The power to detect sex differences in IQ test scores using Multi-Group Covariance and Means Structure Analyses

Research into sex differences in general intelligence, g, has resulted in two opposite views. In the first view, a g-difference is nonexistent, while in the second view, g is associated with a male advantage. Past research using Multi-Group Covariance and Mean Structure Analysis (MG-CMSA) found no sex difference in g. This failure raised the question whether the g-difference is truly absent or whether MG-CMSA lacked statistical power to detect it. The present study used the likelihood ratio test to investigate the power to detect a g-difference in the WAIS-III factor structure with MG-CMSA. Various situations were examined including those reported in the literature. Results showed that power varies greatly among different scenarios. The scenarios based on previous results were associated with power coefficients of about 0.5–0.6. Implications of these findings are discussed in the light of research into sex differences in IQ.     

A framework for ML estimation of parameters of (mixtures of) common reaction time distributions given optional truncation or censoring

We present a framework for distributional reaction time (RT) analysis, based on maximum likelihood (ML) estimation. Given certain information relating to chosen distribution functions, one can estimate the parameters of these distributions and of finite mixtures of these distributions. In addition, left and/or right censoring or truncation may be imposed. Censoring and truncation are useful methods by which to accommodate outlying observations, which are a pervasive problem in RT research. We consider five RT distributions: the Weibull, the ex-Gaussian, the gamma, the log-normal, and the Wald. We employ quasi-Newton optimization to obtain ML estimates. Multicase distributional analyses can be carried out, which enable one to conduct detailed (across or within subjects) comparisons of RT data by means of loglikelihood difference tests. Parameters may be freely estimated, estimated subject to boundary constraints, constrained to be equal (within or over cases), or fixed. To demonstrate the feasibility of ML estimation and to illustrate some of the possibilities offered by the present approach, we present three small simulation studies. In addition, we present three illustrative analyses of real data.     

EZ does it! Extensions of the EZ-diffusion model

In this rejoinder, we address two of Ratcliff’s main concerns with respect to the EZ-diffusion model (Ratcliff, 2008). First, we introduce “robust-EZ,” a mixture model approach to achieve robustness against the presence of response contaminants that might otherwise distort parameter estimates. Second, we discuss an extension of the EZ model that allows the estimation of starting point as an additional parameter. Together with recently developed, user-friendly software programs for fitting the full diffusion model (Vandekerckhove & Tuerlinckx, 2007; Voss & Voss, 2007), the development of the EZ model and its extensions is part of a larger effort to make diffusion model analyses accessible to a broader audience, an effort that is long overdue.     

Differential involvement of left prefrontal cortex in inductive and deductive reasoning

While inductive and deductive reasoning are considered distinct logical and psychological processes, little is known about their respective neural basis. To address this issue we scanned 16 subjects with fMRI, using an event-related design, while they engaged in inductive and deductive reasoning tasks. Both types of reasoning were characterized by activation of left lateral prefrontal and bilateral dorsal frontal, parietal, and occipital cortices. Neural responses unique to each type of reasoning determined from the Reasoning Type (deduction and induction) by Task (reasoning and baseline) interaction indicated greater involvement of left inferior frontal gyrus (BA 44) in deduction than induction, while left dorsolateral (BA 8/9) prefrontal gyrus showed greater activity during induction than deduction. This pattern suggests a dissociation within prefrontal cortex for deductive and inductive reasoning.     

Self-Other Perspective and Sexual Attitudes Affect Estimates of Sexual Risk

This study evaluated the effects of self-other perspective, sexual attitudes, and a sexual victimization history on women’s estimates of sexual risk for a set of vignettes describing situations that they may face when interacting socially with men. One hundred and twenty-four women from a medium sized southwestern university in the US were assigned to two different conditions (imagine self vs. an anonymous woman) and asked to rate how risky the situations described were in terms of having an unwanted sexual experience. Results revealed that participants asked to imagine an anonymous woman estimated sexual risk as higher than participants asked to imagine themselves, and that participants reporting liberal sexual attitudes estimated sexual risk as lower than participants reporting more conservative attitudes.     

Relation of positive memory recall count and accessibility with post-trauma mental health

ABSTRACT

Positive memory encoding and retrieval deficits have an empirical relation with several post-trauma outcomes. Drawing from the Contractor et al. model, we examined relations between positive memory characteristics and post-trauma mental health indicators. A trauma-exposed community sample of 203 participants (M_age?=?35.40 years; 61.10% female) was recruited via Amazon's Mechanical Turk. Participants completed measures of posttraumatic stress disorder (PTSD; PTSD Checklist for DSM-5), depression (Patient Health Questionnaire-9), posttraumatic cognitions (Posttraumatic Cognitions Inventory), affect (Positive and Negative Affect Schedule), count/number of recalled specific positive memories (Autobiographical Memory Test) and accessibility of a specific positive memory (i.e., subjective ease of recalling details of a memory; Memory Experiences Questionnaire-Short Form). Linear regression results indicated that PTSD intrusion severity, PTSD negative alterations in cognitions and mood (NACM) severity, PTSD alterations in arousal and reactivity (AAR) severity, self-blame, and positive affect significantly and negatively predicted the count of specific positive memories. Further, PTSD NACM severity, PTSD AAR severity, negative cognitions about the self, and negative affect significantly and negatively predicted accessibility of a specific positive memory. Thus, count/accessibility of specific positive memories was associated with several post-trauma mental health indicators; this highlights the relevance and potential impact of integrating positive memories into trauma treatment.     

Why g is not an adaptation: a comment on Kanazawa (2004)

In S. Kanazawa's evolutionary theory of general intelligence (g), g is presented as a species-typical information-processing mechanism. This conceptualization of g departs radically from the accepted conceptualization of g as a source of individual differences that is manifest in the positive manifold. Kanazawa's theory is thus problematic in the sense that it concerns a purely hypothetical, and empirically unsupported, conceptualization of g. The authors argue that an evolutionary account of g should address it as a source of individual differences--that is, in a manner that is consistent with the empirical support for g.     

Finite mixture distribution models of simple discrimination learning

Through the application of finite mixture distribution models, we investigated the existence of distinct modes of behavior in learning a simple discrimination. The data were obtained in a repeated measures study in which subjects aged 6 to 10 years carried out a simple discrimination learning task. In contrast to distribution models of exclusively rational learners or exclusively incremental learners, a mixture distribution model of rational learners and slow learners was found to fit the data of all measurement occasions and all age groups. Hence, the finite mixture distribution analysis provides strong support for the existence of distinct modes of learning behavior. The results of a second experiment support this conclusion by crossvalidation of the models that fit the data of the first experiment. The effect of verbally labeling the values on the relevant stimulus dimension and the consistency of behavior over measurement occasions are related to the mixture model estimates.     

Using Two-Level Factor Analysis to Test for Cluster Bias in Ordinal Data

The test for cluster bias is a test of measurement invariance across clusters in 2-level data. This article examines the true positive rates (empirical power) and false positive rates of the test for cluster bias using the likelihood ratio test (LRT) and the Wald test with ordinal data. A simulation study indicates that the scaled version of the LRT that accounts for nonnormality of the data gives untrustworthy results, whereas the unscaled LRT and the Wald test have acceptable false positive rates and perform well in terms of empirical power rate if the amount of cluster bias is large. The test for cluster bias is illustrated with data from research on teacher-student relations.