McGraw-Hill/Irwin © 2004 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 3 Designing the Sample

3-2 Identifying Populations The population consists of those who actually have the information Identify all major factors that qualify knowledgeable respondents List criteria for inclusion and exclusion of respondents

3-3 Specifying Sampling Units The smallest entity that can provide data It’s too broad... If there are multiple, potential respondents It’s too narrow... If responses from multiple individuals would be redundant Use the same units... If results will be compared with other data

3-4 The Sample Frame Need to identify sampling units It must be all-inclusive Excludes any units not in the population Elements be identical to sampling units Clustered sample frames must show cluster boundaries Stratified sample frames must show strata membership

3-5 Reliability and Validity Reliability (Potential) Validity LowHigh Low

3-6 Sample Size Population Variance Sampling Error Sample Reliability Sampling Error Sample Reliability Sampling Factor Relationships

3-7 Confidence Intervals and Probability Levels ± 3 S.E. - 99% C.I. ± 1 S.E. - 68% C.I. ± 2 S.E. - 95% C.I. Mean

3-8 Sample Size and Confidence Intervals Mean Pct. Upper Limit Pct. Lower Limit

% 0% 1% 2% 3% 4% Sample Size Large Samples and Standard Error 3.47%2.45%2.00%1.73%1.41%1.31%1.22%1.15%1.10% ± 2 Standard Errors of the Mean

3-10 Population Variance and Confidence Intervals % Confidence Interval General Public Elementary & High School High School Students Only Sample Size

3-11 Indicators Calling for a Large Sample Serious or costly decisions, based on results. Sponsors demand a high level of confidence. Variance in the population is very large. Sample will be divided into many subsamples. Cost and timing are inelastic to sample size. Time and resources are readily available.

3-12 Indicators Permitting a Large Sample Few major decisions are based on results Only rough estimates of parameters are needed The population is relatively homogeneous Entire sample or large subsamples to analyze Disproportionately high data collection costs Budget and/or timing impose strict limits

3-13 Stratified Designs Stratification Dividing the sample into strata or levels Differential Base Rates Strata proportions differ in the population Differential Confidence Greater confidence needed for certain strata Inter-strata Variance Greater variance between than within strata Differential Strata Variance Greater variance in some strata than others

3-14 Designing a Stratified Sample Select the variables or factors that will define strata Obtain a sample frame showing strata membership Estimate the with and between strata variance Determine desired confidence intervals for each stratum Specify sample size needed for each stratum

3-15 Clustered Designs Interview Data Collection Widely Dispersed Respondents Distance Greatly Increases Costs Sample Is Sufficiently Large

3-16 Clustered and Unclustered Sample Diagrams Unclustered Sample

3-17 Random Sampling N th Name Sampling Random Number Generators Random Number Tables Physical Selection Methods

3-18 Quota Sampling Select Quota Variables Use Combinations Carefully Estimate the Variance Choose Confidence Levels Specify Sample Size Compose Instructions

3-19 Incidence Rates and Qualification Qualifying Observations Respondents are identified by their appearance, location, or behavior. Qualifying Questions Potential respondents are identified by asking preliminary, qualifying questions. Incidence Rate The proportion of all sampling units contacted who meet quota specifications or qualify to respond. Differential Costs The lower the incidence rates for a quota category, the higher the cost per respondent will be.

3-20 Sample Selection Biases 1.Accessibility Bias 2.Affinity Bias 3.Cluster Bias 4.Non-Response Bias 5.Order Bias 6.Self-Selection Bias 7.Termination Bias 8.Visibility Bias

McGraw-Hill/Irwin © 2004 by The McGraw-Hill Companies, Inc. All rights reserved. End of Chapter 3