Chapter 3 Research Design
Focus Types of designs major sources of error in a research design
Definition (p. 83) A framework or blueprint for conducting the marketing research project specifies the details of the procedures necessary for obtaining the information needed to structure and/or solve marketing research problems makes data collection and analyses efficient
Components (p. 83) define the information needed (Ch. 2) design the exploratory, descriptive, and /or causal phases of research (Ch. 3-7) specify measurement procedures (Ch. 8,9) construct and pretest a questionnaire (Ch. 10) specify sampling details (Ch. 11, 12) develop a plan for data analysis (Ch. 14)
Exploratory vs Conclusive Research (p. 83-84) objective: provide insights vs testing hypotheses information needed: loosely defined vs clearly defined research process: unstructured vs structured analysis: qualitative vs quantitative findings: tentative vs conclusive
Exploratory Research Applications (p. 85) define a problem more precisely identify alternative courses of action develop hypotheses isolate key variables and relationships (e.g. Frigidaire) gain insights establish priorities
Types of exploratory studies: Patronage project (p. 86) review of literature to identify relevant demographic and psychographic factors interviews with retail experts case analyses - three best versus three worst stores focus groups to understand store selection criteria
Descriptive Research (p. 87-89) is based on clear statement of the problem and detailed information needs it is therefore preplanned and structured typical descriptive studies market analysis sales and market share studies image studies product usage studies distribution studies pricing studies advertising studies
Example: Advertising Study What % of consumers are aware of the current ad campaign? What do consumers recall of the ad copy? What is the post-exposure brand attitude? What are consumer perceptions of the ad spokesperson? What do consumers like/dislike about the ad?
Two Types of Descriptive Research (p. 89-91) Cross-sectional designs single (surveys) multiple same sample (true panel) different sample (quasi-panel; cohort) Longitudinal designs
Relative Advantages/Disadvantages of Cross-Sectional Designs p. 93-94 detecting change: - amount of data: - accuracy: - representative sampling: + response bias: +
Causal Research (p. 94-95) to test cause-effect relationships e.g. does ad $ increase sales? Does price reduction increase sales? Discussed in a later chapter
Sources of Error (p. 97-98) error = true population value - observed sample value error = random sampling error + non-sampling error random sampling error arises because of drawing a sample from the population the particular sample drawn may differ from the population can be reduced by increasing sample size
Nonsampling error (p.100) attributed to sources other than sampling may be random or non random include error in problem definition, scales, questionnaire design, interviewing methods, data preparation and analysis is made up of nonresponse error and response error
Nonresponse error (p. 100) some of the respondents included in the sample do not respond causes: refusals, not-at-home final sample will end up being different from the planned sample
Response error (p. 100-101) researcher error interviewer error population definition information sought measurement instruments used interviewer error respondent selection questioning cheating respondent error inability to answer unwilling to answer