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Surfing the education wave with official statistics

Explore the role of a National Statistics Office in education, prioritizing investments, current initiatives, examples for classroom use, and the future of education statistics.

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Surfing the education wave with official statistics

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  1. Surfing the education wave with official statistics Sharleen Forbes Statistics New Zealand School of Government, Victoria University

  2. To cover: • The role of a National Statistics Office in education - why surf at all? • Prioritising - what can we afford and where should we invest? • Current initiatives - Community groups • Schools - Tertiary education • Playing with official statistics • Examples for classroom use • Where to in the future? • Providing more sets of real data • New ways of visualising data

  3. Role of Statistics New Zealand • Lead the state sector in production of official statistics (official statistics system responsibility) • Employ large number of statisticians • Not funded specifically for education (promote, partner or facilitate rather than provide) • Need to provide easily understood statistics (Public Good requirement) • Should target informal / second chance education (NSO Workshop: ICOTS 6, Singapore) • Focus on official statistics

  4. Differences between official and other statistics

  5. Prioritising - what can we afford - where should we invest? • Need to balance external demands with internal training needs • Limited funds (need to ‘pick the wave’ - where can we make a difference?)

  6. Current initiatives - community groups • State sector (Official Statistics System) • Certificate of Official Statistics (Level 4) • School of Government and ANZSOG courses • Workshops and seminars • Journalists • JTO compulsory statistics unit(s) • Statistics prize • Small businesses • GoStats! • Maori communities • Pilot projects

  7. Current initiatives - schools • Resources to support the new curriculum • Schools Corner on Statistics New Zealand website (http://www.stats.govt.nz/schools-corner) • CensusAtSchools • Joint funder (http://www.censusatschool.org.nz) • Dataset provision • Census • Official Statistics Surveys • Synthetic Unit Record Files (SURFs)

  8. Current initiatives - tertiary education • Network of Academics in Official Statistics • To provide training and research • Undergraduate student prizes ($1000) • Official Statistics Research Fund • Partnerships with researchers • Vice-Chancellor’s agreement • Confidentialised Unit Record Files (CURFs) • Half-time Professor of Official Statistics • School of Government, Victoria University

  9. Playing with official statistics - Examples • Census data • Official Statistics Survey data • Specially constructed data sets • Confidentialised Unit Record Files (CURFS) • Synthesised Unit Record Files (SURFS)

  10. The statistical investigation (PPDAC) cycle(Creators: Wild and Pfannkuch, Auckland University,1999) • Problem – statement of the research questions • Plan – procedures used to carry out the study • Data – data collection process • Analysis – summaries and analyses of the data to answer the questions posed • Conclusion – about what has been learned.

  11. 1. Census data example • Problem (Question)Is Hamilton ‘greener’ than Wellington? • Plan / DataUse 2006 Census data on ‘the way people travel to work’ to indicate how ‘green’ a city is. (www.stats.govt.nz/census/)

  12. Analysis

  13. Definitions & (Re)classifications • How many and what classes of ‘green’ shall we have? • Have defined ‘green-ness’ by mode of travel to work • Let’s have only 3 classes of ‘green-ness’ • Not green = Driving private or company vehicles • Green= Passenger in private vehicle or using public transport • Very green =Walking, biking or working at home • Omit other categories

  14. More analysis

  15. Conclusion (and classroom questions) • Conclusion • Wellington is ‘greener’ than Hamilton • Questions • Is ‘mode of travel to work’ a good indicator of ‘green-ness’? • What other variables might affect ‘mode of travel’? • Should we use more than one indicator?

  16. Official Statistics Survey data • Problem (questions) • Are fewer people unemployed now than in previous years? • Are you less likely to be unemployed if you have a high level of education ? • Plan / Data Analyse time series data on national unemployment rates • Statistics New Zealand’s Household Labour Force Survey (www.stats.govt.nz)

  17. Analysis - Question a). Time series plots

  18. Conclusions (and classroom questions) • Conclusions • Unemployment has been lower since 2004 than in previous years • Since 2004 unemployment has stayed at roughly the same level (about 4%) • Seasonality is not marked • Questions • What was the cause of the peaks (1991-3 and 1999) in unemployment? • What do the small peaks in 2004 - 2007 reflect? • Should we answer a count question (number unemployed) with a rate (percent unemployed in the labour force)?

  19. Analysis - Question b). Time series plots

  20. Conclusions (and classroom questions) • Conclusions • Pattern over time is similar for all qualification groups. • Unemployment rate always highest for workers with no educational qualifications. • Questions • Which group appears to be the most disadvantaged when unemployment is high? • What appears to be different in recent (compared to past) years between the qualification groups?

  21. Another sample survey example - a simple look at seasonality • Problem (question) Is there an annual pattern in retail sales? • Plan / data Check for seasonality in quarterly summary time series data for monthly retail trade sales (in dollars) • Statistics New Zealand’s Retail Trade Survey (www.stats.govt.nz)

  22. Analysis Time series plot

  23. Conclusions (and classroom questions) • Conclusions • Annual seasonality - peak every December / January • Rising trend over time - plateau in last 3 quarters • Questions • What components of retail trade would contribute most to the December peaks? • What does it mean when the seasonally adjusted and trend lines lie virtually on top of each other? • Easter fell in the March rather than June quarter in 2008. Is there any evidence that this affected the pattern of retail sales?

  24. 3. Specially constructed data sets - Confidentialised datasets (e.g. 2004 Income Survey)

  25. SURFING: Classroom Examples(SURF creator: Pauline Stuart, Statistics NZ) • Using 2004 Income Survey SURF data. • Data available on CD or downloaded from Schools Corner on the Statistics New Zealand website (www.stats.govt.nz/schoolscorner/). • Dataset has 200 records and seven variables: • gender (male, female) • highest education qualification (none, school, vocational, degree) • marital status (married, never, previously, other) • ethnic group (European, Maori, Other) • age (15-45) • hours worked weekly (0-79) • weekly income ($0-$2000).

  26. Example • Background • In this example we let the SURF dataset represent a company’s employees. • Every employee creates the same administration costs regardless of how many hours are worked. • The company is concerned that its staff administration costs are too high. • Problem (questions) • Do most employees work a ‘normal’ (40 hour) week? • What variables are related to the number of hours worked?

  27. Specific questions for secondary school classrooms • What proportion of employees work at least 40 hours per week? (Summary) 2. Are these proportions different for males and females? (Comparison) 3. Do males tend to work more hours per week than females? (Comparison) 4. What is the relationship between hours worked and income? (Relationship between two measurement variables)

  28. Analysis Plan / Data (a).Take a random sample of 35 from the SURF Table: Sample Summary Statistics

  29. Conclusions (and classroom questions) • Conclusions • Only half of all employees work 40 hours or more. • On average (mean) males work longer hours than femalesHours females work vary (standard deviation, inter-quartile range) more than hours males work. • Questions • Are samples of size 17 and 18 large enough? (beware of categorical data) • What does it indicate when the mean and the median are different?

  30. Plan / Data (b).- Resample • Compare between students’ samples (summary statistics) • Combine students’ samples and create new summary statistics • Sample (another 35 say) and compare (or combine) summary statistics

  31. Analysis Plan / Data (c).- Use all the SURF data • How do sample statistics compare with total SURF? • Would a graph be easier to interpret than the table?

  32. Analysis • Graphs of SURF data

  33. Conclusions (and classroom questions) • Conclusions • Use tables for reference, graphs to tell a story. • Females bimodal?: at 5-25 hours (part-time) and 35-50 hours (full-time)? • Males tri-modal?: small at 10-15 hours (part-time), large at 35-55 hours (full-time), small at 60-75 hours (maybe managers)? • Proportions of males and females working 40 hours or more are different. About half of the males do but only about a quarter of the females do. • Questions • What is the ‘clumping’ at 40 hours? • Given the size of the SURF do you think the above patterns will be similar if other SURFs are taken?

  34. Analysis - Question 4.Relationship between hours worked and income?

  35. Conclusion (and classroom questions) • Conclusion • Income increases as work more hours. • Questions • What is the estimated income for someone who doesn’t work? • What extra income (on average) is expected if work an extra hour per week? • Is the (regression) line a good fit to the data?

  36. Other factors related to hours worked?(Sex / Highest qualification / Ethnicity, etc.)Example from a first-year university course Creator: John Harraway, Otago University • Plan / Data • Recategorise highest qualification • Secondary = None OR Secondary (105) =S • Tertiary = Vocational OR Tertiary (95) =T • Do a linear regression in SPSS(equivalent to t-test for difference in means)

  37. AnalysisSPSS regression output • Weekly Income = $(414 + 344Tertiary) • 95% confidence interval for increase in income if have a tertiary qualification is$257 - $431 • T = 7.8, p = 0.000.. • R2 = 0.24 (only about quarter of the variation in the points explained by the best-fitting line)

  38. Conclusion (and classroom question) • Conclusion • Income is higher on average (by $344) if have a tertiary qualification. • Question • Is ‘qualification’ a good explanator of income earned?

  39. Are there multiple factors related to income? • Problem (Question)Are both ‘qualification’ and ‘hours worked’ related to income? • Plan / DataDo a multiple regression (main effects model - no interaction terms) in SPSS using SURF data

  40. AnalysisScatterplot: Income by hours worked and qualification (S = secondary, T = tertiary)

  41. SPSS regression output (valuesextracted & rounded for all 3 models)

  42. Conclusions Weekly Income = $ (-19 + 15xHours + Worked + 183xTertiary) • Conclusions • Both hours worked and highest qualification are related to weekly income earned • Mean increase in income per hour worked is reduced (from $17 to $15) if tertiary also considered • Mean increase in income if have a tertiary qualification is also reduced (from $344 to $183) when adjusted for number of hours worked • 95% confidence interval for the intercept (income when no hours are worked) still contains zero

  43. Classroom questions • Questions • Is there any ‘interaction’ between hours worked and qualification? • Which of the above models fits the data best? • Are there any outliers? • What does a scatterplot of the residuals (distances from the line) indicate?

  44. More resampling Use SURF as sample from CURF population • Bootstrapping • Take repeated samples with replacement (of same size as original, n=200). • Jack-knifing • Take repeated samples dropping one value from original sample each time (n=199). • Calculate mean and standard deviation of sample means • Compare summary statistics with CURF (or full 2004 Income Survey).

  45. Where to from here? • Continue and develop partnerships (academics, teachers, community groups) • More CURFs and SURFs(official launch 1 September 2008 - 2001 Savings Survey SURF www.stats.govt.nz/schools-corner) • Increased free access to data for post-graduate students • Data visualisation (dynamic graphs) • More across-discipline outputs

  46. Animated population pyramids(Creator: Martin Ralphs, Statistics NZ)

  47. Economic structure population pyramid(Office of National Statistics: UK)

  48. Gapminder: www.gapminder.orgGeography, history, demography, econometrics(Creator: Hans Rosling)

  49. Questions and comments • What are your ideas for the future? Contact sharleen.forbes@stats.govt.nz • Thank you.

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