marketing research aaker, kumar, day and leone tenth edition instructor’s presentation slides 1

Marketing ResearchAaker, Kumar, Day and LeoneTenth EditionInstructor’s Presentation Slides

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Marketing Research 10th Edition http://www.drvkumar.com/mr10/

Chapter Sixteen

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Fundamentals of Data Analysis


Data Analysis

• A set of methods and techniques used to obtain

information and insights from data

• Helps avoid erroneous judgments and conclusions

• Can constructively influence the research objectives and

the research design

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Major Data Preparation techniques:

• Data editing

• Coding

• Statistically adjusting the data


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

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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

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Statistically Adjusting the DataWeighting• 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

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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

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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,

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Simple Tabulation

•Consists of counting the number of cases that fall into various categories

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Uses: •Determine empirical distribution (frequency distribution) of the variable in question

•Calculate summary statistics, particularly the mean or percentages

•Aid in "data cleaning" aspects


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

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Frequency Distribution

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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)

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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

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Factors Influencing the Choice of Statistical Technique

Types 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

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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

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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.

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Overview of Statistical TechniquesUnivariate 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

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Classification of Univariate Statistical Techniques

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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

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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)


Classification of Multivariate Statistical Techniques

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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

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marketing research aaker, kumar, day and leone tenth edition instructor’s presentation slides 1

Documents

data slide

sample data

edition http

data editing coding

marketing research aaker

data analysis problems

research objectives

research design