Latent Interaction Effects Research Articles

Latent Interaction Effect in the CLPM Model: A Two-Step Multiple Imputation Analysis

This study aims to estimate the latent interaction effect in the CLPM model through a two-step multiple imputation analysis. The estimation of within × within and between × within latent interaction under the CLPM model framework is compared between the one-step Bayesian LMS method and the two-step multiple imputation analysis through a simulation study. The analysis show that the two-step multiple imputation analysis can provide unbiased estimation parameter, similar to the one-step Bayesian LMS method. This study also uses self-esteem and depression data from NLSY79 to perform a two-step multiple imputation analysis of the CLPM model, as well as an empirical example of latent interaction analysis. Mplus syntax is provided for researchers’ reference.

Investigating Latent Interaction Effects in Multiple-Group Analysis in the Structural Equation Modeling Framework

This study aims to address a gap in the social and behavioral sciences literature concerning interaction effects between latent factors in multiple-group analysis. By comparing two approaches for estimating latent interactions within multiple-group analysis frameworks using simulation studies and empirical data, we assess their relative merits. Our simulation study results demonstrated the superiority of the Latent Moderated Structural Equations (LMS) approach over the Unconstrained Product Indicator (UPI) method in power and providing more accurate estimates of differences in latent interactions between groups. Utilizing multiple-group analysis on real-world data, we evaluated its efficacy in identifying latent interaction differences between groups. Empirical data analysis did not reveal discrepancies between LMS and UPI in terms of detecting differences in latent interactions between boys and girls, although they indicated varying sizes of differences in interaction effects. The study concludes with recommendations for examining differences in latent interactions and suggests avenues for future research. Our findings aim to deepen the understanding of intricate relationships among variables, group differences, and the moderating effects of latent variables, ultimately facilitating the development of more accurate theoretical models.

The Predictive Effect of Depression on Self-Injury: Positive Youth Development as a Moderator

While findings have documented the association between adolescent depression and self-injury, few studies have investigated the moderating effect of positive youth development (PYD) qualities on the association. This study examined concurrent and longitudinal predictive effects of depression and PYD qualities on nonsuicidal self-injury (NSSI) and suicidal self-injury (SSI) among Chinese adolescents. The moderating effects of PYD qualities were also studied. Two waves of data with an approximate 6-month interval were collected from five primary and secondary schools in Chengdu, China. A total of 6,948 adolescents aged 10 to 16 (Mage = 12.91, SD = 1.69 at the first wave, 51.17% boys) formed the working sample. Latent moderated structural equation modeling revealed that depression was a positive concurrent and longitudinal predictor of both NSSI and SSI whereas PYD qualities showed adverse concurrent and longitudinal predictive effects. The latent interaction effects were also significant in both cross-sectional and longitudinal models, with PYD qualities mitigating the positive effects of depression on NSSI and SSI. The results suggest that PYD qualities did not only directly reduce the risk of NSSI and SSI among adolescents but also attenuated the influence of depression on self-injury. These findings provide additional evidence for the protective role of PYD qualities in adolescent development and imply that improving PYD qualities may be a promising way to prevent and treat adolescent self-injury.

Estimating Latent Baseline-by-Treatment Interactions in Statistical Mediation Analysis

Statistical mediation analysis is used to uncover intermediate variables, known as mediators [M], that explain how a treatment [X] changes an outcome [Y]. Often, researchers examine whether baseline levels of M and Y moderate the effect of X on posttest M or Y. However, there is limited guidance on how to estimate baseline-by-treatment interaction (BTI) effects when M and Y are latent variables, which entails the estimation of latent interaction effects. In this paper, we discuss two general approaches for estimating latent BTI effects in mediation analysis: using structural models or scoring latent variables prior to estimating observed BTIs and correcting for unreliability. We present simulation results describing bias, power, type 1 error rates, and interval coverage of the latent BTIs and mediated effects estimated using these approaches. These methods are also illustrated with an applied example. R and Mplus syntax are provided to facilitate the implementation of these approaches.

How empowerment and materialism contribute to anti-consumers’ well-being

PurposeBecause steadily growing consumption is not beneficial for nature and climate and is not the same as increasing well-being, an anti-consumerism movement has formed worldwide. The renouncement of dispensable consumption will, however, only establish itself as a significant lifestyle if consumers do not perceive reduced consumption as a personal sacrifice. Since prior research has not yielded a consistent understanding of the relationship between anti-consumption and personal well-being, this paper aims to examine three factors about which theory implies that they may moderate this relationship: decision-control empowerment, market-control empowerment and the value of materialism.Design/methodology/approachThe analysis is based on data from a large-scale, representative online survey (N = 1,398). Structural equation modelling with latent interaction effects is used to test how three moderators (decision-control empowerment, market-control empowerment and materialism) affect the relationship amongst four types of anti-consumption (e.g. voluntary simplicity) and three different well-being states (e.g. subjective well-being).FindingsWhile both dimensions of empowerment almost always directly promote consumer well-being, significant moderation effects are present in only a few but meaningful cases. Although the materialism value tends to reduce consumers’ well-being, it improves the well-being effect of two anti-consumption styles.Research limitations/implicationsUsing only one sample from a wealthy country is a limitation of the study. Researchers should replicate the findings in different nations and cultures.Practical implicationsConsumer affairs practitioners and commercial marketing for sustainably produced, high-quality and long-lasting goods can benefit greatly from these findings.Social implicationsThis paper shows that sustainable marketing campaigns can more easily motivate consumers to voluntarily reduce their consumption for the benefit of society and the environment if a high level of market-control empowerment can be communicated to them.Originality/valueThis study provides differentiated new insights into the roles of consumer empowerment, i.e. both decision-control empowerment and market-control empowerment, and the value of materialism to frame specific relationships between different anti-consumption types and various well-being states.

Standardized estimates for latent interaction effects: Method comparison and selection strategy

Standardized estimates for latent interaction effects: Method comparison and selection strategy

Interaction Effects in Cross-Lagged Panel Models: SEM with Latent Interactions Applied to Work-Family Conflict, Job Satisfaction, and Gender

Researchers often combine longitudinal panel data analysis with tests of interactions (i.e., moderation). A popular example is the cross-lagged panel model (CLPM). However, interaction tests in CLPMs and related models require caution because stable (i.e., between-level, B) and dynamic (i.e., within-level, W) sources of variation are present in longitudinal data, which can conflate estimates of interaction effects. We address this by integrating literature on CLPMs, multilevel moderation, and latent interactions. Distinguishing stable B and dynamic W parts, we describe three types of interactions that are of interest to researchers: 1) purely dynamic or WxW; 2) cross-level or BxW; and 3) purely stable or BxB. We demonstrate estimating latent interaction effects in a CLPM using a Bayesian SEM in Mplus to apply relationships among work-family conflict and job satisfaction, using gender as a stable B variable. We support our approach via simulations, demonstrating that our proposed CLPM approach is superior to a traditional CLPMs that conflate B and W sources of variation. We describe higher-order nonlinearities as a possible extension, and we discuss limitations and future research directions.

Daily Stress and Behavioral Problems in Chinese Children: The Moderating Roles of Family Functioning and the Classroom Environment.

Grounded in the stress-coping model, our study examined family functioning and the classroom environment as protective factors in the relationship between daily stress and behavioral problems in Chinese children. The participants were 1,450 children (51.7% male, Mage = 10.91 years, SD = 0.96) in the fourth, fifth, and sixth grades at five schools. The children completed the questionnaires measuring daily stress, family functioning, and the classroom environment. Additionally, their parents rated their behavioral problems. The latent moderated structural (LMS) equation approach was used to test moderator effects. After controlling for sex and grade, our results indicate that daily stress positively predicted the children’s behavioral problems. Both family functioning and the classroom environment moderated the relationship between daily stress and behavioral problems. Further assessment of latent interaction effects indicate that buffering effects on behavioral problems were most prominent in conditions involving low stress. In sum, families and schools should not ignore children’s minor stressors, as interventions involving family functioning and favorable classroom environments may help to reduce behavioral problems in children who report low levels of daily stress.

R-squared of a Latent Interaction in Structural Equation Model: A Tutorial of Using R

Mplus (Muthén & Muthén, 1998 - 2017) is one popular statistical software to estimate the latent interaction effects using the latent moderated structural equation approach (LMS). However, the variance explained by a latent interaction that supports the interpretation of estimation results is not currently available from the Mplus output. To relieve human computations and to facilitate interpretations of latent interaction effects in social science research, we developed two functions (LIR & LOIR) in the R package IRmplus to calculate the R-squared of a latent interaction above and beyond the first-order simple main effects in Structural Equation Modeling. This tutorial provides a step-by-step guide for applied researchers to estimating a latent interaction effect in Mplus, and to obtaining the R-squared of a latent interaction effect using the LIR & LOIR functions. Example data and syntax are available online.

Testing Moderation in Business and Psychological Studies with Latent Moderated Structural Equations

Most organizational researchers understand the detrimental effects of measurement errors in testing relationships among latent variables and hence adopt structural equation modeling (SEM) to control for measurement errors. Nonetheless, many of them revert to regression-based approaches, such as moderated multiple regression (MMR), when testing for moderating and other nonlinear effects. The predominance of MMR is likely due to the limited evidence showing the superiority of latent interaction approaches over regression-based approaches combined with the previous complicated procedures for testing latent interactions. In this teaching note, we first briefly explain the latent moderated structural equations (LMS) approach, which estimates latent interaction effects while controlling for measurement errors. Then we explain the reliability-corrected single-indicator LMS (RCSLMS) approach to testing latent interactions with summated scales and correcting for measurement errors, yielding results which approximate those from LMS. Next, we report simulation results illustrating that LMS and RCSLMS outperform MMR in terms of accuracy of point estimates and confidence intervals for interaction effects under various conditions. Then, we show how LMS and RCSLMS can be implemented with Mplus, providing an example-based tutorial to demonstrate a 4-step procedure for testing a range of latent interactions, as well as the decisions at each step. Finally, we conclude with answers to some frequently asked questions when testing latent interactions. As supplementary files to support researchers, we provide a narrated PowerPoint presentation, all Mplus syntax and output files, data sets for numerical examples, and Excel files for conducting the loglikelihood values difference test and plotting the latent interaction effects.

Exploring the Performance of Latent Moderated Structural Equations Approach for Ordered-Categorical Items

ABSTRACT Latent moderated structural equations (LMS) is a popular method in estimating latent interaction effects. Mplus has implemented a variant of LMS (LMS-cat) that uses categorical confirmatory factor analysis to handle ordered-categorical indicators. We conducted a simulation study to examine the performance of the LMS-cat in varying sample size, interaction effect size, missing data rate and scenario, as well as number and symmetry of item response category conditions. Results showed that the LMS-cat is an excellent method in estimating structural parameters (i.e., interaction effect and lower-order effects). However, it could produce highly biased measurement parameters (i.e., factor loadings, and item category thresholds). We illustrated the LMS-cat by testing the interaction effect between participation in teachers` support activities and that of teachers` counterfactual activities (meditation, meditative movement, vigorous physical exercise) on teachers` sense of self-efficacy.

CB-SEM latent interaction: Unconstrained and orthogonalized approaches

Covariance Based – Structural Equation Modelling (CB-SEM) is often used to investigate moderation and latent interaction effects. This study illustrates and compares the application of constrained, unconstrained and orthogonalized CB-SEM approaches to latent variable interaction analysis using AMOS. Although all three techniques provided similar parameter estimates, the orthogonalized approach provided reduced standard errors resulting in identifying a significant latent interaction, suggesting the orthogonalized approach may be better suited for exploratory research.The illustrated example demonstrates three CB-SEM techniques, and the simplicity of the three approaches to test for interaction effects. The three approaches can be comfortably implemented in available software programs. Guidelines and recommendations for the use of the three approaches are identified with a step-wise process of assessing the latent interaction effect in CB-SEM.As far as we are aware this is the first investigation comparing and recommending specific CB-SEM latent variable moderation analysis techniques in marketing research.

How Are We Testing Interactions in Latent Variable Models? Surging Forward or Fighting Shy?:

Latent variable models and interaction effects have both been common in the organizational sciences for some time. Methods for incorporating interactions into latent variable models have existed si...

THE MEDIATION EFFECT OF INERTIA ON SERVICE DURATION

Firms devote large amounts of resources toward customer retention practices since relationship duration is a key driver in enhancing customer lifetime value. We posit that customer inertia plays an important role in determining service duration. In analysing service duration, we incorporate inertia into existing models that feature customer satisfaction, loyalty, and switching costs. A structural equation model is used to show the effect of latent mediation effect of inertia. We find that the mediating role of inertia is significant and the latent interaction effect of loyalty and inertia on service duration is also significant.

Machiavellian Ways to Academic Cheating: A Mediational and Interactional Model.

Academic cheating has become a pervasive practice from primary schools to university. This study aims at investigating this phenomenon through a nomological network which integrates different theoretical frameworks and models, such as trait and social-cognitive theories and models regarding the approaches to learning and contextual/normative environment. Results on a sample of more than 200 Italian university students show that the Amoral Manipulation facet of Machiavellianism, Academic Moral Disengagement, Deep Approach to Learning, and Normative Academic Cheating are significantly associated with Individual Academic Cheating. Moreover, results show a significant latent interaction effect between Normative Academic Cheating and Amoral Manipulation Machiavellianism: “amoral Machiavellians” students are more prone to resort to Academic Cheating in contexts where Academic Cheating is adopted as a practice by their peers, while this effect is not significant in contexts where Academic Cheating is not normative. Results also show that Academic Moral Disengagement and Deep Approach to learning partially mediate the relationship between Amoral Manipulation and Academic Cheating. Practical implications of these results are discussed.

Bullying and Social Anxiety in Chinese Children: Moderating Roles of Trait Resilience and Psychological Suzhi

Research examining the relationship between bullying victimization and social anxiety has mainly been conducted in Western countries, and little is known about the mechanisms underlying this relationship. This study explores the correlation between bullying victimization and social anxiety in a Chinese context and determines the moderating roles of psychological suzhi (a mental quality characterized by being steady, essential and implicit that affects adaptive, developmental, and creative behavior) and trait resilience among victims of bullying. Data were obtained from a stratified sample of 1903 children in the fourth, fifth, and sixth grades. All participants completed measures of bullying victimization, social anxiety, trait resilience, and psychological suzhi. The results indicated that, after controlling for grade, residential area, and parental marital status, bullying victimization positively predicted children’s social anxiety. In addition, multi-group analysis suggested that the association in girls was stronger relative to that observed in boys. Regarding underlying processes, trait resilience moderated the effect of bullying victimization on social anxiety only in girls. Further assessment of the latent interaction effects indicated that the protective effect of trait resilience was stronger for girls experiencing high, relative to low, levels of bullying victimization, and psychological suzhi buffered against the detrimental effects of bullying on children’s social anxiety. Most notably, unlike the moderating effect of resilience, the buffering effect of psychological suzhi against social anxiety was most prominent when bullying victimization was low. Findings underscore the importance of enhancing trait resilience and psychological suzhi in interventions designed to reduce children’s social anxiety.

Analyzing Different Types of Moderated Method Effects in Confirmatory Factor Models for Structurally Different Methods

An extension of two confirmatory factor models for multitrait-multimethod measurement designs with structurally different methods to the analysis of latent interaction effects is presented: the nonlinear latent difference (NL-LD) model and the nonlinear correlated trait–correlated method-minus-one (NL-CTC[M – 1]) model. Both models are compared with regard to (a) the psychometric definition of the latent variables, (b) the capabilities of explaining latent method effects, and (c) the analysis of latent interaction effects. Using the latent moderated structural equation approach, we show how moderated method effects can be examined in the NL-CTC(M – 1) model. This fine-grained analysis of method effects is not feasible using the classical NL-LD model. We propose an extended version of the NL-LD model, which recovers the results of the NL-CTC(M – 1) model. The different versions of the nonlinear multimethod models are illustrated using real data from a multirater study. Finally, the advantages and challenges of incorporating latent interaction effects in complex CFA–MTMM models are discussed.

The role of domain-specific ability self-concepts in the value students attach to school

Expanding the prevalent within-domain perspective, the present study investigated how students' domain-specific ability self-concepts relate to the value they attach to school. With a longitudinal design and a sample of N=1592 lower secondary school students from n=82 classes in different educational tracks, we tested the hypothesis that mathematics and verbal self-concept interact in predicting how students value school. In addition to statistically significant main effects, structural equation modeling revealed the expected latent interaction effect. Response surface methodology demonstrated that students valued school more highly when their ability self-concepts were high in both domains rather than just one; a single low self-concept predisposed students to attach less value to school just as much as low self-concepts in both domains did. Helping all students frame attainable goals, thereby providing them with opportunities to experience success across domains, might increase the value they attach to school.

Capturing heterogeneity and PLS-SEM prediction ability: Alliance governance and innovation

Whether the uses of PLS-SEM latent interaction effect (PLS-LIE), PLS prediction-oriented segmentation (PLS-POS), or PLS-PATHMOX approaches improve prediction ability remains unclear. The present study draws on holdout sample estimations to assess prediction ability for the three approaches. The illustrative empirical model focuses on examining the differences in innovation outcomes that coopetive alliances produce, in which three governance factors may affect the structural model parameter estimates reflecting these differences. The findings suggest improvements in prediction ability with the use of PLS-LIE and PLS-POS but not for PLS-PATHMOX.

Nonparametric Estimates of Gene × Environment Interaction Using Local Structural Equation Modeling.

Gene × environment (G × E) interaction studies test the hypothesis that the strength of genetic influence varies across environmental contexts. Existing latent variable methods for estimating G × E interactions in twin and family data specify parametric (typically linear) functions for the interaction effect. An improper functional form may obscure the underlying shape of the interaction effect and may lead to failures to detect a significant interaction. In this article, we introduce a novel approach to the behavior genetic toolkit, local structural equation modeling (LOSEM). LOSEM is a highly flexible nonparametric approach for estimating latent interaction effects across the range of a measured moderator. This approach opens up the ability to detect and visualize new forms of G × E interaction. We illustrate the approach by using LOSEM to estimate gene × socioeconomic status interactions for six cognitive phenotypes. Rather than continuously and monotonically varying effects as has been assumed in conventional parametric approaches, LOSEM indicated substantial nonlinear shifts in genetic variance for several phenotypes. The operating characteristics of LOSEM were interrogated through simulation studies where the functional form of the interaction effect was known. LOSEM provides a conservative estimate of G × E interaction with sufficient power to detect statistically significant G × E signal with moderate sample size. We offer recommendations for the application of LOSEM and provide scripts for implementing these biometric models in Mplus and in OpenMx under R.