For the exam, you will be asked to use SPSS to run and interpret ANOVA and linear regressions using data from a 2016 study conducted on a Cosmetic Brand’s Facebook page. The relevant abstract from the article that was published about the study can be found below:
Moro, S., Rita, P., & Vala, B. (2016). Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research, 69(9), 3341-3351.
This study presents a research approach using data mining for predicting the performance metrics of posts published in brands' Facebook pages. Twelve posts' performance metrics extracted from a cosmetic company's page including 790 publications were modeled, with the two best results achieving a mean absolute percentage error of around 27%. One of them, the “Lifetime Post Consumers” model, was assessed using sensitivity analysis to understand how each of the seven input features influenced it (category, page total likes, type, month, hour, weekday, paid). The type of content was considered the most relevant feature for the model, with a relevance of 36%. A status post captures around twice the attention of the remaining three types (link, photo, video)….
1. Click “Content” on the D2L course site.
2. Go to “Table of Contents”
3. Click on the “Final Exam Materials” folder/module.
4. Download the dataset “Facebook Metrics”
5. Open SPSS
6. Click “File”
7. Hover over “open”
8. Click “Data”
9. Choose your download folder
10. Open the dataset you just downloaded
11. Be certain to label the variable values for “category” before you run your analysis. The categories are described as: action (special offers and contests), product (direct advertisement, explicit brand content), and inspiration (non-explicit brand-related content):
1=action
2=product
3=inspiration
*******There are multiple extra credit problems on this exam. You may choose to do only select problems or do all of them, but the extra credit maxes out at 10 points (i.e., you cannot get more than 10 extra credit points even if you get all the extra credit problems correct).****************
Part A. Conduct a one-way ANOVA analyzing if category makes a difference in regard to lifetime
Post consumptions (variable #12 on the variable view page). Please make certain you are conducting your analysis using the correct variables since many of them have similar names! It might be useful to widen the column that gives the variable name on SPSS.
Please provide the following: descriptive statistics for your dependent variable (you need to run this separately from your ANOVA), null hypothesis and alternative hypothesis, the f-statistic (including degrees of freedom), and the p-value. All problems worth 1 point unless otherwise noted.
8. Explain your results, including which hypothesis is favored (2pts):
Extra credit (2 pts): What is the critical value associated with the df? (you can look this up in the table on D2L)
Critical value ≈
9. Please attach your output for your descriptive statistics and ANOVA below (3 pts):
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