chapter xiv data preparation and basic data analysis
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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..
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