Psychometrics in Psychological Research: Role Model or Partner in Science?

This is a reaction to Borsboom's (2006) discussion paper on the issue that psychology takes so little notice of the modern developments in psychometrics, in particular, latent variable methods. Contrary to Borsboom, it is argued that latent variables are summaries of interesting data properties, that construct validation should involve studying nomological networks, that psychological research slowly but definitely will incorporate latent variable methods, and that the role of psychometrics in psychology is that of partner, not role model. Requests for reprints should be sent to Klaas Sijtsma, Department of Methodology and Statistics, FSW, Tilburg University, PO Box 90153, 5000 LE, Tilburg, The Netherlands.     

Alcohol use among adults in Kenya: Results from the National Non-Communicable Diseases Risk Factor survey, 2015

The aim of this study was to assess the prevalence and correlates of alcohol use among Kenyan adults. We analysed data from the Kenya cross-sectional national Non-Communicable Diseases Risk Factor survey, 2015. The survey sampled 4 469 adults (median age 38.0 years, interquartile range = 23, age range of 18–69 years). Results indicate that 6.7% were hazardous or harmful alcohol users and 12.8% past month binge-drinkers. In adjusted logistic regression analysis, being male, middle aged, belonging to the Luhya or Kalenjin ethic group, tobacco use, and having hypertension increased the odds for hazardous or harmful alcohol use. Socio-demographic and health factors appear to influence risk for hazardous or harmful alcohol use among adults in Kenya.     

A latent class distance association model for cross‐classified data with a categorical response variable

In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low‐dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented.     

追踪研究中的内生性问题：来源与应对

相对于横断研究,追踪研究中更有可能同时存在多种内生性问题来源。双变量追踪研究在心理学因果分析中发挥了重要的作用,然而其中的内生性问题却未得到应有的关注,这可能会影响推论的准确性。追踪研究中内生性问题的来源视乎模型而定,主要包括遗漏变量、变量选择和样本选择、解释变量的测量误差、动态面板和变量之间的相互关系。本文以代表性追踪模型CLPM为例,展示了内生性问题的影响,讨论了在原模型中运用工具变量来建模以应对内生性问题的可行性,目的是使心理学研究者能够关注追踪研究中的内生性问题,更好地运用追踪模型进行因果分析。     

第二类有中介的调节模型

心理学研究常用有中介的调节模型揭示调节作用通过中介变量间接实现的现象。介绍了第二类有中介的调节模型(meMO-II)的概念及优势; 将meMO-II与其它中介调节混合模型进行了辨析; 给出了meMO-II的建模方法和分析流程, 并用一个实例演示; 介绍了基于潜变量的meMO-II的分析方法、meMO-II建模方法的新进展以及meMO-II的变式。研究有助于推动调节机制研究的发展。     

范畴变量对虚假记忆的影响

以中文词为材料,考察范畴样例重复,样例熟悉性、典型性和范畴大小对虚假再认的影响。实验1表明,随着范畴样例增加,虚假再认也增加。实验2表明,对高熟悉的样例虚假再认多。实验3表明,对高典型样例更易虚假再认。实验4表明,和大范畴样例比,对小范畴样例有更多的虚假再认。整个结果可以用激活扩散理论统一地加以解释     

自我设限及其研究范型和影响因素

自我设限是指个体针对可能来到的失败威胁而事先设计障碍以自我保护或自我提升。自我设限可细分为行为的自我设限、声称的自我设限以及抬举他人,但缺乏对这三种策略的机制、前置因素和后续结果的比较研究。影响自我设限的因素主要有任务性质、评价情境以及性别、自尊、归因倾向等,但对自我设限动机的探讨应更多考察评价情境与其他中介变量的交互作用,以及内隐自尊对自我设限的影响。     

Enhancing Interpretability in Factor Analysis by Means of Mathematical Optimization

Abstract

Exploratory Factor Analysis (EFA) is a widely used statistical technique to discover the structure of latent unobserved variables, called factors, from a set of observed variables. EFA exploits the property of rotation invariance of the factor model to enhance factors’ interpretability by building a sparse loading matrix. In this paper, we propose an optimization-based procedure to give meaning to the factors arising in EFA by means of an additional set of variables, called explanatory variables, which may include in particular the set of observed variables. A goodness-of-fit criterion is introduced which quantifies the quality of the interpretation given this way. Our methodology also exploits the rotational invariance of EFA to obtain the best orthogonal rotation of the factors, in terms of the goodness-of-fit, but making them match to some of the explanatory variables, thus going beyond traditional rotation methods. Therefore, our approach allows the analyst to interpret the factors not only in terms of the observed variables, but in terms of a broader set of variables. Our experimental results demonstrate how our approach enhances interpretability in EFA, first in an empirical dataset, concerning volumes of reservoirs in California, and second in a synthetic data example.     

On the Proper Interpretation and Impact of the AHP Axiom of Independence in Single-Criterion and Multiple-Criteria Problems

Some authors have proposed that the analytic hierarchy process (AHP) axiom of independence be relaxed to accommodate observations drawn from (a) examples of pairwise comparisons of alternatives in clusters in single-criterion AHP problems and (b) examples of problems in which the criteria have the same underlying measurement and both the achievement of the goal and the alternatives are measured objectively. We show that the illustrations given are actually single-criterion problems according to the AHP. Thus the AHP axiom of independence is inapplicable in both situations and therefore not violated. We also consider the consequence of failure to distinguish between a criterion as an attribute of alternatives and cluster of alternatives, the two being different in the hierarchic structure. Finally, we discuss transformable problems, which look like multicriteria problems but are actually single-criterion problems, and how failure to recognize this fact may lead to incorrect syntheses and false conclusions.