MKT 500 Lecture Notes - Lecture 9: Simple Random Sample, Nonprobability Sampling, Telephone Directory

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11 Aug 2016
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Mkt 500: Chapter 9
Chapter 9: Selecting the Sample
BASIC CONCEPTS IN SAMPLES AND SAMPLING
Population
- Population: the entire group under study as defined by research objectives
- Researchers must use the description of the population precisely, whereas
managers use it in a more general way
Census
- Census: requires information from everyone in the population
- Ex. If you want to know the average age of members in the population, you
would have to ask each and every population unit his or her age and then
compute the average
- Since researchers realized the impracticality and outright impossibility of
taking a cense of the population they went on to use subsets or samples to
represent the targeted population
Sample and Sample Unit
- Sample: a subset of the population, and the sample unit pertains to the basic
level of investigation
- Sample unit: is the basic level of investigation – Ex. For Weight Watchers it
would be one person – another example would be a survey of hospital
purchases of laser surgery equipment, in this case the sample unit would be
hospitals since they are the ones being researched
Sample Frame and Sample Frame Error
- Sample frame: a master source of sample units in the population
- The sample frame doesn’t always correspond perfectly to the population
- Sample frame error: the degree to which the sample frame fails to account
for all the population
Sampling Error
- Sampling error: is an error in a survey that occurs because a sample is used
- Sampling error is caused by 2 factors:
1. Sample frame error
2. the size of the sample
REASONS FOR TAKING A SAMPLE
- Taking a sample is less expensive than taking a census
- Typical research firms or the typical researcher cannot analyze the huge
amounts of data that is generated using a census
- Although yes computers can be used they still cant handle large sums of data
and slow down
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PROBABILITY VERSUS NONPROBABILITY SAMPLING METHODS
- Probability samples: are samples in which members of the population have a
known chance of being selected in the model
- Nonprobability samples: are samples where the chances of selecting
members from the population into the sample are unknown
- With probability sampling, the method determines the chances of a sample
unit being selected into the sample
- With non probability methods there is no way to determine the probability
even if the population size is known because the selection technique is
subjective
- Nonprobability sampling is sometimes called “haphazard sampling” because
it is prone to human error and even subconscious bias
Probability Sampling Methods
- There are 4 probability sampling methods: simple random sampling,
systematic sampling, cluster sampling, and stratified sampling
Simple Random Sampling
- with simple random sampling, the probability of selection into the sample is
“known” for all members of the population
- Formula: sample size / population size = probability of selection
oThe random device method
The random device involves using an apparatus of some sort to
ensure that every member of the population has the same
chance of being selected into the sample
Ex. Flipping a coin, lottery numbers being selected using ping
pong balls, roulette wheels, a hand dealt in a poker game
oThe random number method
A tractable and more sophisticated application of the simple
random sampling is to use computer-generated numbers based
on the concept of random numbers, which are numbers whose
chance nature is assured
A computer easily handles data sets of thousands of
individuals; it can quickly label each one with a unique number
or designation, generate random numbers, and match the
random numbers with the unique designations of the
individuals in the data set to select or “pull” the sample
oAdvantages and Disadvantages of Simple random sampling
Pros
Provides unbiased estimates of the population
Guarantees that every member of the population has an
equal chance of being selected in the sample meaning
that the resulting sample, no matter what size, will be a
valid representation of the sample
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