Dealing with Missing Responses in Cognitive Diagnostic Modeling

Missing data are a common problem in educational assessment settings. In the implementation of cognitive diagnostic models (CDMs), the presence and/or inappropriate treatment of missingness may yield biased parameter estimates and diagnostic information. Using simulated data, this study evaluates ten approaches for handling missing data in a commonly applied CDM (the deterministic inputs, noisy “and” gate (DINA) model): treating missing data as incorrect (IN), person mean (PM) imputation, item mean (IM) imputation, two-way (TW) imputation, response function (RF) imputation, logistic regression (LR), expectation-maximization (EM) imputation, full information maximum likelihood (FIML) estimation, predictive mean matching (PMM), and random imputation (RI). Specifically, the current study investigates how the estimation accuracy of item parameters and examinees’ attribute profiles from DINA are impacted by the presence of missing data and the selection of missing data methods across conditions. While no single method was found to be superior to other methods across all conditions, the results suggest the use of FIML, PMM, LR, and EM in recovering item parameters. The selected methods, except for PM, performed similarly across conditions regarding attribute classification accuracy. Recommendations for the treatment of missing responses for CDMs are provided. Limitations and future directions are discussed

The Longitudinal Relationship Between Levels of Cognitively Stimulating Leisure Activity and Positive and Negative Affect Among Older Adults With MCI

Background The purpose of this study was to investigate the longitudinal relationship between different levels of cognitively stimulating leisure activity (CSLA) participation and different levels of positive and negative affect among older adults with mild cognitive impairment (MCI). Methods Using a repeated-measured multivariate analysis of covariance (RM-MANCOVA), this study analyzed the Health and Retirement Study (HRS) data from 2012 to 2020 (N = 5932). Results The results presented the following. (a) The high CSLA group showed higher positive affect and lower negative affect than the mid and low groups. Also, the mid-CSLA group presented higher positive affect and lower negative affect than the low CSLA group. (b) Both positive and negative affect showed significant differences between years and indicated a continuously declining slope year by year without exceptions. (c) The high CSLA group not only presented higher positive affect and lower negative affect during the period but also solely showed a rebounding feature in the declining slope on both emotions. Conclusions The findings of this study provide valuable support for the design and implementation of CSLA participation programs and clinical guidelines for older adults with MCI. The results highlight the importance of determining the optimal level of CSLA engagement that is required to promote emotional health and cognitive function in this population. Healthcare professionals and clinical practitioners can leverage the insights gained from this study to develop and deliver effective CSLA interventions tailored to the specific needs and capacities of older adults with MCI

Examining DIF in the Context of CDMs When the Q-Matrix Is Misspecified

The rise in popularity and use of cognitive diagnostic models (CDMs) in educational research are partly motivated by the models’ ability to provide diagnostic information regarding students’ strengths and weaknesses in a variety of content areas. An important step to ensure appropriate interpretations from CDMs is to investigate differential item functioning (DIF). To this end, the current simulation study examined the performance of three methods to detect DIF in CDMs, with particular emphasis on the impact of Q-matrix misspecification on methods’ performance. Results illustrated that logistic regression and Mantel–Haenszel had better control of Type I error than the Wald test; however, high power rates were found using logistic regression and Wald methods, only. In addition to the tradeoff between Type I error control and acceptable power, our results suggested that Q-matrix complexity and item structures yield different results for different methods, presenting a more complex picture of the methods’ performance. Finally, implications and future directions are discussed