diff --git a/.DS_Store b/.DS_Store index a54dd70..0031aa0 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/analysis.Rmd b/analysis.Rmd index c753905..ca3b5b6 100644 --- a/analysis.Rmd +++ b/analysis.Rmd @@ -8,7 +8,7 @@ knit: (function(input_file, encoding) { output: html_document --- -This is a notebook to analyse NNS data from 2014-2023, baswed on data from NSS wesbite. +This is a notebook to analyse NNS data from 2014-2023, based on data from NSS wesbite. If you don't have eyethinkdata tools, install from github @@ -96,8 +96,13 @@ pirateye( d[ucl & tm],x_condition = "year",colour_condition = "theme",dodgewidt So now the psychology sector average for each year is 0, shown by heavy black line. Benchmarked like this, it looks like we had a good period of growth from 2016 onwards, and in the pandemic years we were well above the mean in almost everything. But again, our assessments are ranked below the mean and perhaps trending down +Let's try an interactive plot of the same info: - - +```{r} +library(plotly) +p <- ggplot(d[Institution=="University College London" & tm],aes(x=year,y=rzall,colour=theme))+ + geom_line(data=d[Institution=="University College London" & tm,.(rzall=mean(rzall)),by=.(year,theme)])+ geom_point() +ggplotly(p) +``` diff --git a/analysis.md b/analysis.md deleted file mode 100644 index 7ebe85f..0000000 --- a/analysis.md +++ /dev/null @@ -1,147 +0,0 @@ -NSS results -================ - -This is a notebook to analyse NNS data from 2014-2023, baswed on data -from NSS wesbite. - -If you don’t have eyethinkdata tools, install from github - -``` r -devtools::install_github("dcr-eyethink/eyethinkdata") -``` - -Load in the package and the raw data. Note that this has been filtered -to just psychology courses, and students on first degree only. - -``` r -library(eyethinkdata) -``` - - ## Loading required package: ggplot2 - - ## Loading required package: data.table - -``` r -full_data <- fread("NSS_2014-23.csv") -qkey <- data.table(read.csv("qkey3.csv")) -``` - -First we need to translate the question numbers into the items and -labels. There are 3 slightly difference question sets. Annoying. So we -have to merge info from the question keys to three different sections. -For each, we will have columns for the theme, the full question, and a -one word question key q. - -``` r -d <- rbind( pid_merge(full_data[year<=2016], qkey[,.(QuestionNumber=qnum,theme=theme2014,q=item2014,question=set_full2014)],link="QuestionNumber"), - pid_merge(full_data[year>2016 & year<2023], qkey[,.(QuestionNumber=qnum,theme=theme2017,q=item2017, question=set_full2017)],link="QuestionNumber"), - pid_merge(full_data[year==2023], qkey[,.(QuestionNumber=qnum,theme=theme2023,q=item2023, question=set_full2023)],link="QuestionNumber")) -``` - -Now we want one number that summarises responses to the question. Right -now we have the distribution of responses for each. All the items are -framed positively, ie if they agree with the statement the student is -saying something good about the university. So we’re going to code into -a response variable between -1 and 1 that is positive if they are -agreeing, and negative if they are disagreeing. This is different to how -the NSS processes this. They have a positivity score, which I assume -just calculates the % of responses that are agreeing in anyway. That -seems to ignore some of the graded information we have in this dataset -tbough. - -For pre 2023, each question has 5 columns giving % responses for the 5 -options from strongly agree to strongly disagree. Convert these first to -numbers, and then sum to a -1 to 1 scale in a variable r. - -``` r -d[year<2023,ans_sd:=ifelse(A1=="",0,as.numeric(gsub(A1,replacement = "",pattern="%")))] -d[year<2023,ans_d:=ifelse(A2=="",0,as.numeric(gsub(A2,replacement = "",pattern="%")))] -d[year<2023,ans_n:=ifelse(A3=="",0,as.numeric(gsub(A3,replacement = "",pattern="%")))] -d[year<2023,ans_a:=ifelse(A4=="",0,as.numeric(gsub(A4,replacement = "",pattern="%")))] -d[year<2023,ans_sa:=ifelse(A5=="",0,as.numeric(gsub(A5,replacement = "",pattern="%")))] -d[year<2023,r:= (ans_sa + ans_a*.5 + ans_d *-.5 + ans_sd*-1)/100] -``` - -For 2023 onwards, We have total number of responses for 4 options from -strongly agree to strongly disagree, ie there is no ‘neither agree nor -disagree’ option. Convert these to a -1 to 1 scale as above. - -``` r -d[year>=2023,ans_sa:=ifelse(A1=="",0,as.numeric(A1))] -d[year>=2023,ans_a:=ifelse(A2=="",0,as.numeric(A2))] -d[year>=2023,ans_d:=ifelse(A3=="",0,as.numeric(A3))] -d[year>=2023,ans_sd:=ifelse(A4=="",0,as.numeric(A4))] -d[year>=2023,r:= (ans_sa + ans_a*.5 + ans_d *-.5 + ans_sd*-1)/(ans_sa+ans_a+ans_d+ans_sd)] -``` - -I’m going to exclude the questions that aren’t in the main list, and -that are about the student union. Then let’s check the distribution of -those values over all questions for each year. - -``` r -d <- d[!theme=="union" & !is.na(theme)] -ggplot(d,aes(x=r,colour=year,group=year))+geom_density() -``` - -![](analysis_files/figure-gfm/unnamed-chunk-6-1.png) - -So it broadly looks as though agreement in these statement overall peaks -around the positive, “agree” response, but that over the years, -responses have been slipping down for everyone. - -Now let’s plot UCL’s last 10 years for each theme There are vertical -gray bars here to denote when questionnaire changed, making comparisons -difficult. I am going to exclude the themes for mental_health, personal, -overall satisfaction and freedom as they only have single question each -that were only asked in a handful of years. - -``` r -d[,tm:=ifelse(theme %in% c("mental_health", "personal", "freedom","overall"),FALSE,TRUE)] -# this is a plot element with the new questionnaires marked. We can reuse it -yp <- geom_vline(data=data.table(year=c(2016.5,2022.5)),alpha=0.1,linewidth=3,aes(xintercept=year)) -pirateye(d[Institution=="University College London" & tm],x_condition = "year", - colour_condition = "theme",line = T,dv="r",violin = F,error_bars = F)+yp -``` - -![](analysis_files/figure-gfm/unnamed-chunk-7-1.png) - -So people love our resources! The rankings seem pretty stable over time -here. Rankings seem higher pre 2017 and there is a drop off in 2023, but -as the grey lines show, these changes are confounded by a different set -of questions (and responses). What does seem clear here is that our weak -point is our assessments. These are ranked low and if anything have been -getting worse. - -How do we match up with the average psych dept? Here’s all rankings over -the years, comparing UCL against all other psych depts. - -``` r -d[,ucl:=ifelse(Institution=="University College London",TRUE,FALSE)] -d[,ucl_all:=ifelse(Institution=="University College London","UCL","All other")] -pirateye(d[ (tm) ],x_condition = "year",colour="ucl_all",dodgewidth = 0, - line = T,dv="r",violin = F,error_bars = T,dots=F)+yp -``` - -![](analysis_files/figure-gfm/unnamed-chunk-8-1.png) - -So in general terms: we had a relatively great pandemic! At least up -until 2023 (the cohort that were in their first year during pandemic). -But again the picture is complicated by the change in survey. We can -split those responses by theme and compare against other depts. It’s a -bit messy comparing all individual themes for both, so let’s try and -simplify this by using a baseline. We can z score the r values for each -question, each year. - -``` r -d[, rzall:=scale(r),by=.(year,question)] -pirateye( d[ucl & tm],x_condition = "year",colour_condition = "theme",dodgewidth = 0, - line = T,dv="rzall",violin = F,error_bars = F,dots=F)+yp+geom_hline(aes(yintercept=0)) -``` - -![](analysis_files/figure-gfm/unnamed-chunk-9-1.png) - -So now the psychology sector average for each year is 0, shown by heavy -black line. Benchmarked like this, it looks like we had a good period of -growth from 2016 onwards, and in the pandemic years we were well above -the mean in almost everything. But again, our assessments are ranked -below the mean and perhaps trending down diff --git a/analysis.nb.html b/analysis.nb.html deleted file mode 100644 index df3e1c8..0000000 --- a/analysis.nb.html +++ /dev/null @@ -1,2006 +0,0 @@ - - - - - - - - - - - - - -NSS results - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - -

This is a notebook to analyse NNS data from 2014-2023, baswed on data -from NSS wesbite.

-

If you don’t have eyethinkdata tools, install from github

- - - -
devtools::install_github("dcr-eyethink/eyethinkdata")
- - - -

Load in the package and the raw data. Note that this has been -filtered to just psychology courses, and students on first degree -only.

- - - -
library(eyethinkdata)
- - -
Loading required package: ggplot2
-Loading required package: data.table
-Registered S3 method overwritten by 'data.table':
-  method           from
-  print.data.table     
-data.table 1.14.10 using 1 threads (see ?getDTthreads).  Latest news: r-datatable.com
-**********
-This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
-This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
-**********
- - -
full_data <- fread("NSS_2014-23.csv")
-qkey <-  data.table(read.csv("qkey3.csv"))
- - - -

First we need to translate the question numbers into the items and -labels. There are 3 slightly difference question sets. Annoying. So we -have to merge info from the question keys to three different sections. -For each, we will have columns for the theme, the full question, and a -one word question key q.

- - - -
d <- rbind( pid_merge(full_data[year<=2016], qkey[,.(QuestionNumber=qnum,theme=theme2014,q=item2014,question=set_full2014)],link="QuestionNumber"),
- pid_merge(full_data[year>2016 & year<2023], qkey[,.(QuestionNumber=qnum,theme=theme2017,q=item2017, question=set_full2017)],link="QuestionNumber"),
- pid_merge(full_data[year==2023], qkey[,.(QuestionNumber=qnum,theme=theme2023,q=item2023, question=set_full2023)],link="QuestionNumber"))
-d
- - -
- -
- - -
NA
- - - -

Now we want one number that summarises responses to the question. -Right now we have the distribution of responses for each. All the items -are framed positively, ie if they agree with the statement the student -is saying something good about the university. So we’re going to code -into a response variable between -1 and 1 that is positive if they are -agreeing, and negative if they are disagreeing. This is different to how -the NSS processes this. They have a positivity score, which I assume -just calculates the % of responses that are agreeing in anyway. That -seems to ignore some of the graded information we have in this dataset -tbough.

-

For pre 2023, each question has 5 columns giving % responses for the -5 options from strongly agree to strongly disagree. Convert these first -to numbers, and then sum to a -1 to 1 scale in a variable r.

- - - -
d[year<2023,ans_sd:=ifelse(A1=="",0,as.numeric(gsub(A1,replacement = "",pattern="%")))]
-d[year<2023,ans_d:=ifelse(A2=="",0,as.numeric(gsub(A2,replacement = "",pattern="%")))]
-d[year<2023,ans_n:=ifelse(A3=="",0,as.numeric(gsub(A3,replacement = "",pattern="%")))]
-d[year<2023,ans_a:=ifelse(A4=="",0,as.numeric(gsub(A4,replacement = "",pattern="%")))]
-d[year<2023,ans_sa:=ifelse(A5=="",0,as.numeric(gsub(A5,replacement = "",pattern="%")))]
-d[year<2023,r:= (ans_sa + ans_a*.5  + ans_d *-.5 + ans_sd*-1)/100]
- - - -

For 2023 onwards, We have total number of responses for 4 options -from strongly agree to strongly disagree, ie there is no ‘neither agree -nor disagree’ option. Convert these to a -1 to 1 scale as above.

- - - -
d[year>=2023,ans_sa:=ifelse(A1=="",0,as.numeric(A1))]
-d[year>=2023,ans_a:=ifelse(A2=="",0,as.numeric(A2))]
-d[year>=2023,ans_d:=ifelse(A3=="",0,as.numeric(A3))]
-d[year>=2023,ans_sd:=ifelse(A4=="",0,as.numeric(A4))]
-d[year>=2023,r:= (ans_sa + ans_a*.5  + ans_d *-.5 + ans_sd*-1)/(ans_sa+ans_a+ans_d+ans_sd)]
- - - -

I’m going to exclude the questions that aren’t in the main list, and -that are about the student union. Then let’s check the distribution of -those values over all questions for each year.

- - - -
d <- d[!theme=="union" & !is.na(theme)]
-ggplot(d,aes(x=r,colour=year,group=year))+geom_density()
- - -

- - - -

So it broadly looks as though agreement in these statement overall -peaks around the positive, “agree” response, but that over the years, -responses have been slipping down for everyone.

-

Now let’s plot UCL’s last 10 years for each theme There are vertical -gray bars here to denote when questionnaire changed, making comparisons -difficult. I am going to exclude the themes for mental_health, personal, -overall satisfaction and freedom as they only have single question each -that were only asked in a handful of years.

- - - -
d[,tm:=ifelse(theme %in% c("mental_health", "personal", "freedom","overall"),FALSE,TRUE)]
-# this is a plot element with the new questionnaires marked. We can reuse it
-yp <-  geom_vline(data=data.table(year=c(2016.5,2022.5)),alpha=0.1,linewidth=3,aes(xintercept=year))
-pirateye(d[Institution=="University College London" & tm],x_condition = "year",
-         colour_condition = "theme",line = T,dv="r",violin = F,error_bars = F)+yp
- - -

- - -
NA
-NA
- - - -

So people love our resources! The rankings seem pretty stable over -time here. Rankings seem higher pre 2017 and there is a drop off in -2023, but as the grey lines show, these changes are confounded by a -different set of questions (and responses). What does seem clear here is -that our weak point is our assessments. These are ranked low and if -anything have been getting worse.

-

How do we match up with the average psych dept? Here’s all rankings -over the years, comparing UCL against all other psych depts.

- - - -
d[,ucl:=ifelse(Institution=="University College London",TRUE,FALSE)]
-d[,ucl_all:=ifelse(Institution=="University College London","UCL","All other")]
-pirateye(d[ (tm) ],x_condition = "year",colour="ucl_all",dodgewidth = 0,
-         line = T,dv="r",violin = F,error_bars = T,dots=F)+yp
- - -

- - - -

So in general terms: we had a relatively great pandemic! At least up -until 2023 (the cohort that were in their first year during pandemic). -But again the picture is complicated by the change in survey. We can -split those responses by theme and compare against other depts. It’s a -bit messy comparing all individual themes for both, so let’s try and -simplify this by using a baseline. We can z score the r values for each -question, each year.

- - - -
d[, rzall:=scale(r),by=.(year,question)]
-pirateye( d[ucl & tm],x_condition = "year",colour_condition =  "theme",dodgewidth = 0,
-         line = T,dv="rzall",violin = F,error_bars = F,dots=F)+yp+geom_hline(aes(yintercept=0))
- - -

- - - -

So now the psychology sector average for each year is 0, shown by -heavy black line. Benchmarked like this, it looks like we had a good -period of growth from 2016 onwards, and in the pandemic years we were -well above the mean in almost everything. But again, our assessments are -ranked below the mean and perhaps trending down

- - -
---
title: "NSS results"
output: html_notebook
---

This is a notebook to analyse NNS data from 2014-2023, baswed on data from NSS wesbite. 

If you don't have eyethinkdata tools, install from github

```{r, eval=FALSE}
devtools::install_github("dcr-eyethink/eyethinkdata")
```

Load in the package and the raw data. Note that this has been filtered to just psychology courses, and students on first degree only. 

```{r}
library(eyethinkdata)
full_data <- fread("NSS_2014-23.csv")
qkey <-  data.table(read.csv("qkey3.csv"))
```
First we need to translate the question numbers into the items and labels. There are 3 slightly difference question sets. Annoying. So we have to merge info from the question keys to three different sections. For each, we will have columns for the theme, the full question, and a one word question key q.

```{r}
d <- rbind( pid_merge(full_data[year<=2016], qkey[,.(QuestionNumber=qnum,theme=theme2014,q=item2014,question=set_full2014)],link="QuestionNumber"),
 pid_merge(full_data[year>2016 & year<2023], qkey[,.(QuestionNumber=qnum,theme=theme2017,q=item2017, question=set_full2017)],link="QuestionNumber"),
 pid_merge(full_data[year==2023], qkey[,.(QuestionNumber=qnum,theme=theme2023,q=item2023, question=set_full2023)],link="QuestionNumber"))
d

```
Now we want one number that summarises responses to the question. Right now we have the distribution of responses for each. All the items are framed positively, ie if they agree with the statement the student is saying something good about the university. So we're going to code into a response variable between -1 and 1 that is positive if they are agreeing, and negative if they are disagreeing. This is different to how the NSS processes this. They have a positivity score, which I assume just calculates the % of responses that are agreeing in anyway. That seems to ignore some of the graded information we have in this dataset tbough.

For pre 2023, each question has 5 columns giving % responses for the 5 options from strongly agree to strongly disagree.  Convert these first to numbers, and then sum to a -1 to 1 scale in a variable r. 

```{r}
d[year<2023,ans_sd:=ifelse(A1=="",0,as.numeric(gsub(A1,replacement = "",pattern="%")))]
d[year<2023,ans_d:=ifelse(A2=="",0,as.numeric(gsub(A2,replacement = "",pattern="%")))]
d[year<2023,ans_n:=ifelse(A3=="",0,as.numeric(gsub(A3,replacement = "",pattern="%")))]
d[year<2023,ans_a:=ifelse(A4=="",0,as.numeric(gsub(A4,replacement = "",pattern="%")))]
d[year<2023,ans_sa:=ifelse(A5=="",0,as.numeric(gsub(A5,replacement = "",pattern="%")))]
d[year<2023,r:= (ans_sa + ans_a*.5  + ans_d *-.5 + ans_sd*-1)/100]
```

For 2023 onwards, We have total number of responses for 4 options from strongly agree to strongly disagree, ie there is no 'neither agree nor disagree' option. Convert these to a -1 to 1 scale as above. 

```{r}
d[year>=2023,ans_sa:=ifelse(A1=="",0,as.numeric(A1))]
d[year>=2023,ans_a:=ifelse(A2=="",0,as.numeric(A2))]
d[year>=2023,ans_d:=ifelse(A3=="",0,as.numeric(A3))]
d[year>=2023,ans_sd:=ifelse(A4=="",0,as.numeric(A4))]
d[year>=2023,r:= (ans_sa + ans_a*.5  + ans_d *-.5 + ans_sd*-1)/(ans_sa+ans_a+ans_d+ans_sd)]
```

I'm going to exclude the questions that aren't in the main list, and that are about the student union. Then let's check the distribution of those values over all questions for each year. 

```{r}
d <- d[!theme=="union" & !is.na(theme)]
ggplot(d,aes(x=r,colour=year,group=year))+geom_density()
```
So it broadly looks as though agreement in these statement overall peaks around the positive, "agree" response, but that over the years, responses have been slipping down for everyone.

Now let's plot UCL's last 10 years for each theme There are vertical gray bars here to denote when questionnaire changed, making comparisons difficult. I am going to exclude the themes for mental_health, personal, overall satisfaction and freedom as they only have single question each that were only asked in a handful of years.

```{r}
d[,tm:=ifelse(theme %in% c("mental_health", "personal", "freedom","overall"),FALSE,TRUE)]
# this is a plot element with the new questionnaires marked. We can reuse it
yp <-  geom_vline(data=data.table(year=c(2016.5,2022.5)),alpha=0.1,linewidth=3,aes(xintercept=year))
pirateye(d[Institution=="University College London" & tm],x_condition = "year",
         colour_condition = "theme",line = T,dv="r",violin = F,error_bars = F)+yp
 

```

So people love our resources! The rankings seem pretty stable over time here. Rankings seem higher pre 2017 and there is a drop off in 2023, but as the grey lines show, these changes are confounded by a different set of questions (and responses). What does seem clear here is that our weak point is our assessments. These are ranked low and if anything have been getting worse.

How do we match up with the average psych dept? Here's all rankings over the years, comparing UCL against all other psych depts.

```{r}
d[,ucl:=ifelse(Institution=="University College London",TRUE,FALSE)]
d[,ucl_all:=ifelse(Institution=="University College London","UCL","All other")]
pirateye(d[ (tm) ],x_condition = "year",colour="ucl_all",dodgewidth = 0,
         line = T,dv="r",violin = F,error_bars = T,dots=F)+yp
```

So in general terms: we had a relatively great pandemic! At least up until 2023 (the cohort that were in their first year during pandemic). But again the picture is complicated by the change in survey. We can split those responses by theme and compare against other depts. It's a bit messy comparing all individual themes for both, so let's try and simplify this by using a baseline. We can z score the r values for each question, each year. 

```{r}
d[, rzall:=scale(r),by=.(year,question)]
pirateye( d[ucl & tm],x_condition = "year",colour_condition =  "theme",dodgewidth = 0,
         line = T,dv="rzall",violin = F,error_bars = F,dots=F)+yp+geom_hline(aes(yintercept=0))
```
So now the psychology sector average for each year is 0, shown by heavy black line. Benchmarked like this, it looks like we had a good period of growth from 2016 onwards, and in the pandemic years we were well above the mean in almost everything. But again, our assessments are ranked below the mean and perhaps trending down







- - - -
- - - - - - - - - - - - - - - - diff --git a/analysis/r theme-year.pdf b/analysis/r theme-year.pdf index a72033b..0eda0fc 100644 Binary files a/analysis/r theme-year.pdf and b/analysis/r theme-year.pdf differ diff --git a/analysis/r ucl_all-year.pdf b/analysis/r ucl_all-year.pdf index 41c1ee2..0cfacd8 100644 Binary files a/analysis/r ucl_all-year.pdf and b/analysis/r ucl_all-year.pdf differ diff --git a/analysis/rzall theme-year.pdf b/analysis/rzall theme-year.pdf index 398e39d..6ae4612 100644 Binary files a/analysis/rzall theme-year.pdf and b/analysis/rzall theme-year.pdf differ diff --git a/docs/index.html b/docs/index.html index a549128..606106a 100644 --- a/docs/index.html +++ b/docs/index.html @@ -226,6 +226,1920 @@ } + + + + + + +