1. 15-1 Bus 421: Marketing Research CSU Monterey Bay School of Business

2. Steps in Analyzing Data 1.Collect Data 2.Create a Data Codebook 3.Code and Enter the Data – Typically in an Excel file 4.Load the data into SPSS 5.Run the desired analyses 1-2

3. Coding and Entering the Data Data coding refers to the identification of codes that pertain to the possible responses for each question on the questionnaire – Turn the responses into numbers Data entry refers to the creation of a computer file that holds the raw data taken from all of the questionnaires deemed suitable for analysis 15-3 How would you code age? Income level? Favorite brand?

4. The Data Code Book The data code book identifies all of the variable names and code numbers associated with each possible response to each question that makes up the data set 15-4 Why do we need to keep a code book?

5. Underlying Code Book in SPSS 15-5 Let’s take a look at the Advanced Automobile Concepts Data Set Let’s take a look at the Advanced Automobile Concepts Data Set

6. Five Types of Statistical Analyses Used in Marketing Research 1.Descriptive analysis: used to describe the data set 2.Inferential analysis: used to generate conclusions about the population’s characteristics based on the sample data 3.Differences analysis: used to compare the mean of the responses of one group to that of another group 4.Associative analysis: determines the strength and direction of relationships between two or more variables 5.Predictive analysis: allows one to make forecasts for future events 15-6

7. 15-7 Types of Statistical Analyses Used in Marketing Research

8. Summarizing the Data Set Four functions of data summarization: – Summarizes the data – Applies understandable conceptualizations – Communicates underlying patterns – Generalizes sample findings to the population 15-8 Gets us familiar with the data/sample “The average respondent’s age is 44”

9. Understanding Data Via Descriptive Analysis – Two sets of descriptive measures: 1.Measures of central tendency: used to report a single piece of information that describes the most typical response to a question 15-9

10. Understanding Data Via Descriptive Analysis 2.Measures of variability: used to reveal the typical difference between the values in a set of values 15-10

11. Three Measures of Central Tendency 15-11 MeanMean MedianMedian ModeMode The “average” value The “middle” value The “most frequent” value

12. Formula for the Mean 15-12

13. Three Measures of Variability Frequency distribution reveals the number (or percent) of occurrences of each number or set of numbers Range identifies the maximum and minimum values in a set of numbers Standard deviation indicates the degree of variation in a way that can be translated into a bell-shaped curve distribution 15-13

14. Formula for the Standard Deviation Standard deviation is the – square root of the – average squared deviation (distance) from the mean 15-14 The more spread out the data are, the larger the standard deviation is Note: You don’t need to memorize formulas, but you do need to know how to use them!

15. The Normal Distribution (aka the Bell Curve) 1-15

16. The Normal Distribution (aka the Bell Curve) 1-16

17. Normal Distributions with Different Means and Standard Deviations 1-17

18. Note on Notation Population statistics: Greek letters Mean = μ Standard Deviation = σ Variance = σ 2 1-18

19. Understanding Data Via Descriptive Analysis Measures of Variability: 15-19

20. When to Use a Particular Statistic 15-20