我国城乡家庭教育投入状况的比较研究
wxy
2019-11-04
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)摘要
本文使用CFPS2014 年的数据，在教育期望、教育支出和教育参与三个维度上对当前我国城乡家庭教育 投入的状况进行了比较分析。
思路
- 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
#数据
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
) %>%
dplyr::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 %>%
dplyr::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,
TRUE ~NA_real_))
wish_edu_z <- wish_edu_c %>%
dplyr:: 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,
TRUE ~ NA_real_))%>%
haven::zap_labels() %>%
head()
#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)) %>%
haven::zap_labels() %>%
head()
#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,
TRUE ~NA_real_
)
}
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)
)
#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() %>%
as.data.frame() %>%
rename(city = V1,countryside = V2) %>%
dplyr::select(countryside ,city)knitr::include_graphics("../img/edu.png")
#家庭教育支出对比矩阵
pay_edu %>%
group_by(wa4) %>%
summarize(mean_total = mean(wd5total),
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 715.00000knitr::include_graphics("../img/pay.png")
#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()
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 907.00000knitr::include_graphics("../img/join.png")
#家庭教育期望样本
#wish_edu_z %>%
# ggplot(aes(x= wa4 ,y = wd2_c)) +
# geom_point()
wish_edu_z %>% count(wa4)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:
#> 1 2 3 4 5 6
#> -0.90052 -0.90052 1.48230 0.00008 0.00008 0.00008
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -10.82 3104.42 -0.003 0.997
#> wa4 10.13 3104.42 0.003 0.997
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 7.6382 on 5 degrees of freedom
#> Residual deviance: 3.8191 on 4 degrees of freedom
#> AIC: 7.8191
#>
#> Number of Fisher Scoring iterations: 18
exp(coef(explot_edu_z))
#> (Intercept) wa4
#> 1.994055e-05 2.507453e+04
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:
#> 1 2 3 4 5 6
#> -6.547e-06 -1.197e-05 1.197e-05 6.547e-06 2.110e-08 1.197e-05
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -3.224e+01 1.326e+06 0 1
#> wa4 2.336e+01 5.067e+04 0 1
#> cfps_gender 4.069e+01 5.610e+05 0 1
#> cfps2014_age -1.207e+00 1.056e+05 0 1
#> cfps2014edu 1.207e+00 1.493e+05 0 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 7.6382e+00 on 5 degrees of freedom
#> Residual deviance: 5.1552e-10 on 1 degrees of freedom
#> AIC: 10
#>
#> Number of Fisher Scoring iterations: 23
exp(coef(explot_edu_z_2))
#> (Intercept) wa4 cfps_gender cfps2014_age cfps2014edu
#> 9.960328e-15 1.396053e+10 4.674889e+17 2.992220e-01 3.342002e+00
explot_edu_c <- lm(wd2_c ~ wa4,data = wish_edu_z)#lm的结果为0.5，他为0.3
summary(explot_edu_c)
#>
#> Call:
#> lm(formula = wd2_c ~ wa4, data = wish_edu_z)
#>
#> Residuals:
#> 1 2 3 4 5 6
#> -1.333 -1.333 2.667 -2.000 1.000 1.000
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 11.0000 1.8634 5.903 0.00412 **
#> wa4 2.3333 0.8333 2.800 0.04881 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 2.041 on 4 degrees of freedom
#> Multiple R-squared: 0.6622, Adjusted R-squared: 0.5777
#> F-statistic: 7.84 on 1 and 4 DF, p-value: 0.04881
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:
#> 1 2 3 4 5 6
#> 8.882e-16 -7.143e-01 7.143e-01 -1.143e+00 -7.143e-01 1.857e+00
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 15.1429 16.5289 0.916 0.456
#> wa4 2.2143 0.8207 2.698 0.114
#> cfps_gender 0.7857 6.5652 0.120 0.916
#> cfps2014_age -0.3571 1.1606 -0.308 0.787
#>
#> Residual standard error: 1.773 on 2 degrees of freedom
#> Multiple R-squared: 0.8726, Adjusted R-squared: 0.6815
#> F-statistic: 4.566 on 3 and 2 DF, p-value: 0.1849knitr::include_graphics("../img/edu_lm.png")
#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:
#> 1 2 3 4 5 6
#> 0.14422 0.01905 -0.16327 -0.45404 0.19003 0.26401
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 7.0618 0.2738 25.794 1.34e-05 ***
#> wa4 0.9255 0.1224 7.559 0.00164 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.2999 on 4 degrees of freedom
#> Multiple R-squared: 0.9346, Adjusted R-squared: 0.9182
#> F-statistic: 57.14 on 1 and 4 DF, p-value: 0.001641
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:
#> 1 2 3 4 5 6
#> 6.653e-18 -1.154e-01 1.154e-01 -1.154e-01 1.154e-01 3.662e-17
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.5878 3.2691 0.180 0.8867
#> wa4 0.8956 0.1154 7.758 0.0816 .
#> cfps_gender 2.5999 1.4556 1.786 0.3249
#> cfps2014_age 0.6026 0.3054 1.973 0.2986
#> cfps2014edu -1.1955 0.7209 -1.658 0.3454
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.2309 on 1 degrees of freedom
#> Multiple R-squared: 0.9903, Adjusted R-squared: 0.9515
#> F-statistic: 25.54 on 4 and 1 DF, p-value: 0.1472
fit1 <- vglm(wd503m_c ~ wa4, tobit, data = pay_edu_c, trace = TRUE)
#> VGLM linear loop 1 : loglikelihood = -1.076736
#> VGLM linear loop 2 : loglikelihood = -0.7600138
#> VGLM linear loop 3 : loglikelihood = -0.7420704
#> VGLM linear loop 4 : loglikelihood = -0.7420165
#> VGLM linear loop 5 : loglikelihood = -0.7420165
coef(fit1, matrix = TRUE)
#> mu loglink(sd)
#> (Intercept) 5.850474 -1.295269
#> wa4 1.192436 0.000000
summary(fit1)
#>
#> Call:
#> vglm(formula = wd503m_c ~ wa4, family = tobit, data = pay_edu_c,
#> trace = TRUE)
#>
#> Pearson residuals:
#> mu loglink(sd)
#> 1 -0.4936 -0.53483
#> 2 -0.4936 -0.53483
#> 3 0.9872 -0.01803
#> 4 -0.7941 -0.26121
#> 5 1.7373 1.42698
#> 6 -0.9432 -0.07808
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept):1 5.8505 0.2500 23.405 < 2e-16 ***
#> (Intercept):2 -1.2953 0.2887 -4.487 7.23e-06 ***
#> wa4 1.1924 0.1118 10.667 < 2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Names of linear predictors: mu, loglink(sd)
#>
#> Log-likelihood: -0.742 on 9 degrees of freedom
#>
#> Number of Fisher scoring iterations: 5
#>
#> No Hauck-Donner effect found in any of the estimatesknitr::include_graphics("../img/pay_logit.png")
#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)knitr::include_graphics("../img/join_logit.png")
#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)