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Sampling Design in Applied Marketing Research


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This lecture introduces some of the considerations for effective sampling design in Applied Marketing Research.

Published in: Business

Sampling Design in Applied Marketing Research

  1. 1. Sampling Design Week 6 (1) Dr. Kelly Page Cardiff Business School E: T: @drkellypage T: @caseinsights FB:
  2. 2. <ul><li>Understand the concept of sampling; </li></ul><ul><li>Learn the steps in developing a sampling plan; </li></ul><ul><li>Understand the concepts of sampling error and non-sampling error; </li></ul><ul><li>Understand the differences between probability and non-probability samples; </li></ul>Lecture Objectives
  3. 3. The Concept of Sampling <ul><li>“ The process of obtaining information from a subset of a larger group”, in order to use the results to make estimates of the characteristics of the larger group. </li></ul><ul><li>Representative sample --- > Accurate predictions </li></ul><ul><li>How do we ensure our sample is as representative as possible of the group we need information about? </li></ul>
  4. 4. Definitions <ul><li>Population (of interest): Entire group of people about whom information is needed (also may be called “universe”). </li></ul><ul><li>Census: Collection of data from or about every member of the population of interest. </li></ul><ul><li>Sample: Subset of all the members of a population of interest. </li></ul><ul><li>Sampling Frame: List of population elements from which units to be sampled can be selected, or a specified procedure for generating such a list. </li></ul>
  5. 5. Sampling Error <ul><li>When we choose a sample and generalise findings from the sample to the whole population of interest, it is highly unlikely that our results will be exactly the same as if we had taken a census of the whole population. This is due to: </li></ul><ul><li>Sampling Error: Error that occurs because the sample selected is not perfectly representative of the population. </li></ul><ul><ul><li>E.g., Admin error (execution) or random sampling error </li></ul></ul><ul><li>And/Or </li></ul><ul><li>Non-sampling error: All error other than sampling error. E.g., Measurement error </li></ul>
  6. 6. Developing Sampling Design <ul><li>Define the population of interest </li></ul><ul><li>Choose data collection method </li></ul><ul><li>Identify sampling frame </li></ul><ul><li>Select sampling method </li></ul><ul><li>Determine sample size </li></ul><ul><li>Develop operational procedures for selecting sample elements </li></ul><ul><li>Execute the operational sampling plan </li></ul>
  7. 7. 1. Population of Interest <ul><li>Some possible bases for defining population of interest: </li></ul><ul><ul><li>Geographical area </li></ul></ul><ul><ul><li>Demographic (age, gender, etc.) </li></ul></ul><ul><ul><li>Usage Awareness / knowledge </li></ul></ul><ul><ul><li>Need to consider: </li></ul></ul><ul><ul><ul><li>Is screening required? </li></ul></ul></ul><ul><ul><ul><li>Should anyone be excluded? </li></ul></ul></ul>
  8. 8. 2. Data Collection Method <ul><li>What methods can be used to reach the population of interest? </li></ul><ul><li>What methods can be used to administer the survey? </li></ul><ul><li>What are the resource implications? </li></ul><ul><li>What are the implications regarding representativeness of the sample? </li></ul>
  9. 9. 3. Identify Sampling Frame <ul><li>Listings: </li></ul><ul><ul><li>Electoral register </li></ul></ul><ul><ul><li>Postcode Address File </li></ul></ul><ul><ul><li>Telephone directory </li></ul></ul><ul><ul><li>Specialised consumer lists </li></ul></ul><ul><ul><li>Random-digit dialling list </li></ul></ul><ul><ul><li>Customer or member lists </li></ul></ul><ul><li>Need to consider: </li></ul><ul><ul><li>Who is not included? </li></ul></ul><ul><ul><li>How could this affect representativeness of your sample? </li></ul></ul><ul><li>It may be that no sampling frame exists for your population of interest –then you have to formulate a procedure for finding your population and sampling from it. </li></ul>
  10. 10. 4. Select Sampling Method <ul><li>Probability OR non-probability sample? </li></ul><ul><li>A probability sample is one in which every element of the population of interest has a known, non-zero likelihood of being selected. </li></ul><ul><li>Advantages: </li></ul><ul><ul><li>Ensures sample is representative </li></ul></ul><ul><ul><li>Can calculate sampling error </li></ul></ul><ul><ul><li>Can project results to whole population, with allowance made for sampling error </li></ul></ul>
  11. 11. Probability Sampling <ul><li>Simple Random Sampling (SRS) </li></ul><ul><ul><li>A sample selected by assigning a number to every element of the population and then using some method for randomly selecting elements to be in the sample such as random digit dialing. </li></ul></ul><ul><ul><li>Requires complete list of population of interest. </li></ul></ul><ul><ul><li>For sample size n from population of size N, probability of selection is n / N </li></ul></ul><ul><li>Procedure </li></ul><ul><ul><li>1) Number list. </li></ul></ul><ul><ul><li>2) Using random number table or random number generator, select n numbers. </li></ul></ul><ul><ul><li>3) Sample consists of elements corresponding to these numbers. </li></ul></ul>
  12. 12. Probability Sampling <ul><li>Systematic Sampling </li></ul><ul><ul><li>A sample in which the entire population is numbered and elements are selected using a skip interval – every “ nth” name is selected. </li></ul></ul><ul><ul><li>Requires complete list of population of interest. </li></ul></ul><ul><ul><li>Set skip interval [N / n ] </li></ul></ul><ul><li>Procedure </li></ul><ul><ul><li>1) Number list. </li></ul></ul><ul><ul><li>2) Using random number table or random number generator, select starting element. </li></ul></ul><ul><ul><li>3) From starting element, add skip interval to find second element, and so on until n elements found. </li></ul></ul><ul><li>Simpler, quicker, cheaper than SRS –but beware of hidden patterns in the list! </li></ul>
  13. 13. Probability Sampling <ul><li>Stratified Sampling </li></ul><ul><ul><li>A sample that is forced to be more representative through simple random sampling of mutually exclusive and exhaustive subsets either proportionally or disproportionally. Good for data that are not normally distributed. </li></ul></ul><ul><ul><li>1) Divide population of interest into mutually exclusive and exhaustive subgroups (i.e. every element is in one and only one subgroup – e.g., male and female) </li></ul></ul><ul><ul><li>2) Select a simple random sample from each subgroup. </li></ul></ul><ul><li>Reduces sampling error </li></ul><ul><li>Can ensure subgroup proportions in sample replicate percentage in whole population of interest (proportional allocation) </li></ul><ul><li>Or can weight sample to take into account that what we are investigating may vary to different extents in different subgroups (disproportional allocation) </li></ul>
  14. 14. Probability Sampling <ul><li>Cluster Sampling </li></ul><ul><ul><li>A sample in which the sampling units are selecting from a number of small geographic areas to reduce data collection costs. </li></ul></ul><ul><ul><li>1) Divide population of interest into mutually exclusive and exhaustive subgroups (i.e. every element is in one and only one subgroup). </li></ul></ul><ul><ul><li>2) Select a simple random sample of the subgroups (clusters). </li></ul></ul><ul><ul><li>3) Either sample all elements in each selected subgroup or select a probability sample of elements from each selected subgroup. </li></ul></ul><ul><li>In practice, most often subdivided by geographical area; multistage area sampling uses more than one subdivision </li></ul><ul><li>Problems occur if elements in clusters are similar but clusters are very dissimilar to each other </li></ul><ul><li>Greater cost efficiency but less statistical efficiency </li></ul>
  15. 15. 4. Select Sampling Method <ul><li>A non-probability sample is one in which not everyone has an equal chance of being sampled! </li></ul><ul><li>We cannot calculate sampling error </li></ul><ul><li>We need to look carefully at how the sample has been selected, in order to evaluate: </li></ul><ul><ul><li>How representative it is of the population </li></ul></ul><ul><ul><li>What sampling biases might be present Non-probability samples are often chosen for reasons of cost, time or convenience. </li></ul></ul>
  16. 16. Non-probability <ul><li>Quota sampling </li></ul><ul><li>Very commonly used </li></ul><ul><li>Population divided into subgroups based on demographic criteria, so that proportion of sample in each subgroup mirrors proportion of subgroup in population </li></ul><ul><li>E.g., if population of interest is 40% male, 60% female, the quota for a sample of 500 respondents will be 200 males and 300 females). </li></ul><ul><li>Differs from stratified sampling because: </li></ul><ul><ul><li>Demographic subgroups are not necessarily related to what is being investigated </li></ul></ul><ul><ul><li>Selection of respondents is left to interviewer (not SRS) </li></ul></ul>
  17. 17. Non-probability <ul><li>Convenience sampling </li></ul><ul><ul><li>Using respondents who are willing and accessible </li></ul></ul><ul><ul><li>A sample based on using people who are easily accessible - such as mall intercepts or other high traffic locations. </li></ul></ul><ul><ul><li>saves time and cost </li></ul></ul><ul><li>Judgment sampling </li></ul><ul><ul><li>selection based on judgment of researcher </li></ul></ul><ul><li>Snowball sampling </li></ul><ul><ul><li>select initial respondents and ask them to nominate others </li></ul></ul>
  18. 18. 4. Selecting Sampling Method <ul><li>Consider: </li></ul><ul><ul><li>Nature of research problem </li></ul></ul><ul><ul><li>Objectives </li></ul></ul><ul><ul><li>Time </li></ul></ul><ul><ul><li>Budget </li></ul></ul><ul><ul><li>Other resource constraints </li></ul></ul>
  19. 19. 5. Determine Sample Size <ul><li>Determining the sample size will be based on factors such as: </li></ul><ul><ul><li>Time to Generate Sample </li></ul></ul><ul><ul><li>Scope of the Research </li></ul></ul><ul><ul><li>Budget Available </li></ul></ul><ul><ul><li>Experience with Sampling </li></ul></ul><ul><ul><li>Level of Accuracy Desired </li></ul></ul><ul><ul><li>Your Knowledge of the Population </li></ul></ul><ul><li>Methods: </li></ul><ul><ul><li>Census: Population canvas - not really a “sample”. Asking the entire population </li></ul></ul><ul><ul><li>Judgment: Best guess of “experts” or draw on your experience to determine sample size </li></ul></ul><ul><ul><li>Conventional: What have others done? See what the sample size has been for similar studies </li></ul></ul><ul><ul><li>Arbitrary: Applies some industry accepted “rule of thumb”. Picking “x” percent of the population to be in the sample </li></ul></ul><ul><ul><li>Cost Basis: What can we afford? How much do we want to spend? </li></ul></ul><ul><ul><li>Statistical: Variance, SD, confidence interval play a key role </li></ul></ul>
  20. 20. 6. Operational Procedures <ul><li>Clear instructions required for interviewers </li></ul><ul><li>Non-probability samples –interviewers have a measure of discretion in selecting elements of sample </li></ul><ul><li>Probability samples –instructions need to be detailed and unambiguous to eliminate interviewer discretion </li></ul>
  21. 21. The content of this work is of shared interest between the author, Kelly Page and other parties who have contributed and/or provided support for the generation of the content detailed within. This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 2.0 UK: England & Wales. Kelly Page (cc)