TAS_Regression_Longformat_2
Sher May
2024-10-15
TAS Descriptive statistics
We will be going through
Step 1: Loading Packages
library(tidyverse)
library(readxl)
library(ggplot2)
library (reshape2)
library(writexl)
library (lmerTest)
library(lme4)Step 2: Import the data
TAS_data_long_format_age <- read_excel("TAS_data_long_format_age.xlsx")Step 3: Preview the data
view(TAS_data_long_format_age)head(TAS_data_long_format_age)## # A tibble: 6 × 42
## TAS TAS05 TAS09 TAS15 `1968 Interview Number` `Person Number` Gender
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 1 1 NA 4 180 2
## 2 2 1 1 NA 5 32 2
## 3 2 1 1 NA 6 34 1
## 4 2 1 1 NA 14 30 1
## 5 1 1 NA NA 18 38 2
## 6 2 1 1 NA 47 34 2
## # ℹ 35 more variables: `Individual is sample` <dbl>, `Year ID Number` <dbl>,
## # `Sequence Number` <dbl>, `Relationship to Head` <dbl>,
## # `Release Number` <dbl>, B5A <dbl>, B5D <dbl>, B6C <dbl>, C2D <dbl>,
## # C2E <dbl>, C2F <dbl>, D2D3_month <dbl>, D2D3_year <dbl>,
## # E1_1st_mention <dbl>, E1_2nd_mention <dbl>, E1_3rd_mention <dbl>, E3 <dbl>,
## # G1 <dbl>, G2_month <dbl>, G2_year <dbl>, G10 <dbl>, G11 <dbl>, G30A <dbl>,
## # G41A <dbl>, G41B <dbl>, G41C <dbl>, G41H <dbl>, G41P <dbl>, H1 <dbl>, …2005 & 2009
Step 5: Regression (2005 & 2009)
Filter the data (2005 & 2009)
ids_to_remove_05_09 <- c("656_31" , "2516_31", "672_172", "1168_35", "2047_30", "2526_33", "2672_30", "3197_5")
Long_format_2005_2009 <- TAS_data_long_format_age %>% filter(TAS05 == 1 & TAS09 ==1) %>% filter(Age_18_graduate< 50) %>% unite("TAS_ID", c("1968 Interview Number", "Person Number")) %>% mutate(year_new = case_when(year == 2005 ~ -1, year == 2009 ~ 0,year == 2015 ~ 1)) %>% add_count(TAS_ID, name = "TAS_ID_count") %>% filter(TAS_ID_count == 2) %>% filter(!TAS_ID %in% ids_to_remove_05_09)knitr::kable(head(Long_format_2005_2009[, 1:43]))| TAS | TAS05 | TAS09 | TAS15 | TAS_ID | Gender | Individual is sample | Year ID Number | Sequence Number | Relationship to Head | Release Number | B5A | B5D | B6C | C2D | C2E | C2F | D2D3_month | D2D3_year | E1_1st_mention | E1_2nd_mention | E1_3rd_mention | E3 | G1 | G2_month | G2_year | G10 | G11 | G30A | G41A | G41B | G41C | G41H | G41P | H1 | L7_1st_mention | L7_2nd_mention | L7_3rd_mention | Age_17_graduate | Age_18_graduate | year | year_new | TAS_ID_count |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 1 | 1 | NA | 5_32 | 2 | 2 | 624 | 3 | 30 | 5 | 5 | 5 | 5 | 7 | 7 | 7 | 0 | 0 | 1 | 7 | 0 | 0 | 1 | 5 | 2002 | 1 | 1 | 7 | 7 | 6 | 6 | 7 | 5 | 2 | 1 | 0 | 0 | 20 | 21 | 2005 | -1 | 2 |
| 2 | 1 | 1 | NA | 6_34 | 1 | 2 | 1202 | 51 | 30 | 5 | 2 | 2 | 6 | 1 | 1 | 1 | 0 | 0 | 7 | 0 | 0 | 5 | 1 | 5 | 2002 | 1 | 1 | 0 | 7 | 5 | 7 | 5 | 3 | 1 | 1 | 0 | 0 | 20 | 21 | 2005 | -1 | 2 |
| 2 | 1 | 1 | NA | 14_30 | 1 | 2 | 736 | 51 | 30 | 5 | 4 | 4 | 4 | 2 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 6 | 2003 | 1 | 5 | 6 | 5 | 6 | 6 | 5 | 5 | 2 | 1 | 0 | 0 | 19 | 20 | 2005 | -1 | 2 |
| 2 | 1 | 1 | NA | 47_34 | 2 | 2 | 2516 | 3 | 30 | 5 | 4 | 5 | 6 | 4 | 5 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 5 | 2005 | 5 | 0 | 6 | 3 | 6 | 4 | 7 | 4 | 1 | 1 | 0 | 0 | 17 | 18 | 2005 | -1 | 2 |
| 2 | 1 | 1 | NA | 53_35 | 2 | 2 | 1392 | 3 | 33 | 5 | 4 | 5 | 5 | 3 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 6 | 2002 | 1 | 1 | 7 | 6 | 7 | 7 | 7 | 5 | 1 | 1 | 0 | 0 | 20 | 21 | 2005 | -1 | 2 |
| 2 | 1 | 1 | NA | 53_36 | 2 | 2 | 1616 | 3 | 30 | 5 | 4 | 5 | 7 | 4 | 1 | 1 | 0 | 0 | 6 | 0 | 0 | 1 | 1 | 6 | 2005 | 1 | 5 | 7 | 7 | 7 | 5 | 7 | 6 | 2 | 1 | 0 | 0 | 17 | 18 | 2005 | -1 | 2 |
testing <- Long_format_2005_2009 %>% mutate(Age_18_graduate_new = ifelse(year == 2005, Age_18_graduate + 4, Age_18_graduate))
comparison_data <- testing %>% filter(year %in% c(2005, 2009)) %>% group_by(TAS_ID) %>% filter(n_distinct(year) == 2) %>% mutate(age_difference = ifelse(year == 2009, Age_18_graduate - Age_18_graduate_new[year == 2005], 0)) %>% ungroup()
Long_format_2005_2009_new <- comparison_data %>% filter(age_difference == 0)view(Long_format_2005_2009)Age count - 2005
Long_format_2005_2009 %>% filter(year == 2005) %>% count(Age_18_graduate)## # A tibble: 5 × 2
## Age_18_graduate n
## <dbl> <int>
## 1 18 158
## 2 19 144
## 3 20 138
## 4 21 74
## 5 22 1Age count - 2009
Long_format_2005_2009 %>% filter(year == 2009) %>% count(Age_18_graduate)## # A tibble: 6 × 2
## Age_18_graduate n
## <dbl> <int>
## 1 22 156
## 2 23 142
## 3 24 137
## 4 25 77
## 5 26 2
## 6 27 1B5A: Responsibility for self
B5A_Regression_05_09 <- (lmer(B5A ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009))
B5A_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: B5A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3044.666
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5721
## Residual 0.9114
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 1.66590 0.11385 2.24869
## Age_18_graduate:year_new
## -0.09734summary(B5A_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: B5A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3044.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2819 -0.4738 0.1861 0.5566 1.8311
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.3273 0.5721
## Residual 0.8307 0.9114
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.66590 1.02713 961.78726 1.622 0.10515
## Age_18_graduate 0.11385 0.04407 961.81142 2.583 0.00993 **
## year_new 2.24869 1.14724 531.14171 1.960 0.05051 .
## Age_18_graduate:year_new -0.09734 0.05361 518.99049 -1.816 0.07001 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.685 -0.685
## Ag_18_grd:_ -0.588 0.589 -0.991Long_format_2005_2009$predicted_B5A <- predict(B5A_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_B5A <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = B5A, color = year_factor_B5A)) + geom_point(aes(shape = year_factor_B5A), alpha = 0.5) + geom_line(aes(y = predicted_B5A), size = 1) + labs(title = "Responsibility for Self (B5A) by Age and Year",x = "Age", y = "Responsibility for Self (B5A)", color = "Year", shape = "Year") + theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
B5D: Managing own money
B5D_Regression_05_09 <- lmer(B5D ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
B5D_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: B5D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 2482.523
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.3138
## Residual 0.7425
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 4.44118 0.01216 1.40866
## Age_18_graduate:year_new
## -0.05755summary(B5D_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: B5D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 2482.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4659 -0.3877 0.2144 0.6232 1.4116
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.09847 0.3138
## Residual 0.55124 0.7425
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.44118 0.77033 1006.48090 5.765 1.08e-08 ***
## Age_18_graduate 0.01216 0.03305 1006.48810 0.368 0.713
## year_new 1.40866 0.93240 530.14313 1.511 0.131
## Age_18_graduate:year_new -0.05755 0.04363 520.81411 -1.319 0.188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.722 -0.722
## Ag_18_grd:_ -0.642 0.642 -0.993Long_format_2005_2009$predicted_B5D <- predict(B5D_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_B5D <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = B5D, color = factor(year_factor_B5D))) + geom_point(aes(shape = year_factor_B5D), alpha = 0.5) + geom_line(aes(y = predicted_B5D), size = 1) + labs(title = "Responsibility for Others (B5D) by Age and Year", x = "Age",y = "Managing own money (B5D)", color = "Year", shape = "Year") + theme_minimal()
B6C: Money management skills
B6C_Regression_05_09 <- lmer(B6C ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
B6C_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: B6C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3391.09
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.9231
## Residual 0.9606
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 6.01603 -0.02671 -1.63186
## Age_18_graduate:year_new
## 0.09538summary(B6C_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: B6C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3391.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.09254 -0.50969 0.05855 0.55934 2.40143
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.8521 0.9231
## Residual 0.9227 0.9606
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.01603 1.26627 865.18646 4.751 2.37e-06 ***
## Age_18_graduate -0.02671 0.05433 865.25272 -0.492 0.6231
## year_new -1.63186 1.21459 535.87436 -1.344 0.1797
## Age_18_graduate:year_new 0.09538 0.05657 517.00541 1.686 0.0924 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.627 -0.627
## Ag_18_grd:_ -0.498 0.498 -0.986Long_format_2005_2009$predicted_B6C <- predict(B6C_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_B6C <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = B6C, color = factor(year_factor_B6C))) + geom_point(aes(shape = year_factor_B6C), alpha = 0.5) + geom_line(aes(y = predicted_B6C), size = 1) + labs(title = "Money management skills (B6C) by Age and Year", x = "Age",y = "Money Management skills (B6C)", color = "Year", shape = "Year") + theme_minimal()
C2D: Worry about expenses
C2D_Regression_05_09 <- lmer(C2D ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
C2D_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: C2D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4144.878
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.036
## Residual 1.531
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.2042 -0.1422 3.5872
## Age_18_graduate:year_new
## -0.1431summary(C2D_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: C2D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4144.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.05352 -0.67428 -0.03819 0.66087 2.15265
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.073 1.036
## Residual 2.345 1.531
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.20420 1.76404 948.21934 4.084 4.8e-05 ***
## Age_18_graduate -0.14222 0.07569 948.24886 -1.879 0.0606 .
## year_new 3.58723 1.92893 531.64194 1.860 0.0635 .
## Age_18_graduate:year_new -0.14315 0.09010 518.66847 -1.589 0.1127
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.676 -0.676
## Ag_18_grd:_ -0.575 0.576 -0.990Long_format_2005_2009$predicted_C2D <- predict(C2D_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_C2D <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = C2D, color = factor(year_factor_C2D))) + geom_point(aes(shape = year_factor_C2D), alpha = 0.5) + geom_line(aes(y = predicted_C2D), size = 1) + labs(title = "Worry about expenses (C2D) by Age and Year", x = "Age",y = "Worry about expenses (C2D)", color = "Year", shape = "Year") + theme_minimal()
C2E: Worry about future job
C2E_Regression_05_09 <- lmer(C2E ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
C2E_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: C2E ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4168.965
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.138
## Residual 1.507
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 6.8657 -0.1364 3.2851
## Age_18_graduate:year_new
## -0.1322summary(C2E_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: C2E ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4169
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.94644 -0.68646 -0.06599 0.68139 2.18580
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.295 1.138
## Residual 2.271 1.507
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.86567 1.80024 925.16971 3.814 0.000146 ***
## Age_18_graduate -0.13643 0.07724 925.20861 -1.766 0.077676 .
## year_new 3.28513 1.90012 532.54458 1.729 0.084406 .
## Age_18_graduate:year_new -0.13220 0.08870 518.12430 -1.490 0.136706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.662 -0.661
## Ag_18_grd:_ -0.553 0.554 -0.989Long_format_2005_2009$predicted_C2E <- predict(C2E_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_C2E <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = C2E, color = factor(year_factor_C2E))) + geom_point(aes(shape = year_factor_C2E), alpha = 0.5) + geom_line(aes(y = predicted_C2E), size = 1) + labs(title = "Worry about future job (C2E) by Age and Year", x = "Age",y = "Worry about future job (C2E)", color = "Year", shape = "Year") + theme_minimal()
C2F: Discouraged about future
C2F_Regression_05_09 <- lmer(C2F ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
C2F_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: C2F ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3930.281
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.048
## Residual 1.325
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.43407 -0.09738 2.61108
## Age_18_graduate:year_new
## -0.10546summary(C2F_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: C2F ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3930.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0405 -0.6416 -0.1266 0.5595 2.6536
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.099 1.048
## Residual 1.755 1.325
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.43407 1.60951 914.34658 3.376 0.000766 ***
## Age_18_graduate -0.09738 0.06906 914.39005 -1.410 0.158862
## year_new 2.61108 1.67087 532.97268 1.563 0.118715
## Age_18_graduate:year_new -0.10546 0.07797 517.83867 -1.353 0.176758
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.656 -0.655
## Ag_18_grd:_ -0.543 0.544 -0.989Long_format_2005_2009$predicted_C2F <- predict(C2F_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_C2F <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = C2F, color = factor(year_factor_C2F))) + geom_point(aes(shape = year_factor_C2F), alpha = 0.5) + geom_line(aes(y = predicted_C2F), size = 1) + labs(title = "Discouraged about future (C2F) by Age and Year", x = "Age",y = "Discouraged about future (C2F)", color = "Year", shape = "Year") + theme_minimal()
E3: Work for money
E3_Regression_05_09 <- lmer(E3 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
E3_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: E3 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4526.603
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7878
## Residual 2.0320
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 3.8885 -0.1180 -2.4061
## Age_18_graduate:year_new
## 0.1157summary(E3_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: E3 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4526.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1202 -0.6522 -0.4059 1.1511 3.0815
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.6206 0.7878
## Residual 4.1290 2.0320
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.88846 2.08302 1011.40856 1.867 0.0622 .
## Age_18_graduate -0.11798 0.08938 1011.41393 -1.320 0.1871
## year_new -2.40609 2.55094 530.13546 -0.943 0.3460
## Age_18_graduate:year_new 0.11565 0.11940 521.18160 0.969 0.3332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.728 -0.727
## Ag_18_grd:_ -0.650 0.650 -0.993Long_format_2005_2009$predicted_E3 <- predict(E3_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_E3 <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = E3, color = factor(year_factor_E3))) + geom_point(aes(shape = year_factor_E3), alpha = 0.5) + geom_line(aes(y = predicted_E3), size = 1) + labs(title = "Work for money (E3) by Age and Year", x = "Age",y = "Work for money (E3)", color = "Year", shape = "Year") + theme_minimal()
G10: Attended college
G10_Regression_05_09 <- lmer(G10 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
G10_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G10 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3571.999
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.2035
## Residual 0.9522
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 2.42092 -0.03434 -4.14838
## Age_18_graduate:year_new
## 0.21255summary(G10_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G10 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3572
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2583 -0.3188 -0.1355 0.0173 4.8833
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.4483 1.2035
## Residual 0.9067 0.9522
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.42092 1.44992 795.48040 1.670 0.095374 .
## Age_18_graduate -0.03434 0.06221 795.59128 -0.552 0.581069
## year_new -4.14838 1.20985 542.94897 -3.429 0.000652 ***
## Age_18_graduate:year_new 0.21255 0.05611 515.95251 3.788 0.000170 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.587 -0.587
## Ag_18_grd:_ -0.425 0.425 -0.981Long_format_2005_2009$predicted_G10 <- predict(G10_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_G10 <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = G10, color = factor(year_factor_G10))) + geom_point(aes(shape = year_factor_G10), alpha = 0.5) + geom_line(aes(y = predicted_G10), size = 1) + labs(title = "Attended college (G10) by Age and Year", x = "Age",y = "Attended college (G10)", color = "Year", shape = "Year") + theme_minimal()
G11: Attending college
G11_Regression_05_09 <- lmer(G11 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
G11_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G11 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4187.066
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.652
## Residual 1.727
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## -0.8742 0.1656 3.8530
## Age_18_graduate:year_new
## -0.1480summary(G11_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G11 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4187.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8823 -0.7252 -0.2515 1.0132 2.2792
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.4251 0.652
## Residual 2.9837 1.727
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.87418 1.76473 1012.68931 -0.495 0.6205
## Age_18_graduate 0.16557 0.07572 1012.69421 2.187 0.0290 *
## year_new 3.85301 2.16826 530.10042 1.777 0.0761 .
## Age_18_graduate:year_new -0.14801 0.10149 521.25057 -1.458 0.1453
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.730 -0.729
## Ag_18_grd:_ -0.652 0.653 -0.993Long_format_2005_2009$predicted_G11 <- predict(G11_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_G11 <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = G11, color = factor(year_factor_G11))) + geom_point(aes(shape = year_factor_G11), alpha = 0.5) + geom_line(aes(y = predicted_G11), size = 1) + labs(title = "Attending college (G11) by Age and Year", x = "Age",y = "Attending college (G11)", color = "Year", shape = "Year") + theme_minimal()
G30A: Likelihood of well-paying job
G30A_Regression_05_09 <- lmer(G30A ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
G30A_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G30A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3950.965
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.4703
## Residual 1.5731
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.35813 0.02315 -1.15446
## Age_18_graduate:year_new
## 0.08033summary(G30A_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G30A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3951
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2759 -0.2934 0.2836 0.6042 1.1950
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.2212 0.4703
## Residual 2.4748 1.5731
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.35813 1.56970 1020.18601 3.413 0.000667 ***
## Age_18_graduate 0.02315 0.06735 1020.18815 0.344 0.731130
## year_new -1.15446 1.97333 530.09541 -0.585 0.558774
## Age_18_graduate:year_new 0.08033 0.09240 521.95151 0.869 0.385054
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.741 -0.741
## Ag_18_grd:_ -0.668 0.669 -0.994Long_format_2005_2009$predicted_G30A <- predict(G30A_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_G30A <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = G30A, color = factor(year_factor_G30A))) + geom_point(aes(shape = year_factor_G30A), alpha = 0.5) + geom_line(aes(y = predicted_G30A), size = 1) + labs(title = "Likelihood of well-paying job (G30A) by Age and Year", x = "Age",y = "Likelihood of well-paying job (G30A)", color = "Year", shape = "Year") + theme_minimal()
G41A: Importance of job status
G41A_Regression_05_09 <- lmer(G41A ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
G41A_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G41A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3919.711
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.254
## Residual 1.213
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.04478 -0.10568 1.17726
## Age_18_graduate:year_new
## -0.07101summary(G41A_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G41A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3919.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.51544 -0.49192 0.08004 0.58992 2.24588
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.573 1.254
## Residual 1.471 1.213
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.04478 1.65629 845.65850 4.253 2.34e-05 ***
## Age_18_graduate -0.10568 0.07106 845.73516 -1.487 0.137
## year_new 1.17726 1.53532 537.08503 0.767 0.444
## Age_18_graduate:year_new -0.07101 0.07144 516.41723 -0.994 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.616 -0.616
## Ag_18_grd:_ -0.479 0.479 -0.985Long_format_2005_2009$predicted_G41A <- predict(G41A_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_G41A <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = G41A, color = factor(year_factor_G41A))) + geom_point(aes(shape = year_factor_G41A), alpha = 0.5) + geom_line(aes(y = predicted_G41A), size = 1) + labs(title = "Importance of job status (G41A) by Age and Year", x = "Age",y = "Importance of job status (G41A)", color = "Year", shape = "Year") + theme_minimal()
G41B: Importance of decision-making
G41B_Regression_05_09 <- lmer(G41B ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
G41B_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G41B ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3203.244
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.6522
## Residual 0.9691
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 6.76995 -0.04942 1.83844
## Age_18_graduate:year_new
## -0.09059summary(G41B_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G41B ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3203.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0434 -0.5194 0.1352 0.6767 2.2311
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.4253 0.6522
## Residual 0.9391 0.9691
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.76995 1.11446 949.21255 6.075 1.8e-09 ***
## Age_18_graduate -0.04942 0.04782 949.24167 -1.033 0.302
## year_new 1.83844 1.22050 531.61437 1.506 0.133
## Age_18_graduate:year_new -0.09059 0.05701 518.70145 -1.589 0.113
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.677 -0.676
## Ag_18_grd:_ -0.576 0.577 -0.990Long_format_2005_2009$predicted_G41A <- predict(G41A_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_G41A <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = G41A, color = factor(year_factor_G41A))) + geom_point(aes(shape = year_factor_G41A), alpha = 0.5) + geom_line(aes(y = predicted_G41A), size = 1) + labs(title = "Importance of decision-making (G41A) by Age and Year", x = "Age",y = "Importance of decision-making (G41A)", color = "Year", shape = "Year") + theme_minimal()
G41C: Importance of challending work
G41C_Regression_05_09 <- lmer(G41C ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
G41C_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G41C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3174.302
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7018
## Residual 0.9277
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 6.04100 -0.01944 1.28671
## Age_18_graduate:year_new
## -0.05893summary(G41C_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G41C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3174.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7996 -0.5155 0.0239 0.6544 2.1193
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.4925 0.7018
## Residual 0.8607 0.9277
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.04100 1.10888 924.76945 5.448 6.54e-08 ***
## Age_18_graduate -0.01944 0.04758 924.80852 -0.409 0.683
## year_new 1.28671 1.16969 532.55863 1.100 0.272
## Age_18_graduate:year_new -0.05893 0.05460 518.11246 -1.079 0.281
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.662 -0.661
## Ag_18_grd:_ -0.553 0.553 -0.989Long_format_2005_2009$predicted_G41C <- predict(G41C_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_G41C <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = G41C, color = factor(year_factor_G41C))) + geom_point(aes(shape = year_factor_G41C), alpha = 0.5) + geom_line(aes(y = predicted_G41C), size = 1) + labs(title = "Importance of challenging work (G41C) by Age and Year", x = "Age",y = "Importance of challenging work (G41C)", color = "Year", shape = "Year") + theme_minimal()
G41H: Importance of healthcare benefits
G41H_Regression_05_09 <- lmer(G41H ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
G41H_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G41H ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3005.093
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5851
## Residual 0.8831
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.46418 -0.05113 1.51063
## Age_18_graduate:year_new
## -0.07159summary(G41H_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G41H ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3005.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8123 -0.4241 0.3822 0.4934 2.0014
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.3423 0.5851
## Residual 0.7799 0.8831
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.46418 1.01082 952.19046 7.384 3.34e-13 ***
## Age_18_graduate -0.05113 0.04337 952.21840 -1.179 0.239
## year_new 1.51063 1.11212 531.51172 1.358 0.175
## Age_18_graduate:year_new -0.07159 0.05196 518.78005 -1.378 0.169
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.679 -0.678
## Ag_18_grd:_ -0.579 0.579 -0.991Long_format_2005_2009$predicted_G41H <- predict(G41H_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_G41H <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = G41H, color = factor(year_factor_G41H))) + geom_point(aes(shape = year_factor_G41H), alpha = 0.5) + geom_line(aes(y = predicted_G41H), size = 1) + labs(title = "Importance of healthcare benefits (G41H) by Age and Year", x = "Age",y = "Importance of healthcare benefits (G41H)", color = "Year", shape = "Year") + theme_minimal()
G41P: Importance of job central to identity
G41P_Regression_05_09 <- lmer(G41P ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
G41P_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G41P ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3885.689
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.9854
## Residual 1.3156
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 8.6667 -0.1837 3.7573
## Age_18_graduate:year_new
## -0.1887summary(G41P_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G41P ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3885.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.71354 -0.54639 0.08629 0.60167 2.13141
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.9711 0.9854
## Residual 1.7307 1.3156
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.66674 1.56701 926.95710 5.531 4.15e-08 ***
## Age_18_graduate -0.18368 0.06723 926.99526 -2.732 0.00642 **
## year_new 3.75726 1.65853 532.41854 2.265 0.02389 *
## Age_18_graduate:year_new -0.18869 0.07742 518.11486 -2.437 0.01514 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.663 -0.663
## Ag_18_grd:_ -0.555 0.555 -0.990Long_format_2005_2009$predicted_G41P <- predict(G41P_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_G41P <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = G41P, color = factor(year_factor_G41P))) + geom_point(aes(shape = year_factor_G41P), alpha = 0.5) + geom_line(aes(y = predicted_G41P), size = 1) + labs(title = "Importance of job central to identity (G41P) by Age and Year", x = "Age",y = "Importance of job central to identity (G41P)", color = "Year", shape = "Year") + theme_minimal()
H1: General Health
H1_Regression_05_09 <- lmer(H1 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
H1_Regression_05_09## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: H1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 2572.206
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5770
## Residual 0.6656
## Number of obs: 1030, groups: TAS_ID, 515
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 1.28369 0.03819 -0.42058
## Age_18_graduate:year_new
## 0.01668summary(H1_Regression_05_09)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: H1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 2572.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.07297 -0.64053 -0.05106 0.50366 3.10299
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.3329 0.5770
## Residual 0.4430 0.6656
## Number of obs: 1030, groups: TAS_ID, 515
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.28369 0.83847 891.92235 1.531 0.126
## Age_18_graduate 0.03819 0.03598 891.97574 1.062 0.289
## year_new -0.42058 0.84044 534.13397 -0.500 0.617
## Age_18_graduate:year_new 0.01668 0.03919 517.41345 0.426 0.671
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.643 -0.642
## Ag_18_grd:_ -0.523 0.523 -0.988Long_format_2005_2009$predicted_H1 <- predict(H1_Regression_05_09, re.form = NA)
Long_format_2005_2009$year_factor_H1 <- factor(Long_format_2005_2009$year)ggplot(Long_format_2005_2009, aes(x = Age_18_graduate, y = H1, color = factor(year_factor_H1))) + geom_point(aes(shape = year_factor_H1), alpha = 0.5) + geom_line(aes(y = predicted_H1), size = 1) + labs(title = "General Health (H1) by Age and Year", x = "Age",y = "General Health (H1)", color = "Year", shape = "Year") + theme_minimal()
2009 & 2015
Step 6: Regression (2009 & 2015)
Filter the data (2009 & 2015)
ids_to_remove_09_15 <- c("1315_34", "5379_32", "565_32", "2109_31", "2921_33", "3211_3","6820_36")
Long_format_2009_2015 <- TAS_data_long_format_age %>% filter(TAS09 == 1 & TAS15 ==1) %>% filter(Age_18_graduate < 3000) %>% unite("TAS_ID", c("1968 Interview Number", "Person Number")) %>% mutate(year_new = case_when(year == 2005 ~ -1, year == 2009 ~ 0,year == 2015 ~ 1)) %>% add_count(TAS_ID, name = "TAS_ID_count") %>% filter(TAS_ID_count == 2) %>% filter(!TAS_ID %in% ids_to_remove_09_15)change_error_age <- Long_format_2009_2015 %>% group_by(TAS_ID) %>% mutate(Age_18_graduate = case_when(Age_18_graduate == 2033 ~ Age_18_graduate[year == 2009] + 6, Age_18_graduate == 2027 ~ Age_18_graduate[year == 2015] - 6, TRUE ~ Age_18_graduate)) %>% ungroup() %>% filter(Age_18_graduate < 100)Long_format_2009_2015_new <-change_error_age %>% group_by(TAS_ID) %>% mutate(age_difference = ifelse(all(c(2009, 2015) %in% year), Age_18_graduate[year == 2015] - Age_18_graduate[year == 2009], NA)) %>% ungroup()view(Long_format_2009_2015_new)knitr::kable(head(Long_format_2009_2015_new[, 1:44]))| TAS | TAS05 | TAS09 | TAS15 | TAS_ID | Gender | Individual is sample | Year ID Number | Sequence Number | Relationship to Head | Release Number | B5A | B5D | B6C | C2D | C2E | C2F | D2D3_month | D2D3_year | E1_1st_mention | E1_2nd_mention | E1_3rd_mention | E3 | G1 | G2_month | G2_year | G10 | G11 | G30A | G41A | G41B | G41C | G41H | G41P | H1 | L7_1st_mention | L7_2nd_mention | L7_3rd_mention | Age_17_graduate | Age_18_graduate | year | year_new | TAS_ID_count | age_difference |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | NA | 1 | 1 | 4_39 | 2 | 2 | 13 | 3 | 60 | 3 | 4 | 5 | 4 | 6 | 7 | 5 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 5 | 2008 | 1 | 5 | 5 | 6 | 5 | 2 | 7 | 6 | 2 | 1 | 0 | 0 | 18 | 19 | 2009 | 0 | 2 | 6 |
| 2 | NA | 1 | 1 | 7_40 | 2 | 2 | 3836 | 2 | 22 | 3 | 2 | 2 | 7 | 7 | 3 | 4 | 0 | 0 | 6 | 0 | 0 | 5 | 1 | 6 | 2007 | 5 | 0 | 5 | 5 | 2 | 5 | 6 | 5 | 3 | 1 | 0 | 0 | 19 | 20 | 2009 | 0 | 2 | 6 |
| 2 | NA | 1 | 1 | 7_41 | 1 | 2 | 576 | 2 | 30 | 3 | 3 | 4 | 7 | 4 | 5 | 4 | 0 | 0 | 3 | 0 | 0 | 5 | 1 | 5 | 2009 | 5 | 0 | 7 | 5 | 6 | 5 | 7 | 5 | 2 | 1 | 0 | 0 | 17 | 18 | 2009 | 0 | 2 | 6 |
| 2 | NA | 1 | 1 | 10_34 | 2 | 2 | 3276 | 3 | 30 | 3 | 4 | 5 | 6 | 4 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 6 | 2008 | 1 | 5 | 7 | 7 | 5 | 4 | 7 | 5 | 2 | 2 | 0 | 0 | 18 | 19 | 2009 | 0 | 2 | 6 |
| 2 | NA | 1 | 1 | 14_31 | 2 | 2 | 713 | 1 | 10 | 3 | 5 | 5 | 7 | 4 | 4 | 4 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 6 | 2005 | 5 | 0 | 6 | 5 | 7 | 6 | 7 | 2 | 4 | 1 | 0 | 0 | 21 | 22 | 2009 | 0 | 2 | 6 |
| 2 | NA | 1 | 1 | 22_30 | 2 | 2 | 907 | 2 | 30 | 3 | 5 | 1 | 4 | 3 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 5 | 2006 | 1 | 1 | 7 | 6 | 6 | 6 | 6 | 6 | 1 | 2 | 0 | 0 | 20 | 21 | 2009 | 0 | 2 | 6 |
Long_format_2009_2015_new %>% filter(year == 2009) %>% count(Age_18_graduate)## # A tibble: 6 × 2
## Age_18_graduate n
## <dbl> <int>
## 1 15 1
## 2 18 153
## 3 19 132
## 4 20 135
## 5 21 87
## 6 22 2Long_format_2009_2015_new %>% filter(year == 2015) %>% count(Age_18_graduate)## # A tibble: 6 × 2
## Age_18_graduate n
## <dbl> <int>
## 1 21 1
## 2 24 153
## 3 25 132
## 4 26 135
## 5 27 87
## 6 28 2B5A: Responsibility for self
B5A_Regression_09_15 <- lmer(B5A ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
B5A_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: B5A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 3080.791
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.3952
## Residual 1.0192
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 0.2485 0.1660 3.4163
## Age_18_graduate:year_new
## -0.1325summary(B5A_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: B5A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 3080.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2417 -0.5044 0.2942 0.5022 5.0538
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.1562 0.3952
## Residual 1.0387 1.0192
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.24851 0.84838 998.92782 0.293 0.769643
## Age_18_graduate 0.16605 0.04386 998.92782 3.786 0.000162 ***
## year_new 3.41627 1.30696 531.82584 2.614 0.009205 **
## Age_18_graduate:year_new -0.13251 0.05784 507.99991 -2.291 0.022367 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.538 0.537
## Ag_18_grd:_ 0.658 -0.659 -0.987Long_format_2009_2015_new$predicted_B5A <- predict(B5A_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_B5A <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = B5A, color = year_factor_B5A)) + geom_point(aes(shape = year_factor_B5A), alpha = 0.5) + geom_line(aes(y = predicted_B5A), size = 1) + labs(title = "Responsibility for Self (B5A) by Age and Year",x = "Age", y = "Responsibility for Self (B5A)", color = "Year", shape = "Year") + theme_minimal()
#### B5D: Managing own money
B5D_Regression_09_15 <- lmer(B5D ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
B5D_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: B5D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 2597.364
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.3068
## Residual 0.8050
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 2.1950 0.1139 2.3322
## Age_18_graduate:year_new
## -0.1055summary(B5D_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: B5D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 2597.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2157 -0.2926 0.2141 0.5456 5.3594
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.09414 0.3068
## Residual 0.64798 0.8050
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.19499 0.66859 999.90890 3.283 0.00106 **
## Age_18_graduate 0.11389 0.03457 999.90890 3.295 0.00102 **
## year_new 2.33215 1.03218 531.63902 2.259 0.02426 *
## Age_18_graduate:year_new -0.10551 0.04568 507.99987 -2.310 0.02131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.540 0.539
## Ag_18_grd:_ 0.660 -0.661 -0.987Long_format_2009_2015_new$predicted_B5D <- predict(B5D_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_B5D <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = B5D, color = factor(year_factor_B5D))) + geom_point(aes(shape = year_factor_B5D), alpha = 0.5) + geom_line(aes(y = predicted_B5D), size = 1) + labs(title = "Responsibility for Others (B5D) by Age and Year", x = "Age",y = "Managing own money (B5D)", color = "Year", shape = "Year") + theme_minimal()
B6C: Money Management Skills
B6C_Regression_09_15 <- lmer(B6C ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
B6C_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: B6C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 3270.6
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.8448
## Residual 0.9395
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.03415 0.02331 -0.67400
## Age_18_graduate:year_new
## 0.01762summary(B6C_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: B6C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 3270.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7025 -0.5537 0.1037 0.5922 2.5653
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.7137 0.8448
## Residual 0.8827 0.9395
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.03415 0.98062 846.75339 5.134 3.53e-07 ***
## Age_18_graduate 0.02331 0.05070 846.75339 0.460 0.646
## year_new -0.67400 1.21872 555.85654 -0.553 0.580
## Age_18_graduate:year_new 0.01762 0.05332 507.99993 0.330 0.741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.333 0.333
## Ag_18_grd:_ 0.525 -0.526 -0.976Long_format_2009_2015_new$predicted_B6C <- predict(B6C_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_B6C <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = B6C, color = factor(year_factor_B6C))) + geom_point(aes(shape = year_factor_B6C), alpha = 0.5) + geom_line(aes(y = predicted_B6C), size = 1) + labs(title = "Money management skills (B6C) by Age and Year", x = "Age",y = "Money Management skills (B6C)", color = "Year", shape = "Year") + theme_minimal()
C2D: Worry about expenses
C2D_Regression_09_15 <- lmer(C2D ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
C2D_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: C2D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 4156.593
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.058
## Residual 1.573
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 4.32784 -0.03099 3.28991
## Age_18_graduate:year_new
## -0.13472summary(C2D_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: C2D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 4156.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.86947 -0.71075 -0.04325 0.66788 2.19759
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.119 1.058
## Residual 2.475 1.573
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.32784 1.47134 926.26952 2.941 0.00335 **
## Age_18_graduate -0.03099 0.07607 926.26952 -0.407 0.68382
## year_new 3.28991 2.02830 542.84889 1.622 0.10538
## Age_18_graduate:year_new -0.13472 0.08928 507.99996 -1.509 0.13194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.430 0.429
## Ag_18_grd:_ 0.586 -0.587 -0.982Long_format_2009_2015_new$predicted_C2D <- predict(C2D_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_C2D <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = C2D, color = factor(year_factor_C2D))) + geom_point(aes(shape = year_factor_C2D), alpha = 0.5) + geom_line(aes(y = predicted_C2D), size = 1) + labs(title = "Worry about expenses (C2D) by Age and Year", x = "Age",y = "Worry about expenses (C2D)", color = "Year", shape = "Year") + theme_minimal()
C2E: Worry about future job
C2E_Regression_09_15 <- lmer(C2E ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
C2E_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: C2E ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 4089.805
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.174
## Residual 1.450
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 4.60717 -0.05064 0.97752
## Age_18_graduate:year_new
## -0.04490summary(C2E_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: C2E ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 4089.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.04384 -0.66022 -0.09101 0.59767 2.50769
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.379 1.174
## Residual 2.104 1.450
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.60717 1.44839 878.31825 3.181 0.00152 **
## Age_18_graduate -0.05064 0.07489 878.31825 -0.676 0.49911
## year_new 0.97752 1.87653 550.27657 0.521 0.60263
## Age_18_graduate:year_new -0.04490 0.08231 507.99997 -0.546 0.58564
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.372 0.371
## Ag_18_grd:_ 0.549 -0.550 -0.979Long_format_2009_2015_new$predicted_C2E <- predict(C2E_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_C2E <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = C2E, color = factor(year_factor_C2E))) + geom_point(aes(shape = year_factor_C2E), alpha = 0.5) + geom_line(aes(y = predicted_C2E), size = 1) + labs(title = "Worry about future job (C2E) by Age and Year", x = "Age",y = "Worry about future job (C2E)", color = "Year", shape = "Year") + theme_minimal()
C2F: Discouraged about future
C2F_Regression_09_15 <- lmer(C2F ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
C2F_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: C2F ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 3880.697
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.101
## Residual 1.288
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 3.77834 -0.03594 1.71397
## Age_18_graduate:year_new
## -0.06834summary(C2F_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: C2F ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 3880.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.978 -0.620 -0.159 0.497 2.570
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.213 1.101
## Residual 1.659 1.288
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.77834 1.31523 862.27274 2.873 0.00417 **
## Age_18_graduate -0.03594 0.06800 862.27273 -0.529 0.59726
## year_new 1.71397 1.66866 553.02446 1.027 0.30480
## Age_18_graduate:year_new -0.06834 0.07310 508.00004 -0.935 0.35028
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.352 0.351
## Ag_18_grd:_ 0.537 -0.537 -0.977Long_format_2009_2015_new$predicted_C2F <- predict(C2F_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_C2F <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = C2F, color = factor(year_factor_C2F))) + geom_point(aes(shape = year_factor_C2F), alpha = 0.5) + geom_line(aes(y = predicted_C2F), size = 1) + labs(title = "Discouraged about future (C2F) by Age and Year", x = "Age",y = "Discouraged about future (C2F)", color = "Year", shape = "Year") + theme_minimal()
E3: Work for money
E3_Regression_09_15 <- lmer(E3 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
E3_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: E3 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 4452.719
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7646
## Residual 2.0060
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 11.0610 -0.4656 -6.1822
## Age_18_graduate:year_new
## 0.3087summary(E3_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: E3 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 4452.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9954 -0.6227 -0.3117 1.0886 1.9997
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.5845 0.7646
## Residual 4.0239 2.0060
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 11.06097 1.66610 999.91254 6.639 5.18e-11 ***
## Age_18_graduate -0.46559 0.08614 999.91254 -5.405 8.11e-08 ***
## year_new -6.18217 2.57217 531.63826 -2.403 0.01658 *
## Age_18_graduate:year_new 0.30869 0.11384 507.99983 2.712 0.00692 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.540 0.539
## Ag_18_grd:_ 0.660 -0.661 -0.987Long_format_2009_2015_new$predicted_E3 <- predict(E3_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_E3 <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = E3, color = factor(year_factor_E3))) + geom_point(aes(shape = year_factor_E3), alpha = 0.5) + geom_line(aes(y = predicted_E3), size = 1) + labs(title = "Work for money (E3) by Age and Year", x = "Age",y = "Work for money (E3)", color = "Year", shape = "Year") + theme_minimal()
G10: Attended college
G10_Regression_09_15 <- lmer(G10 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
G10_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G10 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 3744.484
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.439
## Residual 1.003
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.1366 -0.1627 -2.9257
## Age_18_graduate:year_new
## 0.1077summary(G10_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G10 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 3744.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.36686 -0.28770 -0.11102 -0.04614 2.93267
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 2.071 1.439
## Residual 1.007 1.003
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.13662 1.36150 699.37754 3.773 0.000175 ***
## Age_18_graduate -0.16274 0.07039 699.37754 -2.312 0.021074 *
## year_new -2.92569 1.32926 600.91936 -2.201 0.028116 *
## Age_18_graduate:year_new 0.10769 0.05694 508.00012 1.891 0.059153 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.122 0.121
## Ag_18_grd:_ 0.404 -0.404 -0.956Long_format_2009_2015_new$predicted_G10 <- predict(G10_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_G10 <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = G10, color = factor(year_factor_G10))) + geom_point(aes(shape = year_factor_G10), alpha = 0.5) + geom_line(aes(y = predicted_G10), size = 1) + labs(title = "Attended college (G10) by Age and Year", x = "Age",y = "Attended college (G10)", color = "Year", shape = "Year") + theme_minimal()
G11: Attending college
G11_Regression_09_15 <- lmer(G11 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
G11_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G11 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 4086.155
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.8189
## Residual 1.6081
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## -4.1693 0.2799 4.4798
## Age_18_graduate:year_new
## -0.1515summary(G11_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G11 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 4086.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2601 -0.4800 -0.1032 0.7430 2.4904
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.6705 0.8189
## Residual 2.5860 1.6081
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.16925 1.40055 974.67650 -2.977 0.002984 **
## Age_18_graduate 0.27989 0.07241 974.67650 3.865 0.000118 ***
## year_new 4.47981 2.06613 535.80994 2.168 0.030582 *
## Age_18_graduate:year_new -0.15155 0.09126 508.00001 -1.661 0.097399 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.495 0.494
## Ag_18_grd:_ 0.629 -0.630 -0.985Long_format_2009_2015_new$predicted_G11 <- predict(G11_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_G11 <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = G11, color = factor(year_factor_G11))) + geom_point(aes(shape = year_factor_G11), alpha = 0.5) + geom_line(aes(y = predicted_G11), size = 1) + labs(title = "Attending college (G11) by Age and Year", x = "Age",y = "Attending college (G11)", color = "Year", shape = "Year") + theme_minimal()
G30A: Likelihood of well-paying job
G30A_Regression_09_15 <- lmer(G30A ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
G30A_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G30A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 2928.213
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.6287
## Residual 0.8356
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.808168 0.012067 -0.171941
## Age_18_graduate:year_new
## 0.002538summary(G30A_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G30A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 2928.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7023 -0.5416 0.0197 0.5630 2.4352
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.3952 0.6287
## Residual 0.6982 0.8356
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.808168 0.811544 898.600460 7.157 1.71e-12 ***
## Age_18_graduate 0.012067 0.041959 898.600459 0.288 0.774
## year_new -0.171941 1.079342 547.016659 -0.159 0.873
## Age_18_graduate:year_new 0.002538 0.047417 508.000104 0.054 0.957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.396 0.395
## Ag_18_grd:_ 0.564 -0.565 -0.980Long_format_2009_2015_new$predicted_G30A <- predict(G30A_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_G30A <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = G30A, color = factor(year_factor_G30A))) + geom_point(aes(shape = year_factor_G30A), alpha = 0.5) + geom_line(aes(y = predicted_G30A), size = 1) + labs(title = "Likelihood of well-paying job (G30A) by Age and Year", x = "Age",y = "Likelihood of well-paying job (G30A)", color = "Year", shape = "Year") + theme_minimal()
G41A: Importance of job status
G41A_Regression_09_15 <- lmer(G41A ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
G41A_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G41A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 3963.687
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.306
## Residual 1.262
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 10.0791 -0.2561 -4.1094
## Age_18_graduate:year_new
## 0.1972summary(G41A_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G41A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 3963.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7577 -0.5103 0.1394 0.5547 2.4062
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.707 1.306
## Residual 1.594 1.262
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 10.07906 1.40998 801.60773 7.148 1.98e-12 ***
## Age_18_graduate -0.25612 0.07290 801.60773 -3.513 0.000467 ***
## year_new -4.10938 1.64490 565.40070 -2.498 0.012764 *
## Age_18_graduate:year_new 0.19721 0.07164 507.99992 2.753 0.006120 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.277 0.276
## Ag_18_grd:_ 0.491 -0.491 -0.972Long_format_2009_2015_new$predicted_G41A <- predict(G41A_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_G41A <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = G41A, color = factor(year_factor_G41A))) + geom_point(aes(shape = year_factor_G41A), alpha = 0.5) + geom_line(aes(y = predicted_G41A), size = 1) + labs(title = "Importance of job status (G41A) by Age and Year", x = "Age",y = "Importance of job status (G41A)", color = "Year", shape = "Year") + theme_minimal()
G41B: Importance of decision-making
G41B_Regression_09_15 <- lmer(G41B ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
G41B_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G41B ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 3296.467
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.8679
## Residual 0.9455
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.64953 -0.09984 -1.63733
## Age_18_graduate:year_new
## 0.07930summary(G41B_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G41B ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 3296.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6607 -0.4440 0.1445 0.6113 2.3327
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.7532 0.8679
## Residual 0.8940 0.9455
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.64953 0.99606 840.29978 7.680 4.43e-14 ***
## Age_18_graduate -0.09984 0.05150 840.29978 -1.939 0.0529 .
## year_new -1.63733 1.22717 557.09282 -1.334 0.1827
## Age_18_graduate:year_new 0.07930 0.05365 508.00008 1.478 0.1401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.326 0.325
## Ag_18_grd:_ 0.520 -0.521 -0.975Long_format_2009_2015_new$predicted_G41A <- predict(G41A_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_G41A <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = G41A, color = factor(year_factor_G41A))) + geom_point(aes(shape = year_factor_G41A), alpha = 0.5) + geom_line(aes(y = predicted_G41A), size = 1) + labs(title = "Importance of decision-making (G41A) by Age and Year", x = "Age",y = "Importance of decision-making (G41A)", color = "Year", shape = "Year") + theme_minimal()
G41C: Importance of challenging work
G41C_Regression_09_15 <- lmer(G41C ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)## boundary (singular) fit: see help('isSingular')G41C_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G41C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 3898.309
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.000
## Residual 1.628
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 6.64091 -0.05644 29.44731
## Age_18_graduate:year_new
## -1.31627
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warningssummary(G41C_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G41C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 3898.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8471 -0.3840 -0.2443 0.5991 3.2133
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.000 0.000
## Residual 2.649 1.628
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.64091 1.26313 1016.00000 5.258 1.78e-07 ***
## Age_18_graduate -0.05644 0.06531 1016.00000 -0.864 0.388
## year_new 29.44731 2.08154 1016.00000 14.147 < 2e-16 ***
## Age_18_graduate:year_new -1.31627 0.09236 1016.00000 -14.252 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.607 0.606
## Ag_18_grd:_ 0.706 -0.707 -0.990
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Long_format_2009_2015_new$predicted_G41C <- predict(G41C_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_G41C <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = G41C, color = factor(year_factor_G41C))) + geom_point(aes(shape = year_factor_G41C), alpha = 0.5) + geom_line(aes(y = predicted_G41C), size = 1) + labs(title = "Importance of challenging work (G41C) by Age and Year", x = "Age",y = "Importance of challenging work (G41C)", color = "Year", shape = "Year") + theme_minimal()
G41H: Importance of healthcare benefits
G41H_Regression_09_15 <- lmer(G41H ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
G41H_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G41H ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 3270.427
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.6937
## Residual 1.0127
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.15402 -0.04149 1.33838
## Age_18_graduate:year_new
## -0.05544summary(G41H_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G41H ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 3270.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0083 -0.2572 0.2522 0.5450 1.7022
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.4812 0.6937
## Residual 1.0255 1.0127
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.15402 0.95265 921.96165 7.510 1.4e-13 ***
## Age_18_graduate -0.04149 0.04925 921.96165 -0.842 0.400
## year_new 1.33838 1.30589 543.48217 1.025 0.306
## Age_18_graduate:year_new -0.05544 0.05747 508.00010 -0.965 0.335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.424 0.423
## Ag_18_grd:_ 0.582 -0.583 -0.982Long_format_2009_2015_new$predicted_G41H <- predict(G41H_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_G41H <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = G41H, color = factor(year_factor_G41H))) + geom_point(aes(shape = year_factor_G41H), alpha = 0.5) + geom_line(aes(y = predicted_G41H), size = 1) + labs(title = "Importance of healthcare benefits (G41H) by Age and Year", x = "Age",y = "Importance of healthcare benefits (G41H)", color = "Year", shape = "Year") + theme_minimal()
G41P: Importance of job central to identity
G41P_Regression_09_15 <- lmer(G41P ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
G41P_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: G41P ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 3885.828
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.012
## Residual 1.336
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 8.8934 -0.2062 -1.6773
## Age_18_graduate:year_new
## 0.1135summary(G41P_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: G41P ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 3885.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.55286 -0.50768 0.09858 0.69027 2.23831
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.024 1.012
## Residual 1.786 1.336
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.89339 1.30095 896.83234 6.836 1.5e-11 ***
## Age_18_graduate -0.20620 0.06726 896.83234 -3.066 0.00224 **
## year_new -1.67733 1.72630 547.29278 -0.972 0.33166
## Age_18_graduate:year_new 0.11353 0.07583 507.99999 1.497 0.13498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.394 0.393
## Ag_18_grd:_ 0.563 -0.564 -0.980Long_format_2009_2015_new$predicted_G41P <- predict(G41P_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_G41P <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = G41P, color = factor(year_factor_G41P))) + geom_point(aes(shape = year_factor_G41P), alpha = 0.5) + geom_line(aes(y = predicted_G41P), size = 1) + labs(title = "Importance of job central to identity (G41P) by Age and Year", x = "Age",y = "Importance of job central to identity (G41P)", color = "Year", shape = "Year") + theme_minimal()
H1: General Health
H1_Regression_09_15 <- lmer(H1 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015_new)
H1_Regression_09_15## Linear mixed model fit by REML ['lmerModLmerTest']
## Formula: H1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
## REML criterion at convergence: 2591.558
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5568
## Residual 0.6963
## Number of obs: 1020, groups: TAS_ID, 510
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 0.78685 0.06963 2.10813
## Age_18_graduate:year_new
## -0.09461summary(H1_Regression_09_15)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: H1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015_new
##
## REML criterion at convergence: 2591.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4233 -0.6347 -0.0865 0.5028 3.4160
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.3100 0.5568
## Residual 0.4849 0.6963
## Number of obs: 1020, groups: TAS_ID, 510
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.78685 0.69194 881.87428 1.137 0.2558
## Age_18_graduate 0.06963 0.03578 881.87428 1.946 0.0519 .
## year_new 2.10813 0.90062 549.68931 2.341 0.0196 *
## Age_18_graduate:year_new -0.09461 0.03952 507.99996 -2.394 0.0170 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.376 0.375
## Ag_18_grd:_ 0.551 -0.552 -0.979Long_format_2009_2015_new$predicted_H1 <- predict(H1_Regression_09_15, re.form = NA)
Long_format_2009_2015_new$year_factor_H1 <- factor(Long_format_2009_2015_new$year)ggplot(Long_format_2009_2015_new, aes(x = Age_18_graduate, y = H1, color = factor(year_factor_H1))) + geom_point(aes(shape = year_factor_H1), alpha = 0.5) + geom_line(aes(y = predicted_H1), size = 1) + labs(title = "General Health (H1) by Age and Year", x = "Age",y = "General Health (H1)", color = "Year", shape = "Year") + theme_minimal()
Gender: 2005 & 2015
Step 4: Regression (2005 & 2015)
Filter the data (2005 & 2015)
Long_format_2005_2015 <- TAS_data_long_format_age %>% filter(year==2005| year==2015) %>% filter(Age_18_graduate< 50) %>% mutate(year_new = case_when(year == 2005 ~ -1, year == 2009 ~ 0,year == 2015 ~ 1))knitr::kable(head(Long_format_2005_2015[, 1:43]))| TAS | TAS05 | TAS09 | TAS15 | 1968 Interview Number | Person Number | Gender | Individual is sample | Year ID Number | Sequence Number | Relationship to Head | Release Number | B5A | B5D | B6C | C2D | C2E | C2F | D2D3_month | D2D3_year | E1_1st_mention | E1_2nd_mention | E1_3rd_mention | E3 | G1 | G2_month | G2_year | G10 | G11 | G30A | G41A | G41B | G41C | G41H | G41P | H1 | L7_1st_mention | L7_2nd_mention | L7_3rd_mention | Age_17_graduate | Age_18_graduate | year | year_new |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 1 | 1 | NA | 5 | 32 | 2 | 2 | 624 | 3 | 30 | 5 | 5 | 5 | 5 | 7 | 7 | 7 | 0 | 0 | 1 | 7 | 0 | 0 | 1 | 5 | 2002 | 1 | 1 | 7 | 7 | 6 | 6 | 7 | 5 | 2 | 1 | 0 | 0 | 20 | 21 | 2005 | -1 |
| 2 | 1 | 1 | NA | 6 | 34 | 1 | 2 | 1202 | 51 | 30 | 5 | 2 | 2 | 6 | 1 | 1 | 1 | 0 | 0 | 7 | 0 | 0 | 5 | 1 | 5 | 2002 | 1 | 1 | 0 | 7 | 5 | 7 | 5 | 3 | 1 | 1 | 0 | 0 | 20 | 21 | 2005 | -1 |
| 2 | 1 | 1 | NA | 14 | 30 | 1 | 2 | 736 | 51 | 30 | 5 | 4 | 4 | 4 | 2 | 1 | 1 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 6 | 2003 | 1 | 5 | 6 | 5 | 6 | 6 | 5 | 5 | 2 | 1 | 0 | 0 | 19 | 20 | 2005 | -1 |
| 1 | 1 | NA | NA | 18 | 38 | 2 | 2 | 5647 | 3 | 98 | 5 | 3 | 4 | 3 | 4 | 2 | 2 | 0 | 0 | 3 | 7 | 0 | 5 | 1 | 6 | 2004 | 1 | 5 | 6 | 5 | 5 | 5 | 7 | 5 | 2 | 1 | 0 | 0 | 18 | 19 | 2005 | -1 |
| 2 | 1 | 1 | NA | 47 | 34 | 2 | 2 | 2516 | 3 | 30 | 5 | 4 | 5 | 6 | 4 | 5 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 5 | 2005 | 5 | 0 | 6 | 3 | 6 | 4 | 7 | 4 | 1 | 1 | 0 | 0 | 17 | 18 | 2005 | -1 |
| 2 | 1 | 1 | NA | 53 | 35 | 2 | 2 | 1392 | 3 | 33 | 5 | 4 | 5 | 5 | 3 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 6 | 2002 | 1 | 1 | 7 | 6 | 7 | 7 | 7 | 5 | 1 | 1 | 0 | 0 | 20 | 21 | 2005 | -1 |
B5A: Responsibility for self
coef(summary(lm(B5A ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0315954 0.1018711 39.575457 2.803596e-225
## Gender -0.1979421 0.0638855 -3.098388 1.987583e-03
## year_new 0.2047267 0.0320330 6.391119 2.291367e-10B5D: Managing own money
coef(summary(lm(B5D ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.56326308 0.08011094 56.961793 0.0000000000
## Gender -0.06503111 0.05023925 -1.294428 0.1957479412
## year_new 0.08376591 0.02519060 3.325285 0.0009079731B6C: Money management skills
coef(summary(lm(B6C ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.38049049 0.12029882 44.7260459 4.563815e-265
## Gender -0.03713145 0.07544191 -0.4921860 6.226712e-01
## year_new 0.03260448 0.03782753 0.8619247 3.888883e-01C2D: Worry about expenses
coef(summary(lm(C2D ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.47868097 0.16322806 21.311782 1.194268e-86
## Gender 0.08747606 0.10236374 0.854561 3.929520e-01
## year_new -0.06054635 0.05132647 -1.179632 2.383629e-01C2E: Worry about future job
coef(summary(lm(C2E ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.05782814 0.17001859 17.9852572 9.625869e-65
## Gender 0.29525591 0.10662222 2.7691780 5.699871e-03
## year_new -0.01327876 0.05346173 -0.2483788 8.038807e-01C2F: Discouraged about future
coef(summary(lm(C2F ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.70300273 0.15328352 17.634007 1.489996e-62
## Gender 0.20155433 0.09612731 2.096744 3.620954e-02
## year_new 0.03962929 0.04819945 0.822194 4.111176e-01E3: Work for money
coef(summary(lm(E3 ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.44293314 0.19689620 7.3283952 4.082580e-13
## Gender 0.04978551 0.12347773 0.4031943 6.868718e-01
## year_new -0.25138842 0.06191329 -4.0603303 5.193669e-05G10: Attended college
coef(summary(lm(G10 ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.20322884 0.15242429 14.454578 5.087477e-44
## Gender -0.27530220 0.09558847 -2.880078 4.041110e-03
## year_new -0.09047707 0.04792927 -1.887721 5.928652e-02G11: Attending college
coef(summary(lm(G11 ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8481899 0.16943878 10.9077145 1.448750e-26
## Gender 0.0232750 0.10625861 0.2190411 8.266525e-01
## year_new 0.5452727 0.05327941 10.2342115 1.077770e-23G30A: Likelihood of well-paying job
coef(summary(lm(G30A ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.67676303 0.13936409 40.7333270 2.759724e-234
## Gender 0.04920755 0.08739814 0.5630274 5.735135e-01
## year_new 0.31877671 0.04382253 7.2742650 6.008737e-13G41A: Importance of job status
coef(summary(lm(G41A ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.91622778 0.15069224 32.624293 5.831102e-171
## Gender 0.08934479 0.09450226 0.945425 3.446181e-01
## year_new -0.19273169 0.04738463 -4.067389 5.040812e-05G41B: Importance of decision-making
coef(summary(lm(G41B ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.55242121 0.10837947 51.231303 6.452355e-314
## Gender 0.09262916 0.06796703 1.362854 1.731650e-01
## year_new -0.05062620 0.03407953 -1.485531 1.376462e-01G41C: Importance of healthcare benefits
coef(summary(lm(G41C ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.05045819 0.16259746 31.0611133 8.107273e-159
## Gender 0.04782299 0.10196827 0.4689987 6.391494e-01
## year_new -0.37811595 0.05112818 -7.3954509 2.520449e-13G41H: Importance of healthcare benefits
coef(summary(lm(G41H ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.88255589 0.09269561 63.461000 0.000000e+00
## Gender 0.26887442 0.05813136 4.625291 4.114766e-06
## year_new -0.04569501 0.02914780 -1.567700 1.171950e-01G41P: Importance of job central to identity
coef(summary(lm(G41P ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.92899976 0.14075490 35.018318 1.170615e-189
## Gender 0.01793715 0.08827034 0.203207 8.390051e-01
## year_new -0.05829606 0.04425987 -1.317131 1.880272e-01H1: General Health
coef(summary(lm(H1 ~ Gender + year_new, data = Long_format_2005_2015)))## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.96972544 0.08295675 23.744003 8.884067e-104
## Gender 0.14983206 0.05202392 2.880061 4.041321e-03
## year_new 0.07781306 0.02608545 2.983006 2.907658e-03