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A Review of (Total) Survey Error Models

A Review of (Total) Survey Error Models. William D. Kalsbeek Survey Research Unit University of North Carolina. Purpose. To review the following for existing total survey error (TSE) models:. Composition and Structure Presentation Utility. Presentations of TSE Models.

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A Review of (Total) Survey Error Models

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  1. A Review of (Total) Survey Error Models William D. Kalsbeek Survey Research Unit University of North Carolina

  2. Purpose To review the following for existing total survey error (TSE) models: • Composition and Structure • Presentation • Utility

  3. Presentations of TSE Models • TSE Model (a Definition): * • A postulation to understand or predict, by theory or simulation, the properties or behavior of the survey process • Presentations of TSE: • Practical: • Process origins; plus statistical nature, impact, measurement and/or control of error • Theoretical: • A formulary (usually MSE-based) *Based on Kotz, et al. (1981-89).

  4. Thesis TSE Models • Have organized our thinking on the statistical effects of error sources But • Translation of this understanding into practical improvement has been limited and largely marginalized to individual sources of error

  5. Thesis For the Future: • Greater research emphasis on TSE components and application of TSE findings for a broader array of data systems? • Model re-direction needed?

  6. Sources of Error * • Sampling • Frame • Measurement • Nonresponse (Unit/Item) * One might also view the underlying stochastic model responsible for the data array in model-based inference as a source of error

  7. A Review of TSE Presentations • Tracking presentations for 2+ sources • Structural basis • Various decompositions of MSE • Grouping by number of sources and: • Type of presentation (practical/theoretical) • Source interrelationship (separate/integrated) • Question: • Which parts of the survey process have TSE models accommodated?

  8. Sources of Error • Sampling • Frame • Measurement • Nonresponse (Unit/Item)

  9. ?

  10. Washington Nationals: Season starts: 4/4/05 (at Phillies) Home opener: 4/14/05 (Diamondbacks)

  11. AROUND THE HORN

  12. AROUND THE HORN Total Survey Error

  13. AROUND THE HORN Sampling

  14. AROUND THE HORN Measurement

  15. AROUND THE HORN Frame

  16. AROUND THE HORN Nonresponse Unit Item

  17. AROUND THE HORN Variances

  18. AROUND THE HORN Interfaces

  19. AROUND THE HORN Biases (additive)

  20. A HOME RUN

  21. Two-Source Theoretical (Integrated): • Nonresponse Bias • Hansen and Hurwitz (1946) • Several extension to more complex sample designs • El-Badry (1956) • Rao (1968, 1973) • Rao and Hughes (1983)

  22. Two-Source Theoretical (Integrated): • Measurement Error Model • Hansen, et al. (1951a, 1951b, 1961, and 1964) • Subsequent work by others at the Census Bureau • Forsman (1989) review

  23. Two-Source Theoretical (Integrated): • Multiplicity Estimators: • Birnbaum and Sirken (1965) • Several subsequent papers by Sirken, et al.

  24. Two-Source Theoretical (Integrated): • Model-Based Inference with Missing Data • Little (1995) • Little and Rubin (2002)

  25. Three-Source Theoretical (Integrated): • Platek, et al. (1977, 1983) • Lessler (1983)

  26. All-Source Practical (Separate): • Following Kish (1965) • Anderson, et al (1979) • Groves (1989) • Groves, et al. (2004) • Federal Committee on Statistical Methodology • FCSM (2001) • Kasprzyk & Giesbrecht (2003) • Other error profiles by Bailar and colleagues for Census statistics

  27. All-Source Theoretical (Separate): • Lessler and Kalsbeek (1992) • Sarndahl, Swennsson, and Wretman (1992)

  28. All-Source Theoretical (Integrated): • A general model appended to Lessler and Kalsbeek (1992)

  29. Utility of Existing Models • Provides a theoretical basis in survey practice to: • Structure our thinking • Motivate preventive strategies • Suggest process quality indicators • Suggest measurement approaches • Catalog empirical findings

  30. Limitations of Existing Models * • Compartments and smokestacks • Marginalized treatment of error sources • Plausibility and complexity • Inverse relationship between proximity to reality and complexity • Context and comparability • Breadth of model utility • Lack of Attention • Priorities and cost * Inspiration and insight from Platek and Sarndahl (2001)

  31. Questions for the Future • More emphasis on studying and minimizing TSE? • For the major and minor leagues • Greater integration of TSE and practice? • Cataloging and lessons learned • New directions in TSE model structure? • All sources jointly  TSE • Action-directed models  TQM? • More process indicators

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