Latent class analysis (LCA) has been found to have important applications in social and behavioral sciences for modeling categorical response variables, and nonresponse is typical when collecting data. In this study, the nonresponse mainly included “contingency questions” and real “missing data.” The primary objective of this research was to evaluate the effects of some potential factors on model selection indices in LCA with nonresponse data.
We simulated missing data with contingency questions and evaluated the accuracy rates of eight information criteria for selecting the correct models. The results showed that the main factors are latent class proportions, conditional probabilities, sample size, the number of items, the missing data rate, and the contingency data rate. Interactions of the conditional probabilities with class proportions, sample size, and the number of items are also significant. From our simulation results, the impact of missing data and contingency questions can be amended by increasing the sample size or the number of items. 相似文献
Although distributed teams have been researched extensively in information systems and decision science disciplines, a review of the literature suggests that the dominant focus has been on understanding the factors affecting performance at the team level. There has however been an increasing recognition that specific individuals within such teams are often critical to the team's performance. Consequently, existing knowledge about such teams may be enhanced by examining the factors that affect the performance of individual team members. This study attempts to address this need by identifying individuals who emerge as “stars” in globally distributed teams involved in knowledge work such as information systems development (ISD). Specifically, the study takes a knowledge‐centered view in explaining which factors lead to “stardom” in such teams. Further, it adopts a social network approach consistent with the core principles of structural/relational analysis in developing and empirically validating the research model. Data from U.S.–Scandinavia self‐managed “hybrid” teams engaged in systems development were used to deductively test the proposed model. The overall study has several implications for group decision making: (i) the study focuses on stars within distributed teams, who play an important role in shaping group decision making, and emerge as a result of a negotiated/consensual decision making within egalitarian teams; (ii) an examination of emergent stars from the team members’ point of view reflects the collective acceptance and support dimension decision‐making contexts identified in prior literature; (iii) finally, the study suggests that the social network analysis technique using relational data can be a tool for a democratic decision‐making technique within groups. 相似文献