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# Chapter 9 Sampling.docx

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School
Ryerson University
Department
Marketing
Course
MKT 500
Professor
Tina West
Semester
Winter

Description
MKT500 Marketing Research CHAPTER 9 Sampling BASIC CONCEPT IN SAMPLES AND SAMPLING  Population: entire group under study as specified by the research project  Sample: subset of population that should represent that entire group  Census: defined as an accounting of everyone in the population  Sampling error: any error in a survey that occurs because a sample is used o Caused by two factors: 1. The method of sample selection 2. The size of the sample o Larger samples represent less sampling error than smaller samples, and some sampling methods minimize this error, whereas others do not control it at al regardless of the size of the sample  Sample frame: master list of all members of the population  Sample frame error: exists for sample frames in the forms of mis-, over-, or under-representation of the true population DETERMINING SIZE OF A SAMPLE  Accuracy of a sample: to treat it as a plus-or-minus percentage value o Interpretation of sample accuracy uses the following logic: if a sample size with an accuracy level of =/-5% is used, when one analyzes the survey's findings, they will be about +/-5% of what one would find if census was performed  Sample size and sample accuracy have a curved relationship, meaning as sample size increases accuracy level decreases o Once a sample is greater than 500, large gains in accuracy are not realized with large increases in the size of the sample HOW TO CALCULATE SAMPLE SIZE  Confidence interval formula for sample size 2 2 n = z (pq) / e n = calculated sample size z = standard error associated with the chosen level of confidence p = estimated percentage in the population q = 100% - p e = acceptable error (desired accuracy level)  Confidence interval formula for sample size is based on three elements: 1. Variability (p times q): how much respondents agree in their answer  Use of p=50%,q=50% is a research industry standard because it generates the largest sample size 2. Level of confidence (z)  Customary marketing researchers to use the 95% level of confidence, in which the z is 1.96 3. Desired Accuracy (e): acceptable level of sampling error HOW TO SELECT A REPRESENTATIVE SAMPLE PROBABILITY SAMPLING METHODS  Random sample: every member of the population has an equal chance, or probability, of being selected into that sample  Probability sampling methods: sample methods that use random sampling  Four probability sampling methods: 1. Simple random sampling: the probability of being selected into the sample is "known" as equal for all members of the population Probability of selection = sample size / population size  Random numbers technique: an application of simple random sampling that uses a table of random numbers  Table of random numbers: a list of numbers whose non-systematic (or random) order is assured) 2. Systematic sampling: a way to select a simple random sample from a directory or list that is much more efficient (uses less effort) than with simple random sampling  To use systematic sampling, necessary to obtain hard-copy listing of the population, but not necessary to have a unique identification number assigned to each member of the list MKT500 Marketing Research  Skip interval: goal is to literally "skip" through the list in a systematic way, but to begin at a random starting point in the list Skip interval = population list size / sample size  Random starting point: must use some random number technique to decide on the first name in the sample 3. Cluster sampling: population is divided into subgroups, called “clusters”, that represents the entire population  Area sampling: type of cluster sampling, where researcher subdivides population to be surveyed into geographic areas such as, tracts, cities, neighborhoods, or any other convenient and identifiable geographic designations  One-step area sample
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