1. Chapter Thirteen 13-1

2. Validation & Editing Coding Machine Cleaning of Data Tabulation & Statistical Analysis Data Entry Overview of the Data Analysis Procedure 13-2 Key Terms & Definitions

3. Step One: Validation: Process of ascertaining that interviews actually were conducted as specified. Editing: Process of ascertaining that questionnaires were filled out properly and completely. Skip Pattern: Sequence in which later questions are asked, based on a respondent’s answer to an earlier question or questions. Step Two: Coding: Process of grouping and assigning numeric codes to the various responses to a question. Step Three: Data Entry: Process of converting information to an electronic format. Scanning: Form of data entry in which responses on questionnaires are read in automatically by the data entry device. Overview of the Data Analysis Procedure 13-3 Key Terms & Definitions

4. Step Four: Clean the Data: Check for data entry errors or data entry inconsistencies. Logical or Machine Cleaning: Final computerized error check of data. Error Check Routines: Computer programs that accept instructions from the user to check for logical errors in the data. Step Five: Data Analysis – e.g., One-Way Frequency Tables: Table showing the number of respondents choosing each answer to a survey question. Cross Tabulation Tables - Examination of the responses to one question relative to the responses to one or more other questions. Overview of the Data Analysis Procedure 13-4 Key Terms & Definitions

5. Validation: Once all interviews have been completed, the research firm typically contacts a percentage of the respondents to validate. Phone validation asks four questions: 1.Was the person actually interviewed? 2.Did the person who was interviewed qualify to be interviewed according to the screening questions on the survey? 3.Was the interview conducted in the required manner? 4.Did the interviewer cover the entire survey? Ensuring the interviews are administered properly and completely help to ensure research results and legitimate responses. Data Analysis Procedure in Depth 13-5 Key Terms & Definitions

6. Editing: This gives the research firm a chance to check for interviewer and respondent mistakes by manually checking for the following issues: 1.Whether the interviewer failed to ask certain questions or record answers for certain questions. 2.Whether skip patterns were followed 3.Whether the interviewer paraphrased respondents’ answers to open-ended questions. The person doing the editing must make judgment calls on sub-standard answers to open-ended questions. Data Analysis Procedure in Depth 13-6 Key Terms & Definitions

7. Coding: The process of grouping and assigning numeric codes to various responses to questions. Closed-ended questions are usually pre-coded and need to be tabulated, but open-ended questions create a coding dilemma: 1.List responses providing the client with the full list of actual responses. 2.Consolidate the responses if a number can mean the same thing Consolidated responses require a judgment call on the part of the person doing the coding. Data Analysis Procedure in Depth 13-7 Key Terms & Definitions

8. One-Way Frequency Table 13-8 Key Terms & Definitions

9. The Coding Process 13-6 Key Terms & Definitions 1.List responses 2.Consolidate responses 3.Set codes 4.Enter codes 1.Read responses to individual open-ended questions on questionnaires 2.Match individual responses with consolidated list of response categories

10. The Coding Process 13-7 Key Terms & Definitions

11. One-Way Frequency Tables 13-11 Key Terms & Definitions

12. Bivariate cross tabulation: Cross tabulation two items - “Business Category” and “Gender” Cross Tabulations, Examples 13-12 Key Terms & Definitions

13. Multivariate cross tabulation: Additional filtering criteria - “Veteran Status” Now filtering three items. Cross Tabulations, Examples 13-13 Key Terms & Definitions

14. Cross Tabulations, Examples 13-14 Key Terms & Definitions

15. Cross Tabulations Examples 13-15 Key Terms & Definitions

16. 1.Make hypotheses. 2.Look for what is not there. 3.Scrutinize for the obvious. 4.Keep your mind open. 5.Trust the data. 6.Watch the “n.” Practical Tips for Cross Tabulations To Make things Easier: Key Terms & Definitions 13-16

17. One Way Frequency Tables A table showing the number of respondents choosing each answer to a survey question. Graphic Representations of Data 13-17 Key Terms & Definitions

18. Line Charts: Graphic Representations of Data 13-18 Key Terms & Definitions Good for demonstrating linear relationships Particularly useful for presenting a given measurement taken at several points over time

19. Pie Charts: Graphic Representations of Data 13-19 Key Terms & Definitions Good for special relationships among data points Should total to 100%

20. Bar Charts: Graphic Representations of Data 13-20 Key Terms & Definitions Good for side by side relationships/comparisons Most flexible of the graphs

21. Types of Bar Charts: Graphic Representations of Data Multiple-Row Bar Chart 13-21 Key Terms & Definitions

22. Mean: The sum of the values for all observations of a variable divided by the number of observations. Median: Value below which 50 percent of the observations fall. Mode: The value that occurs most frequently. Mean: The sum of the values for all observations of a variable divided by the number of observations. Median: Value below which 50 percent of the observations fall. Mode: The value that occurs most frequently. Descriptive Statistics 13-22 Key Terms & Definitions

23. Descriptive Statistics 13-23 Key Terms & Definitions

24. Variance: The sums of the squared deviations from the mean divided by the number of observations minus one. The same formula as standard deviation with the squaring. Range: The maximum value for a variable minus the minimum value for that variable. Standard Deviation: Measure of dispersion calculated by subtracting the mean of the series from each value in a series, squaring each result, summing the results, dividing the sum by the number of items minus 1, and taking the square root of this value. Variance: The sums of the squared deviations from the mean divided by the number of observations minus one. The same formula as standard deviation with the squaring. Range: The maximum value for a variable minus the minimum value for that variable. Standard Deviation: Measure of dispersion calculated by subtracting the mean of the series from each value in a series, squaring each result, summing the results, dividing the sum by the number of items minus 1, and taking the square root of this value. Measures of Dispersion 13-24 Key Terms & Definitions These measures indicate how spread out the data are:

25. Measures of Dispersion 13-25 Key Terms & Definitions

26. Descriptive statistics are the most efficient means of summarizing the characteristics of large sets of data. In a statistical analysis, the analyst calculates one number or a few numbers that reveal something about the characteristics of large sets of data. Descriptive Statistics 13-26 Key Terms & Definitions

27. The issue of whether certain measurements are different from one another is central to many questions of marketing managers. Statistical Significance 13-27 Key Terms & Definitions The posttest measure of top-of-mind awareness is slightly higher than the level pretest. Did the top-of-mind awareness really increase? Is there another explanation for the increase? The overall customer satisfaction score increased from 92 percent to 93.5 percent in one three month period Did it really increase? In an awareness test, 28.3 percent of those surveyed have heard of the product on an unaided basis. Is this a good result?

28. The issue of whether certain measurements are different from one another is central to many questions of marketing managers. Statistical Significance 13-28 Key Terms & Definitions Mathematical differences – by definition if the numbers are not exactly the same, they are different. That doesn’t mean they are important or statistically different. Statistical significance – if the difference is large enough to be unlikely to have occurred because of chance or sampling error, then it is considered statistically significant. Managerially important differences – An argument can be made that a difference, even a small one, may have managerial implications.

29. A hypothesis is an assumption or theory that a researcher or manager makes about some characteristics of the population under study. Hypothesis Testing 13-29 Key Terms & Definitions Step One: State the Hypothesis Step Two: Choose the Test Statistic Step Three: Develop the Decision Rule Step Four: Calculate the Value of the Test Statistic Step Five: State the Conclusion This is the point of the survey, questionnaire and testing – to determine if the marketing idea, product, or service has an actionable result.

30. 13-30 Key Terms & Definitions Overview of the Data Analysis ProcedureOverview of the Data Analysis Procedure Validation Editing Skip Pattern Coding Data Entry Intelligent Data Entry Scanning Technology Logical or Machine Cleaning of Data Error Checking Routines Links and button are active when in “Slide Show Mode” Key Terms & Definitions Marginal Report One-way Frequency Table Cross Tabulation Graphic Representations of Data Descriptive Statistics Measures of Central Tendency Measures of Dispersion Mean Median Mode