1. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved.

2. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. CHAPTERCHAPTERCHAPTERCHAPTER 1234 0001 897251 00000 11 Sampling: Theory, Designs, and Issues in Marketing Research 11-2

3. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. The Value of Sampling in Information Research  “Sampling” is an essential part of what it means to be “human.” Therefore, when we “sample” we’re doing what comes “natural.”  The sampling process associated with marketing research is quite complex, based on the scientific method rather than intuition.  Sampling is efficient, enabling a research team to project outcomes from a small group out to a larger target population, saving time and money.  The information gathered from the small group (or sample) allows the research team to make judgments about the larger target population, helping management address the information problem or market opportunity. 11-3

4. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Sampling & The Research Process  A census is a research endeavor that seeks to include data about every member of the defined target population.  A sample is a research endeavor the seeks to make judgments about a larger group of respondents by communicating with a smaller number of people drawn from the total target population. 11-4

5. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Sampling  Cost and time considerations usually make surveying an entire population impractical, so companies take a sample of elements to represent the target population.  It is undeniable to most statistical experts that both small and large samples can be highly accurate—provided the sampling plan is sound.  An important part of the plan is to use a correct sampling frame.

6. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. The Sampling Process Define the Target Population Identify the Sampling Frame Choose the Sampling Method Determine the Sample Size Gather the Data

7. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Identify the Sampling Frame  Sampling Frame: Actual list of each element or member of the target population.  Sampling Biases  Nonresponse Bias: (a.k.a., nonresponse error) Occurs when a high percentage of respondents choose not to participate in the study.  Noncooperation  Noncontact  Passive Refusal  Inaccurate Sampling Methods: An inaccurate sampling frame happens when, for example, samples for surveying a firm’s customers are based on a list provided by the firm. The list may be a list of accounts rather than a list of customers. This would result in a customer with three accounts having a triple probability of being drawn into a sample.  To reduce the chance of sampling bias and to allow for statistical analysis, a random sample should be taken.

8. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Populations and Census  Population: (a.k.a., universe) Is the entire group of people, markets, companies, or products that is being investigated by the researcher.  Finite Population: Has a limited or fixed number of individuals or objects.  Infinite Population: Has an unlimited or non-fixed number of individuals or objects.  Parameter: A measurement used to describe some characteristic of a population.  Census: When a population is sampled in its entirety.

9. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Samples Subset of representative units from the population. The sample is used to represent the population for statistical study, and the findings from the sample are used as the basis for estimating or predicting the characteristics of the population.

10. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Choosing the Sampling Method  Probability Sample: A subset of the population in which the probability of selection is known and nonzero for every sampling unit in the population.  Nonprobability Sampling: Any subset of a population in which the probability of obtaining the sample cannot be computed.

11. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Methods for Determining Sample Size  Blind guessing  Industry rules of thumb  Affordability method  Statistical method

12. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Statistical Method for Computing Sample Size  Desired Precision (+E). Asks, “How precise does the measurement need to be?”  Value Associated with Desired Confidence Level (Z). Asks, “How confident do you want to be that the specified confidence interval takes in the population mean?”  Estimator of the Standard Deviation of the Population (s). Asks, “How heterogeneous are the members that are being investigated?”

13. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Methods to Estimate the Population Standard Deviation  Use information from an earlier study  Conduct a small-scale study of the population

14. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Determining Appropriate Sample Sizes Three factors play a role in determining the “right” sample size: 1)The variability of the population characteristic. 2)The level of confidence desired in the estimate. 3)The degree of precision desired. 11-9

15. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Determining Sample Size: Means Z = standardized value indicating the level of confidence s = estimator of the population standard deviation E = acceptable magnitude of sampling error (that is, precision)

16. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Determining Sample Size: Proportions  Desired Precision (±E): Researchers must determine the largest acceptable difference between the sample proportion and the population proportion, specified as an acceptable degree of sampling error (±E).  Value Associated with Desired Confidence Level (Z): The greater the desired confidence, the larger the sample size must be.  Estimator of the Standard Deviation of the Population (s): This proportion may be estimated in a similar way to that used to approximate the standard deviation when calculating sample size using mean statistics.

17. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Measures of Dispersion  The values in a group of statistical data usually vary in magnitude. The variation of the values is called dispersion.  Measure of dispersion is useful in two ways:  Can show the degree of variation among values in a given data set.  Can supplement an average to describe a group of data or to compare one group of data with another.

18. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved.  The most common types of dispersion expressed:  Range: Difference between the lowest and the highest values.  Standard Deviation and Variance: The most popular measures of variability. They assess the spread (variance) in the data. The standard deviation of a set of values is the square root of the arithmetic mean of the individual deviations squared. Measures of Dispersion – cont’d

19. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Normal Distribution  A frequency distribution represented graphically by a bell-shaped curve that is symmetrical about the mean.  Normal Curve: The bell-shaped curve that depicts the normal distribution. Almost all (99.7 percent) of the normal curve’s values are within +3 standard deviations from its mean.  Since the normal distribution is symmetrical, the midpoint under the curve is the mean of the distribution.  While there are an infinite number of normal distribution curves, statisticians have simplified things for researchers by calculating areas under a special normal distribution curve that has a mean of 0 and a standard deviation of 1. This special curve is called the standard normal distribution curve.

20. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Confidence Interval  Reflects a range of values that we are confident, but not certain, contains the population parameter.  The wider the confidence interval, the more confident we are that the particular interval will contain our parameter. The narrower the confidence interval, the less confident we are that the particular interval will contain our parameter.

21. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Sampling Distribution & The CLT 11-6 Average Household Income Percent of Households  ≈ χ ≈ χ

22. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Sampling & Nonsampling Error  Sampling “error” refers to any type of bias that can be attributed to “messing up” when it comes to drawing a sample.  Sampling “error” refers to any type of bias that can be attributed to “messing up” when it comes to determining the size of the sample.  Nonsampling “error” refers to any type of bias that occurs in the research endeavor regardless of whether a sample or census was used. 11-7

23. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Determining Statistical Precision  General precision refers to the amount of general sampling error associated with the raw data which has been captured by the research team.  Precise precision is the amount of sampling error at a specified level of confidence.  A confidence interval is a statistical range of “values” within which the true value of the defined target population parameter is expected to rest. 11-8

24. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Types of Sampling Methods SAMPLING METHODS Simple random sampling Systematic sampling Stratified sampling Cluster sampling Judgment sampling Convenience sampling Quota sampling Snowball sampling Probability Sampling Nonprobability Sampling

25. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Probabilistic Sampling  Every unit in the target population has a known and nonzero chance of being selected.  Simple Random Sampling: Each element of the population or each possible sample of the same size from the population has an equal chance of being selected.  Systematic Sampling: A sample is drawn by arbitrarily choosing a beginning point in a list and then sequentially selecting every kth element from the list.

26. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved.  Stratified Sampling: Researcher divides population into natural subgroups that are more homogeneous than the population as a whole. Then items are selected for the sample at random or by a systematic method from each subgroup.  Cluster Sampling: Performed by choosing a random sample of subgroups, and all members of the subgroups become part of the sample. Probabilistic Sampling – cont’d

27. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Probability Sampling Methods 11-10a Simple Random Sampling (SRS) Researchers use a table of random numbers, random digit dialing, or some other random selection procedure that ensures that each sampling unit making up the defined target population has a known, equal, nonzero chance of being selected into the sample. Systematic Random Sampling (SWMRS) Using some form of an ordered list of the members of the defined target population, researchers select a random starting point for the first sampled member. After determining what the constant “skip interval” value needs to be to ensure representativeness, the skip interval is applied to select every nth member from the random starting point until the necessary sample is drawn. This sampling method is used such that the entire list is covered, no matter of the starting point. This method accomplishes the same end goal of the SRS method, and is more efficient.

28. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Probability Sampling Methods 11-10b Stratified Random Sampling (STRS) When the defined target population is believed to have a nonnormal (or skewed) distribution for one or more of its distinguishing characteristics (e.g., age, income, product ownership), researchers must identify subpopulations, referred to as strata. After the strata are segmented, a simple random sample is drawn for each stratum. Proportional and disproportional weighting factors may be applied to estimate overall population values. Cluster Sampling This method requires that the defined target population be segmented into geographic areas, each of which is considered to be very similar to the others. Researchers randomly select a few areas, then conduct a census of the elements in each are. As an alternative, researchers can select more areas and take samples from each of those areas. This sampling method is appealing when researchers can easily identify the highly similar areas.

29. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Non-probabilistic Sampling  Violates scientific principles, since the researcher’s personal judgment dominates when selecting sample elements. Therefore, there is no way to determine the probability of selecting a specific element into the sample. As a result, the findings obtained from non-probability sampling techniques are not projectable to the population.  Judgment Sampling: Sample items are selected by using a researcher’s personal judgment.  Convenience Sampling: Selecting sample items that are close at hand or otherwise easy to obtain.

30. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved.  Quota Sampling: Researchers determine the percentage of the target population that possesses the characteristics of interest and then specify the number of these individuals to be included in the sample to reflect their proportion in the population.  Snowball Sampling: Initial respondents provide names of additional respondents to include in a sample. Researchers use this referral method when potential respondents are difficult to locate because they are a tiny part of the entire population. Non-probabilistic Sampling – cont’d

31. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Nonprobability Sampling Methods 11-11a Convenience Sampling A method in which samples are drawn at the convenience of the researcher or interviewer, often as the study is being conducted. The assumptions underlying this method are that the defined target population is homogeneous and the individuals interviewed are similar to the overall target population with regard to the characteristics being studied. Judgment Sampling Participants are selected according to the researcher’s or some other experienced individual’s belief that they will meet the requirements of the study. The underlying assumption is the researcher’s subjective belief that the opinions of a group of perceived experts on the topic of interest are representative of the entire defined target population.

32. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Nonprobability Sampling Methods 11-11b Quota Sampling This method involves the selection of prospective participants according to prespecified quotas regarding demographic characteristics (e.g., age, race, sex, income), specific attitudes (e.g., satisfied/dissatisfied, liking/disliking, great/marginal/no quality), or specific behaviors (e.g., regular/occasional/rare shopper, product user/nonuser, heavy user/light user). The underlying purpose of quota sampling is to provide an assurance that prespecified subgroups of the defined target population are represented on pertinent sampling factors that are determined by the researcher or client.

33. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Nonprobability Sampling Methods 11-11c Snowball Sampling A method that involves the practice of subjectively identifying and qualifying a set of initial prospective respondents who can, in turn, help the researcher identify additional people to be included in the study. After interviewing one person, the interviewer would solicit that person’s help to identify other people with similar characteristics, opinions, or feelings. Members of the defined target population who might not hold similar beliefs or feelings to those of the respondents are less likely to be included in this type of sample.

34. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. A Summary of Comparative Differences 11-12a List of the Population Elements Complete List Necessary None Necessary Information about the Sampling Units Each Unit Identified Need Detail on Habits, Activities, Traits, etc. Sampling Skill Required Skill Required Little Skill Required Time RequirementTime-Consuming Low Time Consumption Cost per Unit Sampled Moderate to HighLow Comparison FactorsProbability SamplingNonprobability Sampling

35. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. A Summary of Comparative Differences 11-12b Estimates of Population Parameters UnbiasedBiased Sample Representativeness Good, Assured Suspect, Undeterminable Accuracy and Reliability Computed with Confidence Intervals Unknown Measurement of Sampling Error Statistical Measures No True Measure Available Comparison FactorsProbability SamplingNonprobability Sampling

36. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Factors to Consider when Selecting a Design 11-13a Selection Factors Questions Research objectives Do the research objectives call for the use of qualitative or quantitative research designs? Degree of accuracy Does the research call for making predictions or inductive inferences about the defined target population or only preliminary insights? Availability of resources Are there tight budget constraints with respect to both dollars and human resources that can be allocated to the research project? Time frameHow quickly does the research project have to be completed?

37. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Factors to Consider when Selecting a Design 11-13b Selection Factors Questions Advanced knowledge of the target population Are there complete lists of the defined target population elements? How easy or difficult is it to generate the required sampling frame of prospective respondents? Scope of the research Is the research going to be international, national, regional, or local? Perceived statistical analysis needs To what extent are accurate statistical projections and/or testing of hypothesized differences in the data structures required?

38. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. The Steps for Developing a Sampling Plan 11-14 Step 1 Define the Target Population Step 2 Select the Data Collection Method Step 3 Identify the Sampling Frame(s) Needed Step 4 Select the Appropriate Sampling Method Step 5 Determine Necessary Sample Sizes and Overall Contract Rates Step 6 Create an Operating Plan for Selecting Sampling Units Step 7 Execute the Operational Plan

39. 1995 7888 4320 000 000001 00023 Copyright © 2003 by The McGraw-Hill Companies, Inc. All rights reserved. Summary of Learning Objectives  Discuss the concept of sampling and list reasons for sampling.  Identify and explain the different roles that sampling plays in the overall information research process.  Identify the fundamental differences between probability and nonprobability sampling methods, and point out their strengths and weaknesses.  Discuss and calculate sampling distributions, standard errors, and confidence intervals and how they are used in assessing the accuracy of a sample.  Identify the criteria involved in determining the appropriate sample design for a given research project.  Discuss the factors that must be considered when determining sample size.  Discuss the methods of calculating appropriate sample sizes.  Identify and explain the steps involved in developing a sampling plan, and design a variety of different sampling plans. 11-15