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comparison of latent class, K-means, and K-median methods for clustering dichotomous data.
Title
A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.
Creator
Brusco, Michael J, Shireman, Emilie, Steinley, Douglas
Abstract/Description
The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted...
Show moreThe problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all 3 methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record
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Date Issued
2017-09-01
Identifier
FSU_pmch_27607543, 10.1037/met0000095, PMC5982597, 27607543, 27607543, 2016-43141-001
Format
Citation
The Impact of Perceived Subgroup Formation on Transactive Memory Systems and Performance in Distributed Teams
Title
The Impact of Perceived Subgroup Formation on Transactive Memory Systems and Performance in Distributed Teams.
Creator
Shen, Yide, Gallivan, Michael J., Tang, Xinlin
Abstract/Description
With distributed teams becoming increasingly common in organizations, improving their performance is a critical challenge for both practitioners and researchers. This research examines how group members' perception of subgroup formation affects team performance in fully distributed teams. The authors propose that individual members' perception about the presence of subgroups within the team has a negative effect on team performance, which manifests itself through decreases in a team's...
Show moreWith distributed teams becoming increasingly common in organizations, improving their performance is a critical challenge for both practitioners and researchers. This research examines how group members' perception of subgroup formation affects team performance in fully distributed teams. The authors propose that individual members' perception about the presence of subgroups within the team has a negative effect on team performance, which manifests itself through decreases in a team's transactive memory system (TMS). Using data from 154 members of 41 fully distributed teams (where no group members were colocated), the authors found that members' perceptions of the existence of subgroups impair the team's TMS and its overall performance. They found these effects to be statistically significant. In addition, decreases in a group's TMS partially mediate the effect of perceived subgroup formation on team performance. The authors discuss the implications of their findings for managerial action, as well as for researchers, and they propose directions for future research.
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Date Issued
2016-03
Identifier
FSU_libsubv1_wos_000385860600004, 10.4018/ijec.2016010104
Format
Citation
Local Optima in Mixture Modeling.
Title
Local Optima in Mixture Modeling.
Creator
Shireman, Emilie M, Steinley, Douglas, Brusco, Michael J
Abstract/Description
It is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model. A real-data illustration and a series of simulations are presented that examine the effect of a variety...
Show moreIt is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model. A real-data illustration and a series of simulations are presented that examine the effect of a variety of data structures on the propensity of local optima and the classification quality of the resulting solution. We show that there is a moderately strong relationship between a solution that has a high proportion of local optima and one that is poorly classified.
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Date Issued
2016-07-01
Identifier
FSU_pmch_27494191, 10.1080/00273171.2016.1160359, PMC5534344, 27494191, 27494191
Format
Citation
method for making inferences in network analysis
Title
A method for making inferences in network analysis: Comment on Forbes, Wright, Markon, and Krueger (2017)..
Creator
Steinley, Douglas, Hoffman, Michaela, Brusco, Michael J, Sher, Kenneth J
Abstract/Description
Forbes, Wright, Markon, and Krueger (2017) make a compelling case for proceeding cautiously with respect to the overinterpretation and dissemination of results using the increasingly popular approach of creating "networks" from co-occurrences of psychopathology symptoms. We commend the authors on their initial investigation and their utilization of cross-validation techniques in an effort to capture the stability of a variety of network estimation methods. Such techniques get at the heart of...
Show moreForbes, Wright, Markon, and Krueger (2017) make a compelling case for proceeding cautiously with respect to the overinterpretation and dissemination of results using the increasingly popular approach of creating "networks" from co-occurrences of psychopathology symptoms. We commend the authors on their initial investigation and their utilization of cross-validation techniques in an effort to capture the stability of a variety of network estimation methods. Such techniques get at the heart of establishing "reproducibility," an increasing focus of concern in both psychology (e.g., Pashler & Wagenmakers, 2012) and science more generally (e.g., Baker, 2016). However, as we will show, the problem is likely worse (or at least more complicated) than they initially indicated. Specifically, for multivariate binary data, the marginal distributions enforce a large degree of structure on the data. We show that some expected measurements-such as commonly used centrality statistics-can have substantially higher values than what would usually be expected. As such, we propose a nonparametric approach to generate confidence intervals through Monte Carlo simulation. We apply the proposed methodology to the National Comorbidity Survey - Replication, provided by Forbes et al., finding that the many of the results are indistinguishable from what would be expected by chance. Further, we discuss the problem of multiple testing and potential issues of applying methods developed for 1-mode networks (e.g., ties within a single set of observations) to 2-mode networks (e.g., ties between 2 distinct sets of entities). When taken together, these issues indicate that the psychometric network models should be employed with extreme caution and interpreted guardedly. (PsycINFO Database Record
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Date Issued
2017-10-01
Identifier
FSU_pmch_29106283, 10.1037/abn0000308, PMC5982585, 29106283, 29106283, 2017-49368-014
Format
Citation