 University of Central Florida

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University of Central Florida
Chapter 12 Basic Sampling Issues Carl McDaniel, Jr. Roger Gates Slides Prepared by Bruce R. Barringer University of Central Florida

Learning Objectives Slide 1 of 2
To understand the concept of sampling. To learn the steps in developing a sampling plan. To understand the differences between probability samples and nonprobability samples. To understand the concepts of sampling error and nonsampling error.

Learning Objectives Slide 2 of 2
To review the types of probability sampling methods. To gain insight into nonprobability sampling methods.

Definitions of Important Terms
Population or Universe The total group of people from whom information is needed. Census Data obtained from or about every member of the population of interest. Sample A subset of the population of interest.

Steps in Developing a Sample Plan
Step 7: Execute Operational Sampling Plan Step 2: Choose Data Collection Method Step 3: Choose Sampling Frames Step 1: Define the Population of Interest Step 6: Develop and Specify Operational Plan Step 5: Determine Sample Size Step 4: Select a Sampling Method

Steps in Developing a Sampling Plan Slide 1 of 8
Step 1: Defining the Population of Interest Bases for defining the population of interest include: Geography Demographics Use Awareness

Steps in Developing a Sampling Plan Slide 2 of 8
Step 2: Choosing a Data Collection Method The selection of a data collection method has implications for the sampling process. Step 3: Choosing a Sampling Frame Sampling frame List of population elements from which to select units to be sampled.

Steps in Developing a Sampling Plan Slide 3 of 8
Step 4: Selecting the Sampling Method Probability samples Samples in which every element of the population has a known, nonzero probability of selection. Nonprobability samples Include the selection of specific elements from the population in a nonrandom manner.

Steps in Developing a Sampling Plan Slide 4 of 8
Step 4: Selecting the Sampling Method (continued) Sampling error: The difference between the sample value and the true value of the population mean.

Steps in Developing a Sampling Plan Slide 5 of 8
Advantages of probability samples Disadvantages of probability samples - The researcher can be sure of obtaining information from a representative cross section of the population of interest. - Sampling error can be computed. - The survey results are projectable to the total population. - They are more expensive than nonprobability samples of the same size in most cases. The rules for selection increase interviewing costs and professional time must be spent in developing the sample design. - Probability samples take more time to design and execute than non- probability samples.

Steps in Developing a Sampling Plan Slide 6 of 8
Advantages of nonprobability samples Disadvantages of nonprobability samples - Nonprobability samples cost less than probability samples. This characteristic of nonprobability samples may have considerable appeal in those situations where accuracy is not of critical importance. - Nonprobability samples ordinarily can be conducted more quickly than probability samples. - Sampling error cannot be computed. - The researcher does not know the degree to which the sample is representative of the population from which it was drawn, but can draw inferences. - The results of nonprobability samples cannot and should not be projected to the total population; this is not true in all cases.

Steps in Developing a Sampling Plan Slide 7 of 8
Step 5: Determine Sample Size Once the sampling method has been chosen, the next step is to determine the appropriate sample size. Step 6: Developing Operational Procedures for Selecting Sample Elements Involves determining whether a probability or nonprobability sample is being used.

Steps in Developing a Sampling Plan Slide 8 of 8
Step 7: Execution of the Sampling Plan The final step in the sampling process involves execution of the operational sampling plan discussed in the previous steps. It is important that this step include adequate checking to make sure that specified procedures are adhered to.

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

Probability Sampling Methods Slide 1 of 4
Simple Random Sampling Is considered to be the purest form of probability 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. Sample Size Probability of Selection = Population Size

Probability Sampling Methods Slide 2 of 4
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

Probability Sampling Methods Slide 3 of 4
Stratified Samples Stratified samples are probability samples that are distinguished by the following procedural steps: First, the original or parent population is divided into two or more mutually exclusive and exhaustive subsets (e.g., male and female). Second, simple random samples of elements from the two or more subsets are chosen independently from each other, either proportionately or disproportionately.

Probability Sampling Methods Slide 4 of 4
Cluster Samples In the case of cluster samples, the sampling units are selected in groups. There are two basic steps in cluster sampling: First, the population of interest is divided into mutually exclusive and exhaustive subsets. Second, a random sample of the subsets is selected.

Nonprobability Sampling Methods Slide 1 of 3
Convenience Samples Nonprobability samples used primarily because they are easy to collect. Judgment Samples Nonprobability samples in which the selection criteria are based on personal judgment that the element is representative of the population under study.

Nonprobability Sampling Methods Slide 2 of 3
Quota Samples Nonprobability samples in which population subgroups are classified on the basis of researcher judgment. Snowball Samples Nonprobability samples in which selection of additional respondents is based on referrals from the initial respondents.

Nonprobability Sampling Methods Slide 3 of 3
Convenience Samples Nonprobability samples used primarily because they are easy to collect. Judgment Samples Nonprobability samples in which the selection criteria are based on personal judgment that the element is representative of the population under study.

Summary of Key Points Slide 1 of 2
The population, or universe, is the total group of people in whose opinions one is interested. A census involves collecting desired information from all the members of the population of interest. A sample is simply a subset of a population.

Summary of Key Points Slide 2 of 2
Probability sampling methods are selected in such a way that every element of the population has a known, nonzero probability of selection. Nonprobability sampling methods include all methods that select specific elements from the population in a nonrandom manner.