After studying this chapter you will be able to: Understand the concept of sampling, learn the steps in developing a sampling plan, understand the concepts of sampling error and nonsampling error, understand the differences between probability samples, and nonprobability samples, understand sampling implications of surveying over the Internet.
CHAPTER Ten Learning Objectives Basic Sampling Issues Copyright © 2004 John Wiley & Sons, Inc Learning Objectives Learning Objectives To understand the concept of sampling To learn the steps in developing a sampling plan To understand the concepts of sampling error and nonsampling error To understand the differences between probability samples, and nonprobability samples To understand sampling implications of surveying over the Internet Learning Objectives 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 Learning Objectives Definition Of Important Terms To understand the concept of sampling Population or Universe The population or population of interest is the total group of people from whom information is needed Defining the population of interest is the first step in the sampling process Requires good logic and judgment Based on the characteristics of current or target customer Sample versus Census Census: Data about every member of the population Sample: A subset of the population Figure 10.1 Learning Objectives Steps in Developing a Sample Plan Step Execute Operational Plan Step Develop Operational Plan Step Determine Sample Size Step Choose Data Collection Method Step1 Define the Population of Interest Step Choose Sampling Frame (4) Select a Sampling Method Learning Objectives 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 Define the characteristics of those that should be excluded Step Two: Choose Data Collection Method Impacts for the sampling process Step Three: Choosing Sampling Frame A list of elements or members from which we select units to be sampled Learning Objectives 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 Probability Samples A known, nonzero probability of selection Learning Objectives Steps In Developing A Sampling Plan To understand the steps in developing a sample plan Nonprobability Samples Elements selected in a nonrandom manner Nonrandomness—selected on the basis of convenience Purposeful nonrandomness—systematically excludes or overrepresents certain subsets of the population Learning Objectives Steps In Developing A Sampling Plan To understand the steps in developing a sample plan 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 Learning Objectives Steps In Developing A Sampling Plan To understand the steps in developing a sample plan 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 Figure 10.2 Learning Objectives Classification of Sampling Methods Sampling methods Probability samples Systemati c Stratified Cluster Simple random Nonprobabilit y samples Convenienc e Judgement Snowball Quota Learning Objectives Steps In Developing A Sampling Plan To distinguish between probability samples and nonprobability samples Step Five: Determine Sample Size • Discussed more in depth in Chapter 11 • Acceptable Error • Levels of Confidence Learning Objectives 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 Step Seven: Execution the Sampling Plan The final step of the operational sampling plan Include adequate checking of specified procedures Learning Objectives To understand the concepts of sampling error and nonsampling error Sampling And Nonsampling Errors Sampling Error The error that results when the same sample is not perfectly representative of the population Two types of sampling error: +- X= s +- ns X = sample mean = true population mean s = sampling error ns = nonsampling error Sampling And Nonsampling Errors Learning Objectives 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 Measurement or Nonsampling Error Includes everything other than sampling error that can cause inaccuracy and bias Probability Sampling Methods Learning Objectives To understand the differences in probability and nonprobability sampling methods Simple Random Sampling The purest form of probability sample Probability of Selection = Sample Size Population Size Systematic Sampling Uses a fixed skip interval to draw elements from a numbered population Skip Interval = Population Size Sample Size Probability Sampling Methods Learning Objectives To understand the differences in probability and nonprobability sampling methods Stratified Samples Probability samples that are distinguished by the following steps: The original population is divided into two or more mutually exclusive and exhaustive subsets Simple random samples of elements from the two or more subsets are chosen independently from each other Probability Sampling Methods Learning Objectives To understand the differences in probability and nonprobability sampling methods Three steps: In implementing a properly stratified sample: Identify salient demographic or classification factors correlated with the behavior of interest Determine what proportions of the population fall into various sub subgroups under each stratum • proportional allocation • disproportional or optimal allocation Select separate simple random samples from each stratum Probability Sampling Methods Learning Objectives To understand the differences in probability and nonprobability sampling methods Cluster Samples Sampling units are selected in groups 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 Nonprobability Sampling Methods Learning Objectives To understand the differences in probability and nonprobability sampling methods Convenience Samples Easy to collect Judgement Samples Based on judgmental selection criteria Quota Samples Demographic characteristics in the same proportion as in the population Snowball Samples Additional respondents selected on referral from initial respondents Learning Objectives Internet Sampling 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 Learning Objectives Internet Sampling To understand sampling implications of surveying over the Internet Disadvantages of Internet Interviewing users of the internet are not representative of the general population no comprehensive and reliable source of email addresses exists Learning Objectives 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 Learning Objectives The End Copyright © 2004 John Wiley & Sons, Inc ... population mean s = sampling error ns = nonsampling error Sampling And Nonsampling Errors Learning Objectives To understand the concepts of sampling error and nonsampling error Sampling Error 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. .. error Sampling And Nonsampling Errors Sampling Error The error that results when the same sample is not perfectly representative of the population Two types of sampling error: +- X= s +- ns X