. "Appendix 2-2: Previous Research on Factors Contributing to Gender Differences Among Faculty." Gender Differences at Critical Transitions in the Careers of Science, Engineering, and Mathematics Faculty. Washington, DC: The National Academies Press, 2010.
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Gender Differences at Critical Transitions in the Careers of Science, Engineering, and Mathematics Faculty
and female faculty members, by rank, at “top 50” departments in several fields. Several scholars turned to their own or a selection of institutions and collected data from institutional research offices, focus groups, or surveys to study this issue (e.g., Montelone et al., 2003; Nerad and Cerny, 1999a; Rosser, 2004; Trower and Bleak, 2004).
Limitations of Cross-Sectional Data Sources
Four major limitations to these types of cross-sectional data sources should be noted. First, although the academic career pathway is a longitudinal process, much of the data available cannot follow the same individual over a long period of time. Some faculty are surveyed in more than one SDR, but the SDR is not a panel study, even though it is longitudinal in its tracking of cohorts. For university studies, it is also possible that faculty would be in more than one survey. Longitudinal data that cover most of an individual faculty member’s career are rare; the most consistently available data are snapshots of faculty at different points in their careers, taken at the time of the survey.2
Large gaps exist between the time periods selected for data collection. While some data collection occurs annually, such as salary surveys conducted by the American Association of University Professors (AAUP) or the American Chemical Society’s (ACS’s) survey of top 50 chemistry departments, most of the data available are not collected annually. Many university gender equity studies appear to be one-time events. The SDR is biennial.3 The NSOPF has been conducted every 5 years since 1988, most recently in 2004.4
Second, the data may be biased or certain data points omitted. Doctoral graduates, for example, who fail to be hired and faculty who leave a university before or after tenure or promotion are less likely to be surveyed. The faculty who leave may exhibit different characteristics than the faculty who stay. As a result, analysis is likely to be restricted to the population of faculty who may be termed “successful” but does not represent all faculty. And it does not allow us to address other critical factors playing a significant role in determining the career paths of men and women in academia. Also, as these survey results are self-reported, data on productivity and job satisfaction may be biased, or faculty may simply misre-member specific quantitative information from earlier stages of their career.
Third, comparability across studies is a major limiting factor, both in comparing surveys from the same series undertaken in different years and comparing
2
This is part of the reason why most of the statistical analyses carried out use regression. A few scholars have used event history or hazard models. See for example Weiss and Lillard (1982), Kahn (1993), and Ginther (2001). See Allison (1984) for an introductory description of the methodology.
3
Conducted on odd numbered years until 2003, thereafter on even numbered years, beginning in 2006.
4
The National Center for Education Statistics also conducted a survey of department chairs during the 1988 NSOPF, but the chairs survey was only done this one time.
