Friend or foe? Postdivorce hostility among recently divorced individuals

High levels of hostility often occur during and postdivorce and may significantly affect the quality of life, parent–child relationships, and social functioning of divorcees. Moreover, hostility may predict aggressive and violent behavior. This study sought to (a) compare average general hostility levels of a large sample of Danish divorcees to the norms of the general adult Danish population, (b) compare general hostility levels between male and female divorcees, and (c) investigate the explanatory value of various sociodemographic and divorce-related factors on postdivorce general hostility and whether these factors differ across gender. Cross-sectional baseline data (N = 1,856) from a larger randomized controlled trial study was used in this study. Normative data from a general sample of Danish adults (N = 2,040) was used for comparisons of hostility levels between our study sample and the Danish background population. This study found that male and female divorcees did not report significantly different hostility levels. However, participants reported significantly higher hostility levels postdivorce than the comparative Danish norm sample. Significant predictors of postdivorce hostility were lower age, lower educational level, infidelity as a reason for divorce, higher degree of postdivorce conflict, worse communication with the former spouse, the former spouse as the initiator of the divorce, and new partner status with neither divorcees having a new partner, or only the former spouse having a new partner. The predictive strength of the factors did not differ across gender. The findings may be especially relevant for interventions targeting problematic outcomes postdivorce (e.g., preventing aggressive behavior).     

Comparison of Common Amplitude Metrics in Event-Related Potential Analysis

Abstract

Waveform data resulting from time-intensive longitudinal designs require careful treatment. In particular, the statistical properties of summary metrics in this area are crucial. We draw on event-related potential (ERP) studies, a field with a relatively long history of collecting and analyzing such data, to illustrate our points. In particular, three summary measures for a component in the average ERP waveform feature prominently in the literature: the maximum (or peak amplitude), the average (or mean amplitude) and a combination (or adaptive mean). We discuss the methodological divide associated with these summary measures. Through both analytic work and simulation study, we explore the properties (e.g., Type I and Type II errors) of these competing metrics for assessing the amplitude of an ERP component across experimental conditions. The theoretical and simulation-based arguments in this article illustrate how design (e.g., number of trials per condition) and analytic (e.g., window location) choices affect the behavior of these amplitude summary measures in statistical tests and highlight the need for transparency in reporting the analytic steps taken. There is an increased need for analytic tools for waveform data. As new analytic methods are developed to address these time-intensive longitudinal data, careful treatment of the statistical properties of summary metrics used for null hypothesis testing is crucial.     

Analysis of Interactions and Nonlinear Effects with Missing Data: A Factored Regression Modeling Approach Using Maximum Likelihood Estimation

Abstract

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x²) or are included in interaction terms (e.g., x·z). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software.     

Attachment to parents and to God: a panel-data approach

ABSTRACT

Research at the nexus of attachment theory and religion has generally been constrained both by data limitations and by a view of attachment style as fixed early in life. I use three waves of data to test key hypotheses from this literature in new ways. Closeness to mother and closeness to father serve as proxies for attachment to parents and closeness to God serves as a proxy for attachment to God. The correspondence hypothesis predicts that people who feel closer to their parents feel closer to God, the compensation hypothesis predicts that people lacking a parent feel closer to God, and the socialized correspondence hypothesis predicts that people with nonreligious parents will feel less close to God if they feel closer to their parents. I find strong evidence in favor of the correspondence hypothesis, but I find no evidence in favor of the compensation hypothesis or the socialized correspondence hypothesis.     

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Abstract

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.