Multivariate Analysis
Develop an individual task and test research hypotheses in the field of sports
management. To test these hypotheses, they will conduct a multiple regression analysis.
Students are also required to present and interpret the findings. They will also discuss how
these findings provide a significant contribution to the study field. The individual task should
address at least the following points:
1. Three or more hypotheses will be developed.
2. Students will gather data to conduct a multivariate analysis of the study hypotheses.
3. Ethical issues should be reported.
4. Reliability and validity issues will be ensured.
5. Multicollinearity issues will be reported.
6. Multiple regression analysis will be performed in Excel or SPSS.
7. Regression findings will be properly presented by using tables and/or figures.
8. Regression findings will be interpreted
Sample Solution
Research Topic: The Impact of Social Media Engagement on Fan Attendance at Sporting Events
Hypotheses:
- Positive Social Media Engagement Effect: There will be a positive relationship between a sports team's average social media engagement (likes, comments, shares) and fan attendance at home games. (H1)
- Opponent Strength Effect: Teams playing stronger opponents will see a higher average fan attendance compared to games against weaker opponents, regardless of social media engagement. (H2)
Full Answer Section
- Ticket Price Effect: Higher ticket prices will have a negative impact on fan attendance, independent of social media engagement and opponent strength. (H3)
Data Collection:
- Data on social media engagement metrics (average likes, comments, shares per post) can be collected from the team's official social media pages for a specific period (e.g., one season).
- Fan attendance data can be obtained from the league's official website or the team's website.
- Opponent strength can be measured using a metric like opponent winning percentage.
- Ticket price data can be obtained from the team's website.
Ethical Considerations:
- Publicly available data will be used, ensuring anonymity.
- Informed consent is not required as data is not collected from individuals.
Reliability and Validity:
- Using official sources for data collection enhances reliability.
- The chosen metrics for social media engagement, opponent strength, and ticket price are widely used and considered valid measures.
Multicollinearity:
- A correlation matrix will be used to check for multicollinearity among independent variables (social media engagement, opponent strength, ticket price). High correlations (>0.8) might necessitate variable removal or transformation.
Multiple Regression Analysis:
- Software like SPSS will be used to conduct a multiple regression analysis. Fan attendance will be the dependent variable, and social media engagement, opponent strength, and ticket price will be the independent variables.
Presentation and Interpretation of Findings:
- Regression results will be presented in a table, showing coefficients, standard errors, p-values, and R-squared (coefficient of determination).
- A significant positive coefficient for social media engagement (H1) would indicate that higher engagement leads to higher attendance.
- A significant positive coefficient for opponent strength (H2) would suggest attendance is influenced by the perceived competitiveness of the game.
- A significant negative coefficient for ticket price (H3) would support the idea that high prices deter fans from attending.
Contribution to the Field:
This study will contribute to sports management by:
- Investigating the increasingly important role of social media in fan engagement and its potential impact on attendance.
- Providing data-driven insights for teams to optimize their social media strategies to boost fan engagement and ultimately, attendance.
- Examining the interplay between social media engagement, opponent strength, and ticket price in influencing fan attendance decisions.