Chapter 9 Developing the Sampling Plan INTRODUCTION Census A type of sampling plan in which data are collected from or about each member of a population When overall population is limited in size it is usually smart to attempt a census even if you cant get information from everyone Most of the time however we develop a sampling plan the process of selecting the people or objects i e companies products and so on to be surveyed interviewed or observed Sample Selection of a subset of elements from a larger group of objects The ability to make inferences about the overall population from a sample of population members depends on how we select the sample Six step procedure for drawing a sample 1 Define the target population 2 Identify the sampling frame 3 Select the sampling procedure 4 Determine the sample size 5 Select the sample elements 6 Collect the data from the designated elements DEFINING THE TARGET POPULATION Population All cases that meet designated specifications for membership in the group Those who qualify are the population elements You must be painfully explicit about who or what qualifies to be a member of the population As the number of criteria for population membership increases so do the cost and time necessary to find them Parameters versus Statistics Our goal with a sample is to determine what is likely to be true for a population based on data obtained from only a subset of that population A sample is easier and less costly to obtain than a census Parameter A character or measure of a population Any population has certain parameters characteristics and we assume that if we could take measurements of these characteristics from all population elements without any kind of error getting into our data that we would know what is true about the population on these parameters Example of parameters include average age portion with a college degree range of incomes attitude toward a new service offering awareness of a new store that has just opened and so on Statistic A characteristic or measure of a sample Sampling error The difference between results obtained from a sample and results that would have been obtained had information been gathered from or about every member of the population Page 1 of 20 IDENTIFYING THE SAMPLING FRAME Sampling frame The list of population elements from which a sample will be drawn the list could consist of geographic areas institutions individuals or other units Perfect sampling frames usually don t exist except in unusual circumstances making developing an acceptable sampling frame one of the most important and creative tasks Example using a phone book as a sampling frame to find all the households in the metropolitan Dallas area Sometimes you ll work with sampling frames that have been developed by companies that specialize in compiling databases and then selling the names addresses phone numbers and or email addresses SELECTING A SAMPLING PROCEDURE Sampling techniques can be divided into two broad categories probability and nonprobability samples Sample designs Nonprobability samples 1 Convenience 2 Judgment Snowball 3 Quota Probability 1 Simple Random 2 Stratified 3 Cluster Area Nonprobability Samples Nonprobability sample A sample that relies on personal judgment in the element selection process We can t estimate the probability that any particular element will be included in the sample It is impossible to assess the degree of sampling error We can t say anything at all about what would have been true for the overall population Managers are taking risks if they choose to use the results from a nonprobability sample We can only talk about the sample not the population Convenience sample A nonprobability sample in which population elements are included in the sample because they were readily available Easy just go out and find a location where a lot of people who are likely to be members of the population are located and do interviews or pass out surveys Commonly used with exploratory research where the goal is to generate insights or to develop a hypothesis Judgment sample A nonprobability sample in which the sample elements are handpicked because they are expected to serve the research purpose Example selecting members through a hiring process rather than randomly Appropriate to use at early stages of research when ideas or insights are being sought and when the researcher realizes its limitations Snowball sample A judgment sample that relies on the researcher s ability to locate an initial set of respondents with the desired characteristics These individuals are then asked to help identify others with the desired characteristics Page 2 of 20 Quote sample A nonprobability 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 Widely used in both consumer and business research and often contain million of panel members The specific sample elements are left to the discretion of the researcher Even though the resulting sample looks like the overall population in key aspects it may not accurately reflect other aspects of the population Probability Samples Probability sample A sample in which each target population element has a known nonzero chance of being included in the sample The chances of each member of the target population being included in the sample may not be equal but everyone has some chance of being included There is a random component in how population elements are selected objectively and not according to the whims of researcher We can make inferences to the larger population population s characteristics based on the results statistics from the sample and estimate the likely amount of sampling error Probability samples depend on the sampling distribution of the particular statistic being considered for the ability to draw inferences about a particular population We have to project the range confidence interval within the population parameter is likely to fall in the population based on the sample statistic Simple random sample A probability sampling plan in which each unit included in the population has a known and equal chance of being selected for the sample Every combination of population elements is a sample possibility Drawing a simple random sample depends mainly on having a good sampling frame Example a computer randomly picking from a list Systematic sample A probability sampling plan
View Full Document
Unlocking...