REFERENCES
- Aiken, L.S., & West, S.G. (1991). Multiple regression: Testing and interpreting interactions. London, UK: Sage.
- Algina, J., & Moulder, B.C. (2001). A note on estimating the Jöreskog-Yang model for latent variable interaction using LISREL 8.3. Structural Equation Modeling: A Multidisciplinary Journal, 8(1), 40–52. http://dx.doi.org/10.1207/S15328007SEM0801_3
- Bollen, K.A., & Paxton, P. (1998). Interactions of latent variables in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 5(3), 267–293. http://dx.doi.org/10.1080/10705519809540105
- Boomgaarden, H.G., Schuck, A.R., Elenbaas, M., & de Vreese, C.H. (2011). Mapping EU attitudes: Conceptual and empirical dimensions of Euroscepticism and EU support. European Union Politics, 12(2), 241–266. http://dx.doi.org/10.1177/1465116510395411
- Brandt, H., Kelava, A., & Klein, A. (2014). A simulation study comparing recent approaches for the estimation of nonlinear effects in SEM under the condition of nonnormality. Structural Equation Modeling: A Multidisciplinary Journal, 21(2), 181–195. http://dx.doi.org/10.1080/10705511.2014.882660
- Busemeyer, J.R., & Jones, L.E. (1983). Analysis of multiplicative combination rules when the causal variables are measured with error. Psychological Bulletin, 93(3), 549–562. http://dx.doi.org/10.1037/0033-2909.93.3.549
- Cham, H., West, S.G., Ma, Y., & Aiken, L.S. (2012). Estimating latent variable interactions with nonnormal observed data: A comparison of four approaches. Multivariate Behavioral Research, 47(6), 840–876. http://dx.doi.org/10.1080/00273171.2012.732901
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
- Cortina, J.M. (1993). Interaction, nonlinearity, and multicollinearity: Implications for multiple regression. Journal of Management, 19(4), 915–922. http://dx.doi.org/10.1177/014920639301900411
- De Vreese, C.H., & Boomgaarden, H.G. (2005). Projecting EU referendums fear of immigration and support for European integration. European Union Politics, 6(1), 59–82. http://dx.doi.org/10.1177/1465116505049608
- Diestel, S., & Schmidt, K.H. (2009). Mediator and moderator effects of demands on self-control in the relationship between work load and indicators of job strain. Work & Stress, 23(1), 60–79. http://dx.doi.org/10.1080/02678370902846686
- Dietrich, J., & Kracke, B. (2009). Career-specific parental behaviors in adolescents' development. Journal of Vocational Behavior, 75(2), 109–119. http://dx.doi.org/10.1016/j.jvb.2009.03.005
- Dimitruk, P., Schermelleh-Engel, K., Kelava, A., & Moosbrugger, H. (2007). Challenges in nonlinear structural equation modeling. Methodology, 3(3), 100–114. http://dx.doi.org/10.1027/1614-2241.3.3.100
- Eisenberger, R., Karagonlar, G., Stinglhamber, F., Neves, P., Becker, T.E., Gonzalez-Morales, M.G., & Steiger-Mueller, M. (2010). Leader–member exchange and affective organizational commitment: The contribution of supervisor's organizational embodiment. Journal of Applied Psychology, 95(6), 1085. http://dx.doi.org/10.1037/a0020858
- European Values Study. (2011). European values study 2008: Integrated dataset (EVS 2008). GESIS Data Archive, Cologne. ZA4800 Data file version 3.0.0. http://dx.doi.org/10.4232/1.11004
- Foldnes, N., & Hagtvet, K.A. (2014). The choice of product indicators in latent variable interaction models: Post hoc analyses. Psychological Methods, 19(3), 444–457. http://dx.doi.org/10.1037/a0035728
- Forero, C.G., & Maydeu-Olivares, A. (2009). Estimation of IRT graded response models: Limited versus full information methods. Psychological Methods, 14(3), 275–299. http://dx.doi.org/10.1037/a0015825
- Gagne, P., & Hancock, G.R. (2006). Measurement model quality, sample size, and solution propriety in confirmatory factor models. Multivariate Behavioral Research, 41(1), 65–83. http://dx.doi.org/10.1207/s15327906mbr4101_5
- Ganzach, Y. (1997). Misleading interaction and curvilinear terms. Psychological Methods, 2(3), 235–247. http://dx.doi.org/10.1037/1082-989X.2.3.235
- Grewal, R., Cote, J.A., & Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing Science, 23(4), 519–529. http://dx.doi.org/10.1287/mksc.1040.0070
- Harring, J.R., Weiss, B.A., & Hsu, J.C. (2012). A comparison of methods for estimating quadratic effects in nonlinear structural equation models. Psychological Methods, 17(2), 193–214. http://dx.doi.org/10.1037/a0027539
- Henson, R.K., & Roberts, J.K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393–416. http://dx.doi.org/10.1177/0013164405282485
- Hooghe, L., & Marks, G. (2004). Does identity or economic rationality drive public opinion on European integration? Political Science and Politics, 37(3), 415–420. http://dx.doi.org/10.1017.S1049096504004585
- Hoogland, J.J., & Boomsma, A. (1998). Robustness studies in covariance structural modeling: An overview and a meta-analysis. Sociological Methods & Research, 26(3), 329–367. http://dx.doi.org/10.1177/0049124198026003003
- 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(2), 348–357. http://dx.doi.org/10.1037/0033-2909.117.2.348
- Jackman, M.G. A., Leite, W.L., & Cochrane, D. J. (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: A Multidisciplinary Journal, 18(2), 274–288. http://dx.doi.org/10.1080/10705511.2011.557342
- Jöreskog, K.G., & Yang, F. (1996). Nonlinear structural equation models: The Kenny-Judd model with interaction effects. In G. Marcoulides & R. Schumacker (Eds.), Advanced structural equation modeling (pp. 57–88). Hillsdale, NJ: Erlbaum.
- Jöreskog, K.G., & Sörbom, D. (2006). LISREL 8.8 for windows [Computer software]. Skokie, IL: Scientific Software International.
- Kelava, A., & Brandt, H. (2009). Estimation of nonlinear latent structural equation models using the extended unconstrained approach. Review of Psychology, 16(2), 123–131.
- Kelava, A., & Nagengast, B. (2012). A Bayesian model for the estimation of latent interaction and quadratic effects when latent variables are non-normally distributed. Multivariate Behavioral Research, 47(5), 717–742. http://dx.doi.org/10.1080/00273171.2012.715560
- Kelava, A., Moosbrugger, H., Dimitruk, P., & Schermelleh-Engel, K. (2008). Multicollinearity and missing constraints: A comparison of three approaches for the analysis of latent nonlinear effects. Methodology, 4(2), 51–66. http://dx.doi.org/10.1027/1614-2241.4.2.51
- Kelava, A., Nagengast, B., & Brandt, H. (2014). A nonlinear structural equation mixture modeling approach for nonnormally distributed latent predictor variables. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 468–481. http://dx.doi.org/10.1080/10705511.2014.915379
- Kelava, A., Werner, C.S., Schermelleh-Engel, K., Moosbrugger, H., Zapf, D., & 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(3), 465–491. http://dx.doi.org/10.1080/10705511.2011.582408
- Kenny, D.A., & Judd, C.M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96(1), 201–210. http://dx.doi.org/10.1037/0033-2909.96.1.201
- Klein, A G., Schermelleh-Engel, K., Moosbrugger, H., & Kelava, A. (2009). Assessing spurious interactions effects. In T. Teo & M.S. Khine (Eds.) Structural equation modeling in educational research: Concepts and applications (pp. 13–28). Rotterdam, The Netherlands: Sense.
- 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. http://dx.doi.org/10.1080/00273170701710205
- Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65(4), 457–474. http://dx.doi.org/10.1007/BF02296338
- Klein, A.G., & Stoolmiller, M. (2003). Detecting latent interaction effects in behavioral data. Methods of Psychological Research Online, 8(2), 113–126.
- Little, T.D., Bovaird, J.A., & Widaman, K.F. (2006). On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 13(4), 497–519. http://dx.doi.org/10.1207/s15328007sem1304_1
- Lubinski, D., & Humphreys, L.G. (1990). Assessing spurious“ moderator effects”: Illustrated substantively with the hypothesized (“synergistic”) relation between spatial and mathematical ability. Psychological Bulletin, 107(3), 385–393. http://dx.doi.org/10.1037/0033-2909.107.3.385
- MacCallum, R.C., & Mar, C.M. (1995). Distinguishing between moderator and quadratic effects in multiple regression. Psychological Bulletin, 118(3), 405–421. http://dx.doi.org/http://dx.doi.org/10.1037/0033-2909.118.3.405
- MacCallum, R.C., Widaman, K.F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4, 84–99. http://dx.doi.org/10.1037/1082-989X.4.1.84
- Markon, K.E. (2010). How things fall apart: Understanding the nature of internalizing through its relationship with impairment. Journal of Abnormal Psychology, 119(3), 447–458. http://dx.doi.org/10.1037/a0019707
- Marsh, H.W., Hau, K.T., 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. http://dx.doi.org/10.1207/s15327906mbr3302_1
- 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. http://dx.doi.org/10.1037/1082-989X.9.3.275
- Marsh, H.W., Wen, Z., & Hau, K.T. (2006). Structural equation models of latent interaction and quadratic effects. In G.R. Hancock & R.O. Mueller (Eds.), Structural equation modeling: A second course (pp. 225–265). Greenwich, CT: Information Age.
- Marsh, H.W., Wen, Z., Hau, K.T., Little, T.D., Bovaird, J.A., & Widaman, K.F. (2007). Unconstrained structural equation models of latent interactions: Contrasting residual-and mean-centered approaches. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 570–580. http://dx.doi.org/10.1080/10705510701303921
- McLaren, L. (2007). Explaining mass-level Euroscepticism: Identity, interests, and institutional distrust. Acta Politica, 42(2), 233–251. http://dx.doi.org/10.1057/palgrave.ap.5500191
- Moosbrugger, H., Schermelleh-Engel, K., & Klein, A. (1997). Methodological problems of estimating latent interaction effects. Methods of Psychological Research Online, 2(2), 95–111.
- Moosbrugger, H., Schermelleh-Engel, K., Kelava, A., & Klein, A.G. (2009). Testing multiple nonlinear effects in structural equation modeling: A comparison of alternative estimation approaches. In T. Teo & M.S. Khine (Eds.), Structural equation modeling in educational research: Concepts and applications. 103–136. Rotterdam, The Netherlands: Sense.
- Moulder, B.C., & Algina, J. (2002). Comparison of methods for estimating and testing latent variable interactions. Structural Equation Modeling: A Multidisciplinary Journal, 9(1), 1–19. http://dx.doi.org/10.1207/S15328007SEM0901_1
- 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: A Multidisciplinary Journal, 9599–620. http://dx.doi.org/10.1207/S15328007SEM0904_8
- Muthén, L.K., & Muthén, B.O. (1998–2011). Mplus user's guide (6th ed.). Los Angeles, CA: Muthén & Muthén.
- Peterson, R.A. (2000). A meta-analysis of variance accounted for and factor loadings in exploratory factor analysis. Marketing Letters, 11261–275. http://dx.doi.org/10.1023/A:1008191211004
- Ping, R. Jr. (1995). A parsimonious estimating technique for interaction and quadratic latent variables. Journal of Marketing Research, 32(3), 336–337. http://dx.doi.org/10.2307/3151985
- Ping, R., Jr. (1996). Latent variable interaction and quadratic effect estimation: A two-step technique using structural equation analysis. Psychological Bulletin, 119(1), 166–175. http://dx.doi.org/10.1037/0033-2909.119.1.166
- R Development Core Team (2012). R: A language and environment for statistical computing version 2.15.2. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/.
- Rigdon, E., Schumacker, R., & Wothke, W. (1998). A comparative review of interaction and nonlinear modeling. In R. Schumacker & G. Marcoulides (Eds.). Interaction and nonlinear effects in structural equation modeling (pp. 1–16). New York, NY: Lawrence Erlbaum.
- Satorra, A., & Bentler, P.M. (1988). Scaling corrections for Chi-square statistics in covariance structure analysis. ASA 1988 Proceedings of the Business and Economic Statistics Section of the American Statistical Association, 308–313. American Statistical Association, Alexandria, VA.
- Schermelleh-Engel, K., Moosbrugger, H., Kelava, A., & Dimitruk, P. (2006, July). Should all nonlinear effects in structural equation models be always analyzed simultaneously? Paper presented at the Second Congress of the European Association of Methodology (EAM), Budapest, Hungary.
- Takeuchi, R., Wang, M., Marinova, S.V., & Yao, X. (2009). Role of domain-specific facets of perceived organizational support during expatriation and implications for performance. Organization Science, 20(3), 621–634. http://dx.doi.org/10.1287/orsc.1080.0403
- Tucker, J.A., Pacek, A.C., & Berinsky, A.J. (2002). Transitional winners and losers: Attitudes toward EU membership in post-communist countries. American Journal of Political Science, 557–571. http://dx.doi.org/10.2307/3088399
- Wall, M.M., & Amemiya, Y. (2001). Generalized appended product indicator procedure for nonlinear structural equation analysis. Journal of Educational and Behavioral Statistics, 26(1), 1–29. http://dx.doi.org/10.3102/10769986026001001
- Wen, Z., Marsh, H.W., & Hau, K.T. (2010). Structural equation models of latent interactions: An appropriate standardized solution and its scale-free properties. Structural Equation Modeling: A Multidisciplinary Journal, 17(1), 1–22. http://dx.doi.org/10.1080/10705510903438872
- Wu, Y., Wen, Z., Marsh, H.W., & Hau, K.T. (2013). A comparison of strategies for forming product indicators for unequal numbers of items in structural equation models of latent interactions. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 551–567. http://dx.doi.org/10.1080/10705511.2013.824772