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Sampling Design in Applied Marketing Research
 

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.

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

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  • 27/07/10

Sampling Design in Applied Marketing Research Sampling Design in Applied Marketing Research Presentation Transcript

  • Sampling Design Week 6 (1) Dr. Kelly Page Cardiff Business School E: pagekl@cardiff.ac.uk T: @drkellypage T: @caseinsights FB: kelly@caseinsights.com
    • Understand the concept of sampling;
    • Learn the steps in developing a sampling plan;
    • Understand the concepts of sampling error and non-sampling error;
    • Understand the differences between probability and non-probability samples;
    Lecture Objectives
  • The Concept of Sampling
    • “ 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.
    • Representative sample --- > Accurate predictions
    • How do we ensure our sample is as representative as possible of the group we need information about?
  • Definitions
    • Population (of interest): Entire group of people about whom information is needed (also may be called “universe”).
    • Census: Collection of data from or about every member of the population of interest.
    • Sample: Subset of all the members of a population of interest.
    • Sampling Frame: List of population elements from which units to be sampled can be selected, or a specified procedure for generating such a list.
  • Sampling Error
    • 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:
    • Sampling Error: Error that occurs because the sample selected is not perfectly representative of the population.
      • E.g., Admin error (execution) or random sampling error
    • And/Or
    • Non-sampling error: All error other than sampling error. E.g., Measurement error
  • Developing Sampling Design
    • Define the population of interest
    • Choose data collection method
    • Identify sampling frame
    • Select sampling method
    • Determine sample size
    • Develop operational procedures for selecting sample elements
    • Execute the operational sampling plan
  • 1. Population of Interest
    • Some possible bases for defining population of interest:
      • Geographical area
      • Demographic (age, gender, etc.)
      • Usage Awareness / knowledge
      • Need to consider:
        • Is screening required?
        • Should anyone be excluded?
  • 2. Data Collection Method
    • What methods can be used to reach the population of interest?
    • What methods can be used to administer the survey?
    • What are the resource implications?
    • What are the implications regarding representativeness of the sample?
  • 3. Identify Sampling Frame
    • Listings:
      • Electoral register
      • Postcode Address File
      • Telephone directory
      • Specialised consumer lists
      • Random-digit dialling list
      • Customer or member lists
    • Need to consider:
      • Who is not included?
      • How could this affect representativeness of your sample?
    • 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.
  • 4. Select Sampling Method
    • Probability OR non-probability sample?
    • A probability sample is one in which every element of the population of interest has a known, non-zero likelihood of being selected.
    • Advantages:
      • Ensures sample is representative
      • Can calculate sampling error
      • Can project results to whole population, with allowance made for sampling error
  • Probability Sampling
    • Simple Random Sampling (SRS)
      • 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.
      • Requires complete list of population of interest.
      • For sample size n from population of size N, probability of selection is n / N
    • Procedure
      • 1) Number list.
      • 2) Using random number table or random number generator, select n numbers.
      • 3) Sample consists of elements corresponding to these numbers.
  • Probability Sampling
    • Systematic Sampling
      • A sample in which the entire population is numbered and elements are selected using a skip interval – every “ nth” name is selected.
      • Requires complete list of population of interest.
      • Set skip interval [N / n ]
    • Procedure
      • 1) Number list.
      • 2) Using random number table or random number generator, select starting element.
      • 3) From starting element, add skip interval to find second element, and so on until n elements found.
    • Simpler, quicker, cheaper than SRS –but beware of hidden patterns in the list!
  • Probability Sampling
    • Stratified Sampling
      • 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.
      • 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)
      • 2) Select a simple random sample from each subgroup.
    • Reduces sampling error
    • Can ensure subgroup proportions in sample replicate percentage in whole population of interest (proportional allocation)
    • Or can weight sample to take into account that what we are investigating may vary to different extents in different subgroups (disproportional allocation)
  • Probability Sampling
    • Cluster Sampling
      • A sample in which the sampling units are selecting from a number of small geographic areas to reduce data collection costs.
      • 1) Divide population of interest into mutually exclusive and exhaustive subgroups (i.e. every element is in one and only one subgroup).
      • 2) Select a simple random sample of the subgroups (clusters).
      • 3) Either sample all elements in each selected subgroup or select a probability sample of elements from each selected subgroup.
    • In practice, most often subdivided by geographical area; multistage area sampling uses more than one subdivision
    • Problems occur if elements in clusters are similar but clusters are very dissimilar to each other
    • Greater cost efficiency but less statistical efficiency
  • 4. Select Sampling Method
    • A non-probability sample is one in which not everyone has an equal chance of being sampled!
    • We cannot calculate sampling error
    • We need to look carefully at how the sample has been selected, in order to evaluate:
      • How representative it is of the population
      • What sampling biases might be present Non-probability samples are often chosen for reasons of cost, time or convenience.
  • Non-probability
    • Quota sampling
    • Very commonly used
    • Population divided into subgroups based on demographic criteria, so that proportion of sample in each subgroup mirrors proportion of subgroup in population
    • 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).
    • Differs from stratified sampling because:
      • Demographic subgroups are not necessarily related to what is being investigated
      • Selection of respondents is left to interviewer (not SRS)
  • Non-probability
    • Convenience sampling
      • Using respondents who are willing and accessible
      • A sample based on using people who are easily accessible - such as mall intercepts or other high traffic locations.
      • saves time and cost
    • Judgment sampling
      • selection based on judgment of researcher
    • Snowball sampling
      • select initial respondents and ask them to nominate others
  • 4. Selecting Sampling Method
    • Consider:
      • Nature of research problem
      • Objectives
      • Time
      • Budget
      • Other resource constraints
  • 5. Determine Sample Size
    • Determining the sample size will be based on factors such as:
      • Time to Generate Sample
      • Scope of the Research
      • Budget Available
      • Experience with Sampling
      • Level of Accuracy Desired
      • Your Knowledge of the Population
    • Methods:
      • Census: Population canvas - not really a “sample”. Asking the entire population
      • Judgment: Best guess of “experts” or draw on your experience to determine sample size
      • Conventional: What have others done? See what the sample size has been for similar studies
      • Arbitrary: Applies some industry accepted “rule of thumb”. Picking “x” percent of the population to be in the sample
      • Cost Basis: What can we afford? How much do we want to spend?
      • Statistical: Variance, SD, confidence interval play a key role
  • 6. Operational Procedures
    • Clear instructions required for interviewers
    • Non-probability samples –interviewers have a measure of discretion in selecting elements of sample
    • Probability samples –instructions need to be detailed and unambiguous to eliminate interviewer discretion
  • 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. http://creativecommons.org/ Kelly Page (cc)