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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 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
• 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.
• 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)