Ginger Lockhart

A comparison of four approaches to account for method effects in latent state trait analyses

Latent state-trait (LST) analysis is frequently applied in psychological research to determine the degree to which observed scores reflect stable person-specific effects, effects of situations and/or person-situation interactions, and random measurement error. Most LST applications use multiple repeatedly measured observed variables as indicators of latent trait and latent state residual factors. In practice, such indicators often show shared indicator-specific (or method) variance over time. In this article, the authors compare 4 approaches to account for such method effects in LST models and discuss the strengths and weaknesses of each approach based on theoretical considerations, simulations, and applications to actual data sets. The simulation study revealed that the LST model with indicator-specific traits (Eid, 1996) and the LST model with M - 1 correlated method factors (Eid, Schneider, & Schwenkmezger, 1999) performed well, whereas the model with M orthogonal method factors used in the early work of Steyer, Ferring, and Schmitt (1992) and the correlated uniqueness approach (Kenny, 1976) showed limitations under conditions of either low or high method-specificity. Recommendations for the choice of an appropriate model are provided.

Sweetened Drink and Snacking Cues in Adolescents: A Study Using Ecological Momentary Assessment

The objective of this study was to identify physical, social, and intrapersonal cues that were associated with the consumption of sweetened beverages and sweet and salty snacks among adolescents from lower SES neighborhoods. Students were recruited from high schools with a minimum level of 25% free or reduced cost lunches. Using ecological momentary assessment, participants (N=158) were trained to answer brief questionnaires on handheld PDA devices: (a) each time they ate or drank, (b) when prompted randomly, and (c) once each evening. Data were collected over 7days for each participant. Participants reported their location (e.g., school grounds, home), mood, social environment, activities (e.g., watching TV, texting), cravings, food cues (e.g., saw a snack), and food choices. Results showed that having unhealthy snacks or sweet drinks among adolescents was associated with being at school, being with friends, feeling lonely or bored, craving a drink or snack, and being exposed to food cues. Surprisingly, sweet drink consumption was associated with exercising. Watching TV was associated with consuming sweet snacks but not with salty snacks or sweet drinks. These findings identify important environmental and intrapersonal cues to poor snacking choices that may be applied to interventions designed to disrupt these food-related, cue-behavior linked habits.

Mediation Analysis in Psychosomatic Medicine Research

This article presents an overview of statistical mediation analysis and its application to psychosomatic medicine research. The article begins with a description of the major approaches to mediation analysis and an evaluation of the strengths and limits of each. Emphasis is placed on longitudinal mediation models, and an application using latent growth modeling is presented. The article concludes with a description of recent developments in mediation analysis and suggestions for the use of mediation for future work in psychosomatic medicine research.

Distinguishing state variability from trait change in longitudinal data: The role of measurement (non)invariance in latent state-trait analyses

Researchers analyzing longitudinal data often want to find out whether the process they study is characterized by (1) short-term state variability, (2) long-term trait change, or (3) a combination of state variability and trait change. Classical latent state-trait (LST) models are designed to measure reversible state variability around a fixed set-point or trait, whereas latent growth curve (LGC) models focus on long-lasting and often irreversible trait changes. In the present article, we contrast LST and LGC models from the perspective of measurement invariance testing. We show that establishing a pure state-variability process requires (1) the inclusion of a mean structure and (2) establishing strong factorial invariance in LST analyses. Analytical derivations and simulations demonstrate that LST models with noninvariant parameters can mask the fact that a trait-change or hybrid process has generated the data. Furthermore, the inappropriate application of LST models to trait change or hybrid data can lead to bias in the estimates of consistency and occasion specificity, which are typically of key interest in LST analyses. Four tips for the proper application of LST models are provided.

First- Versus Second-Order Latent Growth Curve Models: Some Insights From Latent State-Trait Theory

First-order latent growth curve models (FGMs) estimate change based on a single observed variable and are widely used in longitudinal research. Despite significant advantages, second-order latent growth curve models (SGMs), which use multiple indicators, are rarely used in practice, and not all aspects of these models are widely understood. In this article, our goal is to contribute to a better understanding of theoretical and practical differences between FGMs and SGMs. We define the latent variables in FGMs and SGMs explicitly on the basis of latent state–trait (LST) theory and discuss insights that arise from this approach. We show that FGMs imply a strict trait-like conception of the construct under study, whereas SGMs allow for both trait and state components. Based on a simulation study and empirical applications to the Center for Epidemiological Studies Depression Scale (Radloff, 1977) we illustrate that, as an important practical consequence, FGMs yield biased reliability estimates whenever constructs contain state components, whereas reliability estimates based on SGMs were found to be accurate. Implications of the state–trait distinction for the measurement of change via latent growth curve models are discussed.

Analyzing latent state-trait and multiple-indicator latent growth curve models as multilevel structural equation models

Latent state-trait (LST) and latent growth curve (LGC) models are frequently used in the analysis of longitudinal data. Although it is well-known that standard single-indicator LGC models can be analyzed within either the structural equation modeling (SEM) or multilevel (ML; hierarchical linear modeling) frameworks, few researchers realize that LST and multivariate LGC models, which use multiple indicators at each time point, can also be specified as ML models. In the present paper, we demonstrate that using the ML-SEM rather than the SL-SEM framework to estimate the parameters of these models can be practical when the study involves (1) a large number of time points, (2) individually-varying times of observation, (3) unequally spaced time intervals, and/or (4) incomplete data. Despite the practical advantages of the ML-SEM approach under these circumstances, there are also some limitations that researchers should consider. We present an application to an ecological momentary assessment study (N = 158 youths with an average of 23.49 observations of positive mood per person) using the software Mplus (Muthén and Muthén, 1998–2012) and discuss advantages and disadvantages of using the ML-SEM approach to estimate the parameters of LST and multiple-indicator LGC models.

Collapsing factors in multitrait-multimethod models: examining consequences of a mismatch between measurement design and model

Models of confirmatory factor analysis (CFA) are frequently applied to examine the convergent validity of scores obtained from multiple raters or methods in so-called multitrait-multimethod (MTMM) investigations. Many applications of CFA-MTMM and similarly structured models result in solutions in which at least one method (or specific) factor shows non-significant loading or variance estimates. Eid et al. (2008) distinguished between MTMM measurement designs with interchangeable (randomly selected) vs. structurally different (fixed) methods and showed that each type of measurement design implies specific CFA-MTMM measurement models. In the current study, we hypothesized that some of the problems that are commonly seen in applications of CFA-MTMM models may be due to a mismatch between the underlying measurement design and fitted models. Using simulations, we found that models with M method factors (where M is the total number of methods) and unconstrained loadings led to a higher proportion of solutions in which at least one method factor became empirically unstable when these models were fit to data generated from structurally different methods. The simulations also revealed that commonly used model goodness-of-fit criteria frequently failed to identify incorrectly specified CFA-MTMM models. We discuss implications of these findings for other complex CFA models in which similar issues occur, including nested (bifactor) and latent state-trait models.

Two distinct mediated pathways to disordered eating in response to weight stigmatization and their application to prevention programs

ABSTRACT Objective: Disordered eating behaviors among undergraduate women are common and, thus, are an important public health concern. Weight stigmatization, stress, and social withdrawal are often associated with disordered eating behaviors; however, it is unclear whether stress and social withdrawal act as mediators between weight stigmatization and disordered eating. By testing specific pathways to disordered eating, theory-driven prevention programs can be implemented. Methods: Self-reported surveys were administered to 217 undergraduate women during the Fall 2014 semester. Results: There were 2 distinct mediational pathways in response to weight stigmatization. Stress partially mediated the path between weight stigmatization and emotional eating (38%), whereas social withdrawal partially mediated the path between weight stigmatization and dietary restraint (44%). Conclusions: Stress and social withdrawal mediate the relationship between weight stigmatization and disordered eating. The results of this study identified potentially critical risk factors that, if addressed, may improve outcomes of campus-based disordered eating programs for women.

Analyzing Statistical Mediation with Multiple Informants: A New Approach with an Application in Clinical Psychology

Testing mediation models is critical for identifying potential variables that need to be targeted to effectively change one or more outcome variables. In addition, it is now common practice for clinicians to use multiple informant (MI) data in studies of statistical mediation. By coupling the use of MI data with statistical mediation analysis, clinical researchers can combine the benefits of both techniques. Integrating the information from MIs into a statistical mediation model creates various methodological and practical challenges. The authors review prior methodological approaches to MI mediation analysis in clinical research and propose a new latent variable approach that overcomes some limitations of prior approaches. An application of the new approach to mother, father, and child reports of impulsivity, frustration tolerance, and externalizing problems (N = 454) is presented. The results showed that frustration tolerance mediated the relationship between impulsivity and externalizing problems. The new approach allows for a more comprehensive and effective use of MI data when testing mediation models.

The Dynamic Relationship between Unhealthy Weight Control and Adolescent Friendships: A Social Network Approach

Although adolescence marks a vulnerable stage for peer influence on health behavior, little is known about the longitudinal and dynamic relationship between adolescent friendship and weight control. The current study aims to explain these dynamic processes among a sample of 1156 American adolescents in grades 9–11 (48.6% girls, 23.4% European American, 25.2% African American) from the National Longitudinal Study of Adolescent Health. Stochastic actor-oriented models were fit to examine changes in friendship networks and unhealthy weight control across two waves. The findings support a bidirectional relationship where weight control predicts future friendship seeking and friendship seeking predicts future weight control. The findings also indicate that adolescents prefer friends with similar weight control patterns. Taken together, the results of the current study indicate that adolescent friendships play an integral role in the development of unhealthy weight control and thus can be used to identify adolescents at risk and serve as targets within preventive interventions.