1. Aaker, Kumar, Day Ninth Edition Instructor’s Presentation Slides
Marketing Research
Aaker, Kumar, Day
Ninth Edition
Instructor’s Presentation Slides

2. Fundamentals of Data Analysis
Chapter Sixteen
Fundamentals of Data Analysis
Marketing Research 9th Edition
Aaker, Kumar, Day

3. Data Analysis
A set of methods and techniques used to obtain information and insights from data
Helps avoid erroneous judgements and conclusions
Can constructively influence the research objectives and the research design
Major Data Preparation techniques:
Data editing
Coding
Statistically adjusting the data
Marketing Research 9th Edition
Aaker, Kumar, Day

4. Data Editing
Identifies omissions, ambiguities, and errors in responses
Conducted in the field by interviewer and field supervisor and by the analyst prior to data analysis
Problems identified with data editing:
Interviewer Error
Omissions
Ambiguity
Inconsistencies
Lack of Cooperation
Ineligible Respondent
Marketing Research 9th Edition
Aaker, Kumar, Day

5. Coding
Coding closed-ended questions involves specifying how the responses are to be entered
Open-ended questions are difficult to code
Lengthy list of possible responses is generated
Marketing Research 9th Edition
Aaker, Kumar, Day

6. Statistically Adjusting the Data
Weighting
Each response is assigned a number according to a pre-specified rule
Makes sample data more representative of target population on specific characteristics
Modifies number of cases in the sample that possess certain characteristics
Adjusts the sample so that greater importance is attached to respondents with certain characteristics
Marketing Research 9th Edition
Aaker, Kumar, Day

7. Statistically Adjusting the Data (contd.)
Variable Re-specification
Existing data is modified to create new variables
Large number of variables collapsed into fewer variables
Creates variables that are consistent with study objectives
Dummy variables are used (binary, dichotomous, instrumental, quantitative variables)
Use (d-1) dummy variables to specify (d) levels of qualitative variable
Marketing Research 9th Edition
Aaker, Kumar, Day

8. Statistically Adjusting the Data (contd.)
Scale Transformation
Scale values are manipulated to ensure comparability with other scales
Standardization allows the researcher to compare variables that have been measured using different types of scales
Variables are forced to have a mean of zero and a standard deviation of one
Can be done only on interval or ratio scaled data
Standardized score,
Marketing Research 9th Edition
Aaker, Kumar, Day

9. Simple Tabulation
Consists of counting the number of cases that fall into various categories
Uses:
Determine empirical distribution (frequency
distribution) of the variable in question
Calculate summary statistics, particularly the
mean or percentages
Aid in "data cleaning" aspects
Marketing Research 9th Edition
Aaker, Kumar, Day

10. Frequency Distribution
Reports the number of responses that each question received
Organizes data into classes or groups of values
Shows number of observations that fall into each class
Can be illustrated simply as a number or as a percentage or histogram
Response categories may be combined for many questions
Should result in categories with worthwhile number of respondents
Marketing Research 9th Edition
Aaker, Kumar, Day

11. Frequency Distribution
Marketing Research 9th Edition
Aaker, Kumar, Day

12. Descriptive Statistics
Statistics normally associated with a frequency distribution to help summarize information in the frequency table
Includes:
Measures of central tendency mean, median and mode
Measures of dispersion (range, standard deviation, and coefficient of variation)
Measures of shape (skewness and kurtosis)
Marketing Research 9th Edition
Aaker, Kumar, Day

13. Cross Tabulations
Statistical analysis technique to study the relationships among and between variables
Sample is divided to learn how the dependent variable varies from subgroup to subgroup
Frequency distribution for each subgroup is compared to the frequency distribution for the total sample
The two variables that are analyzed must be nominally scaled
Marketing Research 9th Edition
Aaker, Kumar, Day

14. Factors Influencing the Choice of Statistical Technique
Type of Data
Classification of data involves nominal, ordinal, interval and ratio scales of measurement
Nominal scaling is restricted in that mode is the only meaningful measure of central tendency
Both median and mode can be used for ordinal scale
Non-parametric tests can only be run on ordinal data
Mean, median and mode can all be used to measure central tendency for interval and ratio scaled data
Marketing Research 9th Edition
Aaker, Kumar, Day

15. Factors Influencing the Choice of Statistical Technique (Contd.)
Research Design
Depends on:
Whether dependent or independent samples are used
Number of observations per object
Number of groups being analyzed
Number of variables
Control exercised over variable of interest
Marketing Research 9th Edition
Aaker, Kumar, Day

16. Factors Influencing the Choice of Statistical Technique (Contd.)
Assumptions Underlying the Test Statistic
Two-sample t-test :
The samples are independent.
The characteristics of interest in each population have normal distribution.
The two populations have equal variances.
Marketing Research 9th Edition
Aaker, Kumar, Day

17. Overview of Statistical Techniques
Univariate Techniques
Appropriate when there is a single measurement of each of the 'n' sample objects or there are several measurements of each of the `n' observations but each variable is analyzed in isolation
Nonmetric data - measured on nominal or ordinal scale
Metric data - measured on interval or ratio scale
Determine whether single or multiple samples are involved
For multiple samples, choice of statistical test depends on whether the samples are independent or dependent
Marketing Research 9th Edition
Aaker, Kumar, Day

18. Classification of Univariate Statistical Techniques
Marketing Research 9th Edition
Aaker, Kumar, Day

19. Overview of Statistical Techniques (Contd.)
Multivariate Techniques
A collection of procedures for analyzing association between two or more sets of measurements that have been made on each object in one or more samples of objects
Uses:
To group variables or people or objects
To improve the ability to predict variables (such as usage)
To understand relationships between variables (such as
advertising and sales)
Marketing Research 9th Edition
Aaker, Kumar, Day

20. Classification of Multivariate Statistical Techniques
Marketing Research 9th Edition
Aaker, Kumar, Day

21. Classification of Multivariate Techniques (Contd.)
Dependence Techniques
One or more variables can be identified as dependent variables and the remaining as independent variables
Choice of dependence technique depends on the number of dependent variables involved in analysis
Interdependence Techniques
Whole set of interdependent relationships is examined
Further classified as having focus on variable or objects
Marketing Research 9th Edition
Aaker, Kumar, Day