Chapter XIV

Data Preparation and Basic Data Analysis

Important Topics of this ChapterImportant Topics of this Chapter

The Data Preparation ProcessThe Data Preparation Process

Questionnaire CheckingQuestionnaire Checking

EditingEditing

CodingCoding

i. Coding Questionnairesi. Coding Questionnaires

Data CleaningData Cleaning

i. Consistency Checksi. Consistency Checks

ii. Treatment of Missing Responsesii. Treatment of Missing Responses

Selecting a Data Analysis Strategy:Selecting a Data Analysis Strategy:

Descriptive AnalysisDescriptive Analysis

Inferential AnalysisInferential Analysis

Differential AnalysisDifferential Analysis

Associative AnalysisAssociative Analysis

Predictive AnalysisPredictive Analysis

Adjusting the Data

A Classification of Statistical TechniquesA Classification of Statistical Techniques

Understanding data Via Descriptive StatisticsUnderstanding data Via Descriptive Statistics

Measure of Central TendencyMeasure of Central Tendency

ModeMode

MedianMedian

MeanMean

Measure of VariabilityMeasure of Variability

Frequency DistributionFrequency Distribution

RangeRange

Standard DeviationStandard Deviation

Other Descriptive MeasuresOther Descriptive Measures

Measure of SkewnessMeasure of Skewness

KurtosisKurtosis

Obtaining Descriptive Statistics With SPSSObtaining Descriptive Statistics With SPSS

Prepare Preliminary Plan of Data Analysis

Data Preparation ProcessData Preparation ProcessFig. 14.1Fig. 14.1

Check Questionnaire

Edit

Code

Select Data Analysis Strategy

Transcribe

Statistically Adjust the Data

Clean Data

Data ReductionData Reduction

_ Summarization:Summarization:– Condensing the raw data into a few meaningful Condensing the raw data into a few meaningful

computation.computation._ Conceptualization:Conceptualization:

– Visualization of what of these measures represent.Visualization of what of these measures represent._ Communication:Communication:

– Translation of statistical analysis results into a form that is Translation of statistical analysis results into a form that is understandable and, more important, useful to marketing understandable and, more important, useful to marketing manager.manager.

_ Interpolation:Interpolation:– Assessment of data to the populationAssessment of data to the population

Types of Statistical Analysis Types of Statistical Analysis Used in Marketing ResearchUsed in Marketing Research

_ Descriptive Analysis:Descriptive Analysis:– Mean, Mode, Median and Standard deviation.Mean, Mode, Median and Standard deviation.

_ Inferential Analysis:Inferential Analysis:– Hypothesis testing and estimation of true population values.Hypothesis testing and estimation of true population values.

_ Differences Analysis:Differences Analysis:– Determination of significant differences exit in the population.Determination of significant differences exit in the population.

_ Associative Analysis:Associative Analysis:– Investigation of how two and more variables are related.Investigation of how two and more variables are related.

_ Predictive Analysis:Predictive Analysis:– It is used to enhance prediction capabilities of marketing It is used to enhance prediction capabilities of marketing

researcher. Ex: regression analysisresearcher. Ex: regression analysis

Understanding Data Via Understanding Data Via Descriptive AnalysisDescriptive Analysis

_ Measure of Central Tendency:Measure of Central Tendency:– ModeMode

» Highest occurrence in a set of variables.Highest occurrence in a set of variables.

– MedianMedian» Occurrence in the middle of a set values.Occurrence in the middle of a set values.

– Mean:Mean:» Arithmetic average of a set of numbers.Arithmetic average of a set of numbers.

Understanding Data Via Understanding Data Via Descriptive Analysis (cont.)Descriptive Analysis (cont.)

_ Measure of Variability:Measure of Variability:– Frequency Distribution:Frequency Distribution:

» Number of times that each different value appears.Number of times that each different value appears.

– Range:Range:» Identifies the distance between the lowest and the highest Identifies the distance between the lowest and the highest

value in an ordered set of variables.value in an ordered set of variables.

– Standard Deviation:Standard Deviation:» The degree of variation or diversity in the values in a such a The degree of variation or diversity in the values in a such a

way to be translated in a normal bell-shaped distribution.way to be translated in a normal bell-shaped distribution.

Understanding the Data Via Understanding the Data Via Descriptive Statistics (cont.)Descriptive Statistics (cont.)

_ Other Descriptive Measures:Other Descriptive Measures:– Measure of Skewness:Measure of Skewness:

» Reveals the degree of direction of asymmetry in a distribution. Reveals the degree of direction of asymmetry in a distribution. A ‘0’ value indicates symmetric distribution, a negative value A ‘0’ value indicates symmetric distribution, a negative value indicates distribution has tail to the left, a positive value indicates distribution has tail to the left, a positive value indicates distribution has tail to the right.indicates distribution has tail to the right.

– Kurtosis:Kurtosis:» How pointed and peaked a distribution appears. A ‘0’ value How pointed and peaked a distribution appears. A ‘0’ value

indicates distribution is bell shaped, a negative value indicates indicates distribution is bell shaped, a negative value indicates distribution is more flat, a positive value indicated distribution distribution is more flat, a positive value indicated distribution is more peaked than the bell shaped curve.is more peaked than the bell shaped curve.

Earlier Steps (1,2, & 3) of the Marketing Research Process

Known Characteristics of the Data

Properties of Statistical Techniques

Background and Philosophy of the Researcher

Data Analysis Strategy

Selecting a Data Analysis StrategySelecting a Data Analysis StrategyFig. 14.5Fig. 14.5

Univariate Techniques

Metric Data

Independent

A Classification of Univariate TechniquesA Classification of Univariate TechniquesFig. 14.6Fig. 14.6

Non-numeric Data

One Sample Two or More Samples

One Sample Two or More Samples

Related

Independent Related

* t test * Z test

* Frequency*Chi-Square*K-S*Runs* Binomial

* Two- Groups t test

* Z test * One-Way

ANOVA

* Paired* t test

* Chi-Square* Mann-Whitney* Median* K-S* K-W ANOVA

* Sign* Wilcoxon* McNemar* Chi-Square

Multivariate Techniques

Dependence Technique

A Classification of Multivariate TechniquesA Classification of Multivariate TechniquesFig. 14.7Fig. 14.7

More Than One Dependent Variable

* Multivariate Analysis of Variance and Covariance

* Canonical Correlation

* Multiple Discriminant Analysis

* Cross- Tabulation

* Analysis of Variance and Covariance

* Multiple Regression

* Conjoint Analysis

* Factor Analysis

Interdependence Technique

One Dependent Variable

Variable Interdependence

Interobject Similarity

* Cluster Analysis* Multidimensional

Scaling

Nielsen’s Internet Survey: Nielsen’s Internet Survey:

““Does It Carry Any Weight?”Does It Carry Any Weight?”

RIP14.1RIP14.1

The Nielsen Media Research Company, a longtime player in television-related marketing research has come under fire from the various TV networks for its surveying techniques. Additionally, in another potentially large, new revenue business, Internet surveying, Nielsen is encountering serious questions concerning the validity of its survey results. Due to the tremendous impact of electronic commerce on the business world, advertisers need to know how many people are doing business on the Internet in order to decide if it would be lucrative to place their ads online.

Nielsen performed a survey for CommerceNet, a group of companies that includes Sun Microsystems and American Express, to help determine the number of total users on the Internet.

Nielsen’s research stated that 37 million people over the age of 16 have access to the Internet and 24 million have used the Net in the last three months. Where statisticians believe the numbers are flawed is in the weighting used to help match the sample to the population. Weighting must be used to prevent research from being skewed towards one demographic segment. .

The Nielsen survey was weighted for gender but not for education which may have skewed the population towards educated adults.

Nielsen then proceeded to weight the survey by age and income after they had already weighted it for gender. Statisticians also feel that

this is incorrect because weighting must occur simultaneously, not in separate calculations. Nielsen does not believe the concerns about

their sample are legitimate and feel that they have not erred in weighting the survey. However, due to the fact that most third parties

have not endorsed Nielsen’s methods, the validity of their research remains to be established..