我国城乡家庭教育投入状况的比较研究
wxy
2019-10-23
重复刘保中的《我国城乡家庭教育投入状况的比较研究》
library(tidyverse)
library(here)
library(fs)
library(purrr)
library(haven)
library(broom)
library(MASS)
library(VGAM)-三个研究方向的变量筛选，并且需要重编码的重新编码 -对这三个研究方向做描述统计 -对这三个研究方向做回归
儿童表
cfps2014child <- read_dta("../cfps/data/2014AllData/Cfps2014child_170630.dta",
encoding = "GB2312"
)
total <- cfps2014child %>%
filter(! wf301m %in% c(-8,-1,1,2)) #从小学开始，不包括学前教育#家庭教育期望样本
wish_edu <- total %>%
dplyr::select(
wd2,#希望孩子受教育程度
wa4,#孩子现在的户口状况，后面要分农村城市
cfps_gender,
cfps2014_age,
cfps2014edu
) %>%
filter(! wd2 %in% c(-8,-1),
wa4 %in% c(1,3))#只要农业户口和非农业户口的
wish_edu_c <- wish_edu %>%
mutate(wd2_c = case_when(
wd2 == 9 ~ 0,#按年限教育重编码
wd2 == 2 ~ 6,
wd2 == 3 ~ 9,
wd2 == 4 ~ 12,
wd2 == 5 ~ 15,
wd2 == 6 ~ 16,
wd2 == 7 ~ 19,
wd2 == 8 ~ 23,))
wish_edu_cwish_edu_z <- wish_edu_c %>%
mutate(wd2_c_z = case_when(
wd2 %in% c(5,6,7,8) ~ 1,#按专科上下教育重编码
wd2 %in% c(9,2,3,4) ~ 0,))
wish_edu_z %>% head()#家庭教育支出
pay_edu <- total %>%
dplyr::select(
wd503m,#课外辅导费(元)
wd5ckp,#确认教育总支出
wd5total, #过去12个月教育总支出元
wa4,#孩子现在的户口状况，后面要分农村城市
cfps_gender,
cfps2014_age,
cfps2014edu
) %>%
filter (wd5ckp == 5)%>%#确认教育总支出是差不多的
filter(wd503m != -1,
wa4 %in% c(1,3)#只要农业户口和非农业户口的
)%>%
drop_na()
pay_edu_c <- pay_edu %>%
mutate(wd503m_c = if_else(wd503m >0.5,log(wd503m),0.5)) %>%#辅导费与教育总支出取对数
mutate(wd5total_c = if_else(wd5total >0.5,log(wd5total),0.5))
pay_edu_c %>%head()#家庭教育参与
join_edu <- total %>%
dplyr::select(
wf601m,#为孩子学习放弃看电视
wf602m,#常与孩子谈学校里的事
wf603m,#要求孩子完成作业
wf604m,#检查孩子作作业
wf605m,#阻止孩子看电视
wf606m, #限制孩子看的节目
wa4,#孩子现在的户口状况，后面要分农村城市
cfps_gender,
cfps2014_age,
cfps2014edu
) %>%
filter(! wf601m %in% c(-1,-2) )%>%
filter(wf602m != -1 )%>%
filter(wf603m != -1 )%>%
filter(wf604m != -1 )%>%
filter(wf605m != -1 )%>%
filter(! wf606m %in% c(-1,-2),wa4 %in% c(1,3)#只要农业户口和非农业户口的
)
change <- function(x){
case_when(
x == 1 ~ 5,
x == 2 ~ 4,
x == 3 ~ 3,
x == 4 ~ 2,
x == 5 ~ 1,
)
}
join_edu_c <- join_edu %>%
mutate(wf601m_c = change(wf601m),#家庭教育参与重编码
wf602m_c = change(wf602m),
wf603m_c = change(wf603m),
wf604m_c = change(wf604m),
wf605m_c = change(wf605m),
wf606m_c = change(wf606m)
)
join_edu_c %>%head()#家庭教育期望样本对比矩阵
wish_edu_z %>%
group_by(wa4) %>%
summarize(mean_up = mean(wd2_c_z)*100,mean_year = mean(wd2_c),n = n()) %>%
t()
#> [,1] [,2]
#> wa4 1.00000 3.00000
#> mean_up 78.88922 93.66667
#> mean_year 15.55061 16.56000
#> n 3349.00000 900.00000
#家庭教育支出对比矩阵
pay_edu %>%
group_by(wa4) %>%
summarize(mean_total = mean(wd5total),#这里没用取对数的，取了对数和论文不同，但是他前面又说要娶对数，几个意思
mean_outside = mean(wd503m),
odd = mean_outside/mean_total*100, n = n())%>%
t()
#> [,1] [,2]
#> wa4 1.00000 3.00000
#> mean_total 2380.76665 5324.40140
#> mean_outside 271.77843 2488.23916
#> odd 11.41558 46.73275
#> n 2717.00000 715.00000
#家庭教育参与对比矩阵
ratio <- function(x){
a =sum(x %in% c(4,5))
b = sum(x %in% c(1,2,3,4,5))
a/b*100
}
ratio(join_edu_c $ wf601m_c)
#> [1] 70.66356
join_edu_c %>% colnames()
#> [1] "wf601m" "wf602m" "wf603m" "wf604m"
#> [5] "wf605m" "wf606m" "wa4" "cfps_gender"
#> [9] "cfps2014_age" "cfps2014edu" "wf601m_c" "wf602m_c"
#> [13] "wf603m_c" "wf604m_c" "wf605m_c" "wf606m_c"
join_edu_c %>%
group_by(wa4) %>%
summarize(wf601m_c_over2 = ratio(wf601m_c),
wf602m_c_over2 = ratio(wf602m_c),
wf603m_c_over2 = ratio(wf603m_c),
wf604m_c_over2 = ratio(wf604m_c),
wf605m_c_over2 = ratio(wf605m_c),
wf606m_c_over2 = ratio(wf606m_c),
n = n()) %>%
t()
#> [,1] [,2]
#> wa4 1.00000 3.00000
#> wf601m_c_over2 68.74262 77.83903
#> wf602m_c_over2 49.61629 62.95480
#> wf603m_c_over2 84.97639 86.76957
#> wf604m_c_over2 51.91854 64.60860
#> wf605m_c_over2 62.27863 64.60860
#> wf606m_c_over2 35.27155 37.92723
#> n 3388.00000 907.00000
#家庭教育期望样本
#wish_edu_z %>%
# ggplot(aes(x= wa4 ,y = wd2_c)) +
# geom_point()
explot_edu_z <- glm(wd2_c_z ~ wa4,data = wish_edu_z ,family = binomial(link = "logit"))
summary(explot_edu_z)
#>
#> Call:
#> glm(formula = wd2_c_z ~ wa4, family = binomial(link = "logit"),
#> data = wish_edu_z)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -2.3492 0.3617 0.6887 0.6887 0.6887
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.63043 0.09336 6.753 1.45e-11 ***
#> wa4 0.68783 0.07163 9.603 < 2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 4003.4 on 4248 degrees of freedom
#> Residual deviance: 3877.2 on 4247 degrees of freedom
#> AIC: 3881.2
#>
#> Number of Fisher Scoring iterations: 5
exp(coef(explot_edu_z))
#> (Intercept) wa4
#> 1.878424 1.989389
explot_edu_z_2 <- glm(wd2_c_z ~ wa4 + cfps_gender + cfps2014_age+ cfps2014edu,data = wish_edu_z ,family = binomial(link = "logit"))
summary(explot_edu_z_2)
#>
#> Call:
#> glm(formula = wd2_c_z ~ wa4 + cfps_gender + cfps2014_age + cfps2014edu,
#> family = binomial(link = "logit"), data = wish_edu_z)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -2.5504 0.3378 0.6160 0.7093 0.9981
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 1.59092 0.21092 7.543 4.60e-14 ***
#> wa4 0.66660 0.07190 9.271 < 2e-16 ***
#> cfps_gender -0.02877 0.08156 -0.353 0.724
#> cfps2014_age -0.15704 0.02436 -6.447 1.14e-10 ***
#> cfps2014edu 0.56444 0.10936 5.161 2.45e-07 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 4003.4 on 4248 degrees of freedom
#> Residual deviance: 3834.3 on 4244 degrees of freedom
#> AIC: 3844.3
#>
#> Number of Fisher Scoring iterations: 5
exp(coef(explot_edu_z_2))
#> (Intercept) wa4 cfps_gender cfps2014_age cfps2014edu
#> 4.9082784 1.9476081 0.9716437 0.8546697 1.7584691
explot_edu_c <- lm(wd2_c ~ wa4,data = wish_edu_z)
summary(explot_edu_c)
#>
#> Call:
#> lm(formula = wd2_c ~ wa4, data = wish_edu_z)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -15.5506 -0.5600 0.4494 0.4494 7.4494
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 15.04592 0.08859 169.840 <2e-16 ***
#> wa4 0.50469 0.05397 9.352 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 2.875 on 4247 degrees of freedom
#> Multiple R-squared: 0.02018, Adjusted R-squared: 0.01995
#> F-statistic: 87.45 on 1 and 4247 DF, p-value: < 2.2e-16
explot_edu_c_1 <- lm(wd2_c ~ wa4+ cfps_gender + cfps2014_age,data = wish_edu_z)
summary(explot_edu_c_1)
#>
#> Call:
#> lm(formula = wd2_c ~ wa4 + cfps_gender + cfps2014_age, data = wish_edu_z)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -15.2427 -0.7340 0.2551 0.5935 7.8283
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 15.89074 0.20576 77.231 < 2e-16 ***
#> wa4 0.50918 0.05384 9.457 < 2e-16 ***
#> cfps_gender 0.07108 0.08808 0.807 0.42
#> cfps2014_age -0.08188 0.01665 -4.917 9.14e-07 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 2.867 on 4245 degrees of freedom
#> Multiple R-squared: 0.02587, Adjusted R-squared: 0.02518
#> F-statistic: 37.58 on 3 and 4245 DF, p-value: < 2.2e-16#家庭教育支出
#pay_edu_c %>% count(wd5total)
explot_pay_c <- lm(wd5total_c ~ wa4 ,data = pay_edu_c)
summary(explot_pay_c)
#>
#> Call:
#> lm(formula = wd5total_c ~ wa4, data = pay_edu_c)
#>
#> Residuals:
#> <Labelled double>: 过去12个月教育总支出(元)
#> Min 1Q Median 3Q Max
#> -7.2738 -0.8499 0.2322 1.1815 3.7470
#>
#> Labels:
#> value label
#> -10 无法判断
#> -9 缺失
#> -8 不适用
#> -2 拒绝回答
#> -1 不知道
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 6.37514 0.05758 110.72 <2e-16 ***
#> wa4 0.46624 0.03526 13.22 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 1.678 on 3430 degrees of freedom
#> Multiple R-squared: 0.0485, Adjusted R-squared: 0.04823
#> F-statistic: 174.8 on 1 and 3430 DF, p-value: < 2.2e-16
explot_pay_c <- lm(wd5total_c ~ wa4 + cfps_gender + cfps2014_age + cfps2014edu,data = pay_edu_c)
summary(explot_pay_c)
#>
#> Call:
#> lm(formula = wd5total_c ~ wa4 + cfps_gender + cfps2014_age +
#> cfps2014edu, data = pay_edu_c)
#>
#> Residuals:
#> <Labelled double>: 过去12个月教育总支出(元)
#> Min 1Q Median 3Q Max
#> -8.1544 -0.8446 0.2352 1.0923 3.9552
#>
#> Labels:
#> value label
#> -10 无法判断
#> -9 缺失
#> -8 不适用
#> -2 拒绝回答
#> -1 不知道
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 5.31618 0.13532 39.286 < 2e-16 ***
#> wa4 0.44430 0.03453 12.867 < 2e-16 ***
#> cfps_gender -0.03257 0.05596 -0.582 0.5606
#> cfps2014_age 0.03955 0.01651 2.396 0.0166 *
#> cfps2014edu 0.48154 0.07405 6.503 9.02e-11 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 1.637 on 3427 degrees of freedom
#> Multiple R-squared: 0.09442, Adjusted R-squared: 0.09336
#> F-statistic: 89.33 on 4 and 3427 DF, p-value: < 2.2e-16
fit1 <- vglm(wd503m_c ~ wa4, tobit, data = pay_edu_c, trace = TRUE)
#> VGLM linear loop 1 : loglikelihood = -8431.62944
#> VGLM linear loop 2 : loglikelihood = -8372.37044
#> VGLM linear loop 3 : loglikelihood = -8363.70658
#> VGLM linear loop 4 : loglikelihood = -8361.45936
#> VGLM linear loop 5 : loglikelihood = -8360.8535
#> VGLM linear loop 6 : loglikelihood = -8360.68463
#> VGLM linear loop 7 : loglikelihood = -8360.63797
#> VGLM linear loop 8 : loglikelihood = -8360.625
#> VGLM linear loop 9 : loglikelihood = -8360.6214
#> VGLM linear loop 10 : loglikelihood = -8360.6204
#> VGLM linear loop 11 : loglikelihood = -8360.62012
#> VGLM linear loop 12 : loglikelihood = -8360.62004
#> VGLM linear loop 13 : loglikelihood = -8360.62002
#> VGLM linear loop 14 : loglikelihood = -8360.62002
coef(fit1, matrix = TRUE)
#> mu loglink(sd)
#> (Intercept) 0.1521122 1.017163
#> wa4 1.5084092 0.000000
summary(fit1)
#>
#> Call:
#> vglm(formula = wd503m_c ~ wa4, family = tobit, data = pay_edu_c,
#> trace = TRUE)
#>
#> Pearson residuals:
#> Min 1Q Median 3Q Max
#> mu -1.5314 -0.3365 -0.3365 0.7341 2.417
#> loglink(sd) -0.8739 -0.7014 -0.7014 0.6744 6.233
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept):1 0.15211 0.09828 1.548 0.122
#> (Intercept):2 1.01716 0.01443 70.489 <2e-16 ***
#> wa4 1.50841 0.05883 25.640 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Names of linear predictors: mu, loglink(sd)
#>
#> Log-likelihood: -8360.62 on 6861 degrees of freedom
#>
#> Number of Fisher scoring iterations: 14
#>
#> No Hauck-Donner effect found in any of the estimates#家庭教育参与
#join_edu_c %>% colnames()
#aa %>% head ()
aa <- join_edu_c %>%
mutate_at(vars(wf601m_c:wf606m_c), as.factor) #%>%
#dplyr::select(wf601m_c:wf606m_c)
#aa %>%
# polr(. ~ wa4, data = aa, Hess = TRUE) %>%
# summary()
polr(wf601m_c ~ wa4, data = aa, Hess = TRUE)
#> Call:
#> polr(formula = wf601m_c ~ wa4, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> wa4
#> 0.2625907
#>
#> Intercepts:
#> 1|2 2|3 3|4 4|5
#> -2.0132501 -1.2275277 -0.5173662 1.4540002
#>
#> Residual Deviance: 11839.09
#> AIC: 11849.09
polr(wf602m_c ~ wa4, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf602m_c ~ wa4, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.3202 0.03442 9.303
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -1.6446 0.0667 -24.6665
#> 2|3 -0.7303 0.0590 -12.3700
#> 3|4 0.3595 0.0576 6.2451
#> 4|5 2.6048 0.0729 35.7257
#>
#> Residual Deviance: 12371.70
#> AIC: 12381.70
polr(wf603m_c ~ wa4, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf603m_c ~ wa4, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.1437 0.03667 3.919
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -2.9789 0.0923 -32.2616
#> 2|3 -2.2304 0.0749 -29.7874
#> 3|4 -1.5642 0.0661 -23.6765
#> 4|5 1.1539 0.0631 18.2888
#>
#> Residual Deviance: 9545.443
#> AIC: 9555.443
polr(wf604m_c ~ wa4, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf604m_c ~ wa4, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.3004 0.03435 8.743
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -1.1082 0.0608 -18.2272
#> 2|3 -0.4796 0.0576 -8.3215
#> 3|4 0.2379 0.0570 4.1755
#> 4|5 2.1474 0.0673 31.9299
#>
#> Residual Deviance: 12839.09
#> AIC: 12849.09
polr(wf605m_c ~ wa4, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf605m_c ~ wa4, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.07483 0.03468 2.158
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -2.0725 0.0699 -29.6286
#> 2|3 -1.3594 0.0625 -21.7570
#> 3|4 -0.4156 0.0584 -7.1156
#> 4|5 1.9283 0.0668 28.8633
#>
#> Residual Deviance: 11850.26
#> AIC: 11860.26
polr(wf606m_c ~ wa4, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf606m_c ~ wa4, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.1188 0.03326 3.57
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -0.5693 0.0571 -9.9646
#> 2|3 0.0660 0.0566 1.1671
#> 3|4 0.7542 0.0577 13.0629
#> 4|5 2.7584 0.0772 35.7097
#>
#> Residual Deviance: 12823.06
#> AIC: 12833.06
#broom::tidy(mod_mass)#家庭教育参与_2
aa <- join_edu_c %>%
mutate_at(vars(wf601m_c:wf606m_c), as.factor) #%>%
polr(wf601m_c ~ wa4 + cfps_gender + cfps2014_age + cfps2014edu, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf601m_c ~ wa4 + cfps_gender + cfps2014_age +
#> cfps2014edu, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.26350 0.03528 7.4694
#> cfps_gender -0.02358 0.05628 -0.4189
#> cfps2014_age -0.07390 0.01673 -4.4178
#> cfps2014edu 0.02344 0.07353 0.3187
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -2.8057 0.1463 -19.1745
#> 2|3 -2.0166 0.1413 -14.2684
#> 3|4 -1.3024 0.1390 -9.3721
#> 4|5 0.6843 0.1379 4.9629
#>
#> Residual Deviance: 11795.63
#> AIC: 11811.63
polr(wf602m_c ~ wa4+ cfps_gender + cfps2014_age+ cfps2014edu, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf602m_c ~ wa4 + cfps_gender + cfps2014_age +
#> cfps2014edu, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.31678 0.03456 9.1654
#> cfps_gender 0.01107 0.05566 0.1988
#> cfps2014_age -0.09369 0.01672 -5.6027
#> cfps2014edu 0.14179 0.07251 1.9554
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -2.4702 0.1439 -17.1633
#> 2|3 -1.5532 0.1400 -11.0954
#> 3|4 -0.4553 0.1379 -3.3010
#> 4|5 1.8077 0.1419 12.7389
#>
#> Residual Deviance: 12325.94
#> AIC: 12341.94
polr(wf603m_c ~ wa4+ cfps_gender + cfps2014_age+ cfps2014edu, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf603m_c ~ wa4 + cfps_gender + cfps2014_age +
#> cfps2014edu, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.15392 0.03700 4.160
#> cfps_gender 0.09319 0.05985 1.557
#> cfps2014_age -0.10918 0.01780 -6.133
#> cfps2014edu -0.08317 0.07879 -1.056
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -4.2678 0.1668 -25.5792
#> 2|3 -3.5127 0.1573 -22.3248
#> 3|4 -2.8370 0.1526 -18.5916
#> 4|5 -0.0576 0.1445 -0.3986
#>
#> Residual Deviance: 9424.457
#> AIC: 9440.457
polr(wf604m_c ~ wa4+ cfps_gender + cfps2014_age+ cfps2014edu, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf604m_c ~ wa4 + cfps_gender + cfps2014_age +
#> cfps2014edu, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.3409 0.03474 9.815
#> cfps_gender 0.1586 0.05564 2.849
#> cfps2014_age -0.2144 0.01699 -12.624
#> cfps2014edu -0.2185 0.07275 -3.003
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -3.7562 0.1488 -25.2379
#> 2|3 -3.0778 0.1455 -21.1471
#> 3|4 -2.2803 0.1419 -16.0693
#> 4|5 -0.1917 0.1375 -1.3938
#>
#> Residual Deviance: 12280.24
#> AIC: 12296.24
polr(wf605m_c ~ wa4+ cfps_gender + cfps2014_age+ cfps2014edu, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf605m_c ~ wa4 + cfps_gender + cfps2014_age +
#> cfps2014edu, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.08158 0.03485 2.341
#> cfps_gender 0.09402 0.05671 1.658
#> cfps2014_age -0.05063 0.01698 -2.981
#> cfps2014edu -0.08612 0.07469 -1.153
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -2.6987 0.1463 -18.4519
#> 2|3 -1.9813 0.1423 -13.9237
#> 3|4 -1.0311 0.1397 -7.3828
#> 4|5 1.3265 0.1410 9.4056
#>
#> Residual Deviance: 11808.88
#> AIC: 11824.88
polr(wf606m_c ~ wa4+ cfps_gender + cfps2014_age+ cfps2014edu, data = aa, Hess = TRUE) %>%
summary()
#> Call:
#> polr(formula = wf606m_c ~ wa4 + cfps_gender + cfps2014_age +
#> cfps2014edu, data = aa, Hess = TRUE)
#>
#> Coefficients:
#> Value Std. Error t value
#> wa4 0.126907 0.03346 3.7928
#> cfps_gender -0.093784 0.05483 -1.7105
#> cfps2014_age -0.004619 0.01637 -0.2821
#> cfps2014edu -0.154858 0.07172 -2.1592
#>
#> Intercepts:
#> Value Std. Error t value
#> 1|2 -0.8809 0.1349 -6.5295
#> 2|3 -0.2433 0.1343 -1.8118
#> 3|4 0.4470 0.1344 3.3253
#> 4|5 2.4543 0.1434 17.1180
#>
#> Residual Deviance: 12806.06
#> AIC: 12822.06
#broom::tidy(mod_mass)