To view the slide presentation, go to https://jamiepotter17.github.io/PISA_data_exploration/
The data are from the 2012 round of the Programme for International Student Assessment (PISA), a triennial international education survey organised by the Organisation for Economic Co-operation and Development (OECD). The aim is to provide meaningful international comparisons for the educational attainment of 15-16 year olds for participant nations.
Alongside a general questionnaire that quizzed pupils on various aspects of school life and life at home, pupils take a two hour computer-based test. Scores are then scaled so that the average in each of the three areas tested (mathematics, reading, science) have mean 500, standard deviation 100. Typically, the object of most media interest is the ranking that appears at the end, where countries are ranked in terms of their mean scores. The 2012 survey also included a section on problem-solving.
The dataset was downloaded on 06/11/2021 from Udacity's server here.
In the exploration, I found that many things were associated with increased student scores in fairly predictable ways - lower truancy rates, higher parental education level, having one's own room, having a desk, the number of computers, TVs, mobile phones, and books in the household. I found a small possible effect of the sex of the student and mathematics score (with males slightly higher), and a slightly larger possible effect in reading (with females higher). This was the only occasion in which the three response variables differed in an interesting way; the rest of the time, they were tightly intercorrelated. I failed to find anything to do with perseverance or openness to problem-solving, or something involving age.
I found a couple of interactions which I thought were interesting - firstly, I found that wealth interacted with the highest education level achieved by the pupils' parents. At higher wealth levels, the more parental education, the better. But at lower wealth levels, the best performing pupils had parents with a vocational/secondary education level. Secondly, the sex of the student interacted with household wealth levels. Female scores in Mathematics and Science increase 13% faster than they do for males as wealth level increases, indicating an interaction between sex and wealth. But this effect is especially pronounced in reading, where they female scores increase 22% faster than males scores as wealth level increases.
For the presentation, I focus on the elements leading up to the two interactions - sex and wealth. I omit age, truancy, perseverance, and openness to problem-solving entirely. I include the possessions data simply to underline the point that wealth matters, and because I think it's also interesting that the number of books in the household does not have a special relationship with reading.
The key insights are structured as follows:
- Possessions are an indicator of household wealth. In general, the more possession you have, the wealthier you are, and the higher your score in the PISA test.
- Books aren't special. The number of books in your household has no discernible effect specifically on reading beyond being a general indicator of wealth.
- Household wealth interacts with parental education levels. At higher wealth levels, pupils with parents having the highest education levels scored the best. At lower wealth levels, pupils with the highest average test scores come from households where the highest parental education levels are secondary/vocational.
- Males tend to have slightly higher mathematics scores than females, but females have higher reading scores than males.
- Sex interacts with wealth, especially for reading scores. As wealth level increases, female reading scores improve faster than male scores.
The first interaction comes at 3. and concerns the interaction between household wealth and the highest education level of the students' parents. Once that has been addressed, I then talk about the effect of the sex of the pupil, and that leads into the second interaction at 5. concerning sex and wealth.
One thing I tried to do throughout the various plots I used was stick to a consistent colour scheme - orange for mathematics scores, green for reading, and purple for science, with other plots not using these specific colours.