High school completion: Moderators of the gap between indiginous and non-indiginous youth
2021-10-22
Summary
Hi folks, Im just cleaning up the file now, making sure that we will be able to directly convert this to a word document
Descriptives (still working on this)
##
## 0 1
## 9781 589##
## 0 1
## 13108 1143##
## 1 2
## 5149 5221##
## 1 2
## 7231 7020## Variable non-Indigenous Indigenous
## 1 Dropout % 14.82% 23.68%
## 2 Cohort 2003 98.23% 1.77%
## 3 Cohort 2009 96.88% 3.12%
## 4 Girls % 50.37% 52.74%
## 5 Urban % 50.93% 49.07%
## 6 Year 10 or Higher % 90.55% 89.36%
## 7 Achievement Index (Mean) -0.27 [-0.30, -0.23] -1.03 [-1.14, -0.93]
## 8 Socioeconomic Status Index (Mean) 0.31 [0.28, 0.33] -0.09 [-0.15, -0.02]use same as paper
Hypothesis set 1:
H1a: Indigenous Australian Children have higher dropout rates
## Joining, by = "term"A tibble: 7 × 5
Variables Log Odds -95% CI +95% CI p
2 Indigenous 0.901 -1.76 3.56 0.501 3 Cohort -1.75 -3.93 0.437 0.115 4 Grade -5.36 -7.60 -3.13 0
5 Urban 0.698 -1.25 2.64 0.476 6 Gender (Boys) 1.05 -1.89 3.99 0.48 7 SES 0.089 -0.976 1.15 0.869
H1_gender: Is the gap the same for boys and girls
## Joining, by = "term"## # A tibble: 8 × 5
## Variables `Log Odds` `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Intercept 5.99 4.74 7.25 0
## 2 Indigenous 0.644 0.22 1.07 0.003
## 3 Gender (Boys) 0.628 0.485 0.771 0
## 4 Cohort -0.255 -0.43 -0.08 0.005
## 5 Grade -0.756 -0.878 -0.634 0
## 6 Urban -0.527 -0.693 -0.362 0
## 7 SES -0.678 -0.782 -0.575 0
## 8 IndXGen -0.435 -1.06 0.193 0.172H1_location: Is the gap the same for urban and rural
## Joining, by = "term"## # A tibble: 8 × 5
## Variables `Log Odds` `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Intercept 6.00 4.75 7.26 0
## 2 Indigenous 0.229 -0.13 0.588 0.207
## 3 Urban -0.543 -0.709 -0.376 0
## 4 Cohort -0.255 -0.429 -0.08 0.005
## 5 Grade -0.755 -0.876 -0.633 0
## 6 Gender (Boys) 0.613 0.474 0.751 0
## 7 SES -0.678 -0.781 -0.574 0
## 8 IndXLoc 0.439 -0.126 1.00 0.126H1_ses: Is the gap the same for Rich and poor and rural
## Rows: 156 Columns: 2## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): term, Vars##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## Joining, by = "term"## # A tibble: 8 × 5
## Variables `Log Odds` `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Intercept 6.00 4.74 7.25 0
## 2 Indigenous 0.489 0.193 0.784 0.002
## 3 SES -0.686 -0.791 -0.58 0
## 4 Cohort -0.256 -0.431 -0.081 0.005
## 5 Grade -0.755 -0.877 -0.633 0
## 6 Urban -0.527 -0.693 -0.361 0
## 7 Gender (Boys) 0.613 0.474 0.751 0
## 8 IndXSES 0.307 -0.016 0.631 0.062H1_all three: Gap varies as a function of socioeconomic status, gender and locaion
## Joining, by = "term"## # A tibble: 10 × 5
## Variables `Log Odds` `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Intercept 5.99 4.73 7.24 0
## 2 Indigenous 0.529 0.07 0.987 0.024
## 3 SES -0.686 -0.792 -0.581 0
## 4 Urban -0.542 -0.709 -0.375 0
## 5 Gender (Boys) 0.63 0.487 0.772 0
## 6 Cohort -0.254 -0.429 -0.079 0.005
## 7 Grade -0.755 -0.876 -0.633 0
## 8 IndXSES 0.297 -0.018 0.613 0.064
## 9 IndXLoc 0.407 -0.141 0.955 0.143
## 10 IndXGen -0.446 -1.05 0.158 0.145hypotheses 1_ cohort interaction only. This gap narrowed as a function on a change in legislation that increased the effective age of first selection to 17 between 2008 and 2010.
## Joining, by = "term"## # A tibble: 8 × 5
## Variables `Log Odds` `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Intercept 6.00 4.74 7.25 0
## 2 SES -0.678 -0.781 -0.574 0
## 3 Urban -0.528 -0.694 -0.362 0
## 4 Gender (Boys) 0.613 0.474 0.752 0
## 5 Cohort -0.25 -0.431 -0.069 0.007
## 6 Indigenous 0.516 0.159 0.873 0.005
## 7 Grade -0.755 -0.877 -0.634 0
## 8 IndXCoh -0.163 -0.768 0.442 0.593Hypothesis 1_all three interactions and cohort interaction.
## Joining, by = "term"## # A tibble: 11 × 5
## Variables `Log Odds` `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Intercept 5.98 4.72 7.24 0
## 2 Indigenous 0.636 0.131 1.14 0.014
## 3 SES -0.687 -0.792 -0.581 0
## 4 Urban -0.543 -0.711 -0.376 0
## 5 Gender (Boys) 0.63 0.487 0.773 0
## 6 Cohort -0.246 -0.428 -0.065 0.009
## 7 Grade -0.754 -0.876 -0.632 0
## 8 IndXSES 0.311 -0.002 0.623 0.052
## 9 IndXLoc 0.421 -0.125 0.968 0.129
## 10 IndXGen -0.439 -1.04 0.166 0.152
## 11 IndXCoh -0.207 -0.789 0.375 0.48Figures hypothesis 1
hypothesis set 2
Hypothesis 2: Indigenous disadvantage still present when comparing equally advantaged and equally achieving Indigenous and non-Indigenous Youth (controling for achivement)
## # A tibble: 8 × 5
## Parameter Est `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 (Intercept) 2.78 1.46 4.10 < .001
## 2 GEO -0.588 -0.752 -0.424 < .001
## 3 ESCS -0.348 -0.458 -0.237 < .001
## 4 GENDER 0.579 0.428 0.729 < .001
## 5 INDIG 0.055 -0.259 0.369 0.727
## 6 COHORT2 -0.349 -0.528 -0.169 < .001
## 7 GRADE -0.475 -0.603 -0.347 < .001
## 8 ACH1PV -0.782 -0.869 -0.694 < .001Hypothesis 2_ses. Indiginous gap (controlling for achivement) varies as a function of ses
## # A tibble: 9 × 5
## Parameter Est `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 (Intercept) 2.78 1.46 4.10 < .001
## 2 GEO -0.588 -0.751 -0.424 < .001
## 3 GENDER 0.579 0.428 0.729 < .001
## 4 INDIG 0.1 -0.211 0.41 0.523
## 5 ESCS -0.354 -0.466 -0.241 < .001
## 6 COHORT2 -0.349 -0.528 -0.17 < .001
## 7 GRADE -0.475 -0.603 -0.347 < .001
## 8 ACH1PV -0.781 -0.869 -0.694 < .001
## 9 INDIG:ESCS 0.224 -0.158 0.606 0.244hypothess_2_location
## # A tibble: 9 × 5
## Parameter Est `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 (Intercept) 2.78 1.47 4.10 < .001
## 2 GEO -0.607 -0.77 -0.444 < .001
## 3 INDIG -0.182 -0.558 0.194 0.336
## 4 GENDER 0.579 0.428 0.729 < .001
## 5 ESCS -0.347 -0.458 -0.237 < .001
## 6 COHORT2 -0.347 -0.526 -0.168 < .001
## 7 GRADE -0.474 -0.602 -0.346 < .001
## 8 ACH1PV -0.782 -0.869 -0.695 < .001
## 9 GEO:INDIG 0.532 -0.1 1.16 0.096hypothess_2_gender
## # A tibble: 9 × 5
## Parameter Est `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 (Intercept) 2.77 1.45 4.08 < .001
## 2 GEO -0.588 -0.752 -0.424 < .001
## 3 GENDER 0.599 0.443 0.755 < .001
## 4 INDIG 0.342 -0.123 0.807 0.145
## 5 ESCS -0.348 -0.458 -0.237 < .001
## 6 COHORT2 -0.347 -0.527 -0.168 < .001
## 7 GRADE -0.475 -0.603 -0.346 < .001
## 8 ACH1PV -0.783 -0.87 -0.696 < .001
## 9 GENDER:INDIG -0.568 -1.26 0.124 0.104Hypotheis2_legislation–This gap narrowed as a function on a change in legislation that increased the effective age of first selection to 17 between 2008 and 2010.
## # A tibble: 9 × 5
## Parameter Est `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 (Intercept) 2.78 1.46 4.10 < .001
## 2 GEO -0.588 -0.752 -0.424 < .001
## 3 GENDER 0.579 0.428 0.73 < .001
## 4 COHORT2 -0.346 -0.53 -0.162 < .001
## 5 INDIG 0.098 -0.292 0.488 0.617
## 6 ESCS -0.348 -0.458 -0.237 < .001
## 7 GRADE -0.475 -0.603 -0.347 < .001
## 8 ACH1PV -0.782 -0.869 -0.694 < .001
## 9 COHORT2:INDIG -0.077 -0.738 0.584 0.817h2_achivement moderation. Does this gap vary as a function of academic achievement. That is, is the gap between similarly low achieving Indigenous and non-Indigenous children the same as the gap between similarly high achieving Indigenous and non-Indigenous children.
## # A tibble: 9 × 5
## Parameter Est `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 (Intercept) 2.77 1.46 4.09 < .001
## 2 GEO -0.588 -0.752 -0.425 < .001
## 3 GENDER 0.58 0.429 0.731 < .001
## 4 COHORT2 -0.35 -0.53 -0.171 < .001
## 5 INDIG 0.451 0.028 0.874 0.036
## 6 ESCS -0.346 -0.457 -0.236 < .001
## 7 GRADE -0.475 -0.603 -0.347 < .001
## 8 ACH1PV -0.791 -0.879 -0.703 < .001
## 9 INDIG:ACH1PV 0.31 0.042 0.578 0.023h2_everything in from hyp 1 and 2:
## # A tibble: 13 × 5
## Parameter Est `-95% CI` `+95% CI` p
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 (Intercept) 2.76 1.44 4.07 < .001
## 2 GEO -0.609 -0.772 -0.445 < .001
## 3 INDIG 0.501 -0.073 1.08 0.085
## 4 GENDER 0.6 0.444 0.756 < .001
## 5 COHORT2 -0.344 -0.529 -0.159 < .001
## 6 ESCS -0.351 -0.464 -0.238 < .001
## 7 GRADE -0.473 -0.601 -0.345 < .001
## 8 ACH1PV -0.791 -0.879 -0.702 < .001
## 9 GEO:INDIG 0.515 -0.06 1.09 0.077
## 10 INDIG:GENDER -0.518 -1.16 0.119 0.108
## 11 INDIG:COHORT2 -0.095 -0.701 0.511 0.755
## 12 INDIG:ESCS 0.157 -0.214 0.527 0.4
## 13 INDIG:ACH1PV 0.258 -0.022 0.538 0.07Figures hypothesis 2
#Figures faceted hypothesis 2: equal achievment
#Graph summarizing hyp1 results
############################indiginous by location
h2_location_1 <- svyglm(DROPOUT ~ GEO*INDIG+INDIG+COHORT+GENDER+GRADE+GEO+ACH1PV+ESCS,design = lsay,family = quasibinomial())
geo_h2 <- ggeffects::ggpredict(h2_location_1, terms = c("INDIG","GEO"))
geo_h2_out <- data.frame(prob = geo_h2$predicted, ci.low = geo_h2$conf.low, ci.high = geo_h2$conf.high,
indig = rep(c("non-Indigenous","Indigenous"),each=2), geo = rep(c(" Prov ","Urban"), 2))
geo_plot_2 <- geo_h2_out %>%
ggplot(aes(x=geo, y=prob, ymin=ci.low, ymax=ci.high)) +
geom_pointrange() +
#geom_hline(yintercept=0, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
xlab("Indigenous") + ylab("Probability") +
facet_wrap(~indig) +
theme_stata() + xlab("") + ylab("Probability of not completing high-school") +
ggtitle("Geography by Indigenous Status")
#geo_plot
##############################indig by cohort
h2_cohort_1 <- svyglm(DROPOUT ~ COHORT*INDIG+INDIG+COHORT+GENDER+GRADE+GEO+ACH1PV+ESCS,design = lsay,family = quasibinomial())
Cohort_h2_1<- ggeffects::ggpredict(h2_cohort_1, terms = c("INDIG","COHORT"))
Cohort_h2_1_out <- data.frame(prob = Cohort_h2_1$predicted, ci.low = Cohort_h2_1$conf.low, ci.high = Cohort_h2_1$conf.high,
indig = rep(c("non-Indigenous","Indigenous"),each=2), Cohort = rep(c(" 2008","2010"), 2))
cohort_plot_2 <- Cohort_h2_1_out %>%
ggplot(aes(x=Cohort, y=prob, ymin=ci.low, ymax=ci.high)) +
geom_pointrange() +
#geom_hline(yintercept=0, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
xlab("Indigenous") + ylab("Probability") +
facet_wrap(~indig) +
theme_stata() + xlab("") + ylab("Probability of not completing high-school") +
ggtitle("Cohort by Indigenous Status")
#two columns one with seperate , one with all
#####ses by indig
h2_SES_1 <- svyglm(DROPOUT ~ ESCS*INDIG+INDIG+COHORT+GENDER+GRADE+GEO+ACH1PV+ESCS,design = lsay,family = quasibinomial())
ses_h2 <- ggeffects::ggpredict(h2_SES_1, terms = c("INDIG","ESCS [-2,-1,0,1,2]"))
ses_h1_out_1 <- data.frame(prob = ses_h2$predicted, ci.low = ses_h2$conf.low, ci.high = ses_h2$conf.high,
indig = rep(c("non-Indigenous","Indigenous"),each=5), ses = rep(-2:2, 2))
ses_plot_2 <- ses_h1_out_1 %>%
ggplot(aes(x=ses, y=prob, ymin=ci.low, ymax=ci.high)) +
geom_pointrange() +
#geom_hline(yintercept=0, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
xlab("Indigenous") + ylab("Probability") +
facet_wrap(~indig) +
theme_stata() + xlab("SES") + ylab("Probability of not completing high-school") +
ggtitle("SES by Indiginous Status")
####Indiginous x achieve
h2_acheive_1 <- svyglm(DROPOUT ~ ACH1PV*INDIG+INDIG+COHORT+GENDER+GRADE+GEO+ACH1PV+ESCS,design = lsay,family = quasibinomial())
Achieve_h2_1<- ggeffects::ggpredict(h2_acheive_1, terms = c("INDIG","ACH1PV [-2,-1,0,1,2]"))
Achieve_h2_out_1 <- data.frame(prob = Achieve_h2_1$predicted, ci.low = Achieve_h2_1$conf.low, ci.high = Achieve_h2_1$conf.high,
indig = rep(c("non-Indigenous","Indigenous"),each=5), ACH1PV = rep(-2:2, 2))
acheive_plot_2 <- Achieve_h2_out_1 %>%
ggplot(aes(x=ACH1PV, y=prob, ymin=ci.low, ymax=ci.high)) +
geom_pointrange() +
#geom_hline(yintercept=0, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
xlab("Indigenous") + ylab("Probability") +
facet_wrap(~indig) +
theme_stata() + xlab("Achievement") + ylab("Probability of not completing high-school") +
ggtitle("Achievement by Indiginous Status")
###face_wrap cohort and indiginous group indigionous face row n =1 line up with each
tmp<- { cohort_plot_2+ geo_plot_2 +ses_plot_2 + acheive_plot_2 + plot_layout(ncol=2)} + plot_layout(ncol=2)
tmp
ggsave("/Users/jociarrochi/Dropbox/to do/lsay/r syntax/hyp2Facet.png",width=10, height=8, dpi=600, tmp)
tmp