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Data Analytics

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

Individual Factors

DemographicsPast Behavior

PsychographicsSituations

Personal Perceptions

What we think

Affective Response How we feel

BehaviorWhat we

(intend to) do

Social Perceptions

What we think about others and what they

think

TeamSportsMarketing.com

DataType

Examples of Measurement Items

Ratio How many home games will you attend this season? (0-20)

Interval

How passionate are you about the team?Not at all 0 1 2 3 4 5 6 7 8 9 10 Very Passionate

Ordinal

Please rank your top three most favorite teams in order .(List of teams)

Nominal

What season ticket package do you own?__None ___Partial season ___Full season

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

The types of data we collect dictate the kinds of analyses we can conduct.


Are you an avid Lions’ fan? __No __Yes

Are you an avid Lions’ fan? Not at all----------------------------Extremely Avid

1 2 3 4 5

Nominal vs. Continuous Data


Perceptions


Perceptions


Influences on Fan Consumption Behavior

Personal Perceptions Social Perceptions Affective ResponsePlayer image and skills Community pride ExcitementWholesome environment Socialization PleasureCause support Bonding ArousalDrama Perceived Crowding BoredomService quality Social dysfunction DispleasureSportscape environment Social aggression/violence SuspenseVariety Seeking Team social status StressTicket and promotion value Social well-being EnjoymentDestination image Social integration AdorationOutcome uncertainty Camaraderie Vicarious achievementLeisure alternatives Socially-connected (isolated) LikingEscape Celebrity/Player Worship SatisfactionFantasy & flow Familial participation Moods (romantic)Website quality & theme Gender identity Hope


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.

Passion

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.


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.


Single item scales

Attend

TV

Radio

News

Web

PASSION

Methods of Analysis

Independent variable (IV)Dependent variable (DV)


Descriptives

NBA = 82 Games Min Max Mean

Std. Deviation

Fan Passion 0 100 42.84 31.90TV 0 82 29.13 27.61Radio 0 82 12.25 20.35News 0 82 32.93 30.63Internet 0 82 15.88 24.78

Fan Passion


Meaningful Comparisons

Fan Passion in the DFW Market (Single-item passion score)1. Cowboys 64.032. Mavericks 50.393. Rangers 47.834. Stars 34.975. TCU 29.496. SMU 23.027. FC Dallas 17.85

whywehaterankdata


Cross-tabulations

Mavs2 * Cowboys2 Cross-tabulationCowboys2

TotalLow HighMavs2 Low Count 279 340 619

% within Mavs2 45.1% 54.9% 100.0%% within Cowboys2 85.8% 38.9% 51.6%% of Total 23.3% 28.3% 51.6%

High Count 46 535 581% within Mavs2 7.9% 92.1% 100.0%% within Cowboys2 14.2% 61.1% 48.4%% of Total 3.8% 44.6% 48.4%

Total Count 325 875 1200% within Mavs2 27.1% 72.9% 100.0%% within Cowboys2 100.0% 100.0% 100.0%% of Total 27.1% 72.9% 100.0%

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 Cowboys Fans

All Mavs Fans

Mavs

fan

s

Cow

boys

Fan

s


Lovin me some SPSS

SPSS Methods

For all statistical analyses, first click on Analyze

Statistical Analysis

Then click Then click In box click

Crosstabs Descriptives Crosstabs Stats: Chi-SquareCells: Row, Column, Total

ANOVA Compare Means

One-way Anova Options: DescriptivesFactor: Categorical dataDependent: interval data

Correlation Correlate Bivariate None

Multiple Regression

Regression Linear NoneIndependent(s): X-varsDependent: Y-variable

Analysis of Variance

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


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?

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

Does gender influence fan passion?

CowboysMavericksRangersStars


Does gender influence fan passion?

Average Fan Passion Scores

Significant Difference between groups?

Team Males Females F (Significance)Cowboys 66.4 61.6 5.49 (.019)Mavericks 51.9 48.9 2.32 (.128)Rangers 50.3 45.4 6.03 (.014)Stars 36.7 33.3 3.33 (.068)


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.


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.


CorrelationsWhich variables have the strongest correlations with attendance?

** p< .01* p = .07

Attendance Passion (full scale)

Passion (1-item)

AAC Events

Income Household Size

Age

Attendance 1 .406** .366** .409** .08** .112** -.123**Passion 1 .956** .392** -.005 .141** -.200**Passion (1) 1 .350** .021 .132** -.214**AAC Events 1 .196** .110** -.220**Income 1 .116** .052*HH size -.318**

What does the negative correlation between age and passion for the Dallas Mavericks mean?


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)


What do we learn?What predicts attendance?

ModelR2 = 24.0%

DV = Attendance

Unstandardized Coefficients

Standardized Coefficients

t-value Sig.

B Std. Error Beta

IV’s(Constant) -1.419 .494 -2.874 .004Passion .036 .004 .289 10.370 .000AAC Attendance .764 .075 .290 10.171 .000Income .055 .075 .019 .734 .463HH Size .109 .074 .040 1.481 .139Age .003 .007 .010 .365 .715

What if we only used demographics, including marital status, ethnic background, and gender? How much variance is explained?


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)?


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?


What happens to the love?

Dallas Cowboys Average Fan PassionMarital Status (F = 2.65, p = .02) Male FemaleSingle 70.97 64.73Domestic Partner 50.00 68.11Married 67.18 60.67Separated 81.67 41.43Divorced 61.79 58.70Widowed 59.00 57.22OVERALL 66.4 61.6


MANOVA

Back to our model….


MANOVA

Consumption: % of 82 games 42 games

Passion Fan Type TV Radio News Website Attendance

0 Non-fan 0 0 0 0 0

1-20 Inactive 7% 2% 13% 1% 0

20-39 TV Fan 32% 11% 39% 12% 0

40-59 Active 56% 20% 63% 24% 2

60-79 Game 73% 31% 75% 48% 4

80-100 Passionate 83% 51% 82% 69% 7

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


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.


Experimental Design

DV: Socialness of the website

Arousal & Pleasure

Behavior

http://www.rovion.com/gallery/washingtoncapitals-alexovechkin


Karl “Carl” Pearson

Documents

Give yourself time to recharge…renew your thoughts…and get ready for stats