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This article discusses the use of survey questionnaires in psychology and social science research to measure latent traits in study subjects. While structural equation models have traditionally been used for causal analysis, their reliance on knowledge of true causal structure limits their reliability. The article proposes the use of a Bayesian network, which can learn causal structure objectively from data without prior knowledge. The authors develop a learning algorithm based on hill-climbing and bootstrapping that can identify a unique causal structure and quantify associated uncertainty. The algorithm is demonstrated using a dataset from a psychological study on the relationship between symptoms of obsessive-compulsive disorder and depression. The article also mentions the availability of a user-friendly open-source R package for implementing the proposed algorithm. [Extracted from the article]

We proposed and implemented a novel clustering algorithm called LAIR2, which has constant running time average for on-the-fly Scatter/Gather browsing. Our experiments showed that when running on a single processor, the LAIR2 on-line clustering algorithm was several hundred times faster than a parallel Buckshot algorithm running on multiple processors. This paper reports on a study that examined the effectiveness of the LAIR2 algorithm in terms of clustering quality and its impact on retrieval performance. We conducted a user study on 24 subjects to evaluate on-the-fly LAIR2 clustering in Scatter/Gather search tasks by comparing its performance to the Buckshot algorithm, a classic method for Scatter/Gather browsing. Results showed significant differences in terms of subjective perceptions of clustering quality. Subjects perceived that the LAIR2 algorithm produced significantly better quality clusters than the Buckshot method did. Subjects felt that it took less effort to complete the tasks with the LAIR2 system, which was more effective in helping them in the tasks. Interesting patterns also emerged from subjects' comments in the final open-ended questionnaire. We discuss implications and future research. [ABSTRACT FROM AUTHOR]