383.
– Schmidt, P. & Herrmann, J. (2011). Factor Analysis, in International Encyclopedia of Political science Methodology, Sage.
– Schmidt, P. & Herrmann, J. (2011). Structural Equation Models in : International Encyclopedia of Political science Methodology, Sage.
– Seddig, D., Maskileyson, D., & Davidov, E. (2020). The comparability of measures in the ageism module of the fourth round of the European Social Survey, 2008-2009. Survey Research Methods, 14(4), 351-364.
– Sijtsma K. (2009) On the Use, the Misuse, and the very limited Usefulness of Cronbach´s Alpha. Psychometrika 74(1) 107- 120.
– Steinmetz, H. & Schmidt P. & Tina-Booh A. & Wieczorek S. & Schwartz S. H.(2009). Testing invariance using multigroup CFA: differences between educational groups in human values measurement, Quality and Quantity, 599-616.
– Steinmetz, H. & Davidov, E. & Schmidt, P. (2011). Three Approaches to Estimate Latent Interaction Effects: Intention and Perceived Behavioral Control in the Theory of Planned Behavior. Methodological Innovations Online. 6(1), 95-110.
– Van de Schoot, R.& Kluytmans A.&,Tummers,L.& Lugtig,P.& Hox, J.&Muthen,B. Facing off Scylla and Charybdis: a comparison of scalar, partial and the novel possibility of approximate invariance, Frontiers in Psychology ,5, 173 – 187.
– Van der Schoot, R.&Schmidt,P.& Beuckelaer, A.(2015) Measurement Invariance, Special Issue in Frontiers in Psychology, 5.
– Yang-Wallentin, F., Davidov, E., Schmidt, P. &/ Bamberg, S.: Is there any interaction effect between intention and perceived behavioral control?, Methods of Psychological Research Online 2004, 8(2), 127-157.
– Zercher,F.&Schmidt,P.&Ciecuch, J.& Davidov E.(2015) The comparability of the universalism value over time and countries in the European Social Survey: exact vs. approximate invariance, Frontiers in Psychology , 6.733 doi:10.3389/fpsyg.2015.00733
- Substantive References
– Davidov, E. & Schmidt, P. (2007). Working Paper: Are values in the Benelux countries comparable? Testing for equivalence with the European Social Survey 2004-5.
– Davidov, E. & Schmidt, P. & Schwartz, S. H. (2008). Bringing values back in: The adequacy of the European Social Survey to measure values in 20 countries. Public Opinion Quarterly. 72(3). 420-445.
– Davidov, E. & Meuleman, B. & Billiet, J. & Schmidt, P. (2008b). Values and Support for Immigration: A Cross-Country Comparison. European Sociological Review – Oxford Univ Press. 24(5). 583-599.
– Knoppen.D. & Saris,W. (2009) Do we have to combine values in the Schwartz ` Human Values Scale? A Comment on the Davidov Studies. Survey Research Methods, 3, 91-103.
– Wagner, U. & Becker, J. C. & Christ, O. & Pettigrew T. F. & Schmidt, P. (2010) A Longitudinal Test of the Relation between German Nationalism, Patriotism, and Outgroup Derogation. European Sociological Review, 1-14
– Zick, A. & Wolf, C. & Küpper, B. & Davidov, E. & Schmidt, P. & Heitmeyer W. (2008). The Syndrome of Group-Focused Enmity: The Interrelation of Prejudices Tested with Multiple Cross-Sectional and Panel Data. Journal of Social Issues, 64 (2), 363-383.
- Relevant internet homepages:
–concerning the MPLUS software:http://www.statmodel.com/
–concerning the ESS data: http://ess.nsd.uib.no/
–concerning joining the SEMNET discussion group: http://www2.gsu.edu/~mkteer/semnet.html
-concerning the R lavaan package: https://lavaan.ugent.be
Week 1: CONFIRMATORY FACTOR ANALYSIS for COMPARATIVE RESEARCH
Generally
In the practical exercises, we will focus exclusively on the software Mplus Version 8.4. Additionally, we provide syntaxes for all examples in R-lavaan.
In case you have your own data available, we strongly encourage you to use them and work with them over the course of the summer school. We offer time for individual consultations concerning your data and projects and we incorporate presentations of your own models and findings at the end of the course.
DAY 1
Theory and illustration: Types of models, Causality and Data-Sets
Overview of the whole course. Types of models, comparative data-set (European Social Survey) and the concept of values in the ESS. Causality and empirical research, notation, the generalized latent variable model and different types of models, theory testing, use of the Mplus manual, Mplus 8 additions, Transformation of MPLUS syntax into R-lavaan and vice versa. Discussion of the course material.
Practical exercise: CFA with one construct and four indicators
Mplus and the logic of its use, preparing and reading-in data into Mplus. Selected items and constructs: values and attitudes in the three Benelux countries. Confirmatory factor analysis (CFA) with one theoretical construct (factor): tradition and conformity in the Netherlands with four indicators.
Essential Reading: Brown (2015), chapter 3; Byrne (2012), chapters 1 and 2; Davidov & Schmidt (2007); Muthén & Muthén (2012), chapter 1 and 2; Schwartz (2007, 2012).
Additional reading: Davidov, Schmidt, & Schwartz (2008); Marsh et al. (2009).
DAY 2
Theory and illustration: CFA Model specification, estimation and model fit
Foundation of CFA: linear causal modelling, raw data as input, assumptions, types of constraints including formalization, equality constraints. Maximum likelihood estimation (ML) and robust maximum likelihood estimation (MLR). Global and Detailled Model Fit. Ordinal indicators and weighted least squares (WLSMV) estimation. List of presentations of individual projects on Day 10.
Practical exercise: CFA with two constructs and Model Fit
Modelling the values tradition and conformity in the Netherlands with four indicators, estimation and output interpretation. Estimation of 2-factor models. Comparison of factor loadings, measurement errors. Using global and detailed model fit and model modification statistics with ML and MLR.
Essential Reading: Brown (2006), chapters 4 and 7, 238-265; Byrne (2012), chapters 3, 4 and 5; Schmidt & Hermann (2011).
Additional Reading: Muthén & Muthén (2012), chapters 4 and 5; Davidov, Datler, Schmidt, & Schwartz (2011).
DAY 3
Theory and illustration: Simultaneous Confirmatory Factor Analysis, restrictions and identification, comparison of model fit.
Equality constraints. Restrictions, identification, simultaneous confirmatory factor analysis (SCFA) vs. separate confirmatory factor analysis, cross-loadings and measurement error correlations. Comparison of Model-Fit measures and cut-off Values..
Practical exercise: Test Theory models and simultaneous confirmatory factor analysis with three constructs.
CFA with tau-equivalent, parallel and strictly parallel constraints. Output interpretation and comparison of model fit, coefficients and explained variance. SCFA and its modification: Tradition/conformity, universalism and attitude toward immigration. Examination of detailed and global model fit.
Essential Reading: Brown (2015), chapters 3, 4 and 5; Byrne (2012), chapters 3 and 4; Davidov, Schmidt, & Schwartz (2008).
Additional reading: Davidov & Schmidt (2007); Marsh, Hau, & Wen (2004); Muthén & Muthén (2012), chapters 4 and 5; Saris et al. (2009); Saris & Knoppen (2009).
DAY 4
Theory and illustration: Multi-group confirmatory factor analysis (MGCFA) and measurement invariance(MI)
Multiple group confirmatory factor analysis (MGCFA). Configural, metric and scalar invariance in cross cultural research. Classical and new fit measures for testing measurement invariance. The concept of partial invariance.
Practical exercise: MGCFA and MI in the Benelux countries
MGCFA by hand. Multiple group comparisons with the bottom-up approach (MGCFA) across Benelux countries. Using the Mplus convenience feature for a simultaneous test of measurement invariance. Introduction of partial measurement invariance.
Essential Reading: Brown (2015), chapter 7; Byrne (2012), chapter 7; Ciechuch et al. (2016); Davidov (2008); Davidov et al. (2008); Steinmetz et al. (2009); Davidov et al. (2014); Davidov et al. (2015).
Additional Reading: Asparouhov & Muthen (2014); Chen (2007); Davidov & De Beuckelaer (2010); Muthén & Muthén (2012), chapter 5; Munck et al. (2016), Marsh et al. (2016), Ciecuch et al. (2017), Kotzur et al., (2019).
DAY 5
Theory and illustration: Scalar Invariance and latent means. Higher order and Bifactor Models.
Scalar invariance in cross cultural research as a prerequisite of comparing observed and latent means. Mean comparisons of items, indices and latent variables. Drawbacks of the t-test. Using the new alignment procedure in Mplus as an alternative invariance testing procedure for comparisons of many groups (fixed and random). Higher order CFA and Bifactor Models. General strategy for testing measurement models in comparative research. How to report CFA results in comparative research. Using the new alignment procedure in Mplus as an alternative invariance testing procedure for comparisons of many groups (fixed and random)
Practical exercise: CFA with latent means.
CFA with latent means, output interpretation, consolidation of the contents of the first week. Optional: Alignment optimization.
Essential Reading: Brown (2015), chapters 6, 7 and 8; Byrne (2012), chapters 5, 8 and 10; Muthén & Muthén (2012), chapter 5; Van der Schoot et al. (2013); Ciecuch et al. (2014); Davidov et al. (2015).
Additional reading: Byrne & Stewart (2006); Davidov et al. (2011); Davidov et al. (2014, 2015); Lomazzi & Seddig (2020); Reise et al. 2017; Seddig et al. (2020); Steinmetz et al. (2009); Zercher et al. (2015); Zick et al. (2008); McDonald et al. (2002).
Week 2: STRUCTURAL EQUATION MODELS
DAY 6
Theory and illustration: full SEM and Multiple Indicator Multiple causes (MIMIC) Models
Structural equation models (SEM) with latent variables and multiple indicators: specification, identification and estimation. The eight matrices that represent the model, MIMIC Models , causality and equivalent models, a typology of model testing: “The two step strategy“. Fit measures and model modification revisited. :
Practical exercise: Values and Attitudes toward Immigration
Preparation of a full SEM: demographic (control) variables, values and attitudes toward immigration. Comparison of the results for the three countries for the separate analyses with (un)constrained measurement models.
Essential Reading: Byrne (2012), chapter 6; Davidov et al. (2008b); Schmidt & Hermann (2011).
Additional Reading: Anderson & Gerbing (1988); Kline (2011), chapters 6, 7 and 8; Muthén & Muthén (2012), chapter 5.
DAY 7
Theory and illustration: model modification, interpretation of parameters, mediation
Model testing and model modification, detailed and global fit measures, interpretation of parameters, feedback models, decomposition of effects, bootstrapping for testing indirect and total causal effects, full and partial mediation.
Practical exercise: values and attitudes toward immigration
Full SEM with decomposition of effects into indirect and total effects with the Sobel test and bootstrapping, full versus partial mediation, output interpretation.
Essential Reading: Marsh et al. (2004); Muthén & Muthén (2012), chapter 5; Muthen (2012a).
Additional Reading: Paxton et al (2011); Mc Kinnon (2006); Saris et al. (2009), Bou & Satorra (2010); Muthén, Muthén, & Aspourov (2016).
DAY 8
Theory and illustration: Moderation in SEM
Multiple Group Structural equation Modeling (MGSEM) as screening procedure. Direct test of interaction effects, interaction effects/moderation on the observed, observed/latent and latent level, moderated mediation. Direct estimation of interaction effects with QML in Mplus.
Practical exercise: age as moderator of the effect of values on attitudes toward migration
SEM, moderation and direct estimation of interaction (moderation) effects in Mplus, unstandardized and standardized effects, plotting effects.
Essential Reading: Byrne (2012), chapter 9; Davidov et al. (2008b).
Additional Reading: Muthén & Muthén (2010), chapter 5; Davidov et al. (2011); Davidov et al. (2014); Ganzeboom (2009); Yang-Wallentin et al. (2006).
DAY 9
Theory and illustration:missing Values and how to report SEM results
Handling of missing values, pairwise and listwise deletion, full information maximum likelihood estimation (FIML) and multiple Imputation; how to report CFA and SEM results.
Practical exercise:
Preparation for individual presentations on Friday.
Essential Reading: Byrne (2012), chapter 12; Muthén & Muthén (2010), chapter 6 and 11.
Additional Reading: Kline (2011), 289-292 and 356-366; Schaefer & Graham (2002); Muthen & Asparouhov (2012); Kuiper et al. (2013).
DAY 10 Presentation of results of participants
Presentation of the SEM models of the participants using datasets from their projects. 15 minutes presentation, 5-10 minutes discussion for every presentation.
References
- Methodological References
– Anderson, J. C. & Gerbing, D. W. (1988) Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
– Beierlein, C. & Davidov, E. & Schwartz, S. H. & Schmidt, P. & Rammstedt, B. (2012 – erscheint): Testing the discriminant validity of Schwartz‘ Portrait Value Questionnaire items: a replication and extension of Knoppen and Saris (2009). In Survey Research Methods 6(1), 25-36.
– Bollen, K. A. (2002). Latent Variables in Psychology and the Social Sciences. Annual Review of Psychology, 53, 605-634.
– Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7(3), 461-483.
– Brown, T.A. (2015) Confirmatory factor Analysis for Applied Research. Paperback Second Edition, Guilford.
– Byrne, B. M. & Stewart, S. M. (2006) The MACS Approach to Testing for Multigroup Invariance of Second-Order Structure: A Walk Through the Process, Structural Equation Modeling 13(2), 287-321.
– Byrne, B.M. (2012). Structural Equation Modeling with Mplus. Basic Concepts, Applications and Programming.
– Chen, F. F. (2007) Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance, Structural Equation Modeling 14(3), 464-504.
– Ciecuch, J.& Davidov, E.& Schmidt, P.& Algesheimer,& S. Schwartz, S.(2014) Comparing results of an exact vs. an approximate (Bayesian) measurement invariance test: a cross country illustration with a scale to measure 19 values, Frontiers in Psychology,5, 59-68.
– Davidov, E. (2011) Nationalism and Constructive Patriotism: A Longitudinal Test of Comparability in 22 Countries with the ISSP. International Journal of Public Opinions. 23(1):, 88-103.
– Davidov, E. & De Beuckelaer, A. (2010) How Harmful are Survey Translations? – A Test with Schwartz´s Human Values Instrument, International Journal of Public Opinions Research, in Press
– Davidov, E. & Thörner, S. & Schmidt, P. & Gosen, S. & Wolf, C. (2011) Level and change of group-focused enmity in Germany: unconditional and conditional latent growth curve models with four panel waves. AStA Advances in Statistical Analysis. 95(4), 481-500.
– Davidov, E. & Datler, G. & Schmidt, P & Schwartz S. H. (2010) Testing the invariance of values in the Benelux countries with the European Social Survey: Accounting for ordinality. In: E. Davidov&P. Schmidt&J. Billiet (Eds.): Cross-Cultural Analysis: Methods and Applications (European Association of Methodology). Taylor and Francis, 2010
– Davidov,E. & Meuleman B.& Ciecuch J. & Schmidt P. & Billiet J. Equivalence in cross-national research, Annual Review of Sociology 2014.
– Davidov, E. &Ciecuch J.&Meulemann B.&Schmidt,P.& Algesheimer R.&Hausherr M.(2015) The comparability of Measurements of Attitudes toward Immigration in the European Social Survey, Public Opinion Quarterly, 79, 244-266.
– Hoogland, J. J. & Boomsma, A. (1998). Robustness studies in covariance structure modeling. An overview and a metanalysis. Sociological Methods & Research, 26(3), 329-367.
– Ganzeboom H. B. G. (2009) Multiple Indicators Models for social Background. Paper presented at European Survey Research Association, Warsaw, July 2009
– Kline,R. Structural Equation Modeling(2011), Third edition, Guilford.
– Lomazzi, V., & Seddig, D. (2020). Gender role attitudes in the International Social Survey Programme: Cross-national comparability and relationships to cultural values. Cross-Cultural Research, 54(4), 398–431.
– Marsh, H. W. & Muthén, B. & Asparouhov, T. & Lüdtke, O. & Robitzsch, A. & Morin, J. S. & Trautwein, U. (2009) Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students´ Evaluations of University Teaching. Structural Equation Modeling 16(3) 439 – 476.
– 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) Finings. Structural Equation Modeling 11(3) 320- 341.
– Marsh, H.W.(2016)
– Muller, D. & Judd, C. M. & Yzerbyt, V. Y. (2005) When Moderation Is Mediated and Mediation Is Moderated. Journal of Personality and Social Psychology. 89 (6), 852–863.
– Muthén, L. K. & Muthén, B. O. (2012) MPLUS – Statistical Analysis With Latent Variables User’s Guide. Muthén & Muthén
– Muthén, B. O. (2011) Applications of Causally Defined Direct and Indirect Effects in Mediation Analysis using SEM in Mplus. Muthén & Muthén
– Preacher, K. J. & Kelley, K. (2011) Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods. 16 (2), 93-115.
– Saris, W. E. (2001). Measurement models in sociology and political science. In Structural Equation Modeling: present and future. Robert Cudeck, Stephen Du Toit, Dag Soerbom, editors.
– Saris, W. E. & Satorra, A. & van der Veld, W. M. (2009) Testing Structural Equation Models or detection of Misspecifications? Structural Equation Modeling 16(4) 561- 582.
– Schafer, J. L. & Graham, J. W. (2002): Missing Data: Our View of the State of the Art. Psychological Methods. (7(2)) 147–177.
– Scherpenzeel, A.C. & Saris, W. E. (1997). The validity and reliability of survey questions. In Sociological Methods and Research, 25, 347-383.
– Schmidt, P. & Herrmann, J. (2011). Factor Analysis, in International Encyclopedia of Political science Methodology, Sage.
– Schmidt, P. & Herrmann, J. (2011). Structural Equation Models in : International Encyclopedia of Political science Methodology, Sage.
– Seddig, D., Maskileyson, D., & Davidov, E. (2020). The comparability of measures in the ageism module of the fourth round of the European Social Survey, 2008-2009. Survey Research Methods, 14(4), 351-364.
– Sijtsma K. (2009) On the Use, the Misuse, and the very limited Usefulness of Cronbach´s Alpha. Psychometrika 74(1) 107- 120.
– Steinmetz, H. & Schmidt P. & Tina-Booh A. & Wieczorek S. & Schwartz S. H.(2009). Testing invariance using multigroup CFA: differences between educational groups in human values measurement, Quality and Quantity, 599-616.
– Steinmetz, H. & Davidov, E. & Schmidt, P. (2011). Three Approaches to Estimate Latent Interaction Effects: Intention and Perceived Behavioral Control in the Theory of Planned Behavior. Methodological Innovations Online. 6(1), 95-110.
– Van de Schoot, R.& Kluytmans A.&,Tummers,L.& Lugtig,P.& Hox, J.&Muthen,B. Facing off Scylla and Charybdis: a comparison of scalar, partial and the novel possibility of approximate invariance, Frontiers in Psychology ,5, 173 – 187.
– Van der Schoot, R.&Schmidt,P.& Beuckelaer, A.(2015) Measurement Invariance, Special Issue in Frontiers in Psychology, 5.
– Yang-Wallentin, F., Davidov, E., Schmidt, P. &/ Bamberg, S.: Is there any interaction effect between intention and perceived behavioral control?, Methods of Psychological Research Online 2004, 8(2), 127-157.
– Zercher,F.&Schmidt,P.&Ciecuch, J.& Davidov E.(2015) The comparability of the universalism value over time and countries in the European Social Survey: exact vs. approximate invariance, Frontiers in Psychology , 6.733 doi:10.3389/fpsyg.2015.00733
- Substantive References
– Davidov, E. & Schmidt, P. (2007). Working Paper: Are values in the Benelux countries comparable? Testing for equivalence with the European Social Survey 2004-5.
– Davidov, E. & Schmidt, P. & Schwartz, S. H. (2008). Bringing values back in: The adequacy of the European Social Survey to measure values in 20 countries. Public Opinion Quarterly. 72(3). 420-445.
– Davidov, E. & Meuleman, B. & Billiet, J. & Schmidt, P. (2008b). Values and Support for Immigration: A Cross-Country Comparison. European Sociological Review – Oxford Univ Press. 24(5). 583-599.
– Knoppen.D. & Saris,W. (2009) Do we have to combine values in the Schwartz ` Human Values Scale? A Comment on the Davidov Studies. Survey Research Methods, 3, 91-103.
– Wagner, U. & Becker, J. C. & Christ, O. & Pettigrew T. F. & Schmidt, P. (2010) A Longitudinal Test of the Relation between German Nationalism, Patriotism, and Outgroup Derogation. European Sociological Review, 1-14
– Zick, A. & Wolf, C. & Küpper, B. & Davidov, E. & Schmidt, P. & Heitmeyer W. (2008). The Syndrome of Group-Focused Enmity: The Interrelation of Prejudices Tested with Multiple Cross-Sectional and Panel Data. Journal of Social Issues, 64 (2), 363-383.
- Relevant internet homepages:
–concerning the MPLUS software:http://www.statmodel.com/
–concerning the ESS data: http://ess.nsd.uib.no/
–concerning joining the SEMNET discussion group: http://www2.gsu.edu/~mkteer/semnet.html
-concerning the R lavaan package: https://lavaan.ugent.be
