1. Chapter Twelve Fundamentals of Data Analysis

2. Preparing the Data for Analysis Data editing – the process of identifying omissions, ambiguities and errors in the responses Coding – process of assigning numerical values to responses according to a pre-defined system Statistically adjusting the data – the process of modifying the data to enhance its quality for analysis –Weighting, transformations, variable re-specification

3. Preparing the Data for Analysis Problems Identified With Data Editing Omissions Ambiguity Inconsistencies Lack of Cooperation Ineligible Respondent

4. Preparing the Data for Analysis Solutions to such problems

5. Preparing the Data for Analysis Coding closed-ended questions –Relatively simple and straightforward open-ended questions –Define all possible responses and categorize each response and then assign a numerical code –If judgment calls are needed then have several coders do the same task and check inter-coder reliability –Inter-coder reliability

6. Statistical adjustment of data Weighting – –process of enhancing / reducing the importance of certain data by assigning a number –Usually done to increase the representativeness of the sample or achieve study objectives Scale transformations –Manipulation of scales to make them comparable with other scales e.g. converting lbs to kgs. etc. –Z-scores (standardized scales)

7. Preparing the Data for Analysis Variable Re-specification –Existing data modified to create new variables –Large number of variables collapsed into fewer variables –Creates variables that are consistent with research questions Determine if the variable is categorical, rank- order, interval level or ratio level.

8. Categorical Data Analysis - Objectives Describing the sample distribution for the variable (e.g. gender) Frequencies, percentages, quartiles, percentiles, graphs (bar, line, histogram, pie) What are the typical characteristics of the sample? Mode Does the categorical variable bear any relationship with a distribution of another categorical variable (e.g. gender w.r.t. buy the product or not) Cross tabs and chi-square as a measure of association

9. Cross tabulations – example – buyers by age Under 18 yrs. 19-24 yrs.25-34 yrs.Total for sample First time buyers 14%12.5%6.6%11.1% Brand loyals21.9%20%14.5%18.9% Switchers50%53%60% Never bought 14.1%14.5%18.9%10% 100% Distribution of customer types by age: If there were no differences between age groups, then each age group’s distribution would have matched the distribution for the total sample.

10. Crosstabs - conclusions The 25-34 yrs. Group is least likely to be first time buyers than the sample average The under 18 year group is more likely to be a brand loyal than the sample average

11. Rank order data analysis - Objectives What are respondent preferences amongst several competing alternatives? (e.g. rank your preferences amongst ten different brands of cars) –Frequencies, Percentages, Graphs What is the typical preference pattern in the sample (e.g. which car does the sample prefer the most and which one the least?) –Mode

12. Rank order data analysis - Objectives Are two sets of respondent preferences correlated? (e.g. wrist watches brand preferences with car brand preferences) –Spearman’s rank correlation coefficient

13. Interval level / Ratio level data analysis - Objectives What is the average response in the sample (e.g. what is the mean attitude to the brand?) –Mean / Median What is the average variability of the response in the sample (e.g. On an average, how dispersed are the sample’s attitudes to the brand from the mean?) –Standard deviation

14. Interval level / Ratio level data analysis - Objectives Do two or more subgroups in the sample differ from each other on the response / differ from a previously known / hypothesized value E.g. do males like the brand significantly more than the females? T tests, z tests E.g. Does attitude to WU vary by student status (freshman, sophomore, junior, senior) –ANOVA

15. Interval level / Ratio level data analysis - Objectives Are sample responses on two variables correlated? (e.g. are sales related to the advertising expenditure?) –Pearson correlation Can we determine the value of the sample’s response on a variable, if we know the value on another variable? (e.g. If we need to achieve 1 million dollars in sales next year, how much should we spend on advertising?) –Regression analysis