# Instructions – Please use the attached data set, Nike.sav, and Jupyter Notebook,

Instructions

– Please use the attached data set, Nike.sav, and Jupyter Notebook, Nike.ipynb, to answer the factor analysis, cluster analysis, and regression analysis questions below.

– Please submit all of your answers and output in this Word document. Do not submit your answers in Excel or Jupyter notebook files, or any other document besides this one.

– Save the file with the course code, your first initial, your last name, and the assignment name in this format: “MKxxx_JSmith_IA2”

– Complete this assignment solely on your own. Communicating answers with others is cheating. For more details, please refer to the BU Academic Conduct Code.

Survey Background

Imagine that Nike conducted a survey examining consumers’ interest in purchasing Nike running shoes. The questions in the survey are in black font below. Consumers responded to each question on a 9-point scale (as specified beside each question in grey font below):

DV: How interested are you in purchasing Nike running shoes? (1 = Not interested at all, 9 = Very interested)

V1: Life is too short not to take some gambles (1 = Strongly disagree, 9 = Strongly agree)

V2: I love routines (1 = Strongly disagree, 9 = Strongly agree)

V3: Shopping is fun (1 = Strongly disagree, 9 = Strongly agree)

V4: I want to do something different with my life (1 = Strongly disagree, 9 = Strongly agree)

V5: In five years, my income will be a lot higher (1 = Strongly disagree, 9 = Strongly agree)

V6: I would like to take a trip around the world (1 = Strongly disagree, 9 = Strongly agree)

V7: I would rather stick with a brand I usually buy than try something I am not very sure of (1 = Strongly disagree, 9 = Strongly agree)

V8: I am not too heavily in debt (1 = Strongly disagree, 9 = Strongly agree)

V9: I often have too much time on my hands (1 = Strongly disagree, 9 = Strongly agree)

V10: I can do anything I put my mind too (1 = Strongly disagree, 9 = Strongly agree)

V11: I’m a trend-setter (1 = Strongly disagree, 9 = Strongly agree)

V12: I spend for today and let tomorrow bring what it will (1 = Strongly disagree, 9 = Strongly agree)

V13: I want to have more stylish clothes than my friends (1 = Strongly disagree, 9 = Strongly agree)

V14: The US government should not always put America first (1 = Strongly disagree, 9 = Strongly agree)

V15: Variety is the spice of life (1 = Strongly disagree, 9 = Strongly agree)

V16: I always try to use coupons when I can (1 = Strongly disagree, 9 = Strongly agree)

V17: I dress for fashion, not for comfort (1 = Strongly disagree, 9 = Strongly agree)

V18: The main excitement of buying new clothes is showing them off to my friends (1 = Strongly disagree, 9 = Strongly agree)

V19: I combine shopping with eating out (1 = Strongly disagree, 9 = Strongly agree)

V20: There is not enough time in the day (1 = Strongly disagree, 9 = Strongly agree)

V21: Americans should always buy more American products (1 = Strongly disagree, 9 = Strongly agree)

Question Set #1 – Factor analysis (4 points)

Question #1A: As you read above, imagine that Nike conducted a survey examining consumers’ interest in purchasing Nike running shoes. This survey includes the variables labeled V1-V21 in the data file, which captures consumers’ attitudes about various topics. What are the factors that can be extracted from V1-V21? (2 points)

To answer this question, first conduct a factor analysis of variables V1-V21, and paste the factor loadings table here:

Question #1B: Explain your interpretation of each of the factors that you extracted (i.e., the factors extracted in the output that you pasted in response to Question #1A above) in a table with the same format as the sample table below. (2 point)

Create a table with the same format as the sample table below (i.e., in which the factor number is entered in the left-most column, the description of the factor is in the middle column, and the variables that you used when interpreting the factor are in the right-most column). Create a row for each factor that you’ve extracted. (Note: The number of factors in the sample table below may not be the same as the number of factors in the data set.)

Sample Table:

Factor Number

Factor Description

(Use two or three words to describe each factor.)

Note: There’s no single right answer for how to describe this factor – feel free to make up a name that broadly describes the variables you used to interpret this factor.

Variables that you used in interpreting this factor

(Note each of the variables that you used to interpret each factor.)

Factor 1

Summer fun

V18, V1

Factor 2

Family time

V6, V9

Question Set #2 – Regression (4 points)

Question #2A: Save the factor scores. Then, do a linear regression in which you use the factors as the independent variables, and the variable titled “DV” as the dependent variable. This dependent variable is assessing consumers’ interest in purchasing Nike running shoes. Paste the regression output here: (2 point)

Question #2B: Which factors are significantly associated with the dependent variable? (1 point)

To answer this question, create a bullet point list in which you paste each factor number that is significantly associated with the dependent variable.

Question #2C: Interpret the regression results (i.e., explain the direction and magnitude of the relationship between each of the factors that you noted in Question #2B and the dependent variable. For example: people who are 1 point higher on factor x are y points higher/lower on the dependent variable). (1 point)

Question Set #3 – Survey Questions (2 points)

Nike’s management has now received your analysis, and Nike wants to learn even more. Now that they know which specific factors (i.e., which specific motivations/attitudes/opinions) predict interest in purchasing Nike running shoes, they ask you to create ten survey questions to gain further insight into how to better attract people who endorse those factors (i.e., people with those motivations/attitudes/opinions). In particular, they want to gain insight into which running shoe features these consumers might prefer or might not prefer.

Below, create 10 survey questions to gain insight into which specific running shoe features these consumers might prefer or might not prefer. There are no right or wrong answers about which specific running shoes features you choose to ask about – feel free to ask about any features. In addition to writing each question, also write the full and precise response scale that consumers would complete. (In other words, rather than writing “7-point scale ranging from ‘dislike’ to ‘like’,” write the exact labels that participants would see next to each scale point.)

Question Set #4 – Cluster analysis (5 points)

Question #4A: Use all of the factors that you have extracted to cluster the respondents into three groups. Paste the final cluster centers here: (2 point)

Question #4B: Interpret and explain each cluster. (2 points)

To answer this question, for each cluster, first enter the two factor scores with the largest average absolute value into the table below.

Cluster number

The first factor score

(i.e., the factor score with the largest absolute value)

The second factor score

(i.e., the factor score with the second largest absolute value)

1

2

3

Based on the information that you entered into the table above, what do the people in each cluster care about?

Cluster number

Based on the factor score with the largest absolute value, the following statement describes people in this cluster:

Based on the factor score with the second largest absolute value, the following statement describes people in this cluster:

1

2

3

Question #4C: Which cluster is the most interested in purchasing Nike’s running shoes? Please paste your pivot table below. (1 point)

2