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Research and Publications - Department of Gender and Race Studies

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Browsing Research and Publications - Department of Gender and Race Studies by Author "Jiang, Zhehan"

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    Estimating Cognitive Diagnosis Models in Small Samples: Bayes Modal Estimation and Monotonic Constraints
    (Sage, 2021) Ma, Wenchao; Jiang, Zhehan; University of Alabama Tuscaloosa; Peking University
    Despite the increasing popularity, cognitive diagnosis models have been criticized for limited utility for small samples. In this study, the authors proposed to use Bayes modal (BM) estimation and monotonic constraints to stabilize item parameter estimation and facilitate person classification in small samples based on the generalized deterministic input noisy "and" gate (G-DINA) model. Both simulation study and real data analysis were used to assess the utility of the BM estimation and monotonic constraints. Results showed that in small samples, (a) the G-DINA model with BM estimation is more likely to converge successfully, (b) when prior distributions are specified reasonably, and monotonicity is not violated, the BM estimation with monotonicity tends to produce more stable item parameter estimates and more accurate person classification, and (c) the G-DINA model using the BM estimation with monotonicity is less likely to overfit the data and shows higher predictive power.
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    The Use of Multivariate Generalizability Theory to Evaluate the Quality of Subscores
    (Sage, 2018) Jiang, Zhehan; Raymond, Mark; University of Alabama Tuscaloosa
    Conventional methods for evaluating the utility of subscores rely on reliability and correlation coefficients. However, correlations can overlook a notable source of variability: variation in subtest means/difficulties. Brennan introduced a reliability index for score profiles based on multivariate generalizability theory, designated as G, which is sensitive to variation in subtest difficulty. However, there has been little, if any, research evaluating the properties of this index. A series of simulation experiments, as well as analyses of real data, were conducted to investigate G under various conditions of subtest reliability, subtest correlations, and variability in subtest means. Three pilot studies evaluated G in the context of a single group of examinees. Results of the pilots indicated that G indices were typically low; across the 108 experimental conditions, G ranged from .23 to .86, with an overall mean of 0.63. The findings were consistent with previous research, indicating that subscores often do not have interpretive value. Importantly, there were many conditions for which the correlation-based method known as proportion reduction in mean-square error (PRMSE; Haberman, 2006) indicated that subscores were worth reporting, but for which values of G fell into the .50s, .60s, and .70s. The main study investigated G within the context of score profiles for examinee subgroups. Again, not only G indices were generally low, but it was also found that G can be sensitive to subgroup differences when PRMSE is not. Analyses of real data and subsequent discussion address how G can supplement PRMSE for characterizing the quality of subscores.