1 / 47

Variance Estimation in Complex Surveys

Variance Estimation in Complex Surveys. Third International Conference on Establishment Surveys Montreal, Quebec June 18-21, 2007 Presented by: Kirk Wolter, NORC and the University of Chicago. Outline of Lecture –. Introduction (Chapter 1) Textbook Methods (Chapter 1)

butch
Download Presentation

Variance Estimation in Complex Surveys

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Variance Estimation in Complex Surveys Third International Conference on Establishment Surveys Montreal, Quebec June 18-21, 2007 Presented by: Kirk Wolter, NORC and the University of Chicago

  2. Outline of Lecture – • Introduction (Chapter 1) • Textbook Methods (Chapter 1) • Replication-Based Methods • Random Group (Chapter 2) • Balanced Half-Samples (Chapter 3) • Jackknife (Chapter 4) • Bootstrap (Chapter 5) • Taylor Series (Chapter 6) • Generalized Variance Functions (Chapter 7)

  3. Chapter 1: Introduction Notation and Basic Definitions 1. Finite population, - Residents of Canada - Restaurants in Montreal - Farms in Quebec - Schools in Ottawa 2. Sample, - Simple random sampling, without replacement - Systematic sampling - Stratification - Clustering - Double sampling

  4. Chapter 1: Introduction 5. Probability sampling design, - - 8. Characteristic of interest, - -

  5. Chapter 1: Introduction 12. Parameter, - Proportion of residents who are employed - Total production of farms - Trend in price index for restaurants - Regression of sales on area for pharmacies 13. Estimator, -

  6. Chapter 1: Introduction 14. Expectation and variance - - 16. Estimator of variance - - -

  7. Textbook Methods 1. Design: srs wor of size Estimator: Variance Estimator:

  8. Textbook Methods 2. Design: srs wor at both the first and second stages of sampling Estimator: Variance Estimator:

  9. Replication-Based Methods

  10. Chapter 2: The Method of Random Groups • Interpenetrating samples • Replicated samples • Ultimate cluster • Resampling • Random groups

  11. Chapter 2: The Method of Random Groups The Case of Independent Random Groups (i) Draw a sample, No restrictions on the sampling methodology (ii) Replace the first sample Draw second sample, Use same sampling methodology (iii) Repeat until samples are obtained,

  12. Chapter 2: The Method of Random Groups Common estimation procedure: • Editing procedures • Adjustments for nonresponse • Outlier procedures • Estimator of parameter

  13. Chapter 2: The Method of Random Groups Common measurement process: • Field work • Callbacks • Clerical screening and coding • Conversion to machine-readable form

  14. Chapter 2: The Method of Random Groups Estimators of the Parameter of Interest, • Random group estimators • Overall estimators

  15. Chapter 2: The Method of Random Groups Two Examples: Population total Ratio

  16. Chapter 2: The Method of Random Groups Estimators of

  17. Chapter 2: The Method of Random Groups Properties:

  18. Chapter 2: The Method of Random Groups Confidence Intervals:

  19. Chapter 3: Variance Estimation Based on Balanced Half-Samples Description of Basic Techniques L strata Nh units per stratum N size of entire population nh = 2 units selected per stratum srs wr Example: restaurants in Montreal

  20. Chapter 3: Variance Estimation Based on Balanced Half-Samples average number of customers served by Montreal restaurants on a Monday night

  21. Chapter 3: Variance Estimation Based on Balanced Half-Samples Textbook Estimator of Variance

  22. Chapter 3: Variance Estimation Based on Balanced Half-Samples Random Group Estimator of Variance k= 2 independent random groups are available

  23. Chapter 3: Variance Estimation Based on Balanced Half-Samples Half-Sample Methodology

  24. Chapter 3: Variance Estimation Based on Balanced Half-Samples Choosing a Manageable Number, k, of Half-Samples

  25. Chapter 3: Variance Estimation Based on Balanced Half-Samples

  26. Chapter 3: Variance Estimation Based on Balanced Half-Samples Properties of the Balanced Half-Sample Methods

  27. Chapter 3: Variance Estimation Based on Balanced Half-Samples Usage with Multistage Designs

  28. Chapter 3: Variance Estimation Based on Balanced Half-Samples Balanced Half-Sample Methodology

  29. Chapter 3: Variance Estimation Based on Balanced Half-Samples Alternative Half-Sample Estimators of Variance

  30. Chapter 4: The Jackknife Method Quenouille (1949) – bias reduction Tukey (1958) – variance estimation testing interval estimation Resampling

  31. Chapter 4: The Jackknife Method Basic Methodology Random sample Random groups Parameter Estimator

  32. Chapter 4: The Jackknife Method Drop out m Pseudovalue Quenouille’s estimator Variance estimator Special case

  33. Chapter 4: The Jackknife Method Full-sample estimator Variance estimator

  34. Chapter 4: The Jackknife Method Example: ratio

  35. Chapter 4: The Jackknife Method Usage in Stratified Sampling Drop out observation(s) from individual strata

  36. Chapter 4: The Jackknife Method Application to Cluster Sampling Example Drop out ultimate clusters

  37. Chapter 5: The Bootstrap Method

  38. Chapter 5: The Bootstrap Method

  39. Chapter 5: The Bootstrap Method

  40. Chapter 5: The Bootstrap Method

  41. Chapter 5: The Bootstrap Method

  42. Chapter 5: The Bootstrap Method

  43. Chapter 5: The Bootstrap Method

  44. Chapter 6: Taylor Series Methods

  45. Chapter 6: Taylor Series Methods • First-order Taylor series approximation • MSE

  46. Chapter 6: Taylor Series Methods

  47. Chapter 7: Generalized Variance Functions 1. Population total, 2. Estimator of the total, 3. Relative variance, 4.

More Related