1. Data analysis using SPSS and Excel
Hans Baumgartner
Smeal College of Business

2. Steps in the data analysis process
Familiarize yourself with the questionnaire
Input the data and/or read the data into the program you’re planning to use
Screen the raw data carefully and correct any errors
Decide which analyses you want to perform on the cleaned data (depending on your research questions)
Report the results

3. Reading the data
Download the data files from the following directory:
Open the data in SPSS or Excel
Make sure that the data have been read correctly

4. Screening the raw data Check for coding errors and correct any errors
Analyze the wtp and appeal variables
Recode variables if necessary
The variable tix is currently coded as 1=No and 2=Yes; recode to 0=No and 1=Yes and call the resulting variable tix01
Pay attention to missing values
Which variables have missing values?
Identify unusual observations
Analyze the variable ResponseTime
Assess the distribution of the variables
Construct a histogram for the variable wtp

5. Some common analysis types: Single-variable analyses
Frequencies
How many fans chose each of the EPL teams as their favorite team?
Descriptive statistics such as means or proportions
What is the maximum price that a fan is willing to pay on average to watch his or her favorite team play its biggest rival?
What’s the proportion of fans in this sample that have season tickets?

6. Some common analysis types: Bivariate analyses
Crosstabs
How many men and women currently have season tickets?
Comparison of means across groups
Do men and women differ in their identification with their favorite team?
Correlation
How strongly is team identification correlated with the success, talent, entertainment, dedication, admiration and care attributed to the favorite team?

7. Some common analysis types: Multivariate analyses
Regression
Is team identification related to the number of years someone has been supporting his or her favorite team, the distance to the home stadium, gender or age?