Chi Square Analysis

Chi square analysis (Module 2 – TD2)

2a. Using the available SPSS dataset titled “Multiple Stores.sav”, perform a Chi-Square test of independence on any two nominal variables (Please review the background materials to understand the steps of conducting a Chi-square test via SPSS)

2b. Upload as an attachment or copy and paste your SPSS results into the discussion.

2c. Describe results for a Chi-Square for the test of independence based upon your statistical analysis.

Notes:

In this module, we will be considering a statistical test known as Chi-square analysis that compares categorical variables to determine if the groupings of the two variables influence each other. For instance, one might wonder if an urban vs. rural setting might influence whether a cell-phone company’s stores are likely to be in stand-alone buildings or part of a larger complex of buildings. If there was no independence, one would expect the ratio of stand-alone stores vs. complex facilities to be found in similar proportions in both settings but this may not be the case. One way to test this statistically is to perform a Chi-square test.

Here is a tutorial for running a Chi Square test of Independence:

https://www.youtube.com/watch?v=0LOOxxvtGv8

Make sure you only use 2 nominal (also known as categorical or grouped) variables for your Chi square test. You can use any combination of 2 of the below variables listed below. Try to find a combination that has not been used yet by other doctoral learners in the thread. If you can't, that's OK but it would be good to mix things up a bit:  

OWNERSHIP (Franchise or Corporate)

STATE (Which state the store is located out of several choices) [note: you can use this grouped variable if you'd like but it's kind of messy because there are so many states] 

FACTYPE (The type of facility stand alone or shared facility)

SETTING (Urban or Ruran)

PRODMIX (Three product mix options. A, B or C)

Include your tables and a narrative of your findings.

Here is an example of a narrative for reporting a hypothetical Chi square test of independence looking at gender and voting outcomes between two parties:

[Note: the "1" value below refers to the degrees of freedom. N is the sample size. 6.144 refers to the actual Chi Square value. These are all reported in the JASP output]:

A chi square test of independence was calculated comparing the votes for political parties by male and female voters. A significant relationship was found:

Χ² (1, N = 174) = 6.144 , p = .013  

Hence, we reject the null hypothesis. Political voting preference appears to be dependent on gender.

References

• EZSPSS (2019). Easy SPSS Tutorial: Chi Square Test in SPSS, Including Interpretation. Retrieved from https://www.youtube.com/watch?v=A_oV3MB53Y0 (5:50)

• Lund Research. (2018). Chi-Square Test for Association using SPSS Statistics. Retrieved from https://statistics.laerd.com/spss-tutorials/chi-square-test-for-association-using-spss-statistics.php

• Martin, G. (2019). Statistics made easy ! ! ! Learn about the t-test, the chi square test, the p value and more [Video File] Retrieved from https://www.youtube.com/watch?v=I10q6fjPxJ0 (12:49)

• Walter, E. (2020). SPSS Chi Square [Video File]. Retrieved from https://www.youtube.com/watch?v=Y0Oxi6zW-IE (6:04)
• Kent State University (2020). SPSS Tutorials: Chi-square test of independence. Retrieved from https://libguides.library.kent.edu/SPSS/ChiSquare