Convolutions of a Faculty Salary Equity Study

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Convolutions of a Faculty Salary Equity Study
Michael Tumeo, Ph.D. John Kalb, Ph.D. Southern
Methodist University
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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.

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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?

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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.

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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)

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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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Salaries by Years Since Degree
Discipline Area based upon 2-digit CIP Code
Classification
NOTE All charts are based upon the same unit
scale (original)
Years Since Degree
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Salaries by Years Since Degree
Discipline Area based upon 2-digit CIP Code
Classification
NOTE All charts are based upon the same unit
scale (original)
Years Since Degree
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Salaries by Years at the Institution
Discipline Area based upon 2-digit CIP Code
Classification
NOTE All charts are based upon the same unit
scale (original)
Years at Institution
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Salaries by Years at the Institution
Discipline Area based upon 2-digit CIP Code
Classification
NOTE All charts are based upon the same unit
scale (original)
Years at Institution
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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 Degrees
Degree 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 Unstandardized Coefficients t Sig.
B Std. Error
(Constant) -45418.277 6651.084 -6.829 .000
FEMALE -5702.960 2543.721 -2.242 .025
TERMINAL DEGREE 11373.917 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 RATIO 86554.912 4521.985 19.141 .000
STUDY RANK 22630.020 1959.562 11.549 .000
a Dependent Variable Annual Salary
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Studentized Residual Plots
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Studentized Residual Plots
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Influence and Leverage Plot
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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 Change Statistics Change Statistics Change Statistics Change Statistics
Model R R Square Adjusted R Square Std. Error of the Estimate 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_RATIO b
Predictors (Constant), MARKET_RATIO, RANK c
Predictors (Constant), MARKET_RATIO, RANK,
YEARS_AT_INST d Predictors (Constant),
MARKET_RATIO, RANK, YEARS_AT_INST, FEMALE e
Predictors (Constant), MARKET_RATIO, RANK,
YEARS_AT_INST, FEMALE, YEARS_SINCE_DEG f
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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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.