Chapter XIV

1. Chapter XIV Data Preparation and Basic Data Analysis

2. Important Topics of this Chapter The Data Preparation Process Questionnaire Checking Editing Coding i. Coding Questionnaires

3. Data Cleaning i. Consistency Checks ii. Treatment of Missing Responses Selecting a Data Analysis Strategy: Descriptive Analysis Inferential Analysis Differential Analysis Associative Analysis Predictive Analysis Adjusting the Data

4. A Classification of Statistical Techniques Understanding data Via Descriptive Statistics Measure of Central Tendency Mode Median Mean Measure of Variability Frequency Distribution Range Standard Deviation

5. Other Descriptive Measures Measure of Skewness Kurtosis Obtaining Descriptive Statistics With SPSS

6. Data Preparation Process Fig. 14.1 Prepare Preliminary Plan of Data Analysis Check Questionnaire Edit Code Transcribe Clean Data Statistically Adjust the Data Select Data Analysis Strategy

7. Data Reduction • Summarization: • Condensing the raw data into a few meaningful computation. • Conceptualization: • Visualization of what of these measures represent. • Communication: • Translation of statistical analysis results into a form that is understandable and, more important, useful to marketing manager. • Interpolation: • Assessment of data to the population

8. Types of Statistical Analysis Used in Marketing Research • Descriptive Analysis: • Mean, Mode, Median and Standard deviation. • Inferential Analysis: • Hypothesis testing and estimation of true population values. • Differences Analysis: • Determination of significant differences exit in the population. • Associative Analysis: • Investigation of how two and more variables are related. • Predictive Analysis: • It is used to enhance prediction capabilities of marketing researcher. Ex: regression analysis

9. Understanding Data Via Descriptive Analysis • Measure of Central Tendency: • Mode • Highest occurrence in a set of variables. • Median • Occurrence in the middle of a set values. • Mean: • Arithmetic average of a set of numbers.

10. Understanding Data Via Descriptive Analysis (cont.) • Measure of Variability: • Frequency Distribution: • Number of times that each different value appears. • Range: • Identifies the distance between the lowest and the highest value in an ordered set of variables. • Standard Deviation: • The degree of variation or diversity in the values in a such a way to be translated in a normal bell-shaped distribution.

11. Understanding the Data Via Descriptive Statistics (cont.) • Other Descriptive Measures: • Measure of Skewness: • Reveals the degree of direction of asymmetry in a distribution. 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 right. • Kurtosis: • How pointed and peaked a distribution appears. A ‘0’ value indicates distribution is bell shaped, a negative value indicates distribution is more flat, a positive value indicated distribution is more peaked than the bell shaped curve.

12. Selecting a Data Analysis Strategy Fig. 14.5 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

13. A Classification of Univariate Techniques Fig. 14.6 Univariate Techniques Non-numeric Data Metric Data Two or More Samples One Sample Two or More Samples One Sample • Frequency • Chi-Square • K-S • Runs • Binomial * t test * Z test Independent Related * Two- Groups t test * Z test * One-Way ANOVA Independent Related * Paired * t test * Chi-Square * Mann-Whitney * Median * K-S * K-W ANOVA * Sign * Wilcoxon * McNemar * Chi-Square

14. A Classification of Multivariate Techniques Fig. 14.7 Multivariate Techniques Dependence Technique Interdependence Technique One Dependent Variable More Than One Dependent Variable Interobject Similarity Variable Interdependence * Cross- Tabulation * Analysis of Variance and Covariance * Multiple Regression * Conjoint Analysis * Multivariate Analysis of Variance and Covariance * Canonical Correlation * Multiple Discriminant Analysis * Factor Analysis * Cluster Analysis * Multidimensional Scaling

15. Nielsen’s Internet Survey: “Does It Carry Any Weight?” RIP14.1 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.

16. 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.

17. 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.