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.     

Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling

Recently, verbal credibility assessment has been extended to the detection of deceptive intentions, the use of a model statement, and predictive modeling. The current investigation combines these 3 elements to detect deceptive intentions on a large scale. Participants read a model statement and wrote a truthful or deceptive statement about their planned weekend activities (Experiment 1). With the use of linguistic features for machine learning, more than 80% of the participants were classified correctly. Exploratory analyses suggested that liars included more person and location references than truth‐tellers. Experiment 2 examined whether these findings replicated on independent‐sample data. The classification accuracies remained well above chance level but dropped to 63%. Experiment 2 corroborated the finding that liars' statements are richer in location and person references than truth‐tellers' statements. Together, these findings suggest that liars may over‐prepare their statements. Predictive modeling shows promise as an automated veracity assessment approach but needs validation on independent data.     

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.     

Integrating Covariates into Social Relations Models: A Plausible Values Approach for Handling Measurement Error in Perceiver and Target Effects

The Social Relations Model (SRM) is a conceptual and analytical approach to examining dyadic behaviors and interpersonal perceptions within groups. In an SRM, the perceiver effect describes a person's tendency to perceive other group members in a certain way, whereas the target effect measures the tendency to be perceived by others in certain ways. In SRM research, it is often of interest to relate these individual SRM effects to covariates. However, the estimated individual SRM effects might not provide a very reliable measure of the true, unobserved SRM effects, resulting in distorted estimates of associations with other variables. This article introduces a plausible values approach that allows users to correct for measurement error when assessing the association of individual SRM effects with other individual difference variables. In the plausible values approach, the latent, true individual SRM effects are treated as missing values and are imputed from an imputation model by applying Bayesian estimation techniques. In a simulation study, the statistical properties of the plausible values approach are compared with two approaches that have been used in previous research. A data example from educational psychology is presented to illustrate how the plausible values approach can be implemented with the software WinBUGS.     

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.     

抑郁症的脑功能异常:来自静息态功能磁共振成像的证据

静息态功能磁共振成像是指在静息状态下测量的BOLD信号,即受试者安静地躺在扫描仪中,不给受试者任何特定的任务,受试者也不用做任何反应,此时受试者的大脑活动处于自发状态。通过使用该技术可以为抑郁症发作的临床现象提供神经影像学依据,以期为将来抑郁症的治疗提供生物标记。因此,本文综述了大量抑郁症患者在静息态脑功能方面的差异研究,发现了单、双相抑郁症,首、复发抑郁症,早发性、晚发性抑郁症,难治、非难治性抑郁症等不同类型的抑郁症在包括局部一致性(Re Ho)和低频振幅(ALFF)在内的功能分化以及包括功能连接密度(FCD)、功能同伦(VMHC)、复杂网络(Complex Network)和ROI功能连接在内的功能整合两大指标的改变。