BHA & MHA Alumni Survey, 2019
Overall Results
Bottom Line up Front: The programs are on track but require some performance improvement as do all programs.
#####################Read and Pre-Clean the Data#######################
library(psych) #to describe
library(reticulate) #to use Python in R as well## Warning: package 'reticulate' was built under R version 3.5.1mydata=read.csv("C:/Users/lfult/OneDrive - Texas State University/BHA2/alumnisurvey.csv")
#str(mydata)
#########################################################################Descriptive Statistics / Univariate Graphs / Crosstabs
Descriptive analysis is broken down by BHA (n=91), MHA (n=50), BHA/MHA (n=11). For those who earned both the BHA and MHA, their responses are included in all three categories for completeness.
#############################Descriptives 1##############################
describe(mydata[,3:17])## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning
## Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf## vars n mean sd median trimmed mad min
## Progress 1 142 94.11 17.99 100.0 99.58 0.00 24
## Duration..in.seconds. 2 142 1913.75 9673.91 519.5 603.14 278.73 82
## Finished 3 142 NaN NA NA NaN NA Inf
## RecordedDate* 4 142 64.68 36.13 63.0 64.73 44.48 1
## First* 5 142 65.42 35.87 67.5 65.98 45.96 1
## Last* 6 142 68.86 39.42 68.5 68.82 50.41 1
## TexasStateName* 7 142 10.02 13.82 1.0 7.76 0.00 1
## Addess* 8 142 60.96 40.41 60.5 60.50 52.63 1
## City* 9 142 35.60 24.89 34.5 34.89 37.06 1
## State* 10 142 20.04 6.10 21.0 21.52 4.45 1
## Zip* 11 142 59.82 33.51 61.5 60.54 42.25 1
## Email* 12 142 67.57 41.02 67.5 67.50 52.63 1
## Business* 13 142 54.36 38.09 54.5 53.53 51.15 1
## Title* 14 142 48.16 36.08 46.5 46.79 49.67 1
## BusAddress* 15 142 35.32 32.81 28.5 32.51 40.77 1
## max range skew kurtosis se
## Progress 100 76 -3.00 7.70 1.51
## Duration..in.seconds. 91439 91357 8.22 67.69 811.82
## Finished -Inf -Inf NA NA NA
## RecordedDate* 128 127 0.02 -1.17 3.03
## First* 123 122 -0.10 -1.23 3.01
## Last* 137 136 0.01 -1.22 3.31
## TexasStateName* 40 39 1.12 -0.48 1.16
## Addess* 131 130 0.05 -1.27 3.39
## City* 80 79 0.16 -1.38 2.09
## State* 25 24 -1.97 2.83 0.51
## Zip* 116 115 -0.17 -1.17 2.81
## Email* 138 137 0.01 -1.24 3.44
## Business* 121 120 0.09 -1.31 3.20
## Title* 113 112 0.15 -1.33 3.03
## BusAddress* 99 98 0.43 -1.26 2.75par(mfrow=c(1,1))
boxplot(mydata$Q32.MonthstoEmployment~mydata$Degree, horizontal=TRUE, col=c("red", "blue", "green"), main="Time to Employment by Degree")
boxplot(mydata$Q33.AnnualSalary1000s~mydata$Degree, horizontal=TRUE, col=c("red","blue","green"), main="Salary in 1000's by Degree")
plot(mydata$Q33.AnnualSalary1000s~mydata$YearsSinceGrad, ylab="Salary in 1000's", xlab="Years Since Graduation", col="red", pch=20)
abline(lm(mydata$Q33.AnnualSalary1000s~mydata$YearsSinceGrad), col="black")
mygraph=function(x,lab){
ggplot(mydata, aes(x=x, group=Degree))+
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes( label = scales::percent(..prop..),
y= ..prop.. ), stat= "count", vjust=-.1)+
facet_wrap(~Degree)+
scale_y_continuous(labels = scales::percent)+
ylab("Percent")+
xlab(lab)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
guides(fill=FALSE)
}
mygraph(mydata$Q26.Recommend, "Recommend?")
mygraph(mydata$Q16.1.Curriculum, "Curriculum")
mygraph(mydata$Q16.2.FacultyTeaching, "Faculty Teaching")
mygraph(mydata$Q16.3FieldPlacementExperience, "Field Placement Experience")
mygraph(mydata$Q16.4.SOHAFacilities, "SOHA Facilities")
mygraph(mydata$Q16.5.AlumniRelations, "Alumni Relations")
mygraph(mydata$Q18.1.Leadership, "Prepared me for Leadership")
mygraph(mydata$Q18.2.BusinessSkills, "Provided Business Skills")
mygraph(mydata$Q18.3.Professionalism, "Prepared me for Professionalism")
mygraph(mydata$Q18.4.KnowledgeofHCEnvironment, "Provided Knowledge of HC Environment")
mygraph(mydata$Q18.5.CRM, "Prepared for Communication & Relationship Mgt")
mygraph(mydata$Q19.PrepforMgtPosition, "Curriculum Prepared for Mgt Position")
mygraph(mydata$Q20.FieldExpPrepforMgt, "Field Experience Prepared for Mgt Position")
mygraph(mydata$Q22.1.FinMgt, "Course: Financial Mgt")
mygraph(mydata$Q22.2.PatMgtQI, "Course: Patient Mgt & QI")
mygraph(mydata$Q22.3.HIMCourse, "Course: HIM")
mygraph(mydata$Q22.4.HealthLaw, "Course: Health Law")
mygraph(mydata$Q22.5.EthicsOB, "Course: Ethics / OB")
mygraph(mydata$Q22.6.Marketing, "Course: Marketing")
mygraph(mydata$Q22.7.HRM,"HRM")
mygraph(mydata$Q22.8.HCCulture, "Health Care History & Culture")
mygraph(mydata$Q22.9.StratMgt, "Strategic Management")
#########################################################################