Analyzing Enrollment Campaigns with Chi-Square: Is It the Right Approach?

In summary, the conversation discusses testing a hypothesis on the effectiveness of sending both an email and direct mail announcement for a free webinar. Four groups of 100 people each were randomly assigned, with one group receiving no communication, one receiving an email only, one receiving a direct mail piece only, and one receiving both an email and direct mail piece. The question of whether a chi-square analysis is appropriate for this study is raised, and it is suggested to also include a control group for potential follow-up hypotheses. The conversation ends with a suggestion to use a pooled two-proportion z-test to test the effectiveness of the combined announcements against a single announcement.
  • #1
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Homework Statement



I'm trying to test a hypothesis that sending people both an email announcement and direct mail announcement produces significantly more enrollments in a free webinar than email or direct mail alone.
I'd like to do an analysis on these groups created from 400 people selected from our database and randomly assigned.


100 People who received neither email nor direct mail from us
100 People who received an email only
100 People who received a direct mail piece only
100 People who received both and email and direct email piece

Is a chi square analysis the right way to go about this? Do I need the "control group" who received no communication?

Homework Equations



Chi square analysis

The Attempt at a Solution



I think I would set up the groups like this attachment - when I get data. I've just made up some data for now.
Thanks!
 

Attachments

  • enrollment_test.jpg
    enrollment_test.jpg
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  • #2
Hello MIH!

Yup, chi-square is the way to go on this.

While you don't need the control group for the hypothesis you stated, it may be wise to include it anyway. Somebody might ask about it after the study. And if you don't get a significant difference among the groups you are interested in, a reasonable followup hypothesis may be whether the announcements made any difference at all. You'll have the data in hand to address that.
 
  • #3
Thanks so much, Redbelly! :-)
 
  • #4
I may be a bit late to respond...

Anyway, chi-square will tell you whether it matters in general what you do.
It does not really address the hypothesis you've stated.

To test that you need a pooled two-proportion z-test. See e.g. wiki.
You would test the proportion of enrollments with both announcements against the combined proportion of enrollments with a single announcement.
 

Related to Analyzing Enrollment Campaigns with Chi-Square: Is It the Right Approach?

1. What is chi square analysis?

Chi square analysis is a statistical method used to determine whether there is a significant relationship between two categorical variables. It compares the observed frequencies of categories in a data set to the expected frequencies and calculates a chi square value. This value is then used to determine the level of significance between the variables.

2. When should chi square analysis be used?

Chi square analysis should be used when you have two categorical variables and want to determine if there is a significant relationship between them. It is commonly used in biological and social science research to analyze data from experiments or surveys.

3. How is chi square analysis performed?

To perform chi square analysis, you first need to organize your data into a contingency table with the categories of each variable. Then, you calculate the expected frequencies for each cell in the table based on the total sample size and the proportions of each category. Next, you use a formula to calculate the chi square value. Finally, you compare the chi square value to a critical value from a chi square table to determine the level of significance.

4. What is the difference between chi square test and t-test?

Chi square test is used to compare the frequency distributions of categorical variables, while t-test is used to compare the means of continuous variables between two groups. In other words, chi square test is used for categorical data and t-test is used for numerical data.

5. What are the limitations of chi square analysis?

Chi square analysis assumes that the data is representative of the population and that the expected frequencies are not too small. It also cannot determine the directionality or strength of the relationship between variables. Additionally, it is not suitable for analyzing data with more than two categorical variables.

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