 CHAPTER twelve Basic Sampling Issues Copyright © 2002

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CHAPTER twelve Basic Sampling Issues Copyright © 2002
South-Western/Thomson Learning

Learning Objectives 1. To understand the concept of sampling. 2. To learn the steps in developing a sampling plan. 3. To understand the concepts of sampling error and nonsampling error. 4. To distinguish between probability samples, and nonprobability samples. 5. To understand sampling implications of surveying over the Internet.

The Concept of Sampling
To understand the concept of sampling. Sampling Defined:The process of obtaining information from a subset of a larger group. A market researcher takes the results from the sample to make estimates of the larger group. Sampling a small percentage of a population can result in very accurate estimates. It all depends on scientific selection. E.g., 1,000 – 1,500 people polled to predict voting of tens of millions

Definition Of Important Terms
To understand the concept of sampling. Population (or Universe) The total group of people from whom we need to obtain information. Define the target market for the product or service in question. Sample versus Census Census: Data about every member of the population. Sample: A subset of the population

Steps in Developing a Sample Plan
Figure 12.1 Steps in Developing a Sample Plan Step 7. Execute Operational Plan Step 2. Choose Data Collection Method Step1. Define the Population of Interest Step 6. Develop Operational Plan Step 3. Choose Sampling Frame Step 5. Determine Sample Size (4) Select a Sampling Method

Steps In Developing A Sampling Plan
To learn the steps in developing a sample plan. Step One: Defining the Population of Interest Specifying the characteristics from whom information is needed. Often specify in terms of geographic area, demographics, usage, and awareness Define the characteristics of those that should be excluded. Use screening or security questions

Steps In Developing A Sampling Plan
To learn the steps in developing a sample plan. Step Two: Choose Data Collection Method Impacts for the sampling process. E.g., telephone interviews, internet survey, etc. Step Three: Choosing Sampling Frame A list of elements or members from which we select units to be sampled. E.g., choose from phone book, or specify a procedure, such a s questionnaire, that will produce the units to be sampled.

Steps In Developing A Sampling Plan
To learn the steps in developing a sample plan. Name some possible sampling frames for: Patrons of sushi bars Smokers of high-priced cigars Snowboarders Owners of DVD players People who have visited one or more countries in Europe in the last year People with allergies

Steps In Developing A Sampling Plan
To learn the steps in developing a sample plan. Step Four: Select a Sampling Method The selection will depend on: The objectives of the study The financial resources available Time limitations The nature of the problem

Steps In Developing A Sampling Plan
To understand the steps in developing a sample plan. Probability Samples A known, nonzero probability of selection Nonprobability Samples Elements selected in a nonrandom manner (e.g., based on convenience)

Steps In Developing A Sampling Plan
To distinguish between probability samples and nonprobability samples. Advantages of probability samples Obtain information from a representative cross-section Sampling error can be computed. The survey results are projectable to the total population.

Steps In Developing A Sampling Plan
To distinguish between probability samples and nonprobability samples. Disadvantages of probability samples More expensive than nonprobability samples of the same size. Probability samples take more time to design and execute.

Steps In Developing A Sampling Plan
To distinguish between probability samples and nonprobability samples. Step Five: Determine Sample Size For Nonprobability Samples: Available budget Rules of thumb Number of subgroups For Probability Samples: Worry about acceptable error and levels of confidence See Chapter 13.

Steps In Developing A Sampling Plan
To distinguish between probability samples and nonprobability samples. Step Six: Develop of Operational Procedures for Selecting Sample Elements Specify whether a probability or nonprobability sample is being used Procedures more critical for probability sample Step Seven: Execution the Sampling Plan The final step of the operational sampling plan Include adequate checking of specified procedures.

Classification of Sampling Methods Nonprobability samples
Figure 12.3 Classification of Sampling Methods Sampling methods Probability samples Nonprobability samples Systematic Stratified Convenience Snowball Cluster Simple random Judgment Quota

Sampling And Nonsampling Errors
To understand the concepts of sampling error and nonsampling error. Sampling Error The error that results when the same sample is not perfectly representative of the population. Two types of sampling error: + - s ns X = X = sample mean = true population mean s = sampling error ns = nonsampling (or measurement) error

Sampling And Nonsampling Errors
To understand the concepts of sampling error and nonsampling error. 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 Random error: due to chance and cannot be avoided Nonsampling (Measurement) Error Includes all other factors.

Probability Sampling Methods
To understand the differences in probability and nonprobability sampling methods. Simple Random Sampling The purest form of probability sample Sample Size Probability of Selection = Population Size E.g., if population size is 10,000, and sample size is 200, probability of selection is 2%.

Probability Sampling Methods
To understand the differences in probability and nonprobability sampling methods. Systematic Sampling Uses a fixed skip interval to draw elements from a numbered population. Population Size Skip Interval = Sample Size E.g., Pick every 500th name from the phone book. Often simpler, less time-consuming, and less expensive than simple random sampling.

Probability Sampling Methods
To understand the differences in probability and nonprobability sampling methods. Stratified Samples Probability samples that are distinguished by the following steps: 1. The original population is divided into two or more mutually exclusive and exhaustive subsets 2. Simple random samples of elements from the two or more subsets are chosen independently from each other.

Probability Sampling Methods
To understand the differences in probability and nonprobability sampling methods. Probability Sampling Methods Three steps involved 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.

Probability Sampling Methods
To understand the differences in probability and nonprobability sampling methods. 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. E.g., interview everyone in a particular neighborhood (to reduce travel time and add to convenience)

Nonprobability Sampling Methods
To understand the differences in probability and nonprobability sampling methods. Convenience Samples Easy to collect E.g., Ask your own employees, or rent a list of targeted people Judgment Samples Based on judgmental selection criteria E.g., go to a mall to conduct taste tests.

Nonprobability Sampling Methods
To understand the differences in probability and nonprobability sampling methods. Quota Samples Demographic characteristics in the same proportion as in the population E.g., asking males questions in a mall. Snowball Samples Additional respondents selected on referral from initial respondents. Good for “rare” populations, but leads to lesser sample quality.

Sampling Over the Internet
To understand sampling implications of surveying over the Internet. Advantages of Internet sampling: Target respondents can complete the survey at their convenience Data collection is inexpensive The interview can be administered under software control The survey can be completed quickly But… Not generally representative of entire population

Sampling Over the Internet
To understand sampling implications of surveying over the Internet. Don’t Post Surveys in Public Areas Highly biased samples Vested interests quickly

SUMMARY The Concept of Sampling Definition Of Important Terms Steps In Developing A Sampling Plan Sampling And Nonsampling Errors Probability Sampling Methods Nonprobability Sampling Methods Sampling Over the Internet

The End Copyright © 2002 South-Western/Thomson Learning