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Using Statistics to Predict Relationship Success or Failure

based on 6 ratings
Author: Megan Doyle

Grade Level: 9th to 12th; Type: Mathematics/Computer Science

Objective:

In this experiment, students will use statistical analysis to identify variables that affect the duration of high school romances and will analyze data to predict the success or failure of ongoing relationships.

Research Question:

  • Which variables affect the outcome of a high school relationship?

Which factors influence the success or failure of a high school relationship? In this experiment, you will evaluate this question by collecting data from your peers on relationship outcomes. You will then use statistical analysis to identify variables that affect the duration of high school romance.

Materials:

  • Participants (50-100)
  • Computer
  • Spreadsheet software with statistical analysis capabilities (eg, Microsoft Excel)

Experimental Procedure:

  1. Choose five variables that you think might influence the success or failure of a high school relationship and create a questionnaire to give to your classmates that currently are or have been in a relationship. Example variables/questions could include:
    • Are you friends with the same group of people?
    • Were you friends before dating?
    • Do you have similar personalities?
    • Do you go to the same school?
    • Are your families from the same socioeconomic background?
  2. Ask participants to answer your 5 questions for each relationship they have been in. Ask them to also record how long each relationship lasted. If the relationship is currently ongoing, ask the participant to record how long it has lasted to date.
  3. Input the data from at least 50 ended relationships into a spreadsheet.
    • Example: If one of your questions was, “Are you friends with the same group of people?”, then column 1 of your spreadsheet should be, “Friends with the same group of people” and column 2 should be, “Not friends with the same group of people.” Enter the number of months that each relationship lasted into the data cell that corresponds to that participant’s answer. For example, if one participant’s relationship lasted six months and they were not friends with the same group of people, then you should input a six in row 1 of column 2.
  4. Create columns and input relationship data for your other variables, as well.
  5. Calculate the average for each column in your spreadsheet. Which of the variables appear to affect the duration of a high school relationship?
  6. Determine whether there is a statistically significant difference between any of your paired columns by running a t-test. Research how to do this in the program that you are using. Instructions for Microsoft Excel are outlined below:
    • Select an empty cell.
    • Under the “formulas” tab, select “insert function.”
    • Type TTEST in the “search for a function” box.
    • Select TTEST from the menu and click “ok.”
    • Highlight one column of data for “Array1” and the other column for “Array2.”
    • Enter “2” for “tails” since you will want to run a two-tailed (or two-sided) t-test. This is appropriate when the average of one sample may either be smaller or larger than that of the other sample.
    • Enter “2” for “type”. For the purposes of this experiment, we will assume that the samples have equal variance. To evaluate whether your data samples truly have equal variance, consider running an f-test.
  7. What is the output of each t-test? A value that is less than 0.05 means that there is a statistically significant difference between the two columns of data.
  8. Which variables does your statistical analysis support as being important indicators of relationship success or failure? Look back at the data you gathered from participants in ongoing relationships. Which relationships do you believe are likely to fail? When does your model predict that they will fail? (Keep your predictions a secret!)

Terms/Concepts: t-test, statistical significance, f-test, variance

Reference: Microsoft Office. “TTEST.”

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