add 112

112/paper.pdf

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+oid sha256:0a1fac465f75f706e3e2a4d5774f01a112051ae8ed5bc4885413f969cce2de0b
+size 78876

112/replication_package/PIPS_Codebook.rtf

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+
+\f0\fs24 \cf0 CodeBook for \'93The PhD Pipeline Initiative Works\'94\
+Ryan Brutger 12-2-2022\
+\
+The following provides the coding rules for the variables used in the analysis\
+\
+Enrolled: an indicator variable for whether a student had enrolled AND completed the PIPS program when they took the survey (1=yes, 0=no)\
+\
+Enroll_Sem: \
+	0=includes those who did not receive spots in PIPS or who had not yet enrolled in PIPS\
+ 	1=enrolled in spring 2021\
+	2= enrolled fall 2021 but had not yet completed PIPS \
+\
+firstgen: 1= those who self identify as "first generation college student", 0=otherwise\
+\
+\pard\tx220\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\li720\fi-720\pardirnatural\partightenfactor0
+\ls1\ilvl0\cf0 For the race and ethnicity variables, each is an indicator for whether the respondent self-identified\
+with the race or ethnicity identified (respondents could choose more than one) The options were:\
+ \
+{\listtext	\uc0\u8226 	}American Indian or Alaska Native\
+{\listtext	\uc0\u8226 	}Asian or Asian American\
+{\listtext	\uc0\u8226 	}Black or African American\
+{\listtext	\uc0\u8226 	}Hispanic, Latino, Latina, LatinX\
+{\listtext	\uc0\u8226 	}Middle Eastern or Northern African\
+{\listtext	\uc0\u8226 	}Native Hawaiian or Other Pacific Islander\
+{\listtext	\uc0\u8226 	}White\
+{\listtext	\uc0\u8226 	}Another option (please specify)\
+{\listtext	\uc0\u8226 	}Prefer not to say\
+\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
+\cf0 \
+Dependent Variables:\
+\
+Phd_interest: 4=very likely, 3=somewhat likely, 2=somewhat unlikely, 1=very unlikely\
+Prep_app: 4=very prepared, 3=somewhat prepared, 2=not very prepared, 1=not prepared at all\
+Prep_diversity: 4=very prepared, 3=somewhat prepared, 2=not very prepared, 1=not prepared at all\
+Prep_research: 4=very prepared, 3=somewhat prepared, 2=not very prepared, 1=not prepared at all\
+Prep_letters: 4=very prepared, 3=somewhat prepared, 2=not very prepared, 1=not prepared at all\
+\
+The coding for Phd_interest2, Prep_app2, Prep_diversity2, Prep_research2, Prep_letters2 is based on the previously described measured, but it they are dichotomized. If the earlier measure was a 3 or 4, then the dichotomous measure=1, otherwise it equals 0\
+}

112/replication_package/PIPS_ReadMe.rtf

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+
+\f0\fs24 \cf0 ReadMe for \'93The PhD Pipeline Initiative Works\'94\
+Ryan Brutger 12-2-2022\
+\
+The replication files include:\
+\
+1. \'93PIPS_Replication_Code_12-1-2022.R\'94 is the R file necessary to run the analysis for the manuscript and supporting appendix.\
+\
+All of the analyses were carried out using R version 4.0.1 on a Macbook Pro with Intel Core i5 processor using MacOS Catalina V. 10.15.7\
+\
+2. \'93PIPS_Replication_Data_12-2-2022.csv\'94 contains the data file for the analysis.\
+\
+3. \'93PIP_Codebook\'94 includes the coding rules for the variables used in the analysis.}

112/replication_package/PIPS_Replication_Code_12-1-2022.R

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+## Replication Code for "The PhD Pipeline Initiative Works"
+## Ryan Brutger, Last modified: 12-1-2022
+
+## This R file contains the code necessary to replicate the analysis, including those in the main text and supplementary appendix.
+## All of the following analyses were carried out using R version 4.0.1 on a Macbook Pro with Intel Core i5 processor using MacOS Catalina V. 10.15.7
+
+
+#load packages for analysis
+library(foreign)
+library(ggplot2)
+library(stargazer)
+library(xtable)
+
+setwd("ENTER DIRECTORY") # set working directory (change to your directory)
+
+rm(list = ls(all = TRUE))
+pips <- read.csv("PIPS_Replication_Data.csv")
+
+# The following provides the coding rules for the variables used in the analysis
+
+# Enrolled -> an indicator variable for whether a student had enrolled AND completed the PIPS program when they took the survey (1=yes, 0=no)
+# Enroll_Sem -> 0=includes those who did not receive spots in PIPS or who had not yet enrolled in PIPS, 1=enrolled in spring 2021, 2= enrolled fall 2021 but had not yet completed PIPS 
+
+# firstgen -> 1= those who self identify as "first generation college student", 0=otherwise
+# For the race and ethnicty variables, each is an indicator for whether the respondent self-identified with the race or ethnicity (they could choose more than one)
+# The options were: American Indian or Alaska Native; Asian or Asian American; Black or African American; Hispanic, Latino, Latina, LatinX;
+# Middle Eastern or Northern African; Native Hawaiian or Other Pacific Islander; White; Another option (please specify); Prefer not to say
+
+# Dependent Variables:
+#Phd_interest: 4=very likely, 3=somewhat likely, 2=somewhat unlikely, 1=very unlikely
+#Prep_app: 4=very prepared, 3=somewhat prepared, 2=not very prepared, 1=not prepared at all
+#Prep_diversity: 4=very prepared, 3=somewhat prepared, 2=not very prepared, 1=not prepared at all
+#Prep_research: 4=very prepared, 3=somewhat prepared, 2=not very prepared, 1=not prepared at all
+#Prep_letters: 4=very prepared, 3=somewhat prepared, 2=not very prepared, 1=not prepared at all
+
+# For Phd_interest2, Prep_app2, Prep_diversity2, Prep_research2, Prep_letters2 the earlier measures are dichotomized
+# If the earlier measure was a 3 or 4, then the dichotmout measure=1, otherwise it equals 0
+
+
+# Response rate calculations reported on page 5
+# There were 85 students in the lottery who could have taken the pre-PIPS survey
+length(pips$Enrolled[pips$Enrolled==0]) #42
+42/85# 0.49
+
+#At the time of this analysis 38 students had completed PIPS (two dropped during the semester)
+# the Spring 2022 enrolled students were in-progress and had not completed PIPS yet
+length(pips$Enrolled[pips$Enrolled==1]) #20 out of 28 students who had completed PIPS also completed the followup survey
+20/38 # 0.53
+
+# Demographics reported on page 6
+#First Generation
+length(pips$firstgen[pips$firstgen==1 & is.na(pips$firstgen)==FALSE]) #38 first generation 
+length(pips$firstgen[pips$firstgen==0 & is.na(pips$firstgen)==FALSE]) #19 not first generation
+38/(19+38) # 67% first generation
+
+# Race/Ethinicty
+# 11 identify as multiple races/ethnicities, which were counted manually
+sum(pips$hispanic) #25
+sum(pips$white) #16
+sum(pips$middleE) #8
+sum(pips$black) #4
+sum(pips$native) #1
+sum(pips$islander) #0
+
+#Gender (not reported in paper)
+sum(pips$male) #22
+sum(pips$female) #31
+sum(pips$non.binary) #3
+
+# Generate Table 1 of paper
+phd.interest2 <- lm(PhD_interest2 ~ Enrolled, data = pips)
+prep.app2 <- lm(Prep_app2 ~ Enrolled, data = pips)
+prep.diversity2 <- lm(Prep_diversity2 ~ Enrolled, data = pips)
+prep.research2 <- lm(Prep_research2 ~ Enrolled, data = pips)
+prep.letters2 <- lm(Prep_letters2 ~ Enrolled, data = pips)
+
+stargazer(phd.interest2 , prep.app2, prep.diversity2, prep.research2, prep.letters2, omit.stat = c("rsq", "adj.rsq", "ser", "f"), 
+          column.labels = c("PhD Interest", "Prepared \n to Apply", "Prepared \n Personal Statement", "Prepared \n  SOP", "Prepared \n LORs"))
+
+
+#Generate Appendix Table 3 of section 4
+phd.interest <- lm(PhD_interest ~ Enrolled, data = pips)
+prep.app <- lm(Prep_app ~ Enrolled, data = pips)
+prep.diversity <- lm(Prep_diversity ~ Enrolled, data = pips)
+prep.research <- lm(Prep_research ~ Enrolled, data = pips)
+prep.letters <- lm(Prep_letters ~ Enrolled, data = pips)
+
+stargazer(phd.interest , prep.app, prep.diversity, prep.research, prep.letters, omit.stat = c("rsq", "adj.rsq", "ser", "f"), 
+          column.labels = c("PhD Interest", "Prepared \n to Apply", "Prepared \n Personal Statement", "Prepared \n  SOP", "Prepared \n LORs"))
+
+# Generate Appendix Table 4 of section 5
+phd.interest2b <- lm(PhD_interest2 ~ Enrolled + male + white + firstgen, data = pips)
+prep.app2b <- lm(Prep_app2 ~ Enrolled + male + white + firstgen, data = pips)
+prep.diversity2b <- lm(Prep_diversity2 ~ Enrolled + male + white + firstgen, data = pips)
+prep.research2b <- lm(Prep_research2 ~ Enrolled + male + white + firstgen, data = pips)
+prep.letters2b <- lm(Prep_letters2 ~ Enrolled + male + white + firstgen, data = pips)
+
+stargazer(phd.interest2b , prep.app2b, prep.diversity2b, prep.research2b, prep.letters2b, omit.stat = c("rsq", "adj.rsq", "ser", "f"), 
+          column.labels = c("PhD Interest", "Prepared \n to Apply", "Prepared \n Personal Statement", "Prepared \n  SOP", "Prepared \n LORs"))
+
+# Generate Appendix Table 5 of section 6:  
+# Same as Table 1, but limits sample to only those who eventually enrolled in PIPS, comparing those who completed to those who had not yet started
+pipsEnrolled <- subset(pips[pips$Enroll_Sem>0, ])
+
+phd.interest2c <- lm(PhD_interest2 ~ Enrolled, data = pipsEnrolled)
+prep.app2c <- lm(Prep_app2 ~ Enrolled, data = pipsEnrolled)
+prep.diversity2c <- lm(Prep_diversity2 ~ Enrolled, data = pipsEnrolled)
+prep.research2c <- lm(Prep_research2 ~ Enrolled, data = pipsEnrolled)
+prep.letters2c <- lm(Prep_letters2 ~ Enrolled, data = pipsEnrolled)
+
+stargazer(phd.interest2c , prep.app2c, prep.diversity2c, prep.research2c, prep.letters2c, omit.stat = c("rsq", "adj.rsq", "ser", "f"), 
+          column.labels = c("PhD Interest", "Prepared \n to Apply", "Prepared \n Personal Statement", "Prepared \n  SOP", "Prepared \n LORs"))
+

112/replication_package/PIPS_Replication_Data_12-2-2022.csv

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+size 4158

112/should_reproduce.txt

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