Convolutions of a Faculty Salary Equity Study

Michael Tumeo, Ph.D.

John Kalb, Ph.D.

Southern Methodist University

2

Faculty Compensation Overview

• Faculty compensation while not the sole motivator for faculty, is an important magnet for attracting and retaining good faculty as well as and interwoven component to boosting morale (Shuster, Finkelstein, 2006).

• While faculty salary is an important consideration, other factors such a job location, benefits, peer interactions, and non-tangible factors also weigh into the attraction, retention, and morale of faculty.

• Faculty compensation has many facets, but this study will focus on faculty salary specifically.

3

Questions and Answers

• Are there Gender inequities regarding faculty salaries at our institution?– At the 2007 AIR Forum in Kansas City, Porter, Toutkoushian, &

Moore presented a paper in which they show, using NSOPF (National Survey of Postsecondary Faculty) data that gender inequities are pervasive and long-term.

– This then begs the question, “Is the question of gender inequities the right question to ask?” or has this become the “duh” question?

• Perhaps the more appropriate questions become, “Where are the gender inequities? Can they be explained? What can we do about them?”

4

SMU Solution

• Using a multifaceted approach we attempted to explore the answers to the first two questions in hopes of finding a solution to the third.

• We used a graphical analysis, Multiple Regression, and an “inappropriate” ANOVA

• This presentation will walk you through what we did, why we did it, and what we found.

• We will also discuss some of the strengths and weaknesses of each approach and hopefully solicit some ideas for additional analysis.

5

Graphical Approach

• Does time at the institution, or time since degree impact salary equity?

• Do tenure status, and discipline of the faculty member impact salary equity? (only included Tenured and Tenure-Track faculty in analysis) [Non-tenure track faculty unnecessarily complicates an already complicated analysis]

• What is the best way to see the effect of these variables on salary equity?

• KISS method is important so as to not complicate the graphic unnecessarily (using Tenure instead of Rank, for example)

6

Fall 2006 Annual Faculty Salary by Years Since Last Degree

y = 959.08x + 79575

y = 545.22x + 72617

0 10 20 30 40 50 60

Years

Sal

ary

Females Males Linear (Males) Linear (Females)

HIGH

LOW

MODERATE

7

Fall 2006 Annual Faculty Salary by Years at Institution

y = 285x + 95580

y = 249.03x + 78616

0 10 20 30 40 50 60

Years

Sal

ary

Females Males Linear (Males) Linear (Females)

HIGH

LOW

MODERATE

8

Fall 2006 Annual Faculty Salary by Years Since Last Degree

y = 692.13x + 72896

y = 571.96x + 92202

y = -801.98x + 76734

y = 294.57x + 75943

0 10 20 30 40 50 60

Years

Sal

ary

Tenured Females Tenure Track Females Tenured Males Tenure Track Males

Linear ( Tenured Females) Linear (Tenured Males) Linear (Tenure Track Females) Linear (Tenure Track Males)

HIGH

LOW

MODERATE

9

Fall 2006 Annual Faculty Salary by Years at Institution

y = -510.65x + 95236

y = -510.53x + 116985

y = -2044.2x + 74719

y = 1435.5x + 74560

0 10 20 30 40 50 60

Years

Sal

ary

Tenured Females Tenure Track Females Tenured Males Tenure Track Males

Linear ( Tenured Females) Linear (Tenured Males) Linear (Tenure Track Females) Linear (Tenure Track Males)

HIGH

LOW

MODERATE

10

General Trends Found

• Can clearly see in all graphs “apparent” gender salary inequity.

• Time since degree seems to have a larger impact on salary disparity than does time at the institution.

• Both factors of time have a disproportionate effect depending on the tenure status of faculty.

• Provides a wonderful display of salary compression for tenured faculty at an equal rate for both males and females.

• Does not address the discipline question.• Discipline is defined by 2-digit CIP Codes.

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0 10 20 30 40 50

HIGH

LOW

MODERATE

Communication, Journalism, and Related Programs

0 10 20 30 40 50

HIGH

LOW

MODERATE

Education

0 10 20 30 40 50

HIGH

LOW

MODERATE

Engineering

0 10 20 30 40 50

HIGH

LOW

MODERATE

Engineering Technologies/Technicians

Salaries by Years Since Degree

Discipline Area based upon 2-digit CIP Code Classification

Years Since Degree

NOTE: All charts are based upon the same unit scale (original)

Males

Females

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0 10 20 30 40 50

HIGH

LOW

MODERATE

Psychology

0 10 20 30 40 50

HIGH

LOW

MODERATE

Social Sciences

0 10 20 30 40 50

HIGH

LOW

MODERATE

Visual and Performing Arts

Salaries by Years Since Degree

Discipline Area based upon 2-digit CIP Code Classification

Years Since Degree

NOTE: All charts are based upon the same unit scale (original)

0 10 20 30 40 50

HIGH

LOW

MODERATE

Business, Management, Marketing, and Related Support Services

Males

Females

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0 10 20 30 40 50

HIGH

LOW

MODERATE

Communication, Journalism, and Related Programs

0 10 20 30 40 50

HIGH

LOW

MODERATE

Education

0 10 20 30 40 50

HIGH

LOW

MODERATE

Engineering

0 10 20 30 40 50

HIGH

LOW

MODERATE

Engineering Technologies/Technicians

Salaries by Years at the Institution

Discipline Area based upon 2-digit CIP Code Classification

Years at Institution

NOTE: All charts are based upon the same unit scale (original)

Males

Females

14

0 10 20 30 40 50

HIGH

LOW

MODERATE

Psychology

0 10 20 30 40 50

HIGH

LOW

MODERATE

Social Sciences

0 10 20 30 40 50

HIGH

LOW

MODERATE

Visual and Performing Arts

Salaries by Years at the Institution

Discipline Area based upon 2-digit CIP Code Classification

Years at Institution

NOTE: All charts are based upon the same unit scale (original)

0 10 20 30 40 50

HIGH

LOW

MODERATE

Business, Management, Marketing, and Related Support Services

Males

Females

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Multiple Regression Analysis(Enter Method)

• Variables used based upon Luna (2007) and the previous graphical analysis.

• Rank (Professor, Associate, Assistant)• Terminal degree (dummy coded Yes)• Years since degree• Years at Institution• Gender (dummy coded Female)• Market Ratio (account for discipline differences)• Dependent Variable (Annual Salary)

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Table of Terminal and Non-terminal DegreesDegree Type Terminal (Y or N) Degree Type Terminal (Y or N)

AA N MBA N

AMD Y MD Y

AS N MED Y

BA N MFA Y

BBA N MLA N

BFA N MMED N

BJ N MM N

BM N MPA N

BS N MPP N

CERT N MS N

DED Y MSA N

DENG Y MSE N

DM Y MT N

DMA Y MTH N

DME Y PHD Y

DMIN Y SJD Y

DPA Y STD Y

DTH Y THD Y

EDD Y

JD Y

LLB Y

LLM Y

LTR N

MA N

MAST N

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Multiple Regression Coefficients and t-scores

Model Unstandardized Coefficients t Sig.

B Std. Error

(Constant)-45418.277 6651.084 -6.829 .000

FEMALE-5702.960 2543.721 -2.242 .025

TERMINAL DEGREE11373.917 5004.147 2.273 .024

YEARS SINCE DEG 568.848 180.677 3.148 .002

YEARS AT INSTITUTION-1082.334 152.975 -7.075 .000

MARKET RATIO86554.912 4521.985 19.141 .000

STUDY RANK22630.020 1959.562 11.549 .000

a Dependent Variable: Annual Salary

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Studentized Residual PlotsStudentized Residuals Against Years Since Degree

-4.00000

-3.00000

-2.00000

-1.00000

0.00000

1.00000

2.00000

3.00000

4.00000

5.00000

6.00000

0 10 20 30 40 50 60

Years

Stu

den

tize

d R

esid

ual

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Studentized Residual PlotsStudentized Residuals Against Years at Institution

-4.00000

-3.00000

-2.00000

-1.00000

0.00000

1.00000

2.00000

3.00000

4.00000

5.00000

6.00000

0 10 20 30 40 50 60

Years

Stu

den

tize

d R

esid

ual

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Influence and Leverage PlotMeasure of Data Point Influence and Data Point Leverage

(Data in Upper Right Corner are High Influence and High Leverage)

0.00000

0.01000

0.02000

0.03000

0.04000

0.05000

0.06000

0.07000

0.08000

0.09000

0.10000

0.00000 0.01000 0.02000 0.03000 0.04000 0.05000 0.06000 0.07000 0.08000 0.09000 0.10000

Centered Leverage Value

Co

ok'

s D

(In

flu

ence

)

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Multiple Regression Analysis(Stepwise Method)

• Same variables used in the previous analysis

• Interested in model selection

• Most parsimonious model selected using change in R2 rule

• y = -41,625.651 + 89,844.209 * Market Ratio + 26,581.145 * Rank + (-711.610 * Years at Institution).

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Stepwise Data Table

Model R R Square

Adjusted R Square

Std. Error of the Estimate

Change Statistics

R Square Change F Change df1 df2 Sig. F Change

1 .640(a) .410 .408 $29,157.236 .410 312.984 1 451 .000

2 .777(b) .604 .602 $23,916.244 .194 220.322 1 450 .000

3 .799(c) .639 .637 $22,846.032 .035 44.148 1 449 .000

4 .803(d) .644 .641 $22,713.234 .005 6.266 1 448 .013

5 .806(e) .649 .645 $22,572.063 .005 6.621 1 447 .010

6 .808(f) .653 .649 $22,467.606 .004 5.166 1 446 .024

a Predictors: (Constant), MARKET_RATIOb Predictors: (Constant), MARKET_RATIO, RANKc Predictors: (Constant), MARKET_RATIO, RANK, YEARS_AT_INSTd Predictors: (Constant), MARKET_RATIO, RANK, YEARS_AT_INST, FEMALEe Predictors: (Constant), MARKET_RATIO, RANK, YEARS_AT_INST, FEMALE, YEARS_SINCE_DEGf Predictors: (Constant), MARKET_RATIO, RANK, YEARS_AT_INST, FEMALE, YEARS_SINCE_DEG, TERM_DEGREE

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Model Validation

• Condition Index of the Collinearity Diagnostics table yielded a value of 11.6– General Rule (values of 15 or higher = moderate risk

of mulitcollinearity while 30 or higher is a serious risk).

• Two additional Multiple Regressions were run (Forward and Backward) to ensure the Stepwise Regression was not a mathematical artifact.

• Did not do a split sample validation or a cross sample validation, but the model is not being used for predictive purposes so further validation procedures were deemed unnecessary at this time.

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ANOVAThe Final Frontier

• Wanted to explore possible interactions between gender and other factors related to salary equity (finally getting back to the original question)

• Market Ratio was categorized into Market Value (based on Luna 2007, paper)

• 3-way ANOVA with Gender (Female, Male), Market Value (Below Average, Average, Above Average), and Rank (Assistant, Associate, Full) with Dependent Variable (Salary)

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ANOVA Cautionary Notes

• Violated several fundamental rules for an ANOVA, but this was exploratory, so tread lightly.

• ANOVA done on a population, not a sample (All faculty were included because of sample size concerns).

• Not really a true “experimental” design.• Groups size differences at more refined levels

are a concern because of variance differences.• Interpretation of results and generalizations are

very tentative because of these caveats.

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Average Salary for General Groups of Faculty

Females

Males

Assistant

Associate

Full

Below Average

Average

Above Average

Lower Salary Higher Salary

Gender; F = 1.524, p = .218; Not Significant

Rank; F = 39.342, p < .001; Significant

Market Value; F = 107.331, p < .001; Significant

Tukey HSD results show all pairwise comparisons are significantly different.

Tukey HSD results show all pairwise comparisons are significantly different.

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Mean Annual Salaries for Female and Male Faculty by Rank

Assistant Associate Full

Rank

Male

Female

Hig

her

Sal

ary

Low

er S

alar

y Difference = $4,276Difference = $7,050

Difference = $16,902

Gender x Rank InteractionF = 3.429, p < .05

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Average Salary for Market Value based upon Gender and Rank

Below Average Average Above Average

Market Value

Female Assistant Male Assistant Female Associate Male Associate Female Full Male Full

Hig

her

Sal

ary

Low

er S

alar

y

Gender x Rank x Market Value InteractionF = 1.960, p = .100

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Conclusions

• The simple answer to the question of gender salary inequity at SMU is “YES” (a simple question deserves a simple answer after all, right?).

• As you can see the “real” answer is quite a bit more complicated than, simply “Yes”.

• Factors like rank and discipline complicate the picture considerably.

• Complications regarding sampling, and group size differences additionally complicate finding a clear statistical answer.

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Added Factors not Considered

• Additional information regarding faculty standing would be critical to gaining a fuller picture of any potential gender inequities.– Time in rank– Performance measures (publications, class and

supervisor evaluations, service, etc)– Outside job offers– Changing market demands– Etc.

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Lessons Learned and Next Steps

• Discipline specific evaluations may be needed instead of University level evaluations

• Better data about performance measures needed

• Need to explore ways to counter salary compression for both genders

• Need to look more closely at the disparities at the higher ranks to determine the reality of those disparities or if other factors are influencing the apparent salary disparities

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References

• Barbezat, D. A. (2003). From here to seniority: The effect of experience and job tenure on faculty salaries. New Directions for Institutional Research, 117, 21- 47.

• Bellas, M. L. (1997). Disciplinary differences in faculty salaries: Does gender bias play a role? The Journal of Higher Education, 68 (3), 299-321.

• Boudreau, N., Sullivan, J., Balzer, W., Ryan, A. M., Yonker, R., Thorsteinson, T., & Hutchinson. (1997). Should faculty rank be included as a predictor variable

in studies of gender equity in university faculty salaries? Research in Higher Education, 38 (3), 297-312.

• Luna, A. L. (2006). Faculty salary equity cases: combining statistics with the law. The Journal of Higher Education, 77 (2), 193-224.

• Luna, A. L. (2007). Using market ratio factor in faculty salary equity studies. AIR Professional File, 103, 1-16.

• Schuster, J. H., & Finkelstein, M. J. (2006). The American Faculty: The restructuring of Academic Work and Careers. Baltimore, MD: The Johns Hopkins University Press.

• Porter, S. R., Toutkoushian, R. K., & Moore, J. V. (2007) Gender differences in salary for recently-hired faculty, 1998-2004. Scholarly Paper, Presented at the

2007 AIR Forum in Kansas City MO. • Webster, A. L. (1995). Demographic factors affecting faculty salary. Educational and

Psychological Measurement, 55 (5), 728-735.