Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection

Exploratory Mokken scale analysis (MSA) is a popular method for identifying scales from larger sets of items. As with any statistical method, in MSA the presence of outliers in the data may result in biased results and wrong conclusions. The forward search algorithm is a robust diagnostic method for outlier detection, which we adapt here to identify outliers in MSA. This adaptation involves choices with respect to the algorithm's objective function, selection of items from samples without outliers, and scalability criteria to be used in the forward search algorithm. The application of the adapted forward search algorithm for MSA is demonstrated using real data. Recommendations are given for its use in practical scale analysis.     

Detecting Social Desirability Bias Using Factor Mixture Models

Based on the conceptualization that social desirable bias (SDB) is a discrete event resulting from an interaction between a scale's items, the testing situation, and the respondent's latent trait on a social desirability factor, we present a method that makes use of factor mixture models to identify which examinees are most likely to provide biased responses, which items elicit the most socially desirable responses, and which external variables predict SDB. Problems associated with the common use of correlation coefficients based on scales' total scores to diagnose SDB and partial correlations to correct for SDB are discussed. The method is demonstrated with an analysis of SDB in the Attitude toward Interprofessional Service-Learning scale with a sample of students from health-related fields.     

Testing Process Factor Analysis Models Using the Parametric Bootstrap

Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.     

Using the Criterion-Predictor Factor Model to Compute the Probability of Detecting Prediction Bias with Ordinary Least Squares Regression

The study of prediction bias is important and the last five decades include research studies that examined whether test scores differentially predict academic or employment performance. Previous studies used ordinary least squares (OLS) to assess whether groups differ in intercepts and slopes. This study shows that OLS yields inaccurate inferences for prediction bias hypotheses. This paper builds upon the criterion-predictor factor model by demonstrating the effect of selection, measurement error, and measurement bias on prediction bias studies that use OLS. The range restricted, criterion-predictor factor model is used to compute Type I error and power rates associated with using regression to assess prediction bias hypotheses. In short, OLS is not capable of testing hypotheses about group differences in latent intercepts and slopes. Additionally, a theorem is presented which shows that researchers should not employ hierarchical regression to assess intercept differences with selected samples.     

Using Innovative Outliers to Detect Discrete Shifts in Dynamics in Group-Based State-Space Models

Outliers are typically regarded as data anomalies that should be discarded. However, dynamic or “innovative” outliers can be appropriately utilized to capture unusual but substantively meaningful shifts in a system's dynamics. We extend De Jong and Penzer's 1998 approach for representing outliers in single-subject state-space models to a group-based framework. The group-based model enables model predictions concerning the group as a whole while individualized change points are incorporated to capture discrete shifts in each individual's trajectory. An illustrative example using cognitive performance data from N = 38 older adults over 60 days is presented. Results indicated that a subset of older adults showed sudden shifts in learning dynamics in several forms, including sudden jumps in level, slope, and linkage between perceived control and actual performance level. The broader implications of incorporating innovative outliers into studies of intraindividual change are discussed.     

A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis

Psychometrika - Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML estimator’s consistency,...     

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.     

Parsing Heuristic and Forward Search in First‐Graders' Game‐Play Behavior

Seventy‐three children between 6 and 7 years of age were presented with a problem having ambiguous subgoal ordering. Performance in this task showed reliable fingerprints: (a) a non‐monotonic dependence of performance as a function of the distance between the beginning and the end‐states of the problem, (b) very high levels of performance when the first move was correct, and (c) states in which accuracy of the first move was significantly below chance. These features are consistent with a non‐Markov planning agent, with an inherently inertial decision process, and that uses heuristics and partial problem knowledge to plan its actions. We applied a statistical framework to fit and test the quality of a proposed planning model (Monte Carlo Tree Search). Our framework allows us to parse out independent contributions to problem‐solving based on the construction of the value function and on general mechanisms of the search process in the tree of solutions. We show that the latter are correlated with children's performance on an independent measure of planning, while the former is highly domain specific.     

Clustered Common Factor Exploration in Factor Analysis

Psychometrika - The factor analysis (FA) model does not permit unique estimation of the common and unique factor scores. This weakness is notorious as the factor indeterminacy in FA. Luckily, some...     

Detecting Intervention Effects Using a Multilevel Latent Transition Analysis with a Mixture IRT Model

A multilevel latent transition analysis (LTA) with a mixture IRT measurement model (MixIRTM) is described for investigating the effectiveness of an intervention. The addition of a MixIRTM to the multilevel LTA permits consideration of both potential heterogeneity in students’ response to instructional intervention as well as a methodology for assessing stage sequential change over time at both student and teacher levels. Results from an LTA–MixIRTM and multilevel LTA–MixIRTM were compared in the context of an educational intervention study. Both models were able to describe homogeneities in problem solving and transition patterns. However, ignoring a multilevel structure in LTA–MixIRTM led to different results in group membership assignment in empirical results. Results for the multilevel LTA–MixIRTM indicated that there were distinct individual differences in the different transition patterns. The students receiving the intervention treatment outscored their business as usual (i.e., control group) counterparts on the curriculum-based Fractions Computation test. In addition, 27.4 % of the students in the sample moved from the low ability student-level latent class to the high ability student-level latent class. Students were characterized differently depending on the teacher-level latent class.     

验证性因素分析中模型拟合的判断

采用与Hu ＆ Bender不同的设计进行模拟研究,发现在他们推荐的8个指数中,NNFI、CFI、IFI在所有样本量条件下几乎都能得到最低的α与β错误率,而且最佳界值都在0．95左右,属于优良的指数。但Mc与GammaHat的表现却不好,因此不建议使用。他们建议的2指标策略被证明能降低α与β错误率,在样本量较小时尤其值得尝试。     

The Implications of Using Internet Search Engines in Structured Scientific Reviews

Science and Engineering Ethics -     

探索性因素分析因子抽取方法的比较

探索性因素分析决定因子抽取的方法主要有Bartlett法、K1原则、碎石检验法、Aaker原则、PA、MAP等六种,通过对样本1的395名大学生的学习过程问卷调查获得真实数据,运用这六种方法进行因素分析分别抽取7、4、2、4、3、2个因子;应用样本2的383名大学生的问卷调查数据进行验证性因素分析,结果显示,碎石检验方法与MAP方法抽取的二因素模型更理想。研究表明,因子抽取需兼顾“简约性原则”与“完备性原则”,同时要根据一定的理论建构、专业知识和经验来决定因子数。     

A Factor Analysis of Six Commonly Used Instruments Associated With Suicide Using College Students

Determined whether six commonly used inventories which are associated with suicide (Hopelessness Scale, Zung Self-Rating Depression Scale, Scale for Suicide Ideation, Reasons for Living Inventory, Suicide Probability Scale, and the Suicide Ideation Questionnaire) overlap; all six were given to 308 undergraduates. A principal components factor analysis yielded four factors with Eigenvalues greater than 1.00. These four factors were labeled Suicidal/Negative Ideas, Reasons for Living, Self-Doubt, and Suicide Desire. Each scale or subscale had a factor loading of .4 or greater on one factor. Only the Survival and Coping Beliefs subscale of the Reasons for Living Inventory and the Suicide Ideation Questionnaire loaded on two factors. Thus, each of these six instruments accounts for unique variance in suicidality. A suicide screening battery that includes all six inventories would take approximately 20 min for college students to complete, and would be valuable in identifying different aspects of suicidality.     

论因素分析方法的整合

传统的因素分析是探索性的因素分析(Exploratory Factor Analysis,以下简称EFA),现代统计方法增加了验证性的因素分析(Confirmatory Factor Analysis,以下简称CFA)。对二者的关系及其内部的诸多问题进行分析是非常必要的。     

Using Statistical Models of Morphology in the Search for Optimal Units of Representation in the Human Mental Lexicon

Determining optimal units of representing morphologically complex words in the mental lexicon is a central question in psycholinguistics. Here, we utilize advances in computational sciences to study human morphological processing using statistical models of morphology, particularly the unsupervised Morfessor model that works on the principle of optimization. The aim was to see what kind of model structure corresponds best to human word recognition costs for multimorphemic Finnish nouns: a model incorporating units resembling linguistically defined morphemes, a whole‐word model, or a model that seeks for an optimal balance between these two extremes. Our results showed that human word recognition was predicted best by a combination of two models: a model that decomposes words at some morpheme boundaries while keeping others unsegmented and a whole‐word model. The results support dual‐route models that assume that both decomposed and full‐form representations are utilized to optimally process complex words within the mental lexicon.     

平行分析在探索性因素分析中的应用

平行分析是探索性因素分析中用来确定所保留的凶子个数的一种方法。探索性因素分析中常用的确定因子个数的方法有特征值大于1准则和碎石图,但这两种方法又各有不足。平行分析则为探索性因素分析中所保留因子个数的确定提供了另一种新思路。本文详细介绍了平行分析的步骤、潜在逻辑以及进行平行分析所用的软件,并通过实例来说明其在探索性因素分析中如何应用。