FACAIC: Model selection algorithm for the orthogonal factor model using AIC and CAIC

This paper describes the authors' FORTRAN algorithm FACAIC for choosing the number of factors for an orthogonal factor model using Akaike's Information Criterion. FACAIC utilizes the IMSL subroutine OFCOMM.The authors dedicate this algorithm to Professor Hirotugu Akaike in appreciation of his pioneering work on AIC which was originally intended for the factor analysis and other statistical model identification problems.     

Incorporating prior theory in covariance structure analysis: A bayesian approach

A Bayesian approach to the testing of competing covariance structures is developed. The method provides approximate posterior probablities for each model under consideration without prior specification of individual parameter distributions. The method is based on ayesian updating using cross-validated pseudo-likelihoods. Given that the observed variables are the samefor all competing models, the approximate posterior probabilities may be obtained easily from the chi square values and other known constants, using only a hand calculator. The approach is illustrated using and example which illustrates how the prior probabilities can alter the results concerning which model specification is preferred.     

Application of model-selection criteria to some problems in multivariate analysis

A review of model-selection criteria is presented, with a view toward showing their similarities. It is suggested that some problems treated by sequences of hypothesis tests may be more expeditiously treated by the application of model-selection criteria. Consideration is given to application of model-selection criteria to some problems of multivariate analysis, especially the clustering of variables, factor analysis and, more generally, describing a complex of variables.     

Latent variable selection in multidimensional item response theory models using the expectation model selection algorithm

The aim of latent variable selection in multidimensional item response theory (MIRT) models is to identify latent traits probed by test items of a multidimensional test. In this paper the expectation model selection (EMS) algorithm proposed by Jiang et al. (2015) is applied to minimize the Bayesian information criterion (BIC) for latent variable selection in MIRT models with a known number of latent traits. Under mild assumptions, we prove the numerical convergence of the EMS algorithm for model selection by minimizing the BIC of observed data in the presence of missing data. For the identification of MIRT models, we assume that the variances of all latent traits are unity and each latent trait has an item that is only related to it. Under this identifiability assumption, the convergence of the EMS algorithm for latent variable selection in the multidimensional two-parameter logistic (M2PL) models can be verified. We give an efficient implementation of the EMS for the M2PL models. Simulation studies show that the EMS outperforms the EM-based L₁ regularization in terms of correctly selected latent variables and computation time. The EMS algorithm is applied to a real data set related to the Eysenck Personality Questionnaire.     

Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)

This article reviews the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on latent variable models, given their growing use in theory testing and construction. Theoretical statistical results in regression are discussed, and more important issues are illustrated with novel simulations involving latent variable models including factor analysis, latent profile analysis, and factor mixture models. Asymptotically, the BIC is consistent, in that it will select the true model if, among other assumptions, the true model is among the candidate models considered. The AIC is not consistent under these circumstances. When the true model is not in the candidate model set the AIC is efficient, in that it will asymptotically choose whichever model minimizes the mean squared error of prediction/estimation. The BIC is not efficient under these circumstances. Unlike the BIC, the AIC also has a minimax property, in that it can minimize the maximum possible risk in finite sample sizes. In sum, the AIC and BIC have quite different properties that require different assumptions, and applied researchers and methodologists alike will benefit from improved understanding of the asymptotic and finite-sample behavior of these criteria. The ultimate decision to use the AIC or BIC depends on many factors, including the loss function employed, the study's methodological design, the substantive research question, and the notion of a true model and its applicability to the study at hand.     

On the statistical and theoretical basis of signal detection theory and extensions: Unequal variance, random coefficient, and mixture models

Basic results for conditional means and variances, as well as distributional results, are used to clarify the similarities and differences between various extensions of signal detection theory (SDT). It is shown that a previously presented motivation for the unequal variance SDT model (varying strength) actually leads to a related, yet distinct, model. The distinction has implications for other extensions of SDT, such as models with criteria that vary over trials. It is shown that a mixture extension of SDT is also consistent with unequal variances, but provides a different interpretation of the results; mixture SDT also offers a way to unify results found across several types of studies.     

情绪即社会信息模型的理论及应用

情绪即社会信息(EmotionsasSocialInformationmodel,EASI)模型旨在阐释他人情绪如何通过情感反应和推断加工机制影响观察者的决策,以及认知动机和合适性判断在其中的调节作用。文章将EASI模型与相关理论进行了辨析,并基于对63项使用该理论的实证研究的分析,梳理归纳了EASI模型在领导力、团队、顾客服务、谈判及说服领域展开的应用,以及影响EASI模型的边界条件——信息加工程度和合适性判断的具体因素。未来研究需加强系统化验证、深化与其它理论的整合、拓展应用情境和优化测量方法。     

How Emotions Regulate Social Life: The Emotions as Social Information (EASI) Model

ABSTRACT— The idea that emotions regulate social interaction is increasingly popular. But exactly how do emotions do this? To address this question, I draw on research on the interpersonal effects of emotions on behavior in personal relationships, parent–child interactions, conflict, negotiation, and leadership, and propose a new framework that can account for existing findings and guide future research: the emotions as social information (EASI) model. I demonstrate that emotional expressions affect observers' behavior by triggering inferential processes and/or affective reactions in them. The predictive strength of these two processes—which may inspire different behaviors—depends on the observer's information processing and on social-relational factors. Examples of moderators that determine the relative predictive strength of inferences and affective reactions include power, need for cognitive closure, time pressure, display rules, and the appropriateness and target of the emotional expression, which are all discussed.     

The Five Factor Model in personnel selection: Measurement equivalence between applicant and non-applicant groups

This study investigates the measurement equivalence of a Five Factor measure of personality between applicant and non-applicant samples. The Big Five Questionnaire-2 was administered in two samples: A group of volunteers (n = 903), who completed the test for research purposes, and a group applying for jobs, who completed the test during hiring procedures (n = 401). Multiple-group confirmatory factor analysis on item composites was conducted to test for the equivalence of factor covariance and mean structures of twenty facets of the Five Factors. Item-level analyses were carried out through analysis of variance to further examine the issue of measurement invariance. Findings suggested that personality facets have the same measurement unit across applicants and non-applicants, while a lack of equivalence was found in the origin of the scales. Similar results were found at the item-level. Implications for personality assessment are advanced and discussed.     

The WIG (weighted individual and group) shrinkage estimator

Regularization, or shrinkage estimation, refers to a class of statistical methods that constrain the variability of parameter estimates when fitting models to data. These constraints move parameters toward a group mean or toward a fixed point (e.g., 0). Regularization has gained popularity across many fields for its ability to increase predictive power over classical techniques. However, articles published in JEAB and other behavioral journals have yet to adopt these methods. This paper reviews some common regularization schemes and speculates as to why articles published in JEAB do not use them. In response, we propose our own shrinkage estimator that avoids some of the possible objections associated with the reviewed regularization methods. Our estimator works by mixing weighted individual and group (WIG) data rather than by constraining parameters. We test this method on a problem of model selection. Specifically, we conduct a simulation study on the selection of matching‐law‐based punishment models, comparing WIG with ordinary least squares (OLS) regression, and find that, on average, WIG outperforms OLS in this context.     

Properties of hypothesis testing techniques and (Bayesian) model selection for exploration‐based and theory‐based (order‐restricted) hypotheses

In this paper, the performance of six types of techniques for comparisons of means is examined. These six emerge from the distinction between the method employed (hypothesis testing, model selection using information criteria, or Bayesian model selection) and the set of hypotheses that is investigated (a classical, exploration‐based set of hypotheses containing equality constraints on the means, or a theory‐based limited set of hypotheses with equality and/or order restrictions). A simulation study is conducted to examine the performance of these techniques. We demonstrate that, if one has specific, a priori specified hypotheses, confirmation (i.e., investigating theory‐based hypotheses) has advantages over exploration (i.e., examining all possible equality‐constrained hypotheses). Furthermore, examining reasonable order‐restricted hypotheses has more power to detect the true effect/non‐null hypothesis than evaluating only equality restrictions. Additionally, when investigating more than one theory‐based hypothesis, model selection is preferred over hypothesis testing. Because of the first two results, we further examine the techniques that are able to evaluate order restrictions in a confirmatory fashion by examining their performance when the homogeneity of variance assumption is violated. Results show that the techniques are robust to heterogeneity when the sample sizes are equal. When the sample sizes are unequal, the performance is affected by heterogeneity. The size and direction of the deviations from the baseline, where there is no heterogeneity, depend on the effect size (of the means) and on the trend in the group variances with respect to the ordering of the group sizes. Importantly, the deviations are less pronounced when the group variances and sizes exhibit the same trend (e.g., are both increasing with group number).     

It's just (family) business: The impact of familial work experience on perceived qualification and hireability during the selection process

Though scholars have explored the impact of familial work experience (FWE) posthire, research has yet to determine the role of such experience during the selection process. Drawing from Attribution Theory and research on nepotism and family business dynamics, we suggest that the presence of FWE on an applicant's selection materials will lead to reduced perceptions of qualifications and hireability. Through two online experiments (N₁ = 170, N₂ = 251), we found mixed support for this proposition. In our first study focused on cover letters, we found that applicants who reported FWE were not appraised differently than those who did not. In our second study focused on letters of recommendation, however, we found that applicants who reported FWE were perceived as less qualified and less hireable. Moreover, we found that the level of job (i.e., service vs. managerial) moderates these relations, such that managerial applicants with FWE on their letter of recommendation were perceived as less hireable than those who did not. Implications for research and practice are discussed, and future research directions are offered.     

The Personal Disturbance Scale (sAD): normative data and latent structure in a large non-clinical sample

The Personal Disturbance Scale [sAD; Bedford & Foulds (1978) Delusions–Symptoms–States Inventory State of Anxiety and Depression. Windsor: NFER-Nelson] is widely used in diverse settings and yet there are unresolved issues concerning its psychometric properties and normative data for the English speaking version are limited. The sAD was administered to a large sample of the general adult population (N=758). Demographic variables (gender, age, years of education and occupational status) had only very modest influences on sAD scores. Tables are presented for conversion of raw scores on the Anxiety, Depression and Total scales to percentiles. The sAD scales possessed adequate convergent and discriminant validity, as demonstrated by their pattern of correlations with two other measures of depression and anxiety (the DASS and the HADS). Ten competing models of the latent structure of the sAD were derived from theoretical and empirical sources. These models were evaluated using confirmatory factor analysis. The best fitting model (CFI=0.96) had a tripartite structure, and consisted of a general factor of psychological distress/negative affectivity (all items loaded on this factor) plus orthogonal specific factors of anxiety and depression. Correlated errors specified according to previous empirical findings were permitted. The theoretical and practical implications of this latent structure are discussed.     

The Failure to Use Gender Information in Parsing: A Comment on van Berkum, Brown, and Hagoort (1999)

We critically review the empirical evidence published by van Berkum, Brown, and Hagoort (1999a, b) against syntax-first models of sentence parsing. According to van Berkum et al., discourse factors and word gender information are used instantaneously to guide the parser. First, we note that the density of the experimental trials (relative to fillers) and the slow presentation rate of the van Berkum et al. design seem likely to have elicited the use of tactics involving rapid reanalysis of the material. Second, we present new data from a questionnaire study showing that the grammatical gender information of a relative pronoun in Dutch is often completely ignored, even during the wrap-up phase at the end of the sentence.     

外科手术决策过程中的情与法

越是给熟人、领导、要人做手术,出现各种手术相关问题的几率就越高.其原因主要是医生受各种情感因素的影响,降低了对医疗核心制度的执行标准,简化了术前准备,放宽了手术适应证,非理性地扩大了手术范围.解决方法:对待任何病人首先必须恪守法律和医疗规范,在此基础上尽可能与病人进行情感交流,达到医患共同自觉执行相关法律和规章,降低情感因素对手术决策的负性作用.     

The Revised Conners' Parent Rating Scale (CPRS-R): Factor Structure, Reliability, and Criterion Validity

The Conners' Parent Rating Scale (CPRS) is a popular research and clinical tool for obtaining parental reports of childhood behavior problems. The present study introduces a revised CPRS (CPRS-R) which has norms derived from a large, representative sample of North American children, uses confirmatory factor analysis to develop a definitive factor structure, and has an updated item content to reflect recent knowledge and developments concerning childhood behavior problems. Exploratory and confirmatory factor-analytic results revealed a seven-factor model including the following factors: Cognitive Problems, Oppositional, Hyperactivity-Impulsivity, Anxious-Shy, Perfectionism, Social Problems, and Psychosomatic. The psychometric properties of the revised scale appear adequate as demonstrated by good internal reliability coefficients, high test-retest reliability, and effective discriminatory power. Advantages of the CPRS-R include a corresponding factor structure with the Conners' Teacher Rating Scale—Revised and comprehensive symptom coverage for attention deficit hyperactivity disorder (ADHD) and related disorders. Factor congruence with the original CPRS as well as similarities with other parent rating scales are discussed.     

内在锚与外在锚对锚定效应及其双加工机制的影响

通过2（外在锚类型：高锚VS低锚）×2（内在锚类型：有VS无）被试间设计,考察不同锚定信息来源：由外部世界提供的外在锚与个体自身内部产生的内在锚信息对锚定效应及其加工机制的影响。结果发现：（1）当内在锚不存在时,外在高低锚组的估计值有显著差异,当内在锚存在时此种差异变得不显著;（2）当内在锚存在时,外在高低锚组被试的答题反应时有显著差异,内在锚与外在锚一致时反应时比不一致时更快,当内在锚不存在时,此种差异变得不显著。实验结果表明,当锚定调整机制与选择通达机制同时存在时,前者更占优势;锚定信息一致性会影响不同加工机制的启动,一致的信息会激活选择通达机制,不一致的信息则会激活锚定调整机制。