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Data Analytics

Give yourself time to recharge…renew your thoughts…and get ready for stats. Data Analytics. Types of Data. What are some other kinds of data your university would want to collect about fans? Make up questions that would fit each type of data. Types of Data.

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Data Analytics

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  1. Give yourself time to recharge…renew your thoughts…and get ready for stats. Data Analytics

  2. Types of Data • What are some other kinds of data your university would want to collect about fans? • Make up questions that would fit each type of data.

  3. Types of Data • The types of data we collect dictate the kinds of analyses we can conduct.

  4. Nominal vs. Continuous Data Are you an avid Lions’ fan? __No __Yes Are you an avid Lions’ fan? Not at all----------------------------Extremely Avid • 1 2 3 4 5

  5. Perceptions

  6. Perceptions

  7. Influences on Fan Consumption Behavior

  8. Constructs • A construct represents an unobservable psychological trait or state that can be measured indirectly with a collection of related behaviors or opinions that are associated in a meaningful way. • Constructs have clear boundaries that differentiate the concept from other constructs. • Excitement is a construct that represents an emotional response to a stimulus that can be described in affective terms such as exciting, sensational, stimulating, and thrilling. • Excitement is clearly different from boredom, but may be related to other positive emotions such as pleasure.

  9. Passion is a construct. Accuracy is improved with multiple items to measure the multiple facets of the construct. If a construct is simplistic, a single-item measure may capture an acceptable measure of the construct. Passion

  10. Behaviors You can often measure behaviors with single-items, as long as you are very specific. For example: How many of the 82 regular season NBA games did you: • Watch the games on screen (TV, Internet, DVR) • Listen to the games on the radio or Internet. • Follow the results in the newspaper or the Internet. • Visit the team website before, during, or after the game.

  11. Single item scales

  12. Independent variable (IV) Dependent variable (DV) Passion Methods of Analysis

  13. Descriptives Fan Passion

  14. Meaningful Comparisons Fan Passion in the DFW Market (Single-item passion score) • Cowboys 64.03 • Mavericks 50.39 • Rangers 47.83 • Stars 34.97 • TCU 29.49 • SMU 23.02 • FC Dallas 17.85 whywehaterankdata

  15. Cross-tabulations

  16. Cowboys and Mavericks Fans • Mavs fans are Cowboys fans: • 92.1% of Mavs fans are also Cowboys fans. • But, not as many Cowboys fans love the Mavs: • 61.1% of Cowboys fans are also Mavs fans. All Mavs Fans All Cowboys Fans Cowboys Fans Mavs fans

  17. Lovin me some SPSS

  18. Analysis of variance (ANOVA) determines the effect of categorical variables on continuous variables Analysis of Variance

  19. Examples • Do season ticket holders have different perceptions of customer service than non-season ticket holders? • Do members of a specific groups of fans (e.g., students vs. non-students) attend more or less than others? • Do women think there are enough restroom facilities compared to men?

  20. The key thing to remember is that the independent variable (IV) is nominal data. The DV is continuous.

  21. Does gender influence fan passion? Cowboys Mavericks Rangers Stars

  22. Does gender influence fan passion?

  23. We never prove anything… • We don’t ever “prove” anything with statistics, we just provide evidence or support confirming or explaining relationships. • So, we “suggest,” “imply,” or “support” positions with statistics. • Why? Because there’s always a chance (probability) that the relationship doesn’t hold.

  24. Correlations • Correlations determine if a change in one variable is associated with a change in another variable. • Each of the variables must be continuous data. • Correlation coefficients (denoted as “r”) range from -1 to +1. Values near zero suggest little correlation, while numbers closer to +/- 1 indicate stronger correlations.

  25. CorrelationsWhich variables have the strongest correlations with attendance? What does the negative correlation between age and passion for the Dallas Mavericks mean?

  26. Multiple Regression • We conduct multiple regression analyses when we have more than one continuous independent variable and one continuous dependent variable. • You can use dichotomous nominal data by using dummy variables as IVs. • Gender (0,1) • Married/Single (0,1) • Caucasian/Other (0,1)

  27. What do we learn? What predicts attendance? What if we only used demographics, including marital status, ethnic background, and gender? How much variance is explained?

  28. MANOVA • What if we have multiple factors that we want to test? • Age (old/young) X Gender (male/female) • Do old females behave differently than young males, young females, and old men? • Season ticket holders (N/Y) X Type (corporate/personal) • Do corporate STHs behave differently than personal STHs, non-STH (paid), and non-STH (other)?

  29. MANOVA • Does gender (M/F) and marital status (single, domestic partner, married, separated, divorced, widowed) interact to influence fan passion • Does getting married infringe upon being a passionate fan for guys?

  30. What happens to the love?

  31. MANOVA Back to our model….

  32. MANOVA Use MANOVA when you have multiple DVs. Add covariates to control for individual differences such as age, income, gender, etc.

  33. Experimental Design Experimental design manipulates the factors (IVs) and controls for other variables (covariates) that might influence the dependent variable (DV). The goal is to control for all of the other possible explanatory variables so that we can determine the effect that is only due to the change in the manipulated factor.

  34. Experimental Design DV: Socialness of the website Arousal & Pleasure Behavior

  35. Karl “Carl” Pearson

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