Welcome to the Gonski Data Lab. The purpose of this interactive website is to allow users to visually explore relationships and trends between selected outside the school gate factors and the educational performance of students from individual communities. The site uses publicly available data* to compare communities, not schools, in order to better understand how whole of community, outside the school gate influences impact student performance. Read more below
Here are some insights that have already been identified or use the pulldown menus to freely explore the data.
500 results available. Select is focused ,type to refine list, press Down to open the menu,
Researchers and policy makers are deeply interested in understanding what influences student performance and in what context. While student performance is influenced by what happens inside schools it is also heavily influenced by what happens outside the school gate including by the community within which students live, where their community is located, and by their parents and carers.
The purpose of this tool is not to make causal inferences from these relationships and trends but rather to reframe the question about what influences student performance in different communities and to prompt further investigation of those influences.
Each bubble visually represents an individual town or suburb** in NSW and includes the performance of all students from all schools (Government, Catholic and Independent) within that town or suburb. In effect, students in each town or suburb are treated as though they all attend the same school.
Hover over the bubbles to see which town or suburb they represent. Use the pulldown menus to highlight particular towns or suburbs of interest and to change the data sets you might be interested to explore***. Press the play button to see how the data and the relationships have changed over time.
* Available upon request. There is also the capability to add data sets upon request.
** Each bubble represents an ABS Statistical Area. Note that not every town or suburb name has its own named statistical area.
*** Note that all population-related data such as Household Income (Weekly) is from the 2016 ABS Census.
Series of tests focused on basic skills that are administered annually to Australian students
Each variable is an average of school average scores, either weighted or unweighted, within that SA2/year/grade level and particular to a testing domain. Weighting was done by enrolment of the schools in the average. Testing domains are numeracy, reading, writing or narrative, spelling, and grammar. For average, an average of the 5 domains was taken. Where weights are used, domain averages were taken post-weighting.
Mean household income variable was used for 2011 and 2016, respectively. In the years 2008-10, 2012-15, and 2017 an imputation technique was used.
ABS reports weekly household incomes in bands for the 2016 census. This mean was calculated for a particular SA2, or modified group, by taking the sum of the product of the band frequency and the band's middle point in $ and then dividing by the sum of the total SA2's reported frequency.
This is the percent of residents in an SA2, or modified area, where Census respondents indicated their spoken English Proficiency is "Very Well", or "Well". The number of respondents choosing these answers is divided by the total who provided a proficiency response to spoken English. A large proportion of respondents preferred not to state any response to these Census question items.
This measures the ratio of the number of General Practitioners in an SA2 per 1,000 residents. Source: 2016 ABS Census.
An SA2 is Statistical Areas Level 2 (SA2). These are medium-sized general purpose areas built up from whole Statistical Areas Level 1 (SA1). Further information about these regions can be found in the Australian Bureau of Statistics publications.
http://www.abs.gov.au/ausstats/abs@.nsf/mf/1270.0.55.001The Gonski Institute has modified the names and created aggregated communities for some of the SA2's in the data. In general, we combined SA2's that were near each other, and which appeared to sub-divide a community. For example, the 2016 census data distributed Albury into three different SA2's, by East, North and South. Each bubble represents an ABS-defined SA2 or modified community, where multiple SA2's have been combined. SA2s or modified SA2s are assigned a 'Metro' or 'Regional' geography based on their parent SA3.
This is the percent of residents in an SA2, or modified area, where respondents indicated that they identify as indigenous. Respondents could choose from among five answers, Non-Indigenous, Aboriginal, Torres Strait Islander, Aboriginal or Torres Strait Islander, Prefer not to Say. The percent indigenous includes the total of all forms of indigenous identification divided by the total who stated any identification. The percent Aboriginal and percent Torres Strait Islander are not strictly exclusive, as some respondents indicated the category of Aboriginal or Torres Strait Islander.
The unemployment rate is the percent of Census respondents who indicated they were seeking either full-time or part-time work divided by the sum of those who indicated they were employed and the unemployed. Census respondents who indicated they preferred not to state an employment status, or who indicated they were not in the labour force were excluded from this calculation. The adult rate of unemployment is all those age 15 and older who met these criteria. It includes a small portion of those over 65 who indicated they were either still employed or looking for work. We presume those who are retired responded that they were no longer in the labour force. The youth rate of unemployment used the same method of calculation, but included only those in the 15-24 age demographic.
The labour force participation rate is the percent of Census respondents who indicated they were either employed or seeking work divided by the sum of the same and those are not in the labour force. Generally, those not in the labour force refers to students, retired individuals, or others who do not work for a wage.
Population figures are based on 2016 Census responses recorded by the ABS indicating a person's place of usual residence. In some cases aggregates have been taken when SA2s have been combined or modified.
1. Within a school we can consolidate an average for each testing domain (subject area). In some cases this consolidation is not done based on a user's selection. 2. Across schools we average the figure for a given community. The second step is grouped by grade level and calendar year. This is the unweighted average. 3. Across schools we weight the average by school enrolment. We start back at step one and multiply each school's score by the student enrolment. The sum of these figures is then divided by the total community's enrolment. For example, consider three schools A, B, C. A's score is 450, B's is 500, and C's is 550. In an unweighted setting the community's average would be 500. Suppose instead that A's enrolment is 200, B's is 200, and C's is 100. Now, the average is ( (200 * 450) + (200 * 500) + (100 * 550)) / ( 200 + 200 + 100) = 245,000 / 500 = 490. This gives a more accurate picture of the performance of the average student within the community.
The NAPLAN test is scored numerically on a scale of 0 to 1000. The scores are scaled based on students' achievement of the national minimum standards set in each testing domain and year. In general, scores for year 3 students will be lower than year 5, and so on, with year 9 students achieving the highest numeric scores, generally. In 2019, the year 3 score average in reading was 428.8 for NSW. The standard deviation was 87.1. This implies that roughly 67% of schools fall within +/- 87.1 of that average reading figure for the state, and closer to 95% of schools fall within +/- 2 standard deviations (+/- 174.2). NSW was above the national average of 425.3.
https://nap.edu.au/docs/default-source/resources/naplan-2019-national-report.pdf?sfvrsn=2.