Amazon’s Mechanical Turk (MTurk) platform is a popular tool for scholars seeking a reasonably representative population to recruit subjects for academic research that is cheaper than contract work via survey research firms. Numerous scholarly inquiries affirm that the MTurk pool is at least as representative as college student samples; however, questions about the validity of MTurk data persist. Amazon classifies all MTurk Workers into two types: (1) “regular” Workers, and (2) more qualified (and expensive) “master” Workers. In this paper, we evaluate how choice in Worker type impacts the nature of research samples in terms of characteristics/features and performance. Our results identify few meaningful differences between master and regular Workers. However, we do find that master Workers are more likely to be female, older, and Republican, than regular Workers. Additionally, master Workers have far more experience, having spent twice as much time working on MTurk and having completed over seven times the number of assignments. Based on these findings, we recommend that researchers ask for Worker status and number of assignments completed to control for effects related to experience. However, the results imply that budget-conscious scholars will not compromise project integrity by using the wider pool of regular Workers in academic studies.
Political science instructors increasingly use interactive pedagogies that emphasize active learning over traditional lecture formats. I contribute to this effort by developing a data-based teaching method that relies on student-generated data to illustrate course concepts and to serve as a foundation for a variety of activities in political science classrooms. This article summarizes the technique based on my experience in an introductory course in American government. However, given that this method is not intrinsically limited to any topic or area, I also provide examples of how the basic framework may be applied to other subfields in political science. I conclude by calling for the creation of a network of teacher–scholars interested in developing, sharing, and refining best practices related to data-based teaching.
At least partially in response to Donald Trump’s 2016 presidential election (Jordan and Balz 2018), 2018 witnessed a record number of women running for and winning legislative elections across the country. This candidacy surge affords a unique opportunity to evaluate why individuals choose to run for office. Extant literature identifies both individual- and institutional-level determinants of candidate entry, yet little attention has been given to a critical institutional feature that can encourage or discourage women to put their names forward: primary type. This article develops a model of candidate emergence positing that different primary systems—by virtue of including and excluding the participation of various subpopulations of a state’s electorate—will be more or less attractive to potential female candidates relative to potential male candidates. We uncover evidence consistent with our theory: women appear less interested in running in certain types of primaries (e.g., semi-closed) but find other systems more appealing (e.g., nonpartisan). The results also indicate that after considering primary type, women tend to outperform men in the subsequent general election across the board. This study provides encouraging evidence that closing the representation gap is an increasingly achievable goal but that the rules of the electoral game continue to determine who is playing.
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