Empirical Sample Size Guidelines for Use of Latent Difference Score Mediation.

Mediation models are commonly used to identify the mechanisms through which one variable influences another. Among longitudinal mediation methods, latent difference score mediation stands out due to its unique ability to capture non-linear change over time. However, there is limited information regarding sample size demands to achieve adequate power with this method, resulting in few applications of latent difference score mediation. To address this limitation, the current study presents empirically supported sample size guidelines for 10 common latent difference score mediation structural models and 9 unique population models. The results of this study offer researchers with a set of representative sample estimates that may be used when designing studies or seeking funding.

Chronic health conditions and adolescent friendship: perspectives from social network analysis.

OBJECTIVE: The current study uses methods from social network analysis to examine the relationship between chronic health conditions (CHCs) and adolescent friendships. Particular attention is given to the processes of peer marginalization, peer withdrawal and homophily related to CHCs. METHODS: Exponential random graph models were used to investigate the extent to which a CHC is associated with patterns in adolescent friendship connections, while controlling for important social network properties and covariates. The study uses cross-sectional data from six small US high schools (n = 461) within the National Longitudinal Study of Adolescent to Adult Health. RESULTS: Findings demonstrate no significant differences between adolescents with CHCs and adolescents without CHCs in the number of incoming friendship nominations (peer marginalization) or outgoing friendship nominations (peer withdrawal). In addition, similarity in CHCs (homophily) was not significantly related to friendship between two individuals. CONCLUSIONS: In sum, the presence of an adolescent CHC was not significantly associated with adolescent social network structure, including peer marginalization, peer withdrawal, and homophily related to CHCs, after controlling for alternative social network processes. Although previous literature suggests that adolescents with CHCs experience negative social consequences, the current findings demonstrate that the social network structure of adolescents with CHCs did not differ significantly from that of their peers without CHCs. Thus, findings from the current study suggest that CHCs are not related to objective reductions in social connections.

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.

Efficient Exploration of Many Variables and Interactions Using Regularized Regression.

The prevention sciences often face several situations that can compromise the statistical power and validity of a study. Among these, research can (1) have data with many variables, sometimes with low sample sizes, (2) have highly correlated predictors, (3) have unclear theory or empirical evidence related to the research questions, and/or (4) have difficulty selecting the proper covariates in observational studies. Modeling in these situations is difficult-and at times impossible-with conventional methods. Fortunately, regularized regression-a machine learning technique-can aid in exploring datasets that are otherwise difficult to analyze, allowing researchers to draw insights from these data. Although many of these methods have existed for several decades, prevention researchers rarely use them. As a gentle introduction, we discuss the utility of regularized regression to the field of prevention science and apply the technique to a real dataset. The data (n = 7979) for the demonstration consisted of 76 variables (151 including the modeled interactions) from the Youth Risk-Behavior Surveillance System (YRBSS) from 2015. Overall, it is clear that regularized regression can be an important tool in analyzing and gaining insight from data in the prevention sciences.

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.

Network-behavior dynamics of adolescent friendships, alcohol use, and physical activity.

OBJECTIVE: The coevolution of adolescent social networks, alcohol use, and physical activity is studied. Previous research has independently evaluated each behavior, overlooking the potential power of examining their development within a shared social context. The current study extends previous research by examining the dynamics of friendship networks, alcohol use, and physical activity in conjunction, including the concurrent engagement in both behaviors, with a nationally representative sample of U.S. adolescents. Special attention is paid to differing patterns of peer selection and peer assimilation across behaviors. METHOD: Data come from 2 high schools (n = 640; n = 1,156) within the National Longitudinal Study of Adolescent to Adult Health. Longitudinal stochastic actor-based models were used to separate peer selection and assimilation processes to differentiate the mechanisms linking friendships and both behaviors as well as the relationship between alcohol use and physical activity. RESULTS: Findings suggest distinct differences in the importance of peer selection and assimilation processes to adolescent alcohol use and physical activity. In both schools, adolescents selected friends based on similarity in alcohol use, but no selection effect was found for physical activity. Conversely, assimilation to friends' behavior occurred for physical activity, yet evidence for alcohol assimilation was mixed. No significant relationship between alcohol use and physical activity emerged. CONCLUSIONS: Intervention efforts that focus on friend influence in changing health behavior may have particular success with adolescent physical activity. Programs aimed at alcohol use would benefit from including an emphasis on preventing negative friend formations. (PsycINFO Database Record

Prospective Relations among Low-Income African American Adolescents' Maternal Attachment Security, Self-Worth, and Risk Behaviors.

This study examined prospective mediating relations among mother-adolescent attachment security, self-worth, and risk behaviors, including substance use and violence, across ages 13-17 in a sample of 901 low-income African American adolescents. Path analyses revealed that self-worth was a significant mediator between attachment security and risk behaviors, such that earlier attachment security predicted self-worth 1 year later, which in turn, predicted substance use, weapon carrying, and fighting in the 3rd year. Implications for the role of the secure base concept within the context of urban poverty are discussed.

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

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.

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.

Ecological momentary assessment of urban adolescents' technology use and cravings for unhealthy snacks and drinks: differences by ethnicity and sex.

BACKGROUND: Adolescents' technology use is generally associated with food cravings, but it is not clear whether specific types of technology elicit particular types of cravings or whether personal characteristics play a role in these associations. OBJECTIVE: We examined whether momentary associations between four technology types (ie, television, video games, computer messaging, and phone messaging) and cravings for unhealthy snack foods and sweetened drinks were moderated by youths' sex, ethnicity, body mass index, and age. METHODS: Urban adolescents (N=158) aged 14 to 17 years provided momentary information about their technology use and food cravings during the course of 1 week and completed survey reports of their personal characteristics. We used multilevel modeling to determine momentary associations and interactions. RESULTS: Non-Hispanic adolescents showed stronger associations between television exposure and cravings for sweet snacks, salty snacks, and sweetened drinks. Being Hispanic was associated with stronger associations between phone messaging and cravings for sweet snacks, salty snacks, and sweetened drinks. Males showed stronger associations between video game use and salty snack cravings. CONCLUSIONS: As the public health field continues to monitor the effects of technology use on adolescents' eating and overall health, it will be important to determine the extent to which these groups are differentially affected by different forms of technology.

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 deeper 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 CES-D 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.

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.

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.

Body mass index as a predictor of firefighter injury and workers' compensation claims.

OBJECTIVE: To determine the relationship between lifestyle variables including body mass index and filing a workers' compensation claim due to firefighter injury. METHODS: A cross-sectional evaluation of firefighter injury related to workers" compensation claims occurring 5 years after the original Promoting Healthy Lifestyles: Alternative Models' Effects study intervention. RESULTS: A logistic regression analysis for variables predicting filing a workers' compensation claim due to an injury was performed with a total of 433 participants. The odds of filing a compensation claim were almost 3 times higher for firefighters with a body mass index of more than 30 kg/m than firefighters with a normal body mass index (odds ratio, 2.89; P < 0.05). CONCLUSIONS: This study addresses a high-priority area of reducing firefighter injuries and workers' compensation claims. Maintaining a healthy body weight is important to reduce injury and workers' compensation claims among firefighters.

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.

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.