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This document provides estimates of the number and characteristics of political science faculty and students. The data utilized in this report are drawn from a number of sources: National Center for Education Statistics, National Research Council's Survey of Earned Doctorates; National Science Foundation; and two data collection devices of the American Political Science Association: The Guide to Graduate Study in Political Science and The Survey of Departments.In many cases the statistics presented are estimates of the relevant population based on information available on a sample of cases. We shall attempt to be explicit about our definitions and estimation procedures, so that the reader can draw his own conclusions on the usefulness of individual components of this report.

This document provides estimates of the number and characteristics of political science faculty and students. The data utilized in this report are drawn from a number of sources: National Center for Education Statistics, National Research Council's Survey of Earned Doctorates; National Science Foundation; and two data collection devices of the American Political Science Association: The Guide to Graduate Study in Political Science and The Survey of Departments.In many cases the statistics presented are estimates of the relevant population based on information available on a sample of cases. We shall attempt to be explicit about our definitions and estimation procedures, so that the reader can draw his own conclusions on the usefulness of individual components of this report.

Like most future enrollment management leaders, Randall Langston had no idea where his career would be headed when he was an undergraduate. But he discovered a passion for working with students and pursued a master's degree in student affairs.

Genstat allows instructors to create individualized datasets for each student in a research methods class. It also produces answers to statistical problems for each dataset. The data generated may be discrete or continuous. The instructor can control the size, mean, variance, and skew of the datasets. Problems can be generated dealing with measures of central tendency and variance, cross-tabulations, correlations, regression, and ANOVA. This is an otherwise excellent instructional tool, but it is limited by supporting datasets of only six variables or fewer. Since only a single pair of variables may be jointly distributed in any set, Genstat is not intended for instruction on multivariate analysis.

ABSTRACTAlthough classroom cheating violates academic standards of behavior, it occurs frequently. Although the research on cheating is extensive, few researchers have interviewed students directly involved in cheating behaviors. We explore interview responses gathered from a cohort of graduate accounting students, some of whom colluded on an assignment, whereas others did not. We use Latent Dirichlet Allocation (LDA), a powerful text mining algorithm, as our primary tool to explore the underlying topical structure of the interviews and to demarcate subtle differences among students' reactions to and explanations of their experience. Because LDA does not impose or require a priori theories, we use it to provide ideas for future research rather than to test extant theories about classroom collusion. We identify five primary topics that emerged from the accounting students' reflections: (1) general course context (including honor code), (2) the rigor of the assignment, (3) student teams as support mechanisms, (4) the perceived repercussions of cheating (colluding), and (5) personality differences between the tax and audit track students. We find subtle language differences between colluders and noncolluders. Colluders considered the nature of the assignment and the difference between tax and audit majors more significant than noncolluders did. Additionally, the role of teams and the general institutional context were somewhat less relevant for colluders than for noncolluders. We conclude by exploring ethical and pedagogical implications of structuring courses as heavily team based for teaching and future research purposes.