Modelos Sensgenpol

Modelos

Definimos variables:

library(haven)
library(broom)

## Warning: package 'broom' was built under R version 4.4.1

library(dplyr)

## 
## Adjuntando el paquete: 'dplyr'

## The following objects are masked from 'package:stats':
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##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.4.1

## Warning: package 'tidyr' was built under R version 4.4.1

## Warning: package 'lubridate' was built under R version 4.4.1

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## ✔ purrr     1.0.2     ✔ tidyr     1.3.1

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library(knitr)

## Warning: package 'knitr' was built under R version 4.4.1

BDD_Sensgenpol <- read_sav("C:/Users/alvar/OneDrive/Escritorio/BDD Sensgenpol.sav")
myvars <- c("VOX", "PP", "Modern_Sexism", "Populism", "P6_4", "Insatisfacción_afectivo_sexual", "Insatisfacción_economica", "SEXO", "Pareja2", "IDEOLOGIA", "Nativismo", "EDAD", "FORMACIÓN_desagregado", "P27B_1", "P27B_2")
data <- as.data.frame(BDD_Sensgenpol[,myvars])
data$SEXO <- factor(data$SEXO,
                     levels = 1:2,
                     labels= c("Man", "Woman"))
data$Pareja2 <- factor(data$Pareja2,
                        levels = 1:4,
                        labels = c("Has partner", "Don't like or doesn't care", "Would like to have partner", "Would like to have partner and it's important"))
data$Porn_Consumption <- data$P6_4
data$Nivel_educativo <- data$FORMACIÓN_desagregado
data$Masculine_traits <- data$P27B_1
data$Feminine_traits <- data$P27B_2
summary(data)

##       VOX               PP         Modern_Sexism      Populism    
##  Min.   : 0.000   Min.   : 0.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:2.600   1st Qu.:3.400  
##  Median : 0.000   Median : 1.000   Median :3.600   Median :4.000  
##  Mean   : 1.841   Mean   : 2.784   Mean   :3.553   Mean   :3.958  
##  3rd Qu.: 3.000   3rd Qu.: 5.000   3rd Qu.:4.400   3rd Qu.:4.400  
##  Max.   :10.000   Max.   :10.000   Max.   :7.000   Max.   :5.000  
##  NA's   :78       NA's   :117      NA's   :41      NA's   :44     
##       P6_4       Insatisfacción_afectivo_sexual Insatisfacción_economica
##  Min.   :1.000   Min.   : 0.000                 Min.   : 0.00           
##  1st Qu.:1.000   1st Qu.: 1.000                 1st Qu.: 3.00           
##  Median :1.000   Median : 3.000                 Median : 4.00           
##  Mean   :1.658   Mean   : 3.019                 Mean   : 4.45           
##  3rd Qu.:2.000   3rd Qu.: 5.000                 3rd Qu.: 6.00           
##  Max.   :5.000   Max.   :10.000                 Max.   :10.00           
##  NA's   :31      NA's   :81                     NA's   :65              
##     SEXO                                              Pareja2   
##  Man  :557   Has partner                                  :705  
##  Woman:543   Don't like or doesn't care                   :128  
##              Would like to have partner                   :138  
##              Would like to have partner and it's important: 74  
##              NA's                                         : 55  
##                                                                 
##                                                                 
##    IDEOLOGIA        Nativismo          EDAD       FORMACIÓN_desagregado
##  Min.   : 0.000   Min.   : 0.00   Min.   :18.00   Min.   :1.000        
##  1st Qu.: 2.000   1st Qu.: 3.00   1st Qu.:24.00   1st Qu.:2.000        
##  Median : 5.000   Median : 5.00   Median :33.00   Median :4.000        
##  Mean   : 4.238   Mean   : 5.18   Mean   :32.31   Mean   :4.032        
##  3rd Qu.: 6.000   3rd Qu.: 7.00   3rd Qu.:40.00   3rd Qu.:6.000        
##  Max.   :10.000   Max.   :10.00   Max.   :44.00   Max.   :6.000        
##  NA's   :120      NA's   :53                                           
##      P27B_1           P27B_2       Porn_Consumption Nivel_educativo
##  Min.   :  0.00   Min.   :  0.00   Min.   :1.000    Min.   :1.000  
##  1st Qu.: 20.00   1st Qu.: 13.00   1st Qu.:1.000    1st Qu.:2.000  
##  Median : 65.00   Median : 50.00   Median :1.000    Median :4.000  
##  Mean   : 56.18   Mean   : 49.88   Mean   :1.658    Mean   :4.032  
##  3rd Qu.: 90.00   3rd Qu.: 88.00   3rd Qu.:2.000    3rd Qu.:6.000  
##  Max.   :100.00   Max.   :100.00   Max.   :5.000    Max.   :6.000  
##  NA's   :151      NA's   :123      NA's   :31                      
##  Masculine_traits Feminine_traits 
##  Min.   :  0.00   Min.   :  0.00  
##  1st Qu.: 20.00   1st Qu.: 13.00  
##  Median : 65.00   Median : 50.00  
##  Mean   : 56.18   Mean   : 49.88  
##  3rd Qu.: 90.00   3rd Qu.: 88.00  
##  Max.   :100.00   Max.   :100.00  
##  NA's   :151      NA's   :123

Dividimos la muestra en hombres y mujeres

Modelamos PTVs con Modern Sexism como VIs

#PTV VOX
model.VOX <- lm(VOX ~ Modern_Sexism + Populism + Nativismo + SEXO + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data)

tidy(model.VOX) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-2.4029256	0.0004441
Modern_Sexism	0.3702722	0.0000004
Populism	0.0902617	0.4793358
Nativismo	0.0952787	0.0022059
SEXOWoman	0.0980691	0.5770040
Insatisfacción_economica	-0.0178958	0.5903931
IDEOLOGIA	0.5791755	0.0000000
EDAD	-0.0030419	0.7763816
Nivel_educativo	-0.0339008	0.5087979

#PTV VOX HOMBRES
model.VOX.hombres <- lm(VOX ~ Modern_Sexism + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.hombres)

tidy(model.VOX.hombres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-3.3326797	0.0004612
Modern_Sexism	0.3408649	0.0004825
Populism	0.1643757	0.3484208
Nativismo	0.0757581	0.0866055
Insatisfacción_economica	0.0405624	0.3985795
IDEOLOGIA	0.6069630	0.0000000
EDAD	0.0104183	0.4824045
Nivel_educativo	-0.0218646	0.7547407

#PTV VOX MUJERES
model.VOX.mujeres <- lm(VOX ~ Modern_Sexism + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.mujeres)

tidy(model.VOX.mujeres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-1.4592628	0.1357072
Modern_Sexism	0.4075520	0.0004399
Populism	0.0539712	0.7773592
Nativismo	0.1124353	0.0106091
Insatisfacción_economica	-0.0722774	0.1220975
IDEOLOGIA	0.5592419	0.0000000
EDAD	-0.0201621	0.1998947
Nivel_educativo	-0.0406314	0.5936938

#PTV PP
model.PP <- lm(PP ~ Modern_Sexism + Populism + Nativismo + SEXO + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data)

tidy(model.PP) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	0.6990373	0.3408987
Modern_Sexism	0.2430672	0.0023848
Populism	-0.2045478	0.1381360
Nativismo	-0.0563169	0.0943179
SEXOWoman	0.5327372	0.0054694
Insatisfacción_economica	-0.0727427	0.0443147
IDEOLOGIA	0.6497345	0.0000000
EDAD	-0.0077301	0.5041417
Nivel_educativo	-0.0065993	0.9051744

#PTV PP HOMBRES
model.PP.hombres <- lm(PP ~ Modern_Sexism + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.hombres)

tidy(model.PP.hombres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	0.9684912	0.3307229
Modern_Sexism	0.2849248	0.0073354
Populism	-0.1537336	0.4078714
Nativismo	-0.1315528	0.0054432
Insatisfacción_economica	-0.0892352	0.0847017
IDEOLOGIA	0.6150673	0.0000000
EDAD	0.0011311	0.9425160
Nivel_educativo	-0.0891455	0.2312582

#PTV PP MUJERES
model.PP.mujeres <- lm(PP ~ Modern_Sexism + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.mujeres)

tidy(model.PP.mujeres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	0.8222049	0.4425941
Modern_Sexism	0.2526751	0.0460459
Populism	-0.2837909	0.1777238
Nativismo	0.0285145	0.5522179
Insatisfacción_economica	-0.0524586	0.3064016
IDEOLOGIA	0.6698368	0.0000000
EDAD	-0.0213380	0.2173505
Nivel_educativo	0.1094062	0.1882175

Modelamos con Insatisfacción afectivo sexual y luego como deseo de tener pareja como VIs:

Insatisfaccion.VOX <- lm(VOX ~ Insatisfacción_afectivo_sexual + Populism + Nativismo + SEXO + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data)

tidy(Insatisfaccion.VOX) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-1.5580408	0.0233865
Insatisfacción_afectivo_sexual	0.0139717	0.6778904
Populism	0.1940486	0.1331567
Nativismo	0.1277102	0.0000410
SEXOWoman	-0.2051656	0.2293703
Insatisfacción_economica	-0.0480831	0.1772714
IDEOLOGIA	0.6725719	0.0000000
EDAD	-0.0089873	0.4088667
Nivel_educativo	-0.0530704	0.3084777

Insatisfaccion.VOX2 <- lm(VOX ~ Pareja2 + Populism + Nativismo + SEXO + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data)

tidy(Insatisfaccion.VOX2) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-1.7001197	0.0152310
Pareja2Don’t like or doesn’t care	-0.2771355	0.3060032
Pareja2Would like to have partner	-0.2708249	0.3087831
Pareja2Would like to have partner and it’s important	0.5633243	0.0993689
Populism	0.1801965	0.1638172
Nativismo	0.1341462	0.0000172
SEXOWoman	-0.1464643	0.3952129
Insatisfacción_economica	-0.0375604	0.2744267
IDEOLOGIA	0.6606971	0.0000000
EDAD	-0.0035093	0.7558564
Nivel_educativo	-0.0482111	0.3557151

Insatisfaccion.VOX.hombres <- lm(VOX ~ Insatisfacción_afectivo_sexual + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.hombres)

tidy(Insatisfaccion.VOX.hombres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-2.9344475	0.0023207
Insatisfacción_afectivo_sexual	0.0327915	0.4862709
Populism	0.3335660	0.0562280
Nativismo	0.1150286	0.0086206
Insatisfacción_economica	0.0161005	0.7571804
IDEOLOGIA	0.7154129	0.0000000
EDAD	-0.0013451	0.9280058
Nivel_educativo	-0.0214758	0.7610278

Insatisfaccion.VOX.hombres2 <- lm(VOX ~ Pareja2 + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.hombres)

tidy(Insatisfaccion.VOX.hombres2) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-2.8845378	0.0030824
Pareja2Don’t like or doesn’t care	-0.5784705	0.1263687
Pareja2Would like to have partner	-0.2069099	0.5432549
Pareja2Would like to have partner and it’s important	0.9783565	0.0395587
Populism	0.3033087	0.0820746
Nativismo	0.1175566	0.0073460
Insatisfacción_economica	0.0301236	0.5429422
IDEOLOGIA	0.6819679	0.0000000
EDAD	0.0070103	0.6485258
Nivel_educativo	-0.0334688	0.6362013

Insatisfaccion.VOX.mujeres <- lm(VOX ~ Insatisfacción_afectivo_sexual +  Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.mujeres)

tidy(Insatisfaccion.VOX.mujeres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-0.3736793	0.7038591
Insatisfacción_afectivo_sexual	-0.0163188	0.7384953
Populism	0.0697611	0.7215576
Nativismo	0.1304535	0.0033740
Insatisfacción_economica	-0.1129197	0.0219196
IDEOLOGIA	0.6418706	0.0000000
EDAD	-0.0170532	0.2867465
Nivel_educativo	-0.0872062	0.2616139

Insatisfaccion.VOX.mujeres2 <- lm(VOX ~ Pareja2 +  Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.mujeres)

tidy(Insatisfaccion.VOX.mujeres2) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-0.7156591	0.4780242
Pareja2Don’t like or doesn’t care	0.0437156	0.9107445
Pareja2Would like to have partner	-0.3667074	0.3961721
Pareja2Would like to have partner and it’s important	-0.0167589	0.9730366
Populism	0.0834128	0.6722880
Nativismo	0.1440320	0.0013650
Insatisfacción_economica	-0.1097679	0.0233203
IDEOLOGIA	0.6504955	0.0000000
EDAD	-0.0142882	0.3934337
Nivel_educativo	-0.0654618	0.3999399

Insatisfaccion.PP <- lm(PP ~ Insatisfacción_afectivo_sexual +  Populism + Nativismo + SEXO + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data)

tidy(Insatisfaccion.PP) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	1.3070785	0.0746704
Insatisfacción_afectivo_sexual	0.0291378	0.4213823
Populism	-0.1823699	0.1887668
Nativismo	-0.0261021	0.4336077
SEXOWoman	0.3240758	0.0779091
Insatisfacción_economica	-0.1005802	0.0095304
IDEOLOGIA	0.7044282	0.0000000
EDAD	-0.0093162	0.4259041
Nivel_educativo	-0.0141626	0.8000257

Insatisfaccion.PP2 <- lm(PP ~ Pareja2 +  Populism + Nativismo + SEXO + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data)

tidy(Insatisfaccion.PP2) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	1.5447472	0.0397877
Pareja2Don’t like or doesn’t care	-0.0458287	0.8751776
Pareja2Would like to have partner	-0.3149741	0.2705687
Pareja2Would like to have partner and it’s important	-0.0824173	0.8225437
Populism	-0.2214812	0.1121544
Nativismo	-0.0312662	0.3500790
SEXOWoman	0.2988100	0.1076770
Insatisfacción_economica	-0.0710119	0.0567148
IDEOLOGIA	0.7063317	0.0000000
EDAD	-0.0109529	0.3689271
Nivel_educativo	-0.0103546	0.8536532

Insatisfaccion.PP.hombres <- lm(PP ~ Insatisfacción_afectivo_sexual + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.hombres)

tidy(Insatisfaccion.PP.hombres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	1.4703754	0.1444050
Insatisfacción_afectivo_sexual	-0.0196541	0.6947457
Populism	-0.1105729	0.5498279
Nativismo	-0.0909719	0.0511484
Insatisfacción_economica	-0.0778604	0.1681362
IDEOLOGIA	0.6940179	0.0000000
EDAD	-0.0022094	0.8886404
Nivel_educativo	-0.0757809	0.3133941

Insatisfaccion.PP.hombres2 <- lm(PP ~ Pareja2 + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.hombres)

tidy(Insatisfaccion.PP.hombres2) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	1.7805488	0.0861232
Pareja2Don’t like or doesn’t care	-0.2286830	0.5682872
Pareja2Would like to have partner	-0.2252600	0.5377543
Pareja2Would like to have partner and it’s important	-0.4655752	0.3647576
Populism	-0.1251227	0.5031364
Nativismo	-0.1006512	0.0332839
Insatisfacción_economica	-0.0708383	0.1878983
IDEOLOGIA	0.6899604	0.0000000
EDAD	-0.0073616	0.6566831
Nivel_educativo	-0.0801600	0.2928652

Insatisfaccion.PP.mujeres <- lm(PP ~ Insatisfacción_afectivo_sexual + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.mujeres)

tidy(Insatisfaccion.PP.mujeres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	1.3848073	0.1963785
Insatisfacción_afectivo_sexual	0.0837155	0.1175854
Populism	-0.2675448	0.2115189
Nativismo	0.0504902	0.2931565
Insatisfacción_economica	-0.1051103	0.0507686
IDEOLOGIA	0.7132091	0.0000000
EDAD	-0.0201829	0.2481429
Nivel_educativo	0.0670114	0.4251960

Insatisfaccion.PP.mujeres2 <- lm(PP ~ Pareja2 + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.mujeres)

tidy(Insatisfaccion.PP.mujeres2) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	1.5923465	0.1451536
Pareja2Don’t like or doesn’t care	0.0824743	0.8472915
Pareja2Would like to have partner	-0.5108860	0.2718196
Pareja2Would like to have partner and it’s important	0.3044394	0.5648510
Populism	-0.3363462	0.1169520
Nativismo	0.0444181	0.3558432
Insatisfacción_economica	-0.0582660	0.2653519
IDEOLOGIA	0.7250085	0.0000000
EDAD	-0.0196696	0.2784464
Nivel_educativo	0.0878874	0.2928241

Modelamos con Frequency Porn Consumption como VI:

PORN.VOX <- lm(VOX ~ Porn_Consumption + Populism + Nativismo + SEXO + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data)

tidy(PORN.VOX) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-2.6887867	0.0001535
Porn_Consumption	0.4024393	0.0000076
Populism	0.1583115	0.2122372
Nativismo	0.1456368	0.0000019
SEXOWoman	0.0415043	0.8145983
Insatisfacción_economica	-0.0263197	0.4280564
IDEOLOGIA	0.6568372	0.0000000
EDAD	0.0006116	0.9546912
Nivel_educativo	-0.0355156	0.4885683

PORN.VOX.hombres <- lm(VOX ~ Porn_Consumption + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.hombres)

tidy(PORN.VOX.hombres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-3.6715237	0.0002159
Porn_Consumption	0.3649783	0.0012370
Populism	0.2467009	0.1552316
Nativismo	0.1328346	0.0022584
Insatisfacción_economica	0.0409743	0.3961629
IDEOLOGIA	0.6925883	0.0000000
EDAD	0.0103093	0.4911246
Nivel_educativo	-0.0299779	0.6693633

PORN.VOX.mujeres <- lm(VOX ~ Porn_Consumption + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.mujeres)

tidy(PORN.VOX.mujeres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	-1.8427944	0.0671242
Porn_Consumption	0.4783318	0.0018242
Populism	0.1076855	0.5712603
Nativismo	0.1524371	0.0004348
Insatisfacción_economica	-0.0874509	0.0580897
IDEOLOGIA	0.6298075	0.0000000
EDAD	-0.0106087	0.4961482
Nivel_educativo	-0.0345613	0.6483998

PORN.PP <- lm(PP ~ Porn_Consumption + Populism + Nativismo + SEXO + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data)

tidy(PORN.PP) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	0.8070177	0.2889707
Porn_Consumption	0.1814591	0.0620095
Populism	-0.1653696	0.2290947
Nativismo	-0.0226455	0.4926638
SEXOWoman	0.4579934	0.0175101
Insatisfacción_economica	-0.0780923	0.0313553
IDEOLOGIA	0.6994064	0.0000000
EDAD	-0.0071428	0.5401529
Nivel_educativo	-0.0172787	0.7556120

PORN.PP.hombres <- lm(PP ~ Porn_Consumption + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.hombres)

tidy(PORN.PP.hombres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	1.1083918	0.2854297
Porn_Consumption	0.1834733	0.1246518
Populism	-0.0937001	0.6111159
Nativismo	-0.0918518	0.0481955
Insatisfacción_economica	-0.0885012	0.0894538
IDEOLOGIA	0.6821539	0.0000000
EDAD	-0.0009818	0.9506678
Nivel_educativo	-0.0991487	0.1853717

PORN.PP.mujeres <- lm(PP ~ Porn_Consumption + Populism + Nativismo + Insatisfacción_economica + IDEOLOGIA + EDAD + Nivel_educativo, data = data.mujeres)

tidy(PORN.PP.mujeres) %>%
  dplyr::select(term, estimate, p.value) %>%
  kable()

term	estimate	p.value
(Intercept)	0.9062704	0.4121177
Porn_Consumption	0.1786296	0.2960609
Populism	-0.2543135	0.2274730
Nativismo	0.0589971	0.2129267
Insatisfacción_economica	-0.0621895	0.2231486
IDEOLOGIA	0.7164673	0.0000000
EDAD	-0.0178103	0.3019534
Nivel_educativo	0.0933001	0.2626444

Modelos Sensgenpol

Álvaro Calvo

2025-03-11

Modelos

Modelamos PTVs con Modern Sexism como VIs

Modelamos con Insatisfacción afectivo sexual y luego como deseo de tener pareja como VIs:

Modelamos con Frequency Porn Consumption como VI: