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**Aaker, Kumar, Day Ninth Edition Instructor’s Presentation Slides**

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

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**Fundamentals of Data Analysis**

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

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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

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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

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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 Marketing Research 9th Edition Aaker, Kumar, Day

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**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

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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 Marketing Research 9th Edition Aaker, Kumar, Day

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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, Marketing Research 9th Edition Aaker, Kumar, Day

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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

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**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

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**Frequency Distribution**

Marketing Research 9th Edition Aaker, Kumar, Day

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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) Marketing Research 9th Edition Aaker, Kumar, Day

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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 Marketing Research 9th Edition Aaker, Kumar, Day

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**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

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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 Marketing Research 9th Edition Aaker, Kumar, Day

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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. Marketing Research 9th Edition Aaker, Kumar, Day

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**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

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**Classification of Univariate Statistical Techniques**

Marketing Research 9th Edition Aaker, Kumar, Day

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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 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

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**Classification of Multivariate Statistical Techniques**

Marketing Research 9th Edition Aaker, Kumar, Day

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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 Marketing Research 9th Edition Aaker, Kumar, Day

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