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Diagnostic classification models (aka cognitive or skills diagnosis models) have shown great promise for evaluating mastery on a multidimensional profile of skills as assessed through examinee responses, but continued development and application of these models has been hindered by a lack of readily available software. In this article we demonstrate how diagnostic classification models may be estimated as confirmatory latent class models using Mplus, thus bridging the gap between the technical presentation of these models and their practical use for assessment in research and applied settings. Using a sample English test of three grammatical skills, we describe how diagnostic classification models can be phrased as latent class models within Mplus and how to obtain the syntax and output needed for estimation and interpretation of the model parameters. We also have written a freely available SAS program that can be used to automatically generate the Mplus syntax. We hope this work will ultimately result in greater access to diagnostic classification models throughout the testing community, from researchers to practitioners. 相似文献
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The purpose of this article is to demonstrate constraining the nominal response model in Mplus software to calibrate data under the partial credit model (PCM) and generalized partial credit model (GPCM). Currently, many researchers are uncertain if the PCM and GPCM can be estimated within Mplus. Through model constraint commands in Mplus, we demonstrate that both models can be estimated in recent versions of this software. We present an example of this approach with data from 522 respondents on a subset of items from the Math Self-Efficacy Scale (Betz & Hackett, 1983). It is demonstrated that the presented model code is a viable way of estimating the models in Mplus. 相似文献
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MplusAutomation is a package for R that facilitates complex latent variable analyses in Mplus involving comparisons among many models and parameters. More specifically, MplusAutomation provides tools to accomplish 3 objectives: to create and manage Mplus syntax for groups of related models; to automate the estimation of many models; and to extract, aggregate, and compare fit statistics, parameter estimates, and ancillary model outputs. We provide an introduction to the package using applied examples including a large-scale simulation study. By reducing the effort required for large-scale studies, a broad goal of MplusAutomation is to support methodological developments in structural equation modeling using Mplus. 相似文献
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The aim of this study is to investigate interrelationships between overexcitability and learning patterns from the perspective of personality development according to Dabrowski’s theory of positive disintegration. To this end, Bayesian structural equation modeling (BSEM) is applied which allows for the simultaneous inclusion in the measurement model of all, approximate zero cross-loadings and residual covariances based on zero-mean, small-variance priors, and represents substantive theory better. Our BSEM analysis with a sample of 516 students in higher education yields positive results regarding the validity of the model, in contrast to a frequentist approach to validation, and reveals that overexcitability – the degree and nature of which is characteristic of the potential for advanced personality development, according to Dabrowski’s theory – is substantially related to the way in which information is processed, as well as to the regulation strategies that are used for this purpose and to study motivation. Overexcitability is able to explain variations in learning patterns to varying degrees, ranging from weakly (3.3% for reproduction-directed learning for the female group) to rather strongly (46.1% for meaning-directed learning for males), with intellectual overexcitability representing the strongest indicator of deep learning. This study further argues for the relevance of including emotion dynamics – taking into account their multilevelness – in the study of the learning process. 相似文献
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John J. McArdle Fumiaki Hamagami Janice Y. Chang Earl S. Hishinuma 《Structural equation modeling》2013,20(4):608-629
The scientific literature consistently supports a negative relationship between adolescent depression and educational achievement, but we are certainly less sure on the causal determinants for this robust association. In this article we present multivariate data from a longitudinal cohort-sequential study of high school students in Hawai‘i (following McArdle, 2008; McArdle, Johnson, Hishinuma, Miyamoto, & Andrade, 2001). We first describe the full set of data on academic achievements and self-reported depression. We then carry out and present a progression of analyses in an effort to determine the accuracy, size, and direction of the dynamic relationships among depression and academic achievement, including gender and ethnic group differences. We apply 3 recently available forms of longitudinal data analysis: (a) Dealing with incomplete data—We apply these methods to cohort-sequential data with relatively large blocks of data that are incomplete for a variety of reasons (Little & Rubin, 1987; McArdle & Hamagami, 1992). (b) Ordinal measurement models (Muthén & Muthén, 2006)—We use a variety of statistical and psychometric measurement models, including ordinal measurement models, to help clarify the strongest patterns of influence. (c) Dynamic structural equation models (DSEMs; McArdle, 2008). We found the DSEM approach taken here was viable for a large amount of data, the assumption of an invariant metric over time was reasonable for ordinal estimates, and there were very few group differences in dynamic systems. We conclude that our dynamic evidence suggests that depression affects academic achievement, and not the other way around. We further discuss the methodological implications of the study. 相似文献
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Rens van de Schoot Herbert Hoijtink Michael N. Hallquist Paul A. Boelen 《Structural equation modeling》2013,20(4):593-609
Researchers in the behavioral and social sciences often have expectations that can be expressed in the form of inequality constraints among the parameters of a structural equation model resulting in an informative hypothesis. The questions they would like an answer to are “Is the hypothesis Correct” or “Is the hypothesis incorrect?” We demonstrate a Bayesian approach to compare an inequality-constrained hypothesis with its complement in an SEM framework. The method is introduced and its utility is illustrated by means of an example. Furthermore, the influence of the specification of the prior distribution is examined. Finally, it is shown how the approach proposed can be implemented using Mplus. 相似文献
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Natacha Carragher Jonathan Templin Phillip Jones Boaz Shulruf Gary Velan 《Educational Measurement》2019,38(1):89-91
In this ITEMS module, we provide a didactic overview of the specification, estimation, evaluation, and interpretation steps for diagnostic measurement/classification models (DCMs), which are a promising psychometric modeling approach. These models can provide detailed skill‐ or attribute‐specific feedback to respondents along multiple latent dimensions and hold theoretical and practical appeal for a variety of fields. We use a current unified modeling framework—the log‐linear cognitive diagnosis model (LCDM)—as well as a series of quality‐control checklists for data analysts and scientific users to review the foundational concepts, practical steps, and interpretational principles for these models. We demonstrate how the models and checklists can be applied in real‐life data‐analysis contexts. A library of macros and supporting files for Excel, SAS, and Mplus are provided along with video tutorials for key practices. 相似文献
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Tihomir Asparouhov 《Structural equation modeling》2013,20(4):495-508
This article presents a new method for multiple-group confirmatory factor analysis (CFA), referred to as the alignment method. The alignment method can be used to estimate group-specific factor means and variances without requiring exact measurement invariance. A strength of the method is the ability to conveniently estimate models for many groups. The method is a valuable alternative to the currently used multiple-group CFA methods for studying measurement invariance that require multiple manual model adjustments guided by modification indexes. Multiple-group CFA is not practical with many groups due to poor model fit of the scalar model and too many large modification indexes. In contrast, the alignment method is based on the configural model and essentially automates and greatly simplifies measurement invariance analysis. The method also provides a detailed account of parameter invariance for every model parameter in every group. 相似文献
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S. Mason Garrison 《Structural equation modeling》2018,25(6):972-977
Behavior genetic modeling is a prominent application of multi-group structural equation modeling (SEM). It decomposes phenotypic variance into genetic and environmental sources by leveraging the covariation within and between kin pairs. Although any SEM program with multi-group capabilities can be employed, the software program, Mx, has dominated behavior genetics research. Indeed, even though Mx has not been maintained since 2011, it remains the most popular SEM program in Behavior Genetics articles published in 2016 and 2017. Given the persistence of Mx, the aim of this article is to understand Mx’s performance relative to other popular behavior genetic programs. Through this process, programs employed in behavior genetics research are identified, and their relevant technical features and accessibility are compared. Finally, the relative strengths and limitations of the programs are discussed, and recommendations are provided for behavior genetics researchers. 相似文献
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