Cover Image

HARDBACK
$48.95



View/Hide Left Panel

Appendix 3-2
Estimated Adjusted Mean Effects and Differences for the Probability That There Are No Female Applicantsa

Differences Across Effect Levels

Estimated Mean Difference (Lower 95%, Upper 95% Confidence Limits)

Biology – Chemistryb

0.22 (–0.08, 0.51)

Biology – Mathematics

0.50 ( 0.01, 0.99)

Biology – Electrical engineering

0.23 (–0.12, 0.57)

Biology – Physics

0.22 (–0.11, 0.54)

Biology – Civil engineering

0.13 (–0.07, 0.34)

Tenured – Tenure-track

0.81 (0.71, 0.92)

Private institution – Public institution

0.66 (0.49, 0.84)

Top 10 department – Next 10 depts.

0.27 (0.10, 0.44)

Next 10 departments – Remaining depts.

0.81 (0.59, 1.03)

M – F search committee chair

0.24 (–0.16, 0.63)

a The sample size used to fit this model was 667. The effects fit were: (1) indicator variables for discipline (Biology, Chemistry, Civil Engineering, Electrical Engineering, Mathematics, and Physics, (2) indicator variables for Tenured, Tenure-track, (3) indicator variables for private institution, public institution, (4) indicator variables for top ten departments, second ten departments, and remainder, and (5) an indicator variable as to whether the committee chair was female.

b The estimated adjusted mean differences can be interpreted using Biology – Chemistry as an example. For those individuals in Biology, there is an estimated probability of having no female applicants given, or conditional on, the values for the remaining predictors in the logistic regression model. There is an analogous set of estimated conditional probabilities for Chemistry, again conditional on the predictors in the model. For each set of predictors, one can compute the difference of the estimated probabilities, and then one can average these differences in estimated probabilities over the estimated distribution of the predictors. The result is an estimated average difference of probabilities.

SOURCE: Departmental survey conducted by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 284
Appendix 3-2 Estimated Adjusted Mean Effects and Differences for the Probability That There Are No Female Applicantsa Estimated Mean Difference (Lower 95%, Upper 95% Differences Across Effect Levels Confidence Limits) Biology – Chemistryb 0.22 (–0.08, 0.51) Biology – Mathematics 0.50 ( 0.01, 0.99) Biology – Electrical engineering 0.23 (–0.12, 0.57) Biology – Physics 0.22 (–0.11, 0.54) Biology – Civil engineering 0.13 (–0.07, 0.34) Tenured – Tenure-track 0.81 (0.71, 0.92) Private institution – Public institution 0.66 (0.49, 0.84) Top 10 department – Next 10 depts. 0.27 (0.10, 0.44) Next 10 departments – Remaining depts. 0.81 (0.59, 1.03) M – F search committee chair 0.24 (–0.16, 0.63) a The sample size used to fit this model was 667. The effects fit were: (1) indicator variables for discipline (Biology, Chemistry, Civil Engineering, Electrical Engineering, Mathematics, and Physics, (2) indicator variables for Tenured, Tenure-track, (3) indicator variables for private institution, public institution, (4) indicator variables for top ten departments, second ten departments, and remainder, and (5) an indicator variable as to whether the committee chair was female. b The estimated adjusted mean differences can be interpreted using Biology – Chemistry as an example. For those individuals in Biology, there is an estimated probability of having no female appli- cants given, or conditional on, the values for the remaining predictors in the logistic regression model. There is an analogous set of estimated conditional probabilities for Chemistry, again conditional on the predictors in the model. For each set of predictors, one can compute the difference of the estimated probabilities, and then one can average these differences in estimated probabilities over the estimated distribution of the predictors. The result is an estimated average difference of probabilities. SOURCE: Departmental survey conducted by the Committee on Gender Differences in Careers of Science, Engineering, and Mathematics Faculty.