School Climate For Diversity Scale
Andy Pinedo
12/14/2021
#Load packages
#load packages
library(tidyverse)
library(psych)
library(ggdist)
library(gghalves)
library(ggcorrplot)
library(RColorBrewer)
library(lavaan)
library(openssl)
library(ggpubr)
library(rstatix)
library(ggprism)
library(patchwork)
library(magrittr)
library(GGally)
library(knitr)#read in data
#read in data and filter out people who did not provide assent or left it blank
ESSD <- read.csv("ESSD.W1.DSet.clean.csv") %>% filter(assent != "2")#gender breakdown of the sample
table (ESSD$gender)##
## 1 2 3 4
## 238 199 7 4table (ESSD$gender, ESSD$school) %>% kable()| 1 | 2 |
|---|---|
| 112 | 126 |
| 83 | 116 |
| 2 | 5 |
| 3 | 1 |
#gender write-ins
table(ESSD$gender_3_TEXT) %>% kable()| Var1 | Freq |
|---|---|
| Both | 1 |
| Gender Fluid | 1 |
| Genderfluid | 1 |
| Non - binary | 1 |
| Non-binary | 1 |
| Non-Binary | 1 |
| Non-binary | 1 |
#racial breakdown of the sample
table(ESSD$er1) %>% kable()| Var1 | Freq |
|---|---|
| 1 | 5 |
| 1,2,3,4,5,6,7 | 1 |
| 1,4,7 | 1 |
| 3 | 2 |
| 3,4 | 1 |
| 3,7 | 1 |
| 4 | 297 |
| 4,5 | 6 |
| 4,5,6 | 2 |
| 4,6 | 6 |
| 4,8 | 3 |
| 5 | 29 |
| 5,6 | 2 |
| 6 | 8 |
| 6,8 | 1 |
| 7 | 8 |
| 8 | 65 |
#race write-in responses
table(ESSD$er1_8_TEXT) %>% kable()| Var1 | Freq |
|---|---|
| Mexican/ American | 1 |
| America / Mexican | 1 |
| American | 1 |
| American/ Mexican | 1 |
| American/Mexican | 1 |
| American/Mexican | 1 |
| Don’t know rest | 1 |
| Filipino | 1 |
| Hispanic | 4 |
| Hispanic | 6 |
| Hispanic/Latino | 3 |
| I’m Mexican | 1 |
| Latina/American | 1 |
| Latino | 1 |
| Latino | 2 |
| Latino/American | 1 |
| Mexican | 5 |
| Mexican | 2 |
| Mexican american | 1 |
| Mexican American | 4 |
| Mexican American | 12 |
| MEXICAN AMERICAN | 1 |
| Mexican and Salvador | 1 |
| Mexican, American | 1 |
| Mexican/ American | 1 |
| Mexican/American | 2 |
| Mexican/American | 5 |
| Mexico | 1 |
| White | 1 |
| White, Mexican | 1 |
#Mean age of the sample
describe(ESSD$age) %>% kable()| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 447 | 13.91946 | 0.4264271 | 14 | 13.96657 | 0 | 13 | 17 | 4 | 0.3989576 | 8.452974 | 0.0201693 |
#Item Codebook str_1r Students here have a lot of stereotypes about your racial or ethnic group. str_2r Teachers and principals believe negative stereotypes about your racial/ethnic group. str_3r Your racial or cultural group is represented in stereotypical ways in textbooks and class materials.
pcc_1 Your classes teach you about diverse cultures and traditions. pcc_2 In school you get to do things that help you learn about people of different races and cultures. pcc_3 Your textbooks show people of many different races/ethnicities.
cult_1 In your classes you’ve learned new things about your culture. cult_2 At your school, you have chances to learn about the history and traditions of your culture. cult_3 At your school, you have participated in activities that teach you more about your cultural background.
crit_1 Your teachers encourage awareness of social issues affecting your community. crit_2 In your classes you have learned about racial inequality in the United States. crit_3 You have opportunities to learn about social justice.
Response Options: 1 = Not at all true; 2 = A little true; 3 = Somewhat true; 4 = Very true; 5 = Completely true
Treated as a continuous variable
#Descriptive statistics for School Climate for Diversity Scale items
#Create matrix of crit soc items
cc_soc_vars <- ESSD[ , c('crit_1', 'crit_2', 'crit_3', 'cult_1', 'cult_2', 'cult_3', 'str_1r', 'str_2r', 'str_3r', 'cbs_1', 'cbs_2', 'cbs_3', 'pcc_1', 'pcc_2', 'pcc_3')]
describe(cc_soc_vars) %>% kable()| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| crit_1 | 1 | 417 | 2.997602 | 1.165404 | 3 | 2.997015 | 1.4826 | 1 | 5 | 4 | 0.0137338 | -0.7209386 | 0.0570701 |
| crit_2 | 2 | 424 | 3.617924 | 1.179117 | 4 | 3.729412 | 1.4826 | 1 | 5 | 4 | -0.5930965 | -0.4818832 | 0.0572630 |
| crit_3 | 3 | 423 | 3.182033 | 1.142650 | 3 | 3.197640 | 1.4826 | 1 | 5 | 4 | -0.0831016 | -0.7684268 | 0.0555575 |
| cult_1 | 4 | 426 | 3.286385 | 1.298924 | 3 | 3.356725 | 1.4826 | 1 | 5 | 4 | -0.2764276 | -1.0341767 | 0.0629331 |
| cult_2 | 5 | 427 | 3.470726 | 1.204826 | 4 | 3.556851 | 1.4826 | 1 | 5 | 4 | -0.4456470 | -0.7410155 | 0.0583056 |
| cult_3 | 6 | 424 | 2.320755 | 1.301370 | 2 | 2.170588 | 1.4826 | 1 | 5 | 4 | 0.6513422 | -0.7400155 | 0.0632001 |
| str_1r | 7 | 426 | 2.586854 | 1.237917 | 3 | 2.502924 | 1.4826 | 1 | 5 | 4 | 0.3059753 | -0.8556516 | 0.0599773 |
| str_2r | 8 | 421 | 1.698337 | 1.091841 | 1 | 1.462908 | 0.0000 | 1 | 5 | 4 | 1.5578738 | 1.5265275 | 0.0532131 |
| str_3r | 9 | 422 | 2.289099 | 1.214256 | 2 | 2.159763 | 1.4826 | 1 | 5 | 4 | 0.5710610 | -0.6205825 | 0.0591091 |
| cbs_1 | 10 | 423 | 2.957447 | 1.303690 | 3 | 2.946903 | 1.4826 | 1 | 5 | 4 | -0.0559133 | -1.0609892 | 0.0633876 |
| cbs_2 | 11 | 420 | 2.902381 | 1.219372 | 3 | 2.877976 | 1.4826 | 1 | 5 | 4 | 0.1468721 | -0.8557284 | 0.0594993 |
| cbs_3 | 12 | 423 | 2.638298 | 1.466578 | 3 | 2.548673 | 1.4826 | 1 | 5 | 4 | 0.2427654 | -1.3729458 | 0.0713075 |
| pcc_1 | 13 | 427 | 3.435597 | 1.137268 | 4 | 3.501458 | 1.4826 | 1 | 5 | 4 | -0.3810130 | -0.5663893 | 0.0550362 |
| pcc_2 | 14 | 429 | 3.475525 | 1.124279 | 4 | 3.536232 | 1.4826 | 1 | 5 | 4 | -0.3770954 | -0.6082189 | 0.0542807 |
| pcc_3 | 15 | 429 | 3.682984 | 1.109523 | 4 | 3.782609 | 1.4826 | 1 | 5 | 4 | -0.6130781 | -0.3475921 | 0.0535683 |
#Create Matrix for School Climate for Diversity Scale
#create a correlation matrix for cs vars
CCsocmatrix <-cor(cc_soc_vars, use = "complete.obs")
ggcorrplot(CCsocmatrix, hc.order = TRUE, type = "lower", lab = TRUE, ggtheme = ggplot2::theme_gray,
colors = c("#6D9EC1", "white", "#E46726"))
#CFA for School Racial Climate Scale
#CFA for School Engagement Measures
SRC_CFA <- 'CC_soc =~ 1*crit_1 + crit_2 + crit_3
Ctr_soc =~ 1*cult_1 + cult_2 + cult_3
Stp_soc =~ 1*str_1r + str_2r + str_3r
CB_soc =~ 1*cbs_1 + cbs_2 + cbs_3
PRC_soc =~ 1*pcc_1 + pcc_2 + pcc_3'
SRC_CFA <- sem(SRC_CFA, data = ESSD, estimator = "MLR")
summary(SRC_CFA, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-9 ended normally after 43 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 40
##
## Used Total
## Number of observations 396 457
##
## Model Test User Model:
## Standard Robust
## Test Statistic 171.088 156.492
## Degrees of freedom 80 80
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.093
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 2036.757 1777.010
## Degrees of freedom 105 105
## P-value 0.000 0.000
## Scaling correction factor 1.146
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.953 0.954
## Tucker-Lewis Index (TLI) 0.938 0.940
##
## Robust Comparative Fit Index (CFI) 0.956
## Robust Tucker-Lewis Index (TLI) 0.943
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8640.721 -8640.721
## Scaling correction factor 1.080
## for the MLR correction
## Loglikelihood unrestricted model (H1) -8555.177 -8555.177
## Scaling correction factor 1.089
## for the MLR correction
##
## Akaike (AIC) 17361.442 17361.442
## Bayesian (BIC) 17520.698 17520.698
## Sample-size adjusted Bayesian (BIC) 17393.777 17393.777
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.054 0.049
## 90 Percent confidence interval - lower 0.043 0.038
## 90 Percent confidence interval - upper 0.065 0.060
## P-value RMSEA <= 0.05 0.283 0.536
##
## Robust RMSEA 0.051
## 90 Percent confidence interval - lower 0.039
## 90 Percent confidence interval - upper 0.063
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.052 0.052
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CC_soc =~
## crit_1 1.000 0.757 0.644
## crit_2 1.021 0.119 8.614 0.000 0.774 0.651
## crit_3 1.051 0.107 9.802 0.000 0.796 0.703
## Ctr_soc =~
## cult_1 1.000 1.090 0.844
## cult_2 0.904 0.047 19.431 0.000 0.985 0.822
## cult_3 0.771 0.058 13.319 0.000 0.841 0.642
## Stp_soc =~
## str_1r 1.000 0.789 0.635
## str_2r 0.910 0.110 8.243 0.000 0.717 0.655
## str_3r 1.054 0.133 7.895 0.000 0.831 0.683
## CB_soc =~
## cbs_1 1.000 0.815 0.630
## cbs_2 1.288 0.142 9.041 0.000 1.050 0.855
## cbs_3 1.000 0.128 7.802 0.000 0.816 0.557
## PRC_soc =~
## pcc_1 1.000 0.902 0.786
## pcc_2 0.921 0.067 13.696 0.000 0.831 0.732
## pcc_3 0.861 0.069 12.457 0.000 0.776 0.696
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## CC_soc ~~
## Ctr_soc 0.594 0.074 8.001 0.000 0.720 0.720
## Stp_soc 0.113 0.055 2.051 0.040 0.189 0.189
## CB_soc 0.128 0.057 2.263 0.024 0.207 0.207
## PRC_soc 0.546 0.068 8.088 0.000 0.799 0.799
## Ctr_soc ~~
## Stp_soc 0.307 0.065 4.715 0.000 0.358 0.358
## CB_soc 0.092 0.064 1.428 0.153 0.103 0.103
## PRC_soc 0.749 0.072 10.435 0.000 0.762 0.762
## Stp_soc ~~
## CB_soc 0.105 0.050 2.079 0.038 0.163 0.163
## PRC_soc 0.209 0.056 3.755 0.000 0.293 0.293
## CB_soc ~~
## PRC_soc 0.122 0.058 2.111 0.035 0.166 0.166
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .crit_1 0.808 0.073 11.119 0.000 0.808 0.585
## .crit_2 0.812 0.089 9.074 0.000 0.812 0.576
## .crit_3 0.650 0.073 8.913 0.000 0.650 0.506
## .cult_1 0.479 0.057 8.455 0.000 0.479 0.287
## .cult_2 0.466 0.057 8.149 0.000 0.466 0.324
## .cult_3 1.009 0.074 13.671 0.000 1.009 0.588
## .str_1r 0.922 0.090 10.199 0.000 0.922 0.597
## .str_2r 0.686 0.097 7.074 0.000 0.686 0.571
## .str_3r 0.791 0.105 7.550 0.000 0.791 0.534
## .cbs_1 1.012 0.109 9.245 0.000 1.012 0.604
## .cbs_2 0.407 0.122 3.324 0.001 0.407 0.269
## .cbs_3 1.482 0.144 10.299 0.000 1.482 0.690
## .pcc_1 0.505 0.061 8.328 0.000 0.505 0.383
## .pcc_2 0.599 0.074 8.141 0.000 0.599 0.464
## .pcc_3 0.643 0.062 10.444 0.000 0.643 0.516
## CC_soc 0.574 0.084 6.811 0.000 1.000 1.000
## Ctr_soc 1.188 0.098 12.179 0.000 1.000 1.000
## Stp_soc 0.622 0.105 5.947 0.000 1.000 1.000
## CB_soc 0.665 0.115 5.794 0.000 1.000 1.000
## PRC_soc 0.814 0.089 9.183 0.000 1.000 1.000
##
## R-Square:
## Estimate
## crit_1 0.415
## crit_2 0.424
## crit_3 0.494
## cult_1 0.713
## cult_2 0.676
## cult_3 0.412
## str_1r 0.403
## str_2r 0.429
## str_3r 0.466
## cbs_1 0.396
## cbs_2 0.731
## cbs_3 0.310
## pcc_1 0.617
## pcc_2 0.536
## pcc_3 0.484#Additional Visualizations ##crit_1 historgram
#histogram for crit_1
Crit1Hist <- ggplot(ESSD) +
geom_histogram(aes(x = crit_1), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Crit1Hist
##crit_2 historgram
#histogram for crit_1
Crit2Hist <- ggplot(ESSD) +
geom_histogram(aes(x = crit_2), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Crit2Hist
##crit_3 historgram
#histogram for crit_1
Crit3Hist <- ggplot(ESSD) +
geom_histogram(aes(x = crit_3), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Crit3Hist
##cult_1 historgram
#histogram for cult_1
Cult1Hist <- ggplot(ESSD) +
geom_histogram(aes(x = cult_1), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Cult1Hist
##cult_2 historgram
#histogram for cult_1
Cult2Hist <- ggplot(ESSD) +
geom_histogram(aes(x = cult_2), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Cult2Hist
##cult_3 historgram
#histogram for cult_1
Cult3Hist <- ggplot(ESSD) +
geom_histogram(aes(x = cult_3), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Cult3Hist
##pcc_1 historgram
#histogram for pcc_1
Pcc1Hist <- ggplot(ESSD) +
geom_histogram(aes(x = pcc_1), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Pcc1Hist
##pcc_2 historgram
#histogram for pcc_2
Pcc2Hist <- ggplot(ESSD) +
geom_histogram(aes(x = pcc_2), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Pcc2Hist
##pcc_3 historgram
#histogram for pcc_3
Pcc3Hist <- ggplot(ESSD) +
geom_histogram(aes(x = pcc_3), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Pcc3Hist
##str_1r historgram
#histogram for str_1r
Str1Hist <- ggplot(ESSD) +
geom_histogram(aes(x = str_1r), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Str1Hist
##str_2r historgram
#histogram for str_2r
Str2Hist <- ggplot(ESSD) +
geom_histogram(aes(x = str_2r), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Str2Hist
##str_3r historgram
#histogram for str_3r
Str3Hist <- ggplot(ESSD) +
geom_histogram(aes(x = str_3r), bins = 5, col="black", fill = 'darkolivegreen4')
#see histogram
Str3Hist