Alphas
Mutability
##
## Reliability analysis
## Call: psych::alpha(x = as.data.frame(cbind(Mut1, Mut2, Mut3, Mut4,
## Mut5, Mut6)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.82 0.44 4.7 0.018 5 1 0.48
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.78 0.82 0.85
## Duhachek 0.78 0.82 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Mut1 0.78 0.78 0.76 0.42 3.6 0.023 0.0253 0.45
## Mut2 0.79 0.80 0.78 0.44 4.0 0.021 0.0330 0.52
## Mut3 0.78 0.78 0.77 0.42 3.6 0.023 0.0286 0.45
## Mut4 0.76 0.77 0.75 0.40 3.3 0.025 0.0229 0.46
## Mut5 0.78 0.78 0.77 0.42 3.6 0.023 0.0265 0.47
## Mut6 0.85 0.85 0.83 0.54 5.9 0.015 0.0047 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Mut1 232 0.76 0.78 0.73 0.65 5.1 1.3
## Mut2 232 0.72 0.72 0.64 0.58 5.0 1.5
## Mut3 232 0.77 0.78 0.72 0.66 5.3 1.3
## Mut4 232 0.83 0.83 0.81 0.72 4.9 1.5
## Mut5 231 0.78 0.78 0.73 0.65 4.8 1.5
## Mut6 231 0.51 0.50 0.33 0.30 4.8 1.5
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## Mut1 0.01 0.02 0.05 0.22 0.34 0.20 0.15 0.01
## Mut2 0.03 0.02 0.09 0.25 0.26 0.16 0.21 0.01
## Mut3 0.01 0.02 0.05 0.18 0.29 0.23 0.22 0.01
## Mut4 0.04 0.02 0.09 0.22 0.31 0.17 0.16 0.01
## Mut5 0.02 0.06 0.10 0.20 0.30 0.16 0.16 0.01
## Mut6 0.02 0.06 0.10 0.20 0.26 0.22 0.14 0.01Voice futility
##
## Reliability analysis
## Call: psych::alpha(x = as.data.frame(cbind(Vf1, Vf2, Vf3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.79 0.73 0.55 3.7 0.025 2.5 1.2 0.5
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.73 0.78 0.82
## Duhachek 0.73 0.78 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Vf1 0.66 0.66 0.50 0.50 2.0 0.044 NA 0.50
## Vf2 0.63 0.64 0.47 0.47 1.8 0.047 NA 0.47
## Vf3 0.82 0.82 0.69 0.69 4.5 0.024 NA 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Vf1 232 0.84 0.86 0.78 0.67 2.2 1.3
## Vf2 233 0.86 0.87 0.80 0.68 2.4 1.4
## Vf3 233 0.81 0.78 0.58 0.52 2.9 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## Vf1 0.40 0.26 0.16 0.14 0.03 0.01 0.01 0.01
## Vf2 0.32 0.30 0.16 0.13 0.05 0.03 0.01 0.00
## Vf3 0.25 0.22 0.15 0.20 0.13 0.03 0.02 0.00Confidence in gaining status
##
## Reliability analysis
## Call: psych::alpha(x = as.data.frame(cbind(Conf1, Conf2, Conf3)), check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.61 0.62 0.57 0.35 1.6 0.045 4.8 0.99 0.25
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.51 0.61 0.69
## Duhachek 0.52 0.61 0.70
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Conf1 0.39 0.39 0.25 0.25 0.65 0.079 NA 0.25
## Conf2 0.34 0.34 0.20 0.20 0.51 0.086 NA 0.20
## Conf3 0.75 0.75 0.60 0.60 2.99 0.033 NA 0.60
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Conf1 233 0.79 0.80 0.68 0.50 4.7 1.3
## Conf2 232 0.80 0.82 0.72 0.54 4.7 1.2
## Conf3 233 0.66 0.64 0.30 0.25 5.1 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## Conf1 0.02 0.04 0.08 0.32 0.27 0.17 0.10 0.00
## Conf2 0.02 0.02 0.08 0.35 0.31 0.13 0.09 0.01
## Conf3 0.02 0.03 0.07 0.23 0.26 0.24 0.16 0.00Voice
##
## Reliability analysis
## Call: psych::alpha(x = as.data.frame(cbind(V1, V2, V3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.87 0.75 9.1 0.011 5.6 1.1 0.78
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.9 0.92
## Duhachek 0.88 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## V1 0.88 0.89 0.79 0.79 7.7 0.015 NA 0.79
## V2 0.87 0.87 0.78 0.78 7.0 0.016 NA 0.78
## V3 0.81 0.81 0.69 0.69 4.4 0.024 NA 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## V1 233 0.89 0.90 0.82 0.77 5.8 1.2
## V2 233 0.91 0.91 0.83 0.79 5.4 1.3
## V3 233 0.94 0.94 0.90 0.86 5.6 1.2
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## V1 0.00 0.00 0.01 0.13 0.22 0.27 0.36 0
## V2 0.01 0.01 0.04 0.19 0.27 0.23 0.24 0
## V3 0.01 0.00 0.01 0.17 0.24 0.29 0.28 0Task visibility
##
## Reliability analysis
## Call: psych::alpha(x = as.data.frame(cbind(Task.visibility_1, Task.visibility_2,
## Task.visibility_3)))
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.82 0.69 6.6 0.015 5.1 1.1 0.67
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.87 0.89
## Duhachek 0.84 0.87 0.90
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Task.visibility_1 0.78 0.78 0.64 0.64 3.6 0.028 NA 0.64
## Task.visibility_2 0.80 0.80 0.67 0.67 4.1 0.026 NA 0.67
## Task.visibility_3 0.85 0.85 0.75 0.75 5.9 0.019 NA 0.75
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Task.visibility_1 233 0.90 0.91 0.84 0.78 5.0 1.3
## Task.visibility_2 233 0.89 0.90 0.82 0.76 5.2 1.2
## Task.visibility_3 233 0.87 0.87 0.75 0.70 5.0 1.3
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## Task.visibility_1 0.01 0.03 0.05 0.22 0.33 0.22 0.14 0
## Task.visibility_2 0.02 0.01 0.04 0.19 0.33 0.26 0.15 0
## Task.visibility_3 0.02 0.02 0.09 0.24 0.28 0.21 0.15 0Demographics
Correlations + Means and SDs
## The ability to suppress reporting of reporting confidence intervals has been deprecated in this version.
## The function argument show.conf.interval will be removed in a later version.##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4 5 6 7 8 9
## 1. mut 4.97 1.04
##
## 2. voicefut 2.52 1.21 -.38**
## [-.49, -.27]
##
## 3. conf 4.81 0.99 .17* -.30**
## [.04, .29] [-.41, -.17]
##
## 4. voice 5.60 1.11 .19** -.29** .40**
## [.07, .32] [-.40, -.17] [.28, .50]
##
## 5. taskvisib 5.07 1.15 .30** -.30** .37** .24**
## [.18, .42] [-.41, -.17] [.26, .48] [.12, .36]
##
## 6. Gain.and.loss.status_1 4.54 1.29 .20** -.14* .65** .31** .23**
## [.07, .32] [-.26, -.01] [.57, .72] [.19, .42] [.11, .35]
##
## 7. Gain.and.loss.status_2 2.81 1.31 -.18** .43** -.38** -.35** -.37** -.08
## [-.31, -.06] [.32, .53] [-.49, -.27] [-.46, -.23] [-.47, -.25] [-.21, .05]
##
## 8. GPA 3.67 0.38 -.03 .04 -.04 .01 -.05 -.05 .19**
## [-.16, .10] [-.09, .18] [-.17, .10] [-.12, .14] [-.18, .08] [-.19, .08] [.05, .31]
##
## 9. PoliticalSpectrum 5.83 1.87 -.02 -.11 -.01 .06 -.02 -.02 -.06 -.07
## [-.14, .11] [-.23, .02] [-.14, .12] [-.07, .19] [-.15, .11] [-.15, .11] [-.19, .07] [-.21, .06]
##
## 10. USLength 17.45 6.40 -.02 .09 -.08 -.11 -.01 -.15* .06 -.08 .29**
## [-.15, .10] [-.04, .21] [-.21, .05] [-.24, .02] [-.14, .12] [-.27, -.02] [-.07, .19] [-.21, .06] [.17, .40]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## Country of origin
## countryBorn
## America Australia Bulgaria Canada China Egypt Honduras India indonesia Indonesia Japan Kyrgyzstan Lebanon Mexico Nigeria North Macedonia Peru Russia Saudi Arabia Singapore South Korea Spain Taiwan the netherlands Tokyo U.S. U.S.A UK Ukraine United Kingdom United States United States United States of America us US usa USA USA Uzbekistan
## 1 2 1 1 4 4 1 1 7 1 1 1 1 1 1 1 1 2 1 1 4 1 4 1 1 1 6 1 1 1 1 83 4 10 2 18 5 54 1 1Gender
## Gender
## Man Woman
## 103 131Race
## Race
## Asian Black Hispanic Other White
## 110 5 40 27 52Analysis
Code legend
| R Code | Construct | Coding |
|---|---|---|
| mut | Mutability | 1 (not at all) –> 7 (very much so) |
| voice | Voice | 1 (not at all) –> 7 (very much so) |
| voicefut | Futility | 1 (not at all) –> 7 (very much so) |
| conf | Confidence in gaining status | 1 (not at all) –> 7 (very much so) |
| taskvisib | Task visibility | 1 (not at all) –> 7 (very much so) |
| Gain.and.loss.status_1 | Gain status | 1 (not at all) –> 7 (very much so) |
| Gain.and.loss.status_2 | Lose status | 1 (not at all) –> 7 (very much so) |
| Gender | Gender | 1 = Woman, 4 = Man, 3 = “Other” |
| fromusfinal | Nationality | “Not US”, “US” |
| USLength | Length of time in US | Numeric |
| Race | Race | 1 = White, 2 = Black, 4 = Hispanic, 6 = Asian, 12 = Native American, 10 = Pacific Islander, 5 = Other |
| SexualOrientation | Sexual Orientation | 1 = Straight, 2 = Lesbian, 3 = Gay, 4 = Bisexual, 5 = Asexual, 6 = Unsure |
| PoliticalSpectrum | Political spectrum | 1 = Conservative –> 9 = Liberal |
| GPA | GPA | 0.0 –> 4.0 |
Main Effects
##
## Call:
## lm(formula = voice ~ mut, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8181 -0.7229 0.1774 0.9210 2.0472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.57203 0.35017 13.056 < 2e-16 ***
## mut 0.20768 0.06894 3.013 0.00288 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.086 on 230 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.03796, Adjusted R-squared: 0.03378
## F-statistic: 9.076 on 1 and 230 DF, p-value: 0.00288##
## Call:
## lm(formula = voicefut ~ mut, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1825 -0.7673 -0.0971 0.6441 3.4573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.75454 0.36068 13.182 < 2e-16 ***
## mut -0.44914 0.07101 -6.325 1.3e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.118 on 230 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1482, Adjusted R-squared: 0.1445
## F-statistic: 40.01 on 1 and 230 DF, p-value: 1.302e-09##
## Call:
## lm(formula = conf ~ mut, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9261 -0.6617 -0.1008 0.5420 2.4441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.02715 0.31501 12.784 <2e-16 ***
## mut 0.15864 0.06201 2.558 0.0112 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9768 on 230 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02766, Adjusted R-squared: 0.02344
## F-statistic: 6.544 on 1 and 230 DF, p-value: 0.01117##
## Call:
## lm(formula = taskvisib ~ mut, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4214 -0.7488 -0.0320 0.7497 2.9237
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.40366 0.35343 9.630 < 2e-16 ***
## mut 0.33629 0.06958 4.833 2.45e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.096 on 230 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.09221, Adjusted R-squared: 0.08826
## F-statistic: 23.36 on 1 and 230 DF, p-value: 2.455e-06##
## Call:
## lm(formula = Gain.and.loss.status_1 ~ mut, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0458 -0.5951 -0.1853 0.6508 2.8556
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.32487 0.40868 8.136 2.68e-14 ***
## mut 0.24585 0.08055 3.052 0.00254 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.266 on 227 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.03942, Adjusted R-squared: 0.03519
## F-statistic: 9.316 on 1 and 227 DF, p-value: 0.002542##
## Call:
## lm(formula = Gain.and.loss.status_2 ~ mut, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1880 -0.9170 0.0830 0.9668 4.4314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.96220 0.41745 9.491 < 2e-16 ***
## mut -0.23226 0.08221 -2.825 0.00515 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.294 on 227 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.03397, Adjusted R-squared: 0.02971
## F-statistic: 7.981 on 1 and 227 DF, p-value: 0.005147Controls
##
## Call:
## lm(formula = voice ~ mut + Gender + fromusfinal + USLength +
## Race + SexualOrientation + PoliticalSpectrum + GPA, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3386 -0.7798 0.1086 0.8059 2.4735
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.70667 1.12181 4.196 4.07e-05 ***
## mut 0.19847 0.07446 2.665 0.00831 **
## GenderWoman -0.05325 0.16405 -0.325 0.74582
## fromusfinalUS 0.22039 0.16884 1.305 0.19326
## USLength -0.02789 0.01325 -2.105 0.03650 *
## RaceBlack -0.04232 0.51316 -0.082 0.93436
## RaceHispanic -0.28168 0.22436 -1.256 0.21073
## RaceOther -0.15735 0.24564 -0.641 0.52253
## RaceWhite -0.04920 0.20233 -0.243 0.80812
## SexualOrientationBisexual -0.14227 0.69498 -0.205 0.83800
## SexualOrientationGay 0.52747 0.91808 0.575 0.56624
## SexualOrientationStraight -0.22685 0.65441 -0.347 0.72922
## SexualOrientationUnsure -0.25960 0.86093 -0.302 0.76331
## PoliticalSpectrum 0.07219 0.04466 1.617 0.10751
## GPA 0.05512 0.20047 0.275 0.78364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 203 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.08211, Adjusted R-squared: 0.01881
## F-statistic: 1.297 on 14 and 203 DF, p-value: 0.2114##
## Call:
## lm(formula = voicefut ~ mut + Gender + fromusfinal + USLength +
## Race + SexualOrientation + PoliticalSpectrum + GPA, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2375 -0.7235 -0.1155 0.6736 3.2782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.41455 1.12027 3.941 0.000112 ***
## mut -0.48455 0.07436 -6.516 5.58e-10 ***
## GenderWoman -0.18215 0.16382 -1.112 0.267511
## fromusfinalUS 0.09798 0.16861 0.581 0.561811
## USLength 0.01972 0.01323 1.491 0.137630
## RaceBlack 0.20238 0.51246 0.395 0.693311
## RaceHispanic -0.12127 0.22405 -0.541 0.588904
## RaceOther 0.05139 0.24531 0.209 0.834273
## RaceWhite -0.06444 0.20205 -0.319 0.750109
## SexualOrientationBisexual 0.32289 0.69402 0.465 0.642253
## SexualOrientationGay 1.13481 0.91682 1.238 0.217230
## SexualOrientationStraight 0.48552 0.65351 0.743 0.458377
## SexualOrientationUnsure 0.98859 0.85975 1.150 0.251554
## PoliticalSpectrum -0.09081 0.04460 -2.036 0.043010 *
## GPA 0.07360 0.20019 0.368 0.713533
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.106 on 203 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.2145, Adjusted R-squared: 0.1603
## F-statistic: 3.959 on 14 and 203 DF, p-value: 5.209e-06##
## Call:
## lm(formula = conf ~ mut + Gender + fromusfinal + USLength + Race +
## SexualOrientation + PoliticalSpectrum + GPA, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7294 -0.7103 -0.1066 0.5711 2.3712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5841356 1.0161923 4.511 1.09e-05 ***
## mut 0.1197914 0.0674523 1.776 0.0772 .
## GenderWoman -0.0254903 0.1486004 -0.172 0.8640
## fromusfinalUS 0.1073736 0.1529452 0.702 0.4835
## USLength -0.0134506 0.0120023 -1.121 0.2638
## RaceBlack 0.0009332 0.4648484 0.002 0.9984
## RaceHispanic -0.4689574 0.2032326 -2.307 0.0220 *
## RaceOther -0.1307597 0.2225166 -0.588 0.5574
## RaceWhite -0.0640228 0.1832816 -0.349 0.7272
## SexualOrientationBisexual 0.4594343 0.6295435 0.730 0.4664
## SexualOrientationGay 0.8458991 0.8316388 1.017 0.3103
## SexualOrientationStraight 0.3917048 0.5927972 0.661 0.5095
## SexualOrientationUnsure 0.8037694 0.7798749 1.031 0.3039
## PoliticalSpectrum 0.0042628 0.0404523 0.105 0.9162
## GPA -0.1301868 0.1815933 -0.717 0.4743
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.004 on 203 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.06694, Adjusted R-squared: 0.002587
## F-statistic: 1.04 on 14 and 203 DF, p-value: 0.415##
## Call:
## lm(formula = taskvisib ~ mut + Gender + fromusfinal + USLength +
## Race + SexualOrientation + PoliticalSpectrum + GPA, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4423 -0.6776 -0.0474 0.7653 2.7923
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.433115 1.142610 3.880 0.000141 ***
## mut 0.323618 0.075844 4.267 3.04e-05 ***
## GenderWoman 0.027546 0.167087 0.165 0.869219
## fromusfinalUS -0.010023 0.171972 -0.058 0.953583
## USLength 0.002710 0.013495 0.201 0.841068
## RaceBlack -0.424387 0.522677 -0.812 0.417772
## RaceHispanic 0.108766 0.228515 0.476 0.634609
## RaceOther -0.082868 0.250198 -0.331 0.740827
## RaceWhite 0.098560 0.206082 0.478 0.632985
## SexualOrientationBisexual -0.753398 0.707861 -1.064 0.288443
## SexualOrientationGay 0.416267 0.935097 0.445 0.656679
## SexualOrientationStraight -0.757019 0.666543 -1.136 0.257405
## SexualOrientationUnsure -1.075751 0.876894 -1.227 0.221328
## PoliticalSpectrum 0.001376 0.045485 0.030 0.975896
## GPA -0.083092 0.204184 -0.407 0.684477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.128 on 203 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.113, Adjusted R-squared: 0.05184
## F-statistic: 1.847 on 14 and 203 DF, p-value: 0.03405##
## Call:
## lm(formula = Gain.and.loss.status_1 ~ mut + Gender + fromusfinal +
## USLength + Race + SexualOrientation + PoliticalSpectrum +
## GPA, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8397 -0.7890 -0.1036 0.7540 2.7333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.63056 1.30265 3.555 0.000472 ***
## mut 0.19430 0.08642 2.248 0.025647 *
## GenderWoman -0.08245 0.19112 -0.431 0.666618
## fromusfinalUS 0.05050 0.19676 0.257 0.797718
## USLength -0.02971 0.01549 -1.918 0.056509 .
## RaceBlack 0.14352 0.59393 0.242 0.809307
## RaceHispanic -0.42485 0.26701 -1.591 0.113156
## RaceOther -0.07481 0.28426 -0.263 0.792675
## RaceWhite -0.21680 0.23541 -0.921 0.358199
## SexualOrientationBisexual 0.99641 0.80426 1.239 0.216829
## SexualOrientationGay 0.59656 1.06289 0.561 0.575248
## SexualOrientationStraight 0.47909 0.75748 0.632 0.527796
## SexualOrientationUnsure 1.76122 0.99648 1.767 0.078678 .
## PoliticalSpectrum -0.00960 0.05177 -0.185 0.853085
## GPA -0.24144 0.23216 -1.040 0.299619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.282 on 200 degrees of freedom
## (19 observations deleted due to missingness)
## Multiple R-squared: 0.1005, Adjusted R-squared: 0.03751
## F-statistic: 1.596 on 14 and 200 DF, p-value: 0.08273##
## Call:
## lm(formula = Gain.and.loss.status_2 ~ mut + Gender + fromusfinal +
## USLength + Race + SexualOrientation + PoliticalSpectrum +
## GPA, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8791 -0.9399 -0.0015 0.7269 4.3815
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3911705 1.2968009 1.844 0.06668 .
## mut -0.2406101 0.0859189 -2.800 0.00560 **
## GenderWoman -0.4010355 0.1909078 -2.101 0.03692 *
## fromusfinalUS 0.1916783 0.1956085 0.980 0.32832
## USLength 0.0087410 0.0154619 0.565 0.57248
## RaceBlack -0.8671438 0.5923061 -1.464 0.14476
## RaceHispanic -0.1218100 0.2631393 -0.463 0.64393
## RaceOther -0.0004183 0.2838622 -0.001 0.99883
## RaceWhite -0.2152175 0.2349344 -0.916 0.36073
## SexualOrientationBisexual -0.6775723 0.8018149 -0.845 0.39909
## SexualOrientationGay -1.7137994 1.0595456 -1.617 0.10735
## SexualOrientationStraight -0.4294575 0.7550003 -0.569 0.57012
## SexualOrientationUnsure 0.8028686 0.9934148 0.808 0.41994
## PoliticalSpectrum -0.0380270 0.0516738 -0.736 0.46265
## GPA 0.6378222 0.2315567 2.754 0.00642 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.278 on 200 degrees of freedom
## (19 observations deleted due to missingness)
## Multiple R-squared: 0.1412, Adjusted R-squared: 0.08107
## F-statistic: 2.349 on 14 and 200 DF, p-value: 0.004973Interactions
DV: Voice
Gender
##
## Call:
## lm(formula = voice ~ mut * Gender, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8171 -0.7319 0.2070 0.8974 2.1226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.78230 0.58435 8.184 1.94e-14 ***
## mut 0.17247 0.11241 1.534 0.126
## GenderWoman -0.31679 0.73298 -0.432 0.666
## mut:GenderWoman 0.05221 0.14324 0.364 0.716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.09 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.03913, Adjusted R-squared: 0.02648
## F-statistic: 3.095 on 3 and 228 DF, p-value: 0.02774Voice Fut
##
## Call:
## lm(formula = voice ~ mut * voicefut, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0486 -0.7417 0.1601 0.8897 2.1504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.90219 0.85822 6.877 5.8e-11 ***
## mut 0.06009 0.15948 0.377 0.707
## voicefut -0.32438 0.30823 -1.052 0.294
## mut:voicefut 0.01838 0.06114 0.301 0.764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.058 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.09404, Adjusted R-squared: 0.08212
## F-statistic: 7.889 on 3 and 228 DF, p-value: 4.971e-05Confidence
##
## Call:
## lm(formula = voice ~ mut * conf, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1929 -0.6561 0.0088 0.6761 2.1441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.71056 1.54828 -0.459 0.646717
## mut 0.82097 0.28784 2.852 0.004740 **
## conf 1.15902 0.31420 3.689 0.000282 ***
## mut:conf -0.13886 0.05735 -2.421 0.016254 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9986 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1934, Adjusted R-squared: 0.1828
## F-statistic: 18.23 on 3 and 228 DF, p-value: 1.23e-10## JOHNSON-NEYMAN INTERVAL
##
## When conf is OUTSIDE the interval [5.00, 10.83], the slope of mut is p < .05.
##
## Note: The range of observed values of conf is [2.00, 7.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of mut when conf = 3.827611 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.29 0.09 3.27 0.00
##
## Slope of mut when conf = 4.816092 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.15 0.06 2.36 0.02
##
## Slope of mut when conf = 5.804573 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.01 0.08 0.18 0.86
GPA
##
## Call:
## lm(formula = voice ~ mut * GPA, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8745 -0.7557 0.2217 0.8946 2.1139
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.7989 3.7132 2.100 0.0369 *
## mut -0.4087 0.6489 -0.630 0.5295
## GPA -0.8925 1.0203 -0.875 0.3827
## mut:GPA 0.1713 0.1787 0.959 0.3388
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.102 on 214 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.04256, Adjusted R-squared: 0.02914
## F-statistic: 3.171 on 3 and 214 DF, p-value: 0.02522From US Final
##
## Call:
## lm(formula = voice ~ mut * fromusfinal, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8778 -0.6929 0.1818 0.8876 2.1155
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.65100 0.47143 9.866 <2e-16 ***
## mut 0.18801 0.09149 2.055 0.041 *
## fromusfinalUS -0.20360 0.71031 -0.287 0.775
## mut:fromusfinalUS 0.05040 0.14044 0.359 0.720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.09 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.03893, Adjusted R-squared: 0.02629
## F-statistic: 3.079 on 3 and 228 DF, p-value: 0.02833Political Spectrum
##
## Call:
## lm(formula = voice ~ mut * PoliticalSpectrum, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7608 -0.7216 0.1875 0.9461 2.1454
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.39240 1.25599 2.701 0.00743 **
## mut 0.39251 0.24410 1.608 0.10922
## PoliticalSpectrum 0.19651 0.20050 0.980 0.32808
## mut:PoliticalSpectrum -0.03053 0.03892 -0.784 0.43361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.086 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.04558, Adjusted R-squared: 0.03302
## F-statistic: 3.629 on 3 and 228 DF, p-value: 0.01372Task Visibility
##
## Call:
## lm(formula = voice ~ mut * taskvisib, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9531 -0.7246 0.1293 0.9216 2.1278
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.29779 1.47070 0.882 0.3785
## mut 0.69497 0.30254 2.297 0.0225 *
## taskvisib 0.68609 0.27522 2.493 0.0134 *
## mut:taskvisib -0.10279 0.05499 -1.869 0.0629 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.063 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.08579, Adjusted R-squared: 0.07376
## F-statistic: 7.132 on 3 and 228 DF, p-value: 0.0001341## JOHNSON-NEYMAN INTERVAL
##
## When taskvisib is INSIDE the interval [-20.07, 5.40], the slope of mut is p < .05.
##
## Note: The range of observed values of taskvisib is [1.00, 7.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of mut when taskvisib = 3.928365 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.29 0.11 2.76 0.01
##
## Slope of mut when taskvisib = 5.076149 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.17 0.07 2.39 0.02
##
## Slope of mut when taskvisib = 6.223934 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.06 0.09 0.64 0.52
Gain status
##
## Call:
## lm(formula = voice ~ mut * Gain.and.loss.status_1, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9992 -0.7059 0.0284 0.7261 2.0995
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.75045 1.13761 2.418 0.0164 *
## mut 0.33937 0.21651 1.567 0.1184
## Gain.and.loss.status_1 0.47253 0.24379 1.938 0.0538 .
## mut:Gain.and.loss.status_1 -0.04334 0.04509 -0.961 0.3374
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.047 on 225 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1173, Adjusted R-squared: 0.1056
## F-statistic: 9.97 on 3 and 225 DF, p-value: 3.386e-06Lose status
##
## Call:
## lm(formula = voice ~ mut * Gain.and.loss.status_2, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1183 -0.7564 0.1178 0.8451 2.8838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.10459 0.80864 8.786 4.03e-16 ***
## mut -0.13282 0.14981 -0.887 0.37625
## Gain.and.loss.status_2 -0.79790 0.26140 -3.052 0.00254 **
## mut:Gain.and.loss.status_2 0.10142 0.04953 2.047 0.04178 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.024 on 225 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1553, Adjusted R-squared: 0.144
## F-statistic: 13.79 on 3 and 225 DF, p-value: 2.754e-08## JOHNSON-NEYMAN INTERVAL
##
## The Johnson-Neyman interval could not be found. Is the p value for your interaction term below the specified alpha?
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of mut when Gain.and.loss.status_2 = 1.494057 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.12 0.08 1.54 0.13
##
## Slope of mut when Gain.and.loss.status_2 = 2.807860 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.09 0.06 1.60 0.11
##
## Slope of mut when Gain.and.loss.status_2 = 4.121663 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.07 0.09 0.78 0.44
DV: Voice Futility
Gender
##
## Call:
## lm(formula = voicefut ~ mut * Gender, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2413 -0.7477 -0.1429 0.6656 3.4340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.6621 0.5965 9.492 < 2e-16 ***
## mut -0.6052 0.1148 -5.274 3.1e-07 ***
## GenderWoman -1.3799 0.7482 -1.844 0.0665 .
## mut:GenderWoman 0.2353 0.1462 1.609 0.1089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.113 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1643, Adjusted R-squared: 0.1533
## F-statistic: 14.94 on 3 and 228 DF, p-value: 6.482e-09Confidence
##
## Call:
## lm(formula = voicefut ~ mut * conf, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6549 -0.7452 -0.0929 0.6372 3.4225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.92355 1.66093 5.975 8.79e-09 ***
## mut -1.15331 0.30878 -3.735 0.000237 ***
## conf -1.11475 0.33706 -3.307 0.001094 **
## mut:conf 0.15346 0.06152 2.494 0.013329 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.071 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.2253, Adjusted R-squared: 0.2151
## F-statistic: 22.11 on 3 and 228 DF, p-value: 1.331e-12## JOHNSON-NEYMAN INTERVAL
##
## When conf is OUTSIDE the interval [6.17, 17.57], the slope of mut is p < .05.
##
## Note: The range of observed values of conf is [2.00, 7.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of mut when conf = 3.827611 (- 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.57 0.10 -5.95 0.00
##
## Slope of mut when conf = 4.816092 (Mean):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.41 0.07 -5.99 0.00
##
## Slope of mut when conf = 5.804573 (+ 1 SD):
##
## Est. S.E. t val. p
## ------- ------ -------- ------
## -0.26 0.09 -2.95 0.00
GPA
##
## Call:
## lm(formula = voicefut ~ mut * GPA, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2301 -0.7420 -0.0862 0.6149 3.5650
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.67212 3.75369 0.978 0.329
## mut -0.33123 0.65600 -0.505 0.614
## GPA 0.30519 1.03144 0.296 0.768
## mut:GPA -0.03582 0.18066 -0.198 0.843
##
## Residual standard error: 1.114 on 214 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.1603, Adjusted R-squared: 0.1486
## F-statistic: 13.62 on 3 and 214 DF, p-value: 3.635e-08From US Final
##
## Call:
## lm(formula = voicefut ~ mut * fromusfinal, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3996 -0.7550 -0.1384 0.6870 3.4149
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.30806 0.48288 8.922 < 2e-16 ***
## mut -0.37734 0.09371 -4.027 7.7e-05 ***
## fromusfinalUS 0.94574 0.72756 1.300 0.195
## mut:fromusfinalUS -0.15243 0.14385 -1.060 0.290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.117 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1585, Adjusted R-squared: 0.1474
## F-statistic: 14.31 on 3 and 228 DF, p-value: 1.412e-08Political Spectrum
##
## Call:
## lm(formula = voicefut ~ mut * PoliticalSpectrum, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2756 -0.7210 -0.1258 0.6250 3.4058
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.47179 1.28920 4.244 3.19e-05 ***
## mut -0.50792 0.25055 -2.027 0.0438 *
## PoliticalSpectrum -0.12004 0.20580 -0.583 0.5603
## mut:PoliticalSpectrum 0.00944 0.03995 0.236 0.8134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.115 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1608, Adjusted R-squared: 0.1497
## F-statistic: 14.56 on 3 and 228 DF, p-value: 1.041e-08Task Visibility
##
## Call:
## lm(formula = voicefut ~ mut * taskvisib, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9644 -0.7166 -0.0887 0.6792 3.6179
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.580763 1.520035 3.671 0.0003 ***
## mut -0.399655 0.312693 -1.278 0.2025
## taskvisib -0.231807 0.284456 -0.815 0.4160
## mut:taskvisib 0.004076 0.056839 0.072 0.9429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.099 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.185, Adjusted R-squared: 0.1742
## F-statistic: 17.25 on 3 and 228 DF, p-value: 3.969e-10Gain status
##
## Call:
## lm(formula = voicefut ~ mut * Gain.and.loss.status_1, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3394 -0.7043 -0.1041 0.5956 3.4155
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.04327 1.21414 4.977 1.28e-06 ***
## mut -0.63850 0.23107 -2.763 0.0062 **
## Gain.and.loss.status_1 -0.30615 0.26019 -1.177 0.2406
## mut:Gain.and.loss.status_1 0.04635 0.04812 0.963 0.3365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.117 on 225 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1535, Adjusted R-squared: 0.1422
## F-statistic: 13.6 on 3 and 225 DF, p-value: 3.478e-08Lose status
##
## Call:
## lm(formula = voicefut ~ mut * Gain.and.loss.status_2, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5661 -0.6337 -0.1205 0.6579 3.5038
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.78721 0.80911 3.445 0.000682 ***
## mut -0.25246 0.14990 -1.684 0.093518 .
## Gain.and.loss.status_2 0.56029 0.26155 2.142 0.033252 *
## mut:Gain.and.loss.status_2 -0.04133 0.04956 -0.834 0.405202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.024 on 225 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.288, Adjusted R-squared: 0.2785
## F-statistic: 30.33 on 3 and 225 DF, p-value: < 2.2e-16DV: Confidence
Gender
##
## Call:
## lm(formula = conf ~ mut * Gender, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.92936 -0.64817 -0.09049 0.55864 2.51836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.52519 0.52425 8.632 1.05e-15 ***
## mut 0.06991 0.10085 0.693 0.489
## GenderWoman -0.76680 0.65759 -1.166 0.245
## mut:GenderWoman 0.13673 0.12851 1.064 0.288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9779 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0341, Adjusted R-squared: 0.02139
## F-statistic: 2.683 on 3 and 228 DF, p-value: 0.04754GPA
##
## Call:
## lm(formula = conf ~ mut * GPA, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.89420 -0.68300 -0.09627 0.55202 2.49633
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.48043 3.36556 0.737 0.462
## mut 0.48463 0.58817 0.824 0.411
## GPA 0.44887 0.92479 0.485 0.628
## mut:GPA -0.09388 0.16198 -0.580 0.563
##
## Residual standard error: 0.9989 on 214 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.02558, Adjusted R-squared: 0.01192
## F-statistic: 1.873 on 3 and 214 DF, p-value: 0.1352From US Final
##
## Call:
## lm(formula = conf ~ mut * fromusfinal, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9430 -0.6651 -0.1061 0.5392 2.4542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.97839 0.42423 9.378 <2e-16 ***
## mut 0.17023 0.08233 2.068 0.0398 *
## fromusfinalUS 0.12283 0.63920 0.192 0.8478
## mut:fromusfinalUS -0.02926 0.12638 -0.232 0.8171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9809 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02802, Adjusted R-squared: 0.01523
## F-statistic: 2.191 on 3 and 228 DF, p-value: 0.08995## SIMPLE SLOPES ANALYSIS
##
## Slope of mut when fromusfinal = Not USA:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.17 0.08 2.07 0.04
##
## Slope of mut when fromusfinal = US:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.14 0.10 1.47 0.14
Political Spectrum
##
## Call:
## lm(formula = conf ~ mut * PoliticalSpectrum, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9468 -0.6479 -0.1065 0.5439 2.4910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.17983 1.13268 2.807 0.00543 **
## mut 0.33097 0.22014 1.503 0.13410
## PoliticalSpectrum 0.14074 0.18081 0.778 0.43716
## mut:PoliticalSpectrum -0.02866 0.03510 -0.817 0.41499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9796 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.03056, Adjusted R-squared: 0.01781
## F-statistic: 2.396 on 3 and 228 DF, p-value: 0.06902Task Visibility
##
## Call:
## lm(formula = conf ~ mut * taskvisib, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.65120 -0.46884 -0.06926 0.55661 2.53076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.07523 1.27311 3.201 0.00156 **
## mut -0.16986 0.26190 -0.649 0.51726
## taskvisib 0.09927 0.23825 0.417 0.67733
## mut:taskvisib 0.04225 0.04761 0.887 0.37577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9203 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.1444, Adjusted R-squared: 0.1332
## F-statistic: 12.83 on 3 and 228 DF, p-value: 8.935e-08Gain status
##
## Call:
## lm(formula = conf ~ mut * Gain.and.loss.status_1, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.15439 -0.54634 -0.00904 0.48079 2.41307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.77982 0.80897 4.672 5.12e-06 ***
## mut -0.21907 0.15396 -1.423 0.1562
## Gain.and.loss.status_1 0.17810 0.17336 1.027 0.3054
## mut:Gain.and.loss.status_1 0.05815 0.03206 1.814 0.0711 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7443 on 225 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.4298, Adjusted R-squared: 0.4222
## F-statistic: 56.53 on 3 and 225 DF, p-value: < 2.2e-16Lose status
##
## Call:
## lm(formula = conf ~ mut * Gain.and.loss.status_2, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.57186 -0.47898 -0.08813 0.56349 2.36553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.80104 0.71857 6.681 1.84e-10 ***
## mut 0.15451 0.13312 1.161 0.247
## Gain.and.loss.status_2 -0.16081 0.23228 -0.692 0.489
## mut:Gain.and.loss.status_2 -0.02142 0.04402 -0.487 0.627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9098 on 225 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1564, Adjusted R-squared: 0.1452
## F-statistic: 13.91 on 3 and 225 DF, p-value: 2.381e-08DV: Task Visibility
Gender
##
## Call:
## lm(formula = taskvisib ~ mut * Gender, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2669 -0.6439 -0.0748 0.7589 3.2545
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3498 0.5847 7.440 2.04e-12 ***
## mut 0.1529 0.1125 1.359 0.1755
## GenderWoman -1.5027 0.7334 -2.049 0.0416 *
## mut:GenderWoman 0.2964 0.1433 2.068 0.0398 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.091 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.109, Adjusted R-squared: 0.09724
## F-statistic: 9.294 on 3 and 228 DF, p-value: 8.011e-06## SIMPLE SLOPES ANALYSIS
##
## Slope of mut when Gender = Man:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.15 0.11 1.36 0.18
##
## Slope of mut when Gender = Woman:
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.45 0.09 5.06 0.00
GPA
##
## Call:
## lm(formula = taskvisib ~ mut * GPA, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4257 -0.7191 -0.0292 0.7962 2.9770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.0408 3.7548 1.875 0.0621 .
## mut -0.2364 0.6562 -0.360 0.7190
## GPA -0.9780 1.0317 -0.948 0.3442
## mut:GPA 0.1541 0.1807 0.853 0.3946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.114 on 214 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.08808, Adjusted R-squared: 0.0753
## F-statistic: 6.89 on 3 and 214 DF, p-value: 0.0001887From US Final
##
## Call:
## lm(formula = taskvisib ~ mut * fromusfinal, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3993 -0.7018 -0.0526 0.7479 2.8186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.57238 0.47576 7.509 1.34e-12 ***
## mut 0.30449 0.09233 3.298 0.00113 **
## fromusfinalUS -0.38170 0.71684 -0.532 0.59491
## mut:fromusfinalUS 0.07330 0.14173 0.517 0.60556
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.1 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.09333, Adjusted R-squared: 0.0814
## F-statistic: 7.824 on 3 and 228 DF, p-value: 5.415e-05Political Spectrum
##
## Call:
## lm(formula = taskvisib ~ mut * PoliticalSpectrum, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4220 -0.7409 -0.0502 0.7506 2.9069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.553409 1.272641 2.792 0.00568 **
## mut 0.312950 0.247336 1.265 0.20706
## PoliticalSpectrum -0.024946 0.203155 -0.123 0.90238
## mut:PoliticalSpectrum 0.003856 0.039438 0.098 0.92221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.101 on 228 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.09232, Adjusted R-squared: 0.08038
## F-statistic: 7.73 on 3 and 228 DF, p-value: 6.12e-05Gain status
##
## Call:
## lm(formula = taskvisib ~ mut * Gain.and.loss.status_1, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6487 -0.6668 -0.0623 0.6434 2.5902
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.15505 1.17106 0.986 0.32503
## mut 0.62665 0.22288 2.812 0.00536 **
## Gain.and.loss.status_1 0.54426 0.25096 2.169 0.03115 *
## mut:Gain.and.loss.status_1 -0.07324 0.04641 -1.578 0.11598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.077 on 225 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1305, Adjusted R-squared: 0.1189
## F-statistic: 11.26 on 3 and 225 DF, p-value: 6.57e-07## JOHNSON-NEYMAN INTERVAL
##
## When Gain.and.loss.status_1 is INSIDE the interval [-11.14, 5.97], the slope of mut is p < .05.
##
## Note: The range of observed values of Gain.and.loss.status_1 is [1.00, 7.00]
##
## SIMPLE SLOPES ANALYSIS
##
## Slope of mut when Gain.and.loss.status_1 = 3.256951 (- 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.39 0.09 4.20 0.00
##
## Slope of mut when Gain.and.loss.status_1 = 4.545852 (Mean):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.29 0.07 4.20 0.00
##
## Slope of mut when Gain.and.loss.status_1 = 5.834752 (+ 1 SD):
##
## Est. S.E. t val. p
## ------ ------ -------- ------
## 0.20 0.09 2.18 0.03
Lose status
##
## Call:
## lm(formula = taskvisib ~ mut * Gain.and.loss.status_2, data = mutpresurv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6286 -0.5321 0.0160 0.6001 3.0330
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.95833 0.82332 4.808 2.79e-06 ***
## mut 0.37720 0.15253 2.473 0.0141 *
## Gain.and.loss.status_2 -0.07982 0.26614 -0.300 0.7645
## mut:Gain.and.loss.status_2 -0.03849 0.05043 -0.763 0.4462
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.042 on 225 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1928, Adjusted R-squared: 0.1821
## F-statistic: 17.92 on 3 and 225 DF, p-value: 1.836e-10