Data and Descriptive Statistics

The first source of data used for this research is a record-level dataset of every time a person participates in an NSLN course between the 2010/11 and 2012/13 academic years. This data was supplied by NSLN. It contains an identifier for each participant, the school they were working in at the time, and the length and subject-focus of the course they attended. Individuals are free to participate in multiple NSLN courses if desired. Most participants (61%) only attend one NSLN course but many do attend more than one, and 10% attend four or more. Figure 13 in Appendix D shows the full distribution of dosage across participants, across all years in the data, and in the first year that an individual participates. In this analysis, I look at the effect of first observed participation one and two years after the first year that a teacher or department first participates. This has the disadvantage of ignoring variation in dosage that occurs through repeated participation. However, it also has the important advantage of avoiding the endogeneity of repeat participation, in the sense that those participants who have a positive first experience of NSLN are more likely to participate in future, and vice versa. In order to investigate the role of dosage, I investigate the effect of different numbers of days attended within the first year of participation in some of my models, which should keep the endogeneity of repeat participation to a minimum, while providing some insight into dose- response patterns.

The second source of data is National Pupil Database (NPD) and School Workforce Census (SWC) administrative data. Information on pupils and schools comes from the NPD, which is an

administrative dataset containing information on all pupils in England since before 2010. This includes information on school type (e.g. academy), school location, pupil demographics and prior attainment, and school Ofsted rating. Information on teachers is taken from the SWC, which contains information on teachers’ demographic characteristics (e.g. age, ethnicity, gender), professional

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characteristics (e.g. years of experience, pay, degree subject) and class assignments, including which subjects they teach. In total, 3,016 records of the 26,776 (11%) obtained from NSLN could not be matched to teachers in the SWC. A large part of this is likely because NSLN courses are open to (non- teaching) science technicians working in schools and other science educators not based in publicly- funded schools, neither of whom would show up in SWC data.

The SWC also provides the information from which I construct my primary outcome variable, wastage, and my secondary outcome variable, turnover. Wastage measures teachers leaving the profession. When I am analysing individual-level data, the wastage variable takes the value one in period t for any individual who is teaching in a state funded school in England in period t-1 but is not in period t, and zero otherwise. When I am analysing data at the departmental level, the wastage variables for department d in period t is defined as the proportion of teachers who were teaching in department d in period t-1 who are no longer teaching in department d in period t. Turnover measures teachers leaving their school. When I am analysing individual level data, the turnover variables takes the value one in period t for any teacher who is teaching in school s in period t-1 but is no longer teaching in school s in period t, and zero otherwise. When I am analysing data at the departmental level, the turnover variable for department d in period t is defined as the proportion of teachers who were teaching in period t-1 in department d who are no longer teaching in department d in period t. Figure 12 shows the retention of the 2010 cohort of newly qualified teachers (NQTs) in their original 2010 school and in the profession overall. It shows that science teachers leave their original school and the profession at a slightly faster rate than teachers of other subjects.

Table 11 compares the characteristics of science teachers in general, science teachers who participate in NSLN courses and science teachers who participate in more than two days of NSLN courses in total. Compared to science teachers in general, NLSN participants are more likely to be male, unqualified, or on a temporary contract and tend to be younger, less experienced, have been at their school for less time and are paid slightly less. They also tend to work in schools with more

disadvantaged pupils and slightly lower attainment. Heavy NSLN participants (those that take part in two days or more) are even younger and less experienced. This suggests that NSLN courses may be being used strategically to improve the knowledge of junior science teachers. These observable characteristics are controlled for in all models in sections 4.3 and 4.4. Information on the distributions of these variables for participants can be found in Table 33 in Appendix D.

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Figure 12: Science and non-science teacher retention for the 2010 NQT cohort

Notes: N = 16,048. The 2010 NQT cohort are the teachers who received newly qualified teacher status in the 2010/11 academic year. Teachers still in 2010 school are those who remain in the school in which they worked in the 2010/11 academic year.

Table 11: Comparing science teachers by participation levels

Non-NSLN Science Teachers NSLN Science Teachers Heavy NSLN Science Teachers

NSLN Days Attended 2010-13 (mean) 0 1.2 6.9

Female (%) 63.4 17.7 8.3

Age (mean) 39.4 37.3 36.5

Qualified Status (%) 63.4 17.7 8.3

Permanent Contract (%) 63.4 17.8 8.4

Years Since Qualified (mean) 12.6 10.3 9.3

Years at Current School (mean) 6.9 5.9 5.4

Annual Pay £1,000 (mean) 38 35.4 34.7

Free School Meals (%) 14 16 16

Best 8 GCSE Point Score (mean) 353 349 347

N 22,672 6,342 2,980

Notes: Table includes only science teachers who were in service in 2012. Science teachers are those who teach science for most of their timetabled hours. Heavy participants are those that take part in more than two days of NLSN courses between 2010 and 2013.