Faculty Salary Equity Cases: Combining Statistics with the Law

Luna, Andrew L.

doi:10.1353/jhe.2006.0015

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…Consequently, the use and application of advanced statistical procedures allowed researchers to ask more complicated questions regarding salary equity. Over the past four decades, institutional researchers have devoted considerable attention to three primary methodological issues when designing salary‐equity studies: who to study, what variables to include in the model, and what data analysis techniques to use (Barbezat, ; Ferber & Loeb, ; Luna, ; Toutkoushian & Hoffman, ). The section that follows will address these three considerations and provide an overview of findings from relevant faculty salary‐equity studies.…”

Section: Salary‐equity Studies: Methodological Considerationsmentioning

confidence: 99%

“…The first important factor that researchers have considered is who should be included in salary‐equity studies. The most frequent disagreements on the subject often concern the inclusion of part‐time, temporary, and/or non‐tenure‐track faculty in samples (Luna, ). Some researchers believe that these individuals should be included in analyses on the premise that women are disproportionately overrepresented in nontraditional faculty appointments.…”

Section: Salary‐equity Studies: Methodological Considerationsmentioning

confidence: 99%

“…On the other hand, others contend that these groups are distinctive, and the inclusion of part‐time, temporary, and/or non‐tenure‐track faculty in the study will lead to biased results (Snyder, Hyer, & McLaughlin, ). Luna () concludes that courts tend to agree with the latter argument, namely that those with nonregular appointments ought to be regarded as separate from full‐time faculty.…”

Section: Salary‐equity Studies: Methodological Considerationsmentioning

confidence: 99%

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Using Salary as a Measure of Glass Ceiling Effects: Lessons for Institutional Researchers

Rosser

Mamiseishvili

2014

New Drctns for Instit Rsrch

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Section: Salary‐equity Studies: Methodological Considerationsmentioning

confidence: 99%

Section: Salary‐equity Studies: Methodological Considerationsmentioning

confidence: 99%

Section: Salary‐equity Studies: Methodological Considerationsmentioning

confidence: 99%

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Using Salary as a Measure of Glass Ceiling Effects: Lessons for Institutional Researchers

Rosser

Mamiseishvili

2014

New Drctns for Instit Rsrch

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“…That is, the underlying difficulty here is that rank and productivity measures are themselves genderbiased, or "tainted" variables. There is a universal acceptance that tainted or biased variables should not be used in salary regression analyses, as they underestimate the level of inequity; see, for example, Luna (2006), Johnson, Riggs, and Downey (1987), Barbezat (1991Barbezat ( , 2002. Indeed, Finkelstein (1980), who coined the term, in his review of the judicial responses to these models is adamant that "If there has been discrimination in promotions, salary is likely to be a tainted variable, the effect of its inclusion being to conceal discrimination with respect to awards of salary. "…”

Section: Rankmentioning

confidence: 99%

“…Little attention seems to have been given to the question of collinearity among the choices of predictor variables, despite the fact that several of the variables used would intuitively seem to be highly correlated. Luna (2006) suggested collinearity problems were insufficient to undermine a legal case being reviewed. Earlier, Billard, Cooper, and Kaluba (1994) showed that for a particular dataset, the use of one or both of the predictor variables, X 1 = years since degree and X 2 = years employed at the current institution, produced very similar predictions.…”

Section: Beware Of Collinearitymentioning

confidence: 99%

Study of Salary Differentials by Gender and Discipline

Billard

2017

Statistics and Public Policy

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Although it is 45 years since legislation made gender discrimination on university campuses illegal, salary inequities continue to exist today. The seminal work in studying the existence of salary inequities is that of the American Association of University Professors (AAUP), by Scott (1977) and Gray (1980). Subsequently, innumerable analyses based on versions of their multiple regression model have been published. Salary is the dependent variable and is modeled to depend on various independent predictor variables such as years employed. Often, indicator terms, for gender and/or discipline are included in the model as independent predicator variables. Unfortunately, many of these studies are not well grounded in basic statistical science. The most glaring omission is the failure to include indicator by predictor interaction terms in the model when required. The present work draws attention to the broader implications of using these models incorrectly, and the difficulties that ensue when they are not built on an appropriate sound statistical framework. Another issue surrounds the inclusion of "tainted" predictor variables that are themselves gender-biased, the most contentious being the (intuitive) choice of rank. Therefore, a brief look at this issue is included; unfortunately, it is shown that rank still today seems to persist as a tainted variable.

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2011

ASHE Higher Education Report

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POLICY INITIATIVES, CURRICULAR REFORM, RESEARCH, and grassroots organizing have all contributed to advancing equity and shaping women's status in U.S. higher education. Nearly four or more decades have passed since key legislation, including the Equal Pay Act, Title VII, and Title IX, was passed and efforts were begun to broaden and create more inclusive curricula. Significant gains have been made in women's access to and representation in higher education as evidenced by enrollment figures and graduation rates. Yet these measures are only part of the full gender equity picture. For instance, when taken in the aggregate, enrollment data do not portray the persistent lack of gender parity among students studying engineering, computer science, and other science and technology fields, nor do they depict the quality of classroom and campus experiences. Even today, data reveal that women studying and working in postsecondary institutions bump up against glass ceilings and sticky floors, experience pay disparities linked to gender, and experience the threat and reality of sexual harassment and violence on campus.Since 1988 more than half of all undergraduate students have been women, and 60 percent of students in graduate and professional programs in 2007-08 were women (King, 2010). These improvements in women's access to and representation in higher education are indeed impressive and worthy of note. Headlines based on these data alone, however-that is, drawing conclusions that equity has been achieved based on the overall proportion of faculty or students who are female-fail to acknowledge ways in which gender representation tends to be stratified across types of institutions and by rank Published online in Wiley Online Library (wileyonlinelibrary.com) •

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Faculty Salary Equity Cases: Combining Statistics with the Law

Cited by 13 publications

References 26 publications

Using Salary as a Measure of Glass Ceiling Effects: Lessons for Institutional Researchers

Using Salary as a Measure of Glass Ceiling Effects: Lessons for Institutional Researchers

Study of Salary Differentials by Gender and Discipline

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