1. Introduction to Secondary Data Analysis Young Ik Cho, PhD Research Associate Professor Survey Research Laboratory University of Illinois at Chicago Fall, 2009

2. Survey Research Laboratory 2 of 20 What is secondary data? Data collected by a person or organization other than the users of the data

3. Survey Research Laboratory 3 of 20 Advantages of Secondary Data Unobtrusive Fast & inexpensive Avoid data collection problems Provide bases for comparison

4. Survey Research Laboratory 4 of 20 Disadvantages of Secondary Data Data availability Level of observation Quality of documentation Data quality control Outdated data

5. Survey Research Laboratory 5 of 20 Data Sources  Inter-university Consortium for Political and Social Research (ICPSR) http://www.icpsr.umich.edu/icpsrweb/ICPSR/  National Center for Health Statistics (NCHS) http://www.cdc.gov/nchs/surveys.htm  Center for Medicare and Medicaid Services (CMS) http://www.cms.hhs.gov/home/rsds.asp  US Census Bureau http://www.census.gov/main/www/access.html

6. Survey Research Laboratory 6 of 20 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.)

7. Survey Research Laboratory 7 of 20 Data Available for Use with Survey Documentation and Analysis (SDA): http://www.icpsr.umich.edu/icpsrweb/ICPSR/access/sda.jsp Aging Data National Archive of Computerized Data on Aging (NACDA) http://www.icpsr.umich.edu/NACDA/ Holding about 160 survey data including: 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 Data Sources (cont.)

8. Survey Research Laboratory 8 of 20 SDA (continued): Substance Abuse Data Substance Abuse and Mental Health Data Archive (http://www.icpsr.umich.edu/SAMHDA/) 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 Data Sources (cont.)

9. Survey Research Laboratory 9 of 20 SDA (continued): Criminal Justice Data National Archive of Criminal Justice Data (NACJD) (http://www.icpsr.umich.edu/NACJD/) International Crime Data Homicide Data National Crime Victimization Survey Data Corrections Data Data Sources (cont.)

10. Survey Research Laboratory 10 of 20 Evaluation of Data Sources Evaluation of Data Sources Purpose of the study Sponsor/collector of the data Mode of data collection Sampling procedures Consistency of data with other sources

11. Survey Research Laboratory 11 of 20 Evaluation of Data Sources (cont.) Documentation Number of observations Number of variables Coding scheme Summary statistics

12. Survey Research Laboratory 12 of 20 Types of Survey Sample Design Simple Random Sampling Systematic Sampling Complex sample designs ▪stratified designs ▪cluster designs ▪mixed mode designs

13. Survey Research Laboratory 13 of 20 Types of Survey Sample Design 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)

14. Survey Research Laboratory 14 of 20 Types of Survey Sample Design Systematic Random Sampling  the selection of every k th element from a sampling frame with the sampling interval k (=N/n).

15. Survey Research Laboratory 15 of 20 Types of Survey Sample Design Stratified sample  The population is first divided into non- overlapping subpopulations: strata such as gender, race or SES.  Sample from each strata.  Works most effectively when the variance is smaller within the strata than in the sample as a whole.

16. Survey Research Laboratory 16 of 20 Types of Survey Sample Design 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 Coefficient (ICC)

17. Survey Research Laboratory 17 of 20 Why complex survey design? Increased efficiency Decreased costs

18. Survey Research Laboratory 18 of 20 Sample Weights 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

19. Survey Research Laboratory 19 of 20 Types of Sample Weights Post-stratification weights: designed to bring the sample proportions in demographic subgroups into agreement with the population proportion in the subgroups.

20. Survey Research Laboratory 20 of 20 Types of Sample Weights (cont.) Non-response weights: designed to inflate the weights of survey respondents to compensate for nonrespondents with similar characteristics.

21. Survey Research Laboratory 21 of 20 Types of Sample Weights (cont.) “Blow-up” (expansion) weights: provide estimates for the total population of interest

22. Survey Research Laboratory 22 of 20 Types of Sample Weights (cont.) Replicate weights: A series of weight variables that are used instead of PSUs and strata in an effort to protect the respondents' identity. Selection weight and the replicate weights must be used for the correct calculation of the point estimate and its standard error.

23. Survey Research Laboratory 23 of 20 Complex Survey Design Effect 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 biased estimates.

24. Survey Research Laboratory 24 of 20 Complex Survey Design Effect The ratio of the design-based standard error to the SRS standard error of a variable: Deff=SE(des)/SE(srs) Deff= 1 + ρ (n – 1) where the ρ is the interclass correlation and n is the number of elements in the cluster.

25. Survey Research Laboratory How can we adjust for the design effects? Find variables identifying the primary sampling units (psu), the strata, and the weight(s). Use appropriate software to adjust for the design effect. 25 of 20

26. Survey Research Laboratory 26 of 20 Syntax Examples of Design-based Analysis in SAS, STATA & SUDAAN SAS proc surveyreg data=nhanes; strata strata; cluster psu; class sex race; model fatintk = age sex race; weight finalwt STATA svyset strata strata svyset psu psu svyset pweight finalwt svyreg fatitk age male black hispanic

27. Survey Research Laboratory 27 of 20 Syntax Examples of Design-based Analysis in STATA, SUDAAN & SAS 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;