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Introduction to Survey Data Analysis

Introduction to Survey Data Analysis. Linda K. Owens, PhD Assistant Director for Sampling & Analysis Survey Research Laboratory University of Illinois at Chicago. Focus of the seminar. Data cleaning/missing data Sampling bias reduction. When analyzing survey data.

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Introduction to Survey Data Analysis

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  1. Introduction to Survey Data Analysis Linda K. Owens, PhD Assistant Director for Sampling & Analysis Survey Research Laboratory University of Illinois at Chicago

  2. Focus of the seminar • Data cleaning/missing data • Sampling bias reduction

  3. When analyzing survey data... • 1. Understand & evaluate survey design • 2. Screen the data • 3. Adjust for sampling design

  4. 1. Understand & evaluate survey • Conductor of survey • Sponsor of survey • Measured variables • Unit of analysis • Mode of data collection • Dates of data collection

  5. 1. Understand & evaluate survey • Geographic coverage • Respondent eligibility criteria • Sample design • Sample size & response rate

  6. Levels of measurement • Nominal • Ordinal • Interval • Ratio

  7. 2. Data screening • ALWAYS examine raw frequency distributions for… • (a) out-of-range values (outliers) • (b) missing values

  8. 2. Data screening • Out-of-range values: • Delete data • Recode values

  9. Missing data: • can reduce effective sample size • may introduce bias

  10. Reasons for missing data • Refusals (question sensitivity) • Don’t know responses (cognitive problems, memory problems) • Not applicable • Data processing errors • Questionnaire programming errors • Design factors • Attrition in panel studies

  11. Effects of ignoring missing data • Reduced sample size – loss of statistical power • Data may no longer be representative– introduces bias • Difficult to identify effects

  12. Assumptions on missing data • Missing completely at random (MCAR) • Missing at random (MAR) • Ignorable • Nonignorable

  13. Missing completely at random (MCAR) • Being missing is independent from any variables. • Cases with complete data are indistinguishable from cases with missing data. • Missing cases are a random sub-sample of original sample.

  14. Missing at random (MAR) • The probability of a variable being observed is independent of the true value of that variable controlling for one or more variables. • Example: Probability of missing income is unrelated to income within levels of education.

  15. Ignorable missing data • The data are MAR. • The missing data mechanism is unrelated to the parameters we want to estimate.

  16. Nonignorable missing data • The pattern of data missingness is non-MAR.

  17. Methods of handling missing data • Listwise (casewise) deletion: uses only complete cases • Pairwise deletion: uses all available cases • Dummy variable adjustment: Missing value indicator method • Mean substitution: substitute mean value computed from available cases (cf. unconditional or conditional)

  18. Methods of handling missing data • Regression methods: predict value based on regression equation with other variables as predictors • Hot deck: identify the most similar case to the case with a missing and impute the value

  19. Methods of handling missing data • Maximum likelihood methods: use all available data to generate maximum likelihood-based statistics.

  20. Methods of handling missing data • Multiple imputation: combines the methods of ML to produce multiple data sets with imputed values for missing cases

  21. Multiple Imputation Software • SAS—user written IVEware, experimental MI & MIANALYZE • STATA—user written ICE • R—user written libraries and functions • SOLAS • S-PLUS

  22. Methods of handling missing data: summary • Listwise deletion assumes MCAR or MAR • Dummy variable adjustment is biased, don’t use • Conditional mean substitution assumes MCAR within cells • Hot Deck & Regression improvement on mean substitution but... • Multiple Imputation is least biased but computationally intensive

  23. Missing Data Final Point • All methods of imputation underestimate true variance • artificial increase in sample size • values treated as though obtained by data collection • Rao (1996) On variance estimation with imputed survey data. Jrn. Am. Stat. Assoc. 91:499-506 • Fay (1996) in bibliography

  24. Types of survey sample designs • Simple Random Sampling • Systematic Sampling • Complex sample designs • stratified designs • cluster designs • mixed mode designs

  25. Why complex sample designs? • Increased efficiency • Decreased costs

  26. Why complex sample designs? • Statistical software packages with an assumption of SRS underestimate the sampling variance. • Not accounting for the impact of complex sample design can lead to a biased estimate of the sampling variance (Type I error).

  27. Sample weights • Used to adjust for differing probabilities of selection. • In theory, simple random samples are self-weighted. • In practice, simple random samples are likely to also require adjustments for nonresponse.

  28. Types of sample weights • Poststratification weights: designed to bring the sample proportions in demographic subgroups into agreement with the population proportion in the subgroups. • Nonresponse weights: designed to inflate the weights of survey respondents to compensate for nonrespondents with similar characteristics. • “Blow-up” (expansion) weights: provide estimates for the total population of interest.

  29. Syntax examples of design-based analysis in STATA, SUDAAN, & SAS STATA svyset strata strata svyset psu psu svyset pweight finalwt svyreg fatitk age male black hispanic SUDAAN proc regress data=”c:\nhanes.sav” filetype=spss desgn=wr; nest strata psu; weight finalwt subpgroup sex race; levels 2 3; model fatintk = age sex race;

  30. Syntax examples of design-based analysis in STATA, SUDAAN, & SAS • SAS • proc surveyreg data=nhanes; • strata strata; • cluster psu; • class sex race; • model fatintk = age sex race; • weight finalwt

  31. In summary, when analyzing survey data... • Understand & evaluate survey design • Screen the data – deal with missing data & outliers. • If necessary, adjust for study design using weights and appropriate computer software.

  32. Bibliography • On website with slides • Big names in area of missing data are: • Roderick Little • Donald B. Rubin • Paul Allison • See also McKnight et.al. Missing Data: A Gentle Introduction • Large government datasets (NSFG, NHANES, NHIS) often include detailed methodological documentation on imputation and weight construction.

  33. Thank You! • www.srl.uic.edu

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