Fitting growth curve models in the Bayesian framework.

Growth curve modeling is a popular methodological tool due to its flexibility in simultaneously analyzing both within-person effects (e.g., assessing change over time for one person) and between-person effects (e.g., comparing differences in the change trajectories across people). This paper is a practical exposure to fitting growth curve models in the hierarchical Bayesian framework. First the mathematical formulation of growth curve models is provided. Then we give step-by-step guidelines on how to fit these models in the hierarchical Bayesian framework with corresponding computer scripts (JAGS and R). To illustrate the Bayesian GCM approach, we analyze a data set from a longitudinal study of marital relationship quality. We provide our computer code and example data set so that the reader can have hands-on experience fitting the growth curve model.

Challenges to the Pair Bond: Neural and Hormonal Effects of Separation and Reunion in a Monogamous Primate.

Social monogamy at its most basic is a group structure in which two adults form a unit and share a territory. However, many socially monogamous pairs display attachment relationships known as pair bonds, in which there is a mutual preference for the partner and distress upon separation. The neural and hormonal basis of this response to separation from the adult pair mate is under-studied. In this project, we examined this response in male titi monkeys (Callicebus cupreus), a socially monogamous New World primate. Males underwent a baseline scan, a short separation (48 h), a long separation (approximately 2 weeks), a reunion with the female pair mate and an encounter with a female stranger (with nine males completing all five conditions). Regional cerebral glucose metabolism was measured via positron emission tomography (PET) imaging using [18F]-fluorodeoxyglucose (FDG) co-registered with structural magnetic resonance imaging (MRI), and region of interest (ROI) analysis was carried out. In addition, plasma was collected and assayed for cortisol, oxytocin (OT), vasopressin (AVP), glucose and insulin concentrations. Cerebrospinal fluid (CSF) was collected and assayed for OT and AVP. We used generalized estimating equations (GEE) to examine significant changes from baseline. Short separations were characterized by decreases in FDG uptake, in comparison to baseline, in the lateral septum (LS), ventral pallidum (VP), paraventricular nucleus of the hypothalamus (PVN), periaqueductal gray (PAG), and cerebellum, as well as increases in CSF OT, and plasma cortisol and insulin. Long separations differed from baseline in reduced FDG uptake in the central amygdala (CeA), reduced whole brain FDG uptake, increased CSF OT and increased plasma insulin. The response on encounter with a stranger female depended on whether or not the male had previously reproduced with his pair mate, suggesting that transitions to fatherhood contribute to the neurobiology underlying response to a novel female. Reunion with the partner appeared to stimulate coordinated release of central and peripheral OT. The observed changes suggest the involvement of OT and AVP systems, as well as limbic and striatal areas, during separation and reunion from the pair mate.

Do People Agree on What Makes One Feel Loved? A Cognitive Psychometric Approach to the Consensus on Felt Love.

This pragmatic study examines love as a mode of communication. Our focus is on the receiver side: what makes an individual feel loved and how felt love is defined through daily interactions. Our aim is to explore everyday life scenarios in which people might experience love, and to consider people's converging and diverging judgments about which scenarios indicate felt love. We apply a cognitive psychometric approach to quantify a receiver's ability to detect, understand, and know that they are loved. Through crowd-sourcing, we surveyed lay participants about whether various scenarios were indicators of felt love. We thus quantify these responses to make inference about consensus judgments of felt love, measure individual levels of agreement with consensus, and assess individual response styles. More specifically, we (1) derive consensus judgments on felt love; (2) describe its characteristics in qualitative and quantitative terms, (3) explore individual differences in both (a) participant agreement with consensus, and (b) participant judgment when uncertain about shared knowledge, and (4) test whether individual differences can be meaningfully linked to explanatory variables. Results indicate that people converge towards a shared cognitive model of felt love. Conversely, respondents showed heterogeneity in knowledge of consensus, and in dealing with uncertainty. We found that, when facing uncertainty, female respondents and people in relationships more frequently judge scenarios as indicators of felt love. Moreover, respondents from smaller households tend to know more about consensus judgments of felt love, while respondents from larger households are more willing to guess when unsure of consensus.

Alternative Models for Small Samples in Psychological Research: Applying Linear Mixed Effects Models and Generalized Estimating Equations to Repeated Measures Data.

Unavoidable sample size issues beset psychological research that involves scarce populations or costly laboratory procedures. When incorporating longitudinal designs these samples are further reduced by traditional modeling techniques, which perform listwise deletion for any instance of missing data. Moreover, these techniques are limited in their capacity to accommodate alternative correlation structures that are common in repeated measures studies. Researchers require sound quantitative methods to work with limited but valuable measures without degrading their data sets. This article provides a brief tutorial and exploration of two alternative longitudinal modeling techniques, linear mixed effects models and generalized estimating equations, as applied to a repeated measures study (n = 12) of pairmate attachment and social stress in primates. Both techniques provide comparable results, but each model offers unique information that can be helpful when deciding the right analytic tool.