Analyses
Note: For these analyses, I filtered on participants who identified as women.
Compare Difference in Year
My job makes good use of my skills and abilities
##
## Welch Two Sample t-test
##
## data: useskills by year
## t = -9, df = 4427, p-value <0.0000000000000002
## alternative hypothesis: true difference in means between group 2016 and group 2021 is not equal to 0
## 95 percent confidence interval:
## -0.25 -0.16
## sample estimates:
## mean in group 2016 mean in group 2021
## 3.9 4.1I have the resources to do my job well.
##
## Welch Two Sample t-test
##
## data: resourceswell by year
## t = -9, df = 4454, p-value <0.0000000000000002
## alternative hypothesis: true difference in means between group 2016 and group 2021 is not equal to 0
## 95 percent confidence interval:
## -0.26 -0.17
## sample estimates:
## mean in group 2016 mean in group 2021
## 3.7 3.9During your career, would you like to: Continue in your current job and role at the same level of responsibility
##
## Welch Two Sample t-test
##
## data: liketo_contjob by year
## t = -0.1, df = 4775, p-value = 0.9
## alternative hypothesis: true difference in means between group 2016 and group 2021 is not equal to 0
## 95 percent confidence interval:
## -0.053 0.046
## sample estimates:
## mean in group 2016 mean in group 2021
## 3.8 3.8During your career, would you like to: Take on new challenges, assignments, or roles in your current job
##
## Welch Two Sample t-test
##
## data: liketo_newchall by year
## t = -14, df = 4136, p-value <0.0000000000000002
## alternative hypothesis: true difference in means between group 2016 and group 2021 is not equal to 0
## 95 percent confidence interval:
## -0.40 -0.31
## sample estimates:
## mean in group 2016 mean in group 2021
## 3.7 4.0During your career, would you like to: Take on supervisory or managerial responsibilities.
##
## Welch Two Sample t-test
##
## data: liketo_super by year
## t = 18, df = 4981, p-value <0.0000000000000002
## alternative hypothesis: true difference in means between group 2016 and group 2021 is not equal to 0
## 95 percent confidence interval:
## 0.41 0.50
## sample estimates:
## mean in group 2016 mean in group 2021
## 3.8 3.4During your career, would you like to: Reduce work hours or responsibilities
##
## Welch Two Sample t-test
##
## data: liketo_reduc by year
## t = 70, df = 3999, p-value <0.0000000000000002
## alternative hypothesis: true difference in means between group 2016 and group 2021 is not equal to 0
## 95 percent confidence interval:
## 1.7 1.8
## sample estimates:
## mean in group 2016 mean in group 2021
## 3.8 2.1In the past 2 years, an agency official (e.g., supervisor, manager, senior leader, etc.) in my work unit has discriminated in favor or against someone in a personnel action based upon…
##
## Welch Two Sample t-test
##
## data: discsex by year
## t = 136, df = 3666, p-value <0.0000000000000002
## alternative hypothesis: true difference in means between group 2016 and group 2021 is not equal to 0
## 95 percent confidence interval:
## 1.6 1.6
## sample estimates:
## mean in group 2016 mean in group 2021
## 2.7 1.1Graphs
Compare Difference in Gender Composition
Note: all responses are RELATIVE to the response option “1 Subs. M>W”; this means that people indicated there were substantially more men than women.
My job makes good use of my skills and abilities
##
## Call:
## lm(formula = useskills ~ gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.173 -0.173 -0.038 0.854 1.005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0670 0.0163 249.10 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W 0.0793 0.0257 3.09 0.002 **
## gendercomp_text3 W=M 0.1056 0.0237 4.45 0.0000087 ***
## gendercomp_text4 Sl. W>M -0.0287 0.0301 -0.95 0.340
## gendercomp_text5 Subs. W>M -0.0717 0.0310 -2.31 0.021 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 13063 degrees of freedom
## Multiple R-squared: 0.00374, Adjusted R-squared: 0.00344
## F-statistic: 12.3 on 4 and 13063 DF, p-value: 0.000000000585I have the resources to do my job well.
##
## Call:
## lm(formula = resourceswell ~ gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.918 -0.779 0.218 1.082 1.266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.78163 0.01734 218.10 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W 0.07927 0.02727 2.91 0.0037 **
## gendercomp_text3 W=M 0.13612 0.02520 5.40 0.000000067 ***
## gendercomp_text4 Sl. W>M -0.00227 0.03192 -0.07 0.9432
## gendercomp_text5 Subs. W>M -0.04780 0.03292 -1.45 0.1465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 13063 degrees of freedom
## Multiple R-squared: 0.00369, Adjusted R-squared: 0.00338
## F-statistic: 12.1 on 4 and 13063 DF, p-value: 0.000000000844During your career, would you like to: Continue in your current job and role at the same level of responsibility
##
## Call:
## lm(formula = liketo_contjob ~ gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.886 -0.792 0.208 1.114 1.292
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7922 0.0195 194.03 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W 0.0237 0.0307 0.77 0.44082
## gendercomp_text3 W=M 0.0939 0.0284 3.31 0.00095 ***
## gendercomp_text4 Sl. W>M -0.0314 0.0360 -0.87 0.38249
## gendercomp_text5 Subs. W>M -0.0847 0.0371 -2.28 0.02248 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 13063 degrees of freedom
## Multiple R-squared: 0.00212, Adjusted R-squared: 0.00181
## F-statistic: 6.93 on 4 and 13063 DF, p-value: 0.0000145During your career, would you like to: Take on new challenges, assignments, or roles in your current job
##
## Call:
## lm(formula = liketo_newchall ~ gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0523 -0.0523 0.0357 1.0274 1.2089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.96429 0.01748 226.73 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W 0.00827 0.02750 0.30 0.76370
## gendercomp_text3 W=M 0.08805 0.02541 3.47 0.00053 ***
## gendercomp_text4 Sl. W>M -0.08543 0.03219 -2.65 0.00796 **
## gendercomp_text5 Subs. W>M -0.17319 0.03320 -5.22 0.00000018 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 13063 degrees of freedom
## Multiple R-squared: 0.00536, Adjusted R-squared: 0.00505
## F-statistic: 17.6 on 4 and 13063 DF, p-value: 0.0000000000000211During your career, would you like to: Take on supervisory or managerial responsibilities.
##
## Call:
## lm(formula = liketo_super ~ gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.584 -0.584 0.416 1.416 1.608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3922 0.0202 168.02 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W 0.0685 0.0317 2.16 0.03096 *
## gendercomp_text3 W=M 0.1358 0.0293 4.63 0.00000371 ***
## gendercomp_text4 Sl. W>M 0.1918 0.0372 5.16 0.00000025 ***
## gendercomp_text5 Subs. W>M 0.1361 0.0383 3.55 0.00039 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 13063 degrees of freedom
## Multiple R-squared: 0.00289, Adjusted R-squared: 0.00259
## F-statistic: 9.48 on 4 and 13063 DF, p-value: 0.00000012During your career, would you like to: Reduce work hours or responsibilities
##
## Call:
## lm(formula = liketo_reduc ~ gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.85 -1.24 -0.24 0.76 2.76
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2396 0.0201 111.42 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W 0.0908 0.0316 2.87 0.0041 **
## gendercomp_text3 W=M 0.2027 0.0292 6.94 0.0000000000041 ***
## gendercomp_text4 Sl. W>M 0.5602 0.0370 15.14 < 0.0000000000000002 ***
## gendercomp_text5 Subs. W>M 0.6061 0.0382 15.88 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 13063 degrees of freedom
## Multiple R-squared: 0.0304, Adjusted R-squared: 0.0301
## F-statistic: 102 on 4 and 13063 DF, p-value: <0.0000000000000002In the past 2 years, an agency official (e.g., supervisor, manager, senior leader, etc.) in my work unit has discriminated in favor or against someone in a personnel action based upon…
##
## Call:
## lm(formula = discsex ~ gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.929 -0.455 -0.274 0.545 1.726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.2737 0.0125 101.59 <0.0000000000000002 ***
## gendercomp_text2 Sl. M>W 0.0635 0.0197 3.22 0.0013 **
## gendercomp_text3 W=M 0.1810 0.0182 9.93 <0.0000000000000002 ***
## gendercomp_text4 Sl. W>M 0.5347 0.0231 23.16 <0.0000000000000002 ***
## gendercomp_text5 Subs. W>M 0.6555 0.0238 27.54 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.78 on 13063 degrees of freedom
## Multiple R-squared: 0.0804, Adjusted R-squared: 0.0801
## F-statistic: 285 on 4 and 13063 DF, p-value: <0.0000000000000002Graphs
Interactions
Pre-registered: Gender composition by year
My job makes good use of my skills and abilities
##
## Call:
## lm(formula = useskills ~ year * gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.228 -0.228 -0.007 0.783 1.220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.96791 0.05226 75.92 < 0.0000000000000002 ***
## year2021 0.10971 0.05499 2.00 0.0461 *
## gendercomp_text2 Sl. M>W -0.18804 0.06981 -2.69 0.0071 **
## gendercomp_text3 W=M 0.03883 0.06411 0.61 0.5447
## gendercomp_text4 Sl. W>M -0.00374 0.06524 -0.06 0.9543
## gendercomp_text5 Subs. W>M -0.02773 0.06556 -0.42 0.6723
## year2021:gendercomp_text2 Sl. M>W 0.33817 0.07511 4.50 0.0000068 ***
## year2021:gendercomp_text3 W=M 0.10101 0.06911 1.46 0.1439
## year2021:gendercomp_text4 Sl. W>M 0.01682 0.07501 0.22 0.8225
## year2021:gendercomp_text5 Subs. W>M -0.01144 0.07628 -0.15 0.8808
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 13058 degrees of freedom
## Multiple R-squared: 0.0125, Adjusted R-squared: 0.0118
## F-statistic: 18.3 on 9 and 13058 DF, p-value: <0.0000000000000002Estimated marginal means
Comparing groups across years
Comparing years across groups
I have the resources to do my job well.
##
## Call:
## lm(formula = resourceswell ~ year * gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.976 -0.718 0.202 1.024 1.520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6283 0.0555 65.39 < 0.0000000000000002 ***
## year2021 0.1697 0.0584 2.91 0.00366 **
## gendercomp_text2 Sl. M>W -0.1483 0.0741 -2.00 0.04547 *
## gendercomp_text3 W=M 0.0748 0.0681 1.10 0.27204
## gendercomp_text4 Sl. W>M 0.0896 0.0693 1.29 0.19597
## gendercomp_text5 Subs. W>M 0.0726 0.0696 1.04 0.29709
## year2021:gendercomp_text2 Sl. M>W 0.2957 0.0797 3.71 0.00021 ***
## year2021:gendercomp_text3 W=M 0.1031 0.0734 1.40 0.16013
## year2021:gendercomp_text4 Sl. W>M -0.0648 0.0796 -0.81 0.41556
## year2021:gendercomp_text5 Subs. W>M -0.1110 0.0810 -1.37 0.17045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 13058 degrees of freedom
## Multiple R-squared: 0.0131, Adjusted R-squared: 0.0124
## F-statistic: 19.2 on 9 and 13058 DF, p-value: <0.0000000000000002Estimated marginal means
Comparing groups across years
Comparing years across groups
During your career, would you like to: Continue in your current job and role at the same level of responsibility.
##
## Call:
## lm(formula = liketo_contjob ~ year * gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.959 -0.799 0.201 1.134 1.350
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7273 0.0628 59.36 <0.0000000000000002 ***
## year2021 0.0719 0.0661 1.09 0.2763
## gendercomp_text2 Sl. M>W -0.0774 0.0839 -0.92 0.3562
## gendercomp_text3 W=M 0.2322 0.0770 3.02 0.0026 **
## gendercomp_text4 Sl. W>M 0.1205 0.0784 1.54 0.1242
## gendercomp_text5 Subs. W>M 0.0197 0.0788 0.25 0.8029
## year2021:gendercomp_text2 Sl. M>W 0.1310 0.0902 1.45 0.1466
## year2021:gendercomp_text3 W=M -0.1652 0.0830 -1.99 0.0466 *
## year2021:gendercomp_text4 Sl. W>M -0.2203 0.0901 -2.44 0.0145 *
## year2021:gendercomp_text5 Subs. W>M -0.1422 0.0916 -1.55 0.1208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 13058 degrees of freedom
## Multiple R-squared: 0.00384, Adjusted R-squared: 0.00316
## F-statistic: 5.6 on 9 and 13058 DF, p-value: 0.0000000955Estimated marginal means
Comparing groups across years
Comparing years across groups
During your career, would you like to: Take on new challenges, assignments, or roles in your current job.
##
## Call:
## lm(formula = liketo_newchall ~ year * gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1111 -0.1111 0.0095 0.9143 1.5367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6604 0.0557 65.72 < 0.0000000000000002 ***
## year2021 0.3364 0.0586 5.74 0.0000000096 ***
## gendercomp_text2 Sl. M>W -0.1971 0.0744 -2.65 0.00807 **
## gendercomp_text3 W=M 0.1749 0.0683 2.56 0.01046 *
## gendercomp_text4 Sl. W>M 0.0605 0.0695 0.87 0.38445
## gendercomp_text5 Subs. W>M -0.0132 0.0699 -0.19 0.85028
## year2021:gendercomp_text2 Sl. M>W 0.2860 0.0800 3.57 0.00035 ***
## year2021:gendercomp_text3 W=M -0.0607 0.0736 -0.82 0.41009
## year2021:gendercomp_text4 Sl. W>M -0.0668 0.0799 -0.84 0.40327
## year2021:gendercomp_text5 Subs. W>M -0.0798 0.0813 -0.98 0.32619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 13058 degrees of freedom
## Multiple R-squared: 0.0238, Adjusted R-squared: 0.0231
## F-statistic: 35.4 on 9 and 13058 DF, p-value: <0.0000000000000002Estimated marginal means
Comparing groups across years
Comparing years across groups
During your career, would you like to: Take on supervisory or managerial responsibilities.
##
## Call:
## lm(formula = liketo_super ~ year * gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9271 -0.7888 0.0925 1.0729 1.6623
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.78877 0.06422 59.00 < 0.0000000000000002 ***
## year2021 -0.43901 0.06757 -6.50 0.000000000085 ***
## gendercomp_text2 Sl. M>W -0.07598 0.08578 -0.89 0.376
## gendercomp_text3 W=M 0.13836 0.07877 1.76 0.079 .
## gendercomp_text4 Sl. W>M 0.11869 0.08016 1.48 0.139
## gendercomp_text5 Subs. W>M -0.01730 0.08056 -0.21 0.830
## year2021:gendercomp_text2 Sl. M>W 0.13098 0.09229 1.42 0.156
## year2021:gendercomp_text3 W=M -0.06816 0.08492 -0.80 0.422
## year2021:gendercomp_text4 Sl. W>M -0.11296 0.09217 -1.23 0.220
## year2021:gendercomp_text5 Subs. W>M 0.00528 0.09373 0.06 0.955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 13058 degrees of freedom
## Multiple R-squared: 0.0242, Adjusted R-squared: 0.0236
## F-statistic: 36 on 9 and 13058 DF, p-value: <0.0000000000000002Estimated marginal means
Comparing groups across years
Comparing years across groups
During your career, would you like to: Reduce work hours or responsibilities
##
## Call:
## lm(formula = liketo_reduc ~ year * gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9177 -1.0405 -0.0428 0.9212 2.9620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7406 0.0537 69.61 <0.0000000000000002 ***
## year2021 -1.6619 0.0565 -29.39 <0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.1054 0.0718 -1.47 0.1419
## gendercomp_text3 W=M 0.1770 0.0659 2.69 0.0072 **
## gendercomp_text4 Sl. W>M 0.1370 0.0671 2.04 0.0412 *
## gendercomp_text5 Subs. W>M 0.0278 0.0674 0.41 0.6805
## year2021:gendercomp_text2 Sl. M>W 0.0672 0.0772 0.87 0.3845
## year2021:gendercomp_text3 W=M -0.2130 0.0711 -3.00 0.0027 **
## year2021:gendercomp_text4 Sl. W>M -0.1778 0.0771 -2.30 0.0212 *
## year2021:gendercomp_text5 Subs. W>M 0.0161 0.0784 0.20 0.8378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 13058 degrees of freedom
## Multiple R-squared: 0.33, Adjusted R-squared: 0.329
## F-statistic: 713 on 9 and 13058 DF, p-value: <0.0000000000000002Estimated marginal means
Comparing groups across years
Comparing years across groups
In the past 2 years, an agency official (e.g., supervisor, manager, senior leader, etc.) in my work unit has discriminated in favor or against someone in a personnel action based upon…
##
## Call:
## lm(formula = discsex ~ year * gendercomp_text, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8016 -0.1226 -0.0899 -0.0796 1.9204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6845 0.0233 115.46 < 0.0000000000000002 ***
## year2021 -1.5619 0.0245 -63.84 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.1876 0.0311 -6.04 0.0000000016 ***
## gendercomp_text3 W=M 0.1171 0.0285 4.11 0.0000403790 ***
## gendercomp_text4 Sl. W>M 0.1051 0.0290 3.62 0.00030 ***
## gendercomp_text5 Subs. W>M 0.1146 0.0292 3.93 0.0000858377 ***
## year2021:gendercomp_text2 Sl. M>W 0.1447 0.0334 4.33 0.0000150309 ***
## year2021:gendercomp_text3 W=M -0.1498 0.0307 -4.87 0.0000011130 ***
## year2021:gendercomp_text4 Sl. W>M -0.1127 0.0334 -3.38 0.00074 ***
## year2021:gendercomp_text5 Subs. W>M 0.0104 0.0339 0.31 0.75909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.45 on 13058 degrees of freedom
## Multiple R-squared: 0.694, Adjusted R-squared: 0.694
## F-statistic: 3.29e+03 on 9 and 13058 DF, p-value: <0.0000000000000002Estimated marginal means
Comparing groups across years
Comparing years across groups
Women of color
My job makes good use of my skills and abilities
##
## Call:
## lm(formula = useskills ~ year * gendercomp_text * minority, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.290 -0.235 -0.037 0.798 1.532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.95122 0.09052 43.65 < 0.0000000000000002 ***
## year2021 0.11516 0.09630 1.20 0.2318
## gendercomp_text2 Sl. M>W -0.48369 0.12141 -3.98 0.00006813 ***
## gendercomp_text3 W=M -0.04981 0.11369 -0.44 0.6613
## gendercomp_text4 Sl. W>M -0.14880 0.11072 -1.34 0.1790
## gendercomp_text5 Subs. W>M -0.11597 0.10980 -1.06 0.2909
## minorityNon-minority 0.03252 0.11087 0.29 0.7693
## year2021:gendercomp_text2 Sl. M>W 0.69131 0.13355 5.18 0.00000023 ***
## year2021:gendercomp_text3 W=M 0.27298 0.12421 2.20 0.0280 *
## year2021:gendercomp_text4 Sl. W>M 0.30506 0.13092 2.33 0.0198 *
## year2021:gendercomp_text5 Subs. W>M 0.10863 0.12983 0.84 0.4028
## year2021:minorityNon-minority -0.00722 0.11746 -0.06 0.9510
## gendercomp_text2 Sl. M>W:minorityNon-minority 0.42830 0.14824 2.89 0.0039 **
## gendercomp_text3 W=M:minorityNon-minority 0.11780 0.13768 0.86 0.3922
## gendercomp_text4 Sl. W>M:minorityNon-minority 0.23012 0.13706 1.68 0.0932 .
## gendercomp_text5 Subs. W>M:minorityNon-minority 0.14773 0.13696 1.08 0.2808
## year2021:gendercomp_text2 Sl. M>W:minorityNon-minority -0.49257 0.16174 -3.05 0.0023 **
## year2021:gendercomp_text3 W=M:minorityNon-minority -0.23072 0.14973 -1.54 0.1234
## year2021:gendercomp_text4 Sl. W>M:minorityNon-minority -0.44155 0.16029 -2.75 0.0059 **
## year2021:gendercomp_text5 Subs. W>M:minorityNon-minority -0.16502 0.16097 -1.03 0.3053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 12463 degrees of freedom
## (585 observations deleted due to missingness)
## Multiple R-squared: 0.0177, Adjusted R-squared: 0.0162
## F-statistic: 11.8 on 19 and 12463 DF, p-value: <0.0000000000000002
I have the resources to do my job well.
##
## Call:
## lm(formula = resourceswell ~ year * gendercomp_text * minority,
## data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.146 -0.721 0.218 1.055 1.701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7398 0.0965 38.77 < 0.0000000000000002 ***
## year2021 0.1381 0.1026 1.35 0.17835
## gendercomp_text2 Sl. M>W -0.4411 0.1294 -3.41 0.00065 ***
## gendercomp_text3 W=M -0.0920 0.1211 -0.76 0.44787
## gendercomp_text4 Sl. W>M -0.0261 0.1180 -0.22 0.82473
## gendercomp_text5 Subs. W>M -0.0425 0.1170 -0.36 0.71630
## minorityNon-minority -0.1707 0.1181 -1.45 0.14842
## year2021:gendercomp_text2 Sl. M>W 0.5492 0.1423 3.86 0.00011 ***
## year2021:gendercomp_text3 W=M 0.3595 0.1324 2.72 0.00662 **
## year2021:gendercomp_text4 Sl. W>M 0.0463 0.1395 0.33 0.74000
## year2021:gendercomp_text5 Subs. W>M -0.0642 0.1383 -0.46 0.64255
## year2021:minorityNon-minority 0.0744 0.1252 0.59 0.55240
## gendercomp_text2 Sl. M>W:minorityNon-minority 0.4328 0.1580 2.74 0.00616 **
## gendercomp_text3 W=M:minorityNon-minority 0.2451 0.1467 1.67 0.09484 .
## gendercomp_text4 Sl. W>M:minorityNon-minority 0.1775 0.1460 1.22 0.22422
## gendercomp_text5 Subs. W>M:minorityNon-minority 0.1918 0.1459 1.31 0.18887
## year2021:gendercomp_text2 Sl. M>W:minorityNon-minority -0.3777 0.1723 -2.19 0.02843 *
## year2021:gendercomp_text3 W=M:minorityNon-minority -0.3730 0.1595 -2.34 0.01940 *
## year2021:gendercomp_text4 Sl. W>M:minorityNon-minority -0.1824 0.1708 -1.07 0.28550
## year2021:gendercomp_text5 Subs. W>M:minorityNon-minority -0.0927 0.1715 -0.54 0.58904
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 12463 degrees of freedom
## (585 observations deleted due to missingness)
## Multiple R-squared: 0.0169, Adjusted R-squared: 0.0154
## F-statistic: 11.3 on 19 and 12463 DF, p-value: <0.0000000000000002
During your career, would you like to: Continue in your current job and role at the same level of responsibility.
##
## Call:
## lm(formula = liketo_contjob ~ year * gendercomp_text * minority,
## data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.962 -0.790 0.167 1.126 1.669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8049 0.1094 34.78 < 0.0000000000000002 ***
## year2021 -0.0833 0.1164 -0.72 0.47439
## gendercomp_text2 Sl. M>W -0.4737 0.1467 -3.23 0.00125 **
## gendercomp_text3 W=M 0.1576 0.1374 1.15 0.25148
## gendercomp_text4 Sl. W>M -0.0549 0.1338 -0.41 0.68170
## gendercomp_text5 Subs. W>M -0.1574 0.1327 -1.19 0.23564
## minorityNon-minority -0.1260 0.1340 -0.94 0.34694
## year2021:gendercomp_text2 Sl. M>W 0.5421 0.1614 3.36 0.00078 ***
## year2021:gendercomp_text3 W=M -0.0105 0.1501 -0.07 0.94399
## year2021:gendercomp_text4 Sl. W>M -0.0630 0.1582 -0.40 0.69061
## year2021:gendercomp_text5 Subs. W>M 0.0594 0.1569 0.38 0.70518
## year2021:minorityNon-minority 0.2375 0.1419 1.67 0.09430 .
## gendercomp_text2 Sl. M>W:minorityNon-minority 0.5924 0.1791 3.31 0.00095 ***
## gendercomp_text3 W=M:minorityNon-minority 0.1214 0.1664 0.73 0.46549
## gendercomp_text4 Sl. W>M:minorityNon-minority 0.2989 0.1656 1.80 0.07114 .
## gendercomp_text5 Subs. W>M:minorityNon-minority 0.3002 0.1655 1.81 0.06972 .
## year2021:gendercomp_text2 Sl. M>W:minorityNon-minority -0.6123 0.1955 -3.13 0.00173 **
## year2021:gendercomp_text3 W=M:minorityNon-minority -0.2278 0.1809 -1.26 0.20800
## year2021:gendercomp_text4 Sl. W>M:minorityNon-minority -0.2824 0.1937 -1.46 0.14485
## year2021:gendercomp_text5 Subs. W>M:minorityNon-minority -0.3074 0.1945 -1.58 0.11413
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 12463 degrees of freedom
## (585 observations deleted due to missingness)
## Multiple R-squared: 0.0067, Adjusted R-squared: 0.00518
## F-statistic: 4.42 on 19 and 12463 DF, p-value: 0.00000000041
During your career, would you like to: Take on new challenges, assignments, or roles in your current job.
##
## Call:
## lm(formula = liketo_newchall ~ year * gendercomp_text * minority,
## data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.246 -0.246 0.031 0.932 1.695
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.69919 0.09704 38.12 < 0.0000000000000002 ***
## year2021 0.38754 0.10324 3.75 0.00017 ***
## gendercomp_text2 Sl. M>W -0.39399 0.13015 -3.03 0.00247 **
## gendercomp_text3 W=M 0.18814 0.12188 1.54 0.12272
## gendercomp_text4 Sl. W>M -0.07822 0.11869 -0.66 0.50991
## gendercomp_text5 Subs. W>M -0.17428 0.11771 -1.48 0.13874
## minorityNon-minority -0.06911 0.11885 -0.58 0.56096
## year2021:gendercomp_text2 Sl. M>W 0.51127 0.14317 3.57 0.00036 ***
## year2021:gendercomp_text3 W=M -0.02910 0.13316 -0.22 0.82703
## year2021:gendercomp_text4 Sl. W>M 0.04810 0.14036 0.34 0.73183
## year2021:gendercomp_text5 Subs. W>M -0.06004 0.13918 -0.43 0.66618
## year2021:minorityNon-minority -0.04846 0.12592 -0.38 0.70036
## gendercomp_text2 Sl. M>W:minorityNon-minority 0.29974 0.15892 1.89 0.05931 .
## gendercomp_text3 W=M:minorityNon-minority -0.00787 0.14759 -0.05 0.95746
## gendercomp_text4 Sl. W>M:minorityNon-minority 0.24091 0.14693 1.64 0.10111
## gendercomp_text5 Subs. W>M:minorityNon-minority 0.27805 0.14683 1.89 0.05828 .
## year2021:gendercomp_text2 Sl. M>W:minorityNon-minority -0.32561 0.17339 -1.88 0.06043 .
## year2021:gendercomp_text3 W=M:minorityNon-minority -0.05266 0.16052 -0.33 0.74286
## year2021:gendercomp_text4 Sl. W>M:minorityNon-minority -0.20535 0.17184 -1.20 0.23211
## year2021:gendercomp_text5 Subs. W>M:minorityNon-minority -0.08377 0.17257 -0.49 0.62740
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 12463 degrees of freedom
## (585 observations deleted due to missingness)
## Multiple R-squared: 0.0287, Adjusted R-squared: 0.0273
## F-statistic: 19.4 on 19 and 12463 DF, p-value: <0.0000000000000002
During your career, would you like to: Take on supervisory or managerial responsibilities.
##
## Call:
## lm(formula = liketo_super ~ year * gendercomp_text * minority,
## data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.998 -0.779 0.158 1.070 1.725
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8211 0.1112 34.35 <0.0000000000000002 ***
## year2021 -0.2644 0.1183 -2.23 0.025 *
## gendercomp_text2 Sl. M>W -0.2562 0.1492 -1.72 0.086 .
## gendercomp_text3 W=M 0.1084 0.1397 0.78 0.438
## gendercomp_text4 Sl. W>M -0.0469 0.1361 -0.35 0.730
## gendercomp_text5 Subs. W>M -0.1468 0.1349 -1.09 0.277
## minorityNon-minority -0.0528 0.1362 -0.39 0.698
## year2021:gendercomp_text2 Sl. M>W 0.3775 0.1641 2.30 0.021 *
## year2021:gendercomp_text3 W=M 0.0919 0.1526 0.60 0.547
## year2021:gendercomp_text4 Sl. W>M 0.0600 0.1609 0.37 0.709
## year2021:gendercomp_text5 Subs. W>M 0.0107 0.1595 0.07 0.946
## year2021:minorityNon-minority -0.2293 0.1443 -1.59 0.112
## gendercomp_text2 Sl. M>W:minorityNon-minority 0.2667 0.1822 1.46 0.143
## gendercomp_text3 W=M:minorityNon-minority 0.0505 0.1692 0.30 0.765
## gendercomp_text4 Sl. W>M:minorityNon-minority 0.2762 0.1684 1.64 0.101
## gendercomp_text5 Subs. W>M:minorityNon-minority 0.2209 0.1683 1.31 0.189
## year2021:gendercomp_text2 Sl. M>W:minorityNon-minority -0.3328 0.1988 -1.67 0.094 .
## year2021:gendercomp_text3 W=M:minorityNon-minority -0.2340 0.1840 -1.27 0.204
## year2021:gendercomp_text4 Sl. W>M:minorityNon-minority -0.2877 0.1970 -1.46 0.144
## year2021:gendercomp_text5 Subs. W>M:minorityNon-minority -0.0587 0.1978 -0.30 0.767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 12463 degrees of freedom
## (585 observations deleted due to missingness)
## Multiple R-squared: 0.0371, Adjusted R-squared: 0.0356
## F-statistic: 25.3 on 19 and 12463 DF, p-value: <0.0000000000000002
During your career, would you like to: Reduce work hours or responsibilities
##
## Call:
## lm(formula = liketo_reduc ~ year * gendercomp_text * minority,
## data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.962 -0.997 -0.027 0.880 3.003
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.69106 0.09333 39.55 <0.0000000000000002 ***
## year2021 -1.57007 0.09929 -15.81 <0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.34690 0.12517 -2.77 0.0056 **
## gendercomp_text3 W=M 0.27138 0.11722 2.32 0.0206 *
## gendercomp_text4 Sl. W>M 0.11943 0.11415 1.05 0.2955
## gendercomp_text5 Subs. W>M -0.00523 0.11321 -0.05 0.9631
## minorityNon-minority 0.06098 0.11431 0.53 0.5937
## year2021:gendercomp_text2 Sl. M>W 0.35592 0.13769 2.58 0.0097 **
## year2021:gendercomp_text3 W=M -0.27231 0.12807 -2.13 0.0335 *
## year2021:gendercomp_text4 Sl. W>M -0.21022 0.13498 -1.56 0.1194
## year2021:gendercomp_text5 Subs. W>M 0.12041 0.13385 0.90 0.3684
## year2021:minorityNon-minority -0.13591 0.12110 -1.12 0.2618
## gendercomp_text2 Sl. M>W:minorityNon-minority 0.36745 0.15284 2.40 0.0162 *
## gendercomp_text3 W=M:minorityNon-minority -0.12303 0.14194 -0.87 0.3861
## gendercomp_text4 Sl. W>M:minorityNon-minority 0.06107 0.14131 0.43 0.6656
## gendercomp_text5 Subs. W>M:minorityNon-minority 0.08007 0.14121 0.57 0.5707
## year2021:gendercomp_text2 Sl. M>W:minorityNon-minority -0.42515 0.16676 -2.55 0.0108 *
## year2021:gendercomp_text3 W=M:minorityNon-minority 0.08271 0.15438 0.54 0.5921
## year2021:gendercomp_text4 Sl. W>M:minorityNon-minority 0.01066 0.16526 0.06 0.9486
## year2021:gendercomp_text5 Subs. W>M:minorityNon-minority -0.20490 0.16597 -1.23 0.2170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 12463 degrees of freedom
## (585 observations deleted due to missingness)
## Multiple R-squared: 0.342, Adjusted R-squared: 0.341
## F-statistic: 341 on 19 and 12463 DF, p-value: <0.0000000000000002
In the past 2 years, an agency official (e.g., supervisor, manager, senior leader, etc.) in my work unit has discriminated in favor or against someone in a personnel action based upon…
##
## Call:
## lm(formula = discsex ~ year * gendercomp_text * minority, data = mps_w)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8337 -0.1069 -0.0906 0.1663 1.9315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.5935 0.0403 64.42 < 0.0000000000000002 ***
## year2021 -1.4393 0.0428 -33.60 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.3532 0.0540 -6.54 0.000000000063 ***
## gendercomp_text3 W=M 0.1530 0.0506 3.03 0.00249 **
## gendercomp_text4 Sl. W>M 0.1444 0.0492 2.93 0.00337 **
## gendercomp_text5 Subs. W>M 0.1651 0.0488 3.38 0.00072 ***
## minorityNon-minority 0.1382 0.0493 2.80 0.00507 **
## year2021:gendercomp_text2 Sl. M>W 0.3171 0.0594 5.34 0.000000095597 ***
## year2021:gendercomp_text3 W=M -0.2196 0.0552 -3.97 0.000070861281 ***
## year2021:gendercomp_text4 Sl. W>M -0.1665 0.0582 -2.86 0.00425 **
## year2021:gendercomp_text5 Subs. W>M -0.0278 0.0577 -0.48 0.63036
## year2021:minorityNon-minority -0.1855 0.0522 -3.55 0.00039 ***
## gendercomp_text2 Sl. M>W:minorityNon-minority 0.2446 0.0659 3.71 0.00021 ***
## gendercomp_text3 W=M:minorityNon-minority -0.0609 0.0612 -1.00 0.31969
## gendercomp_text4 Sl. W>M:minorityNon-minority -0.0424 0.0610 -0.70 0.48693
## gendercomp_text5 Subs. W>M:minorityNon-minority -0.0674 0.0609 -1.11 0.26873
## year2021:gendercomp_text2 Sl. M>W:minorityNon-minority -0.2468 0.0719 -3.43 0.00060 ***
## year2021:gendercomp_text3 W=M:minorityNon-minority 0.1112 0.0666 1.67 0.09492 .
## year2021:gendercomp_text4 Sl. W>M:minorityNon-minority 0.0608 0.0713 0.85 0.39401
## year2021:gendercomp_text5 Subs. W>M:minorityNon-minority 0.0339 0.0716 0.47 0.63608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.45 on 12463 degrees of freedom
## (585 observations deleted due to missingness)
## Multiple R-squared: 0.705, Adjusted R-squared: 0.704
## F-statistic: 1.56e+03 on 19 and 12463 DF, p-value: <0.0000000000000002
Gender composition by men
My job makes good use of my skills and abilities
##
## Call:
## lm(formula = useskills ~ year * gendercomp_text * sex, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.255 -0.228 0.020 0.815 1.220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.96791 0.05264 75.38 < 0.0000000000000002 ***
## year2021 0.10971 0.05539 1.98 0.0476 *
## gendercomp_text2 Sl. M>W -0.18804 0.07031 -2.67 0.0075 **
## gendercomp_text3 W=M 0.03883 0.06457 0.60 0.5476
## gendercomp_text4 Sl. W>M -0.00374 0.06571 -0.06 0.9547
## gendercomp_text5 Subs. W>M -0.02773 0.06603 -0.42 0.6745
## sexMale 0.02900 0.06195 0.47 0.6398
## year2021:gendercomp_text2 Sl. M>W 0.33817 0.07565 4.47 0.0000079 ***
## year2021:gendercomp_text3 W=M 0.10101 0.06961 1.45 0.1468
## year2021:gendercomp_text4 Sl. W>M 0.01682 0.07555 0.22 0.8238
## year2021:gendercomp_text5 Subs. W>M -0.01144 0.07683 -0.15 0.8817
## year2021:sexMale -0.14375 0.07014 -2.05 0.0404 *
## gendercomp_text2 Sl. M>W:sexMale 0.05421 0.08196 0.66 0.5083
## gendercomp_text3 W=M:sexMale -0.04071 0.07916 -0.51 0.6071
## gendercomp_text4 Sl. W>M:sexMale -0.01326 0.08641 -0.15 0.8781
## gendercomp_text5 Subs. W>M:sexMale -0.18157 0.09211 -1.97 0.0487 *
## year2021:gendercomp_text2 Sl. M>W:sexMale -0.02358 0.09848 -0.24 0.8108
## year2021:gendercomp_text3 W=M:sexMale 0.19315 0.09062 2.13 0.0331 *
## year2021:gendercomp_text4 Sl. W>M:sexMale 0.22270 0.10172 2.19 0.0286 *
## year2021:gendercomp_text5 Subs. W>M:sexMale 0.32221 0.10673 3.02 0.0025 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.0151, Adjusted R-squared: 0.0144
## F-statistic: 20 on 19 and 24768 DF, p-value: <0.0000000000000002
I have the resources to do my job well.
##
## Call:
## lm(formula = resourceswell ~ year * gendercomp_text * sex, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.976 -0.703 0.202 1.024 1.520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6283 0.0556 65.21 < 0.0000000000000002 ***
## year2021 0.1697 0.0585 2.90 0.00375 **
## gendercomp_text2 Sl. M>W -0.1483 0.0743 -1.99 0.04607 *
## gendercomp_text3 W=M 0.0748 0.0683 1.10 0.27339
## gendercomp_text4 Sl. W>M 0.0896 0.0695 1.29 0.19723
## gendercomp_text5 Subs. W>M 0.0726 0.0698 1.04 0.29845
## sexMale 0.0637 0.0655 0.97 0.33050
## year2021:gendercomp_text2 Sl. M>W 0.2957 0.0800 3.70 0.00022 ***
## year2021:gendercomp_text3 W=M 0.1031 0.0736 1.40 0.16131
## year2021:gendercomp_text4 Sl. W>M -0.0648 0.0799 -0.81 0.41687
## year2021:gendercomp_text5 Subs. W>M -0.1110 0.0812 -1.37 0.17165
## year2021:sexMale -0.1572 0.0741 -2.12 0.03396 *
## gendercomp_text2 Sl. M>W:sexMale 0.0529 0.0866 0.61 0.54131
## gendercomp_text3 W=M:sexMale -0.0459 0.0837 -0.55 0.58349
## gendercomp_text4 Sl. W>M:sexMale -0.1230 0.0913 -1.35 0.17808
## gendercomp_text5 Subs. W>M:sexMale -0.1039 0.0974 -1.07 0.28603
## year2021:gendercomp_text2 Sl. M>W:sexMale 0.0089 0.1041 0.09 0.93187
## year2021:gendercomp_text3 W=M:sexMale 0.1311 0.0958 1.37 0.17101
## year2021:gendercomp_text4 Sl. W>M:sexMale 0.3071 0.1075 2.86 0.00429 **
## year2021:gendercomp_text5 Subs. W>M:sexMale 0.2738 0.1128 2.43 0.01524 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.0139, Adjusted R-squared: 0.0131
## F-statistic: 18.3 on 19 and 24768 DF, p-value: <0.0000000000000002
During your career, would you like to: Continue in your current job and role at the same level of responsibility.
##
## Call:
## lm(formula = liketo_contjob ~ year * gendercomp_text * sex, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.959 -0.781 0.201 1.134 1.371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7273 0.0625 59.61 < 0.0000000000000002 ***
## year2021 0.0719 0.0658 1.09 0.2743
## gendercomp_text2 Sl. M>W -0.0774 0.0835 -0.93 0.3542
## gendercomp_text3 W=M 0.2322 0.0767 3.03 0.0025 **
## gendercomp_text4 Sl. W>M 0.1205 0.0781 1.54 0.1227
## gendercomp_text5 Subs. W>M 0.0197 0.0784 0.25 0.8021
## sexMale 0.2006 0.0736 2.73 0.0064 **
## year2021:gendercomp_text2 Sl. M>W 0.1310 0.0899 1.46 0.1449
## year2021:gendercomp_text3 W=M -0.1652 0.0827 -2.00 0.0457 *
## year2021:gendercomp_text4 Sl. W>M -0.2203 0.0897 -2.45 0.0141 *
## year2021:gendercomp_text5 Subs. W>M -0.1422 0.0913 -1.56 0.1193
## year2021:sexMale -0.3415 0.0833 -4.10 0.000042 ***
## gendercomp_text2 Sl. M>W:sexMale -0.0263 0.0974 -0.27 0.7872
## gendercomp_text3 W=M:sexMale -0.2158 0.0940 -2.29 0.0218 *
## gendercomp_text4 Sl. W>M:sexMale -0.1608 0.1026 -1.57 0.1171
## gendercomp_text5 Subs. W>M:sexMale -0.2868 0.1094 -2.62 0.0088 **
## year2021:gendercomp_text2 Sl. M>W:sexMale 0.0818 0.1170 0.70 0.4846
## year2021:gendercomp_text3 W=M:sexMale 0.2714 0.1077 2.52 0.0117 *
## year2021:gendercomp_text4 Sl. W>M:sexMale 0.3460 0.1208 2.86 0.0042 **
## year2021:gendercomp_text5 Subs. W>M:sexMale 0.3805 0.1268 3.00 0.0027 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.00565, Adjusted R-squared: 0.00489
## F-statistic: 7.41 on 19 and 24768 DF, p-value: <0.0000000000000002
During your career, would you like to: Take on new challenges, assignments, or roles in your current job.
##
## Call:
## lm(formula = liketo_newchall ~ year * gendercomp_text * sex,
## data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1571 -0.1571 0.0095 0.8971 1.5367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6604 0.0551 66.48 < 0.0000000000000002 ***
## year2021 0.3364 0.0579 5.81 0.0000000064 ***
## gendercomp_text2 Sl. M>W -0.1971 0.0735 -2.68 0.0074 **
## gendercomp_text3 W=M 0.1749 0.0675 2.59 0.0096 **
## gendercomp_text4 Sl. W>M 0.0605 0.0687 0.88 0.3790
## gendercomp_text5 Subs. W>M -0.0132 0.0691 -0.19 0.8486
## sexMale 0.1470 0.0648 2.27 0.0233 *
## year2021:gendercomp_text2 Sl. M>W 0.2860 0.0791 3.61 0.0003 ***
## year2021:gendercomp_text3 W=M -0.0607 0.0728 -0.83 0.4047
## year2021:gendercomp_text4 Sl. W>M -0.0668 0.0790 -0.85 0.3978
## year2021:gendercomp_text5 Subs. W>M -0.0798 0.0804 -0.99 0.3206
## year2021:sexMale -0.0916 0.0734 -1.25 0.2120
## gendercomp_text2 Sl. M>W:sexMale 0.0859 0.0857 1.00 0.3164
## gendercomp_text3 W=M:sexMale -0.1333 0.0828 -1.61 0.1074
## gendercomp_text4 Sl. W>M:sexMale -0.1550 0.0904 -1.72 0.0863 .
## gendercomp_text5 Subs. W>M:sexMale -0.2574 0.0963 -2.67 0.0076 **
## year2021:gendercomp_text2 Sl. M>W:sexMale -0.1203 0.1030 -1.17 0.2428
## year2021:gendercomp_text3 W=M:sexMale 0.1239 0.0948 1.31 0.1912
## year2021:gendercomp_text4 Sl. W>M:sexMale 0.2120 0.1064 1.99 0.0463 *
## year2021:gendercomp_text5 Subs. W>M:sexMale 0.3456 0.1116 3.10 0.0020 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.026, Adjusted R-squared: 0.0253
## F-statistic: 34.9 on 19 and 24768 DF, p-value: <0.0000000000000002
During your career, would you like to: Take on supervisory or managerial responsibilities.
##
## Call:
## lm(formula = liketo_super ~ year * gendercomp_text * sex, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9567 -0.9563 0.0437 1.0433 1.9491
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.78877 0.06343 59.73 < 0.0000000000000002 ***
## year2021 -0.43901 0.06674 -6.58 0.000000000049 ***
## gendercomp_text2 Sl. M>W -0.07598 0.08472 -0.90 0.3698
## gendercomp_text3 W=M 0.13836 0.07781 1.78 0.0754 .
## gendercomp_text4 Sl. W>M 0.11869 0.07918 1.50 0.1339
## gendercomp_text5 Subs. W>M -0.01730 0.07957 -0.22 0.8279
## sexMale 0.16798 0.07465 2.25 0.0244 *
## year2021:gendercomp_text2 Sl. M>W 0.13098 0.09116 1.44 0.1508
## year2021:gendercomp_text3 W=M -0.06816 0.08387 -0.81 0.4165
## year2021:gendercomp_text4 Sl. W>M -0.11296 0.09103 -1.24 0.2147
## year2021:gendercomp_text5 Subs. W>M 0.00528 0.09258 0.06 0.9545
## year2021:sexMale -0.26243 0.08452 -3.10 0.0019 **
## gendercomp_text2 Sl. M>W:sexMale -0.04289 0.09875 -0.43 0.6641
## gendercomp_text3 W=M:sexMale -0.13880 0.09538 -1.46 0.1456
## gendercomp_text4 Sl. W>M:sexMale -0.16580 0.10412 -1.59 0.1113
## gendercomp_text5 Subs. W>M:sexMale -0.28753 0.11099 -2.59 0.0096 **
## year2021:gendercomp_text2 Sl. M>W:sexMale -0.07452 0.11867 -0.63 0.5300
## year2021:gendercomp_text3 W=M:sexMale 0.02108 0.10920 0.19 0.8469
## year2021:gendercomp_text4 Sl. W>M:sexMale 0.06769 0.12257 0.55 0.5808
## year2021:gendercomp_text5 Subs. W>M:sexMale 0.09517 0.12861 0.74 0.4593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.0506, Adjusted R-squared: 0.0499
## F-statistic: 69.5 on 19 and 24768 DF, p-value: <0.0000000000000002
During your career, would you like to: Reduce work hours or responsibilities
##
## Call:
## lm(formula = liketo_reduc ~ year * gendercomp_text * sex, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.977 -0.968 -0.038 0.921 3.050
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7406 0.0533 70.21 < 0.0000000000000002 ***
## year2021 -1.6619 0.0561 -29.65 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.1054 0.0712 -1.48 0.13848
## gendercomp_text3 W=M 0.1770 0.0654 2.71 0.00675 **
## gendercomp_text4 Sl. W>M 0.1370 0.0665 2.06 0.03944 *
## gendercomp_text5 Subs. W>M 0.0278 0.0668 0.42 0.67783
## sexMale 0.2367 0.0627 3.78 0.00016 ***
## year2021:gendercomp_text2 Sl. M>W 0.0672 0.0766 0.88 0.38033
## year2021:gendercomp_text3 W=M -0.2130 0.0704 -3.02 0.00250 **
## year2021:gendercomp_text4 Sl. W>M -0.1778 0.0765 -2.32 0.02008 *
## year2021:gendercomp_text5 Subs. W>M 0.0161 0.0778 0.21 0.83638
## year2021:sexMale -0.3140 0.0710 -4.42 0.0000098 ***
## gendercomp_text2 Sl. M>W:sexMale -0.0524 0.0829 -0.63 0.52725
## gendercomp_text3 W=M:sexMale -0.1862 0.0801 -2.32 0.02015 *
## gendercomp_text4 Sl. W>M:sexMale -0.2428 0.0874 -2.78 0.00549 **
## gendercomp_text5 Subs. W>M:sexMale -0.3591 0.0932 -3.85 0.00012 ***
## year2021:gendercomp_text2 Sl. M>W:sexMale 0.1042 0.0997 1.05 0.29572
## year2021:gendercomp_text3 W=M:sexMale 0.1902 0.0917 2.07 0.03815 *
## year2021:gendercomp_text4 Sl. W>M:sexMale 0.3639 0.1029 3.53 0.00041 ***
## year2021:gendercomp_text5 Subs. W>M:sexMale 0.2639 0.1080 2.44 0.01455 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.393, Adjusted R-squared: 0.392
## F-statistic: 843 on 19 and 24768 DF, p-value: <0.0000000000000002
In the past 2 years, an agency official (e.g., supervisor, manager, senior leader, etc.) in my work unit has discriminated in favor or against someone in a personnel action based upon…
##
## Call:
## lm(formula = discsex ~ year * gendercomp_text * sex, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8373 -0.1539 -0.0899 0.1718 1.9204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6845 0.0251 106.76 < 0.0000000000000002 ***
## year2021 -1.5619 0.0265 -59.03 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.1876 0.0336 -5.59 0.0000000234 ***
## gendercomp_text3 W=M 0.1171 0.0308 3.80 0.00015 ***
## gendercomp_text4 Sl. W>M 0.1051 0.0314 3.35 0.00082 ***
## gendercomp_text5 Subs. W>M 0.1146 0.0315 3.63 0.00028 ***
## sexMale 0.1528 0.0296 5.16 0.0000002454 ***
## year2021:gendercomp_text2 Sl. M>W 0.1447 0.0361 4.00 0.0000625908 ***
## year2021:gendercomp_text3 W=M -0.1498 0.0333 -4.51 0.0000066501 ***
## year2021:gendercomp_text4 Sl. W>M -0.1127 0.0361 -3.12 0.00180 **
## year2021:gendercomp_text5 Subs. W>M 0.0104 0.0367 0.28 0.77675
## year2021:sexMale 0.0989 0.0335 2.95 0.00318 **
## gendercomp_text2 Sl. M>W:sexMale 0.1446 0.0392 3.69 0.00022 ***
## gendercomp_text3 W=M:sexMale -0.1262 0.0378 -3.34 0.00085 ***
## gendercomp_text4 Sl. W>M:sexMale -0.1632 0.0413 -3.95 0.0000769238 ***
## gendercomp_text5 Subs. W>M:sexMale -0.1967 0.0440 -4.47 0.0000078431 ***
## year2021:gendercomp_text2 Sl. M>W:sexMale -0.2776 0.0470 -5.90 0.0000000037 ***
## year2021:gendercomp_text3 W=M:sexMale -0.0620 0.0433 -1.43 0.15231
## year2021:gendercomp_text4 Sl. W>M:sexMale -0.0480 0.0486 -0.99 0.32350
## year2021:gendercomp_text5 Subs. W>M:sexMale -0.1486 0.0510 -2.92 0.00356 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.49 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.7, Adjusted R-squared: 0.7
## F-statistic: 3.05e+03 on 19 and 24768 DF, p-value: <0.0000000000000002
Gender Composition by year and participant gender
Note: all responses are RELATIVE to the response option “1 Subs. M>W”; this means that people indicated there were substantially more men than women.
My job makes good use of my skills and abilities
Regression
##
## Call:
## lm(formula = useskills ~ gendercomp_text * sex * year, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.255 -0.228 0.020 0.815 1.220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.96791 0.05264 75.38 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.18804 0.07031 -2.67 0.0075 **
## gendercomp_text3 W=M 0.03883 0.06457 0.60 0.5476
## gendercomp_text4 Sl. W>M -0.00374 0.06571 -0.06 0.9547
## gendercomp_text5 Subs. W>M -0.02773 0.06603 -0.42 0.6745
## sexMale 0.02900 0.06195 0.47 0.6398
## year2021 0.10971 0.05539 1.98 0.0476 *
## gendercomp_text2 Sl. M>W:sexMale 0.05421 0.08196 0.66 0.5083
## gendercomp_text3 W=M:sexMale -0.04071 0.07916 -0.51 0.6071
## gendercomp_text4 Sl. W>M:sexMale -0.01326 0.08641 -0.15 0.8781
## gendercomp_text5 Subs. W>M:sexMale -0.18157 0.09211 -1.97 0.0487 *
## gendercomp_text2 Sl. M>W:year2021 0.33817 0.07565 4.47 0.0000079 ***
## gendercomp_text3 W=M:year2021 0.10101 0.06961 1.45 0.1468
## gendercomp_text4 Sl. W>M:year2021 0.01682 0.07555 0.22 0.8238
## gendercomp_text5 Subs. W>M:year2021 -0.01144 0.07683 -0.15 0.8817
## sexMale:year2021 -0.14375 0.07014 -2.05 0.0404 *
## gendercomp_text2 Sl. M>W:sexMale:year2021 -0.02358 0.09848 -0.24 0.8108
## gendercomp_text3 W=M:sexMale:year2021 0.19315 0.09062 2.13 0.0331 *
## gendercomp_text4 Sl. W>M:sexMale:year2021 0.22270 0.10172 2.19 0.0286 *
## gendercomp_text5 Subs. W>M:sexMale:year2021 0.32221 0.10673 3.02 0.0025 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.0151, Adjusted R-squared: 0.0144
## F-statistic: 20 on 19 and 24768 DF, p-value: <0.0000000000000002Estimated marginal means
Tukey HSD - comparing gender composition across p. gender and year
Tukey HSD - comparing year across p. gender and gender composition
Tukey HSD - comparing p. gender across year and gender composition
Graphs
I have the resources to do my job well.
Regression
##
## Call:
## lm(formula = resourceswell ~ gendercomp_text * sex * year, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.976 -0.703 0.202 1.024 1.520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6283 0.0556 65.21 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.1483 0.0743 -1.99 0.04607 *
## gendercomp_text3 W=M 0.0748 0.0683 1.10 0.27339
## gendercomp_text4 Sl. W>M 0.0896 0.0695 1.29 0.19723
## gendercomp_text5 Subs. W>M 0.0726 0.0698 1.04 0.29845
## sexMale 0.0637 0.0655 0.97 0.33050
## year2021 0.1697 0.0585 2.90 0.00375 **
## gendercomp_text2 Sl. M>W:sexMale 0.0529 0.0866 0.61 0.54131
## gendercomp_text3 W=M:sexMale -0.0459 0.0837 -0.55 0.58349
## gendercomp_text4 Sl. W>M:sexMale -0.1230 0.0913 -1.35 0.17808
## gendercomp_text5 Subs. W>M:sexMale -0.1039 0.0974 -1.07 0.28603
## gendercomp_text2 Sl. M>W:year2021 0.2957 0.0800 3.70 0.00022 ***
## gendercomp_text3 W=M:year2021 0.1031 0.0736 1.40 0.16131
## gendercomp_text4 Sl. W>M:year2021 -0.0648 0.0799 -0.81 0.41687
## gendercomp_text5 Subs. W>M:year2021 -0.1110 0.0812 -1.37 0.17165
## sexMale:year2021 -0.1572 0.0741 -2.12 0.03396 *
## gendercomp_text2 Sl. M>W:sexMale:year2021 0.0089 0.1041 0.09 0.93187
## gendercomp_text3 W=M:sexMale:year2021 0.1311 0.0958 1.37 0.17101
## gendercomp_text4 Sl. W>M:sexMale:year2021 0.3071 0.1075 2.86 0.00429 **
## gendercomp_text5 Subs. W>M:sexMale:year2021 0.2738 0.1128 2.43 0.01524 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.0139, Adjusted R-squared: 0.0131
## F-statistic: 18.3 on 19 and 24768 DF, p-value: <0.0000000000000002Estimated marginal means
Tukey HSD - comparing gender composition across p. gender and year
Tukey HSD - comparing year across p. gender and gender composition
Tukey HSD - comparing p. gender across year and gender composition
Graphs
During your career, would you like to: Continue in your current job and role at the same level of responsibility
Regression
##
## Call:
## lm(formula = liketo_contjob ~ gendercomp_text * sex * year, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.959 -0.781 0.201 1.134 1.371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7273 0.0625 59.61 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.0774 0.0835 -0.93 0.3542
## gendercomp_text3 W=M 0.2322 0.0767 3.03 0.0025 **
## gendercomp_text4 Sl. W>M 0.1205 0.0781 1.54 0.1227
## gendercomp_text5 Subs. W>M 0.0197 0.0784 0.25 0.8021
## sexMale 0.2006 0.0736 2.73 0.0064 **
## year2021 0.0719 0.0658 1.09 0.2743
## gendercomp_text2 Sl. M>W:sexMale -0.0263 0.0974 -0.27 0.7872
## gendercomp_text3 W=M:sexMale -0.2158 0.0940 -2.29 0.0218 *
## gendercomp_text4 Sl. W>M:sexMale -0.1608 0.1026 -1.57 0.1171
## gendercomp_text5 Subs. W>M:sexMale -0.2868 0.1094 -2.62 0.0088 **
## gendercomp_text2 Sl. M>W:year2021 0.1310 0.0899 1.46 0.1449
## gendercomp_text3 W=M:year2021 -0.1652 0.0827 -2.00 0.0457 *
## gendercomp_text4 Sl. W>M:year2021 -0.2203 0.0897 -2.45 0.0141 *
## gendercomp_text5 Subs. W>M:year2021 -0.1422 0.0913 -1.56 0.1193
## sexMale:year2021 -0.3415 0.0833 -4.10 0.000042 ***
## gendercomp_text2 Sl. M>W:sexMale:year2021 0.0818 0.1170 0.70 0.4846
## gendercomp_text3 W=M:sexMale:year2021 0.2714 0.1077 2.52 0.0117 *
## gendercomp_text4 Sl. W>M:sexMale:year2021 0.3460 0.1208 2.86 0.0042 **
## gendercomp_text5 Subs. W>M:sexMale:year2021 0.3805 0.1268 3.00 0.0027 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.00565, Adjusted R-squared: 0.00489
## F-statistic: 7.41 on 19 and 24768 DF, p-value: <0.0000000000000002Estimated marginal means
Tukey HSD - comparing gender composition across p. gender and year
Tukey HSD - comparing year across p. gender and gender composition
Tukey HSD - comparing p. gender across year and gender composition
Graphs
During your career, would you like to: Take on new challenges, assignments, or roles in your current job
Regression
##
## Call:
## lm(formula = liketo_newchall ~ gendercomp_text * sex, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0583 -0.0583 0.0357 0.9959 1.2089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.96429 0.01730 229.09 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W 0.00827 0.02721 0.30 0.76131
## gendercomp_text3 W=M 0.08805 0.02515 3.50 0.00046 ***
## gendercomp_text4 Sl. W>M -0.08543 0.03186 -2.68 0.00733 **
## gendercomp_text5 Subs. W>M -0.17319 0.03285 -5.27 0.00000014 ***
## sexMale -0.01580 0.02837 -0.56 0.57751
## gendercomp_text2 Sl. M>W:sexMale -0.12411 0.04210 -2.95 0.00320 **
## gendercomp_text3 W=M:sexMale 0.02176 0.03881 0.56 0.57509
## gendercomp_text4 Sl. W>M:sexMale 0.14101 0.04592 3.07 0.00214 **
## gendercomp_text5 Subs. W>M:sexMale 0.19083 0.04614 4.14 0.00003548 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 24778 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.00531, Adjusted R-squared: 0.00494
## F-statistic: 14.7 on 9 and 24778 DF, p-value: <0.0000000000000002Estimated marginal means
Tukey HSD - comparing gender composition across p. gender and year
Tukey HSD - comparing year across p. gender and gender composition
Tukey HSD - comparing p. gender across year and gender composition
Graphs
During your career, would you like to: Take on supervisory or managerial responsibilities.
Regression
##
## Call:
## lm(formula = liketo_super ~ gendercomp_text * sex * year, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9567 -0.9563 0.0437 1.0433 1.9491
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.78877 0.06343 59.73 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.07598 0.08472 -0.90 0.3698
## gendercomp_text3 W=M 0.13836 0.07781 1.78 0.0754 .
## gendercomp_text4 Sl. W>M 0.11869 0.07918 1.50 0.1339
## gendercomp_text5 Subs. W>M -0.01730 0.07957 -0.22 0.8279
## sexMale 0.16798 0.07465 2.25 0.0244 *
## year2021 -0.43901 0.06674 -6.58 0.000000000049 ***
## gendercomp_text2 Sl. M>W:sexMale -0.04289 0.09875 -0.43 0.6641
## gendercomp_text3 W=M:sexMale -0.13880 0.09538 -1.46 0.1456
## gendercomp_text4 Sl. W>M:sexMale -0.16580 0.10412 -1.59 0.1113
## gendercomp_text5 Subs. W>M:sexMale -0.28753 0.11099 -2.59 0.0096 **
## gendercomp_text2 Sl. M>W:year2021 0.13098 0.09116 1.44 0.1508
## gendercomp_text3 W=M:year2021 -0.06816 0.08387 -0.81 0.4165
## gendercomp_text4 Sl. W>M:year2021 -0.11296 0.09103 -1.24 0.2147
## gendercomp_text5 Subs. W>M:year2021 0.00528 0.09258 0.06 0.9545
## sexMale:year2021 -0.26243 0.08452 -3.10 0.0019 **
## gendercomp_text2 Sl. M>W:sexMale:year2021 -0.07452 0.11867 -0.63 0.5300
## gendercomp_text3 W=M:sexMale:year2021 0.02108 0.10920 0.19 0.8469
## gendercomp_text4 Sl. W>M:sexMale:year2021 0.06769 0.12257 0.55 0.5808
## gendercomp_text5 Subs. W>M:sexMale:year2021 0.09517 0.12861 0.74 0.4593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.0506, Adjusted R-squared: 0.0499
## F-statistic: 69.5 on 19 and 24768 DF, p-value: <0.0000000000000002Estimated marginal means
Tukey HSD - comparing gender composition across p. gender and year
Tukey HSD - comparing year across p. gender and gender composition
Tukey HSD - comparing p. gender across year and gender composition
Graphs
During your career, would you like to: Reduce work hours or responsibilities
Regression
##
## Call:
## lm(formula = liketo_reduc ~ gendercomp_text * sex * year, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.977 -0.968 -0.038 0.921 3.050
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7406 0.0533 70.21 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.1054 0.0712 -1.48 0.13848
## gendercomp_text3 W=M 0.1770 0.0654 2.71 0.00675 **
## gendercomp_text4 Sl. W>M 0.1370 0.0665 2.06 0.03944 *
## gendercomp_text5 Subs. W>M 0.0278 0.0668 0.42 0.67783
## sexMale 0.2367 0.0627 3.78 0.00016 ***
## year2021 -1.6619 0.0561 -29.65 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W:sexMale -0.0524 0.0829 -0.63 0.52725
## gendercomp_text3 W=M:sexMale -0.1862 0.0801 -2.32 0.02015 *
## gendercomp_text4 Sl. W>M:sexMale -0.2428 0.0874 -2.78 0.00549 **
## gendercomp_text5 Subs. W>M:sexMale -0.3591 0.0932 -3.85 0.00012 ***
## gendercomp_text2 Sl. M>W:year2021 0.0672 0.0766 0.88 0.38033
## gendercomp_text3 W=M:year2021 -0.2130 0.0704 -3.02 0.00250 **
## gendercomp_text4 Sl. W>M:year2021 -0.1778 0.0765 -2.32 0.02008 *
## gendercomp_text5 Subs. W>M:year2021 0.0161 0.0778 0.21 0.83638
## sexMale:year2021 -0.3140 0.0710 -4.42 0.0000098 ***
## gendercomp_text2 Sl. M>W:sexMale:year2021 0.1042 0.0997 1.05 0.29572
## gendercomp_text3 W=M:sexMale:year2021 0.1902 0.0917 2.07 0.03815 *
## gendercomp_text4 Sl. W>M:sexMale:year2021 0.3639 0.1029 3.53 0.00041 ***
## gendercomp_text5 Subs. W>M:sexMale:year2021 0.2639 0.1080 2.44 0.01455 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.393, Adjusted R-squared: 0.392
## F-statistic: 843 on 19 and 24768 DF, p-value: <0.0000000000000002Estimated marginal means
Tukey HSD - comparing gender composition across p. gender and year
Tukey HSD - comparing year across p. gender and gender composition
Tukey HSD - comparing p. gender across year and gender composition
Graphs
In the past 2 years, an agency official (e.g., supervisor, manager, senior leader, etc.) in my work unit has discriminated in favor or against someone in a personnel action based upon…
Regression
##
## Call:
## lm(formula = discsex ~ gendercomp_text * sex * year, data = mps_full)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8373 -0.1539 -0.0899 0.1718 1.9204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6845 0.0251 106.76 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W -0.1876 0.0336 -5.59 0.0000000234 ***
## gendercomp_text3 W=M 0.1171 0.0308 3.80 0.00015 ***
## gendercomp_text4 Sl. W>M 0.1051 0.0314 3.35 0.00082 ***
## gendercomp_text5 Subs. W>M 0.1146 0.0315 3.63 0.00028 ***
## sexMale 0.1528 0.0296 5.16 0.0000002454 ***
## year2021 -1.5619 0.0265 -59.03 < 0.0000000000000002 ***
## gendercomp_text2 Sl. M>W:sexMale 0.1446 0.0392 3.69 0.00022 ***
## gendercomp_text3 W=M:sexMale -0.1262 0.0378 -3.34 0.00085 ***
## gendercomp_text4 Sl. W>M:sexMale -0.1632 0.0413 -3.95 0.0000769238 ***
## gendercomp_text5 Subs. W>M:sexMale -0.1967 0.0440 -4.47 0.0000078431 ***
## gendercomp_text2 Sl. M>W:year2021 0.1447 0.0361 4.00 0.0000625908 ***
## gendercomp_text3 W=M:year2021 -0.1498 0.0333 -4.51 0.0000066501 ***
## gendercomp_text4 Sl. W>M:year2021 -0.1127 0.0361 -3.12 0.00180 **
## gendercomp_text5 Subs. W>M:year2021 0.0104 0.0367 0.28 0.77675
## sexMale:year2021 0.0989 0.0335 2.95 0.00318 **
## gendercomp_text2 Sl. M>W:sexMale:year2021 -0.2776 0.0470 -5.90 0.0000000037 ***
## gendercomp_text3 W=M:sexMale:year2021 -0.0620 0.0433 -1.43 0.15231
## gendercomp_text4 Sl. W>M:sexMale:year2021 -0.0480 0.0486 -0.99 0.32350
## gendercomp_text5 Subs. W>M:sexMale:year2021 -0.1486 0.0510 -2.92 0.00356 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.49 on 24768 degrees of freedom
## (1028 observations deleted due to missingness)
## Multiple R-squared: 0.7, Adjusted R-squared: 0.7
## F-statistic: 3.05e+03 on 19 and 24768 DF, p-value: <0.0000000000000002Estimated marginal means
Tukey HSD - comparing gender composition across p. gender and year
Tukey HSD - comparing year across p. gender and gender composition
Tukey HSD - comparing p. gender across year and gender composition
Graphs
