There is increasing interest in the early roots and influencing factors of leadership potential from a life span development perspective. This conceptual and empirical work extends traditional approaches focusing on adults in organizational settings. From the perspective of early influences on leader development, the goal of this study was to examine the effects of overparenting on adolescent leader emergence, influencing mechanisms, and sex differences. Students (N = 1,255) from 55 classrooms in 13 junior high schools participated, with additional responses from their parents, peers, and teachers. The results indicated that overparenting is negatively related to adolescent leader emergence as indicated by parent ratings, teacher ratings, and peer nominations in addition to leader role occupancy. The negative effects of overparenting on leader emergence (perceived and actual) were serially mediated by self-esteem and leader self-efficacy. In addition, sex difference analysis revealed that male adolescents received more overparenting and showed less leader emergence (perceived and actual) than female adolescents. Female adolescents’ self-esteem was more likely to be negatively related to overparenting, and female adolescents’ leader emergence (perceived and actual) was more strongly related to their leader self-efficacy when compared with male adolescents. Implications for life span leader development theory, for youth and adult leadership development practices, and for parenting practices on future generations are discussed.

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Information about the psychometric properties of items can be highly useful in assessment development, for example, in item response theory (IRT) applications and computerized adaptive testing. Although literature on parameter recovery in unidimensional IRT abounds, less is known about parameter recovery in multidimensional IRT (MIRT), notably when tests exhibit complex structures or when latent traits are nonnormal. The current simulation study focuses on investigation of the effects of complex item structures and the shape of examinees' latent trait distributions on item parameter recovery in compensatory MIRT models for dichotomous items. Outcome variables included bias and root mean square error. Results indicated that when latent traits were skewed, item parameter recovery was generally adversely impacted. In addition, the presence of complexity contributed to decreases in the precision of parameter recovery, particularly for discrimination parameters along one dimension when at least one latent trait was generated as skewed.

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.

The presence of missing responses in assessment settings is inevitable and may yield biased parameter estimates in psychometric modeling if ignored or handled improperly. Many methods have been proposed to handle missing responses in assessment data that are often dichotomous or polytomous. Their applications remain nominal, however, partly due to that (1) there is no sufficient support in the literature for an optimal method; (2) many practitioners and researchers are not familiar with these methods; and (3) these methods are usually not employed by psychometric software and missing responses need to be handled separately. This article introduces and reviews the commonly used missing response handling methods in psychometrics, along with the literature that examines and compares the performance of these methods. Further, the use of the TestDataImputation package in R is introduced and illustrated with an example data set and a simulation study. Corresponding R codes are provided.