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The Use of Hot Decking and Propensity Score Matching in Comparing Student Outcomes
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The Use of Hot Decking and Propensity Score Matching in Comparing Student Outcomes

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The performance of institutions such as colleges and schools are of increasing interest to funders and stakeholders because they require accurate estimates of success rates and retention rates. …

The performance of institutions such as colleges and schools are of increasing interest to funders and stakeholders because they require accurate estimates of success rates and retention rates. However it is important that the information these estimates are based upon is consistently applied on a like-with-like basis. Students who attend different institutions do not have the same background characteristics and if this isn’t taken into account certain institutions may be misplaced in terms of performance.

This study uses the technique of causal inference and employs the methods of hot-decking and propensity score matching since these methods can provide a true like-with-like comparison. The study compares the retention rates of Scottish entrants who studied at non-Scottish higher education institutions (HEI’s) against the retention rates of Scottish entrants who studied at Scottish HEI’s.

A basic method, which didn’t take account of background characteristics, came to the following conclusion: if those entrants who studied outside Scotland had studied in Scotland they would have been 5% more likely to drop out of their studies. However, the methods of hot-decking and propensity score matching, which perform a true like-with-like comparison, came to the conclusion that the difference in drop-out was less than the 5% quoted by the basic method. Hot-decking provided an estimate of 2% and propensity score matching provided an estimate of 4%.

Further work is required to assess the sensitivity of the results to the assumptions employed in the models. However, the estimates from this causal inference study help to provide an understanding of the impact of different background characteristics upon the performance of institutions.

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  • 1. The Use of Hot Decking and Propensity Score Matching in Comparing Student Outcomes. By Ian Morton, RSS 2009 Conference, Thursday 10 th September 2009. Joint authors: Kay Penny at Edinburgh Napier University, Zeg Ashraf & John Duffy at Scottish Funding Council.
  • 2. Contents
    • Description of the problem.
    • How performance has been tackled in the past.
    • Solution:
      • Data, methods and programs;
      • Results and conclusions; and
      • Further work.
  • 3. What is the problem ?
    • Student retention at higher education institutions.
    • Key point: We do not know what would have happened to those who studied outside Scotland if they had studied in Scotland – this is what we want to estimate.
  • 4. How performance has been tackled in the past
    • Example is with school and college league tables. Constructed using performance indicators.
    • They do not properly compare like-with-like.
      • Pupils at school w have different background characteristics to pupils at schools x, y, z etc, etc.
    • References:
      • Wilson, D. and Piebalga, A. (2008) Accurate performance measure but meaningless ranking exercise? An analysis of the English school league tables .
      • Goldstein, H. and Leckie, G. (2009) School league tables: what can they really tell us? Significance 5(2).
  • 5. Our solution
    • We still want to compare like-with-like but it isn’t a league table.
    • One set of treatments (recipients in hot decking) and one set of controls (donors in hot decking)
      • study outside Scotland (treatment) and;
      • study in Scotland (control).
    • Use causal inference
      • Rosenbaum, P. R. and Rubin, D. B. (1983) The central role of the propensity score in observational studies for causal effects.
      • Longford, N. T. and Rubin, D. B. (2005) Performance assessment and league tables. Comparing like with like .
  • 6. Data
    • age, institution, deprivation, gender, nationality, accommodation, funding, aim, subject, qualifications.
    • Approx. 32,000 Scottish students (entrants at HEI’s).
    • Donor pool (controls): those who study in Scotland approx. 87% continue after first year.
    • Recipient pool (treatments): those who study outside Scotland approx. 92% continue after first year.
    • Tempting to say that “Scottish students are 5% less likely to continue if they study in Scotland rather than the rest of UK”.
    • BUT we shouldn’t compare these against each other. They have different background characteristics and it wouldn’t be like-with-like.
  • 7. Basic information
    • 41% of the subjects are male and 59% are female;
    • 24% are aged 17 or less, 31% are 18 years of age;
    • 85% have an aim of obtaining a first degree;
    • 33% live with parents, 18% stay in institution maintained properties, and 28% live in their own home;
    • 86% are funded by a funding council, the remainder are funded by other means; and
    • 25% are studying at a pre-Robbins institution (e.g. Cambridge), 25% at a post-Robbins (e.g. Lancaster) and 42% are at a post-92 type of institution (e.g. Edinburgh Napier).
  • 8. Likelihood of continuing
  • 9. Methods 1 - Hot decking
    • Hot decking gets its name because the method is like comparing a deck of computer cards (they were perforated to identify certain characteristics).
    • Use donor pool to impute outcome for the recipients. Carry out five different sets of imputations (in a multiple imputation).
    • Penny, K. I., Ashraf, M. Z., and Duffy, J. C. (2007) The Use of Hot Deck Imputation to Compare Performance of Further Education Colleges .
    • Note: Due to time constraints we will not go into detail on hot decking. Focus more on propensity score matching.
  • 10. Methods 2 – propensity score matching in general
    • Rosenbaum and Rubin (1983) proposed the use of propensity scores.
    • Aim: ensure “balance” between those who have received a treatment and those who haven’t (controls).
    • Require: from those controls who balance with the treatments, calculate proportion continuing to study.
    • Two steps: logistic and then matching.
  • 11. Methods 2.1 – propensity score
    • Use logistic regression to estimate probability (propensity score) of the treatment:
    • Provides a probability (propensity score) for all students; treatment and control.
    • The logistic equation is below, where Ti is the treatment status, Xi are the observations and h ( Xi ) is made up of the covariates (age, institution, deprivation etc).
  • 12. Methods 2.2 - matching
    • Need to match treatments to controls by propensity score.
    • There are different matching methods
    • Once a match has been found for the treatments in the controls, use information on the latter's outcome for inference.
  • 13. Methods 2.3 - Diagram of matching (caliper)
  • 14. Results – logistic output for propensity score
  • 15. Overall results – graph of 95% confidence intervals
    • Notes: psm – propensity score matching, nn – nearest neighbour, wo – without, wi – with, rep – replacement.
  • 16. Conclusion
    • We estimate that 88.2%-90% who actually studied outside Scotland would continue their studies if they had studied in Scotland;
    • Scottish students would be 2%-3.8% less likely to continue their studies if they had stayed at home, whereas an incorrect like-with-like comparison has over-estimated the difference;
    • Background characteristics are important factors and make a difference on outcomes such as student retention; and
    • Why do Scottish students drop-out if they study in Scotland ? For other educational research.
  • 17. Further work
    • Hot deck method. How sensitive is the imputation, can the program be speeded up ?
    • PSM method. How sensitive is the method?
    • What is the best method ? Hot deck or PSM ?
    • What is the best matching algorithm ?
    • Which students would do worse if they had stayed at home ?
    • Application to other areas e.g. finance.
  • 18. A re-cap
    • Description of the problem.
    • How it has been tackled in the past.
    • How we solve it.
    • Findings.
    • Further work.
  • 19. References 1
    • Morton, I.D. (2009) The Use of Hot Decking and Propensity Score Matching in Comparing Student Outcomes Dissertation for MSc in Applied Statistics, Napier University, July 2009.
    • Penny, K., Morton, I.D., Ashraf, M.Z. and Duffy, J.C. (2009) The Use Of Propensity Score Matching in Comparing Student Progression in Higher Education To be presented at the EURISBIS’09 conference, Cagliari, Italy, May 30 – June 3, 2009.
  • 20. Questions ?

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