Primary Market Research

1. Primary Market Research Sampling

2. Module 4b: Objectives Participants will: • define population, sample and sampling; • identify target population for PMR; • explain the rationale for choosing sampling method and size; • justify use of non-probabilistic sampling for PMR; • explain application of non-probabilistic sampling to PMR.

3. Sampling andPrimary Market Research • Main input for product-planning: Needs and expectations of Product customers • Product customers : end-users, clinicians, caregivers, and/or other stakeholders • Primary market research captures relevant information from all of them

4. Target [or Reference] Population • Group of people/objects that meet the criteria or have the characteristics relevant to research purpose • For PMR: All product customers that possess the information, knowledge and experience we are seeking; plus other pre-defined criteria such as- low vision, wheeled chair user, left-handed, …… (human factors) working women, elderly over 65 living alone.... ( demographics)

5. Population Types Homogeneous Population • Group members with relevant characteristics are uniformly distributed throughout a Homogeneous population. • Ex: End-users of AugCom devices having similar experience and perspectives on use.

6. Population Types [contd.] Heterogeneous population Distinct sub-groups make up a Heterogeneous population; relevant characteristics are uniformly distributed within sub-groups, not across sub-groups • Ex: Population comprised of users of AugCom devices, their caregivers and clinicians, all having related but different perspectives on device use

7. Heterogeneity vs. Homogeneity • The information that people possess and which is sought by PMR= Dependent or Outcome variable • The “variety” in the information comes from differences between people on other characteristics: age, experience….[ independent variables] • Ind. Variables =>distinct subgroups => Heterogeneity in the population

8. What is Sampling? • Sample -part or subset of the whole group or target populationthat research is focusing on. • Sampling - procedure by which to choose elements (people or objects) from the target population to make up a sample that has the same characteristics as the parent group. • Purpose: to describe or draw conclusions about the population through the sample, without having to study the entire group.

9. Sample Characteristics • Sample data should let us confidently draw conclusions about population, so a sample should represent the target population characteristics. • Representation is required in experiment-based research, because it allows accurate statistical generalizations about the population. • In PMR, the reason for representation is to increase our credibility in using sample findings to describe the larger target population, rather than statistical generalizations.

10. Sample Characteristics • In a sample representing a heterogeneous population subgroups will have the same relative frequency [proportions] as in the population with respect to relevant characteristics. Ex: The number of wheelchair users, clinicians and caregivers in a mixed focus group sample should reflect their proportions relative to each other in the larger, mixed population

11. Sample Size is Important • How many to include (sample size) is important for representing populations. • Smaller the sample, more difficult to assure inclusion of members of the smaller sub-groups of population. • Especially true of heterogeneous samples. Ex. Those who use specific features like switch scanning might not get into a small mix of AugCom users.

12. Restrictions on Sample Size Practical constraints reduce potential sample size: • Target population is reduced to accessible population, for not everyone is accessible for sampling. Ex. people in remote areas. • Not everyone in the accessible population can or will participate. • Not everyone selected will show up.

13. Sample size for PMR • Experimental Research: Statistical analysis methods define the minimum sample size required for accurate generalization. • PMR:Optimal sample size is guided by: • Information needs. Ex: In designing this product, do we need broad coverage of all features or narrow, in-depth focus on specific features? • Sample type. Heterogeneous samples needs to be bigger. • Cost, logistics [scheduling, availability]

14. Sampling Methods:Two Choices • Random or probability sampling • Non-probabilistic Sampling

15. Random orProbability Sampling • The preferred way of Experiment-based research. • Most reliable way (i.e., with least error) of generalizing from sample data • No selector bias. Every person/object selected from the population has an equal chance of being included in the sample. • Types: Simple random, systematic, stratified, disproportional, cluster …[Portney & Watkins, 1993]

16. Random Sampling Procedures • Simple Random: Start by choosing an element at random from the target population, continue to do so until the desired number of elements are selected for the sample. Use of a random number table is a good tool to draw elements • Others: also draw elements randomly from the target population, but a pre-defined condition modifies the drawing. See next slide.

17. Modified Forms of Random Sampling Systematic: randomly draws every nth element from an organized target population. Ex. from a telephone directory, a dictionary of words … Stratified: randomly draws from sub-groups or strata. Ex. randomly choose 5 students from every classroom of a school Cluster/ multistage: randomly draws pre-defined “clusters” of elements. Ex. Draw “n” schools from city schools, then “m” classes in each school. Others: …..

18. Non-Probabilistic Sampling • Population units have unknown probabilities of being included in the sample. • Allows for selector bias • Often a necessary alternative due to reality constraints: cost, timeliness, sample size, access to target population, … • Types: Convenience, Quota, Purposive, Snowball….

19. Purposive Sampling • Researcher hand-picks people/objects purposefully allowing pre-defined characteristics/ criteria (Ex. special human factors) to be included in the sample. • Its logic and power highly suit PMR research purpose – more concerned with validly describing the sample and target population, than with statistical generalization. • Often used successfully in qualitative evaluations.

20. Non-Probabilistic Sampling: Other Forms • Quota sampling pre-establishes inclusion of a certain quantity or “quota”of elements in its sub-groups to represent the corresponding population subgroup characteristics • Snowball or chain samples are built as the researcher carries out the selection process, getting referrals through sample members. • Convenience Sampling includes elements based on availability – Ex. every one that you can stop at a supermarket parking lot

21. Information Needs for PMR • Context : Product planning and development • Sample data are used for - Formative Purpose: - Data on needs and expectations guide designing decisions while product is still in development [in the “forming”] SummativePurpose: - Data on product evaluation help end-of-the-development (disseminating/ marketing) decisions.

22. Sampling Considerations for PMR • PMR needs information both for Formative and Summative decisions • PMR Samples should include: -“information-rich” cases - preferably from every population sub- group. However….. 3. Product customer universe is often heterogeneous with a considerable number of important subgroups.

23. Sampling Considerations for PMR In light of its information needs, using ProbabilitySampling for PMR might imply: • Either a small sample that excludes an important minority subgroup; • Or a sample of cost prohibitive magnitude that includes all important groups. Purposive sampling is a more useful alternative for constructing valid PMR samples of optimal size.

24. Sampling Considerations for PMR Useful alternatives: • Maximum variant sample – mixed group with information-rich cases drawn from every subgroup of [heterogeneous] population. Ex: Group of Hearing aid users, caregivers and clinicians b. Separate homogeneous samples of information-rich members for each subgroup Ex: caregiver samples, user samples, manufacturer samples…

25. Sampling Considerations for PMR c. Intensity samples: include cases that intensely, but not extremely, manifest the information.Ex: industry experts related to Wheeled mobility technology d. Random purposeful samples: smaller random samples from a larger purposeful group. Increases credibility in generalizing [not statistically] to the target group e. Others: Critical case, snowball … [Patton, 1990]

26. A Practical Sampling Alternative Combine purposive, quotaand snowballsampling into your sampling rationale: [VIEW Example] • Before recruiting, prepare a Sampling frame or matrix to define how you will draw information-rich cases and distribute them in your sample. • Define column and row headings by the different criteria (or characteristics) levels. Ex: columns to represent physical ability levels (high and low) to operate an AAC device, & rows for environmentaldemands (high and low) on device use

27. A Practical Sampling Alternative [cont’d] 3. Define “quotas” or optimal numbers of people to fill the cells with, after weighing the corresponding proportions [known or estimated] of target population subgroups against reality (time, cost and logistical) constraints 4. Fill each cell purposively with the desired numbers by recruiting people that meet criteria as defined. Use Snowball strategy for recruitment,if necessary.

28. Where Do You Use Samples in PMR? PMR collects information through: -focus groups interviews -surveys -“one-on-one” or telephone interviews

29. Recruitment • Sampling frame defines what and how many specific types of people you want to include • Recruitment implements the selection plan. -contact individuals -get commitment -schedule and logistics

30. Recruitment Challenges Quite often, not everyone approached by recruiter meets the criteria, and not everyone that meets the criteria is readily identifiable or accessible. Use the Snowballapproach. Get people through a “chain referral” process to fill in the pre-set sampling frame. This adds the snowball rationale to thepurposive-quota rationale begun at the sample planning stage.

31. Recruitment Guidelines • Define sampling matrix first and then select people by recruiting. Plans for criteria, population characteristics, number, etc. should precede recruitment, so rational adjustments can be made when the plan cannot be fully achieved. • “Over-sample”- allow for bigger proportion of underrepresented segments • “Over-recruit” - counteract sample attrition; anticipate logistical, scheduling problems. • Recruitment takes time - start early

32. Sampling for PMR:An Example The attached example of sampling protocols for the “caller-connect” device illustrates the foregoing rationale for focus group interviews

33. Sampling protocols: The Case of the “Caller -connect” Device • Purpose of the Focus Group interviews: To obtain information useful for “Concept Refinement ” • features/characteristics of a device that meets the need of people that leave telephone off the hook for various reasons [stress, functional limitations, cognitive impairment, forgetfulness by older and child family members]

34. Sampling protocols: The case of the “Caller -connect” Device • Step one: define target population • driving question is “What features should make up this "off-the-hook" device? • seeks input for a "universal design" • universe to include expertise from specific "groups" e.g. families with children/elderly leaving phone off the hook; with various functional needs; and with relevant demographics. Basically, purposive sampling makes sense.

35. Sampling protocols: The case of the “Caller -connect” Device • Step two: make a sampling plan or chart and define what proportions to include • hearing "all" subgroups of interest impractical Alternatively, define several independent subsets of universe and then draw a sampling chart for each subset • 3 groups defined -- persons with disabilities, elderly, younger adults with children

38. Related Issues Questions, Comments, Suggestions?