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Sampling Techniques in Marketing Research, Exams of Nursing

Walden UniversityNursing

A comprehensive overview of various sampling techniques used in marketing research, including probability sampling methods such as simple random sampling, systematic sampling, cluster sampling, and stratified sampling, as well as non-probability sampling methods. It covers key concepts like probability of selection, random device method, skip interval, one-step and two-step area sampling approaches, and weighted mean calculations. The document serves as a valuable resource for students and researchers studying marketing research methodologies, as it covers the essential steps in developing a sample plan, from defining the population to validating the sample. By understanding these sampling techniques, researchers can ensure the representativeness of their data and draw reliable conclusions from their studies.

Typology: Exams

2023/2024

Available from 08/17/2024

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MKT340 exam 2 actual solution
Probability samples - answer โœ…โœ…samples in
which members of the population have a
known chance of being selected into the
sample
Non-probability samples - answer
โœ…โœ…instances in which the chances
(probability) of selecting members from the
population into the sample are unknown
Simple random sampling - answer โœ…โœ…The
researcher uses random numbers from a
computer, random digit dialing, or some
other random selection procedure that
guarantees each member of the population
in the sample frame has an identical chance
of being selected into the sample.
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MKT340 exam 2 actual solution

Probability samples - answer โœ…โœ… samples in which members of the population have a known chance of being selected into the sample Non-probability samples - answer โœ…โœ… instances in which the chances (probability) of selecting members from the population into the sample are unknown Simple random sampling - answer โœ…โœ… The researcher uses random numbers from a computer, random digit dialing, or some other random selection procedure that guarantees each member of the population in the sample frame has an identical chance of being selected into the sample.

Systematic sampling - answer โœ…โœ… Using a sample frame that lists members of the population, the researcher selects a random starting point for the first sample member. A constant skip interval is calculated by dividing the number of population members in the sample frame by the sample size, then this is used to select every other sample member from the sample frame. This procedure accomplishes the same end as simple random sampling, and it is more efficient. Cluster sampling - answer โœ…โœ… The sample frame is divided into groups called clusters, which must be very similar to each other. The researcher can then randomly select a few clusters and perform a census of each one (one stage). If desired, the researcher can then randomly select more clusters and take samples from each one (two stage). This method is desirable when highly similar clusters can be easily identified,

Skip interval - answer โœ…โœ… = population list size / sample size One-Step Area Sample Approach - answer โœ…โœ… the researcher may believe that the various geographic areas (clusters) are sufficiently identical for results in just one area to be generalized to the full population. But the researcher would need to select one area randomly and perform a census of its members. Two-step area sample approach - answer โœ…โœ… for the first step, the researcher could select a random sample of areas, then for the second step, he or she could decide on a probability method to sample individuals within the chosen areas. The two-step area sample approach is preferable to the one- step approach, because there is always the possibility that a single cluster may be less representative than the researcher believes.

Weighted mean - answer โœ…โœ… = (meanA) (proportionA)+(meanB) (proportionB) Steps in sample plan - answer โœ…โœ… 1. Define the population

  1. Obtain a sample frame
  2. Decide on the sample method
  3. Decide on the sample size
  4. Draw the sample
  5. Validate the sample