Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels.

In multilevel models, disaggregating predictors into level-specific parts (typically accomplished via centering) benefits parameter estimates and their interpretations. However, the importance of level-specificity has been sparsely addressed in multilevel literature concerning collinearity. In this study, we develop novel insights into the interactivity of centering and collinearity in multilevel models. After integrating the broad literatures on centering and collinearity, we review level-specific and conflated correlations in multilevel data. Next, by deriving formal relationships between predictor collinearity and multilevel model estimates, we demonstrate how the consequences of collinearity change across different centering specifications and identify data characteristics that may exacerbate or mitigate those consequences. We show that when all or some level-1 predictors are uncentered, slope estimates can be greatly biased by collinearity. Disaggregation of all predictors eliminates the possibility that fixed effect estimates will be biased due to collinearity alone; however, under some data conditions, collinearity is associated with biased standard errors and random effect (co)variance estimates. Finally, we illustrate the importance of disaggregation for diagnosing collinearity in multilevel data and provide recommendations for the use of level-specific collinearity diagnostics. Overall, the necessity of disaggregation for identifying and managing collinearity's consequences in multilevel models is clarified in novel ways.

Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model.

Variability in treatment effects is common in intervention studies using cluster randomized controlled trial (C-RCT) designs. Such variability is often examined in multilevel modeling (MLM) to understand how treatment effects (TRT) differ based on the level of a covariate (COV), called TRT × COV. In detecting TRT × COV effects using MLM, relationships between covariates and outcomes are assumed to vary across clusters linearly. However, this linearity assumption may not hold in all applications and an incorrect assumption may lead to biased statistical inference about TRT × COV effects. In this study, we present generalized additive mixed model (GAMM) specifications in which cluster-specific functional relationships between covariates and outcomes can be modeled using by-variable smooth functions. In addition, the implementation for GAMM specifications is explained using the mgcv R package (Wood, 2021). The usefulness of the GAMM specifications is illustrated using intervention data from a C-RCT. Results of simulation studies showed that parameters and by-variable smooth functions were recovered well in various multilevel designs and the misspecification of the relationship between covariates and outcomes led to biased estimates of TRT × COV effects. Furthermore, this study evaluated the extent to which the GAMM can be treated as an alternative model to MLM in the presence of a linear relationship.

Recruiting Fathers for Parenting Research: An Evaluation of Eight Recruitment Methods and an Exploration of Fathers' Motivations for Participation.

Objective: We evaluated eight recruitment methods (Craigslist, Facebook ads, Google AdWords, in-person, newspaper, parenting magazines, ResearchMatch, and direct mailing) in terms of their ability to accrue fathers of 3- to 7-year-old children into a laboratory-based behavioral trial for parents. The trial was related to child obesity risk and parental health behaviors. Design: Each recruitment method was implemented such that half its occurrences advertised for fathers only, and half advertised for mothers and fathers. Methods were evaluated in terms of number of fathers recruited, cost- and time-efficiency, response rates, and demographic characteristics of individuals recruited. We also assessed fathers' and mothers' motivations for participating in the study. 101 fathers and 260 mothers were recruited. Results: Father-targeted ads were essential for father recruitment; 79% of accruals from father-targeted ads were male, whereas only 14% of accruals from parent-targeted ads were male. Craigslist, ResearchMatch, and Facebook ads were the most cost-efficient for accruing fathers. A greater proportion of fathers was motivated by increasing fathers' representation in research (16%) compared to mothers who wished to increase mothers' representation in research (5.4%). Similar proportions of fathers and mothers were motivated by improving their parenting knowledge and improving their child's health. Conclusions: Future researchers should employ father-targeted recruitment materials (rather than parent-targeted) that capitalize on fathers' unique motivations for participating in research.

Centering categorical predictors in multilevel models: Best practices and interpretation.

The topic of centering in multilevel modeling (MLM) has received substantial attention from methodologists, as different centering choices for lower-level predictors present important ramifications for the estimation and interpretation of model parameters. However, the centering literature has focused almost exclusively on continuous predictors, with little attention paid to whether and how categorical predictors should be centered, despite their ubiquity across applied fields. Alongside this gap in the methodological literature, a review of applied articles showed that researchers center categorical predictors infrequently and inconsistently. Algebraically and statistically, continuous and categorical predictors behave the same, but researchers using them do not, and for many, interpreting the effects of categorical predictors is not intuitive. Thus, the goals of this tutorial article are twofold: to clarify why and how categorical predictors should be centered in MLM, and to explain how multilevel regression coefficients resulting from centered categorical predictors should be interpreted. We first provide algebraic support showing that uncentered coding variables result in a conflated blend of the within- and between-cluster effects of a multicategorical predictor, whereas appropriate centering techniques yield level-specific effects. Next, we provide algebraic derivations to illuminate precisely how the within- and between-cluster effects of a multicategorical predictor should be interpreted under dummy, contrast, and effect coding schemes. Finally, we provide a detailed demonstration of our conclusions with an empirical example. Implications for practice, including relevance of our findings to categorical control variables (i.e., covariates), interaction terms with categorical focal predictors, and multilevel latent variable models, are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Modelling multilevel nonlinear treatment-by-covariate interactions in cluster randomized controlled trials using a generalized additive mixed model.

A cluster randomized controlled trial (C-RCT) is common in educational intervention studies. Multilevel modelling (MLM) is a dominant analytic method to evaluate treatment effects in a C-RCT. In most MLM applications intended to detect an interaction effect, a single interaction effect (called a conflated effect) is considered instead of level-specific interaction effects in a multilevel design (called unconflated multilevel interaction effects), and the linear interaction effect is modelled. In this paper we present a generalized additive mixed model (GAMM) that allows an unconflated multilevel interaction to be estimated without assuming a prespecified form of the interaction. R code is provided to estimate the model parameters using maximum likelihood estimation and to visualize the nonlinear treatment-by-covariate interaction. The usefulness of the model is illustrated using instructional intervention data from a C-RCT. Results of simulation studies showed that the GAMM outperformed an alternative approach to recover an unconflated logistic multilevel interaction. In addition, the parameter recovery of the GAMM was relatively satisfactory in multilevel designs found in educational intervention studies, except when the number of clusters, cluster sizes, and intraclass correlations were small. When modelling a linear multilevel treatment-by-covariate interaction in the presence of a nonlinear effect, biased estimates (such as overestimated standard errors and overestimated random effect variances) and incorrect predictions of the unconflated multilevel interaction were found.

Decision-making about genetic health information among family dyads: a systematic literature review.

Decisions involving two individuals (i.e., dyadic decision-making) have been increasingly studied in healthcare research. There is evidence of bi-directional influences in decision-making processes among spousal, provider-patient and parent-child dyads. Genetic information can directly impact biologically related individuals. Thus, it is important to understand dyadic decision-making about genetic health information among family members. This systematic literature review aimed to identify literature examining decision-making among family dyads. Peer-reviewed publications were included if they reported quantitative empirical research on dyadic decision-making about genetic information, published between January 1998 and August 2020 and written in English. The search was conducted in 6 databases and returned 3167 articles, of which 15 met the inclusion criteria. Most studies were in the context of cancer genetic testing (n = 8) or reproductive testing or screening (n = 5). Studies reported two broad categories of decisions with dyadic influence: undergoing screening or testing (n = 10) and sharing information with family (n = 5). Factors were correlated between dyads such as attitudes, knowledge, behaviors and psychological wellbeing. Emerging evidence shows that dyad members influence each other when making decisions about receiving or sharing genetic information. Our findings emphasize the importance of considering both members of a dyad in intervention design and clinical interactions.

Zoom or In-Person: An Ecological Momentary Assessment Study of Time with Friends and Depressive Symptoms on Affect in Emerging Adults.

INTRODUCTION: Depression is associated with increased negative affect (NA) and low positive affect (PA), as well as interpersonal difficulties. Although most studies examine symptoms and affect at only one time point, ecological momentary assessment (EMA) captures data on affect and activity in real time and across contexts. The present study used EMA to explore the links between in-person and virtual social interactions, depressive symptoms, and momentary affect. METHODS: Emerging adults (N=86) completed a self-report measure of both general depression and dysphoria symptoms, followed by EMA surveys 8 times daily for one week, reporting momentary affect (PA and NA) and social context (in-person and virtual interactions with friends). RESULTS: In-person, but not virtual, presence of friends was associated with increased PA overall. Depressive symptoms were linked to less time with in-person friends and elevated NA. There was also a significant interaction between in-person presence of friends and dysphoria in predicting NA, such that presence of in-person friends was associated with lower NA only for those low in dysphoria. DISCUSSION: Findings suggest that time with in-person friends is associated with increased PA overall and decreased NA only for those lower in depressive symptoms, but these effects do not generalize to virtual interactions. Those with greater depressive symptoms may be less responsive to positive stimuli and experience less of an effect of friends' presence on affect. Alternatively, maladaptive social behaviors characteristic of depression may influence the effect of social interactions on affect.

Investigating the Efficacy of Genetic, Environmental, and Multifactorial Risk Information When Communicating Obesity Risk to Parents of Young Children.

BACKGROUND: Effectively communicating with parents about children's obesity risk is of critical importance for preventive medicine and public health. PURPOSE: The current study investigates the efficacy of communications focused on two primary causes of obesity: genes and environment. METHODS: We compared parental feeding responses to messages focused on (i) genetics alone, (ii) family environment alone, (iii) genetics-family environment interaction (G × FE), and (iv) no causal message. We also examined whether parental guilt mediates the effect of message type on feeding. Our sample consisted of 190 parents, half mothers and half fathers, of children 3-7 years old. After receiving one of the four types of messages, parents chose foods for their child using the Virtual Reality Buffet measure. Parents responded to questionnaires in the lab and at 1-week follow-up. RESULTS: In the VR Buffet, parents did not feed their children differently in message provision conditions versus control. There were, however, differences among message provision conditions wherein mothers who received any genetic information chose higher-calorie meals in the VR Buffet. At 1-week follow-up, parents who received information about genetics alone reported feeding their child more junk food and fatty meat on self-report food frequency assessments; there were no such differences for sugary beverages, sugary foods, or fast foods. Parental guilt was typically higher for participants who received family environment information alone but did not mediate the relation between information provision and feeding outcomes. CONCLUSIONS: While none of the messages improved feeding above the control condition, GxFE messages were associated with a better overall profile of outcomes. As such, it may be beneficial for messaging for parents about children's obesity risk to include content that reflects the complexity of genetic and environmental contributions to obesity risk.

Parents' genetic attributions for children's eating behaviors: Relationships with beliefs, emotions, and food choice behavior.

Considering genetic influences on children's eating behavior could result in reduced self-efficacy for healthy child feeding and less healthy feeding behavior among parents. Indeed, one's eating behaviors are typically thought of as the volitional aspects of weight management that one can directly control. The current study assessed parental genetic attributions for their child's eating behavior, and relationships between these attributions and self-efficacy, guilt, and feeding behaviors. Participants included 190 parents of a child between 4 and 7 years old. Parents' genetic attributions for child eating behaviors were lower than genetic attributions related to child weight. Higher genetic attributions for child eating behaviors were related to lower self-efficacy for feeding the right amounts of food, higher-calorie food choices for the child in a virtual reality-based buffet simulation, and higher levels of guilt. The current findings suggest that heightened beliefs about role of genetics in children's eating behavior is associated with maladaptive affect and behavior among parents. This should be kept in mind when considering whether, when, and how to provide information to parents highlighting the role of genetics in children's eating behavior.

Path Tortuosity in Virtual Reality: A Novel Approach for Quantifying Behavioral Process in a Food Choice Context.

There is a pressing need to better understand how parents make feeding decisions for their children, but extant measures focus primarily on outcomes rather than examining the process of food choice as it unfolds. This exploratory study examined parents' translational movement as they moved throughout a virtual reality-based buffet restaurant to select a lunch for their child. Our aim was to explore whether translational movement would be related to cognitive and affective variables that underlie motivation, effort, and ultimate choices within food decision-making contexts (e.g., guilt, self-efficacy). Movement data were quantified in terms of path tortuosity: the degree of straightness of one's path while traveling through a space. Greater path tortuosity predicted a reduction in parents' guilt about their child feeding, above and beyond actual food chosen. Results suggest path tortuosity serves as an implicit measure of effort put forth by parents throughout the food decision-making process. Future work should continue to explore the utility of novel metrics that can be obtained from unique data sources, such as location tracking, for elucidating complicated behavioral processes such as food choice.

Effects of Fruit and Vegetable Feeding Messages on Mothers and Fathers: Interactions Between Emotional State and Health Message Framing.

BACKGROUND: There is a pressing need to craft optimal public health messages promoting healthy feeding behaviors among parents. How these messages influence such feeding decisions are affected by multiple interactive factors including emotional states, message framing, and gender, but these factors have not been studied in the domain of parents' feeding of their children. PURPOSE: To evaluate the role of message framing, emotional state, and parent gender on feeding choices that parents make for their children. METHODS: In 2016-2017, 190 parents (126 mothers) of 4- to 7-year-old children were randomly assigned to an anger or fear emotion induction and read either a gain- or loss-framed message about the importance of children's fruit and vegetable (FV) consumption prior to choosing foods for their child from a virtual reality buffet. RESULTS: Mothers in an angry state who received a gain-framed message chose relatively more FV for their child in the virtual buffet, F(3, 180) = 4.77, p = .027. However, fathers in this group did not feed more FV, but rather reported greater intention to improve future FV feeding, F(3, 180) = 4.91, p = .028. CONCLUSIONS: Providing gain-framed messages to parents, particularly mothers, in an anger state may be most effective for motivating healthy dietary choices for children. CLINICAL TRIAL INFORMATION: clinicaltrials.gov NCT02622035.

Mothers' and fathers' cognitive and affective responses to epigenetics concepts.

Advances in our understanding of epigenetics present new opportunities to improve children's health through the counseling of parents about epigenetics concepts. However, it is important to first evaluate how parents respond to this type of information and determine the consequences of educating parents about epigenetics. We have taken an initial step toward this goal by assessing parental responses to an epigenetics learning module. Parents (n = 190, 126 mothers) responded to pre- and post-module survey questions. Prior to the module, parents reported that mothers' lifestyles prior to conception were more important for children's health than fathers' lifestyles prior to conception (t = 4.49, df = 316.5, P < 0.0001). However, after the module, there was no difference between ratings of the importance of mothers' and fathers' preconception lifestyles (t = 1.18, df = 319.8, P = NS). Furthermore, after viewing the module, parents increased their ratings of the importance of both mothers' (t = -5.65, df = 294.8, P < 0.0001) and father's (t = -9.01, df = 287.2, P < 0.0001) preconception lifestyles for child health. After viewing the module, most parents reported feelings of guilt and negativity regarding epigenetics (78 and 55%, respectively). When compared with lean parents, parents with overweight more often reported feelings of guilt (χ 2 =10.27, P = 0.001). This work represents an important first step in evaluating parental responses to epigenetics concepts.

Tracing Physical Behavior in Virtual Reality: A Narrative Review of Applications to Social Psychology.

Virtual reality (VR) offers unique benefits to social psychological research, including a high degree of experimental control alongside strong ecological validity, a capacity to manipulate any variable of interest, and an ability to trace the physical, nonverbal behavior of the user in a very fine-grained and automated manner. VR improves upon traditional behavioral measurement techniques (e.g., observation and coding) on several fronts as data collection is covert, continuous, passive, and occurs within a controlled context. The current review synthesizes extant methods for tracing physical behavior in VR, such as gaze tracking and interpersonal distance measurement, and describes how researchers have applied these methods to understand important phenomena within the context of social psychology. To date, primary areas of application have included the assessment of social approach and avoidance, social evaluation and bias, and engagement. The limitations of behavioral tracing methods in VR, as well as future directions for their continued application and extension, are discussed. This narrative review equips readers with a thorough understanding of behavioral tracing methods that can be implemented in VR, their benefits and drawbacks, the insight they may offer into social processes, and future avenues of work for applying emergent technologies to research questions in social psychology.