Couple adjustment to a stressful life event: a dyadic investigation of the roles of positive reframing and perceived benefits

Background and Objectives: This study examines positive reframing (a form of meaning making), perceived benefits (a form of meanings made) and adjustment in couples who experienced a stressful life event in the past year. This study tested whether couple members’ scores were nonindependent and whether one’s own perceived benefits was predicted by their own positive reframing (actor effect) as well as their partner’s positive reframing (partner effect). Further, this study tested actor and partner effects for the link between perceived benefits and adjustment and whether positive reframing (the initial variable) works through perceived benefits (the mediator) to affect adjustment (the outcome) at the dyadic level.

Design: A standard dyadic design was used.

Methods: Eighty couples completed measures of positive reframing, perceived benefits, and adjustment (depression, anxiety, positive affect, life satisfaction, and relationship satisfaction).

Results: Partners’ scores on study variables were related, and although only actor effects were found for the path between positive reframing and perceived benefits, both actor and partner effects were found for the path between perceived benefits and adjustment. Mediation was found for actor–actor and actor-partner indirect effects.

Conclusions: Results indicate that a greater focus on interpersonal factors is needed to further meaning-making theory and inform practice.     

Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques

Autocorrelation and partial autocorrelation, which provide a mathematical tool to understand repeating patterns in time series data, are often used to facilitate the identification of model orders of time series models (e.g., moving average and autoregressive models). Asymptotic methods for testing autocorrelation and partial autocorrelation such as the 1/T approximation method and the Bartlett's formula method may fail in finite samples and are vulnerable to non-normality. Resampling techniques such as the moving block bootstrap and the surrogate data method are competitive alternatives. In this study, we use a Monte Carlo simulation study and a real data example to compare asymptotic methods with the aforementioned resampling techniques. For each resampling technique, we consider both the percentile method and the bias-corrected and accelerated method for interval construction. Simulation results show that the surrogate data method with percentile intervals yields better performance than the other methods. An R package pautocorr is used to carry out tests evaluated in this study.     

Cognitive diagnosis modelling incorporating item response times

To provide more refined diagnostic feedback with collateral information in item response times (RTs), this study proposed joint modelling of attributes and response speed using item responses and RTs simultaneously for cognitive diagnosis. For illustration, an extended deterministic input, noisy ‘and’ gate (DINA) model was proposed for joint modelling of responses and RTs. Model parameter estimation was explored using the Bayesian Markov chain Monte Carlo (MCMC) method. The PISA 2012 computer-based mathematics data were analysed first. These real data estimates were treated as true values in a subsequent simulation study. A follow-up simulation study with ideal testing conditions was conducted as well to further evaluate model parameter recovery. The results indicated that model parameters could be well recovered using the MCMC approach. Further, incorporating RTs into the DINA model would improve attribute and profile correct classification rates and result in more accurate and precise estimation of the model parameters.     

Two-Stage maximum likelihood estimation in the misspecified restricted latent class model

The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. The analysis is conducted in a general restricted latent class model framework addressing all types of CDMs. Sufficient conditions are proposed under which a consistent classification can be obtained by using a misspecified model. Discussions are also provided on the inconsistency of classification under certain model misspecification scenarios. Simulation studies and a real data application are conducted to illustrate these results. Our findings can provide some guidelines as to when a misspecified simple model or a general model can be used to provide a good classification result.     

Gender role attitudes,relationship efficacy,and self-disclosure in intimate relationships

Drawing from the intimacy process model and data from 5,042 individuals who remained partnered across Waves 1 and 2 of the German Panel Analysis of Intimate Relationships and Family Dynamics (pairfam), this study examined the contributions of traditional gender role attitudes and relationship efficacy in predicting levels of self-disclosure within an intimate relationship. Independent samples t-tests demonstrated females scored higher than males on self-disclosure and relationship efficacy measures but lower on traditional gender role attitudes. An ordinary least squares regression analysis revealed relationship efficacy was a stronger predictor of self-disclosure compared to traditional gender role attitudes, which were not associated with self-disclosure. The findings suggest attitudes with an interpersonal motivational system may be especially important for setting the intimacy process into motion within an intimate union.     

Toward a contemporary quantitative model of punishment

A direct‐suppression, or subtractive, model of punishment has been supported as the qualitatively and quantitatively superior matching law‐based punishment model (Critchfield, Paletz, MacAleese, & Newland, 2003; de Villiers, 1980; Farley, 1980). However, this conclusion was made without testing the model against its predecessors, including the original (Herrnstein, 1961) and generalized (Baum, 1974) matching laws, which have different numbers of parameters. To rectify this issue, we reanalyzed a set of data collected by Critchfield et al. (2003) using information theoretic model selection criteria. We found that the most advanced version of the direct‐suppression model (Critchfield et al., 2003) does not convincingly outperform the generalized matching law, an account that does not include punishment rates in its prediction of behavior allocation. We hypothesize that this failure to outperform the generalized matching law is due to significant theoretical shortcomings in model development. To address these shortcomings, we present a list of requirements that all punishment models should satisfy. The requirements include formal statements of flexibility, efficiency, and adherence to theory. We compare all past punishment models to the items on this list through algebraic arguments and model selection criteria. None of the models presented in the literature thus far meets all of the requirements.     

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male’s emotion dynamics over time, but not in the female’s—which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.     

Age and Sex Differences in Morningness/Eveningness Along the Life Span: A Cross-Sectional Study in Spain

Work psychology has noted the importance of considering the temporal dimension of behavior in organizations. Given that society widely operates on a 24-hr schedule, it is important to know how circadian typologies are distributed in the general population. In this study, diurnal preference was analyzed among 4,175 Spanish participants (61.3% women), 12–59 years old, who completed the Composite Scale of Morningness (Smith, Reilly, & Midkiff, 1989). Several measurement models of the CSM were analyzed using exploratory structural equation modeling and confirmatory factor analysis. The bifactor model showed the best fit, with a general factor (morningness/eveningness) and three subfactors (morningness, morning affect, and time of retiring). The age and sex factorial invariance of the bifactor model were tenable at the latent variance-covariance level. Next, age and sex differences analysis indicated a progressive increase in morningness with age, but over 40 years old, men were more morningness oriented than women were. These results indicate that morningness/eveningness can be considered a multidimensional construct and that psychosocial factors must be considered when estimating the prevalence of morningness/eveningness in different populations or countries.