1 / 27

Chapter 11

Chapter 11. Basic Sampling Issues. What is sampling. Sampling: a way of studying a subset of the population but still ensuring “generalizability” (vs. census – study of entire population) – does the study have external validity?. Definitions of Important Terms.

lecea
Télécharger la présentation

Chapter 11

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 11 Basic Sampling Issues

  2. What is sampling • Sampling: a way of studying a subset of the population but still ensuring “generalizability” (vs. census – study of entire population) – does the study have external validity?

  3. Definitions of Important Terms • Population or Universe – entire set of elements to be studied • Census – all elements that completely make up the population. • Sample – a subset

  4. Steps in Developing a Sample Plan Step 2: Choose a Data collection Method Step 3: Choosing a Sampling Frame Step 1: Define the Population of Interest Step 4: Selecting a Sampling Method Step 5: Sample Size Boundaries Operational Implementability

  5. Sampling Method • Probability samples: Samples in which every element of the population has a known, nonzero probability of selection. • Generalizable • Sampling error • Expensive; More time and effort needed • Non-probability samples: Samples that include elements from the population selected in a nonrandom manner. • Hidden agendas • Biased towards well known members of the population; Biased against unusual population members

  6. Sampling and Nonsampling Errors • Parameter vs. Statistic (Estimate) • Sample statistic: statistic (e.g. mean) computed from sample data • - Population parameter: true value for statistic (e.g. mean) for population (we don’t know this) • - Sampling error: population parameter – sample statistic (we don’t know this) • - Confidence interval: interval in which we can be confident that true value lies, based on sample statistic and its standard error

  7. Advantages Of Probability Samples • Information from a representative cross-section • Sampling error can be computed • Results are projectable to the total population. • Disadvantages Of Probability Samples • More expansive than nonprobabiity samples • Take more time to design and execute.

  8. Disadvantages of Nonprobability Samples • Sampling error cannot be computed • Representativeness of the sample is not known • Results cannot be projected to the population. • Advantages of Nonprobability Samples • Cost less than probability • Can be conducted more quickly • Produces samples that are reasonably representative

  9. Classification of Sampling Methods Sampling Methods Quota Probability Samples Non- probability Cluster Systematic Stratified Convenience Snowball Simple Random Judgment

  10. ns X =  s X = sample mean  = true population mean + + - - s ns = sampling error = nonsampling error Sampling And Nonsampling Errors Remember? Sampling Error The error that results when the same sample is not perfectly representative of the population.

  11. Sampling And Nonsampling Errors • Sampling Error • The error that results when the same sample is not perfectly representative of the population. • Administrative error: problems in the execution of the sample (can be reduced) • Random error: due to chance and cannot be avoided; but can be contolled by random sampling and…..estimated! • Measurement or Nonsampling Error • Includes everything other than sampling error that can cause inaccuracy and bias (data entry, biased q’s, bad analysis etc).

  12. Probability Sampling Methods • Simple Random Sampling • A probability sample is a sample in which every element of the population has a known and equal probability of being selected into the sample- EPSEM. Sample Size Probability of Selection = Population Size

  13. Probability Sampling Methods • Systematic Sampling • Probability sampling in which the entire population is numbered, and elements are drawn using a skip interval. Population Size Skip Interval = Sample Size

  14. Probability Sampling Methods • Stratified Samples • Probability samples that select elements from relevant population subsets to be more representative. • Cluster Samples • Probability sample of geographic areas

  15. Stratified Samples • Probability samples that select elements from relevant population subsets to be more representative. • Three steps: In implementing a properly stratified sample: • 1. Identify salient demographic or classification factors correlated with the behavior of interest. • 2. Determine what proportions of the population fall into various sub subgroups under each stratum. • proportional allocation • disproportional or optimal allocation • 3. Select separate simple random samples from each stratum

  16. Cluster Samples • Sampling units are selected in groups. • 1. The population of interest is divided into mutually exclusive and exhaustive subsets. • A random sample of the subsets is selected. • One-stage cluster—all elements in subset selected • Two-stage cluster—elements selected in some probabilistic manner from the selected subsets

  17. Handout 1 – Baseball Example • 1. Ramon Aviles 0.277 • 2. Larry Bowa 0.267 • 3. Pete Rose 0.282 • 4. Mike Schmidt 0.286 • 5. Manny Trillo 0.292 • 6. John Yukovich 0.161 • Mean = 1.565 / 6 = 0.261

  18. SRS of sample size = 2 • Mean Error • Aviles, Bowa 0.272 +0.011 • Aviles, Rose 0.280 +0.019 • Aviles, Schmidt 0.282 +0.021 • Aviles, Trillo 0.285 +0.024 • Aviles, Yukovich 0.219 -0.042 • Bowa, Rose 0.275 +0.014 • Bowa, Schmidt 0.277 +0.016

  19. SRS of sample size = 2 • Bowa, Trillo 0.280 +0.019 • Bowa, Yukovich 0.214 -0.047 • Rose, Schmidt 0.284 +0.023 • Rose, Trillo 0.287 +0.026 • Rose, Yukovich 0.222 -0.039 • Schmidt, Trillo 0.289 +0.028 • Schmidt, Yukovich 0.224 -0.037 • Trillo, Yukovich 0.227 -0.034

  20. Stratification • Let’s divide the sample into two strata • One with Yukovich and another with all others • Stratum 1: Yukovich • Stratum 2: Aviles, Bowa, Rose, Trillo, Schmidt

  21. Stratified Sampling • 1. Yukovich, Aviles • 2. Yukovich, Bowa • 3. Yukovich, Rose • 4. Yukovich, Schmidt • 5. Yukovich, Trillo • Weight the sample. Why? • For anyone from Stratum 2, multiply their value by 5

  22. Example – Mean computation • Yukovich, Schmidt • Yukovich = 0.161 • Schmidt = 0.286 • Therefore, Schmidt’s value is (0.286 * 5) which is 1.43 • Yukovich + Schmidt = 0.161 + 1.43 = • Mean (Yukovich + Schmidt) = 1.591 / 6 = 0.265

  23. Stratified Sampling • 1. Yukovich Aviles 0.258 -0.003 • 2. Yukovich, Bowa 0.249 -0.012 • 3. Yukovich, Rose 0.262 +0.001 • 4. Yukovich, Schmidt 0.265 +0.004 • 5. Yukovich, Trillo 0.270 +0.009 • What’s happening to errors of estimate?

  24. Nonprobability Sampling Methods • Convenience Samples • Nonprobability samples used primarily because they are easy to collect ; Theory testing • Judgment Samples • Nonprobability samples in which the selection criteria are based on personal judgment that the element is representative of the population under study

  25. Nonprobability Sampling Methods • Snowball Samples • Nonprobability samples in which selection of additional respondents is based on referrals from the initial respondents. • Quota Samples • Nonprobability samples in which a population subgroup is classified on the basis of researcher judgment • Different from Stratified

More Related