1. Essentials of Marketing Research Kumar, Aaker, Day Chapter Four Research Design and Implementation - 2

2. Essentials of Marketing Research Kumar, Aaker, Day Four types of Data  Alphabetical / Categorical / Nominal data: –Information falls only in certain categories, not in-between categories –No inferences possible between groups –Only reporting frequencies, percentages and mode makes sense (descriptive statistics) –Chi Square measure of Association (inferential Statistics) –Examples: gender, age groups, income groups, etc.

3. Essentials of Marketing Research Kumar, Aaker, Day Four types of data  Rank order data: –Ranked according to some logic, e.g. preference, etc. –Again an in-between rank does not make sense. –Difference between say rank 1 and 2 need not necessarily be of the same magnitude as the difference between rank 3 and 4. –Only reporting frequencies, percentages and mode makes sense (descriptive statistics); Spearman Rho coefficient of correlation (Inferential statistics) –Examples: brand preferences, class rank on test, etc.

4. Essentials of Marketing Research Kumar, Aaker, Day Four types of data  Interval Level – Numerical data in which the numbers denote the amount of presence / absence of a trait. – zero point does not necessarily mean complete absence of the trait –In-between numbers make sense –Magnitude of difference between numbers of the scale is constant. –All descriptive and inferential statistics possible –Examples: attitude, satisfaction, temperature, etc.

5. Essentials of Marketing Research Kumar, Aaker, Day Four types of data  Ratio level data –Interval level data with a meaningful zero point meaning complete absence of the trait –Magnitude of the difference between numbers of the scale is constant AND the zero point denotes complete absence of the trait being measured. –All descriptive and inferential statistics possible –Examples: sales, profits, weight, height, etc.

6. Essentials of Marketing Research Kumar, Aaker, Day Type of data?

7. Essentials of Marketing Research Kumar, Aaker, Day Data Collection Methods Table 4-2 Relationship between Data Collection Method and Category of Research Category of Research Category of Research Data Collection Method Exploratory Descriptive Causal Secondary Sources Information Systemab Databanks of otherab organizations organizations Syndicated Servicesab b Primary Sources Qualitative Researchab Surveysba b Experimentsb a

8. Essentials of Marketing Research Kumar, Aaker, Day Research Tactics  Measurement – Generally what questions do we ask so that we get the information we want  Sampling Plan – How do we select a sample for the study such that we maximize its chances of faithfully representing the population of interest  Analysis – confirming that all information being obtained is appropriate and adequate for addressing the RQ / hypothesis

9. Essentials of Marketing Research Kumar, Aaker, Day Errors in Research Design  Assume you are interested in knowing what Winthrop undergrad students feel about the quality of the faculty –What is the population? Size?  Assume you take a sample of 100 students and find the sample mean –Would your sample mean match the population mean? –If not, what is the difference?

10. Essentials of Marketing Research Kumar, Aaker, Day Errors in Research Design  Assume you get a mean figure of 4.0 on a 1 (low quality) to 5 (high quality) scale  The population mean is an unknown figure –Always wise to assume that it is different from the sample mean –assume it is 4.5  The difference of 0.5 (4.5 – 4.0) is the total error in the research design

11. Essentials of Marketing Research Kumar, Aaker, Day Errors in Research design  Sampling errors – difference between measure obtained from the sample and true measure obtained from the population from which the sample is drawn (assuming random sampling is used)  Non-sampling errors –Design errors –Administering errors –Response errors –Non-response errors

12. Essentials of Marketing Research Kumar, Aaker, Day Non-sampling errors – Design Errors  Selection errors – biased sample selection  Population specification error – drawing a sample from the wrong population

13. Essentials of Marketing Research Kumar, Aaker, Day Non-sampling errors – Design Errors  Sampling frame error – using inaccurate sampling frame to create the sample  Surrogate information error – difference between information required for the study and what the researcher seeks

14. Essentials of Marketing Research Kumar, Aaker, Day Non-sampling errors – Design Errors  Measurement error – difference between information sought by the researcher and information generated by a particular measurement procedure used by the researcher

15. Essentials of Marketing Research Kumar, Aaker, Day Non-sampling errors – Design Errors  Experimental error – improper experimental design  Data Analysis error – e.g. wrong data coding or wrong statistical analysis

16. Essentials of Marketing Research Kumar, Aaker, Day Non-sampling errors – Administering Errors  Questioning error – incorrect phrasing of questions to respondents  Recording error – improperly recording the respondents answers  Interference error – does not follow the exact procedure while collecting data

17. Essentials of Marketing Research Kumar, Aaker, Day Non-sampling errors – Response Errors  Respondent supplies (intentionally or unintentionally) incorrect answers to questions –Does not understand the question –“Fatigue or boredom

18. Essentials of Marketing Research Kumar, Aaker, Day Non-sampling errors – Response Errors –Unwillingness to give information –Social desirability bias

19. Essentials of Marketing Research Kumar, Aaker, Day Non-sampling errors – Non-Response Errors  Respondents who did not respond may think differently on the issue  Some members of the sample may have provided incomplete information

20. Essentials of Marketing Research Kumar, Aaker, Day Compare Cost and Timing Estimates with Anticipated Value Proceed Data Collection and Analysis Data collection Field work Data processing Data analysis Statistical analysis Interpretation Conclusions and Recommendations Terminate Revise Implementation RESEARCH DESIGN PROCESS