1. Saturday 20 th April 2013

2. Secondary Data Analysis Please sign the LSYPE data conditions of use as well as the register. Please check that you have access to this file: (you may find it easier to open this presentation, which is on today’s web page, and follow the hyperlink) https://files.warwick.ac.uk/maths_ed/browse/ARM/UKDA-5677-spss/spss/spss12 –If you do not have access please update your details on the Files.Warwick page on the computer at the front of the room. Please load SPSS- should be available from the start menu under ‘IT Services Delivered Applications’

3. Secondary Data Analysis Aim Appreciate the range of secondary data available. Use secondary data for descriptive reporting and analytical modelling. Consider opportunities for using secondary data in your own research.

4. The range of secondary data available. NPD –PLASC LSYPE MCD TIMMS /PIRLS PISA OECD ESRC –National Data Service –National Data Archive –NCRM –ADMIN –NFER –CLS ONS

5. Attitude/ opinion LSYPE Agreement with statement about feelings about school: I am happy when I am at school School is a waste of time for me School work is worth doing Most of the time I don't want to go to school TIMSS/ PIRLS How would you characterize students' regard for school property within your school? How would you characterize students' desire to do well in school within your school? Indicate the extent to which you agree or disagree that this school is located in a safe neighborhood

6. Funding ‘With increasing access to large-scale datasets researchers are now strongly encouraged by funders to preface their studies routinely with an analysis of the relevant population figures (Rendall, 2003), before moving on to work with in-depth data or case studies.’ (Gorard, 2012, p. 79) Gorard, S. (2012). The increasing availability of official datasets: Methods, limitations and opportunities for studies of education. British Journal of Educational Studies, 60(1), 77-92.

7. Funding Comparisons- national and international Availability of large scale data sets including Administrative data sets which can encompass the entire population Research methods/ technology  can model more complex situations. –Multiple variables. –Control for particular variables –Multilevel

8. Context NPD (National Pupil Database) 16 schools Individual mathematics classes. Considered: Favourite/least favourite subject wrt mathematics How ‘student-centred’ the mathematics teaching Noyes, A. (2012). It matters which class you are in: student-centred teaching and the enjoyment of learning mathematics. Research in Mathematics Education, 14(3), 273- 290.

9. Read p281 (A) and p282/4 (B) What type of research question was being asked? Which sections are referring to school level and which class level? What is the role of statistical significance? What is the author indicating about causality? What are the key findings?

11. Controlling for variables Men earn more money than women. What variables might you want to know about in order to investigate this relationship?

12. Ethnicity and SEND Strand & Lindsay (2009) compare the proportion of students from different ethnic groups in SEND categories. White British ‘1’ where above 1 indicated a greater proportion of that group were recorded in that category. Read p.7 (A), p.9 (B), p.11(C) (and tables at the back). Which data sets were used? Which variables were considered? What measure was used? What type of test was used? What was the aim of the test? Which variables were controlled for? What was the main finding? Strand, S. & Lindsay, G. (2009). Ethnic disproportionality in special education: Evidence from an English population study. Journal of Special Education

15. There is far more detailed discussion and breakdown of figures in Strand, S. & Lindsay, G. (2009). Ethnic disproportionality in special education: Evidence from an English population study. Journal of Special Education Page 13 onwards and tables at the end.

16. Levels What is clear from this study is that the schools that are most effective for White British pupils, girls, or those not entitled to FSM are also most effective for Black Caribbean pupils, boys, and those entitled to FSM. But the results also suggest the possibility of an equity-effectiveness trade-off where the most effective schools raise the achievement of all pupil groupings but at the same time can increase the White British– Black Caribbean gap. (Strand, 2010, p.310) Strand, S. (2010). Do some schools narrow the gap? Differential school effectiveness by ethnicity, gender, poverty, and prior achievement. School Effectiveness and School Improvement, 21(3), 289-314.

17. Potential issues No control over variables Access Complex- even finding out the variables held. Missing data Relationship v causality Statistical significance v effect size

18. Effect size The difference in age 11 test score between the two groups of schools (high Black Caribbean schools against all other schools in England) is highly statistically significant, although in terms of effect size relatively small (ES ¼ 0.13). Strand (2010, p.301)

19. Research questions Relationships between variables Group differences Prediction of group membership Structure- eg factor analysis Time course of events

20. UK

21. LSYPE teaching dataset: Variables selected from all sections of the questionnaire for the main Wave 1 study. The questionnaire covers: Household and demographic information Languages spoken in the home Attitudes to the young person's school and involvement in education Extra-curricular classes Year 10 subject choices Special educational needs Parental expectations and aspirations Family activities Parental relationship with young person and contact with services Reasons young person does not live with natural parents Household responsibilities and resources (self-completion) Risk factors (absences, truancy, police contact, bullying) Individual parent questions

22. Research questions LSYPE teaching data set or Design research questions that may be answerable from the data sets. Relationships between variables Group differences Prediction of group membership (Structure- eg factor analysis) (Time course of events)

23. Cleaning data What type of variables do you have? –Categorical…. Ratio –Discrete/ continuous What does the data ‘look like’ –Mean median mode, largest, smallest… –Graph (bar/ histogram) –Anomalies –Missing data Known distribution?

24. Missing data Issues? Options?

25. Selecting tests Independent / dependant variable(s) Continuous/ discrete Type of research question Using Multivariate Statistics (Tabachmick and Fidell, 2007, p.29)

26. Secondary data sources. NPD –PLASC LSYPE MCD TIMMS /PIRLS PISA OECD ESRC –National Data ServiceNational Data Service Nesstar –National Data ArchiveNational Data Archive –NCRMNCRM –ADMINADMIN –NFERNFER –CLSCLS ONS

27. Papers Which data set Type of variables Category of research question (difference, relationship…) Type of test(s) used