Empirical Sample Size Guidelines for Use of Latent Difference Score Mediation

Dynamic change meets mechanisms of change: Examining mediators in the latent change score framework

Researchers in behavioral sciences are often interested in longitudinal behavior change outcomes and the mechanisms that influence changes in these outcomes over time. The statistical models that are typically implemented to address these research questions do not allow for investigation of mechanisms of dynamic change over time. However, latent change score models allow for dynamic change (not just linear or exponential change) over time and have flexibility in parameter constraints that other longitudinal models do not have. Developmental researchers also frequently utilize mediation analyses to investigate mechanisms of influence in longitudinal research implemented in path analytic or latent growth curve models. In this article, we provide three examples of how mediation can be tested in the latent change score framework by combining aspects of traditional mediation models with latent change score models of repeated measures outcomes (and mediators and predictors) with more than two timepoints. We also provide the Mplus syntax to complete these analyses and practical considerations of latent change score mediation (LCSM) models.

Latent Change Score Models for the Study of Development and Dynamics in Organizational Research

The empirical study of change has proven to be one of the most vexing challenges in organizational science. Fortunately, contemporary methodologies originating from developmental psychology may provide a potential solution and are consequently working their way into the literature. In particular, organizational researchers are increasingly employing variations of latent change score (LCS) models to address questions regarding change, development, and dynamics. Although these models may indeed be used to reliably study change, development, and dynamics, many studies utilizing these models—and published in premier outlets—are characterized by questionable methodological choices, improper modeling procedures, and suboptimal research designs. Thus, the purpose of the present article is to (a) provide a critical review of LCS models, (b) outline appropriate modeling procedures (with corresponding Mplus and R syntax), (c) compare and contrast LCS modeling with other analytical techniques, and (d) delineate best practices. Ultimately, we endorse the use of LCS models by organizational researchers interested in studying longitudinal phenomena. However, we also heed researchers to do so judiciously because their misuse may lead to their unwarranted rejection by the field.

The importance of face-to-face contact and reciprocal relationships and their associations with depressive symptoms and life satisfaction

OBJECTIVE

The current study aimed to examine how patterns of interpersonal relational contexts (e.g., face-to-face or technology-based) and processes (e.g., initiated or accepted) relate to depressive symptomology and life satisfaction.

METHODS

Participants were recruited through Amazon's Mechanical Turk (n = 962 adults [52.1% female; aged 18-78; 16.4% Non-White]). Quota sampling was used to closely match the sample demographics to that of the United States Census data. Latent class analyses (LCA) identified classes of interpersonal relations using the Multidimensional Interpersonal Relations Scale. Next, participants' responses on the Beck Depression Inventory and Satisfaction With Life Scale were examined to evaluate differences in depressive symptoms and life satisfaction across classes.

RESULTS

LCA results supported a 4-class model, in which classes were characterized by patterns of relational contexts and processes: Class 1 (50.6%) engagement across all contexts (e.g., face-to-face) and processes (e.g., initiated); Class 2 (12.7%) engagement across all contexts and processes except Facebook; Class 3 (24.0%) engagement in all contexts and only passive processes; and Class 4 (12.7%) engagement in only technology-based contexts and passive processes. Membership in Classes 1 and 2 was associated with lower depressive symptomology and higher life satisfaction as compared to Classes 3 and 4.

CONCLUSION

The findings suggest that patterns of relations differentially relate to depressive symptoms and life satisfaction. The findings suggest that multicontextual (e.g., face-to-face and technology-based) and reciprocal relationships with friends (e.g., initiating and accepting connections) may play an important role in the association between interpersonal relations with life satisfaction and depressive symptoms.