Measure information
| Scale Abbrev | Scale Name |
|---|---|
| lmx_aff | Leader member exchange - affect subscale |
| lmx_loy | Leader member exchange - loyalty subscale |
| lmx_resp | Leader member exchange - respect subscale |
| perceptions_1 | confronter wanted to protect women’s well being |
| perceptions_2 | confronter was moral |
| perceptions_3 | confronter wanted to right a wrong |
| perceptions_4 | confronter wanted to teach perp lesson |
| perceptions_5 | confronter wanted to send perp a message |
| perceptions_6 | confronter was angry with perp |
| perceptions_7 | confronter was disgusted w/ perp |
| perceptions_10 | confronter was comfortable taking a risk to protect women |
| perceptions_23 | confronter was competent |
| comp_1 | competent |
| comp_2 | confident |
| comp_3 | independent |
| warm_1 | warm |
| warm_2 | trustworthy |
| warm_3 | tolerant |
| warm_4 | sincere |
| warm_5 | assertive |
| warm_6 | dominant |
| warm_7 | compassionate |
| warm_8 | caring |
| participant_gender | participant gender |
| sentiment_1 | sentiment |
| ladder_confronter | confronter_power |
| ladder_perpetrator | perpetrator_power |
| ladder_participant | participant_power |
| pwr_confronter_perp | confronter power-perpetrator power |
Responses to Amy’s comments
Graphs of Significant Three Way Interactions
Perceptions 1: Confronter wanted to protect women’s well-being
MOD: Perceptions 4: Confronter wanted to teach perp a lesson
Click Here for Simple Slopes
MOD: Perceptions 10: Confronter was comfortable taking a risk to protect women
Click Here for Simple Slopes
Perceptions 2: Confronter was moral
MOD: Perceptions 4: Confronter wanted to teach perp a lesson
Click Here for Simple Slopes
MOD: Perceptions 10: Confronter was comfortable taking a risk to protect women
Click Here for Simple Slopes
DV: LMX Aff: LMX Affect
MOD: Perceptions 4: Confronter wanted to teach perp a lesson
Click Here for Simple Slopes
MOD: Perceptions 10: Confronter was comfortable taking a risk to protect women
Click Here for Simple Slopes
MOD: Warm 1: Confronter was warm
Click Here for Simple Slopes
MOD: Warm 2: Confronter was trustworthy
Click Here for Simple Slopes
MOD: Warm 7: Confronter was compassionate
Click Here for Simple Slopes
LMX Resp: LMX Respect
LMX Loy: LMX Loyalty
MOD: Warm 3: Confronter was disgusted with perp
Click Here for Simple Slopes
Participant Information
Amount that could recall in each condition
## , , recall = No
##
## cond2
## cond rude sexist
## man 23 30
## woman 24 23
##
## , , recall = Yes
##
## cond2
## cond rude sexist
## man 44 20
## woman 27 38Total retained in each condition after filtering out those who failed attn check and couldn’t recall specified situation
##
## rude sexist
## man 39 14
## woman 22 33Analyses
Main Effects - Just Man vs. Woman
Main Effects - Just Rudeness
Main Effects - Both Rudeness vs. Sexism in the same regression
Controls
I controlled for:
- Age.
- Participant Gender.
- Extant sentiment towards confronter.
- Power of participant in org.
- Power of confronter in org.
- Power of perpetrator in org.
## $`as dv: prot. women\nwell-being.`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.700 -1.088 0.154 1.329 3.242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.36307 0.91652 3.67 0.00040 ***
## condwoman 0.05177 0.38307 0.14 0.89279
## cond2sexist 0.77158 0.39029 1.98 0.05095 .
## Age -0.00394 0.01380 -0.29 0.77586
## participant_genderFemale 1.39175 0.36320 3.83 0.00023 ***
## participant_genderOther 0.68810 1.33345 0.52 0.60703
## sentiment_1 0.44050 0.13184 3.34 0.00119 **
## ladder_participant -0.12303 0.11946 -1.03 0.30570
## ladder_confronter 0.00755 0.10713 0.07 0.94395
## ladder_perpetrator 0.10431 0.08190 1.27 0.20592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.8 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.313, Adjusted R-squared: 0.248
## F-statistic: 4.81 on 9 and 95 DF, p-value: 2.7e-05
##
##
## $`as dv: was moral`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.525 -0.809 0.045 0.865 2.829
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5694 0.7094 6.44 4.9e-09 ***
## condwoman -0.0769 0.2965 -0.26 0.7961
## cond2sexist 0.4531 0.3021 1.50 0.1370
## Age -0.0057 0.0107 -0.53 0.5945
## participant_genderFemale 0.8511 0.2811 3.03 0.0032 **
## participant_genderOther 0.0144 1.0321 0.01 0.9889
## sentiment_1 0.4367 0.1021 4.28 4.5e-05 ***
## ladder_participant -0.0870 0.0925 -0.94 0.3489
## ladder_confronter -0.0966 0.0829 -1.16 0.2472
## ladder_perpetrator 0.1189 0.0634 1.88 0.0639 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.327, Adjusted R-squared: 0.264
## F-statistic: 5.14 on 9 and 95 DF, p-value: 1.16e-05
##
##
## $`as dv: wntd 2\nright a wrong`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.694 -0.914 0.307 1.120 2.559
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.83266 0.84211 5.74 1.1e-07 ***
## condwoman -0.30379 0.35198 -0.86 0.39026
## cond2sexist -0.06923 0.35860 -0.19 0.84733
## Age -0.00316 0.01268 -0.25 0.80395
## participant_genderFemale 0.54708 0.33371 1.64 0.10445
## participant_genderOther 0.44941 1.22520 0.37 0.71458
## sentiment_1 0.41605 0.12114 3.43 0.00088 ***
## ladder_participant -0.28898 0.10976 -2.63 0.00989 **
## ladder_confronter 0.05360 0.09844 0.54 0.58737
## ladder_perpetrator 0.19034 0.07525 2.53 0.01308 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.6 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.248, Adjusted R-squared: 0.176
## F-statistic: 3.47 on 9 and 95 DF, p-value: 0.000955
##
##
## $`as dv: teach perp.\nlesson`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.126 -0.893 0.493 1.092 3.045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.83843 0.85522 5.66 1.6e-07 ***
## condwoman -0.59972 0.35745 -1.68 0.0967 .
## cond2sexist -0.28220 0.36418 -0.77 0.4403
## Age 0.00502 0.01287 0.39 0.6977
## participant_genderFemale -0.02709 0.33891 -0.08 0.9365
## participant_genderOther 0.94344 1.24427 0.76 0.4502
## sentiment_1 0.35684 0.12302 2.90 0.0046 **
## ladder_participant -0.10886 0.11147 -0.98 0.3313
## ladder_confronter -0.05953 0.09997 -0.60 0.5529
## ladder_perpetrator 0.18045 0.07643 2.36 0.0203 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.7 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.184, Adjusted R-squared: 0.106
## F-statistic: 2.37 on 9 and 95 DF, p-value: 0.0181
##
##
## $`as dv: send message`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.019 -0.528 0.249 0.952 2.181
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.58373 0.74741 7.47 3.9e-11 ***
## condwoman -0.48274 0.31239 -1.55 0.13
## cond2sexist -0.18813 0.31828 -0.59 0.56
## Age -0.00709 0.01125 -0.63 0.53
## participant_genderFemale 0.39379 0.29618 1.33 0.19
## participant_genderOther 0.37092 1.08741 0.34 0.73
## sentiment_1 0.09988 0.10752 0.93 0.36
## ladder_participant -0.15303 0.09742 -1.57 0.12
## ladder_confronter 0.12865 0.08737 1.47 0.14
## ladder_perpetrator 0.10328 0.06679 1.55 0.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.122, Adjusted R-squared: 0.0388
## F-statistic: 1.47 on 9 and 95 DF, p-value: 0.172
##
##
## $`as dv: angry\nwith perp.`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.253 -0.587 0.226 1.001 2.016
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.97746 0.77041 6.46 4.4e-09 ***
## condwoman 0.01923 0.32201 0.06 0.952
## cond2sexist -0.49744 0.32807 -1.52 0.133
## Age -0.00576 0.01160 -0.50 0.621
## participant_genderFemale 0.15035 0.30530 0.49 0.624
## participant_genderOther 0.57646 1.12088 0.51 0.608
## sentiment_1 0.23026 0.11082 2.08 0.040 *
## ladder_participant -0.09115 0.10042 -0.91 0.366
## ladder_confronter 0.06361 0.09005 0.71 0.482
## ladder_perpetrator 0.13475 0.06885 1.96 0.053 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.128, Adjusted R-squared: 0.0453
## F-statistic: 1.55 on 9 and 95 DF, p-value: 0.142
##
##
## $`as dv: disgusted\nwith perp.`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.994 -0.980 0.331 1.134 2.158
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.85760 0.76427 6.36 7.2e-09 ***
## condwoman 0.10929 0.31944 0.34 0.733
## cond2sexist -0.36614 0.32546 -1.13 0.263
## Age -0.00708 0.01150 -0.62 0.539
## participant_genderFemale 0.45209 0.30286 1.49 0.139
## participant_genderOther 0.67064 1.11194 0.60 0.548
## sentiment_1 0.19252 0.10994 1.75 0.083 .
## ladder_participant -0.12578 0.09962 -1.26 0.210
## ladder_confronter 0.10668 0.08934 1.19 0.235
## ladder_perpetrator 0.12081 0.06830 1.77 0.080 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.135, Adjusted R-squared: 0.0525
## F-statistic: 1.64 on 9 and 95 DF, p-value: 0.115
##
##
## $`as dv: ok\nw risk`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.412 -0.848 0.209 1.137 3.101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.129766 0.849161 4.86 4.6e-06 ***
## condwoman -0.848133 0.354922 -2.39 0.0188 *
## cond2sexist 0.500287 0.361605 1.38 0.1697
## Age 0.000469 0.012782 0.04 0.9708
## participant_genderFemale 1.089598 0.336506 3.24 0.0017 **
## participant_genderOther 0.948283 1.235452 0.77 0.4447
## sentiment_1 0.293881 0.122152 2.41 0.0181 *
## ladder_participant -0.235122 0.110682 -2.12 0.0362 *
## ladder_confronter 0.088648 0.099260 0.89 0.3741
## ladder_perpetrator 0.171691 0.075884 2.26 0.0259 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.7 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.272, Adjusted R-squared: 0.203
## F-statistic: 3.94 on 9 and 95 DF, p-value: 0.000274
##
##
## $`as dv: comp.`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.241 -1.022 0.071 0.831 2.960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.35916 0.67884 6.42 5.3e-09 ***
## condwoman -0.34032 0.28373 -1.20 0.2333
## cond2sexist 0.21853 0.28907 0.76 0.4515
## Age -0.00528 0.01022 -0.52 0.6063
## participant_genderFemale 0.81527 0.26901 3.03 0.0031 **
## participant_genderOther 0.29310 0.98764 0.30 0.7673
## sentiment_1 0.57582 0.09765 5.90 5.7e-08 ***
## ladder_participant -0.13129 0.08848 -1.48 0.1412
## ladder_confronter 0.02519 0.07935 0.32 0.7516
## ladder_perpetrator 0.14192 0.06066 2.34 0.0214 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.404, Adjusted R-squared: 0.347
## F-statistic: 7.14 on 9 and 95 DF, p-value: 7.62e-08
##
##
## $`as dv: competent`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7794 -0.8695 0.0508 0.7673 2.9868
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.59238 0.62987 7.29 9.1e-11 ***
## condwoman -0.67906 0.26327 -2.58 0.01143 *
## cond2sexist 0.53047 0.26822 1.98 0.05086 .
## Age 0.00183 0.00948 0.19 0.84754
## participant_genderFemale 0.97796 0.24961 3.92 0.00017 ***
## participant_genderOther 0.32485 0.91640 0.35 0.72376
## sentiment_1 0.50854 0.09061 5.61 2.0e-07 ***
## ladder_participant -0.18606 0.08210 -2.27 0.02570 *
## ladder_confronter 0.04153 0.07363 0.56 0.57403
## ladder_perpetrator 0.10331 0.05629 1.84 0.06958 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.432, Adjusted R-squared: 0.378
## F-statistic: 8.04 on 9 and 95 DF, p-value: 9.18e-09
##
##
## $`as dv: confident`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1573 -0.7287 0.0805 0.8442 2.2378
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.84e+00 5.98e-01 8.09 2e-12 ***
## condwoman -3.75e-01 2.50e-01 -1.50 0.13671
## cond2sexist -1.65e-01 2.55e-01 -0.65 0.51827
## Age 8.73e-05 9.01e-03 0.01 0.99229
## participant_genderFemale 4.17e-01 2.37e-01 1.76 0.08219 .
## participant_genderOther 4.93e-01 8.71e-01 0.57 0.57295
## sentiment_1 3.08e-01 8.61e-02 3.58 0.00054 ***
## ladder_participant -1.80e-01 7.80e-02 -2.31 0.02321 *
## ladder_confronter 9.29e-02 7.00e-02 1.33 0.18754
## ladder_perpetrator 1.94e-01 5.35e-02 3.62 0.00047 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.323, Adjusted R-squared: 0.259
## F-statistic: 5.03 on 9 and 95 DF, p-value: 1.53e-05
##
##
## $`as dv: indep.`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.936 -0.782 0.095 0.840 2.570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.084618 0.568867 8.94 3.1e-14 ***
## condwoman -0.191468 0.237768 -0.81 0.4227
## cond2sexist -0.106094 0.242245 -0.44 0.6624
## Age -0.000561 0.008563 -0.07 0.9479
## participant_genderFemale 0.244791 0.225431 1.09 0.2803
## participant_genderOther -0.172258 0.827650 -0.21 0.8356
## sentiment_1 0.387642 0.081832 4.74 7.6e-06 ***
## ladder_participant -0.251193 0.074148 -3.39 0.0010 **
## ladder_confronter 0.134479 0.066496 2.02 0.0460 *
## ladder_perpetrator 0.141590 0.050836 2.79 0.0065 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.344, Adjusted R-squared: 0.282
## F-statistic: 5.55 on 9 and 95 DF, p-value: 4.02e-06
##
##
## $`as dv: warm`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.254 -1.014 -0.048 1.182 3.171
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4161 0.8661 3.94 0.00015 ***
## condwoman -0.5053 0.3620 -1.40 0.16602
## cond2sexist 0.8234 0.3688 2.23 0.02792 *
## Age 0.0143 0.0130 1.10 0.27392
## participant_genderFemale 0.6544 0.3432 1.91 0.05960 .
## participant_genderOther 0.9388 1.2601 0.75 0.45809
## sentiment_1 0.2297 0.1246 1.84 0.06833 .
## ladder_participant -0.1063 0.1129 -0.94 0.34865
## ladder_confronter -0.1515 0.1012 -1.50 0.13779
## ladder_perpetrator 0.1118 0.0774 1.45 0.15172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.7 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.188, Adjusted R-squared: 0.111
## F-statistic: 2.45 on 9 and 95 DF, p-value: 0.015
##
##
## $`as dv: trustworthy`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.463 -0.711 0.009 0.949 2.564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9217 0.6839 5.73 1.2e-07 ***
## condwoman -0.0968 0.2859 -0.34 0.736
## cond2sexist 0.4081 0.2912 1.40 0.164
## Age 0.0133 0.0103 1.29 0.201
## participant_genderFemale 0.5178 0.2710 1.91 0.059 .
## participant_genderOther 0.7349 0.9951 0.74 0.462
## sentiment_1 0.5413 0.0984 5.50 3.2e-07 ***
## ladder_participant -0.1224 0.0891 -1.37 0.173
## ladder_confronter -0.0777 0.0799 -0.97 0.334
## ladder_perpetrator 0.1092 0.0611 1.79 0.077 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.367, Adjusted R-squared: 0.307
## F-statistic: 6.12 on 9 and 95 DF, p-value: 9.4e-07
##
##
## $`as dv: tolerant`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.166 -1.266 -0.047 1.575 3.918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.04844 0.92840 5.44 4.2e-07 ***
## condwoman -0.10362 0.38804 -0.27 0.790
## cond2sexist 0.21731 0.39535 0.55 0.584
## Age -0.01509 0.01397 -1.08 0.283
## participant_genderFemale 0.26367 0.36791 0.72 0.475
## participant_genderOther -0.00205 1.35073 0.00 0.999
## sentiment_1 0.27904 0.13355 2.09 0.039 *
## ladder_participant -0.25246 0.12101 -2.09 0.040 *
## ladder_confronter -0.01145 0.10852 -0.11 0.916
## ladder_perpetrator 0.08912 0.08296 1.07 0.285
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.8 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.127, Adjusted R-squared: 0.044
## F-statistic: 1.53 on 9 and 95 DF, p-value: 0.148
##
##
## $`as dv: sincere`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.416 -0.928 0.171 0.985 2.813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.98461 0.71070 7.01 3.4e-10 ***
## condwoman -0.47209 0.29705 -1.59 0.115
## cond2sexist 0.06667 0.30264 0.22 0.826
## Age -0.00707 0.01070 -0.66 0.510
## participant_genderFemale 0.52954 0.28164 1.88 0.063 .
## participant_genderOther 0.21897 1.03400 0.21 0.833
## sentiment_1 0.54346 0.10223 5.32 7.0e-07 ***
## ladder_participant -0.23860 0.09263 -2.58 0.012 *
## ladder_confronter 0.08142 0.08307 0.98 0.330
## ladder_perpetrator 0.10512 0.06351 1.66 0.101
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.333, Adjusted R-squared: 0.27
## F-statistic: 5.28 on 9 and 95 DF, p-value: 7.99e-06
##
##
## $`as dv: assertive`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.373 -0.663 0.141 1.075 2.077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.24858 0.66597 7.88 5.4e-12 ***
## condwoman -0.37733 0.27836 -1.36 0.178
## cond2sexist -0.17677 0.28360 -0.62 0.535
## Age 0.00438 0.01002 0.44 0.663
## participant_genderFemale 0.05224 0.26391 0.20 0.844
## participant_genderOther 0.92527 0.96893 0.95 0.342
## sentiment_1 0.19592 0.09580 2.05 0.044 *
## ladder_participant -0.16730 0.08680 -1.93 0.057 .
## ladder_confronter 0.10063 0.07785 1.29 0.199
## ladder_perpetrator 0.10841 0.05951 1.82 0.072 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.3 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.152, Adjusted R-squared: 0.0714
## F-statistic: 1.89 on 9 and 95 DF, p-value: 0.0629
##
##
## $`as dv: dominant`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.947 -0.907 0.113 1.011 2.696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.45468 0.75114 5.93 4.9e-08 ***
## condwoman -0.04073 0.31395 -0.13 0.8970
## cond2sexist -0.27967 0.31986 -0.87 0.3841
## Age 0.00373 0.01131 0.33 0.7423
## participant_genderFemale 0.06125 0.29766 0.21 0.8374
## participant_genderOther 1.01821 1.09284 0.93 0.3538
## sentiment_1 0.23680 0.10805 2.19 0.0309 *
## ladder_participant -0.19627 0.09791 -2.00 0.0478 *
## ladder_confronter 0.07200 0.08780 0.82 0.4142
## ladder_perpetrator 0.17872 0.06712 2.66 0.0091 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.171, Adjusted R-squared: 0.0925
## F-statistic: 2.18 on 9 and 95 DF, p-value: 0.0302
##
##
## $`as dv: compass.`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.169 -0.894 0.042 0.984 2.856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.83642 0.81397 4.71 8.3e-06 ***
## condwoman -0.48252 0.34021 -1.42 0.159
## cond2sexist 0.60112 0.34662 1.73 0.086 .
## Age 0.00735 0.01225 0.60 0.550
## participant_genderFemale 0.83668 0.32256 2.59 0.011 *
## participant_genderOther 1.51167 1.18426 1.28 0.205
## sentiment_1 0.27900 0.11709 2.38 0.019 *
## ladder_participant -0.16173 0.10610 -1.52 0.131
## ladder_confronter -0.05687 0.09515 -0.60 0.551
## ladder_perpetrator 0.10563 0.07274 1.45 0.150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.6 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.217, Adjusted R-squared: 0.143
## F-statistic: 2.93 on 9 and 95 DF, p-value: 0.00418
##
##
## $`as dv: caring`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.835 -0.945 0.101 1.222 3.487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.72038 0.83634 4.45 2.3e-05 ***
## condwoman -0.43490 0.34956 -1.24 0.2165
## cond2sexist 0.82360 0.35614 2.31 0.0229 *
## Age 0.00984 0.01259 0.78 0.4362
## participant_genderFemale 0.94979 0.33142 2.87 0.0051 **
## participant_genderOther 1.41918 1.21679 1.17 0.2464
## sentiment_1 0.30204 0.12031 2.51 0.0137 *
## ladder_participant -0.14356 0.10901 -1.32 0.1910
## ladder_confronter 0.00307 0.09776 0.03 0.9750
## ladder_perpetrator 0.01670 0.07474 0.22 0.8237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.6 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.223, Adjusted R-squared: 0.15
## F-statistic: 3.03 on 9 and 95 DF, p-value: 0.00315
##
##
## $`as dv: lmx_aff`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.017 -0.939 0.075 0.878 2.676
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.31755 0.68843 6.27 1.1e-08 ***
## condwoman -0.00587 0.28774 -0.02 0.9838
## cond2sexist 0.17499 0.29316 0.60 0.5520
## Age -0.00622 0.01036 -0.60 0.5498
## participant_genderFemale 0.80963 0.27281 2.97 0.0038 **
## participant_genderOther 0.27831 1.00160 0.28 0.7817
## sentiment_1 0.72433 0.09903 7.31 8.2e-11 ***
## ladder_participant -0.09909 0.08973 -1.10 0.2723
## ladder_confronter -0.04239 0.08047 -0.53 0.5996
## ladder_perpetrator 0.02937 0.06152 0.48 0.6341
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.449, Adjusted R-squared: 0.396
## F-statistic: 8.59 on 9 and 95 DF, p-value: 2.58e-09
##
##
## $`as dv: lmx_resp`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1093 -1.1837 0.0615 1.1419 2.7671
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.297104 0.755354 4.36 3.2e-05 ***
## condwoman 0.264567 0.315714 0.84 0.40414
## cond2sexist 0.458170 0.321658 1.42 0.15761
## Age -0.000722 0.011370 -0.06 0.94951
## participant_genderFemale 0.341932 0.299332 1.14 0.25619
## participant_genderOther 0.776984 1.098971 0.71 0.48129
## sentiment_1 0.541365 0.108658 4.98 2.8e-06 ***
## ladder_participant -0.367615 0.098455 -3.73 0.00032 ***
## ladder_confronter 0.139607 0.088294 1.58 0.11717
## ladder_perpetrator 0.152471 0.067501 2.26 0.02618 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.5 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.394, Adjusted R-squared: 0.336
## F-statistic: 6.85 on 9 and 95 DF, p-value: 1.54e-07
##
##
## $`as dv: lmx_loy`
##
## Call:
## lm(formula = y ~ cond + cond2 + Age + participant_gender + sentiment_1 +
## ladder_participant + ladder_confronter + ladder_perpetrator,
## data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.565 -0.832 0.002 1.150 2.721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.57196 0.71593 6.39 6.2e-09 ***
## condwoman 0.24863 0.29924 0.83 0.408
## cond2sexist 0.16776 0.30487 0.55 0.583
## Age -0.01229 0.01078 -1.14 0.257
## participant_genderFemale 0.58603 0.28371 2.07 0.042 *
## participant_genderOther -0.39364 1.04161 -0.38 0.706
## sentiment_1 0.61801 0.10299 6.00 3.6e-08 ***
## ladder_participant -0.29587 0.09332 -3.17 0.002 **
## ladder_confronter 0.00305 0.08369 0.04 0.971
## ladder_perpetrator 0.15358 0.06398 2.40 0.018 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 95 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.429, Adjusted R-squared: 0.375
## F-statistic: 7.93 on 9 and 95 DF, p-value: 1.18e-08Moderations
Note: I wrote a function which will output all marginal and significant results.
Sexism Manipulation by Gender Manipulation
##
## Call:
## lm(formula = warm_2 ~ cond2 * cond, data = g_i1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.455 -0.769 -0.318 1.545 2.682
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.769 0.262 18.17 <2e-16 ***
## cond2sexist -0.341 0.511 -0.67 0.506
## condwoman -0.451 0.437 -1.03 0.304
## cond2sexist:condwoman 1.477 0.681 2.17 0.032 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.6 on 104 degrees of freedom
## Multiple R-squared: 0.07, Adjusted R-squared: 0.0432
## F-statistic: 2.61 on 3 and 104 DF, p-value: 0.0555Men vs. Women
Sexism vs. Rude
Mediation
psych::mediate(lmx_resp~cond_num+(perceptions_1), z = "cond2_num", data = g_i1_clean) # Does not cross zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = lmx_resp ~ cond_num + (perceptions_1), data = g_i1_clean,
## z = "cond2_num")
##
## The DV (Y) was lmx_resp . The IV (X) was cond_num . The mediating variable(s) = perceptions_1 .
##
## Total effect(c) of cond_num on lmx_resp = -0.86 S.E. = 0.35 t = -2.5 df= 106 with p = 0.014
## Direct effect (c') of cond_num on lmx_resp removing perceptions_1 = -0.47 S.E. = 0.31 t = -1.5 df= 105 with p = 0.14
## Indirect effect (ab) of cond_num on lmx_resp through perceptions_1 = -0.39
## Mean bootstrapped indirect effect = -0.39 with standard error = 0.18 Lower CI = -0.77 Upper CI = -0.07
## R = 0.52 R2 = 0.28 F = 20 on 2 and 105 DF p-value: 2.6e-10
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summarypsych::mediate(lmx_loy~cond_num+(perceptions_1), z = "cond2_num", data = g_i1_clean) # Does not cross zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = lmx_loy ~ cond_num + (perceptions_1), data = g_i1_clean,
## z = "cond2_num")
##
## The DV (Y) was lmx_loy . The IV (X) was cond_num . The mediating variable(s) = perceptions_1 .
##
## Total effect(c) of cond_num on lmx_loy = -0.55 S.E. = 0.34 t = -1.6 df= 106 with p = 0.11
## Direct effect (c') of cond_num on lmx_loy removing perceptions_1 = -0.1 S.E. = 0.29 t = -0.34 df= 105 with p = 0.73
## Indirect effect (ab) of cond_num on lmx_loy through perceptions_1 = -0.45
## Mean bootstrapped indirect effect = -0.44 with standard error = 0.19 Lower CI = -0.84 Upper CI = -0.07
## R = 0.57 R2 = 0.32 F = 25 on 2 and 105 DF p-value: 2.2e-12
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summarypsych::mediate(lmx_aff~cond_num+(perceptions_1), z = "cond2_num", data = g_i1_clean) # Does not cross zero
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = lmx_aff ~ cond_num + (perceptions_1), data = g_i1_clean,
## z = "cond2_num")
##
## The DV (Y) was lmx_aff . The IV (X) was cond_num . The mediating variable(s) = perceptions_1 .
##
## Total effect(c) of cond_num on lmx_aff = -0.51 S.E. = 0.34 t = -1.5 df= 106 with p = 0.13
## Direct effect (c') of cond_num on lmx_aff removing perceptions_1 = -0.01 S.E. = 0.27 t = -0.03 df= 105 with p = 0.97
## Indirect effect (ab) of cond_num on lmx_aff through perceptions_1 = -0.5
## Mean bootstrapped indirect effect = -0.5 with standard error = 0.2 Lower CI = -0.9 Upper CI = -0.09
## R = 0.64 R2 = 0.41 F = 36 on 2 and 105 DF p-value: 4e-16
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summary