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1 RTI International is a trade name of Research Triangle Institute 3040 Cornwallis Road P.O. Box 12194 Research Triangle Park, North Carolina, USA 27709.

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Presentation on theme: "1 RTI International is a trade name of Research Triangle Institute 3040 Cornwallis Road P.O. Box 12194 Research Triangle Park, North Carolina, USA 27709."— Presentation transcript:

1 1 RTI International is a trade name of Research Triangle Institute 3040 Cornwallis Road P.O. Box Research Triangle Park, North Carolina, USA Applying Statistical Methodologies Originally Developed for Household Surveys to the Design and Analysis of Establishment Studies Angela Pitts, Marcus Berzofsky, and Michael Witt 6/19/07

2 2 Topics of Discussion Using Multistage Sampling Techniques Using Area Probability Sampling Techniques Selecting Units Proportional to a Composite Size Measure Selecting Units by Sequential PPS w/ Minimal Replacement Computing Weight Adjustments Using a Model-Based Approach Large Table Production Tasks

3 3 Multistage Sampling Refers to the process of sampling within a previous selected sample Used to decrease data collection cost Used when a sampling frame is not available Often one would stratify within stage depending on known information

4 4 Multistage Sampling (continued) Establishment study examples: O*NET Establishment is PSU; sampling unit is employee National survey of employees within occupations Selected establishments, then occupations, and then employees School-based studies National School Radon Survey – selected districts, then schools, and then classes Educational Longitudinal Study (ELS) – select schools and then students

5 5 Area Probability Sampling Closely related to multistage sampling Refers to the multistage design process of selecting geographic areas at the early stages of design Used to minimize data collection costs and address incomplete sampling frames May be useful for non-response follow-up or response-analysis studies Sometimes clustering can decrease the precision of estimates

6 6 Area Probability Sampling (continued) Establishment study examples: National Study of Assisted Living for the Frail Elderly Estab is SSU; sampling unit is resident Select geographic areas Used outside sources to create a list of assisted living facilities in each area – suitable sampling frame did not exist Select facilities then residents

7 7 Area Probability Sampling (continued) Establishment study examples (continued): National Postsecondary Student Aid Study (NPSAS pre-1994) Estab is SSU; sampling unit is student Select 3-digit zip code areas, then higher education institutions within areas, and then students Cash Payment Study (currently being designed) Estab is SSU; sampling unit is establishment Considering oversampling establishments in a sample of geographic areas for in-person non-response follow-up

8 8 Selecting Units Proportional to CSM Common size measure for establishments is the number of employees, however this may not be suitable for all target population(s) of interest Composite size measure accounts for varying subsampling fractions across subdomains (Folsom et al, 1980) In a sense, the composite size measure represents the value of a PSU relative to other PSUs on the frame and relative to the desired ultimate sample

9 9 Selecting Units Proportional to CSM (continued) Benefits of CSM methodology: Can help equalize between PSU workload (cost efficiency) Can help equalize final probabilities of selection within domains (variance reduction)

10 10 Selecting Units Proportional to CSM (continued) For example, suppose subdomain is part-time/full-time workers Let N i =100 and N pi =70 and N fi =30 Let f p = n p /N p = 0.25 for all i Let f f = n f /N f = 0.80 for all i S i = f p * N pi + f f * N fi = 41.5 Note that a commonly used size measure would be 100 (total number of employees in establishment)

11 11 Selecting Units Proportional to CSM (continued) Establishment study examples: O*NET Subdomain is occupation within establishment Could consider using this for hospital studies Subdomain could be medical personnel in different specialties within hospital

12 12 Selecting Units by Sequential PPS w/ Min. Replacement Application of sequential, with replacement sampling to the PPS environment, developed by Dr. Chromy (1979) Each PSU is guaranteed to be selected within 1 of its expected value (m*S i /S + ) If expected value is less than 1, then PSU i would be selected either 0 or 1 times If expected value is greater than 1, for example 3.2, then PSU i would be selected 3 or 4 times

13 13 Selecting Units by Sequential PPS w/ Min. Replacement (continued) Sorting of the file prior to selection can yield variance reduction and/or better control over domain sample sizes via the implicit stratification SAS has an option for Chromys method in their survey select procedures

14 14 Selecting Units by Sequential PPS w/ Min. Replacement (continued) Establishment study examples: National Inmate Survey Establishment is a PSU; sampling unit is inmate Survey of correctional facilities Size measure was number of inmates in facility Implicitly stratified by region and state to ensure that at least one facility was selected in each state O*NET Composite size measure used based on occupations within establishment Implicitly stratified by industry grouping and establishment size

15 15 Weight Adjustments Use model-based approach, such as the generalized exponential model (GEM), developed by RTI for household surveys (Folsom and Singh, 2000) Can be used with nonresponse, post-stratification, and extreme weight adjustments Allows for a larger number of statistically significant main effects and lower-order interaction terms in the adjustment compared to a weighting class adjustment Potential to decrease bias

16 16 Weight Adjustments (continued) Preset bounds on resulting adjustment can be applied – this minimizes unequal weighting thereby increasing precision Maintains marginal control totals unlike logistic regression (propensity scores) May need to produce multiple weights to perform analyses on different units

17 17 Weight Adjustments (continued) Establishment study examples: Cyanide Survey Estab is PSU; sampling unit is advanced life support providers Survey of ALS providers regarding emergency services related to cyanide Used GEM for both nonresponse and post-stratification adjustments NIOSH Fire Fighter Fatality Investigation and Prevention Program Evaluation Estab is PSU; sampling unit is also PSU Survey of fire departments Used GEM for nonresponse adjustments

18 18 Table Production Technique developed for HH surveys where a large number of tables were required Uses a collection of SAS and Visual Basic programs to create analysis tables Programs produce the analysis data and populate the tables Tables can be of varying sizes and formats Tables are formatted and printer-ready

19 19 Table Production (continued) Establishment study examples: National Inmate Study Generated response rate tables, analysis data tables, and nonresponse tables Cash Payments Study Establishment survey designed to collect information on the use of cash versus credit/debit cards, etc. Generated response rate tables, analysis data tables, and nonresponse tables

20 20 Conclusion HH methods shown have not been commonly used in establishment studies In some instances, these methods are beneficial for establishment studies When list frame not available When only a portion of the establishment is the domain of interest When data collection is done in the field (as opposed to telephone)


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