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# Chapter 9 Sampling.docx

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Ryerson University

Marketing

MKT 500

Tina West

Winter

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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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