Interaction effects between explanatory constructs are an important part of many social theories. Analyses of interaction effects between variables using regression techniques have low power because they do not control for measurement errors. Therefore, latent interaction modeling using structural equation modeling (SEM) has been proposed as a better alternative to test for interaction effects. In contrast to traditional and complicated ‘constrained’ SEM approaches, two recent developments, the unconstrained approach and the residual centering approach, are especially attractive for applied researchers as they are much easier to implement. However, applied researchers still seem to be unsure about how to apply these approaches. In this study, we illustrate the use of the unconstrained and the residual centering approach and compare these approaches with the constrained approach of Algina and Moulder (2001) using data from a field study of 1,442 students. Theoretical background is the theory of planned behavior (Ajzen, 1991) in which we test the proposed interaction between an individual's intention to perform a behavior and perceived behavioral control (PBC) on behavior. The illustration should assist researchers interested in testing interaction effects using structural equation modeling.
AjzenI. (1991) ‘The Theory of Planned Behavior’, Organizational Behavior and Human Decision Processes, 50(2) 179–211.
2.
AjzenI. (2002) ‘Perceived Behavioral Control, Self-Efficacy, Locus Control, and the Theory of Planned Behavior’, Journal of Applied Social Psychology, 32(4) 1–20.
3.
AjzenI. (2005) Attitudes, Personality and Behaviour. Milton-Keynes, England: Open University Press.
4.
AjzenI.FishbeinM. (1980) Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice-Hall.
5.
AjzenI.FishbeinM. (2008) ‘Scaling and Testing Multiplicative Combinations in the Expectancy–Value Model of Attitudes’, Journal of Applied Social Psychology, 38(9) 2222–2247.
6.
AlginaJ.MoulderB. C. (2001) ‘A Note on Estimating the Jöreskog-Yang Model for Latent Variable Interaction using LISREL 8.3’, Structural Equation Modeling, 8(1) 40–52.
ArmingerG.MuthénB. O. (1998) ‘A Bayesian Approach to Nonlinear Latent Variable Models using the Gibbs Sampler and the Metropolis-Hastings Algorithm’, Psychometrika, 63(3) 271–300.
9.
ArmitageC. J.ConnerM. (2001) ‘Efficacy of the Theory of Planned Behavior: A Meta-Analytic Review’, British Journal of Social Psychology, 40(4) 471–499.
10.
BambergS.SchmidtP. (1998) ‘Changing Travel-Mode Choice as Rational Choice: Results from a Longitudinal Intervention Study’, Rationality & Society, 10(2) 223–252.
11.
BollenK. A. (1989) Structural Equations with Latent Variables. New York: John Wiley & Sons.
12.
BollenK. A.PaxtonP. (1998) ‘Two-Stage Least Squares Estimation of Interaction Effects’, in SchumackerR. E.MarcoulidesG. A. (eds.), Interaction and Nonlinear Effects in Structural Equation Modeling (pp. 125–151). Mahwah, NJ: Erlbaum.
13.
BusemeyerJ.JonesL. (1983) ‘Analysis of Multiplicative Combination Rules When the Causal Variables are Measured with Error’, Psychological Bulletin, 93(3) 549–562.
14.
CoendersG.Batista-FoguetJ. M.SarisW. E. (2008) ‘Simple, Efficient and Distribution-Free Approach to Interaction Efects in Complex Structural Equation Models’, Quality & Quantity, 42(3) 369–396.
15.
FinneyS. J.DiStefanoC. (2006) ‘Non-Normal and Categorical Data in Structural Equation mMdeling’, in HancockG. R.MuellerR. O. (eds.), Structural Equation Modeling: A Second Course (pp. 269–314). Greenwich, CT: Information Age.
16.
FishbeinM.AjzenI. (2010) Predicting and Changing Behaviour: The Reasoned Action Approach. New York: Psychology Press.
17.
FloraD. B.CurranP. J. (2004) ‘An Empirical Evaluation of Alternative Methods of Etimation for Confirmatory Factor Analysis with Ordinal Data’, Psychological Methods, 9(4) 466–491.
18.
HooglandJ. H.BoomsmaA. (1998) ‘Robustness Studies in Covariance Structural Modelling. An Overview and a Meta-Analysis’, Sociological Methods and Research, 26(3) 329–367.
19.
JaccardJ.WanC. 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.
20.
JaccardJ.WanC. K. (1996) LISREL Approaches to Interaction Effects in Multiple Regression. Newbury Park: Sage.
21.
JöreskogK. G. (1998) ‘Interaction and Nonlinear Modeling: Issues and Approaches’, in SchumackerR. E.MarcoulidesG. A. (eds.), Interaction and Nonlinear Effects in Structural Equation Modeling (pp. 239–250). Mahwah, NJ: Erlbaum.
22.
JöreskogK.SörbomD.ToitS.ToitM. (2000) LISREL 8: New Statistical Features. Third Edition. Chicago, IL: Scientific Software International.
23.
JöreskogK. G.YangF. (1996) ‘Nonlinear Structural Equation Models: The Kenny-Judd Model with Interaction eEfects’, in MarcoulidesG. A.SchumackerR. E. (eds.), Advanced Structural Equation Modeling: Issues and Techniques (pp. 57–88). Mahwah, NJ: Erlbaum.
24.
KennyD. A.JuddC. M. (1984) ‘Estimating the Nonlinear and Interactive Effects of Latent Variables’, Psychological Bulletin, 96(1) 201–210.
25.
KleinA.MoosbruggerH. (2000) ‘Maximum Likelihood Estimates of Latent Interaction Effects with the LMS-Method’, Psychometrika, 65(4) 457–474.
26.
LittleT. D.BovairdJ. A.WidamanK. F. (2006) ‘On the Merits of Orthogonalizing Powered and Product Terms: Implications for Modeling Interactions among Latent Variables’, Structural Equation Modeling, 13(4) 497–519.
27.
MarcoulidesG. A.SchumackerR. E. (2001) New Developments and Techniques in Structural Equation Modeling. Mahwah, NJ: Erlbaum.
28.
MarshH. W.WenZ.HauK. T. (2004) ‘Structural Equation Models of Latent Interactions: Evaluation of Alternative Estimation Strategies and Indicator Construction’, Psychological Methods, 9(3) 275–300.
29.
MarshH. W.WenZ.HauK. T. (2006) ‘Structural Equation Models of Latent Interaction and Quadratic Effects’, in HancockG. R.MuellerR. O. (eds.) Structural Equation Modeling: A Second Course (pp. 225–265). Greenwich, CT: Information Age.
30.
MarshH. W.WenZ.HauK.-T.LittleT. D.BovairdJ. A.WidamanK. F. (2007) ‘Unconstrained Structural Equation Models of Latent Interactions: Contrasting Residual- and Mean-Centered Approaches’, Structural Equation Modeling, 14(4) 570–580.
31.
PingR. A. (1998) ‘EQS and LISREL Examples Using Survey Data’, in SchumackerR. E.MarcoulidesG.A. (eds.), Interaction and Nonlinear Effects in Structural Equation Modeling (pp. 239–250). Mahwah, NJ: Erlbaum.
32.
ReineckeJ. (2002) ‘Nonlinear Structural Equation Models with the Theory of Planned Behavior: Comparison of Multiple Group and Latent Product Term Analyses’, Quality & Quantity, 36(2) 93–112.
33.
RidgeonE. E.SchumackerR. E.WothkeW. A. (1998) ‘Comparative Review of Interaction and Nonlinear Modeling’, in SchumackerR. E.MarcoulidesG. A. (eds.), Interaction and Nonlinear effects in Structural Equation Modeling (pp.1–16). Mahwah, NJ: Erlbaum.
34.
SatorraA.BentlerP. M. (1994) ‘Corrections to Test Statistics and Standard Errors in Covariance Structure Analysis’, in von EyeA.CloggC. (eds.), Latent Variables Analysis (pp. 399–419). Newbury Park, CA: Sage.
35.
SchaferJ. L.GrahamJ. W. (2002) ‘Missing Data: Our View of the State of the Art’, Psychological Methods, 7(2) 147–177.
36.
Schermelleh-EngelK.KleinA.MoosbruggerH. (1998) ‘Estimating Nonlinear Effects using a Latent Moderated Structural Equations Approach’, in SchumackerR. E.MarcoulidesG. A. (eds.), Interaction and Nonlinear effects in Structural Equation Modeling (pp. 203–238). Mahwah, NJ: Erlbaum.
37.
SchumackerR. E.MarcoulidesG. A. (1998) Interaction and Nonlinear effects in Structural Equation Modeling. Mahwah, NJ: Erlbaum.
38.
Spiegel Documentation (1993). Cars, Traffic and Environment. Hamburg: Augstein GmbH.
39.
Van den PutteB.HoogstratenJ. (1997) ‘Applying Structural Equation Modeling in the Context of the Theory of Reasoned Action: Some Problems and Solutions’, Structural Equation Modeling, 4(4) 320–337.
40.
WallM. M.AmemiyaY. (2000) ‘Estimation for Polytomial Structural Equation Models’, Journal of the American Statistical Association, 95, 929–940.
41.
Yang-JonssonF. (1997) Non-Linear Structural Equation Models. Simulation Studies of the Kenny-Judd Model. Stockholm: Gotab.
42.
Yang-WallentinF.JöreskogK. G. (2001) ‘Robust Standard Errors and Chi-Squares for Interaction Models’, in MarcoulidesG. A.SchumackerR. E. (eds.), New Development and Techniques in Structural Equation Modeling (pp. 159–171). Mahwah, NJ: Erlbaum.
43.
Yang-WallentinF.SchmidtP.BambergS. (2001) ‘Testing Interactions with Three Different Methods in the Theory of Planned Behaviour: Analysis of Traffic Behaviour Data’, in CudeckR.Du ToitS.SörbomD. (eds.), Structural Equation Modeling: Present and Future. A Festschrift in Honor of Karl Jöreskog (pp. 405–424). Lincoln, IL: Scientific Software International.
44.
Yang-WallentinF.SchmidtP.DavidovE.BambergS. (2004) ‘Is There Any Interaction Effect Between Intention and Perceived Behavioral Control?’, Methods of Psychological Research Online, 8(2) 127–157.