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
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
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
Social Mobility