1 / 22

Developing the Sampling Plan

Developing the Sampling Plan. Chapter 9, Student Edition. Learning Objectives. Explain the difference between a parameter and a statistic Explain the difference between a probability sample and a nonprobability sample List the primary types of nonprobability samples

audra
Download Presentation

Developing the Sampling Plan

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. Developing the Sampling Plan Chapter 9, Student Edition MR/Brown & Suter

  2. Learning Objectives • Explain the difference between a parameter and a statistic • Explain the difference between a probability sample and a nonprobability sample • List the primary types of nonprobability samples • List the primary types of probability samples • Discuss the concept of total sampling elements (TSE) • Cite three factors that influence the necessary sample size • Explain the relationship between population size and sample size MR/Brown & Suter

  3. Learning Objectives • Explain the difference between a parameter and a statistic • Explain the difference between a probability sample and a nonprobability sample • List the primary types of nonprobability samples • List the primary types of probability samples • Discuss the concept of total sampling elements (TSE) • Cite three factors that influence the necessary sample size • Explain the relationship between population size and sample size MR/Brown & Suter

  4. Learning Objective 1 • Parameter • A characteristic or measure of a population • If it were possible to take measures from all members of a population without error, a true value of a parameter could be determined • Statistic • A characteristic or measure of a sample • Statistics are calculated from sample data and used to estimate population parameters MR/Brown & Suter

  5. Learning Objectives • Explain the difference between a parameter and a statistic • Explain the difference between a probability sample and a nonprobability sample • List the primary types of nonprobability samples • List the primary types of probability samples • Discuss the concept of total sampling elements (TSE) • Cite three factors that influence the necessary sample size • Explain the relationship between population size and sample size MR/Brown & Suter

  6. Learning Objective 2 • Nonprobability Sample • A sample that relies on personal judgment in the element selection process • Neither sampling error nor the margin of sampling error can be estimated or calculated • Techniques include • Convenience • Judgment • Snowball • Quota • Probability Sample • A sample in which each target population element has a known, nonzero chance of being included in the sample • Techniques include • Simple Random • Systematic • Stratified • Cluster • Area MR/Brown & Suter

  7. Learning Objective 2 • Nonprobability Sample • Neither sampling error nor the margin of sampling error can be estimated or calculated • Inferences cannot be made about the population • Inferences are limited to the sample • Thus, results are not generalizable from the sample to the population • Probability Sample • One can statistically assess level of sampling error • Inferences can be made about the population, and not just the sample • Inferences are not limited to the sample • Thus, results aregeneralizable from the sample to the population MR/Brown & Suter

  8. Learning Objectives • Explain the difference between a parameter and a statistic • Explain the difference between a probability sample and a nonprobability sample • List the primary types of nonprobability samples • List the primary types of probability samples • Discuss the concept of total sampling elements (TSE) • Cite three factors that influence the necessary sample size • Explain the relationship between population size and sample size MR/Brown & Suter

  9. Learning Objective 3 • Convenience Sample (Nonprobability Technique) • Population elements are sampled simply because they are in the right place at the right time • Also called “Accidental” Sample • Example – Television news “question of the day” polls MR/Brown & Suter

  10. Learning Objective 3 • Judgment Sample (Nonprobability Technique) • Population elements are handpicked because they are expected to serve the research purpose • Example – Hire panelists who are knowledgeable about the issue being researched rather than selecting them at random • Snowball Sample (Nonprobability Technique) • Initial sample chosen by a probability technique (e.g., systematic sampling) then the population elements are asked for referrals of others they know who might be interested in participation • Example – A demand study for a new product where initial respondents know people with a high interest level within the product category MR/Brown & Suter

  11. Learning Objective 3 • Quota Sample (Nonprobability Technique) • Sample chosen so that the proportion of sample elements with certain characteristics is about the same as the proportion of the elements with the characteristics in the target population • Stated more simply, certain important characteristics of the population are represented proportionately in the sample • Example – Research Problem: Investigate 100 undergraduate student attitudes toward a controversial new technology fee • Known Population Parameters: Class (30% Freshman, 20% Sophomores, 30% Juniors, 20% Seniors) and Gender (50% Female, 50% Male) • Approach: 10 students will interview 10 friends each for a total of 100 responses MR/Brown & Suter

  12. Learning Objectives • Explain the difference between a parameter and a statistic • Explain the difference between a probability sample and a nonprobability sample • List the primary types of nonprobability samples • List the primary types of probability samples • Discuss the concept of total sampling elements (TSE) • Cite three factors that influence the necessary sample size • Explain the relationship between population size and sample size MR/Brown & Suter

  13. Learning Objective 4 • Simple Random Sample (Probability Technique) • Walking down the street and passing out surveys to unknown people “at random” is “random” in the everyday sense, but not random in a scientific sample sense • Example – Sample is drawn by a computer or from a physical list using a random number table MR/Brown & Suter

  14. Learning Objective 4 • Systematic Sample (Probability Technique) • Sample in which every kth element (k = sampling interval) in the population is selected for the sample pool after a random start • Example – Research Problem: Investigate 250 undergraduate student attitudes toward controversial new technology fee • Known Population: 5000 students published in the campus directory • Approach:k = 5000/250 = 20 or 1 out of every 20 students on campus will be surveyed. Randomly select the first name then count down 20 names. Select that person to be surveyed and then count down 20 names again. Select that person and so on until you get 250 names. MR/Brown & Suter

  15. Learning Objective 4 • Stratified Sample (Probability Technique) • Sample in which (1) the population is divided into mutually exclusive and exhaustive subsets and (2) a simple random sample of elements is chosen independently from each group/subset • Most appropriate when subsets (or strata) are homogeneous within but heterogeneous between with respect to key variables • Example – Phoenix is one subset, Tucson is a second subset, and all other residents within the state of Arizona constitute a third subset MR/Brown & Suter

  16. Learning Objective 4 • Cluster Sample (Probability Technique) • Like stratified sampling, (1) the population is divided into mutually exclusive and exhaustive subsets • Unlike stratified sampling, (2) a simple random sample of subsets (i.e., clusters) is chosen • Most appropriate when subsets (or strata) are heterogeneous within but homogeneous between with respect to key variables • Area Sampling (Probability Technique) • A form of cluster sampling that uses census tracks or city blocks as sampling units MR/Brown & Suter

  17. Learning Objectives • Explain the difference between a parameter and a statistic • Explain the difference between a probability sample and a nonprobability sample • List the primary types of nonprobability samples • List the primary types of probability samples • Discuss the concept of total sampling elements (TSE) • Cite three factors that influence the necessary sample size • Explain the relationship between population size and sample size MR/Brown & Suter

  18. Learning Objective 5 • It is common that information cannot be collected from or about all elements chosen for a sample • Bad contact information • Refusal to participate • Inability to reach the potential respondent • To overcome this inevitable situation, it is usually necessary to draw a larger number of sample elements to ultimately achieve the desired sample size • This larger number of elements is known as total sampling elements (TSE) MR/Brown & Suter

  19. Learning Objectives • Explain the difference between a parameter and a statistic • Explain the difference between a probability sample and a nonprobability sample • List the primary types of nonprobability samples • List the primary types of probability samples • Discuss the concept of total sampling elements (TSE) • Cite three factors that influence the necessary sample size • Explain the relationship between population size and sample size MR/Brown & Suter

  20. Learning Objective 6 • Three basic factors affect the size of sample needed when working with a probability sample • Amount of Diversity or Variation • As diversity/variation increases, larger samples are required • Degree of Precision • As need for precision increases, larger samples are required • Degree of Confidence • Confidence increases as sample size increases • At any given sample size, there is a trade-off between confidence and precision. • Higher precision means lower confidence unless we can increase the sample size MR/Brown & Suter

  21. Learning Objectives • Explain the difference between a parameter and a statistic • Explain the difference between a probability sample and a nonprobability sample • List the primary types of nonprobability samples • List the primary types of probability samples • Discuss the concept of total sampling elements (TSE) • Cite three factors that influence the necessary sample size • Explain the relationship between population size and sample size MR/Brown & Suter

  22. Learning Objective 7 • Size of the population has no bearing on the size of the sample • Desired variation, precision, and confidence drive the sample size • Variation is outside the researcher’s control; it’s an artifact of the population • Precision and Confidence are inversely related • The more similar the population elements, the few people needed regardless of how large the population is MR/Brown & Suter

More Related