Longitudinal interplay of young children's negative affectivity and maternal interaction quality in the context of unequal psychosocial resources

Interaction quality and child temperament predict early and later child development. Research hints at transactional interrelations of both aspects but lacks adequate data to examine this assumption. Maternal psychosocial resources are suspected moderators in this context but rarely taken into account. Drawing on data of the German National Educational Panel Study we conducted a cross-lagged panel analysis on the longitudinal interplay of maternal interaction quality and children's negative affectivity at 6–8, 16–18, and 25–27 months and compared mothers with and without accumulated strains. Both variables showed moderate to high structural and rank order stability over time and low but increasing connections. In the case of accumulated stress factors, interaction quality is clearly impaired and high negative affectivity acts as an additional burden while low negative affectivity helps strained mothers to maintain higher interaction quality but only in the first year of life.     

Efficient nonparametric approaches for estimating the operating characteristics of discrete item responses

Rationale and the actual procedures of two nonparametric approaches, called Bivariate P.D.F. Approach and Conditional P.D.F. Approach, for estimating the operating characteristic of a discrete item response, or the conditional probability, given latent trait, that the examinee's response be that specific response, are introduced and discussed. These methods are featured by the facts that: (a) estimation is made without assuming any mathematical forms, and (b) it is based upon a relatively small sample of several hundred to a few thousand examinees.Some examples of the results obtained by the Simple Sum Procedure and the Differential Weight Procedure of the Conditional P.D.F. Approach are given, using simulated data. The usefulness of these nonparametric methods is also discussed.This research was mostly supported by the Office of Naval Research (N00014-77-C-0360, N00014-81-C-0569, N00014-87-K-0320, N00014-90-J-1456).     

Response preferences of monkeys (Macaca mulatta) within wavelength and line-tilt dimensions

Four rhesus monkeys were tested for preferences within the wavelength and line-tilt dimensions. In the case of wavelength, the response panel was back-illuminated by light of one of the following wavelengths, presented in a random manner: 470, 525, 580, and 635 nm. Similarly, the line-tilt dimension was studied, by presenting a 5 cm by 0.3 cm black bar tilted at 0, 30, 60, or 90 degrees. No preferences were found within this latter dimension; in contrast, marked wavelength preferences existed, the order of preference being 470 (most preferred), 525, 580, and 635 nm (least preferred). These response preferences were resistant to behavioral manipulation; the number of responses to blue and to red in extinction was about equal when red was used as the training stimulus, but vastly different following training on blue. These results indicate that such response preferences must be taken into account in the design of a wide variety of experiments.     

Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we tested the effects of violating an implicit assumption often made in these models; that is, independent variables in the model are not directly related to latent classes. Results indicate that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. In addition, we tested whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a reanalysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted.     

Dealing with Multiple Solutions in Structural Vector Autoregressive Models

Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.     

Time-Varying Treatments in Observational Studies: Marginal Structural Models of the Effects of Early Grade Retention on Math Achievement

One of the main objectives of many empirical studies in the social and behavioral sciences is to assess the causal effect of a treatment or intervention on the occurrence of a certain event. The randomized controlled trial is generally considered the gold standard to evaluate such causal effects. However, for ethical or practical reasons, social scientists are often bound to the use of nonexperimental, observational designs. When the treatment and control group are different with regard to variables that are related to the outcome, this may induce the problem of confounding. A variety of statistical techniques, such as regression, matching, and subclassification, is now available and routinely used to adjust for confounding due to measured variables. However, these techniques are not appropriate for dealing with time-varying confounding, which arises in situations where the treatment or intervention can be received at multiple timepoints. In this article, we explain the use of marginal structural models and inverse probability weighting to control for time-varying confounding in observational studies. We illustrate the approach with an empirical example of grade retention effects on mathematics development throughout primary school.     

A Note on Recurring Misconceptions When Fitting Nonlinear Mixed Models

Nonlinear mixed-effects (NLME) models are used when analyzing continuous repeated measures data taken on each of a number of individuals where the focus is on characteristics of complex, nonlinear individual change. Challenges with fitting NLME models and interpreting analytic results have been well documented in the statistical literature. However, parameter estimates as well as fitted functions from NLME analyses in recent articles have been misinterpreted, suggesting the need for clarification of these issues before these misconceptions become fact. These misconceptions arise from the choice of popular estimation algorithms, namely, the first-order linearization method (FO) and Gaussian-Hermite quadrature (GHQ) methods, and how these choices necessarily lead to population-average (PA) or subject-specific (SS) interpretations of model parameters, respectively. These estimation approaches also affect the fitted function for the typical individual, the lack-of-fit of individuals’ predicted trajectories, and vice versa.     

The relationship between perceived organizational support and proactive behaviour directed towards the organization

Numerous studies have shown that perceived organizational support has positive consequences on both employees and organizations. However, no study has examined the causal relationship between perceived organizational support and proactive behaviour directed towards the organization and few studies have identified potential mechanisms underlying this relationship. We first investigated the direction of causality between perceived organizational support and proactive behaviour, and hypothesized that perceived organizational support leads to proactive behaviour directed towards the organization. Second, based on social exchange and motivational perspectives, we examined two potential mechanisms underlying this relationship, i.e., felt obligation and work engagement. Employees from a Belgian Federal Public Service were invited to take part in a three-wave longitudinal survey. Using a cross-lagged panel design, our results (N = 1963) revealed that Time 2 perceived organizational support was positively related to Time 3 proactive behaviour towards the organization, whereas the reverse was not true. Additionally, our results (N = 1293) showed that Time 2 felt obligation and Time 2 work engagement mediated the positive relationship between Time 1 perceived organizational support and Time 3 proactive behaviour directed towards the organization.     

Happiness and life satisfaction in Rwanda

This study investigated predictors of happiness and life satisfaction in Rwanda. Data from the World Values Survey and gathered from 3 030 Rwandese (age ranging 16 to 90 years, mean age = 34.2, SD = 12.7; females = 50.5%) were pooled for the analysis. For the comparison, international World Values Survey data were utilised. A fixed effects multilevel regression model was used to predict happiness and life satisfaction from gender, health, socio-economic, and some subjective measures. Males had greater self-rated happiness and life satisfaction scores than females. State of health and sense of freedom of choice predicted both happiness and life satisfaction. Valuing of friends, weekly religious attendance, and national pride positively predicted happiness, whereas household’s financial satisfaction, full-time employment, high-income group, being a student, and sense of trust predicted life satisfaction. This study suggests that health status, household’s financial satisfaction and emancipative values could maximise subjective well-being in Rwanda.