Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction.
TL;DR: The traditional constrained approach performed more poorly than did 3 new approaches--unconstrained, generalized appended product indicator, and quasi-maximum-likelihood (QML); the authors' new unconstrained approach was easiest to apply.
Abstract: Interactions between (multiple indicator) latent variables are rarely used because of implementation complexity and competing strategies. Based on 4 simulation studies, the traditional constrained approach performed more poorly than did 3 new approaches--unconstrained, generalized appended product indicator, and quasi-maximum-likelihood (QML). The authors' new unconstrained approach was easiest to apply. All 4 approaches were relatively unbiased for normally distributed indicators, but the constrained and QML approaches were more biased for nonnormal data; the size and direction of the bias varied with the distribution but not with the sample size. QML had more power, but this advantage was qualified by consistently higher Type I error rates. The authors also compared general strategies for defining product indicators to represent the latent interaction factor.
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TL;DR: The theory of SEM, which allows for the analysis of independent observations for both unrelated and family data, the available software for SEM, and an example of SEM analysis are reviewed.
Abstract: Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.
4,203 citations
TL;DR: A general analytical framework for combining moderation and mediation that integrates moderated regression analysis and path analysis is presented that clarifies how moderator variables influence the paths that constitute the direct, indirect, and total effects of mediated models.
Abstract: Studies that combine moderation and mediation are prevalent in basic and applied psychology research. Typically, these studies are framed in terms of moderated mediation or mediated moderation, both of which involve similar analytical approaches. Unfortunately, these approaches have important shortcomings that conceal the nature of the moderated and the mediated effects under investigation. This article presents a general analytical framework for combining moderation and mediation that integrates moderated regression analysis and path analysis. This framework clarifies how moderator variables influence the paths that constitute the direct, indirect, and total effects of mediated models. The authors empirically illustrate this framework and give step-by-step instructions for estimation and interpretation. They summarize the advantages of their framework over current approaches, explain how it subsumes moderated mediation and mediated moderation, and describe how it can accommodate additional moderator and mediator variables, curvilinear relationships, and structural equation models with latent variables.
3,624 citations
Cites background from "Structural equation models of laten..."
...If Z is continuous, then the required analytical procedures are more involved because of the complexities of estimating interactions with continuous latent variables in structural equation modeling (Jo¨reskog & Yang, 1996; Li et al., 1998; Marsh, Wen, & Hau, 2004; Schumacker, & Marcoulides, 1998)....
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TL;DR: This compendium of standards for the use and interpretation of structural equation models (SEMs) removes some of the mystery and uncertainty of the use of SEMs, while conveying the spirit of their possibilities.
Abstract: We provide a comprehensive and user-friendly compendium of standards for the use and interpretation of structural equation models (SEMs). To both read about and do research that employs SEMs, it is necessary to master the art and science of the statistical procedures underpinning SEMs in an integrative way with the substantive concepts, theories, and hypotheses that researchers desire to examine. Our aim is to remove some of the mystery and uncertainty of the use of SEMs, while conveying the spirit of their possibilities.
2,557 citations
TL;DR: Analysis of changes in the mean levels and rank order of the Big Five personality traits in a heterogeneous sample of 14,718 Germans across all of adulthood shows that personality changes throughout the life span, but with more pronounced changes in young and old ages, and that this change is partly attributable to social demands and experiences.
Abstract: Does personality change across the entire life course, and are those changes due to intrinsic maturation or major life experiences? This longitudinal study investigated changes in the mean levels and rank order of the Big Five personality traits in a heterogeneous sample of 14,718 Germans across all of adulthood. Latent change and latent moderated regression models provided 4 main findings: First, age had a complex curvilinear influence on mean levels of personality. Second, the rank-order stability of Emotional Stability, Extraversion, Openness, and Agreeableness all followed an inverted U-shaped function, reaching a peak between the ages of 40 and 60 and decreasing afterward, whereas Conscientiousness showed a continuously increasing rank-order stability across adulthood. Third, personality predicted the occurrence of several objective major life events (selection effects) and changed in reaction to experiencing these events (socialization effects), suggesting that personality can change due to factors other than intrinsic maturation. Fourth, when events were clustered according to their valence, as is commonly done, effects of the environment on changes in personality were either overlooked or overgeneralized. In sum, our analyses show that personality changes throughout the life span, but with more pronounced changes in young and old ages, and that this change is partly attributable to social demands and experiences.
956 citations
Posted Content•
01 Jan 2011TL;DR: In this paper, the authors investigated changes in the mean levels and rank order of the Big Five personality traits in a heterogeneous sample of 14,718 Germans across all of adulthood.
Abstract: Does personality change across the entire life course, and are those changes due to intrinsic maturation or major life experiences? This longitudinal study investigated changes in the mean levels and rank order of the Big Five personality traits in a heterogeneous sample of 14,718 Germans across all of adulthood. Latent change and latent moderated regression models provided four main findings: First, age had a complex curvilinear influence on mean levels of personality. Second, the rank-order stability of Emotional Stability, Extraversion, Openness, and Agreeableness all followed an inverted U-shaped function, reaching a peak between the ages of 40 and 60, and decreasing afterwards, whereas Conscientiousness showed a continuously increasing rank-order stability across adulthood. Third, personality predicted the occurrence of several objective major life events (selection effects) and changed in reaction to experiencing these events (socialization effects), suggesting that personality can change due to factors other than intrinsic maturation.. - Fourth, when events were clustered according to their valence, as is commonly done,. - effects of the environment on changes in personality were either overlooked or. - overgeneralized. In sum, our analyses show that personality changes throughout the life. - span, but with more pronounced changes in young and old ages, and that this change is. - partly attributable to social demands and experiences.
924 citations
References
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01 Jan 1991TL;DR: In this article, the effects of predictor scaling on the coefficients of regression equations are investigated. But, they focus mainly on the effect of predictors scaling on coefficients of regressions.
Abstract: Introduction Interactions between Continuous Predictors in Multiple Regression The Effects of Predictor Scaling on Coefficients of Regression Equations Testing and Probing Three-Way Interactions Structuring Regression Equations to Reflect Higher Order Relationships Model and Effect Testing with Higher Order Terms Interactions between Categorical and Continuous Variables Reliability and Statistical Power Conclusion Some Contrasts Between ANOVA and MR in Practice
27,897 citations
4,225 citations
TL;DR: In this paper, Monte Carlo simulations were used to investigate the performance of three X 2 test statistics in confirmatory factor analysis (CFA): Normal theory maximum likelihood )~2 (ML), Browne's asymptotic distribution free X 2 (ADF), and the Satorra-Bentler rescaled X 2(SB) under varying conditions of sample size, model specification, and multivariate distribution.
Abstract: Monte Carlo computer simulations were used to investigate the performance of three X 2 test statistics in confirmatory factor analysis (CFA). Normal theory maximum likelihood )~2 (ML), Browne's asymptotic distribution free X 2 (ADF), and the Satorra-Bentler rescaled X 2 (SB) were examined under varying conditions of sample size, model specification, and multivariate distribution. For properly specified models, ML and SB showed no evidence of bias under normal distributions across all sample sizes, whereas ADF was biased at all but the largest sample sizes. ML was increasingly overestimated with increasing nonnormality, but both SB (at all sample sizes) and ADF (only at large sample sizes) showed no evidence of bias. For misspecified models, ML was again inflated with increasing nonnormality, but both SB and ADF were underestimated with increasing nonnormality. It appears that the power of the SB and ADF test statistics to detect a model misspecification is attenuated given nonnormally distributed data.
4,168 citations
01 Jan 1995
2,804 citations
TL;DR: This chapter presents a review of applications of structural equation modeling (SEM) published in psychological research journals in recent years and focuses first on the variety of research designs and substantive issues to which SEM can be applied productively.
Abstract: This chapter presents a review of applications of structural equation modeling (SEM) published in psychological research journals in recent years. We focus first on the variety of research designs and substantive issues to which SEM can be applied productively. We then discuss a number of methodological problems and issues of concern that characterize some of this literature. Although it is clear that SEM is a powerful tool that is being used to great benefit in psychological research, it is also clear that the applied SEM literature is characterized by some chronic problems and that this literature can be considerably improved by greater attention to these issues.
2,489 citations