Study 3 - PEF+SDO

Merrick’s Additional Analyses

Exploring the effect of condition and SDO on attraction

Specifying SDO_E as the IV

HE careers

HA careers

Specifying SDO_D as the IV

HE careers

HA careers

Demographics - Gender

## 
##                Man                 NB     PreferNotToSay SomethingNotListed              Woman 
##                373                 12                  5                  1                392

Demographics - Race

## 
##       Black   EastAsian      Latino     MidEast   MonoWhite Multiracial      Native   NotListed     SEAsian  SouthAsian 
##          90          23          55           2         496          78           5           9          15          10

HE-E

##  contrast                estimate    SE  df t.ratio p.value
##  Black - EastAsian         0.5509 0.330 732   1.671  0.4528
##  Black - Latino            0.3051 0.233 732   1.312  0.6837
##  Black - MonoWhite         0.6113 0.156 732   3.928  0.0009
##  Black - Multiracial       0.8395 0.211 732   3.976  0.0007
##  EastAsian - Latino       -0.2458 0.349 732  -0.704  0.9555
##  EastAsian - MonoWhite     0.0604 0.303 732   0.199  0.9996
##  EastAsian - Multiracial   0.2887 0.335 732   0.861  0.9108
##  Latino - MonoWhite        0.3062 0.193 732   1.587  0.5064
##  Latino - Multiracial      0.5345 0.240 732   2.227  0.1709
##  MonoWhite - Multiracial   0.2282 0.167 732   1.369  0.6475
## 
## P value adjustment: tukey method for comparing a family of 5 estimates

HE-A

##  contrast                estimate    SE  df t.ratio p.value
##  Black - EastAsian         0.3169 0.314 732   1.010  0.8508
##  Black - Latino            0.1656 0.221 732   0.749  0.9449
##  Black - MonoWhite         0.3601 0.148 732   2.432  0.1079
##  Black - Multiracial       0.2066 0.201 732   1.028  0.8422
##  EastAsian - Latino       -0.1513 0.332 732  -0.456  0.9911
##  EastAsian - MonoWhite     0.0433 0.288 732   0.150  0.9999
##  EastAsian - Multiracial  -0.1103 0.319 732  -0.346  0.9969
##  Latino - MonoWhite        0.1946 0.184 732   1.060  0.8270
##  Latino - Multiracial      0.0410 0.228 732   0.180  0.9998
##  MonoWhite - Multiracial  -0.1536 0.159 732  -0.968  0.8694
## 
## P value adjustment: tukey method for comparing a family of 5 estimates

SDO-D

##  contrast                estimate    SE  df t.ratio p.value
##  Black - EastAsian        -0.0912 0.262 732  -0.349  0.9968
##  Black - Latino            0.0187 0.184 732   0.101  1.0000
##  Black - MonoWhite        -0.3077 0.123 732  -2.493  0.0933
##  Black - Multiracial      -0.2215 0.167 732  -1.322  0.6773
##  EastAsian - Latino        0.1098 0.277 732   0.397  0.9948
##  EastAsian - MonoWhite    -0.2165 0.240 732  -0.900  0.8967
##  EastAsian - Multiracial  -0.1303 0.266 732  -0.490  0.9883
##  Latino - MonoWhite       -0.3263 0.153 732  -2.132  0.2076
##  Latino - Multiracial     -0.2401 0.190 732  -1.261  0.7150
##  MonoWhite - Multiracial   0.0862 0.132 732   0.652  0.9662
## 
## P value adjustment: tukey method for comparing a family of 5 estimates

Doms

##  contrast                estimate    SE  df t.ratio p.value
##  Black - EastAsian         2.8887 0.469 732   6.155  <.0001
##  Black - Latino            0.1317 0.331 732   0.398  0.9947
##  Black - MonoWhite         0.3258 0.222 732   1.471  0.5818
##  Black - Multiracial       0.1740 0.301 732   0.579  0.9782
##  EastAsian - Latino       -2.7569 0.497 732  -5.551  <.0001
##  EastAsian - MonoWhite    -2.5628 0.432 732  -5.939  <.0001
##  EastAsian - Multiracial  -2.7147 0.477 732  -5.692  <.0001
##  Latino - MonoWhite        0.1941 0.275 732   0.707  0.9550
##  Latino - Multiracial      0.0422 0.342 732   0.124  0.9999
##  MonoWhite - Multiracial  -0.1519 0.237 732  -0.640  0.9684
## 
## P value adjustment: tukey method for comparing a family of 5 estimates

Social Mobility

##  contrast                estimate    SE  df t.ratio p.value
##  Black - EastAsian         0.2768 0.257 732   1.077  0.8184
##  Black - Latino            0.1210 0.181 732   0.668  0.9632
##  Black - MonoWhite         0.3903 0.121 732   3.217  0.0118
##  Black - Multiracial       0.3196 0.165 732   1.942  0.2960
##  EastAsian - Latino       -0.1558 0.272 732  -0.573  0.9790
##  EastAsian - MonoWhite     0.1135 0.236 732   0.480  0.9891
##  EastAsian - Multiracial   0.0428 0.261 732   0.164  0.9998
##  Latino - MonoWhite        0.2693 0.150 732   1.790  0.3801
##  Latino - Multiracial      0.1986 0.187 732   1.062  0.8261
##  MonoWhite - Multiracial  -0.0706 0.130 732  -0.544  0.9827
## 
## P value adjustment: tukey method for comparing a family of 5 estimates

ZeroSum

##  contrast                estimate    SE  df t.ratio p.value
##  Black - EastAsian         0.1435 0.322 732   0.446  0.9918
##  Black - Latino            0.0700 0.227 732   0.309  0.9980
##  Black - MonoWhite         0.3627 0.152 732   2.390  0.1192
##  Black - Multiracial      -0.0116 0.206 732  -0.056  1.0000
##  EastAsian - Latino       -0.0735 0.340 732  -0.216  0.9995
##  EastAsian - MonoWhite     0.2192 0.296 732   0.741  0.9467
##  EastAsian - Multiracial  -0.1551 0.327 732  -0.475  0.9896
##  Latino - MonoWhite        0.2927 0.188 732   1.555  0.5269
##  Latino - Multiracial     -0.0817 0.234 732  -0.349  0.9968
##  MonoWhite - Multiracial  -0.3743 0.163 732  -2.303  0.1451
## 
## P value adjustment: tukey method for comparing a family of 5 estimates

Just World

##  contrast                estimate    SE  df t.ratio p.value
##  Black - EastAsian         0.0601 0.250 732   0.241  0.9993
##  Black - Latino            0.2897 0.176 732   1.646  0.4687
##  Black - MonoWhite         0.2419 0.118 732   2.052  0.2424
##  Black - Multiracial       0.4658 0.160 732   2.913  0.0302
##  EastAsian - Latino        0.2297 0.264 732   0.869  0.9082
##  EastAsian - MonoWhite     0.1818 0.230 732   0.792  0.9330
##  EastAsian - Multiracial   0.4058 0.254 732   1.599  0.4986
##  Latino - MonoWhite       -0.0479 0.146 732  -0.327  0.9975
##  Latino - Multiracial      0.1761 0.182 732   0.969  0.8692
##  MonoWhite - Multiracial   0.2240 0.126 732   1.774  0.3896
## 
## P value adjustment: tukey method for comparing a family of 5 estimates

Check variance of the attractiveness for each job

The condition significantly predicts differences in attractiveness of hierarchy-enhancing and hierarchy-attenuating roles. We want to make sure these significant results are not driven by the variance of a significant occupation

Variability 1

Accountant Business Exec Community health worker Corrections Officer Criminal Lawyer FBI agent

## Variability 2

Criminal prosecutor Human Rights Police Officer Public Defender Social Workers Special Ed

Inequality measures

Verify participants see hierarchy enhancing- and attenuating- as unequal (careerHE_HA in qualtrics)

Variability 1

Accountant Business Exec Community health worker Corrections Officer Criminal Lawyer FBI agent

### Variability 2

Criminal prosecutor Human Rights Police Officer Public Defender Social Workers Special Ed

Daniel’s summary

The higher participants are on dominance (and the lower they are on prestige and status motives), the more attractive they will find HE careers and the less attractive they will find HA careers.

Significant

Measure Hierarchy - Enhancing Hierarchy - Attenuating
Dominance Yes - Positive No
Prestige Yes - Positive No
Zero sum beliefs Marginal - Positive No
Social mobility Yes - Positive Significant - Negative
Just world beliefs Yes - Positive Yes - Positive
Status motives Yes - Positive No
Political Affiliation No Yes - Negative

Independent variables

Dominance

DV: Hierarchy Enhancing motives

## 
## Call:
## lm(formula = he_e ~ dom + condition, data = clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.228  -0.789   0.028   0.906   3.394 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             3.4278     0.0904   37.91 < 0.0000000000000002 ***
## dom                     0.0889     0.0248    3.58              0.00036 ***
## conditioncareer title   0.3103     0.0972    3.19              0.00147 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.36 on 780 degrees of freedom
## Multiple R-squared:  0.0298, Adjusted R-squared:  0.0273 
## F-statistic:   12 on 2 and 780 DF,  p-value: 0.00000745

DV: Hierarchy Attenuating motives

## 
## Call:
## lm(formula = he_a ~ dom + condition, data = clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.766 -0.764  0.174  0.944  2.714 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             4.8167     0.0898   53.62 < 0.0000000000000002 ***
## dom                    -0.0270     0.0247   -1.10                 0.27    
## conditioncareer title  -0.4195     0.0965   -4.35             0.000016 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.35 on 780 degrees of freedom
## Multiple R-squared:  0.0256, Adjusted R-squared:  0.0231 
## F-statistic: 10.2 on 2 and 780 DF,  p-value: 0.0000404

Prestige

DV: Hierarchy Enhancing motives

## 
## Call:
## lm(formula = he_e ~ prest + condition, data = clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.340  -0.826   0.008   0.922   3.396 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             3.1342     0.1530   20.48 < 0.0000000000000002 ***
## prest                   0.1243     0.0337    3.69              0.00024 ***
## conditioncareer title   0.3073     0.0972    3.16              0.00163 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.36 on 780 degrees of freedom
## Multiple R-squared:  0.0308, Adjusted R-squared:  0.0283 
## F-statistic: 12.4 on 2 and 780 DF,  p-value: 0.00000512

DV: Hierarchy Attenuating motives

## 
## Call:
## lm(formula = he_a ~ prest + condition, data = clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.800 -0.779  0.176  0.966  2.670 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             4.6340     0.1521   30.47 < 0.0000000000000002 ***
## prest                   0.0293     0.0335    0.87                 0.38    
## conditioncareer title  -0.4277     0.0966   -4.43             0.000011 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.35 on 780 degrees of freedom
## Multiple R-squared:  0.0251, Adjusted R-squared:  0.0226 
## F-statistic:   10 on 2 and 780 DF,  p-value: 0.0000502

Zerso sum beliefs

DV: Hierarchy Enhancing motives

## 
## Call:
## lm(formula = he_e ~ zerosum + condition, data = clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.290  -0.796  -0.001   0.935   3.392 
## 
## Coefficients:
##                       Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             3.4508     0.1290   26.74 <0.0000000000000002 ***
## zerosum                 0.0627     0.0365    1.72              0.0858 .  
## conditioncareer title   0.3152     0.0979    3.22              0.0013 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.37 on 780 degrees of freedom
## Multiple R-squared:  0.0176, Adjusted R-squared:  0.0151 
## F-statistic: 6.98 on 2 and 780 DF,  p-value: 0.000986

DV: Hierarchy Attenuating motives

## 
## Call:
## lm(formula = he_a ~ zerosum + condition, data = clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.807 -0.787  0.168  0.914  2.700 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             4.6768     0.1274   36.70 < 0.0000000000000002 ***
## zerosum                 0.0254     0.0360    0.71                 0.48    
## conditioncareer title  -0.4274     0.0966   -4.42             0.000011 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.35 on 780 degrees of freedom
## Multiple R-squared:  0.0247, Adjusted R-squared:  0.0222 
## F-statistic: 9.89 on 2 and 780 DF,  p-value: 0.0000574

Social mobility

DV: Hierarchy Enhancing motives

## 
## Call:
## lm(formula = he_e ~ socialmobility + condition, data = clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -16.869  -0.793   0.067   0.861   3.198 
## 
## Coefficients:
##                       Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)             2.4187     0.1518   15.93 <0.0000000000000002 ***
## socialmobility          0.3690     0.0414    8.92 <0.0000000000000002 ***
## conditioncareer title   0.3643     0.0934    3.90              0.0001 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.3 on 780 degrees of freedom
## Multiple R-squared:  0.105,  Adjusted R-squared:  0.103 
## F-statistic: 45.8 on 2 and 780 DF,  p-value: <0.0000000000000002

DV: Hierarchy Attenuating motives

## 
## Call:
## lm(formula = he_a ~ socialmobility + condition, data = clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.880 -0.802  0.164  0.976  2.787 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             5.0472     0.1567   32.21 < 0.0000000000000002 ***
## socialmobility         -0.0891     0.0427   -2.09                0.037 *  
## conditioncareer title  -0.4334     0.0964   -4.50            0.0000079 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.35 on 780 degrees of freedom
## Multiple R-squared:  0.0295, Adjusted R-squared:  0.027 
## F-statistic: 11.9 on 2 and 780 DF,  p-value: 0.00000839

Just world beliefs

DV: Hierarchy Enhancing motives

## 
## Call:
## lm(formula = he_e ~ justworld + condition, data = clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.128  -0.679   0.048   0.783   2.811 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             1.7711     0.1487   11.91 < 0.0000000000000002 ***
## justworld               0.5633     0.0408   13.82 < 0.0000000000000002 ***
## conditioncareer title   0.3965     0.0879    4.51            0.0000074 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.23 on 780 degrees of freedom
## Multiple R-squared:  0.208,  Adjusted R-squared:  0.206 
## F-statistic:  102 on 2 and 780 DF,  p-value: <0.0000000000000002

DV: Hierarchy Attenuating motives

## 
## Call:
## lm(formula = he_a ~ justworld + condition, data = clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.981 -0.755  0.158  0.935  2.800 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             4.4717     0.1632   27.40 < 0.0000000000000002 ***
## justworld               0.0848     0.0447    1.90                0.058 .  
## conditioncareer title  -0.4128     0.0965   -4.28             0.000021 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.35 on 780 degrees of freedom
## Multiple R-squared:  0.0286, Adjusted R-squared:  0.0261 
## F-statistic: 11.5 on 2 and 780 DF,  p-value: 0.0000122

Status motives

DV: Hierarchy Enhancing motives

## 
## Call:
## lm(formula = he_e ~ statusmotives + condition, data = clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.168  -0.833   0.013   0.899   3.807 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             3.0603     0.1302   23.50 < 0.0000000000000002 ***
## statusmotives           0.1324     0.0254    5.21           0.00000025 ***
## conditioncareer title   0.3103     0.0963    3.22               0.0013 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.35 on 780 degrees of freedom
## Multiple R-squared:  0.047,  Adjusted R-squared:  0.0446 
## F-statistic: 19.2 on 2 and 780 DF,  p-value: 0.00000000703

DV: Hierarchy Attenuating motives

## 
## Call:
## lm(formula = he_a ~ statusmotives + condition, data = clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.850 -0.788  0.164  0.949  2.664 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             4.5924     0.1304   35.21 < 0.0000000000000002 ***
## statusmotives           0.0368     0.0255    1.44                 0.15    
## conditioncareer title  -0.4276     0.0964   -4.43             0.000011 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.35 on 780 degrees of freedom
## Multiple R-squared:  0.0267, Adjusted R-squared:  0.0242 
## F-statistic: 10.7 on 2 and 780 DF,  p-value: 0.000026

Political party

1 ~ “Democrat” (Reference condition)
2 ~ “Republican”
3 ~ “Libertarian”
4 ~ “Green Party”
5 ~ “Not listed here”

DV: Hierarchy Enhancing motives

## 
## Call:
## lm(formula = he_e ~ polParty + condition, data = clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.189  -0.818  -0.022   0.919   3.120 
## 
## Coefficients:
##                         Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)               3.5222     0.0814   43.28 < 0.0000000000000002 ***
## polPartyGreen Party      -0.7288     0.5522   -1.32              0.18733    
## polPartyLibertarian       0.2164     0.2425    0.89              0.37247    
## polPartyNot listed here  -0.2653     0.1436   -1.85              0.06512 .  
## polPartyRepublican        0.5432     0.1166    4.66            0.0000037 ***
## conditioncareer title     0.3576     0.0963    3.71              0.00022 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.34 on 777 degrees of freedom
## Multiple R-squared:  0.0549, Adjusted R-squared:  0.0488 
## F-statistic: 9.02 on 5 and 777 DF,  p-value: 0.0000000239

DV: Hierarchy Attenuating motives

## 
## Call:
## lm(formula = he_a ~ polParty + condition, data = clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.034 -0.834  0.089  0.946  2.849 
## 
## Coefficients:
##                         Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)               5.0339     0.0794   63.36 < 0.0000000000000002 ***
## polPartyGreen Party       0.6497     0.5391    1.21              0.22850    
## polPartyLibertarian      -0.8824     0.2367   -3.73              0.00021 ***
## polPartyNot listed here  -0.4952     0.1402   -3.53              0.00044 ***
## polPartyRepublican       -0.6709     0.1138   -5.89         0.0000000056 ***
## conditioncareer title    -0.4561     0.0941   -4.85         0.0000015029 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.31 on 777 degrees of freedom
## Multiple R-squared:  0.083,  Adjusted R-squared:  0.0771 
## F-statistic: 14.1 on 5 and 777 DF,  p-value: 0.000000000000351

Merrick’s Analyses

These analyses just verify the influence our manipulation had on our measures.

As SDO increases, participants will find HE careers more attractive and HA careers as less attractive. We don’t expect there to be moderation by condition

HE Careers

SDO_E

## 
## Call:
## lm(formula = he_e ~ sdo_e * condition, data = clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.085  -0.789   0.039   0.852   3.711 
## 
## Coefficients:
##                             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)                   3.0298     0.1363   22.24 < 0.0000000000000002 ***
## sdo_e                         0.2590     0.0503    5.15           0.00000033 ***
## conditioncareer title         0.6671     0.1810    3.69              0.00024 ***
## sdo_e:conditioncareer title  -0.1442     0.0658   -2.19              0.02878 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.34 on 779 degrees of freedom
## Multiple R-squared:  0.0549, Adjusted R-squared:  0.0513 
## F-statistic: 15.1 on 3 and 779 DF,  p-value: 0.00000000148
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of sdo_e when condition = career title: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.11   0.04     2.71   0.01
## 
## Slope of sdo_e when condition = career description: 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.26   0.05     5.15   0.00

SDO_D

## 
## Call:
## lm(formula = he_e ~ sdo_d * condition, data = clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.297  -0.772   0.047   0.882   3.579 
## 
## Coefficients:
##                             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)                   3.2821     0.1327   24.73 <0.0000000000000002 ***
## sdo_d                         0.1391     0.0444    3.13              0.0018 ** 
## conditioncareer title         0.3833     0.1851    2.07              0.0387 *  
## sdo_d:conditioncareer title  -0.0196     0.0626   -0.31              0.7542    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.36 on 779 degrees of freedom
## Multiple R-squared:  0.0351, Adjusted R-squared:  0.0314 
## F-statistic: 9.46 on 3 and 779 DF,  p-value: 0.00000384

SDO

## 
## Call:
## lm(formula = he_e ~ sdo * condition, data = clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.206  -0.770   0.049   0.858   3.678 
## 
## Coefficients:
##                           Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)                 3.1046     0.1406   22.08 < 0.0000000000000002 ***
## sdo                         0.2175     0.0501    4.34             0.000016 ***
## conditioncareer title       0.5221     0.1928    2.71               0.0069 ** 
## sdo:conditioncareer title  -0.0774     0.0689   -1.12               0.2611    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.35 on 779 degrees of freedom
## Multiple R-squared:  0.0476, Adjusted R-squared:  0.044 
## F-statistic:   13 on 3 and 779 DF,  p-value: 0.0000000277

HA Careers

SDO_E

## 
## Call:
## lm(formula = he_a ~ sdo_e * condition, data = clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.102 -0.772  0.158  0.882  3.109 
## 
## Coefficients:
##                             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)                   5.5958     0.1305   42.88 < 0.0000000000000002 ***
## sdo_e                        -0.3590     0.0482   -7.45     0.00000000000025 ***
## conditioncareer title        -0.7800     0.1733   -4.50     0.00000783339111 ***
## sdo_e:conditioncareer title   0.1483     0.0630    2.35                0.019 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.29 on 779 degrees of freedom
## Multiple R-squared:  0.117,  Adjusted R-squared:  0.114 
## F-statistic: 34.6 on 3 and 779 DF,  p-value: <0.0000000000000002
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of sdo_e when condition = career title: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.21   0.04    -5.19   0.00
## 
## Slope of sdo_e when condition = career description: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.36   0.05    -7.45   0.00

SDO_D

## 
## Call:
## lm(formula = he_a ~ sdo_d * condition, data = clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.788 -0.748  0.117  0.868  3.195 
## 
## Coefficients:
##                             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)                   5.7952     0.1217   47.63 < 0.0000000000000002 ***
## sdo_d                        -0.4075     0.0407  -10.01 < 0.0000000000000002 ***
## conditioncareer title        -0.8171     0.1697   -4.82            0.0000018 ***
## sdo_d:conditioncareer title   0.1461     0.0573    2.55                0.011 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.24 on 779 degrees of freedom
## Multiple R-squared:  0.175,  Adjusted R-squared:  0.171 
## F-statistic: 54.9 on 3 and 779 DF,  p-value: <0.0000000000000002
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of sdo_d when condition = career title: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.26   0.04    -6.47   0.00
## 
## Slope of sdo_d when condition = career description: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.41   0.04   -10.01   0.00

SDO

## 
## Call:
## lm(formula = he_a ~ sdo * condition, data = clean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.792 -0.756  0.111  0.884  3.258 
## 
## Coefficients:
##                           Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)                 5.8319     0.1306   44.67 < 0.0000000000000002 ***
## sdo                        -0.4399     0.0465   -9.45 < 0.0000000000000002 ***
## conditioncareer title      -0.8287     0.1790   -4.63            0.0000043 ***
## sdo:conditioncareer title   0.1586     0.0639    2.48                0.013 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.25 on 779 degrees of freedom
## Multiple R-squared:  0.164,  Adjusted R-squared:  0.161 
## F-statistic:   51 on 3 and 779 DF,  p-value: <0.0000000000000002
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of sdo when condition = career title: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.28   0.04    -6.42   0.00
## 
## Slope of sdo when condition = career description: 
## 
##    Est.   S.E.   t val.      p
## ------- ------ -------- ------
##   -0.44   0.05    -9.45   0.00

Do any of our variables moderate WITH the manipulation on HE or HA

Hierarchy Enhancing as DV

Hierarchy Attenuating as DV