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Secondary Data Analysis

Secondary Data Analysis. Linda K. Owens, PhD Assistant Director for Sampling and Analysis Survey Research Laboratory University of Illinois.

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Secondary Data Analysis

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  1. Secondary Data Analysis Linda K. Owens, PhD Assistant Director for Sampling and Analysis Survey Research Laboratory University of Illinois

  2. Data collected by a person or organization other than the users of the data What is secondary data? 2 of 27

  3. Unobtrusive Fast & inexpensive Avoid data collection problems Provide bases for comparison Advantages of Secondary Data 3 of 27

  4. Data availability Level of observation Quality of documentation Data quality control Outdated data Disadvantages of Secondary Data 4 of 27

  5. Inter-university Consortium for Political and Social Research (ICPSR) http://www.icpsr.umich.edu/index-medium.html National Center for Health Statistics (NCHS) http://www.cdc.gov/nchs/default.htm Center for Medicare and Medicaid Services (CMS) http://cms.hhs.gov/researchers/ US Census Bureau http://www.census.gov/main/www/access.html Data Sources 5 of 27

  6. Examples of Directly Downloadable Data from NCHS: National Health and Nutrition Examination Survey(NHANES) National Ambulatory Medical Care Survey (NAMCS) National Hospital Ambulatory Medical Care Survey (NHAMCS) National Hospital Discharge Survey (NHDS) National Home and Hospice Care Survey (NHHCS) National Nursing Home Survey (NNHS) National Survey of Ambulatory Surgery (NSAS) National Employer Health Insurance Survey (NEHIS) National Vital Statistics System (NVSS) National Health Interview Survey (NHIS) Data Sources (cont.) 6 of 27

  7. Survey Documentation & Analysis • Web-based analysis and documentation • http://sda.berkeley.edu/ • http://www.icpsr.umich.edu/access/sda.html • http://www.icpsr.umich.edu/NACJD/das.html • http://www.icpsr.umich.edu/SAMHDA/ 7 of 27

  8. Data Sources (cont.) • Data Available for Use with Survey Documentation and Analysis (SDA): • Aging Data • Longitudinal Study of Aging, 70 Years and Older, 1984-1990 • National Survey of Self-Care and Aging: Follow-Up, 1994 • National Health and Nutrition Examination Survey II: Mortality Study, 1992 • National Hospital Discharge Survey, 1994-1997 • National Health Interview Survey, 1994, Second Supplement on Aging • Criminal Justice Data • International Crime Data • Homicide Data • National Crime Victimization Survey Data • Corrections Data 8 of 27

  9. Data Sources (cont.) • Data Available for Use with Survey Documentation and Analysis (continued): • Substance Abuse Data • Drug Abuse Warning Network • Monitoring the Future • National Household Survey on Drug Abuse • National Pregnancy and Health Survey • National Treatment Improvement Evaluation Study • Treatment Episode Data Set • Uniform Facility Data Set • Washington, DC Metropolitan Area Drug Study (DC*MADS) 9 of 27

  10. Purpose of the study Sponsor/collector of the data Mode of data collection Sampling procedures Consistency of data with other sources Evaluation of Data Sources 10 of 27

  11. Documentation Number of observations Number of variables Coding scheme Summary statistics Evaluation of Data Sources (cont.) 11 of 27

  12. Simple Random Sampling Systematic Sampling Complex sample designs stratified designs cluster designs mixed mode designs Types of Survey Sample Design 12 of 27

  13. Simple Random Sampling Each member of the population has an equal and known chance of being selected Simple Random Sample With Replacement (SRSWR) Simple Random Sample Without Replacement (SRSWOR) Types of Survey Sample Design 13 of 27

  14. Systematic Random Sampling the selection of every kth element from a sampling frame with the sampling interval k (=N/n). Types of Survey Sample Design 14 of 27

  15. Stratified sample The population is first divided into non-overlapping subpopulations: strata such as gender, race or SES. Sample from each stratum. Proportionate vs. disproportionate Works most effectively when the variance of the dependent variable is smaller within the stratum than in the sample as a whole. Types of Survey Sample Design 15 of 27

  16. Cluster sample Elements are selected in groups or clusters PSU: Primary Sampling Unit.  This is the first unit that is sampled in the design.  For example, school districts from Chicago may be sampled and then schools within districts may be sampled. Homogeneity within cluster: Intracluster correlation (ICC) Types of Survey Sample Design 16 of 27

  17. Increased efficiency Decreased costs Sometimes the only option available Why complex survey design? 17 of 27

  18. Complex designs with clustering and unequal selection probabilities generally increase the sampling variance. Not accounting for the impact of complex sample design can lead to Type I error. Complex Survey Design 18 of 27

  19. “pweight” or selection weight: Used to adjust for differing probabilities of selection (=N/n). In theory, simple random samples are self-weighted In practice, simple random samples are likely to also require adjustments for non-response Sample Weights 19 of 27

  20. Post-stratification weights: Typically used to adjust for minor differences in nonresponse by demographic subgroup. Bring the sample proportions in demographic subgroups into agreement with the population proportion in the subgroups. Requires auxiliary dataset to use as a comparison. Not a fix for bad sample design Types of Sample Weights 20 of 27

  21. Post-Stratification Weights Example 21 of 27

  22. Non-response weights: Designed to inflate the weights of survey respondents to compensate for nonrespondents with similar characteristics. Only useful if nonresponse varies by stratum (unless inflating sample size to population size). Types of Sample Weights (cont.) 22 of 27

  23. “Blow-up” (expansion) weights: Weights sum to population total Provide estimates for the total population of interest Types of Sample Weights (cont.) 23 of 27

  24. Replicate weights: A series of weight variables that are used instead of PSUs and strata in an effort to protect the respondents' identity.Pweight and the replicate weights must be used for the correct calculation of the point estimate and its standard error. Types of Sample Weights (cont.) 24 of 27

  25. Summary of Weights • Weight for probability of selection • Adjust for non-response • Post-stratify • Expand or contract to population/sample totals 25 of 27

  26. 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; Syntax Examples of Design-based Analysis in STATA, SUDAAN & SAS 26 of 27

  27. SAS proc surveyreg data=nhanes; strata strata; cluster psu; class sex race; model fatintk = age sex race; weight finalwt; Syntax Examples of Design-based Analysis in STATA, SUDAAN & SAS 27 of 27

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