## Installing packages if not available
list.of.packages <- c("psych", "dplyr","readxl","stringr","GPArotation")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)## Loading required packages into the environment
library(psych)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(readxl)
library(stringr)
library(GPArotation)
options(warn=-1)
## loading functions to process data
source("support_functions.R")file <- "../robinhood0719/data/raw/nycschoolsurvey_data/2018-public-data-file_parent.xlsx"
sheet <- "Parent # of Resp"
## loading ELA & Math performance data
perf <- read.csv("../robinhood0719/data/interim/perf_data_2006_2019.csv")
## loading survey data
parent_survey_data <- read_survey_data(file,sheet)## filtering
parent_responses <- parent_survey_data[[1]]
parent_responses <- parent_responses[parent_responses$dbn %in% perf$DBN,]
## Defining numerical ratings for responses
used_ratings <- c("Strongly agree","Agree","Disagree","Strongly disagree","Very satisfied","Satisfied","Dissatisfied","Very dissatisfied","Don\'t know","Very Dissatisfied","Very unlikely","Somewhat unlikely","Somewhat likely", "Very likely","Never","Rarely","Sometimes","Often")
rating_values <- c(5,4,2,1,5,4,2,1,0,1,1,2,4,5,1,2,4,5)
## Summarizing rating scores for each question
psr <- get_summarized_scores(parent_responses,parent_survey_data[[2]],used_ratings,rating_values)
psr$year <- 2018
## filtering out questions with >5% missingness
## pre-k/3-K, and high-school specific questions get filtered out
## Also Q7 for which responses can not be coded numerically given its subjective nature
psr <- psr[,apply(psr[,(1:(ncol(psr)-2))],2,FUN=function(x) sum(is.na(x)))*100/nrow(psr) < 5]## parallel plot to determine approximate number of factors
fa.parallel(psr[,1:(ncol(psr)-2)],fa="fa",fm="mle")
## Parallel analysis suggests that the number of factors = 7 and the number of components = NA
## fitting factor model with 8 factors after couple of iterations
pfm <- fa(psr[,1:(ncol(psr)-2)],fm="mle",rotate='oblimin',nfactors=8)
## factor loadings/weights
print(pfm$loadings,cutoff=0)
##
## Loadings:
## ML1 ML3 ML8 ML2 ML7 ML6 ML5 ML4
## 1a. 0.167 0.117 0.149 0.089 0.272 0.455 0.042 -0.014
## 1b. 0.104 0.119 0.343 -0.088 0.103 0.424 0.075 0.135
## 1c. 0.097 0.092 0.061 -0.011 0.621 0.108 0.239 0.016
## 1d. 0.107 0.083 0.072 0.264 0.026 0.544 0.176 -0.048
## 1e. 0.340 0.105 0.074 0.121 0.251 0.213 0.149 -0.011
## 1f. 0.074 0.054 0.080 0.277 0.021 0.169 0.552 -0.021
## 1g. 0.282 0.089 0.058 0.118 0.370 0.203 0.158 0.003
## 1h. 0.120 0.038 0.206 0.110 0.237 -0.035 0.534 0.019
## 1i. 0.886 0.015 -0.001 -0.075 0.100 0.008 0.135 -0.038
## 1j. 0.943 0.027 0.038 0.032 0.010 -0.007 0.044 -0.066
## 1k. 0.943 0.025 0.057 0.067 0.000 -0.033 0.027 -0.077
## 1l. 0.926 0.039 -0.026 0.006 0.024 0.084 -0.004 -0.033
## 2a. -0.036 0.054 0.071 0.506 0.001 0.181 0.255 0.239
## 2b. 0.666 0.060 -0.038 0.090 0.051 0.129 -0.037 0.221
## 2c. 0.239 0.112 -0.121 0.223 0.224 0.064 0.045 0.293
## 2d. 0.228 0.130 -0.060 0.221 0.304 0.219 0.050 0.162
## 2e. 0.425 0.053 0.084 0.259 -0.129 0.062 0.223 0.130
## 2f. 0.115 0.082 0.089 0.264 0.104 0.150 0.197 0.035
## 2g. 0.245 0.069 0.088 0.146 0.129 0.045 0.237 0.176
## 3a. 0.732 0.035 -0.097 0.103 0.082 0.110 -0.139 0.219
## 3b. 0.623 0.049 0.137 0.066 0.080 -0.089 0.037 0.330
## 3c. 0.588 0.062 0.183 0.015 0.033 -0.017 0.050 0.375
## 4a. -0.049 0.017 0.671 0.043 0.060 -0.029 0.079 0.245
## 4b. -0.076 0.001 0.574 0.071 0.062 0.391 -0.020 -0.011
## 4c. 0.104 0.060 0.678 0.157 -0.107 0.121 0.007 -0.116
## 5a. 0.157 0.082 0.125 0.380 0.529 -0.024 -0.107 0.018
## 5b. 0.063 0.048 0.005 0.840 0.081 -0.011 0.018 -0.025
## 5c. -0.001 0.050 0.123 0.774 -0.012 0.062 0.089 -0.001
## 6a. 0.008 -0.018 0.758 0.008 0.185 0.049 -0.040 0.042
## 6b. 0.053 0.001 0.809 0.035 -0.051 -0.069 0.146 -0.063
## 9a. -0.026 1.001 -0.028 0.006 -0.031 -0.009 -0.024 -0.036
## 9b. -0.036 0.998 0.053 -0.020 -0.019 -0.034 0.000 0.016
## 9c. 0.021 0.930 -0.072 -0.027 0.018 0.003 -0.009 -0.012
##
## ML1 ML3 ML8 ML2 ML7 ML6 ML5 ML4
## SS loadings 5.824 3.006 2.831 2.233 1.286 1.150 1.042 0.681
## Proportion Var 0.176 0.091 0.086 0.068 0.039 0.035 0.032 0.021
## Cumulative Var 0.176 0.268 0.353 0.421 0.460 0.495 0.526 0.547
## diagram displaying factor-question associations
fa.diagram(pfm,cut=0.245)
