We will be going through
library(tidyverse)
library(readxl)
library(ggplot2)
library (reshape2)
library(writexl)
library(lme4)TAS_data_long_format_age <- read_excel("TAS_data_long_format_age.xlsx")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>, …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_Regression_05_15 <- lm(B5A ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
B5A_Regression_05_15##
## Call:
## lm(formula = B5A ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 0.15546 0.17709 0.49385
## Age_18_graduate:year_new
## -0.02312summary(B5A_Regression_05_15)##
## Call:
## lm(formula = B5A ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8066 -0.6659 0.3341 0.7345 4.1172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15546 0.44715 0.348 0.728
## Age_18_graduate 0.17709 0.02280 7.768 1.62e-14 ***
## year_new 0.49385 0.44715 1.104 0.270
## Age_18_graduate:year_new -0.02312 0.02280 -1.014 0.311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 1296 degrees of freedom
## Multiple R-squared: 0.1075, Adjusted R-squared: 0.1054
## F-statistic: 52.01 on 3 and 1296 DF, p-value: < 2.2e-16B5D_Regression_05_15 <- lm(B5D ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
B5D_Regression_05_15##
## Call:
## lm(formula = B5D ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 3.045448 0.069444 -0.128830
## Age_18_graduate:year_new
## 0.006945summary(B5D_Regression_05_15)##
## Call:
## lm(formula = B5D ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6736 -0.3617 0.4028 0.6320 0.7084
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.045448 0.360022 8.459 < 2e-16 ***
## Age_18_graduate 0.069444 0.018357 3.783 0.000162 ***
## year_new -0.128830 0.360022 -0.358 0.720522
## Age_18_graduate:year_new 0.006945 0.018357 0.378 0.705238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8921 on 1296 degrees of freedom
## Multiple R-squared: 0.03595, Adjusted R-squared: 0.03372
## F-statistic: 16.11 on 3 and 1296 DF, p-value: 2.77e-10B6C_Regression_05_15 <- lm(B6C ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
B6C_Regression_05_15##
## Call:
## lm(formula = B6C ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 6.31388 -0.05142 -0.98854
## Age_18_graduate:year_new
## 0.05295summary(B6C_Regression_05_15)##
## Call:
## lm(formula = B6C ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4236 -0.4236 -0.2149 0.7851 1.9939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.31388 0.54701 11.543 <2e-16 ***
## Age_18_graduate -0.05142 0.02789 -1.844 0.0654 .
## year_new -0.98854 0.54701 -1.807 0.0710 .
## Age_18_graduate:year_new 0.05295 0.02789 1.899 0.0578 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 1296 degrees of freedom
## Multiple R-squared: 0.003719, Adjusted R-squared: 0.001413
## F-statistic: 1.613 on 3 and 1296 DF, p-value: 0.1846C2D_Regression_05_15 <- lm(C2D ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
C2D_Regression_05_15##
## Call:
## lm(formula = C2D ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 3.570786 0.002447 0.115152
## Age_18_graduate:year_new
## -0.008905summary(C2D_Regression_05_15)##
## Call:
## lm(formula = C2D ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6940 -1.5632 -0.5116 1.4303 3.4820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.570786 0.743479 4.803 1.75e-06 ***
## Age_18_graduate 0.002447 0.037909 0.065 0.949
## year_new 0.115152 0.743479 0.155 0.877
## Age_18_graduate:year_new -0.008905 0.037909 -0.235 0.814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.842 on 1296 degrees of freedom
## Multiple R-squared: 0.001239, Adjusted R-squared: -0.001073
## F-statistic: 0.536 on 3 and 1296 DF, p-value: 0.6577C2E_Regression_05_15 <- lm(C2E ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
C2E_Regression_05_15##
## Call:
## lm(formula = C2E ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 4.25107 -0.03511 0.63715
## Age_18_graduate:year_new
## -0.03063summary(C2E_Regression_05_15)##
## Call:
## lm(formula = C2E ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7051 -1.5333 -0.4421 1.4757 3.8208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.25107 0.77492 5.486 4.94e-08 ***
## Age_18_graduate -0.03511 0.03951 -0.889 0.374
## year_new 0.63715 0.77492 0.822 0.411
## Age_18_graduate:year_new -0.03063 0.03951 -0.775 0.438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.92 on 1296 degrees of freedom
## Multiple R-squared: 0.004197, Adjusted R-squared: 0.001892
## F-statistic: 1.821 on 3 and 1296 DF, p-value: 0.1415C2F_Regression_05_15 <- lm(C2F ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
C2F_Regression_05_15##
## Call:
## lm(formula = C2F ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 3.178093 -0.007862 0.235240
## Age_18_graduate:year_new
## -0.009527summary(C2F_Regression_05_15)##
## Call:
## lm(formula = C2F ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1003 -1.0829 -0.0829 1.0255 4.0272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.178093 0.699068 4.546 5.97e-06 ***
## Age_18_graduate -0.007862 0.035644 -0.221 0.825
## year_new 0.235240 0.699068 0.337 0.737
## Age_18_graduate:year_new -0.009527 0.035644 -0.267 0.789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.732 on 1296 degrees of freedom
## Multiple R-squared: 0.0007165, Adjusted R-squared: -0.001597
## F-statistic: 0.3098 on 3 and 1296 DF, p-value: 0.8183D2D3_month_Regression_05_15 <- lm(D2D3_month ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
D2D3_month_Regression_05_15##
## Call:
## lm(formula = D2D3_month ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## -0.95734 0.04986 -0.95734
## Age_18_graduate:year_new
## 0.04986summary(D2D3_month_Regression_05_15)##
## Call:
## lm(formula = D2D3_month ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.276 -0.279 0.000 0.000 97.422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.95734 1.12349 -0.852 0.394
## Age_18_graduate 0.04986 0.05728 0.870 0.384
## year_new -0.95734 1.12349 -0.852 0.394
## Age_18_graduate:year_new 0.04986 0.05728 0.870 0.384
##
## Residual standard error: 2.784 on 1296 degrees of freedom
## Multiple R-squared: 0.005955, Adjusted R-squared: 0.003654
## F-statistic: 2.588 on 3 and 1296 DF, p-value: 0.05161D2D3_year_Regression_05_15 <- lm(D2D3_year ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
D2D3_year_Regression_05_15##
## Call:
## lm(formula = D2D3_year ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## -64.331 3.544 -64.331
## Age_18_graduate:year_new
## 3.544summary(D2D3_year_Regression_05_15)##
## Call:
## lm(formula = D2D3_year ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -98.17 -27.29 0.00 0.00 1993.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -64.331 63.219 -1.018 0.309
## Age_18_graduate 3.544 3.223 1.100 0.272
## year_new -64.331 63.219 -1.018 0.309
## Age_18_graduate:year_new 3.544 3.223 1.100 0.272
##
## Residual standard error: 156.7 on 1296 degrees of freedom
## Multiple R-squared: 0.01232, Adjusted R-squared: 0.01003
## F-statistic: 5.389 on 3 and 1296 DF, p-value: 0.001096E1_1st_mention_Regression_05_15 <- lm(E1_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
E1_1st_mention_Regression_05_15##
## Call:
## lm(formula = E1_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 6.24591 -0.15753 0.56949
## Age_18_graduate:year_new
## -0.05012summary(E1_1st_mention_Regression_05_15)##
## Call:
## lm(formula = E1_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.743 -2.078 -1.040 3.257 5.791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.24591 1.01639 6.145 1.06e-09 ***
## Age_18_graduate -0.15753 0.05182 -3.040 0.00242 **
## year_new 0.56949 1.01639 0.560 0.57537
## Age_18_graduate:year_new -0.05012 0.05182 -0.967 0.33371
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.519 on 1296 degrees of freedom
## Multiple R-squared: 0.07769, Adjusted R-squared: 0.07556
## F-statistic: 36.39 on 3 and 1296 DF, p-value: < 2.2e-16E1_2nd_mention_Regression_05_15 <- lm(E1_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
E1_2nd_mention_Regression_05_15##
## Call:
## lm(formula = E1_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 3.73127 -0.09728 3.12859
## Age_18_graduate:year_new
## -0.14665summary(E1_2nd_mention_Regression_05_15)##
## Call:
## lm(formula = E1_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4691 -1.5900 -1.4913 -0.2737 6.4824
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.73127 1.13387 3.291 0.00103 **
## Age_18_graduate -0.09728 0.05781 -1.683 0.09268 .
## year_new 3.12859 1.13387 2.759 0.00588 **
## Age_18_graduate:year_new -0.14665 0.05781 -2.537 0.01131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.81 on 1296 degrees of freedom
## Multiple R-squared: 0.02591, Adjusted R-squared: 0.02366
## F-statistic: 11.49 on 3 and 1296 DF, p-value: 1.942e-07E1_3rd_mention_Regression_05_15 <- lm(E1_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
E1_3rd_mention_Regression_05_15##
## Call:
## lm(formula = E1_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## -0.052551 0.003901 0.041526
## Age_18_graduate:year_new
## -0.002452summary(E1_3rd_mention_Regression_05_15)##
## Call:
## lm(formula = E1_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0520 -0.0266 -0.0216 -0.0194 6.9791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.052551 0.150479 -0.349 0.727
## Age_18_graduate 0.003901 0.007673 0.508 0.611
## year_new 0.041526 0.150479 0.276 0.783
## Age_18_graduate:year_new -0.002452 0.007673 -0.320 0.749
##
## Residual standard error: 0.3729 on 1296 degrees of freedom
## Multiple R-squared: 0.0003313, Adjusted R-squared: -0.001983
## F-statistic: 0.1432 on 3 and 1296 DF, p-value: 0.9341E3_Regression_05_15 <- lm(E3 ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
E3_Regression_05_15##
## Call:
## lm(formula = E3 ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 5.52955 -0.19787 -0.27701
## Age_18_graduate:year_new
## 0.01148summary(E3_Regression_05_15)##
## Call:
## lm(formula = E3 ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.038 -1.711 -1.038 2.962 6.962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.52955 0.88351 6.259 5.27e-10 ***
## Age_18_graduate -0.19787 0.04505 -4.392 1.21e-05 ***
## year_new -0.27701 0.88351 -0.314 0.754
## Age_18_graduate:year_new 0.01148 0.04505 0.255 0.799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.189 on 1296 degrees of freedom
## Multiple R-squared: 0.0415, Adjusted R-squared: 0.03928
## F-statistic: 18.71 on 3 and 1296 DF, p-value: 7.011e-12G1_Regression_05_15 <- lm(G1 ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G1_Regression_05_15##
## Call:
## lm(formula = G1 ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 0.9945187 0.0002896 -0.0054813
## Age_18_graduate:year_new
## 0.0002896summary(G1_Regression_05_15)##
## Call:
## lm(formula = G1 ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.00757 -0.00178 0.00000 0.00000 0.99706
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9945187 0.0111933 88.849 <2e-16 ***
## Age_18_graduate 0.0002896 0.0005707 0.507 0.612
## year_new -0.0054813 0.0111933 -0.490 0.624
## Age_18_graduate:year_new 0.0002896 0.0005707 0.507 0.612
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02774 on 1296 degrees of freedom
## Multiple R-squared: 0.00217, Adjusted R-squared: -0.0001397
## F-statistic: 0.9395 on 3 and 1296 DF, p-value: 0.4208G2_month_Regression_05_15 <- lm(G2_month ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G2_month_Regression_05_15##
## Call:
## lm(formula = G2_month ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 7.9424898 -0.1000563 0.2539945
## Age_18_graduate:year_new
## -0.0001595summary(G2_month_Regression_05_15)##
## Call:
## lm(formula = G2_month ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.393 -0.890 -0.393 0.209 93.110
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.9424898 2.1226291 3.742 0.000191 ***
## Age_18_graduate -0.1000563 0.1082288 -0.924 0.355404
## year_new 0.2539945 2.1226291 0.120 0.904771
## Age_18_graduate:year_new -0.0001595 0.1082288 -0.001 0.998825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.26 on 1296 degrees of freedom
## Multiple R-squared: 0.002203, Adjusted R-squared: -0.0001068
## F-statistic: 0.9538 on 3 and 1296 DF, p-value: 0.4138G2_year_Regression_05_15 <- lm(G2_year ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G2_year_Regression_05_15##
## Call:
## lm(formula = G2_year ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 2.028e+03 -1.000e+00 5.000e+00
## Age_18_graduate:year_new
## -9.311e-13summary(G2_year_Regression_05_15)##
## Call:
## lm(formula = G2_year ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.160e-12 -7.100e-13 0.000e+00 0.000e+00 7.314e-10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.028e+03 8.224e-12 2.466e+14 <2e-16 ***
## Age_18_graduate -1.000e+00 4.193e-13 -2.385e+12 <2e-16 ***
## year_new 5.000e+00 8.224e-12 6.080e+11 <2e-16 ***
## Age_18_graduate:year_new -9.311e-13 4.193e-13 -2.220e+00 0.0266 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.038e-11 on 1296 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 2.028e+25 on 3 and 1296 DF, p-value: < 2.2e-16G10_Regression_05_15 <- lm(G10 ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G10_Regression_05_15##
## Call:
## lm(formula = G10 ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 6.0227 -0.2096 -0.3622
## Age_18_graduate:year_new
## 0.0247summary(G10_Regression_05_15)##
## Call:
## lm(formula = G10 ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1469 -1.1469 -0.6983 -0.4071 7.0673
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.02272 0.67882 8.872 < 2e-16 ***
## Age_18_graduate -0.20963 0.03461 -6.057 1.82e-09 ***
## year_new -0.36221 0.67882 -0.534 0.594
## Age_18_graduate:year_new 0.02470 0.03461 0.714 0.476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.682 on 1296 degrees of freedom
## Multiple R-squared: 0.05172, Adjusted R-squared: 0.04952
## F-statistic: 23.56 on 3 and 1296 DF, p-value: 7.525e-15G11_Regression_05_15 <- lm(G11 ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G11_Regression_05_15##
## Call:
## lm(formula = G11 ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## -5.07994 0.34009 -0.74350
## Age_18_graduate:year_new
## 0.04566summary(G11_Regression_05_15)##
## Call:
## lm(formula = G11 ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3631 -1.1200 -0.5057 1.1798 4.0366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.07994 0.70973 -7.158 1.37e-12 ***
## Age_18_graduate 0.34009 0.03619 9.398 < 2e-16 ***
## year_new -0.74350 0.70973 -1.048 0.295
## Age_18_graduate:year_new 0.04566 0.03619 1.262 0.207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.759 on 1296 degrees of freedom
## Multiple R-squared: 0.2172, Adjusted R-squared: 0.2154
## F-statistic: 119.9 on 3 and 1296 DF, p-value: < 2.2e-16G30A_Regression_05_15 <- lm(G30A ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G30A_Regression_05_15##
## Call:
## lm(formula = G30A ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 5.908619 -0.007911 0.258439
## Age_18_graduate:year_new
## 0.003302summary(G30A_Regression_05_15)##
## Call:
## lm(formula = G30A ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4484 -0.4371 0.5516 0.9343 1.6077
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.908619 0.634683 9.310 <2e-16 ***
## Age_18_graduate -0.007911 0.032361 -0.244 0.807
## year_new 0.258439 0.634683 0.407 0.684
## Age_18_graduate:year_new 0.003302 0.032361 0.102 0.919
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.573 on 1296 degrees of freedom
## Multiple R-squared: 0.03903, Adjusted R-squared: 0.0368
## F-statistic: 17.54 on 3 and 1296 DF, p-value: 3.627e-11G41A_Regression_05_15 <- lm(G41A ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G41A_Regression_05_15##
## Call:
## lm(formula = G41A ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 5.58414 -0.02707 -0.50731
## Age_18_graduate:year_new
## 0.01674summary(G41A_Regression_05_15)##
## Call:
## lm(formula = G41A ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3030 -0.8806 0.1504 1.1736 4.1504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.58414 0.68628 8.137 9.43e-16 ***
## Age_18_graduate -0.02707 0.03499 -0.773 0.439
## year_new -0.50731 0.68628 -0.739 0.460
## Age_18_graduate:year_new 0.01674 0.03499 0.478 0.632
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.701 on 1296 degrees of freedom
## Multiple R-squared: 0.01348, Adjusted R-squared: 0.01119
## F-statistic: 5.902 on 3 and 1296 DF, p-value: 0.0005338G41B_Regression_05_15 <- lm(G41B ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G41B_Regression_05_15##
## Call:
## lm(formula = G41B ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 5.119286 0.028469 0.021745
## Age_18_graduate:year_new
## -0.005185summary(G41B_Regression_05_15)##
## Call:
## lm(formula = G41B ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7697 -0.7033 0.2630 1.2294 3.3467
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.119286 0.493473 10.374 <2e-16 ***
## Age_18_graduate 0.028469 0.025161 1.131 0.258
## year_new 0.021745 0.493473 0.044 0.965
## Age_18_graduate:year_new -0.005185 0.025161 -0.206 0.837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.223 on 1296 degrees of freedom
## Multiple R-squared: 0.003559, Adjusted R-squared: 0.001252
## F-statistic: 1.543 on 3 and 1296 DF, p-value: 0.2017G41C_Regression_05_15 <- lm(G41C ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G41C_Regression_05_15##
## Call:
## lm(formula = G41C ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 9.7681 -0.2160 4.7219
## Age_18_graduate:year_new
## -0.2397summary(G41C_Regression_05_15)##
## Call:
## lm(formula = G41C ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8326 -0.8326 0.4456 1.1674 7.0908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.76805 0.65631 14.883 < 2e-16 ***
## Age_18_graduate -0.21599 0.03346 -6.454 1.53e-10 ***
## year_new 4.72185 0.65631 7.195 1.06e-12 ***
## Age_18_graduate:year_new -0.23966 0.03346 -7.162 1.33e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.626 on 1296 degrees of freedom
## Multiple R-squared: 0.2466, Adjusted R-squared: 0.2448
## F-statistic: 141.4 on 3 and 1296 DF, p-value: < 2.2e-16G41H_Regression_05_15 <- lm(G41H ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G41H_Regression_05_15##
## Call:
## lm(formula = G41H ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 6.372599 -0.003932 0.007573
## Age_18_graduate:year_new
## -0.002837summary(G41H_Regression_05_15)##
## Call:
## lm(formula = G41H ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3453 -0.3442 0.6547 0.7484 2.7687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.372599 0.425545 14.975 <2e-16 ***
## Age_18_graduate -0.003932 0.021698 -0.181 0.856
## year_new 0.007573 0.425545 0.018 0.986
## Age_18_graduate:year_new -0.002837 0.021698 -0.131 0.896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.054 on 1296 degrees of freedom
## Multiple R-squared: 0.002776, Adjusted R-squared: 0.0004676
## F-statistic: 1.203 on 3 and 1296 DF, p-value: 0.3075G41P_Regression_05_15 <- lm(G41P ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
G41P_Regression_05_15##
## Call:
## lm(formula = G41P ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 4.564503 0.019148 -0.123732
## Age_18_graduate:year_new
## 0.002194summary(G41P_Regression_05_15)##
## Call:
## lm(formula = G41P ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0443 -0.9934 0.0684 1.0897 4.0897
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.564503 0.640747 7.124 1.74e-12 ***
## Age_18_graduate 0.019148 0.032670 0.586 0.558
## year_new -0.123732 0.640747 -0.193 0.847
## Age_18_graduate:year_new 0.002194 0.032670 0.067 0.946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.588 on 1296 degrees of freedom
## Multiple R-squared: 0.002054, Adjusted R-squared: -0.0002561
## F-statistic: 0.8891 on 3 and 1296 DF, p-value: 0.4461H1_Regression_05_15 <- lm(H1 ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
H1_Regression_05_15##
## Call:
## lm(formula = H1 ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 1.95956 0.01105 -0.17487
## Age_18_graduate:year_new
## 0.01162summary(H1_Regression_05_15)##
## Call:
## lm(formula = H1 ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5101 -0.3741 -0.1243 0.7676 6.7166
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.95956 0.37858 5.176 2.62e-07 ***
## Age_18_graduate 0.01105 0.01930 0.573 0.567
## year_new -0.17487 0.37858 -0.462 0.644
## Age_18_graduate:year_new 0.01162 0.01930 0.602 0.547
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9381 on 1296 degrees of freedom
## Multiple R-squared: 0.008008, Adjusted R-squared: 0.005712
## F-statistic: 3.487 on 3 and 1296 DF, p-value: 0.01528L7_1st_mention_Regression_05_15 <- lm(L7_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
L7_1st_mention_Regression_05_15##
## Call:
## lm(formula = L7_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 1.6603344 0.0058137 -0.0626579
## Age_18_graduate:year_new
## 0.0001903summary(L7_1st_mention_Regression_05_15)##
## Call:
## lm(formula = L7_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8523 -0.8242 -0.7118 0.2582 7.2943
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6603344 0.6019138 2.758 0.00589 **
## Age_18_graduate 0.0058137 0.0306904 0.189 0.84979
## year_new -0.0626579 0.6019138 -0.104 0.91711
## Age_18_graduate:year_new 0.0001903 0.0306904 0.006 0.99505
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.492 on 1296 degrees of freedom
## Multiple R-squared: 0.001304, Adjusted R-squared: -0.001008
## F-statistic: 0.564 on 3 and 1296 DF, p-value: 0.6388L7_2nd_mention_Regression_05_15 <- lm(L7_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
L7_2nd_mention_Regression_05_15##
## Call:
## lm(formula = L7_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## 0.318149 -0.006156 0.269649
## Age_18_graduate:year_new
## -0.007451summary(L7_2nd_mention_Regression_05_15)##
## Call:
## lm(formula = L7_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3429 -0.2884 -0.0757 -0.0731 8.7252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.318149 0.356995 0.891 0.373
## Age_18_graduate -0.006156 0.018202 -0.338 0.735
## year_new 0.269649 0.356995 0.755 0.450
## Age_18_graduate:year_new -0.007451 0.018202 -0.409 0.682
##
## Residual standard error: 0.8846 on 1296 degrees of freedom
## Multiple R-squared: 0.01642, Adjusted R-squared: 0.01415
## F-statistic: 7.214 on 3 and 1296 DF, p-value: 8.405e-05L7_3rd_mention_Regression_05_15 <- lm(L7_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new, data = Long_format_2005_2015)
L7_3rd_mention_Regression_05_15##
## Call:
## lm(formula = L7_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Age_18_graduate year_new
## -0.046459 0.003499 0.095283
## Age_18_graduate:year_new
## -0.004114summary(L7_3rd_mention_Regression_05_15)##
## Call:
## lm(formula = L7_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate:year_new,
## data = Long_format_2005_2015)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0377 -0.0353 -0.0328 -0.0029 6.9647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.046459 0.127344 -0.365 0.715
## Age_18_graduate 0.003499 0.006493 0.539 0.590
## year_new 0.095283 0.127344 0.748 0.454
## Age_18_graduate:year_new -0.004114 0.006493 -0.634 0.526
##
## Residual standard error: 0.3156 on 1296 degrees of freedom
## Multiple R-squared: 0.002663, Adjusted R-squared: 0.0003547
## F-statistic: 1.154 on 3 and 1296 DF, p-value: 0.3263Filter the data (2005 & 2009)
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))knitr::kable(head(Long_format_2005_2009[, 1:42]))| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 |
| 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 |
| 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 |
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 ['lmerMod']
## Formula: B5A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3154.08
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5598
## Residual 0.9117
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 1.7049 0.1126 2.3318
## Age_18_graduate:year_new
## -0.1010summary(B5A_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: B5A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3154.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3007 -0.4818 0.1902 0.6032 1.8125
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.3133 0.5598
## Residual 0.8313 0.9117
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.70494 0.95041 1.794
## Age_18_graduate 0.11265 0.04087 2.756
## year_new 2.33176 1.09860 2.122
## Age_18_graduate:year_new -0.10101 0.05159 -1.958
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.678 -0.678
## Ag_18_grd:_ -0.585 0.586 -0.992B5D_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 ['lmerMod']
## Formula: B5D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 2556.905
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.3082
## Residual 0.7357
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 4.505982 0.009648 1.274845
## Age_18_graduate:year_new
## -0.049783summary(B5D_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: B5D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 2556.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5218 -0.4056 0.2098 0.6399 1.4148
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.09499 0.3082
## Residual 0.54125 0.7357
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 4.505982 0.710308 6.344
## Age_18_graduate 0.009648 0.030547 0.316
## year_new 1.274845 0.880385 1.448
## Age_18_graduate:year_new -0.049783 0.041386 -1.203
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.712 -0.711
## Ag_18_grd:_ -0.633 0.633 -0.993B6C_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 ['lmerMod']
## Formula: B6C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3542.164
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.9431
## Residual 0.9615
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.66943 -0.01262 -1.42473
## Age_18_graduate:year_new
## 0.08129summary(B6C_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: B6C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3542.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.09781 -0.50078 0.03584 0.56357 2.39691
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.8894 0.9431
## Residual 0.9245 0.9615
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.66943 1.18545 4.783
## Age_18_graduate -0.01262 0.05098 -0.247
## year_new -1.42473 1.17360 -1.214
## Age_18_graduate:year_new 0.08129 0.05494 1.479
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.618 -0.617
## Ag_18_grd:_ -0.492 0.493 -0.987C2D_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 ['lmerMod']
## Formula: C2D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4312.391
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.047
## Residual 1.532
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.3809 -0.1487 4.0750
## Age_18_graduate:year_new
## -0.1667summary(C2D_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: C2D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4312.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.07121 -0.67266 -0.04062 0.68227 2.15894
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.096 1.047
## Residual 2.346 1.532
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 7.38089 1.64577 4.485
## Age_18_graduate -0.14870 0.07078 -2.101
## year_new 4.07497 1.85011 2.203
## Age_18_graduate:year_new -0.16675 0.08684 -1.920
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.666 -0.665
## Ag_18_grd:_ -0.567 0.568 -0.991C2E_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 ['lmerMod']
## Formula: C2E ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4340.572
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.162
## Residual 1.503
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.0530 -0.1439 3.3269
## Age_18_graduate:year_new
## -0.1320summary(C2E_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: C2E ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4340.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.96230 -0.69129 -0.03401 0.69871 2.19158
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.351 1.162
## Residual 2.260 1.503
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 7.05301 1.68229 4.193
## Age_18_graduate -0.14389 0.07235 -1.989
## year_new 3.32690 1.82167 1.826
## Age_18_graduate:year_new -0.13197 0.08545 -1.544
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.651 -0.650
## Ag_18_grd:_ -0.544 0.544 -0.990C2F_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 ['lmerMod']
## Formula: C2F ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4091.376
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.066
## Residual 1.321
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 6.1188 -0.1258 3.5870
## Age_18_graduate:year_new
## -0.1496summary(C2F_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: C2F ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4091.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0673 -0.6399 -0.1171 0.5601 2.6563
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.137 1.066
## Residual 1.746 1.321
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.11881 1.50217 4.073
## Age_18_graduate -0.12583 0.06460 -1.948
## year_new 3.58700 1.60325 2.237
## Age_18_graduate:year_new -0.14959 0.07518 -1.990
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.645 -0.644
## Ag_18_grd:_ -0.535 0.536 -0.990D2D3_month_Regression_05_09 <- lmer(D2D3_month ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
D2D3_month_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula:
## D2D3_month ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 1366.727
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.000061
## Residual 0.453916
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 0.155328 -0.004539 0.155328
## Age_18_graduate:year_new
## -0.004539summary(D2D3_month_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula:
## D2D3_month ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 1366.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.1622 -0.1122 -0.0822 0.0000 26.3344
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 3.721e-09 0.000061
## Residual 2.060e-01 0.453916
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.155328 0.404610 0.384
## Age_18_graduate -0.004539 0.017401 -0.261
## year_new 0.155328 0.538715 0.288
## Age_18_graduate:year_new -0.004539 0.025344 -0.179
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.751 -0.750
## Ag_18_grd:_ -0.686 0.687 -0.994D2D3_year_Regression_05_09 <- lmer(D2D3_year ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)## boundary (singular) fit: see help('isSingular')D2D3_year_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula: D2D3_year ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 13299.9
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.0
## Residual 122.4
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 145.13 -5.61 145.13
## Age_18_graduate:year_new
## -5.61
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warningssummary(D2D3_year_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: D2D3_year ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 13299.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.3607 -0.1316 0.0000 0.0000 16.3026
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0 0.0
## Residual 14983 122.4
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 145.129 109.108 1.330
## Age_18_graduate -5.610 4.692 -1.196
## year_new 145.129 145.271 0.999
## Age_18_graduate:year_new -5.610 6.834 -0.821
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.751 -0.750
## Ag_18_grd:_ -0.686 0.687 -0.994
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')E1_1st_mention_Regression_05_09 <- lmer(E1_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
E1_1st_mention_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4999.493
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7132
## Residual 2.3946
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.01515 -0.20699 0.54147
## Age_18_graduate:year_new
## -0.06406summary(E1_1st_mention_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4999.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2630 -0.6618 -0.3933 1.1513 2.3054
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.5086 0.7132
## Residual 5.7341 2.3946
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 7.01515 2.22650 3.151
## Age_18_graduate -0.20699 0.09575 -2.162
## year_new 0.54147 2.85485 0.190
## Age_18_graduate:year_new -0.06406 0.13426 -0.477
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.730 -0.729
## Ag_18_grd:_ -0.657 0.658 -0.994E1_2nd_mention_Regression_05_09 <- lmer(E1_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
E1_2nd_mention_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 5201.966
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7677
## Residual 2.6377
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 6.2083 -0.2058 5.3040
## Age_18_graduate:year_new
## -0.2367summary(E1_2nd_mention_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 5202
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.8752 -0.5170 -0.4777 -0.1993 2.4088
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.5893 0.7677
## Residual 6.9572 2.6377
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.2083 2.4480 2.536
## Age_18_graduate -0.2058 0.1053 -1.955
## year_new 5.3040 3.1440 1.687
## Age_18_graduate:year_new -0.2367 0.1479 -1.601
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.731 -0.730
## Ag_18_grd:_ -0.658 0.659 -0.994E1_3rd_mention_Regression_05_09 <- lmer(E1_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)## boundary (singular) fit: see help('isSingular')E1_3rd_mention_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 478.0027
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.0000
## Residual 0.2992
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## -0.024585 0.001138 0.082147
## Age_18_graduate:year_new
## -0.006068
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warningssummary(E1_3rd_mention_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 478
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.1972 -0.1009 -0.0129 -0.0053 23.2960
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.00000 0.0000
## Residual 0.08951 0.2992
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.024585 0.266686 -0.092
## Age_18_graduate 0.001138 0.011469 0.099
## year_new 0.082147 0.355078 0.231
## Age_18_graduate:year_new -0.006068 0.016705 -0.363
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.751 -0.750
## Ag_18_grd:_ -0.686 0.687 -0.994
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')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 ['lmerMod']
## Formula: E3 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4709.245
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7859
## Residual 2.0398
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 4.39455 -0.13948 -1.37494
## Age_18_graduate:year_new
## 0.06563summary(E3_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: E3 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4709.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1081 -0.6651 -0.4100 1.1438 3.0796
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.6177 0.7859
## Residual 4.1608 2.0398
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 4.39455 1.94710 2.257
## Age_18_graduate -0.13948 0.08374 -1.666
## year_new -1.37494 2.43826 -0.564
## Age_18_graduate:year_new 0.06563 0.11464 0.573
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.717 -0.717
## Ag_18_grd:_ -0.640 0.641 -0.993G1_Regression_05_09 <- lmer(G1 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Problem with Hessian check (infinite or missing values?)G1_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula: G1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: -Inf
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0
## Residual 0
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 1 0 0
## Age_18_graduate:year_new
## 0
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warningsG2_month_Regression_05_09 <- lmer(G2_month ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
G2_month_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula: G2_month ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 5453.352
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5183
## Residual 3.0430
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 3.90228 0.07306 -4.41760
## Age_18_graduate:year_new
## 0.20598summary(G2_month_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: G2_month ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 5453.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5792 -0.2055 -0.1424 0.1070 29.7528
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.2686 0.5183
## Residual 9.2601 3.0430
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.90228 2.75146 1.418
## Age_18_graduate 0.07306 0.11833 0.617
## year_new -4.41760 3.61723 -1.221
## Age_18_graduate:year_new 0.20598 0.17016 1.211
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.744 -0.743
## Ag_18_grd:_ -0.676 0.677 -0.994G2_year_Regression_05_09 <- lmer(G2_year ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 13.0297 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?G2_year_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula: G2_year ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: -52798.07
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 4.146e-13
## Residual 4.182e-12
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 2.027e+03 -1.000e+00 4.000e+00
## Age_18_graduate:year_new
## 7.490e-12
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 2 lme4 warningssummary(G2_year_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: G2_year ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: -52798.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6431 -0.7069 0.2719 0.5438 5.2200
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.719e-25 4.146e-13
## Residual 1.749e-23 4.182e-12
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.027e+03 3.746e-12 5.412e+14
## Age_18_graduate -1.000e+00 1.611e-13 -6.208e+12
## year_new 4.000e+00 4.966e-12 8.056e+11
## Age_18_graduate:year_new 7.490e-12 2.336e-13 3.206e+01
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.749 -0.748
## Ag_18_grd:_ -0.682 0.683 -0.994
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 13.0297 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?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 ['lmerMod']
## Formula: G10 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3778.445
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.2655
## Residual 0.9673
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 3.87117 -0.09284 -3.33952
## Age_18_graduate:year_new
## 0.18346summary(G10_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: G10 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3778.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2822 -0.3147 -0.1337 -0.0303 4.7554
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.6014 1.2655
## Residual 0.9357 0.9673
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.87117 1.38495 2.795
## Age_18_graduate -0.09284 0.05956 -1.559
## year_new -3.33952 1.19289 -2.800
## Age_18_graduate:year_new 0.18346 0.05561 3.299
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.578 -0.577
## Ag_18_grd:_ -0.421 0.421 -0.982G11_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 ['lmerMod']
## Formula: G11 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4363.736
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.6589
## Residual 1.7378
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## -2.58440 0.23585 2.47460
## Age_18_graduate:year_new
## -0.09394summary(G11_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: G11 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4363.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8943 -0.7558 -0.2446 1.0181 2.2748
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.4342 0.6589
## Residual 3.0199 1.7378
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -2.58440 1.65549 -1.561
## Age_18_graduate 0.23585 0.07120 3.313
## year_new 2.47460 2.07685 1.192
## Age_18_graduate:year_new -0.09394 0.09765 -0.962
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.718 -0.717
## Ag_18_grd:_ -0.641 0.642 -0.993G30A_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 ['lmerMod']
## Formula: G30A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4090.941
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.4731
## Residual 1.5617
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.718250 0.008129 -0.343537
## Age_18_graduate:year_new
## 0.041186summary(G30A_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: G30A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4090.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2839 -0.2906 0.2903 0.6121 1.1887
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.2239 0.4731
## Residual 2.4388 1.5617
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.718250 1.454073 3.933
## Age_18_graduate 0.008129 0.062534 0.130
## year_new -0.343537 1.862100 -0.184
## Age_18_graduate:year_new 0.041186 0.087569 0.470
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.729 -0.728
## Ag_18_grd:_ -0.656 0.657 -0.994G41A_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 ['lmerMod']
## Formula: G41A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4075.203
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.238
## Residual 1.221
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.8987 -0.1409 2.3370
## Age_18_graduate:year_new
## -0.1241summary(G41A_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: G41A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4075.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5449 -0.4868 0.1208 0.5924 2.2387
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.534 1.238
## Residual 1.490 1.221
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 7.89869 1.52885 5.166
## Age_18_graduate -0.14086 0.06575 -2.142
## year_new 2.33700 1.49157 1.567
## Age_18_graduate:year_new -0.12413 0.06980 -1.778
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.613 -0.612
## Ag_18_grd:_ -0.484 0.485 -0.987G41B_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 ['lmerMod']
## Formula: G41B ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3314.77
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.6485
## Residual 0.9625
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 6.80032 -0.05037 1.99033
## Age_18_graduate:year_new
## -0.09823summary(G41B_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: G41B ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3314.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0723 -0.5259 0.1336 0.6782 2.2419
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.4206 0.6485
## Residual 0.9264 0.9625
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.80032 1.02987 6.603
## Age_18_graduate -0.05037 0.04429 -1.137
## year_new 1.99033 1.16229 1.712
## Age_18_graduate:year_new -0.09823 0.05456 -1.800
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.668 -0.667
## Ag_18_grd:_ -0.570 0.570 -0.991G41C_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 ['lmerMod']
## Formula: G41C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3298.521
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.6993
## Residual 0.9286
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.82546 -0.01050 1.51722
## Age_18_graduate:year_new
## -0.07307summary(G41C_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: G41C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3298.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7948 -0.5121 0.0450 0.6634 2.1203
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.4890 0.6993
## Residual 0.8624 0.9286
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.82546 1.02975 5.657
## Age_18_graduate -0.01050 0.04428 -0.237
## year_new 1.51722 1.12459 1.349
## Age_18_graduate:year_new -0.07307 0.05276 -1.385
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.654 -0.653
## Ag_18_grd:_ -0.549 0.550 -0.990G41H_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 ['lmerMod']
## Formula: G41H ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3129.926
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5887
## Residual 0.8854
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.12845 -0.03713 1.23106
## Age_18_graduate:year_new
## -0.06056summary(G41H_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: G41H ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3129.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7721 -0.4114 0.3761 0.4914 1.9963
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.3465 0.5887
## Residual 0.7839 0.8854
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 7.12845 0.94361 7.554
## Age_18_graduate -0.03713 0.04058 -0.915
## year_new 1.23106 1.06879 1.152
## Age_18_graduate:year_new -0.06056 0.05018 -1.207
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.669 -0.669
## Ag_18_grd:_ -0.572 0.573 -0.991G41P_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 ['lmerMod']
## Formula: G41P ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 4048.821
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.003
## Residual 1.316
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 8.4519 -0.1741 3.7624
## Age_18_graduate:year_new
## -0.1916summary(G41P_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: G41P ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 4048.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7108 -0.5486 0.0846 0.5894 2.1389
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.005 1.003
## Residual 1.732 1.316
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 8.45187 1.46543 5.768
## Age_18_graduate -0.17409 0.06302 -2.762
## year_new 3.76244 1.59442 2.360
## Age_18_graduate:year_new -0.19162 0.07480 -2.562
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.652 -0.652
## Ag_18_grd:_ -0.547 0.547 -0.990H1_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 ['lmerMod']
## Formula: H1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 2687.195
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5807
## Residual 0.6696
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 1.971502 0.009647 -0.316061
## Age_18_graduate:year_new
## 0.017559summary(H1_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: H1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 2687.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.08257 -0.62790 -0.05667 0.46953 3.09080
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.3372 0.5807
## Residual 0.4483 0.6696
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.971502 0.782789 2.519
## Age_18_graduate 0.009647 0.033664 0.287
## year_new -0.316061 0.814130 -0.388
## Age_18_graduate:year_new 0.017559 0.038157 0.460
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.635 -0.635
## Ag_18_grd:_ -0.520 0.520 -0.989L7_1st_mention_Regression_05_09 <- lmer(L7_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
L7_1st_mention_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 3710.662
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.014
## Residual 1.044
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 3.11145 -0.06353 0.97237
## Age_18_graduate:year_new
## -0.04780summary(L7_1st_mention_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 3710.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6960 -0.3286 -0.1203 0.1395 4.8303
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.028 1.014
## Residual 1.090 1.044
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.11145 1.28133 2.428
## Age_18_graduate -0.06353 0.05510 -1.153
## year_new 0.97237 1.27395 0.763
## Age_18_graduate:year_new -0.04780 0.05965 -0.801
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.619 -0.619
## Ag_18_grd:_ -0.494 0.495 -0.987L7_2nd_mention_Regression_05_09 <- lmer(L7_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
L7_2nd_mention_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 2020.621
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5622
## Residual 0.4205
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 0.68811 -0.02221 0.73246
## Age_18_graduate:year_new
## -0.02862summary(L7_2nd_mention_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 2020.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4515 -0.1850 -0.0447 0.0526 9.9576
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.3161 0.5622
## Residual 0.1768 0.4205
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.68811 0.60970 1.129
## Age_18_graduate -0.02221 0.02622 -0.847
## year_new 0.73246 0.51900 1.411
## Age_18_graduate:year_new -0.02862 0.02419 -1.183
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.575 -0.575
## Ag_18_grd:_ -0.415 0.416 -0.982L7_3rd_mention_Regression_05_09 <- lmer(L7_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2005_2009)
L7_3rd_mention_Regression_05_09## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
## REML criterion at convergence: 1000.921
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.1658
## Residual 0.3481
## Number of obs: 1070, groups: TAS_ID, 547
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 0.0635190 -0.0005072 0.1562729
## Age_18_graduate:year_new
## -0.0057423summary(L7_3rd_mention_Regression_05_09)## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2005_2009
##
## REML criterion at convergence: 1000.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1444 -0.1227 -0.1149 0.0070 16.8497
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.0275 0.1658
## Residual 0.1212 0.3481
## Number of obs: 1070, groups: TAS_ID, 547
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0635190 0.3432082 0.185
## Age_18_graduate -0.0005072 0.0147599 -0.034
## year_new 0.1562729 0.4174168 0.374
## Age_18_graduate:year_new -0.0057423 0.0196176 -0.293
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.999
## year_new 0.702 -0.702
## Ag_18_grd:_ -0.619 0.620 -0.993Filter the data (2009 & 2015)
Long_format_2009_2015 <- TAS_data_long_format_age %>% filter(TAS09 == 1 & TAS15 ==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))knitr::kable(head(Long_format_2009_2015[, 1:42]))| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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 | 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 | 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 | 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 | 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 | 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 |
B5A_Regression_09_15 <- lmer(B5A ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
B5A_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: B5A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2013.711
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.6405
## Residual 0.9671
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## -0.06784 0.18196 8.18856
## Age_18_graduate:year_new
## -0.32077summary(B5A_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: B5A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2013.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9738 -0.5167 0.1773 0.5697 4.1633
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.4103 0.6405
## Residual 0.9353 0.9671
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.06784 0.90650 -0.075
## Age_18_graduate 0.18196 0.04684 3.885
## year_new 8.18856 2.13773 3.830
## Age_18_graduate:year_new -0.32077 0.08942 -3.587
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.307 0.306
## Ag_18_grd:_ 0.413 -0.413 -0.992B5D_Regression_09_15 <- lmer(B5D ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
B5D_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: B5D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 1713.707
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5721
## Residual 0.7241
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 2.1596 0.1158 2.6653
## Age_18_graduate:year_new
## -0.1170summary(B5D_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: B5D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 1713.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4300 -0.3061 0.3472 0.5441 3.9452
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.3273 0.5721
## Residual 0.5243 0.7241
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.15960 0.71918 3.003
## Age_18_graduate 0.11581 0.03716 3.117
## year_new 2.66531 1.64125 1.624
## Age_18_graduate:year_new -0.11695 0.06860 -1.705
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.285 0.284
## Ag_18_grd:_ 0.397 -0.397 -0.992B6C_Regression_09_15 <- lmer(B6C ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
B6C_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: B6C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2094.817
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.8232
## Residual 0.9399
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.387665 0.005874 -0.023141
## Age_18_graduate:year_new
## -0.009996summary(B6C_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: B6C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2094.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.89809 -0.32653 0.06953 0.70376 2.15810
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.6776 0.8232
## Residual 0.8834 0.9399
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.387665 0.971474 5.546
## Age_18_graduate 0.005874 0.050196 0.117
## year_new -0.023141 2.163457 -0.011
## Age_18_graduate:year_new -0.009996 0.090383 -0.111
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.271 0.270
## Ag_18_grd:_ 0.387 -0.387 -0.991C2D_Regression_09_15 <- lmer(C2D ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
C2D_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: C2D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2661.31
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7479
## Residual 1.7593
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 4.13061 -0.02107 2.39751
## Age_18_graduate:year_new
## -0.10212summary(C2D_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: C2D ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2661.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5395 -0.8228 0.1197 0.6316 1.8222
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.5594 0.7479
## Residual 3.0951 1.7593
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 4.13061 1.49865 2.756
## Age_18_graduate -0.02107 0.07743 -0.272
## year_new 2.39751 3.70021 0.648
## Age_18_graduate:year_new -0.10212 0.15511 -0.658
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.349 0.349
## Ag_18_grd:_ 0.446 -0.447 -0.993C2E_Regression_09_15 <- lmer(C2E ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
C2E_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: C2E ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2655.352
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.8696
## Residual 1.6967
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 4.39942 -0.03992 -1.23603
## Age_18_graduate:year_new
## 0.03854summary(C2E_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: C2E ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2655.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.66199 -0.76592 -0.07729 0.63441 2.15336
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.7561 0.8696
## Residual 2.8788 1.6967
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 4.39942 1.49336 2.946
## Age_18_graduate -0.03992 0.07716 -0.517
## year_new -1.23603 3.63453 -0.340
## Age_18_graduate:year_new 0.03854 0.15223 0.253
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.334 0.333
## Ag_18_grd:_ 0.434 -0.434 -0.993C2F_Regression_09_15 <- lmer(C2F ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
C2F_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: C2F ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2486.009
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.030
## Residual 1.336
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 3.233585 -0.008496 3.266891
## Age_18_graduate:year_new
## -0.137321summary(C2F_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: C2F ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2486
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.88719 -0.50994 -0.03786 0.44157 2.54331
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.061 1.030
## Residual 1.786 1.336
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.233585 1.315383 2.458
## Age_18_graduate -0.008496 0.067966 -0.125
## year_new 3.266891 3.017656 1.083
## Age_18_graduate:year_new -0.137321 0.126136 -1.089
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.288 0.288
## Ag_18_grd:_ 0.399 -0.399 -0.992D2D3_month_Regression_09_15 <- lmer(D2D3_month ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
D2D3_month_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula:
## D2D3_month ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 3583.659
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.384
## Residual 3.679
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## -0.76268 0.04214 3.42146
## Age_18_graduate:year_new
## -0.11063summary(D2D3_month_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula:
## D2D3_month ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 3583.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.5086 -0.0291 -0.0091 0.0010 23.1727
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.914 1.384
## Residual 13.538 3.679
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.76268 3.08276 -0.247
## Age_18_graduate 0.04214 0.15928 0.265
## year_new 3.42146 7.66266 0.447
## Age_18_graduate:year_new -0.11063 0.32138 -0.344
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.358 0.357
## Ag_18_grd:_ 0.453 -0.453 -0.993D2D3_year_Regression_09_15 <- lmer(D2D3_year ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
D2D3_year_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: D2D3_year ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 8628.343
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 109.8
## Residual 173.2
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## -250.54 13.56 742.84
## Age_18_graduate:year_new
## -30.89summary(D2D3_year_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: D2D3_year ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 8628.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5447 -0.1414 -0.0659 0.0263 8.7751
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 12045 109.8
## Residual 29981 173.2
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -250.535 160.288 -1.563
## Age_18_graduate 13.564 8.282 1.638
## year_new 742.839 380.507 1.952
## Age_18_graduate:year_new -30.890 15.920 -1.940
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.312 0.312
## Ag_18_grd:_ 0.417 -0.417 -0.992E1_1st_mention_Regression_09_15 <- lmer(E1_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
E1_1st_mention_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 3731.303
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 4.081
## Residual 2.438
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 16.6366 -0.6734 -16.3347
## Age_18_graduate:year_new
## 0.7368summary(E1_1st_mention_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 3731.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5611 -0.3066 -0.1614 0.3406 10.1922
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 16.655 4.081
## Residual 5.942 2.438
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 16.6366 3.5772 4.651
## Age_18_graduate -0.6734 0.1848 -3.643
## year_new -16.3347 6.1467 -2.657
## Age_18_graduate:year_new 0.7368 0.2569 2.868
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.175 0.174
## Ag_18_grd:_ 0.335 -0.335 -0.985E1_2nd_mention_Regression_09_15 <- lmer(E1_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)## boundary (singular) fit: see help('isSingular')E1_2nd_mention_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 3196.76
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.0
## Residual 2.9
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## -1.2021 0.1804 6.0135
## Age_18_graduate:year_new
## -0.3507
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warningssummary(E1_2nd_mention_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 3196.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.9544 -0.7677 -0.4229 1.0414 2.2818
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.000 0.0
## Residual 8.409 2.9
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -1.2021 2.2756 -0.528
## Age_18_graduate 0.1804 0.1176 1.535
## year_new 6.0135 5.7824 1.040
## Age_18_graduate:year_new -0.3507 0.2431 -1.443
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.394 0.393
## Ag_18_grd:_ 0.483 -0.484 -0.994
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')E1_3rd_mention_Regression_09_15 <- lmer(E1_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.117773 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?E1_3rd_mention_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: -3927.888
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 2.372e-01
## Residual 1.998e-08
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 1.914e-02 1.945e-14 -3.996e-14
## Age_18_graduate:year_new
## -3.006e-15
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 2 lme4 warningssummary(E1_3rd_mention_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: E1_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: -3927.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.432e-06 -6.940e-09 -6.940e-09 -6.940e-09 2.425e-06
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 5.627e-02 2.372e-01
## Residual 3.994e-16 1.998e-08
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.914e-02 1.325e-02 1.444
## Age_18_graduate 1.945e-14 4.501e-09 0.000
## year_new -3.996e-14 5.454e-08 0.000
## Age_18_graduate:year_new -3.006e-15 2.428e-09 0.000
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt 0.000
## year_new 0.000 0.001
## Ag_18_grd:_ 0.000 -0.439 -0.898
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.117773 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?E3_Regression_09_15 <- lmer(E3 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
E3_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: E3 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2874.296
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7818
## Residual 2.1165
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 12.1131 -0.5193 -9.5815
## Age_18_graduate:year_new
## 0.4502summary(E3_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: E3 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2874.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3757 -0.7181 -0.4758 0.9867 1.8863
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.6112 0.7818
## Residual 4.4798 2.1165
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 12.11313 1.76954 6.845
## Age_18_graduate -0.51929 0.09143 -5.680
## year_new -9.58148 4.40211 -2.177
## Age_18_graduate:year_new 0.45021 0.18464 2.438
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.359 0.358
## Ag_18_grd:_ 0.454 -0.454 -0.993G1_Regression_09_15 <- lmer(G1 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvaluesG1_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: -5568.38
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 3.952e-02
## Residual 4.665e-08
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 1.002e+00 8.916e-13 3.134e-13
## Age_18_graduate:year_new
## -2.225e-13
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 2 lme4 warningssummary(G1_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: -5568.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.199e-05 -6.700e-08 -6.700e-08 -6.700e-08 4.192e-05
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.562e-03 3.952e-02
## Residual 2.176e-15 4.665e-08
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.002e+00 1.735e-03 577.6
## Age_18_graduate 8.916e-13 1.051e-08 0.0
## year_new 3.134e-13 1.273e-07 0.0
## Age_18_graduate:year_new -2.225e-13 5.667e-09 0.0
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt 0.000
## year_new 0.000 0.001
## Ag_18_grd:_ 0.000 -0.439 -0.898
## optimizer (nloptwrap) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvaluesG2_month_Regression_09_15 <- lmer(G2_month ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
G2_month_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G2_month ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 1508.22
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7448
## Residual 0.4009
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.82670 -0.01153 -1.49962
## Age_18_graduate:year_new
## 0.05981summary(G2_month_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G2_month ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 1508.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1487 -0.3340 0.0437 0.2263 3.5818
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.5547 0.7448
## Residual 0.1607 0.4009
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.82670 0.63022 9.245
## Age_18_graduate -0.01153 0.03256 -0.354
## year_new -1.49962 1.02238 -1.467
## Age_18_graduate:year_new 0.05981 0.04277 1.398
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.160 0.159
## Ag_18_grd:_ 0.330 -0.330 -0.983G2_year_Regression_09_15 <- lmer(G2_year ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 9.97306 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?G2_year_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G2_year ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: -32554.64
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 6.724e-15
## Residual 2.057e-12
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 2.027e+03 -1.000e+00 6.000e+00
## Age_18_graduate:year_new
## -9.269e-14
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 2 lme4 warningssummary(G2_year_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G2_year ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: -32554.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6378 -0.8844 -0.3316 0.5527 2.3215
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 4.521e-29 6.724e-15
## Residual 4.231e-24 2.057e-12
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.027e+03 1.614e-12 1.256e+15
## Age_18_graduate -1.000e+00 8.340e-14 -1.199e+13
## year_new 6.000e+00 4.101e-12 1.463e+12
## Age_18_graduate:year_new -9.269e-14 1.724e-13 -5.380e-01
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.394 0.393
## Ag_18_grd:_ 0.483 -0.484 -0.994
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 9.97306 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?G10_Regression_09_15 <- lmer(G10 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
G10_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G10 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2473.981
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.362
## Residual 1.062
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 5.9449 -0.2045 -4.2356
## Age_18_graduate:year_new
## 0.1753summary(G10_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G10 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2474
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1759 -0.3768 -0.2616 0.1465 2.7746
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.856 1.362
## Residual 1.127 1.062
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.94490 1.32393 4.490
## Age_18_graduate -0.20453 0.06841 -2.990
## year_new -4.23561 2.58747 -1.637
## Age_18_graduate:year_new 0.17527 0.10804 1.622
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.215 0.214
## Ag_18_grd:_ 0.352 -0.352 -0.989G11_Regression_09_15 <- lmer(G11 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
G11_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G11 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2425.074
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.4575
## Residual 1.5190
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## -4.1480 0.2786 6.0329
## Age_18_graduate:year_new
## -0.2079summary(G11_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G11 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2425.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.44385 -0.48949 -0.11978 0.08061 2.69330
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.2093 0.4575
## Residual 2.3075 1.5190
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -4.14802 1.24458 -3.333
## Age_18_graduate 0.27858 0.06431 4.332
## year_new 6.03293 3.11954 1.934
## Age_18_graduate:year_new -0.20786 0.13093 -1.588
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.369 0.369
## Ag_18_grd:_ 0.462 -0.463 -0.993G30A_Regression_09_15 <- lmer(G30A ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
G30A_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G30A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 1822.345
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5407
## Residual 0.8388
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 6.27869 -0.01197 -1.19473
## Age_18_graduate:year_new
## 0.05204summary(G30A_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G30A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 1822.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6334 -0.6763 -0.0251 0.7889 1.4168
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.2923 0.5407
## Residual 0.7036 0.8388
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.27869 0.78013 8.048
## Age_18_graduate -0.01197 0.04031 -0.297
## year_new -1.19473 1.84737 -0.647
## Age_18_graduate:year_new 0.05204 0.07729 0.673
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.310 0.310
## Ag_18_grd:_ 0.415 -0.416 -0.992G41A_Regression_09_15 <- lmer(G41A ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
G41A_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G41A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2455.006
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.220
## Residual 1.149
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 10.4620 -0.2750 -3.1766
## Age_18_graduate:year_new
## 0.1716summary(G41A_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G41A ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2455
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6610 -0.3938 0.1280 0.6089 2.2436
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.487 1.220
## Residual 1.320 1.149
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 10.46203 1.29530 8.077
## Age_18_graduate -0.27499 0.06693 -4.109
## year_new -3.17658 2.72347 -1.166
## Age_18_graduate:year_new 0.17163 0.11372 1.509
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.243 0.243
## Ag_18_grd:_ 0.368 -0.369 -0.990G41B_Regression_09_15 <- lmer(G41B ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
G41B_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G41B ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2045.289
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7573
## Residual 0.9281
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.381505 -0.086371 0.590214
## Age_18_graduate:year_new
## -0.003998summary(G41B_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G41B ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2045.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2818 -0.4789 0.1679 0.6487 1.5267
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.5736 0.7573
## Residual 0.8613 0.9281
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 7.381505 0.932900 7.912
## Age_18_graduate -0.086371 0.048203 -1.792
## year_new 0.590214 2.113662 0.279
## Age_18_graduate:year_new -0.003998 0.088328 -0.045
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.281 0.280
## Ag_18_grd:_ 0.393 -0.394 -0.992G41C_Regression_09_15 <- lmer(G41C ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
G41C_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G41C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2399.71
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.3403
## Residual 1.5169
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 6.29767 -0.03876 25.15586
## Age_18_graduate:year_new
## -1.14267summary(G41C_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G41C ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2399.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6490 -0.4411 0.2511 0.8511 3.2143
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.1158 0.3403
## Residual 2.3009 1.5169
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.29767 1.21981 5.163
## Age_18_graduate -0.03876 0.06303 -0.615
## year_new 25.15586 3.07690 8.176
## Age_18_graduate:year_new -1.14267 0.12923 -8.842
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.379 0.379
## Ag_18_grd:_ 0.471 -0.472 -0.994G41H_Regression_09_15 <- lmer(G41H ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
G41H_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G41H ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 1907.542
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.7324
## Residual 0.7963
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 7.13998 -0.04078 -1.42773
## Age_18_graduate:year_new
## 0.05883summary(G41H_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G41H ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 1907.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8042 -0.2484 0.4041 0.4596 2.1474
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.5364 0.7324
## Residual 0.6342 0.7963
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 7.13998 0.84018 8.498
## Age_18_graduate -0.04078 0.04341 -0.939
## year_new -1.42773 1.84672 -0.773
## Age_18_graduate:year_new 0.05883 0.07714 0.763
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.264 0.263
## Ag_18_grd:_ 0.382 -0.382 -0.991G41P_Regression_09_15 <- lmer(G41P ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
G41P_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: G41P ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2447.521
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.091
## Residual 1.234
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 9.3552 -0.2298 -4.5067
## Age_18_graduate:year_new
## 0.2375summary(G41P_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: G41P ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2447.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3999 -0.4495 0.1099 0.5873 1.7524
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.190 1.091
## Residual 1.523 1.234
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 9.35524 1.28054 7.306
## Age_18_graduate -0.22984 0.06617 -3.474
## year_new -4.50671 2.84500 -1.584
## Age_18_graduate:year_new 0.23747 0.11885 1.998
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.270 0.269
## Ag_18_grd:_ 0.386 -0.386 -0.991H1_Regression_09_15 <- lmer(H1 ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
H1_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: H1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 1674.149
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.5133
## Residual 0.7283
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 0.76615 0.07040 1.31171
## Age_18_graduate:year_new
## -0.06162summary(H1_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: H1 ~ Age_18_graduate + year_new + Age_18_graduate * year_new +
## (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 1674.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.65096 -0.61886 -0.09524 0.69298 2.67585
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.2635 0.5133
## Residual 0.5304 0.7283
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.76615 0.69569 1.101
## Age_18_graduate 0.07040 0.03595 1.959
## year_new 1.31171 1.62368 0.808
## Age_18_graduate:year_new -0.06162 0.06790 -0.908
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.300 0.299
## Ag_18_grd:_ 0.407 -0.408 -0.992L7_1st_mention_Regression_09_15 <- lmer(L7_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
L7_1st_mention_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 2344.999
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 1.150
## Residual 1.029
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 1.55658 0.01921 2.46626
## Age_18_graduate:year_new
## -0.11375summary(L7_1st_mention_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_1st_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 2345
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7364 -0.3982 -0.0922 0.0392 4.5613
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 1.323 1.150
## Residual 1.059 1.029
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.55658 1.19062 1.307
## Age_18_graduate 0.01921 0.06152 0.312
## year_new 2.46626 2.45811 1.003
## Age_18_graduate:year_new -0.11375 0.10263 -1.108
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.236 0.235
## Ag_18_grd:_ 0.364 -0.364 -0.990L7_2nd_mention_Regression_09_15 <- lmer(L7_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)
L7_2nd_mention_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: 1331.688
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.3920
## Residual 0.5575
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 0.322047 -0.009658 0.738235
## Age_18_graduate:year_new
## -0.028423summary(L7_2nd_mention_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_2nd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: 1331.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2017 -0.1663 -0.1431 -0.1345 9.2836
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.1537 0.3920
## Residual 0.3108 0.5575
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.322047 0.532137 0.605
## Age_18_graduate -0.009658 0.027495 -0.351
## year_new 0.738235 1.242455 0.594
## Age_18_graduate:year_new -0.028423 0.051956 -0.547
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.300 0.299
## Ag_18_grd:_ 0.407 -0.408 -0.992L7_3rd_mention_Regression_09_15 <- lmer(L7_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate*year_new + (1 | TAS_ID), data = Long_format_2009_2015)## boundary (singular) fit: see help('isSingular')L7_3rd_mention_Regression_09_15## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
## REML criterion at convergence: -448.7136
## Random effects:
## Groups Name Std.Dev.
## TAS_ID (Intercept) 0.0000
## Residual 0.1673
## Number of obs: 643, groups: TAS_ID, 517
## Fixed Effects:
## (Intercept) Age_18_graduate year_new
## 0.168675 -0.008124 -0.168675
## Age_18_graduate:year_new
## 0.008124
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warningssummary(L7_3rd_mention_Regression_09_15)## Linear mixed model fit by REML ['lmerMod']
## Formula: L7_3rd_mention ~ Age_18_graduate + year_new + Age_18_graduate *
## year_new + (1 | TAS_ID)
## Data: Long_format_2009_2015
##
## REML criterion at convergence: -448.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.1342 -0.0856 -0.0371 0.0000 17.8440
##
## Random effects:
## Groups Name Variance Std.Dev.
## TAS_ID (Intercept) 0.000 0.0000
## Residual 0.028 0.1673
## Number of obs: 643, groups: TAS_ID, 517
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.168675 0.131303 1.285
## Age_18_graduate -0.008124 0.006784 -1.197
## year_new -0.168675 0.333650 -0.506
## Age_18_graduate:year_new 0.008124 0.014027 0.579
##
## Correlation of Fixed Effects:
## (Intr) Ag_18_ yer_nw
## Age_18_grdt -0.998
## year_new -0.394 0.393
## Ag_18_grd:_ 0.483 -0.484 -0.994
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')lm(B5A ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = B5A ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 4.0316 -0.1979 0.2047lm(B5D ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = B5D ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 4.56326 -0.06503 0.08377lm(B6C ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = B6C ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 5.38049 -0.03713 0.03260lm(C2D ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = C2D ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 3.47868 0.08748 -0.06055lm(C2E ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = C2E ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 3.05783 0.29526 -0.01328lm(C2F ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = C2F ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 2.70300 0.20155 0.03963lm(D2D3_month ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = D2D3_month ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 0.2999 -0.1260 0.1051lm(D2D3_year ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = D2D3_year ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 10.1353 0.8905 11.5120lm(E1_1st_mention ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = E1_1st_mention ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 2.7863 0.1345 -0.6118lm(E1_2nd_mention ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = E1_2nd_mention ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 0.99540 0.39807 0.05653lm(E1_3rd_mention ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = E1_3rd_mention ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 2.432e-02 -9.528e-05 -4.208e-03lm(E3 ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = E3 ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 1.44293 0.04979 -0.25139lm(G1 ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G1 ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 1.0029698 -0.0014904 0.0006658lm(G2_month ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G2_month ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 5.4648 0.2927 0.1542lm(G2_year ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G2_year ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 2.008e+03 5.850e-04 3.942e+00lm(G10 ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G10 ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 2.20323 -0.27530 -0.09048lm(G11 ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G11 ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 1.84819 0.02327 0.54527lm(G30A ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G30A ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 5.67676 0.04921 0.31878lm(G41A ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G41A ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 4.91623 0.08934 -0.19273lm(G41B ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G41B ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 5.55242 0.09263 -0.05063lm(G41C ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G41C ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 5.05046 0.04782 -0.37812lm(G41H ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G41H ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 5.8826 0.2689 -0.0457lm(G41P ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = G41P ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 4.92900 0.01794 -0.05830lm(H1 ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = H1 ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 1.96973 0.14983 0.07781lm(L7_1st_mention ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = L7_1st_mention ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 1.84561 -0.04416 -0.05405lm(L7_2nd_mention ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = L7_2nd_mention ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 0.31758 -0.08751 0.10884lm(L7_3rd_mention ~ Gender + year_new, data = Long_format_2005_2015)##
## Call:
## lm(formula = L7_3rd_mention ~ Gender + year_new, data = Long_format_2005_2015)
##
## Coefficients:
## (Intercept) Gender year_new
## 0.0188941 0.0009522 0.0153578