Assignment 5 (1)

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Nov 24, 2024

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Marketing Research MAR 4613 Assignment 5 50 Total Points Due: Sunday, November 19 @ 11:59 PM Submission Guidelines: Upload your submission to Canvas in Word format. Please make sure that you write your name (individual) or the names of the participating group members on the final submission. If you are working individually, you may consult with your classmates, but the writing must be your own. If you are working in a group, make sure that you contribute to the discussion, planning and write-up of the assignment. Please do not free-ride as it would be unfair for others to carry the weight of a non-participating member. Submission on Canvas (MS Word or PDF) by one member is sufficient if you worked in groups. Name(s): Please read the case on the following pages and answer the corresponding questions

In this assignment, you are going examine the factors that influence a consumer’s likelihood to view a YouTube video by a vlogger. The data was collected from 276 American adults using an online survey developed on Qualtrics. Participants in the survey were shown one short video by a vlogger and were asked to respond to a few questions about the vlogger, and the content itself. The videos seen by different participants were not necessarily the same video or by the same vlogger. The data collector has shared results for some of the data that was collected. There are 6 different variables in this dataset as follows: 1. Views – How likely the consumers are to view other videos by the vlogger 2. ProfSetup – The extent to which the vlogger used a professional vlogging setup visible in the video (e.g., professional cameras, microphones, background wall and props) 3. Distinct – How different or similar did consumers find this vlogger versus others that they know of 4. DiverseTopic – How diverse or specialized is the content developed by the vlogger 5. MaxLearning – The extent to which consumers think they can get maximum learning on diverse topics from the YouTube channel of this particular vlogger only 6. ContentQual – The extent to which consumers found the content by the vlogger of good quality Using the dataset and the description of the variables above, you are going to complete the following tasks. WATCH THE WALKTHROUGH VIDEO HERE: https://drive.google.com/file/d/1j1L00juyATlcwANaAkQRvcPLu2yxVoMt/view?usp=sharing Section 1: In this part of the assignment, you are tasked with determining whether having a professional setup (ProfSetup) is related to views received on a video on YouTube (Views). For this, while collecting data from participants, your team show a YouTube video to participants and then asked the following questions:  “ProfSetup” = To what extent does the YouTube video display a typical professional vlogging setup? (1 = Extremely low, 7 = Extremely high)  “Views” = How likely are you to view other videos by this vlogger? (1 = Very unlikely, 7 = Very likely) A. Write down the null and alternate hypothesis for this correlation analysis

B. Using the variables “Views” and “ProfSetup” determine the Pearson correlation coefficient. Please provide the output from SPSS/Excel and explain what is relationship between the two variables. (10 points) Section 2 : In this section you will assess whether people are more likely to view videos from a YouTuber who is very distinct from other YouTubers, or one that is very similar to others. In your data collection exercise, you had asked participants to indicate about the level of similarity the vlogger in the video had compared to other vloggers participants know of.  “Distinct” = To what extent is this vlogger similar or distinct from other vloggers on YouTube that you follow? (1 = Extremely similar, 7 = Extremely distinct)  “Views” = How likely are you to view other videos by this vlogger? (1 = Very unlikely, 7 = Very likely) A. Write down the null and alternate hypothesis for this regression analysis B. Run a bivariate/linear regression analysis on SPSS/Excel to assess whether “Distinct” predicts “Views”. Please provide the three output tables (Model Summary, ANOVA and Coefficients) from SPSS/Excel in the write-up C. Using the coefficients table, interpret the results and explain what it means. In your explanation, be very clear in the wording such that a person who has never conducted regression analysis, is easily able to understand the results. Hint: if you do not recall how to interpret the results, please refer to the slides on chapter 12 (15 points) Section 3 : In order to find whether diverse type of content, and the quality of the content itself, would help a vlogger on YouTube in attracting more views to their YouTube channel, each participant was also asked the following questions in the survey:  DiverseTopic = How diverse, in your opinion, is the vlogger in terms of the topics he/she speaks about? (1 = Very diverse, 7 = Very specialized)

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 MaxLearning = To what extent do you agree with the following statement – “If I wanted to learn about different topics, I would visit the YouTube channel of this vlogger and be able to find content on most of what I am looking for” (1 = Strongly Disagree, 5 = Strongly Agree)  “ContentQual” = To what extent do you think this vlogger makes quality content? (1 = Very low quality, 7 = Very high quality) A. Run a multiple regression analysis on SPSS/Excel to assess whether “DiverseTopic”, “MaxLearning” and “ContentQual” predict “Views”. Please provide the three output tables (Model Summary, ANOVA and Coefficients) from SPSS in the write-up B. Using the coefficients table, interpret the results for each independent variable and explain what it means. In your explanation, be very clear in the wording such that a person who has never conducted regression analysis, is easily able to understand the results. Hint: if you do not recall how to interpret the results, please refer to the slides on chapter 12. Excel users can interpret the coefficients the same way they do in Section 2 above since MS Excel does not produce standardized coefficients described in the slides. (20 points) Section 4 : A. Describe what is multicollinearity and explain one possible solution to deal with multicollinearity in the dataset. (hint: Read the last few slides from chapter 12 if you do not recall what multicollinearity is) (5 points)

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