Single-sex schools

Appendices

8Appendix A Details of methodology and measurement

Methods of estimation

PISA 2009, which is the first wave of LSAY09, uses plausible value methodologies to measure student achievement. It also uses an incomplete balanced matrix design, which means that students answer a sample of, rather than all, test questions. This is why descriptive estimates of student achievement in science in this paper are based on five plausible values for each student and computed with the OECD-recommended analytical techniques, including balanced-repeated replicate (BRR) weights with Fay adjustments (OECD 2009). All analyses have been performed on the data in which missing values had been replaced by the estimates from a multiple chained imputation procedure available in Stata 12 (Royston 2004). The imputation model included, as predictors, all the variables from the analyses in this paper, except for the dependent variables.

Because of the use of imputations and plausible values (Mislevy et al. 1992), all estimates in the multivariate analyses have been obtained using multiple imputation methodology. This involves fitting five sets of models, each with one plausible value, and then combining these values using the Rubin rule (Little & Rubin 1987), as per OECD recommendations (OECD 2007b). For the estimations of multilevel models, MPlus version 6 was used because of its ability to handle complex weights in hierarchical estimations.

The PISA 2009 sample is representative of 15-year-olds, not of students in any particular grade. All analyses of career plans in this paper have been weighted back to the original PISA population, while all analyses of subject choices have been weighted to such subpopulation of PISA students as remained after 1) those who failed to participate in the survey’s subsequent waves and 2) who changed schools after Wave 1, or 3) who did not answer the question about remaining in the original PISA school, were excluded from the analysis. Only student-level weights were used, as per OECD recommendations (OECD 2012b), as PISA data have been collected with a sampling mechanism that is invariant across sample clusters, so school-level weights are not necessary (Asparouhov 2004).

Multivariate analyses in this paper are two-level hierarchical logit models with school-level and student-level covariates (OECD 2012b; Raudenbush & Bryk 2002). Logit models are suitable for predictions regarding binary variables. Here dependent variables denote the chances of studying at least one life science subject in Year 11 and at least one physical science subject in Year 11 as well as expectations of a career related to life science and expectations of a career related to hard science. The two-level logit model has the following functional form:



where Y_ij denotes the dependent variable for an observation for student i in school j, is the average intercept across schools. X is a vector of student-level explanatory variables and β is a vector of regression coefficients corresponding to variables from vector X. Z is a vector of school-level explanatory variables and γ is a vector of regression coefficients corresponding to variables from vector Z. The error component u_0j varies between schools. In multilevel logit models, the individual error term, denoted by e_ij, is omitted due to identification problems (Raudenbush & Bryk 2002).

Measurement

Student characteristics

Dummy (zero-one) variables

1. 
   Female: coded 1 for females and 0 for males
   
2. 
   English spoken at home: coded 1 for students who spoke English at home and 0 for everyone else
   
3. 
   Australian-born to Australian parents: coded 1 for students who were born in Australia and whose both parents were Australian-born.
   
4. 
   Foreign-born student: coded 1 for students born overseas with both parents also born overseas
   
5. 
   Parent foreign-born: coded 1 for students born in Australia with at least one parent born overseas
   
6. 
   NSW, ACT, Victoria, Queensland, South Australia, Western Australia, Tasmania, Northern Territory
   
7. 
   Metropolitan area, provincial town, remote location
   
8. 
   Aboriginal student
   

Other variables

1. 
   Economic and cultural status of family: the PISA variable known as students’ economic, social and cultural status (ESCS). This composite construct comprises the International Socio-Economic Index of Occupational Status (ISEI); the highest level of education of the student’s parents, converted into years of schooling; the PISA index of family wealth; the PISA index of home educational resources; and the PISA index of possessions, including cultural assets such as books of poetry or works of art in the family home (OECD 2007b). This index is standardised to the mean of 0 and the standard deviation of 1 across the OECD countries.
   
2. 
   Academic performance in science: measured by PISA’s five plausible values (OECD 2009; Wu 2005), which indicate ability to use science-related concepts in adult life. Plausible value methodologies, including the use of balanced repeated replication (BRR) weights with Fay’s adjustment (OECD 2007b, p.55, and Chapter 4), have been used in this paper.
   
3. 
   Minutes per week study science: the number of minutes devoted to studying science each week reported in Wave 1. Divided by 100 to facilitate presentation of coefficients.
   
4. 
   Self-confidence in science skills is a single question indicator of how well the student thought they did in science. Five answer categories range from ‘very well’ to ‘very poorly’.
   

School characteristics

Dummy (zero-one) variables

1. 
   Boys-only school and girls-only school are indicators identifying schools with 0% and 100% of female students
   
2. 
   Government school, independent school, Catholic school
   

Other variables

1. 
   Selective admission to school is a three-category question ‘How often student’s record of academic performance (including placement tests) is considered when students are admitted to your school?’: 0 Never, 0.5 Sometimes, 1 Always.
   
2. 
   
   

9Appendix B Details of coding of occupations and subjects

Physical science subjects Life science subjects

6 Chemistry 1 Agricultural science

8 Earth and environmental science 2 Agriculture and horticulture (VET)

9 Earth science 3 Applied science

14 Geology 4 Biological sciences

21 Physical sciences 5 Biology

22 Physics 7 Contemporary issues and science

10 Environmental science

13 Geography

15 Human biological science

17 Life science

18 Marine and aquatic practices (VET)

19 Marine studies

20 Multi-strand science

23 Psychology

25 Science life skills

27 Science 21

28 Scientific studies

29 Senior science

30 Tasmanian natural resources

Note: General science, Integrated science and Other science are not classified as either life or physical science and a small number of students were left out of analysis when this distinction is made.

Physical science occupations ANZSCO (ABS 2006)

Note: these occupations are related to computing, engineering, mathematics or physical sciences. ‘Physical science’ is used as a short label for this entire group of occupations

1350 ICT Managers

1351 ICT Managers

2232 ICT Trainers

2241 Actuaries, mathematicians and statisticians

2300 Design, engineering, science and transport professionals

2310 Air and marine transport professionals

2311 Air transport professionals

2312 Marine transport professionals

2320 Architects, designers, planners and surveyors

2321 Architects and landscape architects

2322 Cartographers and surveyors

2326 Urban and regional planners

2330 Engineering professionals

2331 Chemical and materials engineers

2332 Civil engineering professionals

2333 Electrical engineers

2334 Electronics engineers

2335 Industrial, mechanical and production engineers

2336 Mining engineers

2339 Other engineering professionals

2340 Natural and physical science professionals

2344 Geologists and geophysicists

2349 Other natural and physical science professionals

2600 ICT professionals

2610 Business and systems analysts, and programmers

2611 ICT business and systems analysts

2612 Multimedia specialists and web developers

2613 Software and applications programmers

2620 Database and systems administrators, and ICT security specialists

2621 Database and systems administrators, and ICT security specialists

2630 ICT network and support professionals

2631 Computer network professionals

2632 ICT support and test engineers

2633 Telecommunications engineering professionals

Life science occupations ANZSCO (ABS 2006)

2341 Agricultural and forestry scientists

2343 Environmental scientists

2345 Life scientists

2346 Medical laboratory scientists

2347 Veterinarians

2500 Health professionals

2510 Health diagnostic and promotion professionals

2511 Dieticians

2512 Medical Imaging professionals

2513 Occupational and environmental health professionals

2514 Optometrists and orthoptists

2515 Pharmacists

2519 Other health diagnostic and promotion professionals

2520 Health therapy professionals

2521 Chiropractors and osteopaths

2522 Complementary health therapists

2523 Dental practitioners

2524 Occupational therapists

2525 Physiotherapists

2526 Podiatrists

2527 Speech professionals and audiologists

2530 Medical practitioners

2531 Generalist medical practitioners

2532 Anaesthetists

2533 Internal medicine specialists

2534 Psychiatrists

2535 Surgeons

2539 Other medical practitioners

2540 Midwifery and nursing professionals

2541 Midwives

2542 Nurse educators and researchers

2543 Nurse managers

2544 Registered nurses

The coding of occupations has been conceptually informed by the OECD coding framework for PISA 2006 data (Sikora & Pokropek 2011).

Building researcher capacity initiative

This paper is produced as part of NCVER’s building researcher capacity initiative, which is funded under the National Vocational Education and Training Research (NVETR) Program. The NVETR Program is coordinated and managed by NCVER on behalf of the Australian Government and state and territory governments. Funding is provided through the Department of Industry (formerly the Department of Industry, Innovation, Climate Change, Science, Research and Tertiary Education).

The aims of the building researcher capacity initiative are to attract experienced researchers from outside the sector, encourage early career researchers and support people in the sector to undertake research.

The building researcher capacity initiative includes the following programs: NCVER fellowships, PhD top-up scholarships, postgraduate research papers and community of practice scholarships for VET practitioners. These grants are awarded to individuals through a selection process and are subject to NCVER’s quality assurance process, including peer review.

1^ Reliance on Year 11 data necessitates the imputation of information on subject choices for students who were in Year 11 in 2009 as they were not asked the relevant questions. The advantage of this strategy, as opposed to reliance on data from Year 12 students (see figure 1), is that the entire spectrum of socioeconomic (SES) backgrounds of students is reflected in the analyses. As science engagement is known to be closely related to SES and as low-SES students are more likely to drop out of LSAY by Wave 2, the analysis of data from Year 12 students would be somewhat biased towards higher SES backgrounds.