Organized Output
HP
7/6/2020
Narrative
- How do students’ views of engineering (engemp and engbel) and their long-term career priorities (finances, innovation, and leadership) predict their discipline interests and engineering beliefs (belong1/2 & id)?
- Fairly straightforward analysis, foundational to next two questions
- Might work well as conference paper to set up journal paper?
- A chunk of this analysis has already been done
- Do the relationships between views/priorities and discipline interests differ by gender? E.g., does engemp increase interest in F3 for female/nb students but not for men?
- Moderation analysis, a bit more involved
- Would fit in enged and gender studies spaces
- A chunk of this analysis has already been done
- Are the relationships between views/priorities and beliefs mediated by conformity to masculine norms? I.e., how much of the relationship between v/p and belonging is due to differences in views, and how much can be explained by cmn?
- Mediation analysis with lots of factors, the most complicated analysis/interpretation
- Would fit in enged and gender studies spaces
- Might also need to include moderation by gender? Analysis gets more complicated
Factor Analysis
Disciplines
##
## Call:
## factanal(x = d, factors = 4, rotation = "varimax", cutoff = 0.3)
##
## Uniquenesses:
## aero agri bio chem civil compeng cons elec
## 0.690 0.442 0.542 0.580 0.504 0.249 0.325 0.565
## phys env ind mse mech multi nuc compsci
## 0.505 0.548 0.442 0.457 0.513 0.449 0.523 0.131
## it otherstem nonstem
## 0.369 0.686 0.773
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4
## compeng 0.844
## elec 0.525
## compsci 0.913
## it 0.678
## agri 0.683
## bio 0.673
## chem 0.627
## env 0.546
## civil 0.681
## cons 0.776
## ind 0.604
## aero 0.525
## phys 0.549
## mech 0.642
## mse 0.479 0.440
## multi 0.408 0.458
## nuc 0.413 0.476
## otherstem 0.406
## nonstem
##
## Factor1 Factor2 Factor3 Factor4
## SS loadings 2.749 2.657 2.253 2.050
## Proportion Var 0.145 0.140 0.119 0.108
## Cumulative Var 0.145 0.284 0.403 0.511
##
## Test of the hypothesis that 4 factors are sufficient.
## The chi square statistic is 1737.29 on 101 degrees of freedom.
## The p-value is 4.15e-296##
## Call:
## factanal(x = d, factors = 5, rotation = "varimax", cutoff = 0.3)
##
## Uniquenesses:
## aero agri bio chem civil compeng cons elec
## 0.692 0.453 0.494 0.524 0.502 0.212 0.308 0.535
## phys env ind mse mech multi nuc compsci
## 0.504 0.560 0.444 0.458 0.514 0.432 0.515 0.154
## it otherstem nonstem
## 0.352 0.430 0.545
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## compeng 0.864
## elec 0.549 0.334
## compsci 0.881
## it 0.633 0.365
## civil 0.681
## cons 0.786
## ind 0.598 0.331
## agri 0.302 0.632
## bio 0.702
## chem 0.664
## aero 0.524
## phys 0.548
## mech 0.642
## otherstem 0.660
## nonstem 0.613
## env 0.372 0.477
## mse 0.316 0.434 0.446
## multi 0.388 0.464 0.321
## nuc 0.401 0.477
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 2.573 2.207 2.187 2.055 1.351
## Proportion Var 0.135 0.116 0.115 0.108 0.071
## Cumulative Var 0.135 0.252 0.367 0.475 0.546
##
## Test of the hypothesis that 5 factors are sufficient.
## The chi square statistic is 1072.33 on 86 degrees of freedom.
## The p-value is 4.61e-170Career Vars
##
## Call:
## factanal(x = d, factors = 2, rotation = "varimax", cutoff = 0.3)
##
## Uniquenesses:
## career_money career_known career_helping career_supervising
## 0.870 0.632 0.686 0.445
## career_security career_people career_invent career_developing
## 0.779 0.657 0.421 0.213
##
## Loadings:
## Factor1 Factor2
## career_invent 0.742
## career_developing 0.879
## career_known 0.585
## career_supervising 0.724
## career_money
## career_helping 0.448
## career_security
## career_people 0.406 0.422
##
## Factor1 Factor2
## SS loadings 1.895 1.403
## Proportion Var 0.237 0.175
## Cumulative Var 0.237 0.412
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 985.86 on 13 degrees of freedom.
## The p-value is 1.9e-202##
## Call:
## factanal(x = d, factors = 3, rotation = "varimax", cutoff = 0.3)
##
## Uniquenesses:
## career_money career_known career_helping career_supervising
## 0.005 0.639 0.666 0.398
## career_security career_people career_invent career_developing
## 0.677 0.634 0.415 0.233
##
## Loadings:
## Factor1 Factor2 Factor3
## career_invent 0.737
## career_developing 0.860
## career_known 0.544
## career_supervising 0.754
## career_money 0.986
## career_helping 0.448
## career_security 0.407
## career_people 0.403 0.450
##
## Factor1 Factor2 Factor3
## SS loadings 1.808 1.305 1.220
## Proportion Var 0.226 0.163 0.153
## Cumulative Var 0.226 0.389 0.542
##
## Test of the hypothesis that 3 factors are sufficient.
## The chi square statistic is 427.01 on 7 degrees of freedom.
## The p-value is 3.82e-88Field Vars
CMN Vars
Composite Scores
EAB Composite
Need to find earlier factor analysis or re-run.
Q5. To what extent do you agree or disagree with the following statements:
- Engineering as Empowering/Impactful (engemp)
- Q5a = I can make changes in my community with engineering.
- Q5c = Engineering will give me the tools and resources to make an impact in my community.
- Q5f = I can make an impact in peoples’ lives through engineering.
- Engineering as Beneficial (engbel)
- Q5b = Engineering can improve our society.
- Q5d = Engineering can improve quality of life.
- Q5e = Engineering can be a resource for my community.
- Q5g = Engineering can improve the quality of life in my community.
- Q5h = Engineering knowledge is for the advancement of human welfare. VALUES: 0 through 6 (rating scale); 0 = “Strongly Disagree”, 6 = “Strongly Agree”, = missing
Disciplines Composite
- Factor 1
- Comp Eng
- Elec
- Comp Sci
- IT
- Factor 2
- Civil
- Cons
- Ind
- Factor 3
- Agri
- Bio
- Chem
- Env (.477)
- Factor 4
- Aero
- Phys
- Mech
- MSE (.446, .434 on F3)
- Multi (.464)
- Nuc (.477, .401 on F3)
- Factor 5
- Other STEM
- Non-STEM
Career Composite
Q15. How important are the following factors for your future career satisfaction?
- Finances
- Q15e Having job security and opportunity (security)
- Q15a Making money (money)
- Leadership
- Q15b Becoming well known (known)
- Q15d Supervising others (supervising)
- Q15f Working with people (people)
- Innovation
- Q15g Inventing/designing things (invent)
- Q15h Developing new knowledge and skills (developing)
- Q15c Helping others (helping)
CMN Composites
- Thinking about your own actions, feelings and beliefs, please indicate how much you personally agree or disagree with each statement. There are no correct or wrong answers to the items. You should give the responses that most accurately describe your personal actions, feelings and beliefs. It is best if you respond with your first impression when answering.
(Reverse-coded variables recoded before factor analysis.)
- Stoic
- cmn6-Q13f = I like to talk about my feelings. *
- cmn9-Q13i = I tend to share my feelings. *
- Hetero
- cmn4-Q13d = It would be awful if someone thought I was gay.
- cmn7-Q13g = It is important to me that people think I am heterosexual.
- Risks
- cmn3-Q13c = In general, I do not like risky situations. *
- cmn18-Q13r = I enjoy taking risks.
- Help
- cmn21-Q12u = It bothers me when I have to ask for help.
- cmn17-Q13q = I never ask for help.
- Violence
- cmn8-Q13h = I believe that violence is never justified. *
- cmn12-Q13l = Sometimes violent action is necessary.
- Importance
- cmn11-Q13k = I would hate to be important. *
- cmn16-Q13p = I never do things to be an important person. *
Normality Tests
Univariate Normality
Screen for Multivariate Outliers
##
## No Yes
## 1975 91Preliminary Analyses
Correlation Plot
Correlation Matrix
Variable List
- CMN = Conformity to Masculine Norms
- Help
- Importance
- Homophobia
- Risks
- Violence
- Stoic
- Career = Career Priorities
- Finances
- Leadership
- Innovation
- EF = Engineering Fields of Interest
- Academia (Higher Education)
- Engineering Industry
- Entrepreneurship / Start a Company
- Government / Policy
- K-12 Education
- Law
- Medicine / Health
- Non-profit / NGO
- Other
- ED = Engineering Disciplines
- F1 = compeng, elec, compsci, it
- F2 = civil, cons, ind
- F3 = agri, bio, chem, env
- F4 = aero, phys, mech, mse, multi, nuc
- F5 = otherstem, nonstem
- EB = Engineering Beliefs
- Identity (id)
- Recognition (rec)
- Interest (int)
- Performance/Competence (pc)
- Belonging
- In Engineering (belong1)
- In Class (belong2)
- Engineering Agency Beliefs/Views of Engineering
- Engineering as Empowering/Impactful (engemp)
- Engineering as Beneficial (engbel)
- Identity (id)
Preliminary Regressions
Engineering Beliefs as DVs
Conformity to Masculine Norms as IVs
cmn & belong1
library(strengejacke)## # Attaching packages
## <U+2714> ggeffects 0.15.0 <U+2714> sjlabelled 1.1.6
## <U+2714> sjmisc 2.8.5 <U+2714> sjstats 0.18.0
## <U+2714> sjPlot 2.8.4.9000 <U+2714> esc 0.5.1library(car)
d <- d4
d$id <- (d$engint + d$engrec + d$engpc)/3
Y <- d$belong1
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7469 -0.6361 0.2197 0.9082 2.1884
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.42099 0.14954 29.563 < 2e-16 ***
## cmn_help -0.10173 0.02029 -5.014 5.81e-07 ***
## cmn_importance -0.14187 0.02307 -6.150 9.36e-10 ***
## cmn_hetero 0.04591 0.01502 3.057 0.00226 **
## cmn_risks 0.15374 0.03888 3.955 7.94e-05 ***
## cmn_violence 0.04900 0.03241 1.512 0.13068
## cmn_stoic -0.01948 0.01677 -1.161 0.24563
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.203 on 1968 degrees of freedom
## Multiple R-squared: 0.04859, Adjusted R-squared: 0.04569
## F-statistic: 16.75 on 6 and 1968 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred") #outputcmn & belong2
Y <- d$belong2
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7099 -0.7118 0.1590 0.9230 2.4150
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.43520 0.14315 30.984 < 2e-16 ***
## cmn_help -0.11630 0.01942 -5.988 2.51e-09 ***
## cmn_importance -0.12974 0.02208 -5.876 4.93e-09 ***
## cmn_hetero 0.03290 0.01437 2.289 0.02221 *
## cmn_risks 0.11401 0.03721 3.064 0.00222 **
## cmn_violence 0.07213 0.03102 2.325 0.02016 *
## cmn_stoic 0.01775 0.01605 1.106 0.26904
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.152 on 1968 degrees of freedom
## Multiple R-squared: 0.05194, Adjusted R-squared: 0.04905
## F-statistic: 17.97 on 6 and 1968 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred") #outputcmn & id
Y <- d$id
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3787 -0.5357 0.0749 0.6390 1.9789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.214372 0.110605 38.103 < 2e-16 ***
## cmn_help -0.055234 0.015007 -3.681 0.000239 ***
## cmn_importance -0.130764 0.017062 -7.664 2.81e-14 ***
## cmn_hetero 0.028468 0.011106 2.563 0.010442 *
## cmn_risks 0.132278 0.028754 4.600 4.49e-06 ***
## cmn_violence 0.046469 0.023968 1.939 0.052674 .
## cmn_stoic 0.006957 0.012404 0.561 0.574953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8898 on 1968 degrees of freedom
## Multiple R-squared: 0.0561, Adjusted R-squared: 0.05322
## F-statistic: 19.49 on 6 and 1968 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred") #outputcmn & engbel
Y <- d$engbel
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1535 -0.4064 0.2198 0.5760 1.3105
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.2302869 0.0955884 54.717 < 2e-16 ***
## cmn_help -0.0435362 0.0129694 -3.357 0.000803 ***
## cmn_importance -0.1374041 0.0147454 -9.318 < 2e-16 ***
## cmn_hetero -0.0226883 0.0095984 -2.364 0.018187 *
## cmn_risks 0.1117465 0.0248499 4.497 7.3e-06 ***
## cmn_violence 0.0530748 0.0207142 2.562 0.010474 *
## cmn_stoic 0.0004267 0.0107201 0.040 0.968252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.769 on 1968 degrees of freedom
## Multiple R-squared: 0.06875, Adjusted R-squared: 0.06591
## F-statistic: 24.22 on 6 and 1968 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred") #outputcmn & engemp
Y <- d$engemp
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3275 -0.5680 0.1682 0.7073 1.6063
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.924095 0.112675 43.702 < 2e-16 ***
## cmn_help -0.069538 0.015288 -4.549 5.73e-06 ***
## cmn_importance -0.163198 0.017381 -9.389 < 2e-16 ***
## cmn_hetero -0.003557 0.011314 -0.314 0.7532
## cmn_risks 0.121887 0.029292 4.161 3.30e-05 ***
## cmn_violence 0.048020 0.024417 1.967 0.0494 *
## cmn_stoic 0.027042 0.012636 2.140 0.0325 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9064 on 1968 degrees of freedom
## Multiple R-squared: 0.07601, Adjusted R-squared: 0.0732
## F-statistic: 26.98 on 6 and 1968 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred") #outputCareer Priorities as IVs
career & belong1
Y <- d$belong1
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8270 -0.5306 0.1962 0.7288 2.7352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.49745 0.16502 9.075 < 2e-16 ***
## car_fin 0.07452 0.02867 2.599 0.00942 **
## car_inno 0.59424 0.03103 19.153 < 2e-16 ***
## car_lead -0.03980 0.02549 -1.562 0.11854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.104 on 1971 degrees of freedom
## Multiple R-squared: 0.1969, Adjusted R-squared: 0.1957
## F-statistic: 161.1 on 3 and 1971 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred") #outputcareer & belong2
Y <- d$belong2
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6803 -0.6702 0.1475 0.8688 2.6691
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.371605 0.165152 14.360 <2e-16 ***
## car_fin 0.006802 0.028695 0.237 0.813
## car_inno 0.460877 0.031052 14.842 <2e-16 ***
## car_lead -0.009939 0.025508 -0.390 0.697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.105 on 1971 degrees of freedom
## Multiple R-squared: 0.1251, Adjusted R-squared: 0.1238
## F-statistic: 93.95 on 3 and 1971 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred") #outputcareer & id
Y <- d$id
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2281 -0.4901 0.0749 0.5678 2.3369
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.22557 0.12302 18.090 <2e-16 ***
## car_fin 0.04803 0.02138 2.247 0.0247 *
## car_inno 0.43582 0.02313 18.841 <2e-16 ***
## car_lead -0.02787 0.01900 -1.467 0.1426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8234 on 1971 degrees of freedom
## Multiple R-squared: 0.1904, Adjusted R-squared: 0.1892
## F-statistic: 154.5 on 3 and 1971 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred") #outputcareer & views (engbel)
Y <- d$engbel
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2387 -0.3649 0.1528 0.4742 1.6878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.12520 0.10624 29.416 < 2e-16 ***
## car_fin 0.12986 0.01846 7.035 2.74e-12 ***
## car_inno 0.37369 0.01998 18.707 < 2e-16 ***
## car_lead -0.07665 0.01641 -4.671 3.20e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7111 on 1971 degrees of freedom
## Multiple R-squared: 0.2025, Adjusted R-squared: 0.2012
## F-statistic: 166.8 on 3 and 1971 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred") #outputcareer & views (engemp)
Y <- d$engemp
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4720 -0.4824 0.1030 0.5273 2.3397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.23086 0.12069 18.485 < 2e-16 ***
## car_fin 0.07820 0.02097 3.729 0.000197 ***
## car_inno 0.52502 0.02269 23.137 < 2e-16 ***
## car_lead -0.04065 0.01864 -2.181 0.029333 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8078 on 1971 degrees of freedom
## Multiple R-squared: 0.2651, Adjusted R-squared: 0.264
## F-statistic: 237 on 3 and 1971 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred") #outputCareer Priorities as DVs
Conformity to Masculine Norms as IVs
cmn & career_fin
Y <- d$car_fin
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6969 -0.5108 0.1281 0.6786 1.8082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.42875 0.11370 38.952 < 2e-16 ***
## cmn_help 0.02271 0.01543 1.472 0.1411
## cmn_importance -0.12667 0.01754 -7.222 7.27e-13 ***
## cmn_hetero 0.02893 0.01142 2.534 0.0113 *
## cmn_risks 0.16283 0.02956 5.509 4.08e-08 ***
## cmn_violence 0.04383 0.02464 1.779 0.0754 .
## cmn_stoic -0.02942 0.01275 -2.307 0.0211 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9147 on 1968 degrees of freedom
## Multiple R-squared: 0.04743, Adjusted R-squared: 0.04453
## F-statistic: 16.33 on 6 and 1968 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred")cmn & career_inno
Y <- d$car_inno
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4984 -0.5787 0.0866 0.6681 1.8038
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.52581 0.10883 41.585 < 2e-16 ***
## cmn_help -0.09547 0.01477 -6.465 1.27e-10 ***
## cmn_importance -0.10493 0.01679 -6.250 5.02e-10 ***
## cmn_hetero -0.01107 0.01093 -1.013 0.311
## cmn_risks 0.16610 0.02829 5.870 5.09e-09 ***
## cmn_violence 0.03578 0.02358 1.517 0.129
## cmn_stoic 0.05955 0.01221 4.879 1.15e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8755 on 1968 degrees of freedom
## Multiple R-squared: 0.0837, Adjusted R-squared: 0.08091
## F-statistic: 29.96 on 6 and 1968 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred")cmn & career_lead
Y <- d$car_lead
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4948 -0.6643 -0.0035 0.6895 3.0090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.78025 0.13225 21.023 < 2e-16 ***
## cmn_help -0.04474 0.01794 -2.494 0.0127 *
## cmn_importance -0.19858 0.02040 -9.734 < 2e-16 ***
## cmn_hetero 0.07825 0.01328 5.893 4.45e-09 ***
## cmn_risks 0.20175 0.03438 5.868 5.15e-09 ***
## cmn_violence 0.04388 0.02866 1.531 0.1259
## cmn_stoic 0.11940 0.01483 8.051 1.41e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.064 on 1968 degrees of freedom
## Multiple R-squared: 0.1235, Adjusted R-squared: 0.1208
## F-statistic: 46.2 on 6 and 1968 DF, p-value: < 2.2e-16# plot_model(fit, type = "pred")Discipline Interest as DVs
Conformity to Masculine Norms as IVs
Y <- d$disc1
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1330 -1.3095 0.0659 1.1810 3.8919
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.943029 0.195997 9.914 < 2e-16 ***
## cmn_help 0.029980 0.026593 1.127 0.25972
## cmn_importance 0.011762 0.030234 0.389 0.69729
## cmn_hetero -0.037279 0.019681 -1.894 0.05835 .
## cmn_risks 0.069367 0.050953 1.361 0.17354
## cmn_violence 0.129242 0.042473 3.043 0.00237 **
## cmn_stoic -0.004742 0.021981 -0.216 0.82922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.577 on 1968 degrees of freedom
## Multiple R-squared: 0.009099, Adjusted R-squared: 0.006078
## F-statistic: 3.012 on 6 and 1968 DF, p-value: 0.006217Y <- d$disc2
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2918 -1.0482 0.0935 1.0372 4.1189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7556607 0.1831854 9.584 < 2e-16 ***
## cmn_help 0.0200362 0.0248544 0.806 0.4203
## cmn_importance 0.0163736 0.0282581 0.579 0.5624
## cmn_hetero 0.0930810 0.0183943 5.060 4.58e-07 ***
## cmn_risks 0.1029636 0.0476223 2.162 0.0307 *
## cmn_violence -0.0002702 0.0396967 -0.007 0.9946
## cmn_stoic 0.0255083 0.0205440 1.242 0.2145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.474 on 1968 degrees of freedom
## Multiple R-squared: 0.02132, Adjusted R-squared: 0.01834
## F-statistic: 7.147 on 6 and 1968 DF, p-value: 1.49e-07Y <- d$disc3
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.946 -1.058 0.055 1.053 3.891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.105657 0.173570 12.131 < 2e-16 ***
## cmn_help 0.009695 0.023550 0.412 0.68061
## cmn_importance 0.019457 0.026775 0.727 0.46751
## cmn_hetero -0.050014 0.017429 -2.870 0.00415 **
## cmn_risks 0.088308 0.045123 1.957 0.05048 .
## cmn_violence -0.025072 0.037613 -0.667 0.50512
## cmn_stoic 0.080738 0.019466 4.148 3.5e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.396 on 1968 degrees of freedom
## Multiple R-squared: 0.01533, Adjusted R-squared: 0.01233
## F-statistic: 5.106 on 6 and 1968 DF, p-value: 3.253e-05Y <- d$disc4
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4159 -0.7879 0.2007 0.9028 3.4270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.361836 0.157094 15.035 < 2e-16 ***
## cmn_help 0.027806 0.021314 1.305 0.19219
## cmn_importance 0.004907 0.024233 0.203 0.83954
## cmn_hetero -0.015791 0.015774 -1.001 0.31692
## cmn_risks 0.103209 0.040839 2.527 0.01158 *
## cmn_violence 0.101550 0.034043 2.983 0.00289 **
## cmn_stoic -0.015002 0.017618 -0.852 0.39457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.264 on 1968 degrees of freedom
## Multiple R-squared: 0.01162, Adjusted R-squared: 0.008607
## F-statistic: 3.856 on 6 and 1968 DF, p-value: 0.0007881Y <- d$disc5
fit <- lm(Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks + cmn_violence + cmn_stoic, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_importance + cmn_hetero + cmn_risks +
## cmn_violence + cmn_stoic, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9998 -1.1827 0.2241 0.8778 4.1339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.68976 0.18660 9.056 < 2e-16 ***
## cmn_help 0.05618 0.02532 2.219 0.02660 *
## cmn_importance 0.02156 0.02878 0.749 0.45393
## cmn_hetero -0.08575 0.01874 -4.577 5.02e-06 ***
## cmn_risks 0.15213 0.04851 3.136 0.00174 **
## cmn_violence 0.02060 0.04044 0.509 0.61056
## cmn_stoic 0.05363 0.02093 2.563 0.01046 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.501 on 1968 degrees of freedom
## Multiple R-squared: 0.0205, Adjusted R-squared: 0.01752
## F-statistic: 6.866 on 6 and 1968 DF, p-value: 3.157e-07Career Priorities as IVs
Y <- d$disc1
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7601 -1.3272 0.0927 1.1880 3.5621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.85355 0.23588 7.858 6.37e-15 ***
## car_fin 0.05444 0.04099 1.328 0.184
## car_inno 0.06150 0.04435 1.387 0.166
## car_lead 0.03517 0.03643 0.965 0.335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.579 on 1971 degrees of freedom
## Multiple R-squared: 0.004983, Adjusted R-squared: 0.003468
## F-statistic: 3.29 on 3 and 1971 DF, p-value: 0.01991Y <- d$disc2
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9874 -1.0615 0.1229 1.0265 3.8365
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.72060 0.21992 7.824 8.29e-15 ***
## car_fin 0.06787 0.03821 1.776 0.0759 .
## car_inno -0.04178 0.04135 -1.010 0.3124
## car_lead 0.18272 0.03397 5.379 8.36e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.472 on 1971 degrees of freedom
## Multiple R-squared: 0.02212, Adjusted R-squared: 0.02063
## F-statistic: 14.86 on 3 and 1971 DF, p-value: 1.444e-09Y <- d$disc3
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0008 -1.0651 0.0646 1.0444 3.7079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.06302 0.20825 9.906 < 2e-16 ***
## car_fin -0.11883 0.03618 -3.284 0.001042 **
## car_inno 0.13208 0.03916 3.373 0.000757 ***
## car_lead 0.09408 0.03216 2.925 0.003484 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.394 on 1971 degrees of freedom
## Multiple R-squared: 0.01731, Adjusted R-squared: 0.01581
## F-statistic: 11.57 on 3 and 1971 DF, p-value: 1.617e-07Y <- d$disc4
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4019 -0.7522 0.2137 0.8594 3.1096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.084959 0.186995 11.150 < 2e-16 ***
## car_fin -0.055279 0.032491 -1.701 0.089 .
## car_inno 0.251152 0.035159 7.143 1.28e-12 ***
## car_lead -0.008058 0.028882 -0.279 0.780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.252 on 1971 degrees of freedom
## Multiple R-squared: 0.02912, Adjusted R-squared: 0.02764
## F-statistic: 19.71 on 3 and 1971 DF, p-value: 1.382e-12Y <- d$disc5
fit <- lm(Y ~ car_fin + car_inno + car_lead, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8123 -1.2489 0.2053 0.8348 3.9641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.5257149 0.2252640 11.212 < 2e-16 ***
## car_fin -0.1311335 0.0391401 -3.350 0.000822 ***
## car_inno -0.0003162 0.0423542 -0.007 0.994044
## car_lead 0.1278479 0.0347927 3.675 0.000245 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.508 on 1971 degrees of freedom
## Multiple R-squared: 0.01035, Adjusted R-squared: 0.008846
## F-statistic: 6.872 on 3 and 1971 DF, p-value: 0.0001327Engineering Beliefs as IVs
d$id <- (d$engint + d$engrec + d$engpc)/3
Y <- d$disc1
fit <- lm(Y ~ belong1 + belong2 + id + engbel + engemp, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ belong1 + belong2 + id + engbel + engemp, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9881 -1.2640 0.0828 1.1649 3.6547
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.94875 0.24815 7.853 6.61e-15 ***
## belong1 0.09709 0.04943 1.964 0.049647 *
## belong2 -0.05210 0.04289 -1.215 0.224693
## id 0.22428 0.06183 3.627 0.000294 ***
## engbel -0.09564 0.07763 -1.232 0.218056
## engemp -0.02170 0.07017 -0.309 0.757097
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.567 on 1969 degrees of freedom
## Multiple R-squared: 0.02123, Adjusted R-squared: 0.01875
## F-statistic: 8.544 on 5 and 1969 DF, p-value: 5.138e-08Y <- d$disc2
fit <- lm(Y ~ belong1 + belong2 + id + engbel + engemp, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ belong1 + belong2 + id + engbel + engemp, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8567 -1.0666 0.0967 1.0457 3.6703
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.974568 0.234207 8.431 <2e-16 ***
## belong1 0.040270 0.046653 0.863 0.3881
## belong2 -0.008376 0.040484 -0.207 0.8361
## id 0.104824 0.058357 1.796 0.0726 .
## engbel -0.177242 0.073265 -2.419 0.0156 *
## engemp 0.169825 0.066223 2.564 0.0104 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.479 on 1969 degrees of freedom
## Multiple R-squared: 0.01424, Adjusted R-squared: 0.01173
## F-statistic: 5.687 on 5 and 1969 DF, p-value: 3.245e-05Y <- d$disc3
fit <- lm(Y ~ belong1 + belong2 + id + engbel + engemp, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ belong1 + belong2 + id + engbel + engemp, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8921 -1.0484 0.0637 1.0253 3.5260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.999402 0.221841 9.013 < 2e-16 ***
## belong1 -0.090699 0.044190 -2.052 0.04025 *
## belong2 0.001622 0.038347 0.042 0.96627
## id 0.038779 0.055276 0.702 0.48304
## engbel -0.051155 0.069396 -0.737 0.46112
## engemp 0.192816 0.062726 3.074 0.00214 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.401 on 1969 degrees of freedom
## Multiple R-squared: 0.008842, Adjusted R-squared: 0.006325
## F-statistic: 3.513 on 5 and 1969 DF, p-value: 0.003643Y <- d$disc4
fit <- lm(Y ~ belong1 + belong2 + id + engbel + engemp, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ belong1 + belong2 + id + engbel + engemp, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4087 -0.7815 0.1864 0.8683 2.9830
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.46521 0.19517 7.508 9.08e-14 ***
## belong1 0.08929 0.03888 2.297 0.0217 *
## belong2 -0.05042 0.03374 -1.495 0.1352
## id 0.25945 0.04863 5.335 1.06e-07 ***
## engbel 0.01629 0.06105 0.267 0.7897
## engemp 0.02249 0.05518 0.408 0.6836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.232 on 1969 degrees of freedom
## Multiple R-squared: 0.06001, Adjusted R-squared: 0.05762
## F-statistic: 25.14 on 5 and 1969 DF, p-value: < 2.2e-16Y <- d$disc5
fit <- lm(Y ~ belong1 + belong2 + id + engbel + engemp, d=d)
summary(fit)##
## Call:
## lm(formula = Y ~ belong1 + belong2 + id + engbel + engemp, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5042 -1.1810 0.1930 0.8898 4.1697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.12806 0.23569 9.029 < 2e-16 ***
## belong1 -0.36666 0.04695 -7.810 9.24e-15 ***
## belong2 0.00160 0.04074 0.039 0.9687
## id 0.24972 0.05873 4.252 2.22e-05 ***
## engbel 0.03557 0.07373 0.482 0.6296
## engemp 0.11685 0.06664 1.753 0.0797 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.488 on 1969 degrees of freedom
## Multiple R-squared: 0.03705, Adjusted R-squared: 0.0346
## F-statistic: 15.15 on 5 and 1969 DF, p-value: 1.246e-14Preliminary Moderated Regressions
Gender as Moderator
- Gender frequently moderates the relationship between cmn violence & engineering beliefs
- Gender moderates relationship between career priorities and pc
- No effect of gender on views & other beliefs
- Gender moderates relationship between views and career priorities
Setup
library(strengejacke)
colnames(gender) <- c("Litho","genid","genid2")
dm <- merge(d, gender, by = "Litho", all.x = T)
table(dm$genid)##
## Agender Cisgender Female
## 2 1 493
## Genderqueer Male Multiple Options Selected
## 3 1363 12
## Not Listed Transgender
## 21 3dm$genfin[dm$genid == "Male"] <- 0 #male
dm$genfin[dm$genid == "Agender" | dm$genid == "Genderqueer" | dm$genid == "Multiple Options Selected" | dm$genid == "Not Listed" | dm$genid == "Female"] <- 1 #women and non-binary
dm$genfin <- as.factor(dm$genfin)
table(dm$genfin, useNA = "always")##
## 0 1 <NA>
## 1363 531 81cmn & belong1
Y <- dm$belong1
fit1 <- lm(Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence + cmn_risks + cmn_stoic + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence +
## cmn_risks + cmn_stoic + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7589 -0.6637 0.2180 0.8990 2.1115
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.55367 0.15350 29.666 < 2e-16 ***
## cmn_help -0.09760 0.02047 -4.768 2.00e-06 ***
## cmn_hetero 0.03069 0.01565 1.961 0.050084 .
## cmn_importance -0.14470 0.02336 -6.194 7.19e-10 ***
## cmn_violence 0.04134 0.03291 1.256 0.209211
## cmn_risks 0.14293 0.03934 3.633 0.000287 ***
## cmn_stoic -0.01099 0.01698 -0.647 0.517684
## genfin1 -0.19811 0.06368 -3.111 0.001892 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.192 on 1886 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.05049, Adjusted R-squared: 0.04697
## F-statistic: 14.33 on 7 and 1886 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ cmn_help*genfin + cmn_hetero*genfin + cmn_importance*genfin + cmn_violence*genfin + cmn_risks*genfin + cmn_stoic*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ cmn_help * genfin + cmn_hetero * genfin + cmn_importance *
## genfin + cmn_violence * genfin + cmn_risks * genfin + cmn_stoic *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7863 -0.6480 0.2207 0.8979 2.1295
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.45400 0.17709 25.151 < 2e-16 ***
## cmn_help -0.11320 0.02437 -4.644 3.65e-06 ***
## genfin1 0.11196 0.34643 0.323 0.74660
## cmn_hetero 0.02382 0.01871 1.273 0.20318
## cmn_importance -0.13463 0.02799 -4.810 1.63e-06 ***
## cmn_violence 0.09161 0.04014 2.282 0.02258 *
## cmn_risks 0.14736 0.04590 3.211 0.00135 **
## cmn_stoic -0.02316 0.02040 -1.135 0.25650
## cmn_help:genfin1 0.05399 0.04509 1.197 0.23129
## genfin1:cmn_hetero 0.01802 0.03426 0.526 0.59894
## genfin1:cmn_importance -0.03278 0.05120 -0.640 0.52211
## genfin1:cmn_violence -0.15332 0.07027 -2.182 0.02924 *
## genfin1:cmn_risks -0.01613 0.08955 -0.180 0.85711
## genfin1:cmn_stoic 0.03278 0.03716 0.882 0.37773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.191 on 1880 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.05414, Adjusted R-squared: 0.0476
## F-statistic: 8.278 on 13 and 1880 DF, p-value: < 2.2e-16plot_model(fit2, type = "int")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
cmn & belong2
Y <- dm$belong2
fit1 <- lm(Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence + cmn_risks + cmn_stoic + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence +
## cmn_risks + cmn_stoic + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7380 -0.7120 0.1492 0.9308 2.3227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.54332 0.14777 30.745 < 2e-16 ***
## cmn_help -0.11134 0.01971 -5.650 1.85e-08 ***
## cmn_hetero 0.02247 0.01507 1.491 0.13607
## cmn_importance -0.13140 0.02249 -5.843 6.04e-09 ***
## cmn_violence 0.05841 0.03168 1.844 0.06538 .
## cmn_risks 0.10905 0.03787 2.879 0.00403 **
## cmn_stoic 0.02778 0.01635 1.699 0.08951 .
## genfin1 -0.16906 0.06130 -2.758 0.00588 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.147 on 1886 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.05346, Adjusted R-squared: 0.04995
## F-statistic: 15.22 on 7 and 1886 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ cmn_help*genfin + cmn_hetero*genfin + cmn_importance*genfin + cmn_violence*genfin + cmn_risks*genfin + cmn_stoic*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ cmn_help * genfin + cmn_hetero * genfin + cmn_importance *
## genfin + cmn_violence * genfin + cmn_risks * genfin + cmn_stoic *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8004 -0.7099 0.1473 0.9194 2.3391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.511444 0.170131 26.517 < 2e-16 ***
## cmn_help -0.126197 0.023416 -5.389 7.96e-08 ***
## genfin1 -0.171164 0.332809 -0.514 0.60710
## cmn_hetero 0.011068 0.017977 0.616 0.53821
## cmn_importance -0.118661 0.026889 -4.413 1.08e-05 ***
## cmn_violence 0.123011 0.038561 3.190 0.00145 **
## cmn_risks 0.091252 0.044092 2.070 0.03863 *
## cmn_stoic 0.002927 0.019602 0.149 0.88133
## cmn_help:genfin1 0.054176 0.043313 1.251 0.21116
## genfin1:cmn_hetero 0.032621 0.032915 0.991 0.32178
## genfin1:cmn_importance -0.035056 0.049185 -0.713 0.47610
## genfin1:cmn_violence -0.192830 0.067509 -2.856 0.00433 **
## genfin1:cmn_risks 0.072480 0.086034 0.842 0.39964
## genfin1:cmn_stoic 0.071921 0.035695 2.015 0.04406 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.144 on 1880 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.06103, Adjusted R-squared: 0.05454
## F-statistic: 9.4 on 13 and 1880 DF, p-value: < 2.2e-16plot_model(fit2, type = "int")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
cmn & id
Y <- dm$id
fit1 <- lm(Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence + cmn_risks + cmn_stoic + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ cmn_help + cmn_hetero + cmn_importance + cmn_violence +
## cmn_risks + cmn_stoic + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4120 -0.5413 0.0841 0.6565 1.9402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.27143 0.11457 37.282 < 2e-16 ***
## cmn_help -0.05100 0.01528 -3.338 0.000861 ***
## cmn_hetero 0.01875 0.01168 1.605 0.108737
## cmn_importance -0.13398 0.01744 -7.683 2.47e-14 ***
## cmn_violence 0.04202 0.02456 1.711 0.087255 .
## cmn_risks 0.13141 0.02936 4.476 8.08e-06 ***
## cmn_stoic 0.01277 0.01268 1.008 0.313751
## genfin1 -0.11651 0.04753 -2.451 0.014327 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8894 on 1886 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.05706, Adjusted R-squared: 0.05356
## F-statistic: 16.3 on 7 and 1886 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ cmn_help*genfin + cmn_hetero*genfin + cmn_importance*genfin + cmn_violence*genfin + cmn_risks*genfin + cmn_stoic*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ cmn_help * genfin + cmn_hetero * genfin + cmn_importance *
## genfin + cmn_violence * genfin + cmn_risks * genfin + cmn_stoic *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3884 -0.5453 0.0936 0.6406 1.9475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2069769 0.1320837 31.851 < 2e-16 ***
## cmn_help -0.0643027 0.0181793 -3.537 0.000414 ***
## genfin1 0.0365757 0.2583811 0.142 0.887445
## cmn_hetero 0.0196316 0.0139570 1.407 0.159718
## cmn_importance -0.1283737 0.0208756 -6.149 9.47e-10 ***
## cmn_violence 0.0864753 0.0299374 2.889 0.003915 **
## cmn_risks 0.1254576 0.0342314 3.665 0.000254 ***
## cmn_stoic -0.0005074 0.0152185 -0.033 0.973405
## cmn_help:genfin1 0.0490345 0.0336267 1.458 0.144952
## genfin1:cmn_hetero -0.0074212 0.0255540 -0.290 0.771534
## genfin1:cmn_importance -0.0150330 0.0381856 -0.394 0.693859
## genfin1:cmn_violence -0.1359951 0.0524119 -2.595 0.009540 **
## genfin1:cmn_risks 0.0210468 0.0667936 0.315 0.752719
## genfin1:cmn_stoic 0.0394306 0.0277126 1.423 0.154949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8884 on 1880 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.06208, Adjusted R-squared: 0.05559
## F-statistic: 9.572 on 13 and 1880 DF, p-value: < 2.2e-16plot_model(fit2, type = "int")## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
career & belong1
Y <- dm$belong1
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9020 -0.5462 0.1957 0.7368 2.6409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.58323 0.16832 9.406 < 2e-16 ***
## car_fin 0.07818 0.02910 2.687 0.00728 **
## car_inno 0.58777 0.03166 18.562 < 2e-16 ***
## car_lead -0.03879 0.02576 -1.506 0.13231
## genfin1 -0.26914 0.05614 -4.794 1.76e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.094 on 1889 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.1983, Adjusted R-squared: 0.1966
## F-statistic: 116.8 on 4 and 1889 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8427 -0.5543 0.1843 0.7461 2.5278
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.79127 0.19590 9.144 < 2e-16 ***
## car_fin 0.07935 0.03370 2.355 0.01865 *
## genfin1 -1.08065 0.38200 -2.829 0.00472 **
## car_inno 0.56285 0.03692 15.245 < 2e-16 ***
## car_lead -0.06509 0.03065 -2.124 0.03382 *
## car_fin:genfin1 -0.02632 0.06687 -0.394 0.69392
## genfin1:car_inno 0.11959 0.07215 1.658 0.09756 .
## genfin1:car_lead 0.09807 0.05672 1.729 0.08396 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.092 on 1886 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.2023, Adjusted R-squared: 0.1993
## F-statistic: 68.32 on 7 and 1886 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
##
## [[3]]
career & belong2
Y <- dm$belong2
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7475 -0.6893 0.1144 0.8122 2.5887
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.435024 0.169569 14.360 < 2e-16 ***
## car_fin 0.011633 0.029318 0.397 0.691557
## car_inno 0.454401 0.031900 14.244 < 2e-16 ***
## car_lead -0.007045 0.025954 -0.271 0.786073
## genfin1 -0.207301 0.056554 -3.666 0.000254 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.102 on 1889 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.1248, Adjusted R-squared: 0.1229
## F-statistic: 67.32 on 4 and 1889 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7649 -0.6608 0.1319 0.8426 2.4762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.45080 0.19749 12.410 <2e-16 ***
## car_fin 0.04392 0.03397 1.293 0.1963
## genfin1 -0.32841 0.38511 -0.853 0.3939
## car_inno 0.42652 0.03722 11.459 <2e-16 ***
## car_lead -0.01802 0.03090 -0.583 0.5599
## car_fin:genfin1 -0.13772 0.06741 -2.043 0.0412 *
## genfin1:car_inno 0.12243 0.07273 1.683 0.0925 .
## genfin1:car_lead 0.05354 0.05718 0.936 0.3492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.101 on 1886 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.1279, Adjusted R-squared: 0.1247
## F-statistic: 39.53 on 7 and 1886 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
##
## [[3]]
career & id
Y <- dm$id
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2658 -0.4908 0.0660 0.5598 2.2939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.23972 0.12648 17.708 < 2e-16 ***
## car_fin 0.05171 0.02187 2.365 0.018144 *
## car_inno 0.44174 0.02379 18.565 < 2e-16 ***
## car_lead -0.03295 0.01936 -1.702 0.088866 .
## genfin1 -0.15865 0.04218 -3.761 0.000174 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8221 on 1889 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.1931, Adjusted R-squared: 0.1914
## F-statistic: 113 on 4 and 1889 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2590 -0.5047 0.0715 0.5694 2.2079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.29918 0.14717 15.622 < 2e-16 ***
## car_fin 0.06695 0.02532 2.644 0.00825 **
## genfin1 -0.40652 0.28699 -1.417 0.15679
## car_inno 0.43713 0.02774 15.759 < 2e-16 ***
## car_lead -0.06428 0.02303 -2.791 0.00530 **
## car_fin:genfin1 -0.07456 0.05024 -1.484 0.13795
## genfin1:car_inno 0.04117 0.05420 0.760 0.44765
## genfin1:car_lead 0.11437 0.04261 2.684 0.00733 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8205 on 1886 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.1974, Adjusted R-squared: 0.1945
## F-statistic: 66.28 on 7 and 1886 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
##
## [[3]]
career & pc
Y <- dm$engpc
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3178 -0.6304 0.1330 0.7676 2.3668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.52037 0.15954 15.798 < 2e-16 ***
## car_fin 0.05020 0.02758 1.820 0.0689 .
## car_inno 0.41638 0.03001 13.873 < 2e-16 ***
## car_lead -0.06349 0.02442 -2.600 0.0094 **
## genfin1 -0.26301 0.05321 -4.943 8.37e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 1889 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.1158, Adjusted R-squared: 0.1139
## F-statistic: 61.82 on 4 and 1889 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3869 -0.6350 0.1467 0.7576 2.3370
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.61201 0.18560 14.073 < 2e-16 ***
## car_fin 0.06914 0.03193 2.166 0.030472 *
## genfin1 -0.64129 0.36193 -1.772 0.076580 .
## car_inno 0.40713 0.03498 11.638 < 2e-16 ***
## car_lead -0.10265 0.02904 -3.535 0.000418 ***
## car_fin:genfin1 -0.09410 0.06336 -1.485 0.137663
## genfin1:car_inno 0.06563 0.06836 0.960 0.337091
## genfin1:car_lead 0.14382 0.05374 2.676 0.007506 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.035 on 1886 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.1209, Adjusted R-squared: 0.1176
## F-statistic: 37.05 on 7 and 1886 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
##
## [[3]]
career & int
Y <- dm$engint
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7663 -0.4436 0.2339 0.5669 2.4735
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.26414 0.14166 15.983 < 2e-16 ***
## car_fin 0.10050 0.02449 4.103 4.25e-05 ***
## car_inno 0.55997 0.02665 21.012 < 2e-16 ***
## car_lead -0.10270 0.02168 -4.737 2.34e-06 ***
## genfin1 -0.05447 0.04725 -1.153 0.249
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9208 on 1889 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.2242, Adjusted R-squared: 0.2226
## F-statistic: 136.5 on 4 and 1889 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7637 -0.4381 0.2357 0.5617 2.4271
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.33548 0.16508 14.148 < 2e-16 ***
## car_fin 0.10816 0.02840 3.809 0.000144 ***
## genfin1 -0.33900 0.32190 -1.053 0.292414
## car_inno 0.55458 0.03111 17.825 < 2e-16 ***
## car_lead -0.12592 0.02583 -4.875 1.18e-06 ***
## car_fin:genfin1 -0.04224 0.05635 -0.750 0.453616
## genfin1:car_inno 0.03839 0.06080 0.631 0.527845
## genfin1:car_lead 0.08431 0.04779 1.764 0.077874 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9203 on 1886 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.2262, Adjusted R-squared: 0.2233
## F-statistic: 78.75 on 7 and 1886 DF, p-value: < 2.2e-16career & rec
Y <- dm$engrec
fit1 <- lm(Y ~ car_fin + car_inno + car_lead + genfin, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ car_fin + car_inno + car_lead + genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4584 -0.7339 0.0713 0.8172 3.0237
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.934673 0.181838 10.640 < 2e-16 ***
## car_fin 0.004438 0.031439 0.141 0.88775
## car_inno 0.348861 0.034208 10.198 < 2e-16 ***
## car_lead 0.067322 0.027831 2.419 0.01566 *
## genfin1 -0.158480 0.060646 -2.613 0.00904 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.182 on 1889 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.08593, Adjusted R-squared: 0.084
## F-statistic: 44.4 on 4 and 1889 DF, p-value: < 2.2e-16fit2 <- lm(Y ~ car_fin*genfin + car_inno*genfin + car_lead*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ car_fin * genfin + car_inno * genfin + car_lead *
## genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4473 -0.7413 0.0698 0.8393 2.9236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.95006 0.21191 9.202 <2e-16 ***
## car_fin 0.02355 0.03646 0.646 0.518
## genfin1 -0.23926 0.41323 -0.579 0.563
## car_inno 0.34967 0.03994 8.755 <2e-16 ***
## car_lead 0.03574 0.03316 1.078 0.281
## car_fin:genfin1 -0.08734 0.07234 -1.207 0.227
## genfin1:car_inno 0.01948 0.07804 0.250 0.803
## genfin1:car_lead 0.11499 0.06135 1.874 0.061 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.181 on 1886 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.08812, Adjusted R-squared: 0.08474
## F-statistic: 26.04 on 7 and 1886 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
##
## [[3]]
views & belong1
Y <- dm$belong1
fit1 <- lm(Y ~ engbel + engemp, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ engbel + engemp, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8542 -0.5101 0.1881 0.6598 2.9522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.75537 0.15315 4.932 8.81e-07 ***
## engbel 0.05014 0.04965 1.010 0.313
## engemp 0.71399 0.04196 17.016 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.01 on 1972 degrees of freedom
## Multiple R-squared: 0.328, Adjusted R-squared: 0.3273
## F-statistic: 481.2 on 2 and 1972 DF, p-value: < 2.2e-16# plot_model(fit1, type="pred")
fit2 <- lm(Y ~ engbel*genfin + engemp*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ engbel * genfin + engemp * genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5910 -0.5090 0.1918 0.6090 3.3213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.89458 0.18296 4.890 1.1e-06 ***
## engbel 0.07970 0.05913 1.348 0.1778
## genfin1 -0.76320 0.33593 -2.272 0.0232 *
## engemp 0.66971 0.04955 13.515 < 2e-16 ***
## engbel:genfin1 0.02274 0.11190 0.203 0.8390
## genfin1:engemp 0.07697 0.09610 0.801 0.4233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9912 on 1888 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.3423, Adjusted R-squared: 0.3405
## F-statistic: 196.5 on 5 and 1888 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
views & belong2
Y <- dm$belong2
fit1 <- lm(Y ~ engbel + engemp, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ engbel + engemp, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6863 -0.5373 0.1521 0.7734 2.8415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.09043 0.15450 7.058 2.33e-12 ***
## engbel 0.20154 0.05009 4.023 5.95e-05 ***
## engemp 0.48782 0.04233 11.524 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.019 on 1972 degrees of freedom
## Multiple R-squared: 0.2561, Adjusted R-squared: 0.2554
## F-statistic: 339.5 on 2 and 1972 DF, p-value: < 2.2e-16# plot_model(fit1, type="pred")
fit2 <- lm(Y ~ engbel*genfin + engemp*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ engbel * genfin + engemp * genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7385 -0.5265 0.1604 0.7202 3.1586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.20889 0.18555 6.515 9.28e-11 ***
## engbel 0.20638 0.05996 3.442 0.00059 ***
## genfin1 -0.68801 0.34068 -2.020 0.04357 *
## engemp 0.47209 0.05026 9.394 < 2e-16 ***
## engbel:genfin1 0.03797 0.11349 0.335 0.73795
## genfin1:engemp 0.05707 0.09746 0.586 0.55827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.005 on 1888 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.2724, Adjusted R-squared: 0.2705
## F-statistic: 141.4 on 5 and 1888 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
views & id
Y <- dm$id
fit1 <- lm(Y ~ engbel + engemp, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ engbel + engemp, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6391 -0.4179 0.0821 0.5155 2.3658
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.40610 0.11292 12.452 < 2e-16 ***
## engbel 0.14230 0.03661 3.887 0.000105 ***
## engemp 0.46151 0.03094 14.918 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7448 on 1972 degrees of freedom
## Multiple R-squared: 0.3374, Adjusted R-squared: 0.3367
## F-statistic: 502 on 2 and 1972 DF, p-value: < 2.2e-16# plot_model(fit1, type="pred")
fit2 <- lm(Y ~ engbel*genfin + engemp*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ engbel * genfin + engemp * genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6998 -0.4302 0.0737 0.5157 2.4639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.52454 0.13649 11.170 < 2e-16 ***
## engbel 0.15385 0.04411 3.488 0.000498 ***
## genfin1 -0.60343 0.25061 -2.408 0.016142 *
## engemp 0.43380 0.03697 11.734 < 2e-16 ***
## engbel:genfin1 0.02258 0.08348 0.271 0.786797
## genfin1:engemp 0.06697 0.07170 0.934 0.350393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7394 on 1888 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.3475, Adjusted R-squared: 0.3458
## F-statistic: 201.1 on 5 and 1888 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
views & career priorities (finances)
Y <- dm$car_fin
fit1 <- lm(Y ~ engbel + engemp, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ engbel + engemp, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0588 -0.5226 0.1239 0.6618 1.9718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.40427 0.13780 24.704 < 2e-16 ***
## engbel 0.24419 0.04468 5.465 5.2e-08 ***
## engemp 0.03788 0.03776 1.003 0.316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9089 on 1972 degrees of freedom
## Multiple R-squared: 0.05751, Adjusted R-squared: 0.05655
## F-statistic: 60.17 on 2 and 1972 DF, p-value: < 2.2e-16# plot_model(fit1, type="pred")
fit2 <- lm(Y ~ engbel*genfin + engemp*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ engbel * genfin + engemp * genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0463 -0.5190 0.1207 0.6496 1.9000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.420243 0.167486 20.421 < 2e-16 ***
## engbel 0.229504 0.054126 4.240 2.34e-05 ***
## genfin1 0.145088 0.307522 0.472 0.637
## engemp 0.049800 0.045364 1.098 0.272
## engbel:genfin1 0.004024 0.102440 0.039 0.969
## genfin1:engemp -0.033156 0.087978 -0.377 0.706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9074 on 1888 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.05379, Adjusted R-squared: 0.05128
## F-statistic: 21.46 on 5 and 1888 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
views & career priorities (leadership)
Y <- dm$car_lead
fit1 <- lm(Y ~ engbel + engemp, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ engbel + engemp, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7045 -0.7294 -0.0327 0.7464 2.8984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.54976 0.16792 15.184 < 2e-16 ***
## engbel -0.14051 0.05444 -2.581 0.00993 **
## engemp 0.35255 0.04601 7.663 2.83e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 1972 degrees of freedom
## Multiple R-squared: 0.04815, Adjusted R-squared: 0.04718
## F-statistic: 49.88 on 2 and 1972 DF, p-value: < 2.2e-16# plot_model(fit1, type="pred")
fit2 <- lm(Y ~ engbel*genfin + engemp*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ engbel * genfin + engemp * genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6976 -0.7214 -0.0284 0.7432 2.8910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.60602 0.20495 12.716 < 2e-16 ***
## engbel -0.18040 0.06623 -2.724 0.00651 **
## genfin1 -0.07132 0.37631 -0.190 0.84970
## engemp 0.38320 0.05551 6.903 6.92e-12 ***
## engbel:genfin1 0.11756 0.12535 0.938 0.34845
## genfin1:engemp -0.11143 0.10766 -1.035 0.30078
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.11 on 1888 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.04705, Adjusted R-squared: 0.04452
## F-statistic: 18.64 on 5 and 1888 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]
views & career priorities (innovation)
Y <- dm$car_inno
fit1 <- lm(Y ~ engbel + engemp, d=dm)
summary(fit1)##
## Call:
## lm(formula = Y ~ engbel + engemp, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.842 -0.502 0.147 0.664 2.803
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.33152 0.11922 19.556 <2e-16 ***
## engbel 0.02216 0.03865 0.573 0.566
## engemp 0.47859 0.03266 14.652 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7863 on 1972 degrees of freedom
## Multiple R-squared: 0.2594, Adjusted R-squared: 0.2587
## F-statistic: 345.4 on 2 and 1972 DF, p-value: < 2.2e-16# plot_model(fit1, type="pred")
fit2 <- lm(Y ~ engbel*genfin + engemp*genfin, d=dm)
summary(fit2)##
## Call:
## lm(formula = Y ~ engbel * genfin + engemp * genfin, data = dm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8045 -0.4735 0.0846 0.6621 2.3766
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.26647 0.14394 15.746 < 2e-16 ***
## engbel -0.01686 0.04652 -0.362 0.71710
## genfin1 0.36717 0.26428 1.389 0.16490
## engemp 0.52876 0.03899 13.563 < 2e-16 ***
## engbel:genfin1 0.16397 0.08804 1.863 0.06268 .
## genfin1:engemp -0.22539 0.07561 -2.981 0.00291 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7798 on 1888 degrees of freedom
## (81 observations deleted due to missingness)
## Multiple R-squared: 0.2634, Adjusted R-squared: 0.2615
## F-statistic: 135 on 5 and 1888 DF, p-value: < 2.2e-16plot_model(fit2, type="int")## [[1]]
##
## [[2]]