Challenging socioeconomic status: A cross‐cultural comparison of early executive function

The widely and internationally replicated socioeconomic status (SES) gradient of executive function (EF) implies that intervention approaches may do well to extrapolate conditions and practices from contexts that generate better child outcomes (in this case, higher SES circumstances) and translate these to contexts with comparatively poorer outcomes (often low‐SES populations). Yet, can the reverse also be true? Using data from equivalent assessments of 1,092 pre‐schoolers’ EFs in South Africa and Australia, we evaluated: the SES gradient of EF within each sample; and whether this SES gradient extended cross‐culturally. The oft‐found EF‐SES gradients were replicated in both samples. However, contrary to the inferences of EF‐SES associations found nationally, the most highly disadvantaged South African subsample outperformed middle‐ and high‐SES Australian pre‐schoolers on two of three EFs. This suggests the possibility of EF‐protective and ‐promotive practices within low‐ and middle‐income countries that may aid understandings of the nature and promotion of EFs.     

The Infinitesimal Jackknife with Exploratory Factor Analysis

The infinitesimal jackknife, a nonparametric method for estimating standard errors, has been used to obtain standard error estimates in covariance structure analysis. In this article, we adapt it for obtaining standard errors for rotated factor loadings and factor correlations in exploratory factor analysis with sample correlation matrices. Both maximum likelihood estimation and ordinary least squares estimation are considered.     

Reading through a noisy channel: why there's nothing special about the perception of orthography

The goal of research on how letter identity and order are perceived during reading is often characterized as one of "cracking the orthographic code." Here, we suggest that there is no orthographic code to crack: Words are perceived and represented as sequences of letters, just as in a dictionary. Indeed, words are perceived and represented in exactly the same way as other visual objects. The phenomena that have been taken as evidence for specialized orthographic representations can be explained by assuming that perception involves recovering information that has passed through a noisy channel: the early stages of visual perception. The noisy channel introduces uncertainty into letter identity, letter order, and even whether letters are present or absent. We develop a computational model based on this simple principle and show that it can accurately simulate lexical decision data from the lexicon projects in English, French, and Dutch, along with masked priming data that have been taken as evidence for specialized orthographic representations.     

The problem of model selection uncertainty in structural equation modeling

Model selection in structural equation modeling (SEM) involves using selection criteria to declare one model superior and treating it as a best working hypothesis until a better model is proposed. A limitation of this approach is that sampling variability in selection criteria usually is not considered, leading to assertions of model superiority that may not withstand replication. We illustrate that selection decisions using information criteria can be highly unstable over repeated sampling and that this uncertainty does not necessarily decrease with increases in sample size. Methods for addressing model selection uncertainty in SEM are evaluated, and implications for practice are discussed.     

On effect size

The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qualities of effect sizes. Our definition of effect size is general and subsumes many existing definitions of effect size. We define effect size as a quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest. Our definition of effect size is purposely more inclusive than the way many have defined and conceptualized effect size, and it is unique with regard to linking effect size to a question of interest. Additionally, we review some important developments in the effect size literature and discuss the importance of accompanying an effect size with an interval estimate that acknowledges the uncertainty with which the population value of the effect size has been estimated. We hope that this article will facilitate discussion and improve the practice of reporting and interpreting effect sizes.     

Verbal Sexual Coercion Experiences,Sexual Risk,and Substance Use in Women

Research has linked sexual assault, substance use, and sexually transmitted infection (STI) risk in women. Sexual assault by means of verbal sexual coercion (VSC) is more common than sexual assault by means of physical tactics, but VSC is rarely assessed independently. In addition, past work has established global connections among substance use, sexual assault history, and STI risk; however, assessing substance use during sexual behavior is less common. This study examined the relations among VSC, STI risk behavior, and substance use and attitudes. We hypothesized that women with larger numbers of VSC experiences would report more frequent sexual risk behaviors and substance use and attitudes. Participants with larger numbers of VSC experiences reported larger numbers of anal sex partners, more frequent penile–vaginal sex and sexual activity after substance use, and stronger sex-related alcohol expectancies. These findings suggest that VSC is associated with higher levels of STI risk in women.     

Normal and Exceptional Children's Attitudes Toward Themselves and one Another

In this study 65 junior high school boys and girls (54 normal and 11 exceptional children) evaluated themselves most favorably, normal children as a group less favorably, and exceptional children as a group least favorably of all on the Personal Attribute Inventory for Children. This was so regardless of whether the respondents were normal or exceptional children. Since data were collected from mainstreamed classrooms, it appears that mainstreaming may not be directly deleterious to exceptional children's self-concepts, but has associated with it a negative stigma for exceptional children as a group for both exceptional and normal children. These findings, plus others reported previously, fail to demonstrate that mainstreaming in its present form may be an elixir for exceptional children's social-emotional difficulties.     

Ignoring Clustering in Confirmatory Factor Analysis: Some Consequences for Model Fit and Standardized Parameter Estimates

In many situations, researchers collect multilevel (clustered or nested) data yet analyze the data either ignoring the clustering (disaggregation) or averaging the micro-level units within each cluster and analyzing the aggregated data at the macro level (aggregation). In this study we investigate the effects of ignoring the nested nature of data in confirmatory factor analysis (CFA). The bias incurred by ignoring clustering is examined in terms of model fit and standardized parameter estimates, which are usually of interest to researchers who use CFA. We find that the disaggregation approach increases model misfit, especially when the intraclass correlation (ICC) is high, whereas the aggregation approach results in accurate detection of model misfit in the macro level. Standardized parameter estimates from the disaggregation and aggregation approaches are deviated toward the values of the macro- and micro-level standardized parameter estimates, respectively. The degree of deviation depends on ICC and cluster size, particularly for the aggregation method. The standard errors of standardized parameter estimates from the disaggregation approach depend on the macro-level item communalities. Those from the aggregation approach underestimate the standard errors in multilevel CFA (MCFA), especially when ICC is low. Thus, we conclude that MCFA or an alternative approach should be used if possible.     

Modeling Time-Dependent Association in Longitudinal Data: A Lag as Moderator Approach

We describe a straightforward, yet novel, approach to examine time-dependent association between variables. The approach relies on a measurement-lag research design in conjunction with statistical interaction models. We base arguments in favor of this approach on the potential for better understanding the associations between variables by describing how the association changes with time. We introduce a number of different functional forms for describing these lag-moderated associations, each with a different substantive meaning. Finally, we use empirical data to demonstrate methods for exploring functional forms and model fitting based on this approach.