有关智力的遗传研究演进

智力是被行为遗传学广泛研究的重要内容之一。过去十几年问。有关智力的遗传研究超越了最基本的先天与后天之争,通过发展的遗传分析、多变量遗传分析以及对遗传与环境交互作用的探讨获得了许多重要发现。分子遗传学的发展更是为智力研究展开了新的一页,使得确定与特定认知能力有关的基因成为可能。     

多元整合元分析法评介

在传统元分析中, 每个研究只能提取一个效果量, 以满足各效果量相互独立的条件。若出现单个研究多效果量的情况, 就需要采用多元元分析技术予以处理。多元元分析主要包括多元线性模型法和多元整合法, 其中, 多元整合法是应用最为广泛的一种方法, 可较为有效地解决多效果量非独立性问题, 还可借助传统元分析的固定效应模型和混合效应模型进行统计分析。但多元整合法在应用中还存在着统计软件的开发问题、同质性Q检验显著性的稳定问题、不同质混合效果量的同质性检验的可行性问题。     

优势分析方法及其应用

优势分析是近年来由Budescu等人新发展起来的一种确定多元回归方程中各预测变量相对重要性的方法。与传统方法相比,优势分析突出的特点是,全面比较了在由全模型所衍生出来的所有子模型情况下,各预测变量(X1,X…XP)在解释或预测标准变量у时,它们之间的相对重要性。本文从基本原理以及具体操作过程对这一新的统计分析方法进行了详细介绍。     

Multivariate base rates for the assessment of executive functioning among children and adolescents

This study provides observed base rates of low executive functioning test scores among healthy children and adolescents, stratifies those base rates by narrow intellectual functioning and age groupings, and provides normative classification ranges to aid the interpretation of performances on the Delis-Kaplan Executive Function System (D-KEFS) in clinical practice and research. Participants included 875 children and adolescents between 8 and 19 years old from the D-KEFS normative sample (48% male; 52% female). Among these participants, 838 had complete data and were included in the current study. The racial/ethnic composition of the sample was: White (73.7%), African American (12.4%), Hispanic (11.1%), and other racial/ethnic backgrounds (2.7%). The Overall Test Battery Mean (OTBM) and the prevalence of low scores at various clinical cut-offs were calculated for the 13 primary scores from the D-KEFS Trail Making Test, Verbal Fluency Test, and Color-Word Interference Test. The OTBM and base rates were also calculated separately for those scores reflecting executive functioning (n = 7) and processing speed (n = 6). Healthy children and adolescents commonly obtained low scores on the D-KEFS tests considered here. Younger age, lower estimated full-scale intelligence quotient, and more test scores interpreted were associated with a greater frequency of low scores. Clinicians and researchers are encouraged to consider these multivariate base rates when assessing and attempting to identify executive functioning impairment among children and adolescents with the D-KEFS.     

青少年学习倦怠量表的多元概化分析

运用多元概化理论考察青少年学习倦怠量表（ASBI）的测量学属性。结果发现,ASBI各因子以及全量表的概化系数和可靠性指数均达到较好水平,既可用于常模参照测验,也可用于标准参照测验;各因子的分量比例还不尽完善,低成就感对全域总分的贡献略低于在全量表中的分值比重,对相对误差方差贡献最大的是低成就感,学业疏离对绝对误差方差的贡献最大;今后ASBI的修订工作,可考虑对学业疏离和低成就感因子条目质量的改进,同时适当增加身心耗竭因子的条目数以提高其测量信度。     

An Introduction to Kristof’s Theorem for Solving Least-Square Optimization Problems Without Calculus

Kristof’s Theorem (Kristof, 1970 Kristof, W. (1970). A theorem on the trace of certain matrix products and some applications. Journal of Mathematical Psychology, 7(3), 515–530. https://doi.org/10.1016/0022-2496(70)90037-4[Crossref], [Web of Science ®] , [Google Scholar]) describes a matrix trace inequality that can be used to solve a wide-class of least-square optimization problems without calculus. Considering its generality, it is surprising that Kristof’s Theorem is rarely used in statistics and psychometric applications. The underutilization of this method likely stems, in part, from the mathematical complexity of Kristof’s (1964 Kristof, W. (1964). Die beste orthogonale Transformation zur gegenseitigen Uberfiihrung zweier Faktorenmatrizen. Diagnostica, 10, 87–90. [Google Scholar], 1970 Kristof, W. (1970). A theorem on the trace of certain matrix products and some applications. Journal of Mathematical Psychology, 7(3), 515–530. https://doi.org/10.1016/0022-2496(70)90037-4[Crossref], [Web of Science ®] , [Google Scholar]) writings. In this article, I describe the underlying logic of Kristof’s Theorem in simple terms by reviewing four key mathematical ideas that are used in the theorem’s proof. I then show how Kristof’s Theorem can be used to provide novel derivations to two cognate models from statistics and psychometrics. This tutorial includes a glossary of technical terms and an online supplement with R (R Core Team, 2017 R Core Team (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/ [Google Scholar]) code to perform the calculations described in the text.     

Log-Multiplicative Association Models as Item Response Models

Log-multiplicative association (LMA) models, which are special cases of log-linear models, have interpretations in terms of latent continuous variables. Two theoretical derivations of LMA models based on item response theory (IRT) arguments are presented. First, we show that Anderson and colleagues (Anderson &; Vermunt, 2000; Anderson &; Böckenholt, 2000; Anderson, 2002), who derived LMA models from statistical graphical models, made the equivalent assumptions as Holland (1990) when deriving models for the manifest probabilities of response patterns based on an IRT approach. We also present a second derivation of LMA models where item response functions are specified as functions of rest-scores. These various connections provide insights into the behavior of LMA models as item response models and point out philosophical issues with the use of LMA models as item response models. We show that even for short tests, LMA and standard IRT models yield very similar to nearly identical results when data arise from standard IRT models. Log-multiplicative association models can be used as item response models and do not require numerical integration for estimation.     

A multivariate analysis of social dominance in children

The social dominance behavior of dyads of unacquainted, same-sex 31/2 year-olds was observed in a familiar laboratory playroom under two conditions: A free play situation and a situation where candy was introduced. In each of the two conditions, a principal components analysis was used to explore two issues: the usefulness of the multivariate approach in devising a definition of dominance, and the cross-situational stability of the construct. In the free play session, the first principal component that emerged was consistent with a theoretical definition of dominance. This picture was disrupted by the introduction of candy in the second condition. However, a high correlation was found between the dominance hierarchies established in each situation. It was concluded that the multivariate analysis is a useful method for the study of dominance. The generalizability of social dominance across settings was discussed as a possible explanation for the high cross-situational stability.     

Comparing the Predictive Accuracy of Case Linkage Methods in Serious Sexual Assaults

The empirical support for linkage analysis is steadily increasing, but the question remains as to what method of linking is the most effective. We compared a more theory‐based, dimensional behavioural approach with a rather pragmatic, multivariate behavioural approach with regard to their accuracy in linking serial sexual assaults in a UK sample of serial sexual assaults (n = 90) and one‐off sexual assaults (n = 129). Their respective linkage accuracy was assessed by (1) using seven dimensions derived by non‐parametric Mokken scale analysis (MSA) as predictors in discriminant function analysis (DFA) and (2) 46 crime scene characteristics simultaneously in a naive Bayesian classifier (NBC). The dimensional scales predicted 28.9% of the series correctly, whereas the NBC correctly identified 34.5% of the series. However, a subsequent inclusion of non‐serial offences in the target group decreased the amount of correct links in the dimensional approach (MSA–DFA: 8.9%; NBC: 32.2%). Receiver operating characteristic analysis was used as a more objective comparison of the two methods under both conditions, confirming that each achieved good accuracies (AUCs = .74–.89), but the NBC performed significantly better than the dimensional approach. The consequences for the practical implementation in behavioural case linkage are discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means—the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.