问题解决测验中过程数据的特征抽取与能力评估

基于计算机的问题解决测验可以实时记录被试探索环境和解决问题时的详细行动痕迹, 并保存为过程数据。首先介绍了过程数据的分析流程, 然后从问题解决测验入手, 分别对过程数据的特征抽取和能力估计建模两方面的研究进行了梳理和评价。未来研究应注意：提高分析结果的可解释性; 特征提取时纳入更多信息; 实现更复杂问题情景下的能力评估; 注重方法的实用性; 以及融合与借鉴不同领域的分析方法。     

Subtask analysis of process data through a predictive model

Response process data collected from human–computer interactive items contain detailed information about respondents' behavioural patterns and cognitive processes. Such data are valuable sources for analysing respondents' problem-solving strategies. However, the irregular data format and the complex structure make standard statistical tools difficult to apply. This article develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction, easy clustering and meaningful interpretation. Each subprocess is considered a subtask. The segmentation is based on sequential action predictability using a parsimonious predictive model combined with the Shannon entropy. Simulation studies are conducted to assess the performance of the new method. We use a case study of PIAAC 2012 to demonstrate how exploratory analysis for process data can be carried out with the new approach.     

The Joint Multivariate Modeling of Multiple Mixed Response Sources: Relating Student Performances with Feedback Behavior

The present study concerns a Dutch computer-based assessment, which includes an assessment process about information literacy and a feedback process for students. The assessment is concerned with the measurement of skills in information literacy and the feedback process with item-based support to improve student learning. To analyze students’ feedback behavior (i.e. feedback use and attention time), test performance, and speed of working, a multivariate hierarchical latent variable model is proposed. The model can handle multivariate mixed responses from multiple sources related to different processes and comprehends multiple measurement components for responses and response times. A flexible within-subject latent variable structure is defined to explore multiple individual latent characteristics related to students’ test performance and feedback behavior. Main results of the computer-based assessment showed that feedback-information pages were less visited by well-performing students when they relate to easy items. Students’ attention paid to feedback was positively related to working speed but not to the propensity to use feedback.     

Latent variable models for multivariate longitudinal ordinal responses

The paper proposes a full information maximum likelihood estimation method for modelling multivariate longitudinal ordinal variables. Two latent variable models are proposed that account for dependencies among items within time and between time. One model fits item‐specific random effects which account for the between time points correlations and the second model uses a common factor. The relationships between the time‐dependent latent variables are modelled with a non‐stationary autoregressive model. The proposed models are fitted to a real data set.     

Analyzing Longitudinal Item Response Data via the Pairwise Fitting Method

Multidimensional item response theory (MIRT) models can be applied to longitudinal educational surveys where a group of individuals are administered different tests over time with some common items. However, computational problems typically arise as the dimension of the latent variables increases. This is especially true when the latent variable distribution cannot be integrated out analytically, as with MIRT models for binary data. In this article, based on the pseudolikelihood theory, we propose a pairwise modeling strategy to estimate item and population parameters in longitudinal studies. Our pairwise method effectively reduces the dimensionality of the problem and hence is applicable to longitudinal IRT data with high-dimensional latent variables, which are challenging for classical methods. And in the low-dimensional case, our simulation study shows that it performs comparably with the classical methods. We further illustrate the implementation of the pairwise method using a development study of mathematics levels of junior high school students in which the response data are collected from 65 classes of 8 schools from 4 different school districts in China.     

Uncovering Substantive Patterns in Student Responses in International Large-Scale Assessments—Comparing a Latent Class to a Manifest DIF Approach

In this study, we contrast results from two differential item functioning (DIF) approaches (manifest and latent class) by the number of items and sources of items identified as DIF using data from an international reading assessment. The latter approach yielded three latent classes, presenting evidence of heterogeneity in examinee response patterns. It also yielded more DIF items with larger effect sizes and more consistent item response patterns by substantive aspects (e.g., reading comprehension processes and cognitive complexity of items). Based on our findings, we suggest empirically evaluating the homogeneity assumption in international assessments because international populations cannot be assumed to have homogeneous item response patterns. Otherwise, differences in response patterns within these populations may be under-detected when conducting manifest DIF analyses. Detecting differences in item responses across international examinee populations has implications on the generalizability and meaningfulness of DIF findings as they apply to heterogeneous examinee subgroups.     

A general latent trait model for response processes

The purpose of the current paper is to propose a general multicomponent latent trait model (GLTM) for response processes. The proposed model combines the linear logistic latent trait (LLTM) with the multicomponent latent trait model (MLTM). As with both LLTM and MLTM, the general multicomponent latent trait model can be used to (1) test hypotheses about the theoretical variables that underlie response difficulty and (2) estimate parameters that describe test items by basic substantive properties. However, GLTM contains both component outcomes and complexity factors in a single model and may be applied to data that neither LLTM nor MLTM can handle. Joint maximum likelihood estimators are presented for the parameters of GLTM and an application to cognitive test items is described.This research was partially supported by the National Institute of Education grant number NIE-6-7-0156 to Susan Embretson (Whitely), principal investigator. However the optinions expressed herein do not necessarily reflect the position or policy of the National Institute of Education, and no official endorsement by the National Institute of Education should be inferred.     

Hybrid route choice model incorporating latent cognitive effects of real-time travel information using physiological data

The proliferation of information systems is enabling drivers to receive en route real-time travel information, often from multiple sources, for making informed routing decisions. A robust understanding of route choice behavior under information provision can be leveraged by traffic operators to design information and its delivery systems for managing network-wide traffic. However, most existing route choice models lack the ability to consider the latent cognitive effects of information on drivers and their implications on route choice decisions. This paper presents a hybrid route choice modeling framework that incorporates the latent cognitive effects of real-time information and the effects of several explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors). The latent cognitive effects are estimated by analyzing drivers’ physiological data (i.e., brain electrical activity patterns) measured using an electroencephalogram (EEG). Data was collected for 95 participants in driving simulator experiments designed to elicit realistic route choices using a network-level setup featuring routes with different characteristics (in terms of travel time and driving environment complexity) and dynamic ambient traffic. Averaged EEG band powers in multiple brain regions were used to extract two latent cognitive variables that capture driver’s cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. A Multiple Indicators Multiple Causes model was used to test the effects of several explanatory factors on the latent cognitive variables, and their combined impacts on route choice decisions. The study results highlight the significant effects of driver attributes and information characteristics on latent cognitive effort and of route characteristics on latent cognitive inattention. They also indicate that drivers who are more attentive and exert more cognitive effort are more likely to switch from their current route by complying with the information provided. The study insights can aid traffic operators and information service providers to incorporate human factors and cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers’ route choices.     

组织职业生涯管理与员工心理与行为的关系

通过广泛的开放式问卷调查和访谈,结合国外职业生涯管理的做法,编制了适合我国国情的组织职业生涯管理问卷(简称OCMQ),将问卷在13个国有企业、民营企业、股份制企业的管理者、技术人员中进行了调查,对所获得的449份有效问卷探索性因素分析结果表明：我国组织职业生涯管理的结构主要体现在四个维度：即晋升公平、注重培训、职业自我认识、提供职业信息。后来,利用研制的OCMQ,以及相关问卷职业承诺、组织承诺、工作卷入度、职业满意度、工作绩效等又在11家国有和中外合资企业中进行了调查,399份有效问卷结果进一步验证了OCMQ问卷的结构效度、实证效度和信度;并发现组织职业生涯管理对企业员工的职业承诺、组织承诺、工作绩效、职业满意度、工作卷入度等产生积极的影响,证实了职业生涯管理的价值。     

Generalized multilevel structural equation modeling

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.gllamm can be downloaded from http://www.gllamm.org. The paper was written while Sophia Rabe-Hesketh was employed at and Anders Skrondal was visiting the Department of Biostatistics and Computing, Institute of Psychiatry, King's College London.     

A hierarchical latent response model for inferences about examinee engagement in terms of guessing and item-level non-response

In low-stakes assessments, test performance has few or no consequences for examinees themselves, so that examinees may not be fully engaged when answering the items. Instead of engaging in solution behaviour, disengaged examinees might randomly guess or generate no response at all. When ignored, examinee disengagement poses a severe threat to the validity of results obtained from low-stakes assessments. Statistical modelling approaches in educational measurement have been proposed that account for non-response or for guessing, but do not consider both types of disengaged behaviour simultaneously. We bring together research on modelling examinee engagement and research on missing values and present a hierarchical latent response model for identifying and modelling the processes associated with examinee disengagement jointly with the processes associated with engaged responses. To that end, we employ a mixture model that identifies disengagement at the item-by-examinee level by assuming different data-generating processes underlying item responses and omissions, respectively, as well as response times associated with engaged and disengaged behaviour. By modelling examinee engagement with a latent response framework, the model allows assessing how examinee engagement relates to ability and speed as well as to identify items that are likely to evoke disengaged test-taking behaviour. An illustration of the model by means of an application to real data is presented.     

Latent variables should remain as such: Evidence from a Monte Carlo study

Use of subject scores as manifest variables to assess the relationship between latent variables produces attenuated estimates. This has been demonstrated for raw scores from classical test theory (CTT) and factor scores derived from factor analysis. Conclusions on scores have not been sufficiently extended to item response theory (IRT) theta estimates, which are still recommended for estimation of relationships between latent variables. This is because IRT estimates appear to have preferable properties compared to CTT, while structural equation modeling (SEM) is often advised as an alternative to scores for estimation of the relationship between latent variables. The present research evaluates the consequences of using subject scores as manifest variables in regression models to test the relationship between latent variables. Raw scores and three methods for obtaining theta estimates were used and compared to latent variable SEM modeling. A Monte Carlo study was designed by manipulating sample size, number of items, type of test, and magnitude of the correlation between latent variables. Results show that, despite the advantage of IRT models in other areas, estimates of the relationship between latent variables are always more accurate when SEM models are used. Recommendations are offered for applied researchers.     

Dimensionality of the Latent Structure and Item Selection Via Latent Class Multidimensional IRT Models

With reference to a questionnaire aimed at assessing the performance of Italian nursing homes on the basis of the health conditions of their patients, we investigate two relevant issues: dimensionality of the latent structure and discriminating power of the items composing the questionnaire. The approach is based on a multidimensional item response theory model, which assumes a two-parameter logistic parameterization for the response probabilities. This model represents the health status of a patient by latent variables having a discrete distribution and, therefore, it may be seen as a constrained version of the latent class model. On the basis of the adopted model, we implement a hierarchical clustering algorithm aimed at assessing the actual number of dimensions measured by the questionnaire. These dimensions correspond to disjoint groups of items. Once the number of dimensions is selected, we also study the discriminating power of every item, so that it is possible to select the subset of these items which is able to provide an amount of information close to that of the full set. We illustrate the proposed approach on the basis of the data collected on 1,051 elderly people hosted in a sample of Italian nursing homes.     

Utilizing response times in cognitive diagnostic computerized adaptive testing under the higher-order deterministic input,noisy ‘and’ gate model

Methods of cognitive diagnostic computerized adaptive testing (CD-CAT) under higher-order cognitive diagnosis models have been developed to simultaneously provide estimates of the attribute mastery statuses of examinees for formative assessment and estimates of a latent continuous trait for overall summative evaluation. In a typical CD-CAT environment, examinees are often subject to a time limit, and the examinees’ response times (RTs) for specific test items can be routinely recorded by custom-made programs. Because examinees are individually administered tailored sets of test items from the item pool, they may experience different levels of speededness during testing and different levels of risk of running out of time. In this study, RTs were considered during the item-selection procedure to control the test speededness and the RTs were treated as useful information for improving latent trait estimation in CD-CAT under the higher-order deterministic input, noisy ‘and’ gate (DINA) model. A modified posterior-weighted Kullback–Leibler (PWKL) method that maximizes the item information per time unit and a shadow-test method that assembles a provisional test subject to a specified time constraint were developed. Two simulation studies were conducted to assess the effects of the proposed methods on the quality of CD-CAT for fixed- and variable-length exams. The results show that, compared with the traditional PWKL method, the proposed methods preserve a lower risk of running out of time while ensuring satisfactory attribute estimation and providing more accurate estimates of the latent trait and speed parameters. Finally, several suggestions for future research are proposed.     

使用验证性补偿多维IRT模型进行认知诊断评估

随着人们对测验反馈结果精细化的需求逐渐提高, 具有认知诊断功能的测量方法逐渐受到人们的关注。在认知诊断模型(CDMs)闪耀着光芒的同时, 另一类能够在连续量尺上提供精细反馈的多维IRT模型(MIRTMs)似乎受到些许冷落。为探究MIRTMs潜在的认知诊断功能, 本文以补偿模型为视角, 聚焦于分别属于MIRTMs的多维两参数logistic模型(M2PLM)和属于CDMs的线性logistic模型(LLM); 之后为使两者具有可比性, 可对补偿M2PLM引入验证性矩阵(Q矩阵)来界定题目与维度之间的关系, 进而得到验证性的补偿M2PLM (CC-M2PLM), 并通过把潜在特质按切点划分为跨界属性, 以期使CC-M2PLM展现出其本应具有的认知诊断功能; 预研究表明logistic量尺上的0点可作为相对合理的切点; 然后, 通过模拟研究对比探究CC-M2PLM和LLM的认知诊断功能, 结果表明CC-M2PLM可用于分析诊断测验数据, 且认知诊断功能与直接使用LLM的效果相当; 最后, 以两则实证数据为例来说明CC-M2PLM在实际诊断测验分析中的可行性。     

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.     

Analysis of multivariate mixed longitudinal data: A flexible latent process approach

Multivariate ordinal and quantitative longitudinal data measuring the same latent construct are frequently collected in psychology. We propose an approach to describe change over time of the latent process underlying multiple longitudinal outcomes of different types (binary, ordinal, quantitative). By relying on random‐effect models, this approach handles individually varying and outcome‐specific measurement times. A linear mixed model describes the latent process trajectory while equations of observation combine outcome‐specific threshold models for binary or ordinal outcomes and models based on flexible parameterized non‐linear families of transformations for Gaussian and non‐Gaussian quantitative outcomes. As models assuming continuous distributions may be also used with discrete outcomes, we propose likelihood and information criteria for discrete data to compare the goodness of fit of models assuming either a continuous or a discrete distribution for discrete data. Two analyses of the repeated measures of the Mini‐Mental State Examination, a 20‐item psychometric test, illustrate the method. First, we highlight the usefulness of parameterized non‐linear transformations by comparing different flexible families of transformation for modelling the test as a sum score. Then, change over time of the latent construct underlying directly the 20 items is described using two‐parameter longitudinal item response models that are specific cases of the approach.     

Fitting and Testing Conditional Multinormal Partial Credit Models

A multinormal partial credit model for factor analysis of polytomously scored items with ordered response categories is derived using an extension of the Dutch Identity (Holland in Psychometrika 55:5?C18, 1990). In the model, latent variables are assumed to have a multivariate normal distribution conditional on unweighted sums of item scores, which are sufficient statistics. Attention is paid to maximum likelihood estimation of item parameters, multivariate moments of latent variables, and person parameters. It is shown that the maximum likelihood estimates can be found without the use of numerical integration techniques. More general models are discussed which can be used for testing the model, and it is shown how models with different numbers of latent variables can be tested against each other. In addition, multi-group extensions are proposed, which can be used for testing both measurement invariance and latent population differences. Models and procedures discussed are demonstrated in an empirical data example.     

Marginal maximum likelihood estimation for a psychometric model of discontinuous development

Item response theory models posit latent variables to account for regularities in students' performances on test items. Wilson's “Saltus” model extends the ideas of IRT to development that occurs in stages, where expected changes can be discontinuous, show different patterns for different types of items, or even exhibit reversals in probabilities of success on certain tasks. Examples include Piagetian stages of psychological development and Siegler's rule-based learning. This paper derives marginal maximum likelihood (MML) estimation equations for the structural parameters of the Saltus model and suggests a computing approximation based on the EM algorithm. For individual examinees, empirical Bayes probabilities of learning-stage are given, along with proficiency parameter estimates conditional on stage membership. The MML solution is illustrated with simulated data and an example from the domain of mixed number subtraction. The authors' names appear in alphabetical order. We would like to thank Karen Draney for computer programming, Kikumi Tatsuoka for allowing us to use the mixed-number subtraction data, and Eric Bradlow, Chan Dayton, Kikumi Tatsuoka, and four anonymous referees for helpful suggestions. The first author's work was supported by Contract No. N00014-88-K-0304, R&T 4421552, from the Cognitive Sciences Program, Cognitive and Neural Sciences Division, Office of Naval Research, and by the Program Research Planning Council of Educational Testing Service. The second author's work was supported by a National Academy of Education Spencer Fellowship and by a Junior Faculty Research Grant from the Committee on Research, University of California at Berkeley. A copy of the Saltus computer program can be obtained from the second author.     

Introduction to bifactor polytomous item response theory analysis

A bifactor item response theory model can be used to aid in the interpretation of the dimensionality of a multifaceted questionnaire that assumes continuous latent variables underlying the propensity to respond to items. This model can be used to describe the locations of people on a general continuous latent variable as well as on continuous orthogonal specific traits that characterize responses to groups of items. The bifactor graded response (bifac-GR) model is presented in contrast to a correlated traits (or multidimensional GR model) and unidimensional GR model. Bifac-GR model specification, assumptions, estimation, and interpretation are demonstrated with a reanalysis of data (Campbell, 2008) on the Shared Activities Questionnaire. We also show the importance of marginalizing the slopes for interpretation purposes and we extend the concept to the interpretation of the information function. To go along with the illustrative example analyses, we have made available supplementary files that include command file (syntax) examples and outputs from flexMIRT, IRTPRO, R, Mplus, and STATA. Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.jsp.2016.11.001. Data needed to reproduce analyses in this article are available as supplemental materials (online only) in the Appendix of this article.