Animal Welfare Item





“The rights of animals ought be considered just as important as the rights of humans.”

  • The item was asked in 9-point scale raning from 1 (Completely Disagree) to 9 (Completely Agree).





Demographics of Animal Welfare

Social Class





Figure X. Animal Welfare as grouped by SES

Figure X. Animal Welfare as grouped by SES





Animal Welfare as grouped by SES
SES N Mean SD
Rich 90 -0.46 1.01
Upper Middle Class 395 -0.21 1.02
Middle Middle Class 679 0.07 0.98
Lower Middle Class 298 0.23 0.93
Poor 38 0.23 0.91

Gender





Figure X. Animal Welfare as grouped by Gender

Figure X. Animal Welfare as grouped by Gender





Animal Welfare as grouped by Gender
Gender N Mean SD
Male 740 -0.21 1.03
Female 760 0.20 0.93

Age





Figure X. Animal Welfare as grouped by Age

Figure X. Animal Welfare as grouped by Age





Animal Welfare as grouped by Age
Age N Mean SD
65+ 254 -0.27 1.02
45-54 years 292 -0.01 1.04
35-44 years 263 0.04 1.02
55-64 years 234 0.05 0.98
25-34 years 264 0.07 0.96
18-24 years 193 0.16 0.91

Education





Figure X. Animal Welfare as grouped by Education

Figure X. Animal Welfare as grouped by Education





Animal Welfare as grouped by Education
Education N Mean SD
Graduate 193 -0.31 1.07
Bachelor 310 -0.25 0.98
Some college 471 0.06 0.98
High-school 475 0.19 0.94
Less than High-school 51 0.36 0.98

Income Levels





Figure X. Animal Welfare as grouped by Income Levels

Figure X. Animal Welfare as grouped by Income Levels





Animal Welfare as grouped by Income Levels
Income Levels N Mean SD
$150,000 + 95 -0.40 1.00
$100,000-$149,999 160 -0.23 1.04
$75,000-$99,999 192 -0.21 1.04
$50,000-$74,999 292 0.03 0.96
$35,000-$49,999 227 0.09 1.01
Less than $15,000 178 0.12 0.92
$25,000-$34,999 176 0.18 0.99
$15,000-$24,999 180 0.19 0.94

Ethnicity





Figure X. Animal Welfare as grouped by Ethnicity

Figure X. Animal Welfare as grouped by Ethnicity





Animal Welfare as grouped by Ethnicity
Ethnicity N Mean SD
Other 18 -0.04 1.14
Caucasian/European origin 1237 -0.03 1.01
Native American 13 0.10 0.95
Black/African America 115 0.10 0.90
Asian/Pacific Islander 29 0.16 0.72
Latino 88 0.26 1.01

Occupation





Figure X. Animal Welfare as grouped by Occupation

Figure X. Animal Welfare as grouped by Occupation





Animal Welfare as grouped by Occupation
Occupation N Mean SD
Retired 268 -0.19 0.99
Employed 768 -0.05 1.01
Student 85 0.08 0.86
Parent 104 0.09 1.02
Disabled 98 0.17 1.03
Unemployed 146 0.28 0.96
Full-time caregiver 31 0.48 0.81

Area





Figure X. Animal Welfare as grouped by Area

Figure X. Animal Welfare as grouped by Area





Animal Welfare as grouped by Area
Area N Mean SD
Rural 545 -0.07 0.99
Urban 955 0.04 1.00





Religious Affiliation





Figure X. Animal Welfare as grouped by Religious Affiliation

Figure X. Animal Welfare as grouped by Religious Affiliation





Animal Welfare as grouped by Religious Affiliation
Religious Affiliation N Mean SD
Jewish 52 -0.30 1.09
Christian 1014 -0.06 1.00
Atheist/Agnostic 230 0.12 1.00
Muslim 9 0.13 0.86
No religion 195 0.26 0.92









Political Psychological correlates of Animal Welfare





















Political Behavior and Animal Welfare

Political Orientation

Figure X. Animal Welfare & Political Orientation

Figure X. Animal Welfare & Political Orientation





Figure X. Animal Welfare & Political Orientation

Figure X. Animal Welfare & Political Orientation





  Political Orientation Social Political Orientation Economic Political Orientation Composite Political Orientation
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p
Intercept 6.82 6.54 – 7.11 <0.001 6.56 6.23 – 6.88 <0.001 7.37 7.06 – 7.67 <0.001 6.92 6.64 – 7.19 <0.001
Animal Welfare -0.27 -0.31 – -0.22 <0.001 -0.29 -0.34 – -0.24 <0.001 -0.33 -0.38 – -0.28 <0.001 -0.30 -0.34 – -0.25 <0.001
Observations 1500 1500 1500 1500
R2 / adjusted R2 0.081 / 0.080 0.073 / 0.073 0.109 / 0.108 0.101 / 0.100





  Political Orientation Social Political Orientation Economic Political Orientation Composite Political Orientation
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p
Intercept 4.24 3.72 – 4.77 <0.001 3.90 3.34 – 4.47 <0.001 4.56 3.98 – 5.14 <0.001 4.24 3.73 – 4.74 <0.001
Animal Welfare -0.21 -0.25 – -0.17 <0.001 -0.23 -0.28 – -0.19 <0.001 -0.27 -0.32 – -0.23 <0.001 -0.24 -0.28 – -0.20 <0.001
Age 0.22 0.16 – 0.29 <0.001 0.22 0.15 – 0.30 <0.001 0.23 0.16 – 0.31 <0.001 0.23 0.16 – 0.29 <0.001
Income 0.07 0.01 – 0.13 0.016 -0.02 -0.08 – 0.05 0.617 0.11 0.05 – 0.18 0.001 0.06 0.00 – 0.11 0.050
Religiosity 0.36 0.32 – 0.39 <0.001 0.47 0.43 – 0.51 <0.001 0.27 0.23 – 0.32 <0.001 0.37 0.33 – 0.40 <0.001
Education -0.22 -0.34 – -0.11 <0.001 -0.27 -0.39 – -0.15 <0.001 -0.09 -0.21 – 0.04 0.178 -0.19 -0.30 – -0.08 <0.001
Observations 1500 1500 1500 1500
R2 / adjusted R2 0.290 / 0.288 0.345 / 0.343 0.234 / 0.232 0.326 / 0.323

Religiosity





Figure X. Animal Welfare & Religiosity

Figure X. Animal Welfare & Religiosity





Religiosity & Political Orientation

Figure X. Animal Welfare & Religiosity & Political Orientation

Figure X. Animal Welfare & Religiosity & Political Orientation





Candidate Preferences













Candidate Preferences and Animal Welfare [raw means]
Candidate Preferences N Mean SD Range
Ted Cruz 122 3.83 2.70 1-9
Jeb Bush 83 4.31 2.60 1-9
Rand Paul 44 4.59 3.06 1-9
Gary Johnson 68 5.04 2.60 1-9
Donald Trump 444 5.50 2.64 1-9
Hillary Clinton 371 6.33 2.31 1-9
Bernie Sanders 362 6.37 2.20 1-9





Figure X. Animal Welfare & Candidate Preferences

Figure X. Animal Welfare & Candidate Preferences





Party Preferences





Figure X. Animal Welfare & Party Preferences

Figure X. Animal Welfare & Party Preferences





Party Preferences and Animal Welfare [centered]
Party Preference N Mean SD
Tea Party 68 -0.80 1.01
Constitution Party 14 -0.53 1.23
Libertarian Party 100 -0.27 1.04
Republican Party 508 -0.20 1.02
None 120 0.02 1.08
Don’t know 90 0.16 0.83
Democratic Party 560 0.28 0.88
Green Party 40 0.45 0.71





Party Preferences and Animal Welfare [raw means]
Party Preferences N Mean SD Range
Tea Party 68 3.57 2.62 1-9
Constitution Party 14 4.29 3.20 1-9
Libertarian Party 100 4.96 2.71 1-9
Republican Party 508 5.16 2.64 1-9
None 120 5.71 2.80 1-9
Don’t know 90 6.08 2.15 1-9
Democratic Party 560 6.38 2.29 1-9
Green Party 40 6.83 1.85 1-9





Voting





    2016 [Trump vs. Clinton]   2016 [Trump vs. Clinton] + Supporters   2012 [Romney vs. Obama]   2008 [McCain vs. Obama]
    Odds Ratio CI   Odds Ratio CI   Odds Ratio CI   Odds Ratio CI
(Intercept)   0.38 *** 0.28 – 0.51   0.38 *** 0.28 – 0.51   0.26 *** 0.19 – 0.35   0.30 *** 0.23 – 0.40
Animal Welfare   1.19 *** 1.13 – 1.25   1.19 *** 1.13 – 1.24   1.30 *** 1.25 – 1.37   1.28 *** 1.22 – 1.34
Observations   1103   1148   1236   1206
Notes * p<.05   ** p<.01   *** p<.001









Party Identity





Figure X. Animal Welfare & Party Identity

Figure X. Animal Welfare & Party Identity





Figure X. Animal Welfare, Party Identity and Voting

Figure X. Animal Welfare, Party Identity and Voting





Tables meant for checking the number of observations in each category of the above graph.

Party Identity & Voting
Donald Trump Hilary Clinton
Strong Democrat 4 323
Democrat 27 129
Leaning Democrat 10 65
Independent 17 16
Leaning Republican 58 7
Republican 166 24
Strong Republican 282 7

Party Identity

with both representative (N = 1500) and convinience (2119) samples.





Figure X. Animal Welfare, Party Identity and Voting

Figure X. Animal Welfare, Party Identity and Voting





Tables meant for checking the number of observations in each category of the above graph.

Party Identity and Party Identity & Voting [N = 3619]
Donald Trump Hilary Clinton
Strong Democrat 6 649
Democrat 58 307
Leaning Democrat 18 196
Independent 70 46
Leaning Republican 181 15
Republican 440 57
Strong Republican 748 12





Voting & Party Identity





Figure X. Animal Welfare & Party Identity & Voting Preferences

Figure X. Animal Welfare & Party Identity & Voting Preferences





    2016 [Clinton vs. Trump]   2016 [Trump vs. Clinton] + Supporters
    Odds Ratio CI   Odds Ratio CI
(Intercept)   17.392 *** 8.881 – 35.717   12.046 *** 6.532 – 23.073
Party Identity   0.005 *** 0.003 – 0.008   0.007 *** 0.004 – 0.010
Animal Welfare   0.967   0.879 – 1.061   0.997   0.914 – 1.088
Observations   1103   1148
Notes * p<.05   ** p<.01   *** p<.001





Likeability

Trump’s Likebility





Figure X. Animal Welfare & Trump's Likebility

Figure X. Animal Welfare & Trump’s Likebility





Clinton’s Likebility





Figure X. Animal Welfare & Clinton's Likebility

Figure X. Animal Welfare & Clinton’s Likebility





Johnson’s Likebility





Figure X. Animal Welfare & Johnson's Likebility

Figure X. Animal Welfare & Johnson’s Likebility





Animal Welfare Item (as dependent variable)





Figure X. Animal Welfare & Political Orientation

Figure X. Animal Welfare & Political Orientation





Animal Welfare Item & Ideology



Four multiple regressions, each using one type of ideological self-placement measure as independent variable. R-squared is of relevance in compairing predictors - even if, formally dominance analysis would be required to infer about which explains “better”.



  Animal Welfare
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p
Intercept 7.26 6.96 – 7.56 <0.001 6.92 6.66 – 7.18 <0.001 7.45 7.16 – 7.74 <0.001 7.45 7.15 – 7.75 <0.001
Political Orientation -0.30 -0.35 – -0.25 <0.001
Social Political Orientation -0.25 -0.30 – -0.21 <0.001
Economic Political Orientation -0.33 -0.37 – -0.28 <0.001
Composite Political Orientation -0.34 -0.39 – -0.29 <0.001
Observations 1500 1500 1500 1500
R2 / adjusted R2 0.081 / 0.080 0.073 / 0.073 0.109 / 0.108 0.101 / 0.100





Animal Welfare Item & Ideology - controlling for demographics





  Animal Welfare
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p
Intercept 8.79 8.30 – 9.29 <0.001 8.61 8.12 – 9.09 <0.001 8.75 8.27 – 9.24 <0.001 8.86 8.37 – 9.34 <0.001
Political Orientation -0.29 -0.35 – -0.24 <0.001
Age 0.01 -0.07 – 0.09 0.774 0.01 -0.07 – 0.09 0.853 0.02 -0.06 – 0.10 0.689 0.03 -0.05 – 0.11 0.518
Income -0.07 -0.14 – -0.00 0.042 -0.10 -0.17 – -0.03 0.006 -0.06 -0.13 – 0.01 0.102 -0.07 -0.14 – -0.00 0.041
Religiosity -0.00 -0.05 – 0.05 0.856 0.02 -0.03 – 0.07 0.451 -0.03 -0.07 – 0.02 0.285 0.02 -0.03 – 0.07 0.410
Education -0.41 -0.55 – -0.28 <0.001 -0.42 -0.56 – -0.29 <0.001 -0.37 -0.50 – -0.24 <0.001 -0.41 -0.54 – -0.28 <0.001
Social Political Orientation -0.27 -0.33 – -0.22 <0.001
Economic Political Orientation -0.30 -0.35 – -0.25 <0.001
Composite Political Orientation -0.35 -0.41 – -0.29 <0.001
Observations 1500 1500 1500 1500
R2 / adjusted R2 0.122 / 0.119 0.123 / 0.120 0.140 / 0.137 0.142 / 0.139





Animal Welfare Item, Ideology & Psychological variables





  Animal Welfare
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
Intercept 7.31 7.00 – 7.62 <0.001 5.91 5.57 – 6.25 <0.001 6.58 6.06 – 7.10 <0.001 9.26 8.69 – 9.84 <0.001 8.13 7.61 – 8.66 <0.001
Anti-Egalitarianism -0.42 -0.49 – -0.34 <0.001
Dominance -0.07 -0.16 – 0.02 0.117
System Justification -0.18 -0.28 – -0.08 <0.001
Economic SJ -0.73 -0.84 – -0.62 <0.001
Gender-Specific SJ -0.45 -0.55 – -0.36 <0.001
Observations 1500 1500 1500 1500 1500
R2 / adjusted R2 0.078 / 0.078 0.002 / 0.001 0.009 / 0.008 0.095 / 0.094 0.057 / 0.056





Animal Welfare Item, Ideology & Psychological variables - controlling for demographics





  Animal Welfare Animal Welfare Animal Welfare Animal Welfare
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
Intercept 9.08 8.56 – 9.59 <0.001 8.26 7.69 – 8.83 <0.001 8.33 7.70 – 8.95 <0.001 10.51 9.85 – 11.17 <0.001 9.75 9.11 – 10.38 <0.001
Anti-Egalitarianism -0.37 -0.44 – -0.30 <0.001
Age -0.03 -0.11 – 0.05 0.427 -0.06 -0.14 – 0.02 0.145 -0.04 -0.13 – 0.04 0.298 0.01 -0.07 – 0.09 0.867 -0.02 -0.10 – 0.07 0.711
Income -0.07 -0.14 – -0.00 0.042 -0.10 -0.17 – -0.03 0.008 -0.09 -0.16 – -0.02 0.012 -0.05 -0.12 – 0.02 0.176 -0.06 -0.13 – 0.01 0.119
Religiosity -0.07 -0.12 – -0.03 0.002 -0.11 -0.16 – -0.07 <0.001 -0.11 -0.16 – -0.07 <0.001 -0.07 -0.11 – -0.02 0.005 -0.08 -0.13 – -0.04 <0.001
Education -0.38 -0.51 – -0.24 <0.001 -0.38 -0.51 – -0.24 <0.001 -0.37 -0.51 – -0.24 <0.001 -0.38 -0.51 – -0.25 <0.001 -0.40 -0.54 – -0.27 <0.001
Dominance -0.06 -0.15 – 0.03 0.165
System Justification -0.07 -0.18 – 0.03 0.155
Economic SJ -0.64 -0.76 – -0.52 <0.001
Gender-Specific SJ -0.39 -0.48 – -0.29 <0.001
Observations 1500 1500 1500 1500 1500
R2 / adjusted R2 0.122 / 0.119 0.065 / 0.062 0.065 / 0.062 0.129 / 0.126 0.102 / 0.099









Animal Welfare Item, Ideology & Psychological variables





  Animal Welfare
Predictors Estimates CI p Estimates CI p Estimates CI p
Intercept 8.52 7.96 – 9.07 <0.001 10.38 9.69 – 11.08 <0.001 9.77 9.07 – 10.47 <0.001
Anti-Egalitarianism -0.50 -0.59 – -0.41 <0.001 -0.31 -0.41 – -0.21 <0.001
Dominance 0.27 0.17 – 0.37 <0.001 0.32 0.22 – 0.42 <0.001
Age -0.01 -0.09 – 0.07 0.833 -0.02 -0.10 – 0.07 0.709 0.02 -0.06 – 0.10 0.650
Income -0.07 -0.14 – -0.01 0.033 -0.05 -0.12 – 0.02 0.166 -0.05 -0.12 – 0.02 0.156
Religiosity -0.07 -0.12 – -0.03 0.001 -0.06 -0.11 – -0.01 0.010 -0.05 -0.10 – -0.01 0.020
Education -0.35 -0.48 – -0.22 <0.001 -0.40 -0.53 – -0.27 <0.001 -0.36 -0.49 – -0.23 <0.001
System Justification 0.22 0.11 – 0.34 <0.001 0.18 0.06 – 0.29 0.003
Economic SJ -0.57 -0.71 – -0.42 <0.001 -0.51 -0.67 – -0.34 <0.001
Gender-Specific SJ -0.23 -0.36 – -0.10 <0.001 -0.16 -0.29 – -0.03 0.015
Observations 1500 1500 1500
R2 / adjusted R2 0.138 / 0.134 0.141 / 0.137 0.171 / 0.166

















R & R

Addressing John’s requests

A. Opposite effects of GBD and OEQ on Animal Welfare (when entered simultaneously)



  1. What is especially interesting is that the GBD and OEQ facets of SDO exert opposite effects on animal welfare attitudes (when entered simultaneously). Can you see if this replicates in your (larger but non-representative) data set. If so, I think we should report both of your studies here (either as Study 1 and Study 2 or as Study 1a and 1b) and make a bigger deal (in the intro) of the failure (in previous studies) to distinguish between the 2 facets of SDO.





Testing Linear Regressions, Multiple Regressions, and interaction effects for SDO and its facets (Representative Sample = 1500)


  Model 1 - DV: Animal Welfare (Representative Sample) Model 2 Model 3 Model 4 Model 5 Model 6
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
Intercept 7.31 7.00 – 7.62 <0.001 5.91 5.57 – 6.25 <0.001 6.96 6.60 – 7.32 <0.001 6.82 6.47 – 7.17 <0.001 7.53 6.91 – 8.16 <0.001 7.67 7.35 – 8.00 <0.001
Anti-Egalitarianism -0.42 -0.49 – -0.34 <0.001 -0.56 -0.65 – -0.48 <0.001 -0.77 -0.94 – -0.60 <0.001 -0.80 -0.93 – -0.66 <0.001
Dominance -0.07 -0.16 – 0.02 0.117 0.31 0.20 – 0.41 <0.001 0.06 -0.15 – 0.26 0.602
SDO -0.35 -0.44 – -0.26 <0.001
Interaction OEQ*GBD 0.06 0.02 – 0.11 0.007 0.07 0.05 – 0.10 <0.001
Observations 1500 1500 1500 1500 1500 1500
R2 / adjusted R2 0.078 / 0.078 0.002 / 0.001 0.037 / 0.036 0.099 / 0.098 0.103 / 0.102 0.103 / 0.102





Testing Linear Regressions, Multiple Regressions, and interaction effects for SDO and its facets (Convinient Sample = 2199)


  Model 1 - DV: Animal Welfare (Convinient Sample) Model 2 Model 3 Model 4 Model 5 Model 6
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
Intercept 6.93 6.65 – 7.21 <0.001 5.13 4.85 – 5.41 <0.001 6.37 6.06 – 6.69 <0.001 6.35 6.05 – 6.65 <0.001 6.95 6.39 – 7.50 <0.001 7.37 7.08 – 7.65 <0.001
Anti-Egalitarianism -0.44 -0.51 – -0.38 <0.001 -0.62 -0.69 – -0.54 <0.001 -0.77 -0.91 – -0.63 <0.001 -0.85 -0.96 – -0.75 <0.001
Dominance -0.02 -0.09 – 0.06 0.661 0.36 0.28 – 0.44 <0.001 0.16 -0.02 – 0.34 0.085
SDO -0.33 -0.41 – -0.26 <0.001
Interaction OEQ*GBD 0.05 0.01 – 0.09 0.013 0.08 0.06 – 0.09 <0.001
Observations 2119 2119 2119 2119 2119 2119
R2 / adjusted R2 0.087 / 0.087 0.000 / -0.000 0.034 / 0.033 0.119 / 0.118 0.122 / 0.120 0.120 / 0.119





Model Comparisons (for both Representative/Convenience samples)



Model 4 vs. Model 5: Do we need the interaction term? (Answer: Yes).



res.df rss df sumsq statistic p.value
1497 9130.854 NA NA NA NA
1496 9087.171 1 43.68317 7.191459 0.0074057
res.df rss df sumsq statistic p.value
2116 11930.61 NA NA NA NA
2115 11895.59 1 35.01566 6.225677 0.0126668



Model 5 vs. Model 6: Do we need the inclusion of GBD? (Answer: No).



res.df rss df sumsq statistic p.value
1497 9088.827 NA NA NA NA
1496 9087.171 1 1.655767 0.2725852 0.6016802
res.df rss df sumsq statistic p.value
2116 11912.26 NA NA NA NA
2115 11895.59 1 16.66771 2.963468 0.0853113





Plotting Model Effects





Interaction



via Confidence intervals



via Confidence intervals





via Prediction (based on average scores on Dominance)




Summary


First and foremost, all models considered are poor in explaining the variance of animal rights. The best single predictor model of Animal Welfare (see above) seems to be Economic conservatims (Ideological self-placement), whose R-squared as a single predictor was found to be 0.109. When adding demographics & religiosity, this goes to 0.140, which is still poor. So, it is important for me to understand whether your data and scale managed to capture this much variance when modelling.

Second, when focusing on SDO and its facets, variance explained really floors, so in general, SDO/facets are poor predictors. Now that might be the focus “find what predicts Animal Welfare best”, but I think it is relevant information.

Third, in trying to understand the effect of OEQ/GBD on AW, for both datasets, when adding both OEQ and GBD, the latter falls out of significance. When comparing model 4 and model 5 (with interaction between OEQ & GBD), the inclusion of the interaction term lead to a significantly improved fit over the model 4. Also for both datasets, the term GBD does not seem to explain enough variance of the DV, so it drops out.

The “best model” (model 6), which explains a comparable amount of variance to model 4/5, means that while there is a negative effect of OEQ on Animal Welfare, its impact is significantly influenced by levels of GBD. In specific, and curiously, higher levels of Dominance seem to attenuate the rate by which OEQ affects Animal Welfare (across both datasets). Is this at all relevant?

















A.2 With demographic controls: Testing Linear Regressions, Multiple Regressions, and interaction effects for SDO and its facets



  Animal Welfare Animal Welfare Animal Welfare Animal Welfare Animal Welfare Animal Welfare
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
Intercept 8.83 8.34 – 9.32 <0.001 7.86 7.31 – 8.41 <0.001 8.71 8.18 – 9.25 <0.001 8.27 7.74 – 8.80 <0.001 8.94 8.22 – 9.67 <0.001 9.00 8.50 – 9.49 <0.001
Anti-Egalitarianism -0.39 -0.46 – -0.32 <0.001 -0.52 -0.61 – -0.43 <0.001 -0.72 -0.90 – -0.55 <0.001 -0.73 -0.87 – -0.60 <0.001
Age -0.05 -0.13 – 0.03 0.189 -0.10 -0.18 – -0.02 0.019 -0.08 -0.16 – -0.00 0.044 -0.03 -0.11 – 0.05 0.455 -0.02 -0.10 – 0.06 0.616 -0.02 -0.10 – 0.06 0.619
Income -0.07 -0.14 – 0.00 0.054 -0.09 -0.16 – -0.02 0.012 -0.08 -0.15 – -0.01 0.034 -0.07 -0.14 – -0.00 0.044 -0.07 -0.14 – -0.00 0.049 -0.07 -0.14 – -0.00 0.050
Education -0.37 -0.50 – -0.24 <0.001 -0.37 -0.51 – -0.23 <0.001 -0.38 -0.52 – -0.25 <0.001 -0.34 -0.47 – -0.21 <0.001 -0.35 -0.48 – -0.22 <0.001 -0.35 -0.48 – -0.22 <0.001
Dominance -0.09 -0.17 – -0.00 0.047 0.26 0.16 – 0.36 <0.001 0.02 -0.19 – 0.23 0.849
SDO -0.33 -0.42 – -0.25 <0.001
Interaction OEQ*GBD 0.06 0.02 – 0.11 0.008 0.07 0.04 – 0.09 <0.001
Observations 1500 1500 1500 1500 1500 1500
R2 / adjusted R2 0.117 / 0.114 0.051 / 0.049 0.083 / 0.080 0.132 / 0.129 0.136 / 0.132 0.136 / 0.133



  Animal Welfare Animal Welfare Animal Welfare Animal Welfare Animal Welfare Animal Welfare
Predictors Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p Estimates CI p
Intercept 9.13 8.71 – 9.55 <0.001 7.97 7.50 – 8.44 <0.001 8.92 8.45 – 9.38 <0.001 8.50 8.05 – 8.96 <0.001 9.00 8.36 – 9.64 <0.001 9.31 8.89 – 9.73 <0.001
Anti-Egalitarianism -0.39 -0.44 – -0.33 <0.001 -0.52 -0.59 – -0.45 <0.001 -0.65 -0.79 – -0.52 <0.001 -0.71 -0.82 – -0.60 <0.001
Age -0.24 -0.30 – -0.18 <0.001 -0.27 -0.33 – -0.21 <0.001 -0.26 -0.32 – -0.20 <0.001 -0.23 -0.29 – -0.17 <0.001 -0.23 -0.29 – -0.17 <0.001 -0.23 -0.28 – -0.17 <0.001
Income -0.07 -0.12 – -0.01 0.020 -0.09 -0.15 – -0.03 0.002 -0.08 -0.14 – -0.02 0.006 -0.06 -0.12 – -0.01 0.029 -0.06 -0.12 – -0.00 0.034 -0.06 -0.12 – -0.00 0.035
Education -0.30 -0.42 – -0.18 <0.001 -0.31 -0.43 – -0.19 <0.001 -0.32 -0.44 – -0.20 <0.001 -0.26 -0.38 – -0.15 <0.001 -0.27 -0.39 – -0.16 <0.001 -0.28 -0.39 – -0.16 <0.001
Dominance -0.04 -0.11 – 0.03 0.213 0.28 0.20 – 0.36 <0.001 0.11 -0.06 – 0.28 0.213
SDO -0.30 -0.37 – -0.23 <0.001
Interaction OEQ*GBD 0.04 0.00 – 0.08 0.030 0.06 0.04 – 0.08 <0.001
Observations 2119 2119 2119 2119 2119 2119
R2 / adjusted R2 0.168 / 0.166 0.105 / 0.103 0.131 / 0.130 0.187 / 0.185 0.188 / 0.186 0.188 / 0.186









B. Mediation between Ideology and Animal welfare through ESJ/SDO

  1. Also, can you see if (in your 2 data sets) you find evidence that ESJ mediates the effect of conservatism on support for animal welfare? It would be great if it did, and if SDO does not….

ESJ as Mediator - Mediation between Ideology and Animal welfare through ESJ



## [1] "Bootstrap resampling has begun. This process may take a considerable amount of time if the number of replications is large, which is optimal for the bootstrap procedure."
##                                           Estimate CI.Lower_BCa
## Indirect.Effect                        -0.12494355           NA
## Indirect.Effect.Partially.Standardized -0.04805108           NA
## Index.of.Mediation                     -0.11792391           NA
## R2_4.5                                  0.06111794           NA
## R2_4.6                                  0.01067904           NA
## R2_4.7                                  0.09371245           NA
## Ratio.of.Indirect.to.Total.Effect       0.41550290           NA
## Ratio.of.Indirect.to.Direct.Effect      0.71087247           NA
## Success.of.Surrogate.Endpoint          -1.24246098           NA
## Residual.Based_Gamma                    0.07567333           NA
## Residual.Based.Standardized_gamma       0.09348303           NA
## SOS                                     0.75877509           NA
##                                        CI.Upper_BCa
## Indirect.Effect                                  NA
## Indirect.Effect.Partially.Standardized           NA
## Index.of.Mediation                               NA
## R2_4.5                                           NA
## R2_4.6                                           NA
## R2_4.7                                           NA
## Ratio.of.Indirect.to.Total.Effect                NA
## Ratio.of.Indirect.to.Direct.Effect               NA
## Success.of.Surrogate.Endpoint                    NA
## Residual.Based_Gamma                             NA
## Residual.Based.Standardized_gamma                NA
## SOS                                              NA



## lavaan 0.6-2 ended normally after 17 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          5
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Parameter Estimates:
## 
##   Standard Errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws             991
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   SJ_Eco ~                                                              
##     I_SP_JJ_O  (a)    0.242    0.011   22.997    0.000    0.242    0.542
##   Env_2_REV ~                                                           
##     SJ_Eco     (b)   -0.516    0.071   -7.243    0.000   -0.516   -0.218
##     I_SP_JJ_O (cp)   -0.176    0.032   -5.413    0.000   -0.176   -0.166
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .SJ_Eco            0.847    0.036   23.839    0.000    0.847    0.706
##    .Env_2_REV         5.987    0.173   34.529    0.000    5.987    0.886
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ab               -0.125    0.018   -6.922    0.000   -0.125   -0.118
##     total            -0.301    0.028  -10.859    0.000   -0.301   -0.284
lhs op rhs label est se z pvalue ci.lower ci.upper std.lv std.all std.nox
SJ_Eco ~ Ideo_SP_JJ_Overall a 0.2420232 0.0105239 22.997388 0e+00 0.2213713 0.2642259 0.2420232 0.5421386 0.2209817
Env_2_REV ~ SJ_Eco b -0.5162462 0.0712703 -7.243499 0e+00 -0.6536712 -0.3696897 -0.5162462 -0.2175162 -0.2175162
Env_2_REV ~ Ideo_SP_JJ_Overall cp -0.1757609 0.0324688 -5.413229 1e-07 -0.2362270 -0.1086081 -0.1757609 -0.1658862 -0.0676171
SJ_Eco ~~ SJ_Eco 0.8469518 0.0355277 23.839172 0e+00 0.7843106 0.9241321 0.8469518 0.7060857 0.7060857
Env_2_REV ~~ Env_2_REV 5.9866981 0.1733828 34.528790 0e+00 5.6653068 6.3535499 5.9866981 0.8860446 0.8860446
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.0187716 0.0000000 NA NA 6.0187716 6.0187716 6.0187716 1.0000000 6.0187716
ab := a*b ab -0.1249436 0.0180514 -6.921547 0e+00 -0.1618249 -0.0887215 -0.1249436 -0.1179239 -0.0480671
total := cp+ab total -0.3007044 0.0276914 -10.859138 0e+00 -0.3570631 -0.2469944 -0.3007044 -0.2838101 -0.1156842 



## ************************* PROCESS Procedure for R Based On PROCESS for SAS v2.11 ************************
## Written by Andrew F. Hayes, Ph.D.  http://www.afhayes.com
## Converted For R by Dean Lim.  d.lim@rscbga.com
## *******************************************************************************************************
## Model and Variables
## Model = 4
## Y     =  Env_2_REV
## X     =  Ideo_SP_JJ_Overall
## M     =  SJ_Eco
## Sample size:
## 1500
## *****************************************************************************************
## Outcome: SJ_Eco
## Model Summary
## R R-sq F df1 df2 p
##     0.5421    0.2939  623.5554    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  3.649790  0.056652 64.425167  0.000000  3.538665  3.760915
## Ideo_SP_JJ_Overall  0.242023  0.009692 24.971092  0.000000  0.223012  0.261035
## *****************************************************************************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV    0.3376    0.1140   96.2656    2.0000 1497.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## constant  9.143e+00  2.926e-01  3.125e+01  0.000e+00  8.569e+00  9.717e+00
## SJ_Eco -5.162e-01  6.872e-02 -7.513e+00  9.903e-14 -6.510e-01 -3.815e-01
## Ideo_SP_JJ_Overall -1.758e-01  3.068e-02 -5.730e+00  1.215e-08 -2.359e-01 -1.156e-01
## ********************************* TOTAL EFFECT MODEL *********************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV 2.838e-01 8.055e-02 1.312e+02 1.000e+00 1.498e+03 0.000e+00
## Model
## coeff se t p LLCI ULCI
## constant   7.25867   0.15343  47.30899   0.00000   6.95771   7.55963
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## *************************** TOTAL, DIRECT AND INDIRECT EFFECTS ***************************
## Total effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## Direct effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall -1.758e-01  3.068e-02 -5.730e+00  1.215e-08 -2.359e-01 -1.156e-01
## Indirect effect of X on Y
## Effect Boot SE BootLLCI BootULCI
## SJ_Eco -0.12494  0.01839 -0.16059 -0.08855
## ****************************** ANALYSIS NOTES AND WARNINGS ******************************
## Number of bootstrap samples for bias corrected bootstrap confidence intervals:
## 1000
## Level of confidence for all confidence intervals in output:
## 95













SDO as Mediator - Mediation between Ideology and Animal welfare through SDO



## [1] "Bootstrap resampling has begun. This process may take a considerable amount of time if the number of replications is large, which is optimal for the bootstrap procedure."
##                                            Estimate CI.Lower_BCa
## Indirect.Effect                        -0.037345472           NA
## Indirect.Effect.Partially.Standardized -0.014362408           NA
## Index.of.Mediation                     -0.035247311           NA
## R2_4.5                                  0.032135560           NA
## R2_4.6                                  0.001058789           NA
## R2_4.7                                  0.012449527           NA
## Ratio.of.Indirect.to.Total.Effect       0.124193299           NA
## Ratio.of.Indirect.to.Direct.Effect      0.141804463           NA
## Success.of.Surrogate.Endpoint          -1.103397018           NA
## Residual.Based_Gamma                    0.068819939           NA
## Residual.Based.Standardized_gamma       0.084801483           NA
## SOS                                     0.398960788           NA
##                                        CI.Upper_BCa
## Indirect.Effect                                  NA
## Indirect.Effect.Partially.Standardized           NA
## Index.of.Mediation                               NA
## R2_4.5                                           NA
## R2_4.6                                           NA
## R2_4.7                                           NA
## Ratio.of.Indirect.to.Total.Effect                NA
## Ratio.of.Indirect.to.Direct.Effect               NA
## Success.of.Surrogate.Endpoint                    NA
## Residual.Based_Gamma                             NA
## Residual.Based.Standardized_gamma                NA
## SOS                                              NA



## lavaan 0.6-2 ended normally after 18 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          5
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Parameter Estimates:
## 
##   Standard Errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws             998
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   SDO ~                                                                 
##     I_SP_JJ_O  (a)    0.273    0.013   20.724    0.000    0.273    0.465
##   Env_2_REV ~                                                           
##     SDO        (b)   -0.137    0.054   -2.522    0.012   -0.137   -0.076
##     I_SP_JJ_O (cp)   -0.263    0.032   -8.164    0.000   -0.263   -0.249
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .SDO               1.619    0.055   29.322    0.000    1.619    0.784
##    .Env_2_REV         6.182    0.176   35.120    0.000    6.182    0.915
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ab               -0.037    0.015   -2.495    0.013   -0.037   -0.035
##     total            -0.301    0.028  -10.862    0.000   -0.301   -0.284
lhs op rhs label est se z pvalue ci.lower ci.upper std.lv std.all std.nox
SDO ~ Ideo_SP_JJ_Overall a 0.2725260 0.0131501 20.724197 0.0000000 0.2476443 0.2992331 0.2725260 0.4652039 0.1896223
Env_2_REV ~ SDO b -0.1370345 0.0543411 -2.521746 0.0116774 -0.2428450 -0.0323784 -0.1370345 -0.0757674 -0.0757674
Env_2_REV ~ Ideo_SP_JJ_Overall cp -0.2633589 0.0322601 -8.163616 0.0000000 -0.3247314 -0.2012813 -0.2633589 -0.2485628 -0.1013170
SDO ~~ SDO 1.6185397 0.0551993 29.321762 0.0000000 1.5214005 1.7368052 1.6185397 0.7835853 0.7835853
Env_2_REV ~~ Env_2_REV 6.1820258 0.1760280 35.119551 0.0000000 5.8398000 6.5428150 6.1820258 0.9149535 0.9149535
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.0187716 0.0000000 NA NA 6.0187716 6.0187716 6.0187716 1.0000000 6.0187716
ab := a*b ab -0.0373455 0.0149666 -2.495254 0.0125867 -0.0689100 -0.0081478 -0.0373455 -0.0352473 -0.0143672
total := cp+ab total -0.3007044 0.0276832 -10.862359 0.0000000 -0.3567337 -0.2469938 -0.3007044 -0.2838101 -0.1156842 



## ************************* PROCESS Procedure for R Based On PROCESS for SAS v2.11 ************************
## Written by Andrew F. Hayes, Ph.D.  http://www.afhayes.com
## Converted For R by Dean Lim.  d.lim@rscbga.com
## *******************************************************************************************************
## Model and Variables
## Model = 4
## Y     =  Env_2_REV
## X     =  Ideo_SP_JJ_Overall
## M     =  SDO
## Sample size:
## 1500
## *****************************************************************************************
## Outcome: SDO
## Model Summary
## R R-sq F df1 df2 p
##     0.4652    0.2164  413.7255    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  2.29849  0.07831 29.34934  0.00000  2.14487  2.45211
## Ideo_SP_JJ_Overall  0.27253  0.01340 20.34024  0.00000  0.24624  0.29881
## *****************************************************************************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV 2.916e-01 8.505e-02 6.957e+01 2.000e+00 1.497e+03 0.000e+00
## Model
## coeff se t p LLCI ULCI
## constant  7.573643  0.192148 39.415592  0.000000  7.196734  7.950552
## SDO -0.137035  0.050512 -2.712921  0.006746 -0.236116 -0.037953
## Ideo_SP_JJ_Overall -0.263359  0.029591 -8.900013  0.000000 -0.321403 -0.205315
## ********************************* TOTAL EFFECT MODEL *********************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV 2.838e-01 8.055e-02 1.312e+02 1.000e+00 1.498e+03 0.000e+00
## Model
## coeff se t p LLCI ULCI
## constant   7.25867   0.15343  47.30899   0.00000   6.95771   7.55963
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## *************************** TOTAL, DIRECT AND INDIRECT EFFECTS ***************************
## Total effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## Direct effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall -0.26336  0.02959 -8.90001  0.00000 -0.32140 -0.20532
## Indirect effect of X on Y
## Effect Boot SE BootLLCI BootULCI
## SDO -0.037345  0.015255 -0.067543 -0.007981
## ****************************** ANALYSIS NOTES AND WARNINGS ******************************
## Number of bootstrap samples for bias corrected bootstrap confidence intervals:
## 1000
## Level of confidence for all confidence intervals in output:
## 95









(Convenient Sample) ESJ as Mediator - Mediation between Ideology and Animal welfare through ESJ



## [1] "Bootstrap resampling has begun. This process may take a considerable amount of time if the number of replications is large, which is optimal for the bootstrap procedure."
##                                           Estimate CI.Lower_BCa
## Indirect.Effect                        -0.15075133           NA
## Indirect.Effect.Partially.Standardized -0.05962137           NA
## Index.of.Mediation                     -0.14545270           NA
## R2_4.5                                  0.08208939           NA
## R2_4.6                                  0.01461136           NA
## R2_4.7                                  0.10820505           NA
## Ratio.of.Indirect.to.Total.Effect       0.45923460           NA
## Ratio.of.Indirect.to.Direct.Effect      0.84923074           NA
## Success.of.Surrogate.Endpoint          -1.14824480           NA
## Residual.Based_Gamma                    0.09983243           NA
## Residual.Based.Standardized_gamma       0.12650566           NA
## SOS                                     0.81830020           NA
##                                        CI.Upper_BCa
## Indirect.Effect                                  NA
## Indirect.Effect.Partially.Standardized           NA
## Index.of.Mediation                               NA
## R2_4.5                                           NA
## R2_4.6                                           NA
## R2_4.7                                           NA
## Ratio.of.Indirect.to.Total.Effect                NA
## Ratio.of.Indirect.to.Direct.Effect               NA
## Success.of.Surrogate.Endpoint                    NA
## Residual.Based_Gamma                             NA
## Residual.Based.Standardized_gamma                NA
## SOS                                              NA



## lavaan 0.6-2 ended normally after 17 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          5
## 
##   Number of observations                          2119
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Parameter Estimates:
## 
##   Standard Errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws             992
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   SJ_Eco ~                                                              
##     I_SP_JJ_O  (a)    0.286    0.009   31.866    0.000    0.286    0.615
##   Env_2_REV ~                                                           
##     SJ_Eco     (b)   -0.527    0.061   -8.713    0.000   -0.527   -0.236
##     I_SP_JJ_O (cp)   -0.178    0.029   -6.212    0.000   -0.178   -0.171
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .SJ_Eco            0.798    0.027   29.343    0.000    0.798    0.621
##    .Env_2_REV         5.527    0.135   40.905    0.000    5.527    0.865
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ab               -0.151    0.018   -8.601    0.000   -0.151   -0.145
##     total            -0.328    0.022  -15.204    0.000   -0.328   -0.317
lhs op rhs label est se z pvalue ci.lower ci.upper std.lv std.all std.nox
SJ_Eco ~ Ideo_SP_JJ_Overall a 0.2858855 0.0089715 31.865887 0 0.2695457 0.3041372 0.2858855 0.6153450 0.2522908
Env_2_REV ~ SJ_Eco b -0.5273138 0.0605212 -8.712881 0 -0.6548942 -0.4119707 -0.5273138 -0.2363759 -0.2363759
Env_2_REV ~ Ideo_SP_JJ_Overall cp -0.1775151 0.0285760 -6.212027 0 -0.2303401 -0.1160181 -0.1775151 -0.1712758 -0.0702229
SJ_Eco ~~ SJ_Eco 0.7978443 0.0271903 29.342969 0 0.7472368 0.8530744 0.7978443 0.6213505 0.6213505
Env_2_REV ~~ Env_2_REV 5.5272927 0.1351261 40.904709 0 5.2842586 5.8082811 5.5272927 0.8649660 0.8649660
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 5.9488731 0.0000000 NA NA 5.9488731 5.9488731 5.9488731 1.0000000 5.9488731
ab := a*b ab -0.1507514 0.0175272 -8.600992 0 -0.1880106 -0.1180983 -0.1507514 -0.1454527 -0.0596355
total := cp+ab total -0.3282665 0.0215912 -15.203693 0 -0.3706594 -0.2868450 -0.3282665 -0.3167285 -0.1298583 



## ************************* PROCESS Procedure for R Based On PROCESS for SAS v2.11 ************************
## Written by Andrew F. Hayes, Ph.D.  http://www.afhayes.com
## Converted For R by Dean Lim.  d.lim@rscbga.com
## *******************************************************************************************************
## Model and Variables
## Model = 4
## Y     =  Env_2_REV
## X     =  Ideo_SP_JJ_Overall
## M     =  SJ_Eco
## Sample size:
## 2119
## *****************************************************************************************
## Outcome: SJ_Eco
## Model Summary
## R R-sq F df1 df2 p
##     0.6153    0.3786 1290.0947    1.0000 2117.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  3.524563  0.048757 72.288709  0.000000  3.428947  3.620180
## Ideo_SP_JJ_Overall  0.285885  0.007959 35.917888  0.000000  0.270276  0.301495
## *****************************************************************************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV    0.3675    0.1350  165.1695    2.0000 2116.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## constant  8.773e+00  2.391e-01  3.670e+01  0.000e+00  8.304e+00  9.241e+00
## SJ_Eco -5.273e-01  5.722e-02 -9.216e+00  0.000e+00 -6.395e-01 -4.151e-01
## Ideo_SP_JJ_Overall -1.775e-01  2.658e-02 -6.678e+00  3.093e-11 -2.296e-01 -1.254e-01
## ********************************* TOTAL EFFECT MODEL *********************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV    0.3167    0.1003  236.0509    1.0000 2117.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## constant   6.91395   0.13088  52.82618   0.00000   6.65729   7.17062
## Ideo_SP_JJ_Overall  -0.32827   0.02137 -15.36395   0.00000  -0.37017  -0.28637
## *************************** TOTAL, DIRECT AND INDIRECT EFFECTS ***************************
## Total effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall  -0.32827   0.02137 -15.36395   0.00000  -0.37017  -0.28637
## Direct effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall -1.775e-01  2.658e-02 -6.678e+00  3.093e-11 -2.296e-01 -1.254e-01
## Indirect effect of X on Y
## Effect Boot SE BootLLCI BootULCI
## SJ_Eco -0.15075  0.01791 -0.18416 -0.11291
## ****************************** ANALYSIS NOTES AND WARNINGS ******************************
## Number of bootstrap samples for bias corrected bootstrap confidence intervals:
## 1000
## Level of confidence for all confidence intervals in output:
## 95













(Convenient Sample) SDO as Mediator - Mediation between Ideology and Animal welfare through SDO



## [1] "Bootstrap resampling has begun. This process may take a considerable amount of time if the number of replications is large, which is optimal for the bootstrap procedure."
##                                             Estimate CI.Lower_BCa
## Indirect.Effect                        -0.0237256832           NA
## Indirect.Effect.Partially.Standardized -0.0093833844           NA
## Index.of.Mediation                     -0.0228917693           NA
## R2_4.5                                  0.0317161290           NA
## R2_4.6                                  0.0004627925           NA
## R2_4.7                                  0.0045219532           NA
## Ratio.of.Indirect.to.Total.Effect       0.0722756771           NA
## Ratio.of.Indirect.to.Direct.Effect      0.0779064161           NA
## Success.of.Surrogate.Endpoint          -1.2712062569           NA
## Residual.Based_Gamma                    0.0555085780           NA
## Residual.Based.Standardized_gamma       0.0705390712           NA
## SOS                                     0.3161591779           NA
##                                        CI.Upper_BCa
## Indirect.Effect                                  NA
## Indirect.Effect.Partially.Standardized           NA
## Index.of.Mediation                               NA
## R2_4.5                                           NA
## R2_4.6                                           NA
## R2_4.7                                           NA
## Ratio.of.Indirect.to.Total.Effect                NA
## Ratio.of.Indirect.to.Direct.Effect               NA
## Success.of.Surrogate.Endpoint                    NA
## Residual.Based_Gamma                             NA
## Residual.Based.Standardized_gamma                NA
## SOS                                              NA



## lavaan 0.6-2 ended normally after 18 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          5
## 
##   Number of observations                          2119
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Parameter Estimates:
## 
##   Standard Errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws             995
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   SDO ~                                                                 
##     I_SP_JJ_O  (a)    0.258    0.012   22.372    0.000    0.258    0.453
##   Env_2_REV ~                                                           
##     SDO        (b)   -0.092    0.046   -1.995    0.046   -0.092   -0.051
##     I_SP_JJ_O (cp)   -0.305    0.024  -12.434    0.000   -0.305   -0.294
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .SDO               1.534    0.043   35.531    0.000    1.534    0.795
##    .Env_2_REV         5.736    0.137   41.940    0.000    5.736    0.898
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ab               -0.024    0.012   -1.982    0.048   -0.024   -0.023
##     total            -0.328    0.022  -15.205    0.000   -0.328   -0.317
lhs op rhs label est se z pvalue ci.lower ci.upper std.lv std.all std.nox
SDO ~ Ideo_SP_JJ_Overall a 0.2582323 0.0115427 22.371925 0.0000000 0.2352176 0.2808875 0.2582323 0.4532742 0.1858419
Env_2_REV ~ SDO b -0.0918773 0.0460449 -1.995385 0.0460010 -0.1800506 -0.0018180 -0.0918773 -0.0505031 -0.0505031
Env_2_REV ~ Ideo_SP_JJ_Overall cp -0.3045408 0.0244926 -12.433997 0.0000000 -0.3531843 -0.2526295 -0.3045408 -0.2938368 -0.1204727
SDO ~~ SDO 1.5340903 0.0431761 35.530980 0.0000000 1.4523634 1.6200377 1.5340903 0.7945425 0.7945425
Env_2_REV ~~ Env_2_REV 5.7361913 0.1367727 41.939589 0.0000000 5.4812354 6.0108882 5.7361913 0.8976565 0.8976565
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 5.9488731 0.0000000 NA NA 5.9488731 5.9488731 5.9488731 1.0000000 5.9488731
ab := a*b ab -0.0237257 0.0119722 -1.981739 0.0475085 -0.0465580 -0.0005864 -0.0237257 -0.0228918 -0.0093856
total := cp+ab total -0.3282665 0.0215890 -15.205239 0.0000000 -0.3706466 -0.2868135 -0.3282665 -0.3167285 -0.1298583 



## ************************* PROCESS Procedure for R Based On PROCESS for SAS v2.11 ************************
## Written by Andrew F. Hayes, Ph.D.  http://www.afhayes.com
## Converted For R by Dean Lim.  d.lim@rscbga.com
## *******************************************************************************************************
## Model and Variables
## Model = 4
## Y     =  Env_2_REV
## X     =  Ideo_SP_JJ_Overall
## M     =  SDO
## Sample size:
## 2119
## *****************************************************************************************
## Outcome: SDO
## Model Summary
## R R-sq F df1 df2 p
##     0.4533    0.2055  547.4264    1.0000 2117.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  2.44301  0.06761 36.13459  0.00000  2.31042  2.57559
## Ideo_SP_JJ_Overall  0.25823  0.01104 23.39714  0.00000  0.23659  0.27988
## *****************************************************************************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV    0.3199    0.1023  120.6246    2.0000 2116.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## constant   7.13841   0.16627  42.93256   0.00000   6.81234   7.46448
## SDO  -0.09188   0.04204  -2.18565   0.02895  -0.17431  -0.00944
## Ideo_SP_JJ_Overall  -0.30454   0.02395 -12.71650   0.00000  -0.35151  -0.25758
## ********************************* TOTAL EFFECT MODEL *********************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV    0.3167    0.1003  236.0509    1.0000 2117.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## constant   6.91395   0.13088  52.82618   0.00000   6.65729   7.17062
## Ideo_SP_JJ_Overall  -0.32827   0.02137 -15.36395   0.00000  -0.37017  -0.28637
## *************************** TOTAL, DIRECT AND INDIRECT EFFECTS ***************************
## Total effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall  -0.32827   0.02137 -15.36395   0.00000  -0.37017  -0.28637
## Direct effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall  -0.30454   0.02395 -12.71650   0.00000  -0.35151  -0.25758
## Indirect effect of X on Y
## Effect Boot SE BootLLCI BootULCI
## SDO -0.0237257  0.0113766 -0.0456446 -0.0007479
## ****************************** ANALYSIS NOTES AND WARNINGS ******************************
## Number of bootstrap samples for bias corrected bootstrap confidence intervals:
## 1000
## Level of confidence for all confidence intervals in output:
## 95

























Digging deeper

Anti-Egalitarianism as mediator



## lavaan 0.6-2 ended normally after 18 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          5
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Parameter Estimates:
## 
##   Standard Errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws             997
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   SDO7_AntiEgal ~                                                       
##     I_SP_JJ_O  (a)    0.350    0.016   22.129    0.000    0.350    0.490
##   Env_2_REV ~                                                           
##     SDO7_AntE  (b)   -0.275    0.045   -6.064    0.000   -0.275   -0.185
##     I_SP_JJ_O (cp)   -0.205    0.032   -6.319    0.000   -0.205   -0.193
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .SDO7_AntiEgal     2.329    0.083   28.168    0.000    2.329    0.760
##    .Env_2_REV         6.036    0.175   34.567    0.000    6.036    0.893
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ab               -0.096    0.017   -5.773    0.000   -0.096   -0.091
##     total            -0.301    0.028  -10.863    0.000   -0.301   -0.284
lhs op rhs label est se z pvalue ci.lower ci.upper std.lv std.all std.nox
SDO7_AntiEgal ~ Ideo_SP_JJ_Overall a 0.3495802 0.0157974 22.128932 0 0.3197118 0.3794871 0.3495802 0.4899249 0.1996989
Env_2_REV ~ SDO7_AntiEgal b -0.2750163 0.0453538 -6.063802 0 -0.3659527 -0.1831950 -0.2750163 -0.1852097 -0.1852097
Env_2_REV ~ Ideo_SP_JJ_Overall cp -0.2045641 0.0323724 -6.319082 0 -0.2658744 -0.1402571 -0.2045641 -0.1930712 -0.0786980
SDO7_AntiEgal ~~ SDO7_AntiEgal 2.3288471 0.0826764 28.168226 0 2.1681898 2.4962777 2.3288471 0.7599736 0.7599736
Env_2_REV ~~ Env_2_REV 6.0362793 0.1746253 34.567043 0 5.6975226 6.3933106 6.0362793 0.8933827 0.8933827
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.0187716 0.0000000 NA NA 6.0187716 6.0187716 6.0187716 1.0000000 6.0187716
ab := a*b ab -0.0961403 0.0166549 -5.772502 0 -0.1303230 -0.0639506 -0.0961403 -0.0907389 -0.0369862
total := cp+ab total -0.3007044 0.0276825 -10.862628 0 -0.3568403 -0.2470173 -0.3007044 -0.2838101 -0.1156842 



## [1] "Bootstrap resampling has begun. This process may take a considerable amount of time if the number of replications is large, which is optimal for the bootstrap procedure."
##                                           Estimate CI.Lower_BCa
## Indirect.Effect                        -0.09614027           NA
## Indirect.Effect.Partially.Standardized -0.03697385           NA
## Index.of.Mediation                     -0.09073887           NA
## R2_4.5                                  0.05221902           NA
## R2_4.6                                  0.00680544           NA
## R2_4.7                                  0.06383056           NA
## Ratio.of.Indirect.to.Total.Effect       0.31971686           NA
## Ratio.of.Indirect.to.Direct.Effect      0.46997616           NA
## Success.of.Surrogate.Endpoint          -0.86018709           NA
## Residual.Based_Gamma                    0.09265238           NA
## Residual.Based.Standardized_gamma       0.10399212           NA
## SOS                                     0.64829552           NA
##                                        CI.Upper_BCa
## Indirect.Effect                                  NA
## Indirect.Effect.Partially.Standardized           NA
## Index.of.Mediation                               NA
## R2_4.5                                           NA
## R2_4.6                                           NA
## R2_4.7                                           NA
## Ratio.of.Indirect.to.Total.Effect                NA
## Ratio.of.Indirect.to.Direct.Effect               NA
## Success.of.Surrogate.Endpoint                    NA
## Residual.Based_Gamma                             NA
## Residual.Based.Standardized_gamma                NA
## SOS                                              NA









Dominance as mediator



## lavaan 0.6-2 ended normally after 18 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          5
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Parameter Estimates:
## 
##   Standard Errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws             999
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   SDO7_Dominance ~                                                      
##     I_SP_JJ_O  (a)    0.195    0.015   13.040    0.000    0.195    0.321
##   Env_2_REV ~                                                           
##     SDO7_Dmnn  (b)    0.099    0.049    2.003    0.045    0.099    0.057
##     I_SP_JJ_O (cp)   -0.320    0.030  -10.650    0.000   -0.320   -0.302
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .SDO7_Dominance    1.997    0.064   31.178    0.000    1.997    0.897
##    .Env_2_REV         6.193    0.177   34.965    0.000    6.193    0.917
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ab                0.019    0.010    2.001    0.045    0.019    0.018
##     total            -0.301    0.028  -10.862    0.000   -0.301   -0.284
lhs op rhs label est se z pvalue ci.lower ci.upper std.lv std.all std.nox
SDO7_Dominance ~ Ideo_SP_JJ_Overall a 0.1954718 0.0149907 13.039553 0.0000000 0.1649925 0.2240968 0.1954718 0.3213180 0.1309728
Env_2_REV ~ SDO7_Dominance b 0.0985645 0.0492094 2.002962 0.0451814 0.0082560 0.1991539 0.0985645 0.0565923 0.0565923
Env_2_REV ~ Ideo_SP_JJ_Overall cp -0.3199710 0.0300456 -10.649512 0.0000000 -0.3778125 -0.2626030 -0.3199710 -0.3019942 -0.1230962
SDO7_Dominance ~~ SDO7_Dominance 1.9974674 0.0640670 31.177788 0.0000000 1.8841024 2.1332210 1.9974674 0.8967547 0.8967547
Env_2_REV ~~ Env_2_REV 6.1930140 0.1771194 34.965186 0.0000000 5.8463331 6.5532764 6.1930140 0.9165798 0.9165798
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.0187716 0.0000000 NA NA 6.0187716 6.0187716 6.0187716 1.0000000 6.0187716
ab := a*b ab 0.0192666 0.0096270 2.001308 0.0453592 0.0016656 0.0391606 0.0192666 0.0181841 0.0074121
total := cp+ab total -0.3007044 0.0276839 -10.862050 0.0000000 -0.3568019 -0.2470249 -0.3007044 -0.2838101 -0.1156842 



## [1] "Bootstrap resampling has begun. This process may take a considerable amount of time if the number of replications is large, which is optimal for the bootstrap procedure."
##                                             Estimate CI.Lower_BCa
## Indirect.Effect                         0.0192665805           NA
## Indirect.Effect.Partially.Standardized  0.0074095859           NA
## Index.of.Mediation                      0.0181841365           NA
## R2_4.5                                 -0.0012363243           NA
## R2_4.6                                  0.0003225003           NA
## R2_4.7                                  0.0038659733           NA
## Ratio.of.Indirect.to.Total.Effect      -0.0640714944           NA
## Ratio.of.Indirect.to.Direct.Effect     -0.0602135240           NA
## Success.of.Surrogate.Endpoint          -1.5383516951           NA
## Residual.Based_Gamma                    0.0266315914           NA
## Residual.Based.Standardized_gamma       0.0362830888           NA
## SOS                                    -0.0153488815           NA
##                                        CI.Upper_BCa
## Indirect.Effect                                  NA
## Indirect.Effect.Partially.Standardized           NA
## Index.of.Mediation                               NA
## R2_4.5                                           NA
## R2_4.6                                           NA
## R2_4.7                                           NA
## Ratio.of.Indirect.to.Total.Effect                NA
## Ratio.of.Indirect.to.Direct.Effect               NA
## Success.of.Surrogate.Endpoint                    NA
## Residual.Based_Gamma                             NA
## Residual.Based.Standardized_gamma                NA
## SOS                                              NA









Universalism Nature as mediator



## lavaan 0.6-2 ended normally after 17 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          5
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard Errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws             993
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   PVQ_nat ~                                                             
##     I_SP_JJ_O  (a)   -0.124    0.012  -10.381    0.000   -0.124   -0.263
##   Env_2_REV ~                                                           
##     PVQ_nat    (b)    0.805    0.059   13.572    0.000    0.805    0.358
##     I_SP_JJ_O (cp)   -0.201    0.027   -7.421    0.000   -0.201   -0.189
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .PVQ_nat           1.247    0.045   27.488    0.000    1.247    0.931
##    .Env_2_REV         5.404    0.173   31.206    0.000    5.404    0.800
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ab               -0.100    0.012   -8.297    0.000   -0.100   -0.094
##     total            -0.301    0.028  -10.884    0.000   -0.301   -0.284
lhs op rhs label est se z pvalue ci.lower ci.upper std.lv std.all std.nox
PVQ_nat ~ Ideo_SP_JJ_Overall a -0.1242029 0.0119650 -10.380519 0 -0.1475287 -0.0991088 -0.1242029 -0.2632090 -0.1072869
Env_2_REV ~ PVQ_nat b 0.8048530 0.0593009 13.572367 0 0.6812250 0.9150185 0.8048530 0.3584558 0.3584558
Env_2_REV ~ Ideo_SP_JJ_Overall cp -0.2007393 0.0270512 -7.420723 0 -0.2521823 -0.1477256 -0.2007393 -0.1894613 -0.0772265
PVQ_nat ~~ PVQ_nat 1.2473535 0.0453786 27.487684 0 1.1637186 1.3445847 1.2473535 0.9307210 0.9307210
Env_2_REV ~~ Env_2_REV 5.4043984 0.1731862 31.205702 0 5.0792575 5.7656917 5.4043984 0.7998629 0.7998629
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.0187716 0.0000000 NA NA 6.0187716 6.0187716 6.0187716 1.0000000 6.0187716
ab := a*b ab -0.0999651 0.0120486 -8.296811 0 -0.1265193 -0.0780604 -0.0999651 -0.0943488 -0.0384576
total := cp+ab total -0.3007044 0.0276282 -10.883953 0 -0.3569887 -0.2473208 -0.3007044 -0.2838101 -0.1156842 



## [1] "Bootstrap resampling has begun. This process may take a considerable amount of time if the number of replications is large, which is optimal for the bootstrap procedure."
##                                           Estimate CI.Lower_BCa
## Indirect.Effect                        -0.09996509           NA
## Indirect.Effect.Partially.Standardized -0.03844480           NA
## Index.of.Mediation                     -0.09434880           NA
## R2_4.5                                  0.04713940           NA
## R2_4.6                                  0.00901080           NA
## R2_4.7                                  0.04502314           NA
## Ratio.of.Indirect.to.Total.Effect       0.33243640           NA
## Ratio.of.Indirect.to.Direct.Effect      0.49798460           NA
## Success.of.Surrogate.Endpoint           2.42107343           NA
## Residual.Based_Gamma                    0.04586138           NA
## Residual.Based.Standardized_gamma       0.04563655           NA
## SOS                                     0.58523240           NA
##                                        CI.Upper_BCa
## Indirect.Effect                                  NA
## Indirect.Effect.Partially.Standardized           NA
## Index.of.Mediation                               NA
## R2_4.5                                           NA
## R2_4.6                                           NA
## R2_4.7                                           NA
## Ratio.of.Indirect.to.Total.Effect                NA
## Ratio.of.Indirect.to.Direct.Effect               NA
## Success.of.Surrogate.Endpoint                    NA
## Residual.Based_Gamma                             NA
## Residual.Based.Standardized_gamma                NA
## SOS                                              NA









Raw Enviromental Scale as mediator



## lavaan 0.6-2 ended normally after 15 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          5
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Parameter Estimates:
## 
##   Standard Errors                            Bootstrap
##   Number of requested bootstrap draws             1000
##   Number of successful bootstrap draws            1000
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Env ~                                                                 
##     I_SP_JJ_O  (a)   -0.338    0.014  -23.397    0.000   -0.338   -0.539
##   Env_2_REV ~                                                           
##     Env        (b)    1.025    0.041   25.056    0.000    1.025    0.607
##     I_SP_JJ_O (cp)    0.046    0.027    1.722    0.085    0.046    0.043
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .Env               1.679    0.068   24.536    0.000    1.679    0.709
##    .Env_2_REV         4.448    0.168   26.518    0.000    4.448    0.658
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     ab               -0.347    0.019  -18.266    0.000   -0.347   -0.327
##     total            -0.301    0.028  -10.867    0.000   -0.301   -0.284
lhs op rhs label est se z pvalue ci.lower ci.upper std.lv std.all std.nox
Env ~ Ideo_SP_JJ_Overall a -0.3381334 0.0144517 -23.397435 0.0000000 -0.3672214 -0.3101363 -0.3381334 -0.5391891 -0.2197795
Env_2_REV ~ Env b 1.0253062 0.0409204 25.056085 0.0000000 0.9380977 1.1006518 1.0253062 0.6068601 0.6068601
Env_2_REV ~ Ideo_SP_JJ_Overall cp 0.0459858 0.0267050 1.721994 0.0850706 -0.0046610 0.0987917 0.0459858 0.0434023 0.0176912
Env ~~ Env 1.6788676 0.0684245 24.536074 0.0000000 1.5546610 1.8375135 1.6788676 0.7092751 0.7092751
Env_2_REV ~~ Env_2_REV 4.4475051 0.1677165 26.517996 0.0000000 4.1069471 4.7641079 4.4475051 0.6582406 0.6582406
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.0187716 0.0000000 NA NA 6.0187716 6.0187716 6.0187716 1.0000000 6.0187716
ab := a*b ab -0.3466902 0.0189804 -18.265674 0.0000000 -0.3838782 -0.3084751 -0.3466902 -0.3272123 -0.1333754
total := cp+ab total -0.3007044 0.0276710 -10.867123 0.0000000 -0.3569125 -0.2470861 -0.3007044 -0.2838101 -0.1156842 



## [1] "Bootstrap resampling has begun. This process may take a considerable amount of time if the number of replications is large, which is optimal for the bootstrap procedure."
##                                           Estimate CI.Lower_BCa
## Indirect.Effect                        -0.34669025           NA
## Indirect.Effect.Partially.Standardized -0.13333094           NA
## Index.of.Mediation                     -0.32721234           NA
## R2_4.5                                  0.07921207           NA
## R2_4.6                                  0.08259335           NA
## R2_4.7                                  0.24167105           NA
## Ratio.of.Indirect.to.Total.Effect       1.15292709           NA
## Ratio.of.Indirect.to.Direct.Effect     -7.53906374           NA
## Success.of.Surrogate.Endpoint           0.88930707           NA
## Residual.Based_Gamma                    0.08153751           NA
## Residual.Based.Standardized_gamma       0.09577144           NA
## SOS                                     0.98341240           NA
##                                        CI.Upper_BCa
## Indirect.Effect                                  NA
## Indirect.Effect.Partially.Standardized           NA
## Index.of.Mediation                               NA
## R2_4.5                                           NA
## R2_4.6                                           NA
## R2_4.7                                           NA
## Ratio.of.Indirect.to.Total.Effect                NA
## Ratio.of.Indirect.to.Direct.Effect               NA
## Success.of.Surrogate.Endpoint                    NA
## Residual.Based_Gamma                             NA
## Residual.Based.Standardized_gamma                NA
## SOS                                              NA













Overall Picture of Mediations

## 
## Mediation/Moderation Analysis 
## Call: mediate(y = "Env_2_REV", x = c("Ideo_SP_JJ_Overall"), m = c("SDO", 
##     "SJ_Eco"), data = data, n.obs = 1500, n.iter = 50)
## 
## The DV (Y) was  Env_2_REV . The IV (X) was  Ideo_SP_JJ_Overall . The mediating variable(s) =  SDO SJ_Eco .
## 
## Total effect(c) of  Ideo_SP_JJ_Overall  on  Env_2_REV  =  -0.3   S.E. =  0.03  t  =  -11.46  df=  1496   with p =  3.5e-29
## Direct effect (c') of  Ideo_SP_JJ_Overall  on  Env_2_REV  removing  SDO SJ_Eco  =  -0.18   S.E. =  0.03  t  =  -5.78  df=  1496   with p =  9.1e-09
## Indirect effect (ab) of  Ideo_SP_JJ_Overall  on  Env_2_REV  through  SDO SJ_Eco   =  -0.12 
## Mean bootstrapped indirect effect =  -0.12  with standard error =  0.02  Lower CI =  -0.15    Upper CI =  -0.09
## R = 0.34 R2 = 0.11   F = 64.4 on 3 and 1496 DF   p-value:  3.66e-39 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary
## 
## Mediation/Moderation Analysis 
## Call: mediate(y = "Env_2_REV", x = c("Ideo_SP_JJ_Overall"), m = c("SDO7_AntiEgal", 
##     "SJ_Eco"), data = data, n.obs = 1500, n.iter = 50)
## 
## The DV (Y) was  Env_2_REV . The IV (X) was  Ideo_SP_JJ_Overall . The mediating variable(s) =  SDO7_AntiEgal SJ_Eco .
## 
## Total effect(c) of  Ideo_SP_JJ_Overall  on  Env_2_REV  =  -0.3   S.E. =  0.03  t  =  -11.46  df=  1496   with p =  3.5e-29
## Direct effect (c') of  Ideo_SP_JJ_Overall  on  Env_2_REV  removing  SDO7_AntiEgal SJ_Eco  =  -0.15   S.E. =  0.03  t  =  -4.73  df=  1496   with p =  2.4e-06
## Indirect effect (ab) of  Ideo_SP_JJ_Overall  on  Env_2_REV  through  SDO7_AntiEgal SJ_Eco   =  -0.15 
## Mean bootstrapped indirect effect =  -0.15  with standard error =  0.02  Lower CI =  -0.19    Upper CI =  -0.12
## R = 0.35 R2 = 0.12   F = 68.86 on 3 and 1496 DF   p-value:  1.04e-41 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary
## 
## Mediation/Moderation Analysis 
## Call: mediate(y = "Env_2_REV", x = c("Ideo_SP_JJ_Overall"), m = c("SDO7_Dominance", 
##     "SJ_Eco"), data = data, n.obs = 1500, n.iter = 50)
## 
## The DV (Y) was  Env_2_REV . The IV (X) was  Ideo_SP_JJ_Overall . The mediating variable(s) =  SDO7_Dominance SJ_Eco .
## 
## Total effect(c) of  Ideo_SP_JJ_Overall  on  Env_2_REV  =  -0.3   S.E. =  0.03  t  =  -11.46  df=  1496   with p =  3.5e-29
## Direct effect (c') of  Ideo_SP_JJ_Overall  on  Env_2_REV  removing  SDO7_Dominance SJ_Eco  =  -0.19   S.E. =  0.03  t  =  -6.29  df=  1496   with p =  4e-10
## Indirect effect (ab) of  Ideo_SP_JJ_Overall  on  Env_2_REV  through  SDO7_Dominance SJ_Eco   =  -0.11 
## Mean bootstrapped indirect effect =  -0.11  with standard error =  0.02  Lower CI =  -0.16    Upper CI =  -0.07
## R = 0.36 R2 = 0.13   F = 73.68 on 3 and 1496 DF   p-value:  1.89e-44 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

## 
## Mediation/Moderation Analysis 
## Call: mediate(y = "Env_2_REV", x = c("Ideo_SP_JJ_Overall"), m = c("SDO7_Dominance", 
##     "SDO7_AntiEgal", "SJ_Eco"), data = data, n.obs = 1500, n.iter = 50)
## 
## The DV (Y) was  Env_2_REV . The IV (X) was  Ideo_SP_JJ_Overall . The mediating variable(s) =  SDO7_Dominance SDO7_AntiEgal SJ_Eco .
## 
## Total effect(c) of  Ideo_SP_JJ_Overall  on  Env_2_REV  =  -0.3   S.E. =  0.03  t  =  -11.46  df=  1495   with p =  3.5e-29
## Direct effect (c') of  Ideo_SP_JJ_Overall  on  Env_2_REV  removing  SDO7_Dominance SDO7_AntiEgal SJ_Eco  =  -0.15   S.E. =  0.03  t  =  -4.88  df=  1495   with p =  1.2e-06
## Indirect effect (ab) of  Ideo_SP_JJ_Overall  on  Env_2_REV  through  SDO7_Dominance SDO7_AntiEgal SJ_Eco   =  -0.15 
## Mean bootstrapped indirect effect =  -0.15  with standard error =  0.02  Lower CI =  -0.19    Upper CI =  -0.12
## R = 0.39 R2 = 0.15   F = 66.41 on 4 and 1495 DF   p-value:  9.09e-52 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

Parallel Multiple-Mediator models



SDO & ESJ



## lavaan 0.6-2 ended normally after 22 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          8
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     367.777
##   Degrees of freedom                                 1
##   P-value (Chi-square)                           0.000
## lavaan 0.6-2 ended normally after 22 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          8
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     367.777
##   Degrees of freedom                                 1
##   P-value (Chi-square)                           0.000
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             1437.813
##   Degrees of freedom                                 6
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.744
##   Tucker-Lewis Index (TLI)                      -0.537
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -7963.581
##   Loglikelihood unrestricted model (H1)      -7779.693
## 
##   Number of free parameters                          8
##   Akaike (AIC)                               15943.162
##   Bayesian (BIC)                             15985.668
##   Sample-size adjusted Bayesian (BIC)        15960.254
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.494
##   90 Percent Confidence Interval          0.453  0.538
##   P-value RMSEA <= 0.05                          0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.113
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model          Structured
##   Standard Errors                             Standard
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Env_2_REV ~                                                           
##     I_SP_JJ_O  (c)   -0.181    0.033   -5.413    0.000   -0.181   -0.171
##     SDO       (b1)    0.047    0.050    0.955    0.340    0.047    0.026
##     SJ_Eco    (b2)   -0.547    0.069   -7.967    0.000   -0.547   -0.230
##   SDO ~                                                                 
##     I_SP_JJ_O (a1)    0.273    0.013   20.354    0.000    0.273    0.465
##   SJ_Eco ~                                                              
##     I_SP_JJ_O (a2)    0.242    0.010   24.988    0.000    0.242    0.542
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .Env_2_REV         5.984    0.218   27.386    0.000    5.984    0.882
##    .SDO               1.619    0.059   27.386    0.000    1.619    0.784
##    .SJ_Eco            0.847    0.031   27.386    0.000    0.847    0.706
## 
## R-Square:
##                    Estimate
##     Env_2_REV         0.118
##     SDO               0.216
##     SJ_Eco            0.294
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     SDO.IDE           0.013    0.014    0.954    0.340    0.013    0.012
##     SJ_Eco.IDE       -0.132    0.017   -7.591    0.000   -0.132   -0.125
##     sum.IDE          -0.119    0.022   -5.409    0.000   -0.119   -0.112
##     total            -0.301    0.026  -11.437    0.000   -0.301   -0.283


Bootstrap confidence intervals


Bootstrap confidence intervals
lhs op rhs label est se z pvalue ci.lower ci.upper
Env_2_REV ~ Ideo_SP_JJ_Overall c -0.18 0.03 -5.41 0.00 -0.25 -0.12
Env_2_REV ~ SDO b1 0.05 0.05 0.95 0.34 -0.05 0.14
Env_2_REV ~ SJ_Eco b2 -0.55 0.07 -7.97 0.00 -0.68 -0.41
SDO ~ Ideo_SP_JJ_Overall a1 0.27 0.01 20.35 0.00 0.25 0.30
SJ_Eco ~ Ideo_SP_JJ_Overall a2 0.24 0.01 24.99 0.00 0.22 0.26
Env_2_REV ~~ Env_2_REV 5.98 0.22 27.39 0.00 5.56 6.41
SDO ~~ SDO 1.62 0.06 27.39 0.00 1.50 1.73
SJ_Eco ~~ SJ_Eco 0.85 0.03 27.39 0.00 0.79 0.91
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.02 0.00 NA NA 6.02 6.02
SDO.IDE := a1*b1 SDO.IDE 0.01 0.01 0.95 0.34 -0.01 0.04
SJ_Eco.IDE := a2*b2 SJ_Eco.IDE -0.13 0.02 -7.59 0.00 -0.17 -0.10
sum.IDE := (a1b1)+(a2b2) sum.IDE -0.12 0.02 -5.41 0.00 -0.16 -0.08
total := c+(a1b1)+(a2b2) total -0.30 0.03 -11.44 0.00 -0.35 -0.25



Plot



via PROCESS, by Andrew F. Hayes



## ************************* PROCESS Procedure for R Based On PROCESS for SAS v2.11 ************************
## Written by Andrew F. Hayes, Ph.D.  http://www.afhayes.com
## Converted For R by Dean Lim.  d.lim@rscbga.com
## *******************************************************************************************************
## Model and Variables
## Model = 4
## Y     =  Env_2_REV
## X     =  Ideo_SP_JJ_Overall
## M1    = SDO
## M2    = SJ_Eco
## Sample size:
## 1500
## *****************************************************************************************
## Outcome: SDO
## Model Summary
## R R-sq F df1 df2 p
##     0.4652    0.2164  413.7255    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  2.29849  0.07831 29.34934  0.00000  2.14487  2.45211
## Ideo_SP_JJ_Overall  0.27253  0.01340 20.34024  0.00000  0.24624  0.29881
## *****************************************************************************************
## Outcome: SJ_Eco
## Model Summary
## R R-sq F df1 df2 p
##     0.5421    0.2939  623.5554    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  3.649790  0.056652 64.425167  0.000000  3.538665  3.760915
## Ideo_SP_JJ_Overall  0.242023  0.009692 24.971092  0.000000  0.223012  0.261035
## *****************************************************************************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV    0.3382    0.1144   64.4020    3.0000 1496.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## constant  9.145e+00  2.926e-01  3.125e+01  0.000e+00  8.571e+00  9.719e+00
## SDO  4.741e-02  5.620e-02  8.437e-01  3.990e-01 -6.282e-02  1.576e-01
## SJ_Eco -5.468e-01  7.768e-02 -7.039e+00  2.943e-12 -6.992e-01 -3.944e-01
## Ideo_SP_JJ_Overall -1.813e-01  3.137e-02 -5.779e+00  9.132e-09 -2.428e-01 -1.198e-01
## ********************************* TOTAL EFFECT MODEL *********************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV 2.838e-01 8.055e-02 1.312e+02 1.000e+00 1.498e+03 0.000e+00
## Model
## coeff se t p LLCI ULCI
## constant   7.25867   0.15343  47.30899   0.00000   6.95771   7.55963
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## *************************** TOTAL, DIRECT AND INDIRECT EFFECTS ***************************
## Total effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## Direct effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall -1.813e-01  3.137e-02 -5.779e+00  9.132e-09 -2.428e-01 -1.198e-01
## Indirect effect of X on Y
## Effect Boot SE BootLLCI BootULCI
## TOTAL -0.11942  0.01896 -0.15721 -0.08278
## SDO  0.01292  0.01594 -0.01777  0.04467
## SJ_Eco -0.13234  0.02043 -0.17307 -0.09289
## (C1)  0.14526  0.03135  0.08477  0.20738
## Specific indirect effect contrast definitions
## (C1) SDO minus SJ_Eco
## ****************************** ANALYSIS NOTES AND WARNINGS ******************************
## Number of bootstrap samples for bias corrected bootstrap confidence intervals:
## 10000
## Level of confidence for all confidence intervals in output:
## 95













Anti-Egalitarianism, Dominance & ESJ



## lavaan 0.6-2 ended normally after 24 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         11
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     824.860
##   Degrees of freedom                                 3
##   P-value (Chi-square)                           0.000
## lavaan 0.6-2 ended normally after 24 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         11
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     824.860
##   Degrees of freedom                                 3
##   P-value (Chi-square)                           0.000
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             2167.396
##   Degrees of freedom                                10
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.619
##   Tucker-Lewis Index (TLI)                      -0.270
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -10852.213
##   Loglikelihood unrestricted model (H1)     -10439.783
## 
##   Number of free parameters                         11
##   Akaike (AIC)                               21726.426
##   Bayesian (BIC)                             21784.871
##   Sample-size adjusted Bayesian (BIC)        21749.927
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.427
##   90 Percent Confidence Interval          0.403  0.452
##   P-value RMSEA <= 0.05                          0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.159
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model          Structured
##   Standard Errors                             Standard
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Env_2_REV ~                                                           
##     I_SP_JJ_O  (c)   -0.151    0.034   -4.412    0.000   -0.151   -0.141
##     SDO7_Dmnn (b1)    0.368    0.044    8.405    0.000    0.368    0.208
##     SDO7_AntE (b2)   -0.315    0.041   -7.762    0.000   -0.315   -0.209
##     SJ_Eco    (b3)   -0.461    0.067   -6.853    0.000   -0.461   -0.191
##   SDO7_Dominance ~                                                      
##     I_SP_JJ_O (a1)    0.195    0.015   13.141    0.000    0.195    0.321
##   SDO7_AntiEgal ~                                                       
##     I_SP_JJ_O (a2)    0.350    0.016   21.766    0.000    0.350    0.490
##   SJ_Eco ~                                                              
##     I_SP_JJ_O (a3)    0.242    0.010   24.988    0.000    0.242    0.542
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .Env_2_REV         5.737    0.209   27.386    0.000    5.737    0.824
##    .SDO7_Dominance    1.997    0.073   27.386    0.000    1.997    0.897
##    .SDO7_AntiEgal     2.329    0.085   27.386    0.000    2.329    0.760
##    .SJ_Eco            0.847    0.031   27.386    0.000    0.847    0.706
## 
## R-Square:
##                    Estimate
##     Env_2_REV         0.176
##     SDO7_Dominance    0.103
##     SDO7_AntiEgal     0.240
##     SJ_Eco            0.294
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     SDO7_Dmnnc.IDE    0.072    0.010    7.081    0.000    0.072    0.067
##     SDO7_AntEg.IDE   -0.110    0.015   -7.311    0.000   -0.110   -0.102
##     SJ_Eco.IDE       -0.111    0.017   -6.609    0.000   -0.111   -0.104
##     sumIDE           -0.150    0.025   -6.036    0.000   -0.150   -0.139
##     total            -0.301    0.027  -11.279    0.000   -0.301   -0.280


Bootstrap confidence intervals


Bootstrap confidence intervals
lhs op rhs label est se z pvalue ci.lower ci.upper
Env_2_REV ~ Ideo_SP_JJ_Overall c -0.15 0.03 -4.41 0 -0.22 -0.08
Env_2_REV ~ SDO7_Dominance b1 0.37 0.04 8.41 0 0.28 0.45
Env_2_REV ~ SDO7_AntiEgal b2 -0.31 0.04 -7.76 0 -0.39 -0.24
Env_2_REV ~ SJ_Eco b3 -0.46 0.07 -6.85 0 -0.59 -0.33
SDO7_Dominance ~ Ideo_SP_JJ_Overall a1 0.20 0.01 13.14 0 0.17 0.22
SDO7_AntiEgal ~ Ideo_SP_JJ_Overall a2 0.35 0.02 21.77 0 0.32 0.38
SJ_Eco ~ Ideo_SP_JJ_Overall a3 0.24 0.01 24.99 0 0.22 0.26
Env_2_REV ~~ Env_2_REV 5.74 0.21 27.39 0 5.33 6.15
SDO7_Dominance ~~ SDO7_Dominance 2.00 0.07 27.39 0 1.85 2.14
SDO7_AntiEgal ~~ SDO7_AntiEgal 2.33 0.09 27.39 0 2.16 2.50
SJ_Eco ~~ SJ_Eco 0.85 0.03 27.39 0 0.79 0.91
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.02 0.00 NA NA 6.02 6.02
SDO7_Dominance.IDE := a1*b1 SDO7_Dominance.IDE 0.07 0.01 7.08 0 0.05 0.09
SDO7_AntiEgal.IDE := a2*b2 SDO7_AntiEgal.IDE -0.11 0.02 -7.31 0 -0.14 -0.08
SJ_Eco.IDE := a3*b3 SJ_Eco.IDE -0.11 0.02 -6.61 0 -0.14 -0.08
sumIDE := (a1b1)+(a2b2)+(a3*b3) sumIDE -0.15 0.02 -6.04 0 -0.20 -0.10
total := c+(a1b1)+(a2b2)+(a3*b3) total -0.30 0.03 -11.28 0 -0.35 -0.25



Plot



via PROCESS, by Andrew F. Hayes



## ************************* PROCESS Procedure for R Based On PROCESS for SAS v2.11 ************************
## Written by Andrew F. Hayes, Ph.D.  http://www.afhayes.com
## Converted For R by Dean Lim.  d.lim@rscbga.com
## *******************************************************************************************************
## Model and Variables
## Model = 4
## Y     =  Env_2_REV
## X     =  Ideo_SP_JJ_Overall
## M1    = SDO7_Dominance
## M2    = SDO7_AntiEgal
## M3    = SJ_Eco
## Sample size:
## 1500
## *****************************************************************************************
## Outcome: SDO7_Dominance
## Model Summary
## R R-sq F df1 df2 p
##     0.3213    0.1032  172.4679    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  2.48479  0.08700 28.56057  0.00000  2.31413  2.65545
## Ideo_SP_JJ_Overall  0.19547  0.01488 13.13270  0.00000  0.16628  0.22467
## *****************************************************************************************
## Outcome: SDO7_AntiEgal
## Model Summary
## R R-sq F df1 df2 p
##     0.4899    0.2400  473.1212    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  2.11219  0.09394 22.48435  0.00000  1.92792  2.29646
## Ideo_SP_JJ_Overall  0.34958  0.01607 21.75135  0.00000  0.31805  0.38111
## *****************************************************************************************
## Outcome: SJ_Eco
## Model Summary
## R R-sq F df1 df2 p
##     0.5421    0.2939  623.5554    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  3.649790  0.056652 64.425167  0.000000  3.538665  3.760915
## Ideo_SP_JJ_Overall  0.242023  0.009692 24.971092  0.000000  0.223012  0.261035
## *****************************************************************************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV    0.3884    0.1509   66.4143    4.0000 1495.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## constant  8.690e+00  2.922e-01  2.974e+01  0.000e+00  8.117e+00  9.263e+00
## SDO7_Dominance  3.678e-01  5.098e-02  7.214e+00  8.606e-13  2.678e-01  4.678e-01
## SDO7_AntiEgal -3.146e-01  5.037e-02 -6.244e+00  5.531e-10 -4.134e-01 -2.157e-01
## SJ_Eco -4.605e-01  7.685e-02 -5.993e+00  2.581e-09 -6.113e-01 -3.098e-01
## Ideo_SP_JJ_Overall -1.512e-01  3.096e-02 -4.884e+00  1.154e-06 -2.119e-01 -9.045e-02
## ********************************* TOTAL EFFECT MODEL *********************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV 2.838e-01 8.055e-02 1.312e+02 1.000e+00 1.498e+03 0.000e+00
## Model
## coeff se t p LLCI ULCI
## constant   7.25867   0.15343  47.30899   0.00000   6.95771   7.55963
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## *************************** TOTAL, DIRECT AND INDIRECT EFFECTS ***************************
## Total effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## Direct effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall -1.512e-01  3.096e-02 -4.884e+00  1.154e-06 -2.119e-01 -9.045e-02
## Indirect effect of X on Y
## Effect Boot SE BootLLCI BootULCI
## TOTAL -0.149531  0.020903 -0.190347 -0.108736
## SDO7_Dominance  0.071894  0.011906  0.050239  0.097014
## SDO7_AntiEgal -0.109964  0.020139 -0.149540 -0.070066
## SJ_Eco -0.111461  0.020381 -0.152513 -0.072411
## (C1)  0.181858  0.027739  0.127004  0.237007
## (C2)  0.183356  0.025021  0.135762  0.233885
## (C3)  0.001498  0.032491 -0.061756  0.066334
## Specific indirect effect contrast definitions
## (C1) SDO7_Dominance minus SDO7_AntiEgal
## (C2) SDO7_Dominance minus SJ_Eco
## (C3) SDO7_AntiEgal minus SJ_Eco
## ****************************** ANALYSIS NOTES AND WARNINGS ******************************
## Number of bootstrap samples for bias corrected bootstrap confidence intervals:
## 10000
## Level of confidence for all confidence intervals in output:
## 95













Anti-Egalitarianism & ESJ



## lavaan 0.6-2 ended normally after 22 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          8
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     371.363
##   Degrees of freedom                                 1
##   P-value (Chi-square)                           0.000
## lavaan 0.6-2 ended normally after 22 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          8
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     371.363
##   Degrees of freedom                                 1
##   P-value (Chi-square)                           0.000
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             1499.113
##   Degrees of freedom                                 6
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.752
##   Tucker-Lewis Index (TLI)                      -0.488
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -8230.558
##   Loglikelihood unrestricted model (H1)      -8044.877
## 
##   Number of free parameters                          8
##   Akaike (AIC)                               16477.117
##   Bayesian (BIC)                             16519.623
##   Sample-size adjusted Bayesian (BIC)        16494.209
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.497
##   90 Percent Confidence Interval          0.455  0.540
##   P-value RMSEA <= 0.05                          0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.111
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model          Structured
##   Standard Errors                             Standard
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Env_2_REV ~                                                           
##     I_SP_JJ_O  (c)   -0.149    0.034   -4.415    0.000   -0.149   -0.142
##     SDO7_AntE (b1)   -0.166    0.041   -4.015    0.000   -0.166   -0.112
##     SJ_Eco    (b2)   -0.388    0.068   -5.672    0.000   -0.388   -0.164
##   SDO7_AntiEgal ~                                                       
##     I_SP_JJ_O (a1)    0.350    0.016   21.766    0.000    0.350    0.490
##   SJ_Eco ~                                                              
##     I_SP_JJ_O (a2)    0.242    0.010   24.988    0.000    0.242    0.542
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .Env_2_REV         5.937    0.217   27.386    0.000    5.937    0.890
##    .SDO7_AntiEgal     2.329    0.085   27.386    0.000    2.329    0.760
##    .SJ_Eco            0.847    0.031   27.386    0.000    0.847    0.706
## 
## R-Square:
##                    Estimate
##     Env_2_REV         0.110
##     SDO7_AntiEgal     0.240
##     SJ_Eco            0.294
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     SDO7_AntEg.IDE   -0.058    0.015   -3.948    0.000   -0.058   -0.055
##     SJ_Eco.IDE       -0.094    0.017   -5.531    0.000   -0.094   -0.089
##     sum.IDE          -0.152    0.022   -6.767    0.000   -0.152   -0.144
##     total            -0.301    0.026  -11.542    0.000   -0.301   -0.286


Bootstrap confidence intervals


Bootstrap confidence intervals
lhs op rhs label est se z pvalue ci.lower ci.upper
Env_2_REV ~ Ideo_SP_JJ_Overall c -0.15 0.03 -4.42 0 -0.22 -0.08
Env_2_REV ~ SDO7_AntiEgal b1 -0.17 0.04 -4.02 0 -0.25 -0.08
Env_2_REV ~ SJ_Eco b2 -0.39 0.07 -5.67 0 -0.52 -0.25
SDO7_AntiEgal ~ Ideo_SP_JJ_Overall a1 0.35 0.02 21.77 0 0.32 0.38
SJ_Eco ~ Ideo_SP_JJ_Overall a2 0.24 0.01 24.99 0 0.22 0.26
Env_2_REV ~~ Env_2_REV 5.94 0.22 27.39 0 5.51 6.36
SDO7_AntiEgal ~~ SDO7_AntiEgal 2.33 0.09 27.39 0 2.16 2.50
SJ_Eco ~~ SJ_Eco 0.85 0.03 27.39 0 0.79 0.91
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.02 0.00 NA NA 6.02 6.02
SDO7_AntiEgal.IDE := a1*b1 SDO7_AntiEgal.IDE -0.06 0.01 -3.95 0 -0.09 -0.03
SJ_Eco.IDE := a2*b2 SJ_Eco.IDE -0.09 0.02 -5.53 0 -0.13 -0.06
sum.IDE := (a1b1)+(a2b2) sum.IDE -0.15 0.02 -6.77 0 -0.20 -0.11
total := c+(a1b1)+(a2b2) total -0.30 0.03 -11.54 0 -0.35 -0.25



Plot



via PROCESS, by Andrew F. Hayes



## ************************* PROCESS Procedure for R Based On PROCESS for SAS v2.11 ************************
## Written by Andrew F. Hayes, Ph.D.  http://www.afhayes.com
## Converted For R by Dean Lim.  d.lim@rscbga.com
## *******************************************************************************************************
## Model and Variables
## Model = 4
## Y     =  Env_2_REV
## X     =  Ideo_SP_JJ_Overall
## M1    = SDO7_AntiEgal
## M2    = SJ_Eco
## Sample size:
## 1500
## *****************************************************************************************
## Outcome: SDO7_AntiEgal
## Model Summary
## R R-sq F df1 df2 p
##     0.4899    0.2400  473.1212    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  2.11219  0.09394 22.48435  0.00000  1.92792  2.29646
## Ideo_SP_JJ_Overall  0.34958  0.01607 21.75135  0.00000  0.31805  0.38111
## *****************************************************************************************
## Outcome: SJ_Eco
## Model Summary
## R R-sq F df1 df2 p
##     0.5421    0.2939  623.5554    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  3.649790  0.056652 64.425167  0.000000  3.538665  3.760915
## Ideo_SP_JJ_Overall  0.242023  0.009692 24.971092  0.000000  0.223012  0.261035
## *****************************************************************************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV    0.3483    0.1213   68.8562    3.0000 1496.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## constant  9.023e+00  2.934e-01  3.075e+01  0.000e+00  8.448e+00  9.599e+00
## SDO7_AntiEgal -1.655e-01  4.672e-02 -3.543e+00  4.080e-04 -2.572e-01 -7.388e-02
## SJ_Eco -3.877e-01  7.747e-02 -5.005e+00  6.263e-07 -5.397e-01 -2.357e-01
## Ideo_SP_JJ_Overall -1.490e-01  3.148e-02 -4.734e+00  2.413e-06 -2.108e-01 -8.726e-02
## ********************************* TOTAL EFFECT MODEL *********************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV 2.838e-01 8.055e-02 1.312e+02 1.000e+00 1.498e+03 0.000e+00
## Model
## coeff se t p LLCI ULCI
## constant   7.25867   0.15343  47.30899   0.00000   6.95771   7.55963
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## *************************** TOTAL, DIRECT AND INDIRECT EFFECTS ***************************
## Total effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## Direct effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall -1.490e-01  3.148e-02 -4.734e+00  2.413e-06 -2.108e-01 -8.726e-02
## Indirect effect of X on Y
## Effect Boot SE BootLLCI BootULCI
## TOTAL -0.15170  0.01993 -0.19137 -0.11392
## SDO7_AntiEgal -0.05786  0.01769 -0.09342 -0.02405
## SJ_Eco -0.09384  0.02010 -0.13389 -0.05547
## (C1)  0.03597  0.03220 -0.02641  0.09966
## Specific indirect effect contrast definitions
## (C1) SDO7_AntiEgal minus SJ_Eco
## ****************************** ANALYSIS NOTES AND WARNINGS ******************************
## Number of bootstrap samples for bias corrected bootstrap confidence intervals:
## 10000
## Level of confidence for all confidence intervals in output:
## 95













Anti-Egalitarianism & Dominance



## lavaan 0.6-2 ended normally after 19 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          8
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     427.384
##   Degrees of freedom                                 1
##   P-value (Chi-square)                           0.000
## lavaan 0.6-2 ended normally after 19 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          8
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                     427.384
##   Degrees of freedom                                 1
##   P-value (Chi-square)                           0.000
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             1212.285
##   Degrees of freedom                                 6
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.647
##   Tucker-Lewis Index (TLI)                      -1.121
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -8866.193
##   Loglikelihood unrestricted model (H1)      -8652.500
## 
##   Number of free parameters                          8
##   Akaike (AIC)                               17748.385
##   Bayesian (BIC)                             17790.891
##   Sample-size adjusted Bayesian (BIC)        17765.477
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.533
##   90 Percent Confidence Interval          0.491  0.576
##   P-value RMSEA <= 0.05                          0.000
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.138
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model          Structured
##   Standard Errors                             Standard
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Env_2_REV ~                                                           
##     I_SP_JJ_O  (c)   -0.216    0.031   -7.072    0.000   -0.216   -0.199
##     SDO7_AntE (b1)   -0.426    0.041  -10.391    0.000   -0.426   -0.281
##     SDO7_Dmnn (b2)    0.328    0.044    7.400    0.000    0.328    0.184
##   SDO7_AntiEgal ~                                                       
##     I_SP_JJ_O (a1)    0.350    0.016   21.766    0.000    0.350    0.490
##   SDO7_Dominance ~                                                      
##     I_SP_JJ_O (a2)    0.195    0.015   13.141    0.000    0.195    0.321
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .Env_2_REV         5.875    0.215   27.386    0.000    5.875    0.833
##    .SDO7_AntiEgal     2.329    0.085   27.386    0.000    2.329    0.760
##    .SDO7_Dominance    1.997    0.073   27.386    0.000    1.997    0.897
## 
## R-Square:
##                    Estimate
##     Env_2_REV         0.167
##     SDO7_AntiEgal     0.240
##     SDO7_Dominance    0.103
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     SDO7_AntEg.IDE   -0.149    0.016   -9.377    0.000   -0.149   -0.138
##     SDO7_Dmnnc.IDE    0.064    0.010    6.448    0.000    0.064    0.059
##     sum.IDE          -0.085    0.019   -4.532    0.000   -0.085   -0.078
##     total            -0.301    0.027  -11.196    0.000   -0.301   -0.278


Bootstrap confidence intervals


Bootstrap confidence intervals
lhs op rhs label est se z pvalue ci.lower ci.upper
Env_2_REV ~ Ideo_SP_JJ_Overall c -0.22 0.03 -7.07 0 -0.28 -0.16
Env_2_REV ~ SDO7_AntiEgal b1 -0.43 0.04 -10.39 0 -0.51 -0.35
Env_2_REV ~ SDO7_Dominance b2 0.33 0.04 7.40 0 0.24 0.41
SDO7_AntiEgal ~ Ideo_SP_JJ_Overall a1 0.35 0.02 21.77 0 0.32 0.38
SDO7_Dominance ~ Ideo_SP_JJ_Overall a2 0.20 0.01 13.14 0 0.17 0.22
Env_2_REV ~~ Env_2_REV 5.88 0.21 27.39 0 5.45 6.30
SDO7_AntiEgal ~~ SDO7_AntiEgal 2.33 0.09 27.39 0 2.16 2.50
SDO7_Dominance ~~ SDO7_Dominance 2.00 0.07 27.39 0 1.85 2.14
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.02 0.00 NA NA 6.02 6.02
SDO7_AntiEgal.IDE := a1*b1 SDO7_AntiEgal.IDE -0.15 0.02 -9.38 0 -0.18 -0.12
SDO7_Dominance.IDE := a2*b2 SDO7_Dominance.IDE 0.06 0.01 6.45 0 0.04 0.08
sum.IDE := (a1b1)+(a2b2) sum.IDE -0.08 0.02 -4.53 0 -0.12 -0.05
total := c+(a1b1)+(a2b2) total -0.30 0.03 -11.20 0 -0.35 -0.25



Plot



via PROCESS, by Andrew F. Hayes



## ************************* PROCESS Procedure for R Based On PROCESS for SAS v2.11 ************************
## Written by Andrew F. Hayes, Ph.D.  http://www.afhayes.com
## Converted For R by Dean Lim.  d.lim@rscbga.com
## *******************************************************************************************************
## Model and Variables
## Model = 4
## Y     =  Env_2_REV
## X     =  Ideo_SP_JJ_Overall
## M1    = SDO7_AntiEgal
## M2    = SDO7_Dominance
## Sample size:
## 1500
## *****************************************************************************************
## Outcome: SDO7_AntiEgal
## Model Summary
## R R-sq F df1 df2 p
##     0.4899    0.2400  473.1212    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  2.11219  0.09394 22.48435  0.00000  1.92792  2.29646
## Ideo_SP_JJ_Overall  0.34958  0.01607 21.75135  0.00000  0.31805  0.38111
## *****************************************************************************************
## Outcome: SDO7_Dominance
## Model Summary
## R R-sq F df1 df2 p
##     0.3213    0.1032  172.4679    1.0000 1498.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## Constant  2.48479  0.08700 28.56057  0.00000  2.31413  2.65545
## Ideo_SP_JJ_Overall  0.19547  0.01488 13.13270  0.00000  0.16628  0.22467
## *****************************************************************************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV    0.3612    0.1305   74.8348    3.0000 1496.0000    0.0000
## Model
## coeff se t p LLCI ULCI
## constant  7.345e+00  1.892e-01  3.883e+01  0.000e+00  6.973e+00  7.716e+00
## SDO7_AntiEgal -4.261e-01  4.735e-02 -8.999e+00  0.000e+00 -5.190e-01 -3.332e-01
## SDO7_Dominance  3.277e-01  5.113e-02  6.409e+00  1.964e-10  2.274e-01  4.280e-01
## Ideo_SP_JJ_Overall -2.158e-01  2.935e-02 -7.351e+00  3.213e-13 -2.734e-01 -1.582e-01
## ********************************* TOTAL EFFECT MODEL *********************************
## Outcome: Env_2_REV
## Model Summary
## R R-sq F df1 df2 p
## Env_2_REV 2.838e-01 8.055e-02 1.312e+02 1.000e+00 1.498e+03 0.000e+00
## Model
## coeff se t p LLCI ULCI
## constant   7.25867   0.15343  47.30899   0.00000   6.95771   7.55963
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## *************************** TOTAL, DIRECT AND INDIRECT EFFECTS ***************************
## Total effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall  -0.30070   0.02625 -11.45564   0.00000  -0.35219  -0.24921
## Direct effect of X on Y
## Effect SE t p LLCI ULCI
## Ideo_SP_JJ_Overall -2.158e-01  2.935e-02 -7.351e+00  3.213e-13 -2.734e-01 -1.582e-01
## Indirect effect of X on Y
## Effect Boot SE BootLLCI BootULCI
## TOTAL -0.08491  0.01694 -0.11862 -0.05248
## SDO7_AntiEgal -0.14896  0.01964 -0.18891 -0.11104
## SDO7_Dominance  0.06405  0.01182  0.04216  0.08823
## (C1) -0.21301  0.02764 -0.26942 -0.16023
## Specific indirect effect contrast definitions
## (C1) SDO7_AntiEgal minus SDO7_Dominance
## ****************************** ANALYSIS NOTES AND WARNINGS ******************************
## Number of bootstrap samples for bias corrected bootstrap confidence intervals:
## 10000
## Level of confidence for all confidence intervals in output:
## 95













(with residual variances correlating for the mediators) Parallel Multiple-Mediator models



SDO & ESJ



## lavaan 0.6-2 ended normally after 25 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          9
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## lavaan 0.6-2 ended normally after 25 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          9
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             1437.813
##   Degrees of freedom                                 6
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -7779.693
##   Loglikelihood unrestricted model (H1)      -7779.693
## 
##   Number of free parameters                          9
##   Akaike (AIC)                               15577.386
##   Bayesian (BIC)                             15625.205
##   Sample-size adjusted Bayesian (BIC)        15596.614
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent Confidence Interval          0.000  0.000
##   P-value RMSEA <= 0.05                             NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model          Structured
##   Standard Errors                             Standard
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Env_2_REV ~                                                           
##     I_SP_JJ_O  (c)   -0.181    0.031   -5.787    0.000   -0.181   -0.171
##     SDO       (b1)    0.047    0.056    0.845    0.398    0.047    0.026
##     SJ_Eco    (b2)   -0.547    0.078   -7.048    0.000   -0.547   -0.230
##   SDO ~                                                                 
##     I_SP_JJ_O (a1)    0.273    0.013   20.354    0.000    0.273    0.465
##   SJ_Eco ~                                                              
##     I_SP_JJ_O (a2)    0.242    0.010   24.988    0.000    0.242    0.542
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .SDO ~~                                                                
##    .SJ_Eco            0.546    0.033   16.368    0.000    0.546    0.466
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .Env_2_REV         5.984    0.218   27.386    0.000    5.984    0.886
##    .SDO               1.619    0.059   27.386    0.000    1.619    0.784
##    .SJ_Eco            0.847    0.031   27.386    0.000    0.847    0.706
## 
## R-Square:
##                    Estimate
##     Env_2_REV         0.114
##     SDO               0.216
##     SJ_Eco            0.294
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     SDO.IDE           0.013    0.015    0.844    0.399    0.013    0.012
##     SJ_Eco.IDE       -0.132    0.020   -6.784    0.000   -0.132   -0.125
##     sum.IDE          -0.119    0.019   -6.439    0.000   -0.119   -0.113
##     total            -0.301    0.026  -11.463    0.000   -0.301   -0.284


Bootstrap confidence intervals


Bootstrap confidence intervals
lhs op rhs label est se z pvalue ci.lower ci.upper
Env_2_REV ~ Ideo_SP_JJ_Overall c -0.18 0.03 -5.79 0.0 -0.24 -0.12
Env_2_REV ~ SDO b1 0.05 0.06 0.84 0.4 -0.06 0.16
Env_2_REV ~ SJ_Eco b2 -0.55 0.08 -7.05 0.0 -0.70 -0.39
SDO ~ Ideo_SP_JJ_Overall a1 0.27 0.01 20.35 0.0 0.25 0.30
SJ_Eco ~ Ideo_SP_JJ_Overall a2 0.24 0.01 24.99 0.0 0.22 0.26
SDO ~~ SJ_Eco 0.55 0.03 16.37 0.0 0.48 0.61
Env_2_REV ~~ Env_2_REV 5.98 0.22 27.39 0.0 5.56 6.41
SDO ~~ SDO 1.62 0.06 27.39 0.0 1.50 1.73
SJ_Eco ~~ SJ_Eco 0.85 0.03 27.39 0.0 0.79 0.91
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.02 0.00 NA NA 6.02 6.02
SDO.IDE := a1*b1 SDO.IDE 0.01 0.02 0.84 0.4 -0.02 0.04
SJ_Eco.IDE := a2*b2 SJ_Eco.IDE -0.13 0.02 -6.78 0.0 -0.17 -0.09
sum.IDE := (a1b1)+(a2b2) sum.IDE -0.12 0.02 -6.44 0.0 -0.16 -0.08
total := c+(a1b1)+(a2b2) total -0.30 0.03 -11.46 0.0 -0.35 -0.25



Plot













Anti-Egalitarianism, Dominance & ESJ



## lavaan 0.6-2 ended normally after 36 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         14
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## lavaan 0.6-2 ended normally after 36 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                         14
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             2167.396
##   Degrees of freedom                                10
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)             -10439.783
##   Loglikelihood unrestricted model (H1)     -10439.783
## 
##   Number of free parameters                         14
##   Akaike (AIC)                               20907.566
##   Bayesian (BIC)                             20981.951
##   Sample-size adjusted Bayesian (BIC)        20937.477
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent Confidence Interval          0.000  0.000
##   P-value RMSEA <= 0.05                             NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model          Structured
##   Standard Errors                             Standard
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Env_2_REV ~                                                           
##     I_SP_JJ_O  (c)   -0.151    0.031   -4.892    0.000   -0.151   -0.143
##     SDO7_Dmnn (b1)    0.368    0.051    7.226    0.000    0.368    0.211
##     SDO7_AntE (b2)   -0.315    0.050   -6.255    0.000   -0.315   -0.212
##     SJ_Eco    (b3)   -0.461    0.077   -6.003    0.000   -0.461   -0.194
##   SDO7_Dominance ~                                                      
##     I_SP_JJ_O (a1)    0.195    0.015   13.141    0.000    0.195    0.321
##   SDO7_AntiEgal ~                                                       
##     I_SP_JJ_O (a2)    0.350    0.016   21.766    0.000    0.350    0.490
##   SJ_Eco ~                                                              
##     I_SP_JJ_O (a3)    0.242    0.010   24.988    0.000    0.242    0.542
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .SDO7_Dominance ~~                                                      
##    .SDO7_AntiEgal      1.074    0.062   17.263    0.000    1.074    0.498
##    .SJ_Eco             0.434    0.035   12.264    0.000    0.434    0.334
##  .SDO7_AntiEgal ~~                                                       
##    .SJ_Eco             0.658    0.040   16.425    0.000    0.658    0.468
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .Env_2_REV         5.737    0.209   27.386    0.000    5.737    0.849
##    .SDO7_Dominance    1.997    0.073   27.386    0.000    1.997    0.897
##    .SDO7_AntiEgal     2.329    0.085   27.386    0.000    2.329    0.760
##    .SJ_Eco            0.847    0.031   27.386    0.000    0.847    0.706
## 
## R-Square:
##                    Estimate
##     Env_2_REV         0.151
##     SDO7_Dominance    0.103
##     SDO7_AntiEgal     0.240
##     SJ_Eco            0.294
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     SDO7_Dmnnc.IDE    0.072    0.011    6.332    0.000    0.072    0.068
##     SDO7_AntEg.IDE   -0.110    0.018   -6.012    0.000   -0.110   -0.104
##     SJ_Eco.IDE       -0.111    0.019   -5.837    0.000   -0.111   -0.105
##     sumIDE           -0.150    0.019   -7.751    0.000   -0.150   -0.141
##     total            -0.301    0.026  -11.463    0.000   -0.301   -0.284


Bootstrap confidence intervals


Bootstrap confidence intervals
lhs op rhs label est se z pvalue ci.lower ci.upper
Env_2_REV ~ Ideo_SP_JJ_Overall c -0.15 0.03 -4.89 0 -0.21 -0.09
Env_2_REV ~ SDO7_Dominance b1 0.37 0.05 7.23 0 0.27 0.47
Env_2_REV ~ SDO7_AntiEgal b2 -0.31 0.05 -6.25 0 -0.41 -0.22
Env_2_REV ~ SJ_Eco b3 -0.46 0.08 -6.00 0 -0.61 -0.31
SDO7_Dominance ~ Ideo_SP_JJ_Overall a1 0.20 0.01 13.14 0 0.17 0.22
SDO7_AntiEgal ~ Ideo_SP_JJ_Overall a2 0.35 0.02 21.77 0 0.32 0.38
SJ_Eco ~ Ideo_SP_JJ_Overall a3 0.24 0.01 24.99 0 0.22 0.26
SDO7_Dominance ~~ SDO7_AntiEgal 1.07 0.06 17.26 0 0.95 1.20
SDO7_Dominance ~~ SJ_Eco 0.43 0.04 12.26 0 0.36 0.50
SDO7_AntiEgal ~~ SJ_Eco 0.66 0.04 16.43 0 0.58 0.74
Env_2_REV ~~ Env_2_REV 5.74 0.21 27.39 0 5.33 6.15
SDO7_Dominance ~~ SDO7_Dominance 2.00 0.07 27.39 0 1.85 2.14
SDO7_AntiEgal ~~ SDO7_AntiEgal 2.33 0.09 27.39 0 2.16 2.50
SJ_Eco ~~ SJ_Eco 0.85 0.03 27.39 0 0.79 0.91
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.02 0.00 NA NA 6.02 6.02
SDO7_Dominance.IDE := a1*b1 SDO7_Dominance.IDE 0.07 0.01 6.33 0 0.05 0.09
SDO7_AntiEgal.IDE := a2*b2 SDO7_AntiEgal.IDE -0.11 0.02 -6.01 0 -0.15 -0.07
SJ_Eco.IDE := a3*b3 SJ_Eco.IDE -0.11 0.02 -5.84 0 -0.15 -0.07
sumIDE := (a1b1)+(a2b2)+(a3*b3) sumIDE -0.15 0.02 -7.75 0 -0.19 -0.11
total := c+(a1b1)+(a2b2)+(a3*b3) total -0.30 0.03 -11.46 0 -0.35 -0.25



Plot













Anti-Egalitarianism & ESJ



## lavaan 0.6-2 ended normally after 24 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          9
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## lavaan 0.6-2 ended normally after 24 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          9
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             1499.113
##   Degrees of freedom                                 6
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -8044.877
##   Loglikelihood unrestricted model (H1)      -8044.877
## 
##   Number of free parameters                          9
##   Akaike (AIC)                               16107.754
##   Bayesian (BIC)                             16155.573
##   Sample-size adjusted Bayesian (BIC)        16126.982
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent Confidence Interval          0.000  0.000
##   P-value RMSEA <= 0.05                             NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model          Structured
##   Standard Errors                             Standard
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Env_2_REV ~                                                           
##     I_SP_JJ_O  (c)   -0.149    0.031   -4.740    0.000   -0.149   -0.141
##     SDO7_AntE (b1)   -0.166    0.047   -3.548    0.000   -0.166   -0.111
##     SJ_Eco    (b2)   -0.388    0.077   -5.011    0.000   -0.388   -0.163
##   SDO7_AntiEgal ~                                                       
##     I_SP_JJ_O (a1)    0.350    0.016   21.766    0.000    0.350    0.490
##   SJ_Eco ~                                                              
##     I_SP_JJ_O (a2)    0.242    0.010   24.988    0.000    0.242    0.542
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .SDO7_AntiEgal ~~                                                      
##    .SJ_Eco            0.658    0.040   16.425    0.000    0.658    0.468
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .Env_2_REV         5.937    0.217   27.386    0.000    5.937    0.879
##    .SDO7_AntiEgal     2.329    0.085   27.386    0.000    2.329    0.760
##    .SJ_Eco            0.847    0.031   27.386    0.000    0.847    0.706
## 
## R-Square:
##                    Estimate
##     Env_2_REV         0.121
##     SDO7_AntiEgal     0.240
##     SJ_Eco            0.294
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     SDO7_AntEg.IDE   -0.058    0.017   -3.501    0.000   -0.058   -0.055
##     SJ_Eco.IDE       -0.094    0.019   -4.913    0.000   -0.094   -0.089
##     sum.IDE          -0.152    0.019   -7.983    0.000   -0.152   -0.143
##     total            -0.301    0.026  -11.463    0.000   -0.301   -0.284


Bootstrap confidence intervals


Bootstrap confidence intervals
lhs op rhs label est se z pvalue ci.lower ci.upper
Env_2_REV ~ Ideo_SP_JJ_Overall c -0.15 0.03 -4.74 0 -0.21 -0.09
Env_2_REV ~ SDO7_AntiEgal b1 -0.17 0.05 -3.55 0 -0.26 -0.07
Env_2_REV ~ SJ_Eco b2 -0.39 0.08 -5.01 0 -0.54 -0.24
SDO7_AntiEgal ~ Ideo_SP_JJ_Overall a1 0.35 0.02 21.77 0 0.32 0.38
SJ_Eco ~ Ideo_SP_JJ_Overall a2 0.24 0.01 24.99 0 0.22 0.26
SDO7_AntiEgal ~~ SJ_Eco 0.66 0.04 16.43 0 0.58 0.74
Env_2_REV ~~ Env_2_REV 5.94 0.22 27.39 0 5.51 6.36
SDO7_AntiEgal ~~ SDO7_AntiEgal 2.33 0.09 27.39 0 2.16 2.50
SJ_Eco ~~ SJ_Eco 0.85 0.03 27.39 0 0.79 0.91
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.02 0.00 NA NA 6.02 6.02
SDO7_AntiEgal.IDE := a1*b1 SDO7_AntiEgal.IDE -0.06 0.02 -3.50 0 -0.09 -0.03
SJ_Eco.IDE := a2*b2 SJ_Eco.IDE -0.09 0.02 -4.91 0 -0.13 -0.06
sum.IDE := (a1b1)+(a2b2) sum.IDE -0.15 0.02 -7.98 0 -0.19 -0.11
total := c+(a1b1)+(a2b2) total -0.30 0.03 -11.46 0 -0.35 -0.25



Plot













Anti-Egalitarianism & Dominance



## lavaan 0.6-2 ended normally after 26 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          9
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
## lavaan 0.6-2 ended normally after 26 iterations
## 
##   Optimization method                           NLMINB
##   Number of free parameters                          9
## 
##   Number of observations                          1500
## 
##   Estimator                                         ML
##   Model Fit Test Statistic                       0.000
##   Degrees of freedom                                 0
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic             1212.285
##   Degrees of freedom                                 6
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    1.000
##   Tucker-Lewis Index (TLI)                       1.000
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -8652.500
##   Loglikelihood unrestricted model (H1)      -8652.500
## 
##   Number of free parameters                          9
##   Akaike (AIC)                               17323.001
##   Bayesian (BIC)                             17370.820
##   Sample-size adjusted Bayesian (BIC)        17342.229
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.000
##   90 Percent Confidence Interval          0.000  0.000
##   P-value RMSEA <= 0.05                             NA
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.000
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Information saturated (h1) model          Structured
##   Standard Errors                             Standard
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##   Env_2_REV ~                                                           
##     I_SP_JJ_O  (c)   -0.216    0.029   -7.361    0.000   -0.216   -0.204
##     SDO7_AntE (b1)   -0.426    0.047   -9.011    0.000   -0.426   -0.287
##     SDO7_Dmnn (b2)    0.328    0.051    6.417    0.000    0.328    0.188
##   SDO7_AntiEgal ~                                                       
##     I_SP_JJ_O (a1)    0.350    0.016   21.766    0.000    0.350    0.490
##   SDO7_Dominance ~                                                      
##     I_SP_JJ_O (a2)    0.195    0.015   13.141    0.000    0.195    0.321
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##  .SDO7_AntiEgal ~~                                                      
##    .SDO7_Dominance    1.074    0.062   17.263    0.000    1.074    0.498
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##    .Env_2_REV         5.875    0.215   27.386    0.000    5.875    0.870
##    .SDO7_AntiEgal     2.329    0.085   27.386    0.000    2.329    0.760
##    .SDO7_Dominance    1.997    0.073   27.386    0.000    1.997    0.897
## 
## R-Square:
##                    Estimate
##     Env_2_REV         0.130
##     SDO7_AntiEgal     0.240
##     SDO7_Dominance    0.103
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
##     SDO7_AntEg.IDE   -0.149    0.018   -8.326    0.000   -0.149   -0.141
##     SDO7_Dmnnc.IDE    0.064    0.011    5.766    0.000    0.064    0.060
##     sum.IDE          -0.085    0.016   -5.414    0.000   -0.085   -0.080
##     total            -0.301    0.026  -11.463    0.000   -0.301   -0.284


Bootstrap confidence intervals


Bootstrap confidence intervals
lhs op rhs label est se z pvalue ci.lower ci.upper
Env_2_REV ~ Ideo_SP_JJ_Overall c -0.22 0.03 -7.36 0 -0.27 -0.16
Env_2_REV ~ SDO7_AntiEgal b1 -0.43 0.05 -9.01 0 -0.52 -0.33
Env_2_REV ~ SDO7_Dominance b2 0.33 0.05 6.42 0 0.23 0.43
SDO7_AntiEgal ~ Ideo_SP_JJ_Overall a1 0.35 0.02 21.77 0 0.32 0.38
SDO7_Dominance ~ Ideo_SP_JJ_Overall a2 0.20 0.01 13.14 0 0.17 0.22
SDO7_AntiEgal ~~ SDO7_Dominance 1.07 0.06 17.26 0 0.95 1.20
Env_2_REV ~~ Env_2_REV 5.87 0.21 27.39 0 5.45 6.30
SDO7_AntiEgal ~~ SDO7_AntiEgal 2.33 0.09 27.39 0 2.16 2.50
SDO7_Dominance ~~ SDO7_Dominance 2.00 0.07 27.39 0 1.85 2.14
Ideo_SP_JJ_Overall ~~ Ideo_SP_JJ_Overall 6.02 0.00 NA NA 6.02 6.02
SDO7_AntiEgal.IDE := a1*b1 SDO7_AntiEgal.IDE -0.15 0.02 -8.33 0 -0.18 -0.11
SDO7_Dominance.IDE := a2*b2 SDO7_Dominance.IDE 0.06 0.01 5.77 0 0.04 0.09
sum.IDE := (a1b1)+(a2b2) sum.IDE -0.08 0.02 -5.41 0 -0.12 -0.05
total := c+(a1b1)+(a2b2) total -0.30 0.03 -11.46 0 -0.35 -0.25



Plot