Identifying expectations about the strength of causal relationships

When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people’s a priori beliefs about causal systems, with recent research focusing on people’s expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the form of such prior distributions have been made previously, many different distributions are possible, making it difficult to test such proposals exhaustively. In Experiment 1 we used iterated learning—a method in which participants make inferences about data generated based on their own responses in previous trials—to estimate participants’ prior beliefs about the strengths of causes. This method produced estimated prior distributions that were quite different from those previously proposed in the literature. Experiment 2 collected a large set of human judgments on the strength of causal relationships to be used as a benchmark for evaluating different models, using stimuli that cover a wider and more systematic set of contingencies than previous research. Using these judgments, we evaluated the predictions of various Bayesian models. The Bayesian model with priors estimated via iterated learning compared favorably against the others. Experiment 3 estimated participants’ prior beliefs concerning different causal systems, revealing key similarities in their expectations across diverse scenarios.     

Fitting direct covariance structures by the MSTRUCT modeling language of the CALIS procedure

This paper demonstrates the usefulness and flexibility of the general structural equation modelling (SEM) approach to fitting direct covariance patterns or structures (as opposed to fitting implied covariance structures from functional relationships among variables). In particular, the MSTRUCT modelling language (or syntax) of the CALIS procedure (SAS/STAT version 9.22 or later: SAS Institute, 2010) is used to illustrate the SEM approach. The MSTRUCT modelling language supports a direct covariance pattern specification of each covariance element. It also supports the input of additional independent and dependent parameters. Model tests, fit statistics, estimates, and their standard errors are then produced under the general SEM framework. By using numerical and computational examples, the following tests of basic covariance patterns are illustrated: sphericity, compound symmetry, and multiple‐group covariance patterns. Specification and testing of two complex correlation structures, the circumplex pattern and the composite direct product models with or without composite errors and scales, are also illustrated by the MSTRUCT syntax. It is concluded that the SEM approach offers a general and flexible modelling of direct covariance and correlation patterns. In conjunction with the use of SAS macros, the MSTRUCT syntax provides an easy‐to‐use interface for specifying and fitting complex covariance and correlation structures, even when the number of variables or parameters becomes large.     

A general proof of consistency of heuristic classification for cognitive diagnosis models

The Asymptotic Classification Theory of Cognitive Diagnosis (Chiu et al., 2009, Psychometrika, 74, 633–665) determined the conditions that cognitive diagnosis models must satisfy so that the correct assignment of examinees to proficiency classes is guaranteed when non‐parametric classification methods are used. These conditions have only been proven for the Deterministic Input Noisy Output AND gate model. For other cognitive diagnosis models, no theoretical legitimization exists for using non‐parametric classification techniques for assigning examinees to proficiency classes. The specific statistical properties of different cognitive diagnosis models require tailored proofs of the conditions of the Asymptotic Classification Theory of Cognitive Diagnosis for each individual model – a tedious undertaking in light of the numerous models presented in the literature. In this paper a different way is presented to address this task. The unified mathematical framework of general cognitive diagnosis models is used as a theoretical basis for a general proof that under mild regularity conditions any cognitive diagnosis model is covered by the Asymptotic Classification Theory of Cognitive Diagnosis.     

Development and Initial Validation of a Scale Measuring the Beliefs of Educators Regarding Response to Intervention

This article presents information on the development and initial validation of the 16-item Response to Intervention (RTI) Beliefs Scale. The scale is designed to measure the extent to which educators working in schools hold beliefs consistent with the tenets of RTI. The authors administered the instrument to 2,430 educators in 62 elementary schools in the fall of 2007 and 2,443 educators in 68 elementary schools in the spring of 2008. Exploratory, single-level confirmatory, and multilevel confirmatory factor analysis procedures were used to examine construct validity. Results supported a correlated 3-factor model (Academic Abilities and Performance of Students with Disabilities, Data-Based Decision Making, and Functions of Core and Supplemental Instruction) at both the school and educator levels of analysis. Furthermore, the factor scores derived from the model demonstrated significant, positive relations to RTI implementation. Reliability estimates for two of the three factor scores exceeded.70. Implications for research on educator beliefs and implementation of RTI as well as implications for school psychologists supporting RTI implementation are discussed.     

A comparative assessment of the factor structures and psychometric properties of the GHQ-12 and the GHQ-20 based on data from a Finnish population-based sample

The General Health Questionnaire (GHQ) is a self-report questionnaire designed to identify psychological distress. Psychometric properties of two versions of GHQ-12 and GHQ-20 were assessed in a large population-based sample of Finnish twins, ages 22 to 27 (n= 4580). Participants were randomized into two subgroups, namely Twin1 (n= 2294) and Twin2 (n= 2286). The GHQ-12 data were assessed using Confirmatory Factor Analysis (CFA). The factor structure of the GHQ-20 was first assessed with Exploratory Factor Analysis (EFA) in the Twin1 dataset, and the results obtained were then subjected to CFA in Twin1 and Twin2 datasets. The CFA of the GHQ-12 indicated that the best fit and the simplest solution were provided by the three-factor solution in both subpopulations. Analyses of the GHQ-20 suggested that the four-factor structure was superior to the three-factor model. This result is also theoretically justifiable. Compared to the 12-item version, GHQ-20 provides additional fourth factor of anhedonia, suggesting some discriminative power.     

Beta-MPT: Multinomial processing tree models for addressing individual differences

Traditionally, multinomial processing tree (MPT) models are applied to groups of homogeneous participants, where all participants within a group are assumed to have identical MPT model parameter values. This assumption is unreasonable when MPT models are used for clinical assessment, and it often may be suspect for applications to ordinary psychological experiments. One method for dealing with parameter variability is to incorporate random effects assumptions into a model. This is achieved by assuming that participants’ parameters are drawn independently from some specified multivariate hyperdistribution. In this paper we explore the assumption that the hyperdistribution consists of independent beta distributions, one for each MPT model parameter. These beta-MPT models are ‘hierarchical models’, and their statistical inference is different from the usual approaches based on data aggregated over participants. The paper provides both classical (frequentist) and hierarchical Bayesian approaches to statistical inference for beta-MPT models. In simple cases the likelihood function can be obtained analytically; however, for more complex cases, Markov Chain Monte Carlo algorithms are constructed to assist both approaches to inference. Examples based on clinical assessment studies are provided to demonstrate the advantages of hierarchical MPT models over aggregate analysis in the presence of individual differences.     

A Signal Detection Model for Multiple-Choice Exams

A model for multiple-choice exams is developed from a signal-detection perspective. A correct alternative in a multiple-choice exam can be viewed as being a signal embedded in noise (incorrect alternatives). Examinees are assumed to have perceptions of the plausibility of each alternative, and the decision process is to choose the most plausible alternative. It is also assumed that each examinee either knows or does not know each item. These assumptions together lead to a signal detection choice model for multiple-choice exams. The model can be viewed, statistically, as a mixture extension, with random mixing, of the traditional choice model, or similarly, as a grade-of-membership extension. A version of the model with extreme value distributions is developed, in which case the model simplifies to a mixture multinomial logit model with random mixing. The approach is shown to offer measures of item discrimination and difficulty, along with information about the relative plausibility of each of the alternatives. The model, parameters, and measures derived from the parameters are compared to those obtained with several commonly used item response theory models. An application of the model to an educational data set is presented.     

《大学生主观幸福感量表》的编制

根据国内外关于主观幸福感的文献和已有的研究经验,编制了大学生主观幸福感问卷。以往大多数研究认为,主观幸福感由生活满意度和情绪体验两个基本成分构成,本研究认为主观幸福感还应包括社会性行为和人际关系等成分。对376名大学生进行初测,通过探索性因素分析,表明大学生主观幸福感由自我满意、家庭满意、消极情绪、积极情绪、精力、生活满意、社会性行为和人际关系8个维度构成,共41个项目。对511名大学生施测41个项目的主观幸福感问卷,进行验证性因素分析,结果表明,上述结构是合理的。