The prospects and limitations of latent variable models in educational psychology

Handbook of Educational Psychology: Third Edition

Volumn , Issue , 2015, Pages 41-58

  Nagengast, Benjamin ^a,b   Trautwein, Ulrich ^a,b  

^a UNIVERSITY OF TÜBINGEN   (Germany)

^b Graduate School on Learning Educational Achievement and Life Course Development ^*  (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84941659151     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (179)

1. Aiken, L. S., West, S. G., & Millsap, R. E. (2008). Doctoral training in statistics, measurement, and methodology in psychology: Replication and extension of Aiken, West, Sechrest, and Reno's (1990) survey of PhD programs in North America. American Psychologist, 63, 32-50.
2. Asparouhov, T., & Muthén, B. O. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16, 397-438.
3. Atkinson, J. W. (1957). Motivational determinants of risk-taking behavior. Psychological Review, 64, 359-372.
4. Bachman, J. G., Staff, J., O'Malley, P. M., Schulenberg, J. E., & Freedman-Doan, P. (2011). Twelfth-grade student work intensity linked to later educational attainment and substance use: New longitudinal evidence. Developmental Psychology, 47, 344-363.
5. Bandalos, D. L., & Finney, S. J. (2001). Item parceling issues in structural equation modeling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 269-296). Mahwah, NJ: Erlbaum.
6. Baron, 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.
7. Bauer, D. J. (2005). A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling, 12, 513-535.
8. Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models:New procedures and recommendations. Psychological Methods, 11, 142-163.
9. Becker, M., Lüdtke, O., Trautwein, U., Köller, O., & Baumert, J. (2012). The differential effects of school tracking: Do academic-track schools make students smarter? Journal of Educational Psychology, 104, 682-699.
10. Becker, M., Neumann, M., Tetzner, J., Böse, S., Knoppick, H., Maaz, K.,... Lehmann, R. (2014). Is early ability grouping good for high-achieving students' psychosocial development? Effects of the transition into academically selective schools. Journal of Educational Psychology, 106, 555-568.
11. Blanton, H., & Jaccard, J. (2006). Tests of multiplicative models in psychology:A case study using the unified theory of implicit attitudes, stereotypes, self-esteem, and self-concept. Psychological Review, 113, 155-166.
12. Bollen, K. A. (1989). Structural equations with latent variables. New York:Wiley.
13. Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111-150.
14. Brunner, M., Keller, U., Hornung, C., Reichert, M., & Martin, R. (2009). The cross-cultural generalizability of a new structural model of academic self-concepts. Learning and Individual Differences, 19, 387-403.
15. Bulotsky-Shearer, R. J., Dominguez, X., & Bell, E. R. (2012). Preschool classroom behavioral context and school readiness outcomes for low-income children: A multilevel examination of child-and classroom-level influences. Journal of Educational Psychology, 104, 421.
16. Busemeyer, J. R., & Jones, L. E. (1983). Analysis of multiplicative combination rules when the causal variables are measured with error. Psychological Bulletin, 93, 549-562.
17. Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245-276.
18. Chen, F. F. (2008). What happens if we compare chopsticks with forks? The impact of making inappropriate comparisons in cross-cultural research. Journal of Personality and Social Psychology, 95, 1005-1018.
19. Cochran, W. G., & Rubin, D. B. (1973). Controlling bias in observational studies: A review. Sankhya-A, 35, 417-446.
20. Cook, T. D., Steiner, P., & Pohl, S. (2009). How bias reduction is affected by covariate choice, unreliability, and mode of data analysis: Results from two types of within-study comparisons. Multivariate Behavioral Research, 44, 828-847.
21. Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods:A handbook for research on interactions. Oxford, UK: Irvington.
22. De Meyer, J., Tallir, I. B., Soenens, B., Vansteenkiste, M., Aelterman, N., Van den Berghe, L.,... & Haerens, L. (2014). Does observed controlling teaching behavior relate to students' motivation in physical education? Journal of Educational Psychology, 106, 541-554.
23. Dicke, T., Parker, P. D., Marsh, H. W., Kunter, M., Schmeck, A., & Leutner, D. (2014). Self-efficacy in classroom management, classroom disturbances, and emotional exhaustion: A moderated mediation analysis of teacher candidates. Journal of Educational Psychology, 106, 569-583.
24. DiStefano, C. (2002). The impact of categorization with confirmatory factor analysis. Structural Equation Modeling, 9, 327-346.
25. Dolan, C. V., Oort, F. J., Stoel, R. D., & Wicherts, J. M. (2009). Testing measurement invariance in the target rotated multigroup exploratory factor model. Structural Equation Modeling: A Multidisciplinary Journal, 16, 295-314.
26. Eccles (Parsons), J. S. (1983). Expectancies, values, and academic behaviours. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75-146). San Francisco, CA: W. H. Freeman.
27. Elliot, A. J., & McGregor, H. A. (2001). A 2×2 achievement goal framework. Journal of Personality and Social Psychology, 80, 501-519.
28. Elliot, A. J., Murayama, K., & Pekrun, R. (2011). A 3×2 achievement goal model. Journal of Educational Psychology, 103, 632.
29. Enders, C., & Tofighi, D. (2007). Centering predictor variables in crosssectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121-138.
30. Fauth, B., Decristan, J., Rieser, S., Klieme, E., & Büttner, G. (2014). Student ratings of teaching quality in primary school: Dimensions and prediction of student outcomes. Learning and Instruction, 29, 1-9.
31. Feather, N. T. (1982). Expectancy-value approaches: Present status and future directions. In N. T. Feather (Ed.), Expectations and actions:Expectancy-value models in psychology (pp. 395-420). Hillsdale, NJ:Erlbaum.
32. Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9, 466-491.
33. Foster, E. M. (2010a). The U-shaped relationship between complexity and usefulness: A commentary. Developmental Psychology, 46, 1760-1766.
34. Foster, E. M. (2010b). Causal inference and developmental psychology. Developmental Psychology, 46, 1454-1480.
35. Freedman, D. A. (1987). As others see us-a case-study in path-analysis. Journal of Educational Statistics, 12, 101-128.
36. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement:Potential of the concept, state of the evidence. Review of Educational Research, 74, 59-109.
37. Friedrich, A., Jonkmann, K., Nagengast, B., Schmitz, B., & Trautwein, U. (2013). Teachers' and students' perceptions of self-regulated learning and math competence: Differentiation and agreement. Learning and Individual Differences, 27, 26-34.
38. Fuchs, L. S., Geary, D. C., Fuchs, D., Compton, D. L., & Hamlett, C. L. (2014a). Sources of individual differences in emerging competence with numeration understanding versus multidigit calculation skill. Journal of Educational Psychology, 106, 482-498.
39. Fuchs, L. S., Schumacher, R. F., Sterba, S. K., Long, J., Namkung, J., Malone, A.,... & Changas, P. (2014b). Does working memory moderate the effects of fraction intervention? An aptitude-treatment interaction. Journal of Educational Psychology, 106, 499-514.
40. Gardiner, J. C., Luo, Z. & Roman, L. A. (2009). Fixed effects, random effects and GEE: What are the differences. Statistics and Medicine, 28, 221-239.
41. Goos, M., Van Damme, J., Onghena, P., Petry, K., & de Bilde, J. (2013). First-grade retention in the Flemish educational context: Effects on children's academic growth, psychosocial growth, and school career throughout primary education. Journal of School Psychology, 51, 323-347.
42. Grouzet, F. M., Otis, N., & Pelletier, L. G. (2006). Longitudinal crossgender factorial invariance of the Academic Motivation Scale. Structural Equation Modeling, 13, 73-98.
43. Guay, F., Morin, A. J. S., Litalien, D., Valois, P., & Vallerand, R. J. (2015). Application of exploratory structural equation modeling to evaluate the Academic Motivation Scale. Journal of Experimental Education, 83, 51-82.
44. Harackiewicz, J. M., Canning, E. A., Tibbetts, Y., Giffen, C. J., Blair, S. S., Rouse, D. I., & Hyde, J. S. (2014). Closing the social class achievement gap for first-generation students in undergraduate biology. Journal of Educational Psychology, 106, 375-389.
45. Harackiewicz, J. M., Rozek, C. S., Hulleman, C. S., & Hyde, J. S. (2012). Helping parents to motivate adolescents in mathematics and science: An experimental test of a utility-value intervention. Psychological Science, 23, 899-906.
46. Harker, R., & Tymms, P. (2004). The effects of student composition on school outcomes. School Effectiveness and School Improvement, 15, 177-199.
47. Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation. In J. J. Heckman & E. E. Leamer (Eds.), Handbook of econometrics (Vol. 6, pt. 2, pp. 4779-4874). Amsterdam, the Netherlands: North-Holland.
48. Hektner, J. M., Schmidt, J. A., & Csikszentmihalyi, M. (2006). Experience sampling method: Measuring the quality of everyday life. Thousand Oaks, CA: Sage.
49. Henson, R. K., Hull, D. M., & Williams, C. S. (2010). Methodology in our education research culture. Educational Researcher, 39, 229-240.
50. Holland, P. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945-960.
51. Holzberger, D., Philipp, A., & Kunter, M. (2014). Predicting teachers' instructional behaviors: The interplay between self-efficacy and intrinsic needs. Contemporary Educational Psychology, 39, 100-111.
52. Hong, G., & Raudenbush, S. W. (2006). Evaluating kindergarten retention policy. Journal of the American Statistical Association, 101, 901-910.
53. Hong, G., & Raudenbush, S. (2008). Causal inference for timevarying instructional treatments. Journal of Educational and Behavioral Statistics, 33, 333-362.
54. Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179-185.
55. Hughes, J., Chen, Q., Thoemmes, F., & Kwok, O. (2010). An investigation of the relationship between retention in first grade and performance on high stakes tests in 3rd grade. Educational Evaluation and Policy Analysis, 32, 166-182.
56. Hulleman, C. S., & Harackiewicz, J. M. (2009). Promoting interest and performance in high school science classes. Science, 326, 1410-1412.
57. Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15, 309-334.
58. Imbens, G., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142, 615-635.
59. Jacobs, J. E., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A. (2002). Changes in children's self-competence and values: Gender and domain differences across grades one through twelve. Child Development, 73, 509-527.
60. Jak, S., Oort, F. J., & Dolan, C. V. (2013). A test for cluster bias: Detecting violations of measurement invariance across clusters in multilevel data. Structural Equation Modeling, 20, 265-282.
61. Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183-202.
62. Jöreskog, K. G. (1970). A general method for analysis of covariance structures. Biometrika, 57, 239-251.
63. Jöreskog, K. G., & Yang, F. (1996). Nonlinear structural equation models:The Kenny-Judd model with interaction effects. In G. Markoulides & R. Schumacker (Eds.), Advanced structural equation modeling (pp. 57-87). Mahwah, NJ: Erlbaum.
64. Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141-151.
65. Kelava, A., & Brandt, H. (2009). Estimation of nonlinear latent structural equation models using the extended unconstrained approach. Review of Psychology, 16, 123-131.
66. Kelava, A., & Nagengast, B. (2012). A bayesian model for the estimation of latent interaction and quadratic effects when latent variables are nonnormally distributed. Multivariate Behavioral Research, 47, 717-742.
67. Kelava, A., Nagengast, B., & Brandt, H. (2014). A nonlinear structural equation mixture modeling approach for non-normally distributed latent predictor variables. Structural Equation Modeling, 21, 468-481.
68. Kelava, A., Werner, C. S., Schermelleh-Engel, K., Moosbrugger, H., Zapf, D., Ma, Y., Cham, H., Aiken, L., & West, S. G. (2011). Advanced nonlinear latent variable modeling: Distribution analytic LMS and QML estimators of interaction and quadratic effects. Structural Equation Modeling: A Multidisciplinary Journal, 18, 465-491.
69. Kenny, D. A., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96, 201-210.
70. Klein, A. G., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65, 457-474.
71. Klein, A. G., & Muthén, B. O. (2007). Quasi maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects. Multivariate Behavioral Research, 42, 647-674.
72. Kunter, M., Klusmann, U., Baumert, J., Richter, D., Voss, T., & Hachfeld, A. (2013). Professional competence of teachers: Effects on instructional quality and student development. Journal of Educational Psychology, 105, 805-820.
73. Lee, V., & Bryk, A. (1986). Effects of single-sex secondary schools on student achievement and attitudes. Journal of Educational Psychology, 78, 381-395.
74. Liem, G. A. D., Martin, A. J., Anderson, M., Gibson, R., & Sudmalis, D. (2014). The role of arts-related information and communication technology use in problem solving and achievement: findings from the programme for international student assessment. Journal of Educational Psychology, 106, 348-363.
75. Little, T. D., Rhemtulla, M., Gibson, K., & Schoemann, A. M. (2013). Why the items versus parcels controversy needn't be one. Psychological Methods, 18, 285-300.
76. Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Oxford, UK: Addison-Wesley.
77. Lüdtke, O., Marsh, H. W., Robitzsch, A., & Trautwein, U. (2011). A 2x2 taxonomy of multilevel latent contextual models: Accuracy and bias tradeoffs in full and partial error-correction models. Psychological Methods, 16, 444-467.
78. Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U., Asparouhov, T., & Muthén, B. (2008). The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 13, 203-229.
79. MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York: Erlbaum.
80. Mainhard, M. T., Brekelmans, M., & Wubbels, T. (2011). Coercive and supportive teacher behaviour: Within-and across-lesson associations with the classroom social climate. Learning and Instruction, 21, 345-354.
81. Markus, K. A., & Borsboom, D. (2013). Frontiers of test validity theory:Measurement, causation, and meaning. New York: Routledge.
82. Marsh, H. W. (1989). Effects of attending single-sex and coeducational high schools on achievement, attitudes, behaviors, and sex differences. Journal of Educational Psychology, 81, 70-85.
83. Marsh, H. W., Abduljabbar, A. S., Parker, P. D., Morin, A. J. S., Abdelfattah, F., & Nagengast, B. (2014). The big-fish-little-pond effect in mathematics:A cross-cultural comparison of U.S. and Saudi Arabian TIMSS responses. Journal of Cross-Cultural Psychology, 45, 777-804.
84. Marsh, H. W., & Hau, K. (2003). Big-fish-little-pond effect on academic self-concept. A cross-cultural (26-country) test of the negative effects of academically selective schools. American Psychologist, 58, 364-376.
85. Marsh, H. W., & Hau, K.-T. (2007). Applications of latent-variable models in educational psychology: The need for methodological-substantive synergies. Contemporary Educational Psychology, 32, 151-170.
86. Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First-and higher order factor models and their invariance across groups. Psychological Bulletin, 97, 562-582.
87. Marsh, H. W., Liem, G. A. D., Martin, A. J., Morin, A. J. S., & Nagengast, B. (2011). Methodological measurement fruitfulness of exploratory structural equation modeling (ESEM): New approaches to key substantive issues in motivation and engagement. Journal of Psychoeducational Assessment, 29, 322-346.
88. Marsh, H. W., Lüdtke, O., Muthén, B., Asparouhov, T., Morin, A. J. S., Trautwein, U., & Nagengast, B. (2010). A new look at the Big Five factor structure through exploratory structural equation modeling. Psychological Assessment, 22, 471-491.
89. Marsh, H. W., Lüdtke, O., Nagengast, B., Trautwein, U., Morin, A. J. S., Abduljabbar, A. S. & Köller, O. (2012a). Classroom climate and contextual effects: Conceptual and methodological issues in the evaluation of group-level effects. Educational Psychologist, 47(2), 106-124.
90. Marsh, H. W., Lüdtke, O., Nagengast, B., Morin, A. J. S., & Davier, Von, M. (2013). Why item parcels are (almost) never appropriate: Two wrongs do not make a right-camouflaging misspecification with item parcels in CFA models. Psychological Methods, 18, 257-284.
91. Marsh, H. W., Muthén, B. O., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A. J. S., & Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students' evaluations of university teaching. Structural Equation Modeling, 16, 439-476.
92. Marsh, H. W., Nagengast, B., Morin, A. J. S., Parada, R. H., Craven, R. G., & Hamilton, L. R. (2011b). Construct validity of the multidimensional structure of bullying and victimization: An application of exploratory structural equation modeling. Journal of Educational Psychology, 103, 701-732.
93. Marsh, H.W., & Shavelson, R. (1985). Self-concept: Its multifaceted, hierarchical structure. Educational Psychologist, 20, 107-125.
94. 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.
95. Marsh, H.W., Wen, Z., Hau, K.-T., & Nagengast, B. (2013). Structural equation models of latent interaction and quadratic effects. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling. A second course (2nd ed., pp. 395-438). Greenwich, CT: Information Age.
96. Marsh, H.W., Wen, Z., Nagengast, B., & Hau, K.-T. (2012b). Structural equation models of latent interactions. In R.D. Hoyle (Ed.), Handbook of structural equation modeling (pp. 436-458). New York: Guilford Press.
97. Martin, A. J. (2007). Examining a multidimensional model of student motivation and engagement using a construct validation approach. British Journal of Educational Psychology, 77, 413-440.
98. Martin, A. J. (2011). Prescriptive statements and educational practice: What can structural equation modeling (SEM) offer? Educational Psychology Review, 23, 235-244.
99. Matsueda, R. L. (2012). Key advances in the history of structural equation modeling. In R. D. Hoyle (Ed.), Handbook of structural equation modeling (pp. 17-42). New York: Guilford Press.
100. Mayer, A., Nagengast, B., Fletcher, J., & Steyer, R. (2014). Analyzing average and conditional effects with multigroup multilevel structural equation models. Frontiers in Psychology, 5, 304.
101. Meece, J. L., Wigfield, A., & Eccles, J. S. (1990). Predictors of math anxiety and its influence on young adolescents course enrolment intentions and performance in mathematics. Journal of Educational Psychology, 82, 60-70.
102. Mehta, P. D., & Neale, M. C. (2005). People are variables too: Multilevel structural equations modeling. Psychological Methods, 10, 259-284.
103. Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525-543.
104. Michell, J. (1997). Quantitative science and the definition of measurement in psychology. British Journal of Psychology, 88, 355-383.
105. Midgley, C., Kaplan, A., Middleton, M., Maehr, M. L., Urdan, T., Anderman, L. H., et al. (1998). The development and validation of scales assessing students' achievement goal orientations. Contemporary Educational Psychology, 23, 113-131.
106. Millsap, R.E. (2011). Statistical approaches to measurement invariance. New York: Routledge.
107. Monahan, K. C., Lee, J. M., & Steinberg, L. (2011). Revisiting the impact of part-time work on adolescent adjustment: Distinguishing between selection and socialization using propensity score matching. Child Development, 82, 96-112.
108. Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference:Methods and principles for social research. Cambridge: Cambridge University Press.
109. Morin, A.J.S., Marsh, H.W., & Nagengast, B. (2013). Exploratory structural equation modeling: An introduction. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course, 2nd ed. Greenwich, CT: IAP.
110. Murayama, K., & Elliot, A. (2009). The joint influence of personal achievement goals and classroom goal structures on achievement-relevant outcomes. Journal of Educational Psychology, 101, 432-447.
111. Muthén, B. O. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29, 81-117.
112. Muthén, B. O. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus. Retrieved from http://www.statmodel.com/papers.shtml (accessed February 18, 2015).
113. Muthén, B. O., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17, 313-335.
114. Muthén, B. O., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189.
115. Muthén, B. O., & Kaplan, D. (1992). A comparison of some methodologies for the factor analysis of non-normal Likert variables: A note on the size of the model. British Journal of Mathematical and Statistical Psychology, 45, 19-30.
116. Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user's guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
117. Muthén, B. O., & Satorra, A. (1995). Complex sample data in structural equation modeling. Sociological Methodology, 25, 267-316.
118. Muthén, B. O., du Toit, S. H. C., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. Retrieved from http://www.gseis.ucla.edu/faculty/muthen/articles/Article_075.pdf (accessed February 18, 2015).
119. Nagengast, B. (2009). Causal inference in multilevel designs. Unpublished doctoral dissertation. Jena: Friedrich-Schiller-Universität, Faculty of Social and Behavioral Sciences.
120. Nagengast, B., & Marsh, H. W. (2012). Big fish in little ponds aspire more:Mediation and cross-cultural generalizability of school-average ability effects on self-concept and career aspirations in science. Journal of Educational Psychology, 104, 1033-1053.
121. Nagengast, B., & Marsh, H.W. (2013). Motivation and engagement in science around the globe: Testing measurement invariance with multigroup structural equation models across 57 countries using PISA 2006. In L. Rutkowski, M. von Davier, & D. Rutkowski (Eds.), Analysis of international large-scale assessment data. New York: Taylor & Francis.
122. Nagengast, B., Marsh, H.W., Chiorri, C., & Hau, K.-T. (2014). Character building or subversive consequences of employment during high school:Causal effects based on propensity score models for categorical treatments. Journal of Educational Psychology, 106, 584-603.
123. Nagengast, B., Marsh, H. W., Scalas, L. F., Xu, M. K., Hau, K.-T., & Trautwein, U. (2011). Who took the "x" out of expectancy-value theory? A psychological mystery, a substantive-methodological synergy, and a cross-national generalization. Psychological Science, 22, 1058-1066.
124. Neyman, J. (1923/1990). On the application of probability theory to agricultural experiments. Essay on principles. Statistical Science, 5, 465-480.
125. Olsson, U. (1979). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44, 443-460.
126. Orkibi, H., Ronen, T., & Assoulin, N. (2014). The subjective well-being of Israeli adolescents attending specialized school classes. Journal of Educational Psychology, 106, 515-526.
127. Pearl, J. (2012). The mediation formula: A guide to the assessment of causal pathways in non-linear models. Prevention Science, 13, 426-436.
128. Pekrun, R. (2006). The control-value theory of achievement emotions:Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315-341.
129. Pituch, K. A., & Stapleton, L. M. (2011). Hierarchical linear and structural equation modeling approaches to mediation analysis in randomized field experiments. In M. Williams & P. Vogt (Eds.), The SAGE handbook of innovation in social research methods (pp. 590-619). Thousand Oaks, CA: SAGE.
130. Pituch, K. A., & Stapleton, L. M. (2012). Distinguishing between crossand cluster-level mediation processes in the cluster randomized trial. Sociological Methods & Research, 41, 630-670.
131. Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2011). Alternative methods for assessing mediation in multilevel data: the advantages of multilevel SEM. Structural Equation Modeling, 18, 161-182.
132. Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15, 209-233.
133. Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel structural equation modelling. Psychometrika, 69, 167-190.
134. Rabe-Hesketh, S., Skrondal, A. & Zheng, X. (2012). Multilevel structural equation modeling. In R. D. Hoyle (Ed.), Handbook of structural equation modeling (pp. 512-531). New York: Guilford Press.
135. Raykov, T. (2012). Propensity score analysis with fallible covariates: A note on a latent variable modeling approach. Educational and Psychological Measurement, 72, 715-733.
136. Reckase, M. D. (1997). The past and future of multidimensional item response theory. Applied Psychological Measurement, 21, 25-36.
137. Reeve, J., & Lee, W. (2014). Students' classroom engagement produces longitudinal changes in classroom motivation. Journal of Educational Psychology, 106, 527-540.
138. Reinhart, A. L., Haring, S. H., Levin, J. R., Patall, E. A., & Robinson, D. H. (2013). Models of not-so-good behavior: Yet another way to squeeze causality and recommendations for practice out of correlational data. Journal of Educational Psychology, 105, 241-247.
139. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41.
140. Rubin, D. B. (1973). The use of matching and regression adjustment to remove bias in observational studies. Biometrics, 29, 185-203.
141. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology, 66, 688-701.
142. Rubin, D. B. (1977). Assignment to treatment group on the basis of a covariate. Journal of Educational Statistics, 2, 1-26.
143. Rubin, D. B. (1978). Bayesian-inference for causal effects: The role of randomization. Annals of Statistics, 6, 34-58.
144. Rubin, D. B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services & Outcomes Research Methodology, 2, 169-188.
145. Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322-331.
146. Rubin, D. B. (2008). For objective causal inference, design trumps analysis. The Annals of Applied Statistics, 2, 808-840.
147. Ryu, E., & West, S. G. (2009). Level-specific evaluation of model fit in multilevel structural equation modeling. Structural Equation Modeling, 16, 583-601.
148. Schafer, J. L., & Kang, J. (2008). Average causal effects from nonrandomized studies: A practical guide and simulated example. Psychological Methods, 13, 279-313.
149. Schweig, J. (2014). Cross-level measurement invariance in school and classroom environment surveys. Educational Evaluation and Policy Analysis, 36, 259-280.
150. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental design for generalized causal inference. Boston:Houghton Mifflin.
151. Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling:Multilevel, longitudinal and structural equation models. Boca Raton, FL: Chapman & Hall/CRC.
152. Snijders, T.A.B., & Bosker, R.J. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Thousand Oaks, CA: Sage.
153. Sobel, M. (2008). Identification of causal parameters in randomized studies with mediating variables. Journal of Educational and Behavioral Statistics, 33, 230-251.
154. Sörbom, D. (1974). A general method for studying differences in factor means and factor structure between groups. British Journal of Mathematical and Statistical Psychology, 27, 229-239.
155. Steyer, R., & Eid, M. (2001). Messen und Testen (2. Aufl.). Berlin: Springer.
156. Steyer, R., Gabler, S., von Davier, A. A., Nachtigall, C., & Buhl, T. (2000). Causal regression models I: Individual and average causal effects. Methods of Psychological Research Online, 5, 39-71.
157. Steyer, R., Mayer, A. & Fiege, C. (in press). Causal inference on total, direct, and indirect effects. In A. C. Michalos (Ed.), Encyclopedia of quality of life and well-being research. New York: Springer.
158. Stieff, M., Dixon, B. L., Ryu, M., Kumi, B. C., & Hegarty, M. (2014). Strategy training eliminates sex differences in spatial problem solving in a STEM domain. Journal of Educational Psychology, 106, 390-402.
159. Stuart, E. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25, 1-21.
160. Subramanian, S. V., & O'Malley, A. J. (2010). Modeling neighborhood effects: The futility of comparing mixed and marginal approaches. Epidemiology, 21, 475-478.
161. Thoemmes, F., & Kim, E. S. (2011). A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research, 46, 90-118.
162. Thurstone, L. L. (1947). Multiple factor analysis. Chicago, IL: University of Chicago.
163. Tolman, E. C. (1938). The determiners of behavior at a choice point. Psychological Review, 45, 1-35.
164. Tolman, E. C. (1955). Principles of performance. Psychological Review, 62, 315-326.
165. Trautwein, U., & Baeriswyl, F. (2007). Wenn leistungsstarke Klassenkameraden ein Nachteil sind: Referenzgruppeneffekte bei Übergangsentscheidungen [When high-achieving classmates put students at a disadvantage: Reference group effects at the transition to secondary schooling]. Zeitschrift für Pädagogische Psychologie, 21, 119-133.
166. Trautwein, U., Marsh, H. W., Nagengast, B., Lüdtke, O., Nagy, G., & Jonkmann, K. (2012). Probing for the multiplicative term in modern expectancy-value theory: A latent interaction modeling study. Journal of Educational Psychology, 104, 763-777.
167. Valeri, L., & VanderWeele, T. J. (2013). Mediation analysis allowing for exposure-mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods, 18, 137-150.
168. Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4-70.
169. van der Linden, W. J., & Hambleton, R. K. (Eds.). (1997). Handbook of modern item response theory. New York: Springer.
170. VanderWeele, T. J. (2010). Direct and indirect effects for neighbourhood-based clustered and longitudinal data. Sociological Methods & Research, 38, 515-544.
171. VanderWeele, T. J., & Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions and composition. Statistics and its Interface, 2, 457-468.
172. Waasdorp, T. E., Bradshaw, C. P., & Duong, J. (2011). The link between parents' perceptions of the school and their responses to school bullying:Variation by child characteristics and the forms of victimization. Journal of Educational Psychology, 103, 324-335.
173. Wagner, W., Göllner, R., Helmke, A., Trautwein, U., & Lüdtke, O. (2013). Construct validity of student perceptions of instructional quality is high, but not perfect: Dimensionality and generalizability of domainindependent assessments. Learning and Instruction, 28, 1-11.
174. Wang, M.-T., Willett, J. B., & Eccles, J. S. (2011). The assessment of school engagement: Examining dimensionality and measurement invariance by gender and race/ethnicity. Journal of School Psychology, 49, 465-480.
175. Watt, H. M., Shapka, J. D., Morris, Z. A., Durik, A. M., Keating, D. P., & Eccles, J. S. (2012). Gendered motivational processes affecting high school mathematics participation, educational aspirations, and career plans: a comparison of samples from Australia, Canada, and the United States. Developmental Psychology, 48, 1594-1611.
176. What Works Clearinghouse (2008). Evidence standards for reviewing studies (version 1.0). Washington, DC: What Works Clearinghouse.
177. Widaman, K.F., & Reise, S.P. (1997). Exploring the measurement invariance of psychological instruments: Applications in the substance use domain. In K. J. Bryant, M. Windle, & S. G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281-324). Washington, DC: American Psychological Association.
178. Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68-81.
179. Wirth, R. J., & Edwards, M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12, 58-79.