library(haven)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(janitor)
##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test
library(sjPlot)
## Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
##
## The following object is masked from 'package:dplyr':
##
## recode
##
## The following object is masked from 'package:purrr':
##
## some
library(ggplot2)
library(patchwork)
library(ggpubr)
library(gtsummary)
library(haven)
library(stats)
library(survey)
## Loading required package: grid
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
##
## Loading required package: survival
##
## Attaching package: 'survey'
##
## The following object is masked from 'package:graphics':
##
## dotchart
library(marginaleffects)
library(readr)
library(kableExtra)
##
## Attaching package: 'kableExtra'
##
## The following object is masked from 'package:dplyr':
##
## group_rows
usa_00043 <- read_csv("usa_00043.csv")
## Rows: 16545837 Columns: 28
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (28): YEAR, MULTYEAR, SAMPLE, SERIAL, CBSERIAL, HHWT, CLUSTER, STRATA, G...
##
## 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.
dat1 <- usa_00043
colnames(dat1) <- tolower(colnames(dat1))
dat1 <- na.omit(dat1)
dat1$educd <- as.numeric(dat1$educd)
dat1$degree <- Recode(
dat1$educd,
recodes = "000:061='No High School Degree'; 062:064='High School Diploma'; 065:100='Some College'; 100:116='BA/BS Degree'"
)
dat1$degree <- factor(
dat1$degree,
levels = c('No High School Degree', 'High School Diploma', 'Some College', 'BA/BS Degree')
)
degreetable <- tabyl(dat1$degree)
dat1$empstat <- as.numeric(dat1$empstat)
dat1$employmentstatus <- recode(dat1$empstat, "1='Employed'; 2='Unemployed'; 3='Not in labor force'; else=NA")
dat1$employmentstatus <- factor(dat1$employmentstatus, levels = c("Employed", "Unemployed", "Not in labor force"), exclude = NULL)
dat1$insured <- Recode(dat1$hcovany, recodes = "2=1;else=0")
tabyl(dat1$insured)
## dat1$insured n percent
## 0 1953809 0.1180846
## 1 14592028 0.8819154
blkamerica <- dat1 %>%
filter((raced == 200 | raced == 210) & (bpld == 99900 | bpld < 99900))
blkamerica$group <- "usablack"
blkafrican <- dat1 %>%
filter((raced == 200 | raced == 210) & bpld >= 60000 & bpld < 69999 & bpld != 99900)
blkafrican$group <- "africanblack"
blkcaribbean <- dat1 %>%
filter((raced == 200 | raced == 210) &
(bpld >= 21000 & bpld < 39999 |
bpld >= 9000 & bpld < 10000 |
bpld >= 200 & bpld < 300)
)
blkcaribbean$group <- "blackcaribbean"
blkcombined <- rbind(blkamerica, blkafrican, blkcaribbean)
blkcombined$group <- relevel(factor(blkcombined$group), ref = "usablack")
tabyl(blkcombined$group)
## blkcombined$group n percent
## usablack 1656642 0.89330539
## africanblack 83657 0.04511008
## blackcaribbean 114209 0.06158453
ageprobs<- blkcombined %>%
group_by(age, group) %>%
summarize(p=mean(insured),n=n())
## `summarise()` has grouped output by 'age'. You can override using the `.groups`
## argument.
degreeprobs<- blkcombined %>%
group_by(degree, group) %>%
summarize(p=mean(insured),n=n())
## `summarise()` has grouped output by 'degree'. You can override using the
## `.groups` argument.
empstprobs<- blkcombined %>%
group_by(employmentstatus, group) %>%
summarize(p=mean(insured),n=n())
## `summarise()` has grouped output by 'employmentstatus'. You can override using
## the `.groups` argument.
summary_table <- blkcombined %>%
select(insured, degree, employmentstatus, age, group) %>%
tbl_summary(
by = group,
statistic = list(insured ~ "{p}% ({n})",
degree ~ "{p}% ({n})",
age ~ "{mean} ({sd})")
) %>%
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(all_stat_cols() ~ "**Group**") %>%
modify_caption("**Statistics for Health Insured, Employment Status, Educational Attainment, and Age of Native Blacks and Foreign-born Black African, Caribbean, and Latin American Individuals**") %>%
add_overall()
summary_table
Statistics for Health Insured, Employment Status, Educational Attainment, and Age of Native Blacks and Foreign-born Black African, Caribbean, and Latin American Individuals
| Variable |
Overall, N = 1,854,508 |
Group
|
| usablack, N = 1,656,642 |
africanblack, N = 83,657 |
blackcaribbean, N = 114,209 |
| insured |
83% (1,545,051) |
83% (1,380,076) |
83% (69,506) |
84% (95,469) |
| degree |
|
|
|
|
| No High School Degree |
14% (250,802) |
14% (224,848) |
11% (9,043) |
15% (16,911) |
| High School Diploma |
31% (568,375) |
31% (518,815) |
19% (16,070) |
29% (33,490) |
| Some College |
32% (600,996) |
33% (543,575) |
28% (23,282) |
30% (34,139) |
| BA/BS Degree |
23% (434,335) |
22% (369,404) |
42% (35,262) |
26% (29,669) |
| employmentstatus |
|
|
|
|
| Employed |
1,176,356 (63%) |
1,025,561 (62%) |
64,922 (78%) |
85,873 (75%) |
| Unemployed |
106,157 (5.7%) |
95,568 (5.8%) |
4,345 (5.2%) |
6,244 (5.5%) |
| Not in labor force |
571,995 (31%) |
535,513 (32%) |
14,390 (17%) |
22,092 (19%) |
| age |
46 (12) |
46 (12) |
43 (11) |
47 (11) |
model1 <- glm(insured ~ group, data = blkcombined, family = binomial)
summary(model1)
##
## Call:
## glm(formula = insured ~ group, family = binomial, data = blkcombined)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.607444 0.002083 771.578 <2e-16 ***
## groupafricanblack -0.015817 0.009455 -1.673 0.0943 .
## groupblackcaribbean 0.020697 0.008257 2.507 0.0122 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1672337 on 1854507 degrees of freedom
## Residual deviance: 1672327 on 1854505 degrees of freedom
## AIC: 1672333
##
## Number of Fisher Scoring iterations: 3
model2 <- glm(insured ~ group + age, data = blkcombined, family = binomial)
summary(model2)
##
## Call:
## glm(formula = insured ~ group + age, family = binomial, data = blkcombined)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1708628 0.0075777 22.548 < 2e-16 ***
## groupafricanblack 0.0498502 0.0095376 5.227 1.73e-07 ***
## groupblackcaribbean -0.0354729 0.0083411 -4.253 2.11e-05 ***
## age 0.0326331 0.0001712 190.604 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1672337 on 1854507 degrees of freedom
## Residual deviance: 1634860 on 1854504 degrees of freedom
## AIC: 1634868
##
## Number of Fisher Scoring iterations: 4
model3 <- glm(insured ~ group * (age + employmentstatus + degree) , data = blkcombined, family = binomial)
summary(model3)
##
## Call:
## glm(formula = insured ~ group * (age + employmentstatus + degree),
## family = binomial, data = blkcombined)
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.1742039 0.0098003
## groupafricanblack 0.7880146 0.0480630
## groupblackcaribbean -0.0256990 0.0417059
## age 0.0385711 0.0001861
## employmentstatusUnemployed -1.1226116 0.0078445
## employmentstatusNot in labor force -0.8169998 0.0048685
## degreeHigh School Diploma 0.2168545 0.0060959
## degreeSome College 0.6200079 0.0064959
## degreeBA/BS Degree 1.2683247 0.0084161
## groupafricanblack:age -0.0175129 0.0009061
## groupblackcaribbean:age -0.0060053 0.0007661
## groupafricanblack:employmentstatusUnemployed 0.1026171 0.0358319
## groupblackcaribbean:employmentstatusUnemployed -0.0302257 0.0304305
## groupafricanblack:employmentstatusNot in labor force 0.0623501 0.0231058
## groupblackcaribbean:employmentstatusNot in labor force 0.0617262 0.0200133
## groupafricanblack:degreeHigh School Diploma -0.1884445 0.0326525
## groupblackcaribbean:degreeHigh School Diploma 0.1146123 0.0237861
## groupafricanblack:degreeSome College -0.3875054 0.0316720
## groupblackcaribbean:degreeSome College 0.1298763 0.0252040
## groupafricanblack:degreeBA/BS Degree -0.6326153 0.0317656
## groupblackcaribbean:degreeBA/BS Degree 0.0283775 0.0294973
## z value Pr(>|z|)
## (Intercept) -17.775 < 2e-16 ***
## groupafricanblack 16.395 < 2e-16 ***
## groupblackcaribbean -0.616 0.53777
## age 207.271 < 2e-16 ***
## employmentstatusUnemployed -143.109 < 2e-16 ***
## employmentstatusNot in labor force -167.813 < 2e-16 ***
## degreeHigh School Diploma 35.574 < 2e-16 ***
## degreeSome College 95.447 < 2e-16 ***
## degreeBA/BS Degree 150.701 < 2e-16 ***
## groupafricanblack:age -19.328 < 2e-16 ***
## groupblackcaribbean:age -7.839 4.55e-15 ***
## groupafricanblack:employmentstatusUnemployed 2.864 0.00419 **
## groupblackcaribbean:employmentstatusUnemployed -0.993 0.32058
## groupafricanblack:employmentstatusNot in labor force 2.698 0.00697 **
## groupblackcaribbean:employmentstatusNot in labor force 3.084 0.00204 **
## groupafricanblack:degreeHigh School Diploma -5.771 7.87e-09 ***
## groupblackcaribbean:degreeHigh School Diploma 4.818 1.45e-06 ***
## groupafricanblack:degreeSome College -12.235 < 2e-16 ***
## groupblackcaribbean:degreeSome College 5.153 2.56e-07 ***
## groupafricanblack:degreeBA/BS Degree -19.915 < 2e-16 ***
## groupblackcaribbean:degreeBA/BS Degree 0.962 0.33603
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1672337 on 1854507 degrees of freedom
## Residual deviance: 1527222 on 1854487 degrees of freedom
## AIC: 1527264
##
## Number of Fisher Scoring iterations: 5
coefficients <- coef(model1)
mod1logodds <- exp(coefficients)
mod1logodds
## (Intercept) groupafricanblack groupblackcaribbean
## 4.9900422 0.9843079 1.0209126
coefficients <- coef(model2)
mod2logodds <- exp(coefficients)
mod2logodds
## (Intercept) groupafricanblack groupblackcaribbean age
## 1.1863280 1.0511136 0.9651489 1.0331714
coefficients <- coef(model3)
mod2logodds <- exp(coefficients)
mod2logodds
## (Intercept)
## 0.8401256
## groupafricanblack
## 2.1990262
## groupblackcaribbean
## 0.9746285
## age
## 1.0393246
## employmentstatusUnemployed
## 0.3254288
## employmentstatusNot in labor force
## 0.4417550
## degreeHigh School Diploma
## 1.2421633
## degreeSome College
## 1.8589427
## degreeBA/BS Degree
## 3.5548921
## groupafricanblack:age
## 0.9826396
## groupblackcaribbean:age
## 0.9940127
## groupafricanblack:employmentstatusUnemployed
## 1.1080670
## groupblackcaribbean:employmentstatusUnemployed
## 0.9702265
## groupafricanblack:employmentstatusNot in labor force
## 1.0643349
## groupblackcaribbean:employmentstatusNot in labor force
## 1.0636711
## groupafricanblack:degreeHigh School Diploma
## 0.8282465
## groupblackcaribbean:degreeHigh School Diploma
## 1.1214385
## groupafricanblack:degreeSome College
## 0.6787480
## groupblackcaribbean:degreeSome College
## 1.1386875
## groupafricanblack:degreeBA/BS Degree
## 0.5312008
## groupblackcaribbean:degreeBA/BS Degree
## 1.0287840
ageprobs$num <-ageprobs$p*(1-ageprobs$p)
ageprobs$sep <- sqrt(ageprobs$num/degreeprobs$n)
## Warning in ageprobs$num/degreeprobs$n: longer object length is not a multiple
## of shorter object length
ageprobs$me <- 2*ageprobs$sep
ggplot(data =ageprobs, aes(x = age , y = p, ymin=p-me, ymax=p+me, fill=group,color = group, group = group)) +
geom_line() + ylim(0,1) + geom_ribbon(alpha=0.3,aes(color=NULL)) +
labs(title="Health Insured By Age",
subtitle="Native Black Foreign-born Black African/Carribean/Latin American Respondents",
caption="Source: 2017 & 2022 ACS 5-Year File") +
xlab(label="Age") +
ylab(label="Insured Rate")
degreeprobs$num <- degreeprobs$p*(1-degreeprobs$p)
degreeprobs$sep <- sqrt(degreeprobs$num/degreeprobs$n)
degreeprobs$me <- 2*degreeprobs$sep
ggplot(data =degreeprobs, aes(x = degree , y = p, ymin=p-me, ymax=p+me, fill=group,color = group, group = group)) +
geom_line() + ylim(0,1) + geom_ribbon(alpha=0.3,aes(color=NULL)) +
labs(title="Health Insured Rates By Education Level",
subtitle="Native Black/Foreign-born Black African/Carribean/Latin American Respondents",
caption="Source: 2017 & 2022 ACS 5-Year File") +
xlab(label="Education Attainment") +
ylab(label="Insured Rate")
p2 <- plot_predictions(model2, condition = c("age", "group")) +
geom_line() +
ylim(0.50, 1) +
geom_ribbon(alpha = 0., aes(color = NULL)) +
labs(
title = "Health Insured Rates by Age",
subtitle = "Native Black/Foreign-born African/Caribbean/Latin American Respondents Ages 25 to 65",
caption = "Source: 2017 & 2022 ACS 5-Year File",
xlab = "Age",
ylab = "Insured Rate"
)
p2
tidy_model <- tidy(model1)
des.table <- tidy_model %>%
mutate(
Odds_Ratio = exp(estimate),
Odds_Ratio_CI_Lower = exp(estimate - 1.96 * std.error),
Odds_Ratio_CI_Upper = exp(estimate + 1.96 * std.error),
P_Value = p.adjust(p.value, method = "holm")
) %>%
select(
term,
Estimate = estimate,
Standard_Error = std.error,
Z_Value = statistic,
P_Value,
Odds_Ratio,
Odds_Ratio_CI_Lower,
Odds_Ratio_CI_Upper
)
print(des.table)
## # A tibble: 3 × 8
## term Estimate Standard_Error Z_Value P_Value Odds_Ratio Odds_Ratio_CI_Lower
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 (Inter… 1.61 0.00208 772. 0 4.99 4.97
## 2 groupa… -0.0158 0.00945 -1.67 0.0943 0.984 0.966
## 3 groupb… 0.0207 0.00826 2.51 0.0244 1.02 1.00
## # ℹ 1 more variable: Odds_Ratio_CI_Upper <dbl>
tidy_model <- tidy(model1)
des.table <- tidy_model %>%
mutate(
Odds_Ratio = exp(estimate),
Odds_Ratio_CI_Lower = exp(estimate - 1.96 * std.error),
Odds_Ratio_CI_Upper = exp(estimate + 1.96 * std.error),
P_Value = p.adjust(p.value, method = "holm")
) %>%
select(
term,
Estimate = estimate,
Standard_Error = std.error,
Z_Value = statistic,
P_Value,
Odds_Ratio,
Odds_Ratio_CI_Lower,
Odds_Ratio_CI_Upper
)
des.table %>%
kable("html", align = c("l", "c", "c", "c", "c", "c", "c", "c")) %>%
add_header_above(c("**Logistic Regression Results for Health Insurance Status**" = 8), align = "center") %>%
kable_styling(full_width = FALSE, position = "center")
|
Logistic Regression Results for Health Insurance Status
|
|
term
|
Estimate
|
Standard_Error
|
Z_Value
|
P_Value
|
Odds_Ratio
|
Odds_Ratio_CI_Lower
|
Odds_Ratio_CI_Upper
|
|
(Intercept)
|
1.6074444
|
0.0020833
|
771.57845
|
0.0000000
|
4.9900422
|
4.9697078
|
5.010460
|
|
groupafricanblack
|
-0.0158166
|
0.0094545
|
-1.67291
|
0.0943449
|
0.9843079
|
0.9662357
|
1.002718
|
|
groupblackcaribbean
|
0.0206970
|
0.0082569
|
2.50663
|
0.0243777
|
1.0209126
|
1.0045237
|
1.037569
|
tidy_model <- tidy(model2)
des.table2 <- tidy_model %>%
mutate(
Odds_Ratio = exp(estimate),
Odds_Ratio_CI_Lower = exp(estimate - 1.96 * std.error),
Odds_Ratio_CI_Upper = exp(estimate + 1.96 * std.error),
P_Value = p.adjust(p.value, method = "holm")
) %>%
select(
term,
Estimate = estimate,
Standard_Error = std.error,
Z_Value = statistic,
P_Value,
Odds_Ratio,
Odds_Ratio_CI_Lower,
Odds_Ratio_CI_Upper
)
des.table2 %>%
kable("html", align = c("l", "c", "c", "c", "c", "c", "c", "c")) %>%
add_header_above(c("**Logistic Regression Results for Health Insurance Status and Age of Native Blacks and Foreign-born Black African, Caribbean, and Latin American **" = 8), align = "center") %>%
kable_styling(full_width = FALSE, position = "center")
|
Logistic Regression Results for Health Insurance Status and Age
of Native Blacks and Foreign-born Black African, Caribbean, and Latin
American
|
|
term
|
Estimate
|
Standard_Error
|
Z_Value
|
P_Value
|
Odds_Ratio
|
Odds_Ratio_CI_Lower
|
Odds_Ratio_CI_Upper
|
|
(Intercept)
|
0.1708628
|
0.0075777
|
22.547999
|
0.00e+00
|
1.1863280
|
1.1688384
|
1.2040793
|
|
groupafricanblack
|
0.0498502
|
0.0095376
|
5.226691
|
3.00e-07
|
1.0511136
|
1.0316469
|
1.0709477
|
|
groupblackcaribbean
|
-0.0354729
|
0.0083411
|
-4.252800
|
2.11e-05
|
0.9651489
|
0.9494985
|
0.9810573
|
|
age
|
0.0326331
|
0.0001712
|
190.603710
|
0.00e+00
|
1.0331714
|
1.0328248
|
1.0335182
|
tidy_model <- tidy(model3)
des.table3 <- tidy_model %>%
mutate(
Odds_Ratio = exp(estimate),
Odds_Ratio_CI_Lower = exp(estimate - 1.96 * std.error),
Odds_Ratio_CI_Upper = exp(estimate + 1.96 * std.error),
P_Value = p.adjust(p.value, method = "holm")
) %>%
select(
term,
Estimate = estimate,
Standard_Error = std.error,
Z_Value = statistic,
P_Value,
Odds_Ratio,
Odds_Ratio_CI_Lower,
Odds_Ratio_CI_Upper
)
des.table3 %>%
kable("html", align = c("l", "c", "c", "c", "c", "c", "c", "c")) %>%
add_header_above(c("**Logistic Regression Results for Health Insurance Status, Employment Status, Educational Attainment, and Age of Native Blacks and Foreign-born Black African, Caribbean, and Latin American**" = 8), align = "center") %>%
kable_styling(full_width = FALSE, position = "center")
|
Logistic Regression Results for Health Insurance Status,
Employment Status, Educational Attainment, and Age of Native Blacks and
Foreign-born Black African, Caribbean, and Latin American
|
|
term
|
Estimate
|
Standard_Error
|
Z_Value
|
P_Value
|
Odds_Ratio
|
Odds_Ratio_CI_Lower
|
Odds_Ratio_CI_Upper
|
|
(Intercept)
|
-0.1742039
|
0.0098003
|
-17.7753655
|
0.0000000
|
0.8401256
|
0.8241420
|
0.8564192
|
|
groupafricanblack
|
0.7880146
|
0.0480630
|
16.3954690
|
0.0000000
|
2.1990262
|
2.0013286
|
2.4162531
|
|
groupblackcaribbean
|
-0.0256990
|
0.0417059
|
-0.6161943
|
0.9617348
|
0.9746285
|
0.8981281
|
1.0576449
|
|
age
|
0.0385711
|
0.0001861
|
207.2713812
|
0.0000000
|
1.0393246
|
1.0389456
|
1.0397038
|
|
employmentstatusUnemployed
|
-1.1226116
|
0.0078445
|
-143.1086513
|
0.0000000
|
0.3254288
|
0.3204635
|
0.3304710
|
|
employmentstatusNot in labor force
|
-0.8169998
|
0.0048685
|
-167.8127176
|
0.0000000
|
0.4417550
|
0.4375597
|
0.4459905
|
|
degreeHigh School Diploma
|
0.2168545
|
0.0060959
|
35.5738773
|
0.0000000
|
1.2421633
|
1.2274103
|
1.2570936
|
|
degreeSome College
|
0.6200079
|
0.0064959
|
95.4466206
|
0.0000000
|
1.8589427
|
1.8354249
|
1.8827619
|
|
degreeBA/BS Degree
|
1.2683247
|
0.0084161
|
150.7013149
|
0.0000000
|
3.5548921
|
3.4967328
|
3.6140187
|
|
groupafricanblack:age
|
-0.0175129
|
0.0009061
|
-19.3282563
|
0.0000000
|
0.9826396
|
0.9808961
|
0.9843862
|
|
groupblackcaribbean:age
|
-0.0060053
|
0.0007661
|
-7.8386365
|
0.0000000
|
0.9940127
|
0.9925213
|
0.9955064
|
|
groupafricanblack:employmentstatusUnemployed
|
0.1026171
|
0.0358319
|
2.8638431
|
0.0209268
|
1.1080670
|
1.0329166
|
1.1886850
|
|
groupblackcaribbean:employmentstatusUnemployed
|
-0.0302257
|
0.0304305
|
-0.9932703
|
0.9617348
|
0.9702265
|
0.9140504
|
1.0298551
|
|
groupafricanblack:employmentstatusNot in labor force
|
0.0623501
|
0.0231058
|
2.6984588
|
0.0278645
|
1.0643349
|
1.0172091
|
1.1136441
|
|
groupblackcaribbean:employmentstatusNot in labor force
|
0.0617262
|
0.0200133
|
3.0842647
|
0.0122434
|
1.0636711
|
1.0227553
|
1.1062238
|
|
groupafricanblack:degreeHigh School Diploma
|
-0.1884445
|
0.0326525
|
-5.7712108
|
0.0000001
|
0.8282465
|
0.7769002
|
0.8829863
|
|
groupblackcaribbean:degreeHigh School Diploma
|
0.1146123
|
0.0237861
|
4.8184656
|
0.0000101
|
1.1214385
|
1.0703563
|
1.1749586
|
|
groupafricanblack:degreeSome College
|
-0.3875054
|
0.0316720
|
-12.2349573
|
0.0000000
|
0.6787480
|
0.6378944
|
0.7222180
|
|
groupblackcaribbean:degreeSome College
|
0.1298763
|
0.0252040
|
5.1529992
|
0.0000021
|
1.1386875
|
1.0838033
|
1.1963511
|
|
groupafricanblack:degreeBA/BS Degree
|
-0.6326153
|
0.0317656
|
-19.9151296
|
0.0000000
|
0.5312008
|
0.4991365
|
0.5653248
|
|
groupblackcaribbean:degreeBA/BS Degree
|
0.0283775
|
0.0294973
|
0.9620353
|
0.9617348
|
1.0287840
|
0.9709917
|
1.0900159
|