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Introduction to Secondary Data Analysis. Young Ik Cho, PhD Research Associate Professor Survey Research Laboratory University of Illinois at Chicago Fall, 2009. Data collected by a person or organization other than the users of the data. What is secondary data?.

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introduction to secondary data analysis

Introduction to Secondary Data Analysis

Young Ik Cho, PhD

Research Associate Professor

Survey Research Laboratory

University of Illinois at Chicago

Fall, 2009

advantages of secondary data
Unobtrusive

Fast & inexpensive

Avoid data collection problems

Provide bases for comparison

Advantages of Secondary Data

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disadvantages of secondary data
Data availability

Level of observation

Quality of documentation

Data quality control

Outdated data

Disadvantages of Secondary Data

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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

Data Sources

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slide6
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.)

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slide7

Data Sources (cont.)

  • 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

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slide8

Data Sources (cont.)

  • 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

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slide9

Data Sources (cont.)

  • 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

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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

Evaluation of Data Sources

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evaluation of data sources cont
Documentation

Number of observations

Number of variables

Coding scheme

Summary statistics

Evaluation of Data Sources (cont.)

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types of survey sample design
Simple Random Sampling

Systematic Sampling

Complex sample designs

stratified designs

cluster designs

mixed mode designs

Types of Survey Sample Design

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types of survey sample design13
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

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types of survey sample design14
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

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types of survey sample design15
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.

Types of Survey Sample Design

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types of survey sample design16
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)

Types of Survey Sample Design

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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

Sample Weights

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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.Types of Sample Weights

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

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types of sample weights cont22
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.Types of Sample Weights (cont.)

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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.

Complex Survey Design Effect

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complex survey design effect24
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.

Complex Survey Design Effect

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how can we adjust for the design effects
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.

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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

Syntax Examples of Design-based Analysis in SAS, STATA & SUDAAN

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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;

Syntax Examples of Design-based Analysis in STATA, SUDAAN & SAS

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