#load required libraries

library(ggplot2)
library(lattice)
library(stats)
library(Matrix)
library(car)#used for type III, Anova()
library(ca) #correspondence analysis, ca()
library(agricolae)#used for HSD.test()
library(phia)
library(FactoMineR)#multiple correspondence analysis, MCA()
library(MASS)
library(gdata) #used to rename some variables
library(psych) #descriptive stats for table 1
library(plyr) 
#read in data file

prepost.ms1 <- read.csv("manuscript1data.csv", na.strings="")
str(prepost.ms1)

#############################################################
#paired t-tests for table 1
#############################################################
names(prepost.ms1)

table(prepost.ms1$Q2aPre,prepost.ms1$Q3aPre)

Q3.pair <- na.omit(prepost.ms1[,c(21,22)])
	t.test(Q3.pair$Q3aPre,Q3.pair$Q3aPost,paired=TRUE,alternative="less")
	describe(Q3.pair)
Q4.pair <- na.omit(prepost.ms1[,c(23,24)])
	t.test(Q4.pair$Q4aPre,Q4.pair$Q4aPost,paired=TRUE,alternative="less")
	describe(Q4.pair)
Q5a.pair <- na.omit(prepost.ms1[,c(25,26)])
	t.test(Q5a.pair$Q5aPre,Q5a.pair$Q5aPost,paired=TRUE,alternative="less")
	describe(Q5a.pair)
Q5b.pair <- na.omit(prepost.ms1[,c(27,28)])
	t.test(Q5b.pair$Q5bPre,Q5b.pair$Q5bPost,paired=TRUE,alternative="less")
	describe(Q5b.pair)
Q5c.pair <- na.omit(prepost.ms1[,c(29,30)])
	t.test(Q5c.pair$Q5cPre,Q5c.pair$Q5cPost,paired=TRUE,alternative="less")
	describe(Q5c.pair)
Q5d.pair <- na.omit(prepost.ms1[,c(31,32)])
	t.test(Q5d.pair$Q5dPre,Q5d.pair$Q5dPost,paired=TRUE,alternative="less")
	describe(Q5d.pair)
Q5e.pair <- na.omit(prepost.ms1[,c(33,34)])
	t.test(Q5e.pair$Q5ePre,Q5e.pair$Q5ePost,paired=TRUE,alternative="less")
	describe(Q5e.pair)
Q8.pair <- na.omit(prepost.ms1[,c(35,36)])
	t.test(Q8.pair$Q8Pre,Q8.pair$Q8Post,paired=TRUE,alternative="less")
	describe(Q8.pair)
Q10a.pair <- na.omit(prepost.ms1[,c(41,42)])
	t.test(Q10a.pair$Q10aPre,Q10a.pair$Q10aPost,paired=TRUE,alternative="less")
	describe(Q10a.pair)
Q10b.pair <- na.omit(prepost.ms1[,c(46,47)])
	t.test(Q10b.pair$OldQ10apre,Q10b.pair$OldQ10apost,paired=TRUE,alternative="less")
	describe(Q10b.pair)

#############################################################
#Preparing data for rmANOVAs
#############################################################

#make stacked variables for repeated measures anova

make.rm<-function(constant,repeated,data,contrasts) {
  if(!missing(constant) && is.vector(constant)) {
   if(!missing(repeated) && is.vector(repeated)) {
    if(!missing(data)) {
     dd<-dim(data)
     replen<-length(repeated)
     if(missing(contrasts))
      prepost<-
       ordered(sapply(paste("T",1:length(repeated),sep=""),rep,dd[1]))
     else
      prepost<-matrix(sapply(contrasts,rep,dd[1]),ncol=dim(contrasts)[2])
     if(length(constant) == 1) cons.col<-rep(data[,constant],replen)
     else cons.col<-lapply(data[,constant],rep,replen)
     new.df<-data.frame(cons.col,
      repeated.question=as.vector(data.matrix(data[,repeated])),
      prepost)
     return(new.df)
    }
   }
  }
  cat("Usage: make.rm(constant, repeated, data [, contrasts])\n")
  cat("\tWhere 'constant' is a vector of indices of non-repeated data and\n")
  cat("\t'repeated' is a vector of indices of the repeated measures data.\n")
  }

	sciskill.sub <- na.omit(prepost.ms1[,c(1,2,3,5,7,9,11,13,15,17,19,21,22)])
		sciskill<-make.rm(constant=c(1:11), repeated=c(12:13), data=sciskill.sub)
	confidence.sub <- na.omit(prepost.ms1[,c(1,2,3,5,7,9,11,13,15,17,19,23,24)])
		confidence<-make.rm(constant=c(1:11), repeated=c(12:13), data=confidence.sub)
	inds.rsrch.sub <- na.omit(prepost.ms1[,c(1,2,3,5,7,9,11,13,15,17,19,25,26)])
		inds.rsrch<-make.rm(constant=c(1:11), repeated=c(12:13), data=inds.rsrch.sub)
	mentor.rsrch.sub <- na.omit(prepost.ms1[,c(1,2,3,5,7,9,11,13,15,17,19,27,28)])
		mentor.rsrch<-make.rm(constant=c(1:11), repeated=c(12:13), data=mentor.rsrch.sub)
	analyze.sub <- na.omit(prepost.ms1[,c(1,2,3,5,7,9,11,13,15,17,19,29,30)])
		analyze<-make.rm(constant=c(1:11), repeated=c(12:13), data=analyze.sub)
	writeup.sub <- na.omit(prepost.ms1[,c(1,2,3,5,7,9,11,13,15,17,19,31,32)])
		writeup<-make.rm(constant=c(1:11), repeated=c(12:13), data=writeup.sub)
	present.sub <- na.omit(prepost.ms1[,c(1,2,3,5,7,9,11,13,15,17,19,33,34)])
		present<-make.rm(constant=c(1:11), repeated=c(12:13), data=present.sub)
	goals.sub <- na.omit(prepost.ms1[,c(1,2,3,5,7,9,11,13,15,17,19,35,36)])
		goals<-make.rm(constant=c(1:11), repeated=c(12:13), data=goals.sub)
	enviro.sub <- na.omit(prepost.ms1[,c(1,2,3,5,7,9,11,13,15,17,19,41,42)])
		enviro<-make.rm(constant=c(1:11), repeated=c(12:13), data=enviro.sub)
	STEM.sub <- na.omit(prepost.ms1[,c(1,2,3,5,7,9,11,13,15,17,19,46,47)])
		STEM<-make.rm(constant=c(1:11), repeated=c(12:13), data=STEM.sub)

#stacked variables for figure 11
	sciskill.8sub <- na.omit(prepost.ms1[,c(1,2,35,21,22)])
		sciskill8 <-make.rm(constant=c(1:3), repeated=c(4:5), data=sciskill.8sub)
	confidence.8sub <- na.omit(prepost.ms1[,c(1,2,35,23,24)])
		confidence8 <-make.rm(constant=c(1:3), repeated=c(4:5), data=confidence.8sub)
	inds.rsrch.8sub <- na.omit(prepost.ms1[,c(1,2,35,25,26)])
		inds.rsrch8 <-make.rm(constant=c(1:3), repeated=c(4:5), data=inds.rsrch.8sub)
	mentor.rsrch.8sub <- na.omit(prepost.ms1[,c(1,2,35,27,28)])
		mentor.rsrch8 <-make.rm(constant=c(1:3), repeated=c(4:5), data=mentor.rsrch.8sub)
	analyze.8sub <- na.omit(prepost.ms1[,c(1,2,35,29,30)])
		analyze8 <-make.rm(constant=c(1:3), repeated=c(4:5), data=analyze.8sub)
	writeup.8sub <- na.omit(prepost.ms1[,c(1,2,35,31,32)])
		writeup8 <-make.rm(constant=c(1:3), repeated=c(4:5), data=writeup.8sub)
	present.8sub <- na.omit(prepost.ms1[,c(1,2,35,33,34)])
		present8 <-make.rm(constant=c(1:3), repeated=c(4:5), data=present.8sub)

#############################################################
#rmANOVA 
#############################################################

#Multiple Correspondence Analysis used to check for multicolinearity

#restructure dataset for MCA
survey1 <- prepost.ms1
survey1[c(1:2,4,6,8,10,12,14,16,18,20:55)] <- list(NULL)
exp <- na.omit(survey1)
exp [,1][exp [,1]==0] <- "No.Lab"
exp [,1][exp [,1]==1] <- "Lab"
exp [,2][exp [,2]==0] <- "No.Field"
exp [,2][exp [,2]==1] <- "Field"
exp [,3][exp [,3]==0] <- "No.Team"
exp [,3][exp [,3]==1] <- "Team"
exp [,4][exp [,4]==0] <- "No.Mentor"
exp [,4][exp [,4]==1] <- "Mentor"
exp [,5][exp [,5]==0] <- "No.Present"
exp [,5][exp [,5]==1] <- "Present"
exp [,6][exp [,6]==0] <- "No.Contribute"
exp [,6][exp [,6]==1] <- "Contribute"
exp [,7][exp [,7]==0] <- "No.Author"
exp [,7][exp [,7]==1] <- "Author"
exp [,8][exp [,8]==0] <- "No.Intern"
exp [,8][exp [,8]==1] <- "Intern"
exp [,9][exp [,9]==0] <- "No.Respect"
exp [,9][exp [,9]==1] <- "Respect"
exp <- as.data.frame(unclass(exp ))
str(exp)

res.mca = MCA(exp)


#######
#Research Skills
#######
	
total.mt.lm <- lm(cbind(sciskill, confidence, indsrsrch, mentorrsrch, analyze, writeup, present, goals) ~ (prepost + 
			as.factor(Q1aPre) + as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student)), total.mt)
	summary(total.mt.lm)
total.mt.anova <- Anova(total.mt.lm, type="III", singular.ok = T)
	print(total.mt.anova)


#Question 3a - Perception of their scientific skills

#model using MiniTab stacking technique, fewer observations
sciskill.mt.lm <- lm(repeated.question ~ prepost + as.factor(Q1aPre) + 
			as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student), sciskill.mt)
	summary(sciskill.mt.lm)
sciskill.mt.anova <- Anova(sciskill.mt.lm, type="III", singular.ok = T) ##this is the exact output that I recieved from MiniTab
	print(sciskill.mt.anova)
#model using revised stacking technique, 48 more observations
#This new method will be continued from here on out
sciskill.lm <- lm(repeated.question ~ prepost + as.factor(Q1aPre) + 
			as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student), sciskill)
	summary(sciskill.lm)
sciskill.anova <- Anova(sciskill.lm, type="III", singular.ok = T) 
	print(sciskill.anova)
		HSD.test(sciskill.lm,"prepost",console=TRUE)
		HSD.test(sciskill.lm,"as.factor(Q1aPre)",console=TRUE)
		HSD.test(sciskill.lm,"as.factor(Q2aPre)",console=TRUE)
			with(sciskill, interaction.plot(prepost, Q1aPre,  repeated.question, 
 			 ylim = c(1, 4), lty = c(1, 3), lwd = 2,
 			 ylab = "Scientific skills", xlab = "pre/post", trace.label = "Research Team", ))
	
#Question 4a - Confidence in their scientific skills
confidence.lm <- lm(repeated.question ~ Student + prepost + as.factor(Q1aPre) + 
			as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student), sciskill)

confidence.anova <- Anova(confidence.lm, type="III", singular.ok = T) 
	print(confidence.anova)
		HSD.test(confidence.lm,"prepost",console=TRUE)
		HSD.test(confidence.lm,"as.factor(Q1aPre)",console=TRUE)
		HSD.test(confidence.lm,"as.factor(Q2aPre)",console=TRUE)

#######
#Interdisciplinary Research
#######

#Question 5a - Ability to conduct interdisciplinary research with a resaerch team
inds.rsrch.lm <- lm(repeated.question ~ Student + prepost + as.factor(Q1aPre) + 
			as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student), inds.rsrch)
inds.rsrch.anova <- Anova(inds.rsrch.lm, type="III", singular.ok = T) 
	print(inds.rsrch.anova)
		HSD.test(inds.rsrch.lm,"prepost",console=TRUE)
		HSD.test(inds.rsrch.lm,"as.factor(Q1aPre)",console=TRUE)
		HSD.test(inds.rsrch.lm,"as.factor(Q2aPre)",console=TRUE)

#Question 5b - Ability to conduct research supervised by a mentor
mentor.rsrch.lm <- lm(repeated.question ~ Student + prepost + as.factor(Q1aPre) + 
			as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student), mentor.rsrch)
mentor.rsrch.anova <- Anova(mentor.rsrch.lm, type="III", singular.ok = T) 
	print(mentor.rsrch.anova)
		HSD.test(mentor.rsrch.lm,"prepost",console=TRUE)
		HSD.test(mentor.rsrch.lm,"as.factor(Q2aPre)",console=TRUE)

#######
#Creating Research Products
#######

t.test(prepost.ms1$Q5cPre,prepost.ms1$Q5cPost,paired=TRUE)
t.test(analyze.sub$Q5cPre, analyze.sub$Q5cPost, paired=TRUE)


#Question 5c - Ability to analyze data
analyze.lm <- lm(repeated.question ~ Student + prepost + as.factor(Q1aPre) + 
			as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student), analyze)
analyze.anova <- Anova(analyze.lm, type="III", singular.ok = T) 
	print(analyze.anova)
		HSD.test(analyze.lm,"as.factor(Q1aPre)",console=TRUE)
		HSD.test(analyze.lm,"as.factor(Q1cPre)",group=TRUE, console=TRUE)
		HSD.test(analyze.lm,"as.factor(Q1fPre)",console=TRUE)
		HSD.test(analyze.lm,"as.factor(Q2aPre)",console=TRUE)

#Question 5d - Ability to write-up results
writeup.lm <- lm(repeated.question ~ Student + prepost + as.factor(Q1aPre) + 
			as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student), writeup)

writeup.anova <- Anova(writeup.lm, type="III", singular.ok = T) 
	print(writeup.anova)
		HSD.test(writeup.lm,"as.factor(Q1aPre)",console=TRUE)
		HSD.test(writeup.lm,"as.factor(Q1fPre)",console=TRUE)

#Question 5e - Ability to present results
present.lm <- lm(repeated.question ~ Student + prepost + as.factor(Q1aPre) + 
			as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student), present)
present.anova <- Anova(present.lm, type="III", singular.ok = T) 
	print(present.anova)
		HSD.test(present.lm,"prepost",console=TRUE)
		HSD.test(present.lm,"as.factor(Q1ePre)",console=TRUE)
		HSD.test(present.lm,"as.factor(Q1fPre)",console=TRUE)
		HSD.test(present.lm,"as.factor(Q2aPre)",console=TRUE)

#######
#Persistance in science
#######

#Question 3a - Perception of their scientific skills
sciskill.8.lm <- lm(repeated.question ~ Student + prepost + as.factor(Q8aPre) + prepost*as.factor(Q8aPre) +(1|Student), sciskill8)
sciskill.8.anova <- Anova(sciskill.8.lm, type="III", singular.ok = T) 
	print(sciskill.8.anova)
		HSD.test(sciskill.8.lm,"prepost",console=TRUE)
		HSD.test(sciskill.8.lm,"as.factor(Q8aPre)",console=TRUE)
		with(sciskill8, interaction.plot(prepost, as.factor(Q8aPre),  repeated.question, 
 			 ylim = c(1, 4), lty = c(1,2,3), lwd = 2,
 			 ylab = "Scientific skills", xlab = "pre/post", trace.label = "Clarity", ))

#Question 4a - Confidence in their scientific skills
confidence.8.lm <- lm(repeated.question ~ Student + prepost + Q8aPre + prepost*Q8aPre +(1|Student), confidence8)
confidence.8.anova <- Anova(confidence.8.lm, type="III", singular.ok = T) 
	print(confidence.8.anova)	
		HSD.test(confidence.8.lm,"prepost",console=TRUE)
		HSD.test(confidence.8.lm,"Q8aPre",console=TRUE)
		#Interaction plot
		with(confidence8, interaction.plot(prepost, Q8aPre,  repeated.question, 
 			 ylim = c(1, 4), lty = c(1,2,3), lwd = 2,
 			 ylab = "Confidence in skills", xlab = "pre/post", trace.label = "Clarity", ))

#Question 5a - Ability to conduct interdisciplinary research with a resaerch team
inds.rsrch.8.lm <- lm(repeated.question ~ Student + prepost + Q8aPre + prepost*Q8aPre +(1|Student), inds.rsrch8)
inds.rsrch.8.anova <- Anova(inds.rsrch.8.lm, type="III", singular.ok = T) 
	print(inds.rsrch.8.anova)

#Question 5b - Ability to conduct research supervised by a mentor
mentor.rsrch.8.lm <- lm(repeated.question ~ Student + prepost + Q8aPre + prepost*Q8aPre +(1|Student), mentor.rsrch8)
mentor.rsrch.8.anova <- Anova(mentor.rsrch.8.lm, type="III", singular.ok = T) 
	print(mentor.rsrch.8.anova)
		HSD.test(mentor.rsrch.8.lm,"prepost",console=TRUE)

#Question 5c - Ability to analyze data
analyze.8.lm <- lm(repeated.question ~ Student + prepost + Q8aPre + prepost*Q8aPre +(1|Student), analyze8)
analyze.8.anova <- Anova(analyze.8.lm, type="III", singular.ok = T) 
	print(analyze.8.anova)
		HSD.test(analyze.8.lm,"prepost",console=TRUE)
		HSD.test(analyze.8.lm,"Q8aPre",console=TRUE)

#Question 5d - Ability to write-up results
writeup.8.lm <- lm(repeated.question ~ Student + prepost + Q8aPre + prepost*Q8aPre +(1|Student), writeup8)
writeup.8.anova <- Anova(writeup.8.lm, type="III", singular.ok = T) 
	print(writeup.8.anova)
		HSD.test(writeup.8.lm,"prepost",console=TRUE)
		HSD.test(writeup.8.lm,"Q8aPre",console=TRUE)
		#Interaction plot
		with(writeup8, interaction.plot(prepost, Q8aPre,  repeated.question, 
 			 ylim = c(1, 5), lty = c(1,2,3), lwd = 2,
 			 ylab = "Write-Up Scientific Results", xlab = "pre/post", trace.label = "Clarity", ))

#Question 5e - Ability to present results
present.8.lm <- lm(repeated.question ~ Student + prepost + as.factor(Q8aPre) + prepost*as.factor(Q8aPre) +(1|Student), present8)
present.8.anova <- Anova(present.8.lm, type="III", singular.ok = T) 
	print(present.8.anova)
		HSD.test(present.8.lm,"prepost",console=TRUE)
		HSD.test(present.8.lm,"as.factor(Q8aPre)",console=TRUE)	
		with(writeup8, interaction.plot(prepost, as.factor(Q8aPre),  repeated.question, 
 			 ylim = c(1, 5), lty = c(1,2,3), lwd = 2,
 			 ylab = "Write-Up Scientific Results", xlab = "pre/post", trace.label = "Clarity", ))

enviro.lm <- lm(repeated.question ~ Student + prepost + as.factor(Q1aPre) + 
			as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student), enviro)
enviro.anova <- Anova(enviro.lm, type="III", singular.ok = T) 
	print(enviro.anova)
		HSD.test(enviro.lm,"as.factor(Q1bPre)",console=TRUE)

STEM.lm <- lm(repeated.question ~ Student + prepost + as.factor(Q1aPre) + 
			as.factor(Q1bPre) + as.factor(Q1cPre) + as.factor(Q1dPre) + 
			as.factor(Q1ePre) + as.factor(Q1fPre) + as.factor(Q1gPre) + 
			as.factor(Q1hPre) + as.factor(Q2aPre) + as.factor(prepost)*as.factor(Q1aPre) + 
			as.factor(prepost)*as.factor(Q1bPre) + as.factor(prepost)*as.factor(Q1cPre) + 
			as.factor(prepost)*as.factor(Q1dPre) + as.factor(prepost)*as.factor(Q1ePre) + 
			as.factor(prepost)*as.factor(Q1fPre) + as.factor(prepost)*as.factor(Q1gPre) + 
			as.factor(prepost)*as.factor(Q1hPre) + as.factor(prepost)*as.factor(Q2aPre) +
			(1|Student), STEM)
STEM.anova <- Anova(STEM.lm, type="III", singular.ok = T) 
	print(STEM.anova)
		
		with(STEM, interaction.plot(STEM, as.factor(Q5dPre),  repeated.question, 
 			 ylim = c(1, 5), lty = c(1,2,3), lwd = 2,
 			 ylab = "STEM Research Field", xlab = "pre/post", trace.label = "Liklihood", ))


Q10a.pair <- na.omit(prepost.ms1[,c(41,42,5)])
Q10a.field <- Q10a.pair[Q10a.pair$Q1bPre %in% 1,]
Q10a.nofield <- Q10a.pair[Q10a.pair$Q1bPre %in% 0,]
	t.test(Q10a.field$Q10aPre,Q10a.field$Q10aPost,paired=TRUE,alternative="less")
		describe(Q10a.field)
	t.test(Q10a.nofield$Q10aPre,Q10a.nofield$Q10aPost,paired=TRUE,alternative="less")
		describe(Q10a.nofield)

Q10b.pair <- na.omit(prepost.ms1[,c(46,47)])
	t.test(Q10b.pair$OldQ10apre,Q10b.pair$OldQ10apost,paired=TRUE,alternative="less")
	describe(Q10b.pair)


#############################################################
#Correspondence Analysis - Long term career plans
#############################################################

names(prepost.ms1)
summary(Q9)
str(Q9)

Q9 <- droplevels(na.omit(prepost.ms1[38:39]))
mytable <- table(Q9$Q9aPre,Q9$Q9aPost) # create a 2 way table
prop.table(mytable, 1) # row percentages
prop.table(mytable, 2) # column percentages
fit <- ca(mytable)
print(fit) # basic results 
summary(fit) # extended results 
plot(fit, mass = TRUE, contrib = "absolute", map =
  	"rowgreen", arrows = c(FALSE, TRUE)) # asymmetric map
plot(fit, xlab="Profession (39.8%)", ylab="Career path directly after graduation (27.9%)" ,mass=TRUE) # symmetric map - we are interested in this plot over the asymentrical