Considerations
Our supplemental analyses should include analyses of other considerations
Diversity
##
## Call:
## lm(formula = div_cond ~ cond_end + cond_race, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.353 -1.449 0.359 1.647 3.551
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.641 0.168 21.71 < 0.0000000000000002 ***
## cond_endStrong endorsement 1.713 0.200 8.56 0.00000000000000026 ***
## cond_raceWhite -0.192 0.200 -0.96 0.34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.97 on 388 degrees of freedom
## Multiple R-squared: 0.16, Adjusted R-squared: 0.155
## F-statistic: 36.8 on 2 and 388 DF, p-value: 0.00000000000000223Qualifications (negative)
##
## Call:
## lm(formula = considerations_1 ~ cond_end + cond_race, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.524 -0.524 -0.068 0.932 1.667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.068 0.109 55.80 < 0.0000000000000002 ***
## cond_endStrong endorsement -0.735 0.130 -5.67 0.000000029 ***
## cond_raceWhite 0.191 0.130 1.47 0.14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.28 on 388 degrees of freedom
## Multiple R-squared: 0.0799, Adjusted R-squared: 0.0751
## F-statistic: 16.8 on 2 and 388 DF, p-value: 0.0000000968Skills (negative)
##
## Call:
## lm(formula = considerations_2 ~ cond_end + cond_race, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.336 -0.984 0.016 0.902 1.778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.984 0.116 51.63 < 0.0000000000000002 ***
## cond_endStrong endorsement -0.762 0.138 -5.51 0.000000065 ***
## cond_raceWhite 0.114 0.138 0.83 0.41
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.36 on 388 degrees of freedom
## Multiple R-squared: 0.0735, Adjusted R-squared: 0.0687
## F-statistic: 15.4 on 2 and 388 DF, p-value: 0.000000371Fit with the position’s needs (negative)
##
## Call:
## lm(formula = considerations_3 ~ cond_end + cond_race, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.092 -0.436 -0.092 0.867 1.605
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.0920 0.1086 56.12 < 0.0000000000000002 ***
## cond_endStrong endorsement -0.6970 0.1295 -5.38 0.00000013 ***
## cond_raceWhite 0.0409 0.1293 0.32 0.75
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.28 on 388 degrees of freedom
## Multiple R-squared: 0.0695, Adjusted R-squared: 0.0647
## F-statistic: 14.5 on 2 and 388 DF, p-value: 0.000000852Workload
##
## Call:
## lm(formula = considerations_6 ~ cond_end + cond_race, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.332 -1.332 0.047 1.668 3.047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3317 0.1638 26.44 <0.0000000000000002 ***
## cond_endStrong endorsement -0.3147 0.1954 -1.61 0.11
## cond_raceWhite -0.0639 0.1951 -0.33 0.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.93 on 388 degrees of freedom
## Multiple R-squared: 0.00703, Adjusted R-squared: 0.00191
## F-statistic: 1.37 on 2 and 388 DF, p-value: 0.255Interactions
Mediator~IV*DV
Endorsement condition BY Selecting the Black Candidate ON Diversity Considerations
Regression
##
## Call:
## lm(formula = div_cond ~ cond_end * selectedblackcand, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.827 -0.838 0.173 1.052 4.162
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.838 0.101 28.03 <0.0000000000000002 ***
## cond_endStrong endorsement 0.110 0.185 0.60 0.552
## selectedblackcand 2.989 0.208 14.40 <0.0000000000000002 ***
## cond_endStrong endorsement:selectedblackcand 0.627 0.284 2.21 0.028 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.27 on 387 degrees of freedom
## Multiple R-squared: 0.654, Adjusted R-squared: 0.651
## F-statistic: 244 on 3 and 387 DF, p-value: <0.0000000000000002Posthocs
Comparing Black candidates at different levels of endorsement condition
Plot
DV~IV*Mediator
Endorsement condition BY Diversity Considerations ON selecting the black candidate
##
## Call:
## glm(formula = selectedblackcand ~ cond_end * div_cond, family = "binomial",
## data = introvert_pilot_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.014 0.945 -7.42 0.00000000000012 ***
## cond_endStrong endorsement -4.334 2.112 -2.05 0.04 *
## div_cond 1.340 0.194 6.91 0.00000000000486 ***
## cond_endStrong endorsement:div_cond 0.939 0.404 2.32 0.02 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 533.70 on 390 degrees of freedom
## Residual deviance: 176.53 on 387 degrees of freedom
## AIC: 184.5
##
## Number of Fisher Scoring iterations: 7## SIMPLE SLOPES ANALYSIS
##
## Slope of div_cond when cond_end = Strong endorsement:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 2.28 0.35 6.43 0.00
##
## Slope of div_cond when cond_end = Qualified endorsement:
##
## Est. S.E. z val. p
## ------ ------ -------- ------
## 1.34 0.19 6.91 0.00
Main Analyses
Selecting Black candidate
##
## Call:
## glm(formula = selectedblackcand ~ cond_end + cond_race, family = "binomial",
## data = introvert_pilot_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1221 0.1944 -5.77 0.000000007893583 ***
## cond_endStrong endorsement 1.7346 0.2244 7.73 0.000000000000011 ***
## cond_raceWhite -0.0892 0.2239 -0.40 0.69
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 533.70 on 390 degrees of freedom
## Residual deviance: 468.09 on 388 degrees of freedom
## AIC: 474.1
##
## Number of Fisher Scoring iterations: 4## (Intercept) cond_endStrong endorsement cond_raceWhite
## 0.3256 5.6668 0.9147Text below from: https://stackoverflow.com/questions/41384075/r-calculate-and-interpret-odds-ratio-in-logistic-regression
The odds ratio for the value of the intercept is the odds of a “success” (in your data, this is the odds of taking the product) when x = 0 (i.e. zero thoughts). The odds ratio for your coefficient is the increase in odds above this value of the intercept when you add one whole x value (i.e. x=1; one thought). Using the menarche data. Your odds ratio of 2.07 implies that a 1 unit increase in ‘Thoughts’ increases the odds of taking the product by a factor of 2.07.
For us: our odds ration of 5.67 implies that, relative to the qualified endorsement condition, participants were five times more likely to select the Black candidate than the Whtie candidate.
Selecting information technology committee
##
## Call:
## glm(formula = selectedcand_qual ~ cond_end, family = "binomial",
## data = introvert_pilot_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.686 0.148 4.65 0.000003381403 ***
## cond_endStrong endorsement -1.470 0.217 -6.79 0.000000000012 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 542.04 on 390 degrees of freedom
## Residual deviance: 492.80 on 389 degrees of freedom
## AIC: 496.8
##
## Number of Fisher Scoring iterations: 4## (Intercept) cond_endStrong endorsement
## 1.986 0.230The odds of participants in the Strong (vs. qualified) endorsement condition choosing the candidate who has the information technology experience is 23% lower.
Mediation
Diversity considerations (Likert)
Choosing the Black candidate
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = selectedblackcand ~ cond_end_num + (div_cond),
## data = introvert_pilot_clean)
##
## The DV (Y) was selectedblackcand . The IV (X) was cond_end_num . The mediating variable(s) = div_cond .
##
## Total effect(c) of cond_end_num on selectedblackcand = 0.4 S.E. = 0.05 t = 8.7 df= 389 with p = 0.000000000000000093
## Direct effect (c') of cond_end_num on selectedblackcand removing div_cond = 0.1 S.E. = 0.03 t = 3.1 df= 388 with p = 0.0021
## Indirect effect (ab) of cond_end_num on selectedblackcand through div_cond = 0.3
## Mean bootstrapped indirect effect = 0.3 with standard error = 0.03 Lower CI = 0.23 Upper CI = 0.37
## R = 0.81 R2 = 0.65 F = 362.9 on 2 and 388 DF p-value: 3.33e-112
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryChoosing the candidate who does IT work
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = selectedcand_qual ~ cond_end_num + (div_cond),
## data = introvert_pilot_clean)
##
## The DV (Y) was selectedcand_qual . The IV (X) was cond_end_num . The mediating variable(s) = div_cond .
##
## Total effect(c) of cond_end_num on selectedcand_qual = -0.35 S.E. = 0.05 t = -7.39 df= 389 with p = 0.00000000000088
## Direct effect (c') of cond_end_num on selectedcand_qual removing div_cond = -0.07 S.E. = 0.04 t = -1.88 df= 388 with p = 0.061
## Indirect effect (ab) of cond_end_num on selectedcand_qual through div_cond = -0.28
## Mean bootstrapped indirect effect = -0.28 with standard error = 0.03 Lower CI = -0.35 Upper CI = -0.22
## R = 0.74 R2 = 0.54 F = 232.2 on 2 and 388 DF p-value: 0.0000000000000000000000000000000000000000000000000000000000000000000000000000000000000305
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryQualifications
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = considerations_1 ~ cond_end_num + (div_cond),
## data = introvert_pilot_clean)
##
## The DV (Y) was considerations_1 . The IV (X) was cond_end_num . The mediating variable(s) = div_cond .
##
## Total effect(c) of cond_end_num on considerations_1 = -0.73 S.E. = 0.13 t = -5.61 df= 389 with p = 0.000000039
## Direct effect (c') of cond_end_num on considerations_1 removing div_cond = -0.26 S.E. = 0.13 t = -2.03 df= 388 with p = 0.043
## Indirect effect (ab) of cond_end_num on considerations_1 through div_cond = -0.47
## Mean bootstrapped indirect effect = -0.47 with standard error = 0.07 Lower CI = -0.62 Upper CI = -0.33
## R = 0.49 R2 = 0.24 F = 61.14 on 2 and 388 DF p-value: 0.0000000000000000000000000000000217
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summarySkills
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = considerations_2 ~ cond_end_num + (div_cond),
## data = introvert_pilot_clean)
##
## The DV (Y) was considerations_2 . The IV (X) was cond_end_num . The mediating variable(s) = div_cond .
##
## Total effect(c) of cond_end_num on considerations_2 = -0.76 S.E. = 0.14 t = -5.49 df= 389 with p = 0.000000074
## Direct effect (c') of cond_end_num on considerations_2 removing div_cond = -0.37 S.E. = 0.14 t = -2.61 df= 388 with p = 0.0093
## Indirect effect (ab) of cond_end_num on considerations_2 through div_cond = -0.39
## Mean bootstrapped indirect effect = -0.39 with standard error = 0.07 Lower CI = -0.53 Upper CI = -0.26
## R = 0.41 R2 = 0.17 F = 40.1 on 2 and 388 DF p-value: 0.000000000000000000000137
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryFit with needs
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = considerations_3 ~ cond_end_num + (div_cond),
## data = introvert_pilot_clean)
##
## The DV (Y) was considerations_3 . The IV (X) was cond_end_num . The mediating variable(s) = div_cond .
##
## Total effect(c) of cond_end_num on considerations_3 = -0.7 S.E. = 0.13 t = -5.38 df= 389 with p = 0.00000013
## Direct effect (c') of cond_end_num on considerations_3 removing div_cond = -0.37 S.E. = 0.13 t = -2.73 df= 388 with p = 0.0066
## Indirect effect (ab) of cond_end_num on considerations_3 through div_cond = -0.33
## Mean bootstrapped indirect effect = -0.33 with standard error = 0.07 Lower CI = -0.47 Upper CI = -0.21
## R = 0.39 R2 = 0.15 F = 34.64 on 2 and 388 DF p-value: 0.0000000000000000000741
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryDiversity considerations (Binary)
Choosing the Black candidate
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = selectedblackcand ~ cond_end_num + (rank_goals),
## data = introvert_pilot_clean)
##
## The DV (Y) was selectedblackcand . The IV (X) was cond_end_num . The mediating variable(s) = rank_goals .
##
## Total effect(c) of cond_end_num on selectedblackcand = 0.4 S.E. = 0.05 t = 8.7 df= 389 with p = 0.000000000000000093
## Direct effect (c') of cond_end_num on selectedblackcand removing rank_goals = 0.1 S.E. = 0.04 t = 2.39 df= 388 with p = 0.017
## Indirect effect (ab) of cond_end_num on selectedblackcand through rank_goals = 0.3
## Mean bootstrapped indirect effect = 0.3 with standard error = 0.06 Lower CI = 0.2 Upper CI = 0.43
## R = 0.7 R2 = 0.49 F = 183.2 on 2 and 388 DF p-value: 0.0000000000000000000000000000000000000000000000000000000000000000000000000539
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryChoosing the candidate who does IT work
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = selectedcand_qual ~ cond_end_num + (rank_goals),
## data = introvert_pilot_clean)
##
## The DV (Y) was selectedcand_qual . The IV (X) was cond_end_num . The mediating variable(s) = rank_goals .
##
## Total effect(c) of cond_end_num on selectedcand_qual = -0.35 S.E. = 0.05 t = -7.39 df= 389 with p = 0.00000000000088
## Direct effect (c') of cond_end_num on selectedcand_qual removing rank_goals = -0.08 S.E. = 0.05 t = -1.84 df= 388 with p = 0.066
## Indirect effect (ab) of cond_end_num on selectedcand_qual through rank_goals = -0.27
## Mean bootstrapped indirect effect = -0.27 with standard error = 0.05 Lower CI = -0.39 Upper CI = -0.17
## R = 0.61 R2 = 0.37 F = 113.9 on 2 and 388 DF p-value: 0.0000000000000000000000000000000000000000000000000000653
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryQualifications
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = considerations_1 ~ cond_end_num + (rank_goals),
## data = introvert_pilot_clean)
##
## The DV (Y) was considerations_1 . The IV (X) was cond_end_num . The mediating variable(s) = rank_goals .
##
## Total effect(c) of cond_end_num on considerations_1 = -0.73 S.E. = 0.13 t = -5.61 df= 389 with p = 0.000000039
## Direct effect (c') of cond_end_num on considerations_1 removing rank_goals = 0.01 S.E. = 0.12 t = 0.04 df= 388 with p = 0.97
## Indirect effect (ab) of cond_end_num on considerations_1 through rank_goals = -0.73
## Mean bootstrapped indirect effect = -0.74 with standard error = 0.17 Lower CI = -1.12 Upper CI = -0.45
## R = 0.58 R2 = 0.34 F = 98.89 on 2 and 388 DF p-value: 0.0000000000000000000000000000000000000000000000147
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summarySkills
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = considerations_2 ~ cond_end_num + (rank_goals),
## data = introvert_pilot_clean)
##
## The DV (Y) was considerations_2 . The IV (X) was cond_end_num . The mediating variable(s) = rank_goals .
##
## Total effect(c) of cond_end_num on considerations_2 = -0.76 S.E. = 0.14 t = -5.49 df= 389 with p = 0.000000074
## Direct effect (c') of cond_end_num on considerations_2 removing rank_goals = -0.19 S.E. = 0.14 t = -1.33 df= 388 with p = 0.18
## Indirect effect (ab) of cond_end_num on considerations_2 through rank_goals = -0.57
## Mean bootstrapped indirect effect = -0.57 with standard error = 0.16 Lower CI = -0.92 Upper CI = -0.3
## R = 0.46 R2 = 0.21 F = 51.68 on 2 and 388 DF p-value: 0.000000000000000000000000000403
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryFit with needs
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = considerations_3 ~ cond_end_num + (rank_goals),
## data = introvert_pilot_clean)
##
## The DV (Y) was considerations_3 . The IV (X) was cond_end_num . The mediating variable(s) = rank_goals .
##
## Total effect(c) of cond_end_num on considerations_3 = -0.7 S.E. = 0.13 t = -5.38 df= 389 with p = 0.00000013
## Direct effect (c') of cond_end_num on considerations_3 removing rank_goals = -0.23 S.E. = 0.14 t = -1.69 df= 388 with p = 0.093
## Indirect effect (ab) of cond_end_num on considerations_3 through rank_goals = -0.46
## Mean bootstrapped indirect effect = -0.47 with standard error = 0.14 Lower CI = -0.79 Upper CI = -0.22
## R = 0.42 R2 = 0.18 F = 41.28 on 2 and 388 DF p-value: 0.0000000000000000000000359
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryDiversity considerations (Parallel Likert + Binary)
Choosing the Black candidate
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = selectedblackcand ~ cond_end_num + (div_cond) +
## (rank_goals), data = introvert_pilot_clean)
##
## The DV (Y) was selectedblackcand . The IV (X) was cond_end_num . The mediating variable(s) = div_cond rank_goals .
##
## Total effect(c) of cond_end_num on selectedblackcand = 0.4 S.E. = 0.05 t = 8.7 df= 389 with p = 0.000000000000000093
## Direct effect (c') of cond_end_num on selectedblackcand removing div_cond rank_goals = 0.03 S.E. = 0.03 t = 0.9 df= 387 with p = 0.37
## Indirect effect (ab) of cond_end_num on selectedblackcand through div_cond rank_goals = 0.37
## Mean bootstrapped indirect effect = 0.37 with standard error = 0.04 Lower CI = 0.29 Upper CI = 0.46
## R = 0.83 R2 = 0.69 F = 292 on 3 and 387 DF p-value: 1.92e-115
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryChoosing the candidate who does IT work
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = selectedcand_qual ~ cond_end_num + (div_cond) +
## (rank_goals), data = introvert_pilot_clean)
##
## The DV (Y) was selectedcand_qual . The IV (X) was cond_end_num . The mediating variable(s) = div_cond rank_goals .
##
## Total effect(c) of cond_end_num on selectedcand_qual = -0.35 S.E. = 0.05 t = -7.39 df= 389 with p = 0.00000000000088
## Direct effect (c') of cond_end_num on selectedcand_qual removing div_cond rank_goals = -0.02 S.E. = 0.04 t = -0.43 df= 387 with p = 0.67
## Indirect effect (ab) of cond_end_num on selectedcand_qual through div_cond rank_goals = -0.34
## Mean bootstrapped indirect effect = -0.34 with standard error = 0.04 Lower CI = -0.43 Upper CI = -0.26
## R = 0.75 R2 = 0.57 F = 169.7 on 3 and 387 DF p-value: 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000932
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryQualifications
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = considerations_1 ~ cond_end_num + (div_cond) +
## (rank_goals), data = introvert_pilot_clean)
##
## The DV (Y) was considerations_1 . The IV (X) was cond_end_num . The mediating variable(s) = div_cond rank_goals .
##
## Total effect(c) of cond_end_num on considerations_1 = -0.73 S.E. = 0.13 t = -5.61 df= 389 with p = 0.000000039
## Direct effect (c') of cond_end_num on considerations_1 removing div_cond rank_goals = 0.06 S.E. = 0.12 t = 0.49 df= 387 with p = 0.62
## Indirect effect (ab) of cond_end_num on considerations_1 through div_cond rank_goals = -0.79
## Mean bootstrapped indirect effect = -0.8 with standard error = 0.16 Lower CI = -1.16 Upper CI = -0.54
## R = 0.6 R2 = 0.36 F = 71.46 on 3 and 387 DF p-value: 0.00000000000000000000000000000000000000000000276
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summarySkills
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = considerations_2 ~ cond_end_num + (div_cond) +
## (rank_goals), data = introvert_pilot_clean)
##
## The DV (Y) was considerations_2 . The IV (X) was cond_end_num . The mediating variable(s) = div_cond rank_goals .
##
## Total effect(c) of cond_end_num on considerations_2 = -0.76 S.E. = 0.14 t = -5.49 df= 389 with p = 0.000000074
## Direct effect (c') of cond_end_num on considerations_2 removing div_cond rank_goals = -0.14 S.E. = 0.14 t = -0.96 df= 387 with p = 0.34
## Indirect effect (ab) of cond_end_num on considerations_2 through div_cond rank_goals = -0.62
## Mean bootstrapped indirect effect = -0.62 with standard error = 0.14 Lower CI = -0.93 Upper CI = -0.39
## R = 0.48 R2 = 0.23 F = 37.65 on 3 and 387 DF p-value: 0.0000000000000000000000000132
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryFit with needs
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = considerations_3 ~ cond_end_num + (div_cond) +
## (rank_goals), data = introvert_pilot_clean)
##
## The DV (Y) was considerations_3 . The IV (X) was cond_end_num . The mediating variable(s) = div_cond rank_goals .
##
## Total effect(c) of cond_end_num on considerations_3 = -0.7 S.E. = 0.13 t = -5.38 df= 389 with p = 0.00000013
## Direct effect (c') of cond_end_num on considerations_3 removing div_cond rank_goals = -0.18 S.E. = 0.14 t = -1.33 df= 387 with p = 0.19
## Indirect effect (ab) of cond_end_num on considerations_3 through div_cond rank_goals = -0.51
## Mean bootstrapped indirect effect = -0.52 with standard error = 0.13 Lower CI = -0.79 Upper CI = -0.3
## R = 0.44 R2 = 0.19 F = 30.33 on 3 and 387 DF p-value: 0.000000000000000000000569
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summaryModeration
Diversity considerations
Selected Black candidate
##
## Call:
## glm(formula = selectedblackcand ~ cond_end_num * div_cond, family = "binomial",
## data = introvert_pilot_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.014 0.945 -7.42 0.00000000000012 ***
## cond_end_num -4.334 2.112 -2.05 0.04 *
## div_cond 1.340 0.194 6.91 0.00000000000486 ***
## cond_end_num:div_cond 0.939 0.404 2.32 0.02 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 533.70 on 390 degrees of freedom
## Residual deviance: 176.53 on 387 degrees of freedom
## AIC: 184.5
##
## Number of Fisher Scoring iterations: 7Qualifications
##
## Call:
## lm(formula = considerations_1 ~ cond_end_num * div_cond, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.957 -0.534 0.065 0.865 2.267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.6559 0.1685 39.50 < 0.0000000000000002 ***
## cond_end_num 0.8822 0.2846 3.10 0.00208 **
## div_cond -0.1397 0.0419 -3.33 0.00094 ***
## cond_end_num:div_cond -0.2611 0.0584 -4.47 0.00001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.14 on 387 degrees of freedom
## Multiple R-squared: 0.277, Adjusted R-squared: 0.271
## F-statistic: 49.4 on 3 and 387 DF, p-value: <0.0000000000000002Skills - Marginal
##
## Call:
## lm(formula = considerations_2 ~ cond_end_num * div_cond, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.767 -0.671 0.099 0.980 2.329
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.3765 0.1879 33.94 < 0.0000000000000002 ***
## cond_end_num 0.7403 0.3174 2.33 0.02020 *
## div_cond -0.0951 0.0467 -2.04 0.04248 *
## cond_end_num:div_cond -0.2542 0.0651 -3.90 0.00011 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.27 on 387 degrees of freedom
## Multiple R-squared: 0.203, Adjusted R-squared: 0.196
## F-statistic: 32.8 on 3 and 387 DF, p-value: <0.0000000000000002Fit with needs
##
## Call:
## lm(formula = considerations_3 ~ cond_end_num * div_cond, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.987 -0.648 0.094 0.841 2.094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.4166 0.1786 35.92 < 0.0000000000000002 ***
## cond_end_num 0.5348 0.3017 1.77 0.07713 .
## div_cond -0.0859 0.0444 -1.93 0.05388 .
## cond_end_num:div_cond -0.2062 0.0619 -3.33 0.00095 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 387 degrees of freedom
## Multiple R-squared: 0.175, Adjusted R-squared: 0.169
## F-statistic: 27.4 on 3 and 387 DF, p-value: 0.000000000000000438Workload
##
## Call:
## lm(formula = considerations_6 ~ cond_end_num * div_cond, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.267 -1.427 0.132 1.223 3.412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3082 0.2805 11.80 < 0.0000000000000002 ***
## cond_end_num 0.4820 0.4737 1.02 0.310
## div_cond 0.2798 0.0698 4.01 0.000073 ***
## cond_end_num:div_cond -0.2429 0.0972 -2.50 0.013 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.89 on 387 degrees of freedom
## Multiple R-squared: 0.0471, Adjusted R-squared: 0.0397
## F-statistic: 6.37 on 3 and 387 DF, p-value: 0.000318Rank Goals
Selected Black candidate
##
## Call:
## glm(formula = selectedblackcand ~ cond_end_num * rank_goals,
## family = "binomial", data = introvert_pilot_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.609 0.274 -5.88 0.0000000042 ***
## cond_end_num 0.727 0.439 1.66 0.09777 .
## rank_goals 3.114 0.828 3.76 0.00017 ***
## cond_end_num:rank_goals 1.619 1.351 1.20 0.23075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 267.70 on 195 degrees of freedom
## Residual deviance: 156.23 on 192 degrees of freedom
## (195 observations deleted due to missingness)
## AIC: 164.2
##
## Number of Fisher Scoring iterations: 6Qualifications
##
## Call:
## lm(formula = considerations_1 ~ cond_end_num * rank_goals, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.250 -0.437 -0.220 0.750 2.562
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.2500 0.1169 53.48 <0.0000000000000002 ***
## cond_end_num -0.0305 0.2136 -0.14 0.8867
## rank_goals -1.1591 0.3645 -3.18 0.0017 **
## cond_end_num:rank_goals -0.6229 0.4384 -1.42 0.1569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.15 on 192 degrees of freedom
## (195 observations deleted due to missingness)
## Multiple R-squared: 0.324, Adjusted R-squared: 0.313
## F-statistic: 30.6 on 3 and 192 DF, p-value: 0.000000000000000318Skills - Marginal
##
## Call:
## lm(formula = considerations_2 ~ cond_end_num * rank_goals, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.031 -0.521 -0.031 0.901 2.479
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.0313 0.1253 48.13 <0.0000000000000002 ***
## cond_end_num 0.0907 0.2291 0.40 0.693
## rank_goals -0.7585 0.3908 -1.94 0.054 .
## cond_end_num:rank_goals -0.8426 0.4700 -1.79 0.075 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.23 on 192 degrees of freedom
## (195 observations deleted due to missingness)
## Multiple R-squared: 0.229, Adjusted R-squared: 0.217
## F-statistic: 19 on 3 and 192 DF, p-value: 0.0000000000806Fit with needs
##
## Call:
## lm(formula = considerations_3 ~ cond_end_num * rank_goals, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.156 -0.156 0.188 0.844 2.188
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1563 0.1236 49.82 <0.0000000000000002 ***
## cond_end_num -0.0831 0.2259 -0.37 0.71
## rank_goals -0.6108 0.3854 -1.58 0.11
## cond_end_num:rank_goals -0.6499 0.4635 -1.40 0.16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.21 on 192 degrees of freedom
## (195 observations deleted due to missingness)
## Multiple R-squared: 0.182, Adjusted R-squared: 0.169
## F-statistic: 14.2 on 3 and 192 DF, p-value: 0.0000000203Workload
##
## Call:
## lm(formula = considerations_6 ~ cond_end_num * rank_goals, data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.818 -1.281 0.208 1.208 3.208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.281 0.196 21.80 <0.0000000000000002 ***
## cond_end_num -0.013 0.359 -0.04 0.97
## rank_goals 0.537 0.613 0.88 0.38
## cond_end_num:rank_goals -1.014 0.737 -1.38 0.17
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.92 on 192 degrees of freedom
## (195 observations deleted due to missingness)
## Multiple R-squared: 0.018, Adjusted R-squared: 0.00262
## F-statistic: 1.17 on 3 and 192 DF, p-value: 0.322Exploration
Ranking: Right personality
##
## Call:
## glm(formula = rank_personality ~ cond_end, family = "binomial",
## data = introvert_pilot_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.516 0.368 -6.84 0.0000000000077 ***
## cond_endStrong endorsement -0.840 0.693 -1.21 0.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 84.736 on 195 degrees of freedom
## Residual deviance: 83.118 on 194 degrees of freedom
## (195 observations deleted due to missingness)
## AIC: 87.12
##
## Number of Fisher Scoring iterations: 6## (Intercept) cond_endStrong endorsement
## 0.08081 0.43169Ranking: Qualified candidate
##
## Call:
## glm(formula = rank_qualified ~ cond_end, family = "binomial",
## data = introvert_pilot_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.533 0.253 6.06 0.0000000014 ***
## cond_endStrong endorsement -1.827 0.332 -5.51 0.0000000357 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 255.49 on 195 degrees of freedom
## Residual deviance: 221.56 on 194 degrees of freedom
## (195 observations deleted due to missingness)
## AIC: 225.6
##
## Number of Fisher Scoring iterations: 4## (Intercept) cond_endStrong endorsement
## 4.6316 0.1609Ranking: Fulfilling diversity goal
##
## Call:
## glm(formula = rank_goals ~ cond_end, family = "binomial", data = introvert_pilot_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.166 0.318 -6.81 0.00000000001 ***
## cond_endStrong endorsement 2.324 0.383 6.07 0.00000000127 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 239.80 on 195 degrees of freedom
## Residual deviance: 193.71 on 194 degrees of freedom
## (195 observations deleted due to missingness)
## AIC: 197.7
##
## Number of Fisher Scoring iterations: 4## (Intercept) cond_endStrong endorsement
## 0.1146 10.2173The top management team will be happy with my decision
##
## Call:
## lm(formula = considerations_1.1 ~ cond_end * selectedblackcand_text,
## data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.864 -0.864 0.136 1.082 2.493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.306 0.102 52.21 < 0.0000000000000002 ***
## cond_endStrong endorsement -0.798 0.186 -4.30 0.000022 ***
## selectedblackcand_textSelected Black candidate 0.613 0.208 2.94 0.0035 **
## cond_endStrong endorsement:selectedblackcand_textSelected Black candidate 0.744 0.285 2.61 0.0094 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.27 on 387 degrees of freedom
## Multiple R-squared: 0.129, Adjusted R-squared: 0.123
## F-statistic: 19.2 on 3 and 387 DF, p-value: 0.0000000000129
The candidate I selected will be happy about my decision
##
## Call:
## lm(formula = considerations_2.1 ~ cond_end * selectedblackcand,
## data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.186 -0.186 0.045 0.814 1.045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1146 0.0753 81.25 <0.0000000000000002 ***
## cond_endStrong endorsement -0.1594 0.1376 -1.16 0.25
## selectedblackcand 0.2527 0.1543 1.64 0.10
## cond_endStrong endorsement:selectedblackcand -0.0215 0.2112 -0.10 0.92
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.943 on 387 degrees of freedom
## Multiple R-squared: 0.0148, Adjusted R-squared: 0.00718
## F-statistic: 1.94 on 3 and 387 DF, p-value: 0.123This decision was difficult to make
##
## Call:
## lm(formula = considerations_3.1 ~ cond_end * selectedblackcand,
## data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.955 -1.803 0.197 1.483 3.878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.803 0.154 24.73 <0.0000000000000002 ***
## cond_endStrong endorsement 0.153 0.281 0.54 0.587
## selectedblackcand -0.680 0.315 -2.16 0.032 *
## cond_endStrong endorsement:selectedblackcand 0.242 0.432 0.56 0.576
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.93 on 387 degrees of freedom
## Multiple R-squared: 0.0175, Adjusted R-squared: 0.00989
## F-statistic: 2.3 on 3 and 387 DF, p-value: 0.077I feel good about the decision I made
##
## Call:
## lm(formula = considerations_4.1 ~ cond_end * selectedblackcand,
## data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.650 -0.650 0.350 0.873 1.873
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.650 0.110 51.47 <0.0000000000000002 ***
## cond_endStrong endorsement -0.187 0.201 -0.93 0.3521
## selectedblackcand 0.575 0.225 2.55 0.0110 *
## cond_endStrong endorsement:selectedblackcand -0.910 0.308 -2.96 0.0033 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.38 on 387 degrees of freedom
## Multiple R-squared: 0.0583, Adjusted R-squared: 0.051
## F-statistic: 7.99 on 3 and 387 DF, p-value: 0.0000352
I feel guilty about the decision I made
##
## Call:
## lm(formula = considerations_5.1 ~ cond_end * selectedblackcand,
## data = introvert_pilot_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.712 -1.274 -0.653 1.288 4.726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.274 0.134 17.01 <0.0000000000000002 ***
## cond_endStrong endorsement 0.368 0.244 1.51 0.133
## selectedblackcand -0.621 0.274 -2.26 0.024 *
## cond_endStrong endorsement:selectedblackcand 0.691 0.375 1.84 0.066 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.68 on 387 degrees of freedom
## Multiple R-squared: 0.04, Adjusted R-squared: 0.0325
## F-statistic: 5.37 on 3 and 387 DF, p-value: 0.00125