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Testing Your Hypothesis

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- In your previous assignments you were supposed to develop two hypotheses that examine a relationship between two variables.
- For example:
- The researcher wishes to determine if there is a significant relationship between the age of the worker and the number of repetitive strain injuries they have had over the past year.

- In your final portion of the project, you will be testing your hypotheses to see if there are significant relationships between variables in your study.

- The Null Hypothesis states “There is no significant relationship between …..”
- Represented by H0

- Represented by H1

- When testing a research hypothesis statistically, we go at it somewhat backwards.
- Using the blue block hypotheses:
- Null Hypothesis: There is no significant relationship between ….
- Alternative Hypothesis: There is a significant relationship between ….

- The statistical procedure really tests if the null hypothesis is true or not.

- Null Hypothesis: There is no significant relationship between ….
- Alternative Hypothesis: There is a significant relationship between ….
- If our statistical is significant, we reject the null hypothesis and accept the alternative.
- If our statistical is not significant, we accept the null hypothesis.

- In order to statistically prove the relationship exists, we are really proving because the statement “There is no significant relationship between ….“ is false, the alternative statement “There is a significant relationship between ….” must be true.

- Using a problem statement where you are testing for a relationship between two variables, the following process is followed:
- The researcher wishes to determine if there is a significant relationship between the age of the worker and the number of repetitive strain injuries they have had over the past year.
- Null Hypothesis: There is no significant relationship between the age of the worker and the number of repetitive strain injuries they have had over the past year.
- Alternative Hypothesis: There is a significant relationship between the age of the worker and the number of repetitive strain injuries they have had over the past year.

- For Pearson, Point Biserial, and Spearman Correlations
- First calculate what your correlation coefficient (r) is
- Next, use a t-test to determine if the correlation coefficient is equal to zero or not.
- Remember correlation coefficients (r) can range from -1.00 to +1.00 with 0 representing no correlation present
- If we prove our r is not equal to 0 (no correlation exists), then a significant correlation must exist

- For Phi and Chi Squared procedures:
- Use a Chi-square distribution and you will compare your obtained Phi or Chi Squared result to a cutoff score on the Chi Squared Table

- H0: There is no significant relationship between the age of the worker and the number of repetitive strain injuries they have had over the past year.
- When it is time to run the correlation procedure (i.e.: Pearson Correlation, we are testing r=0)

- H1: There is a significant relationship between the age of the worker and the number of repetitive strain injuries they have had over the past year.
- When it is time to run the correlation procedure (i.e.: Pearson Correlation, we are testing r ≠ 0)

- For Pearson, Point Biserial, Spearman Rank
- To determine if your correlation coefficient is significant, you will be using a t-test to do so
- Review Module 6 on how to run this test and determine significance
- Null Hypothesis: r = 0
- Alternative Hypothesis: r ≠ 0

- You will be using an Alpha level = .05 in your significance tests
- You will be taking a 5% chance of committing a Type I error
- You will be taking a 5% chance of saying a significant correlation exists when it really doesn’t

- In Module 6, you will find examples of the various correlation procedures
- You should know by now which correlation procedure you should be using for your project.
- If you determined you need to run either Eta, Gamma, or Mann-Whitney:
- Due to the complexity of the math required to run these procedures by hand, you will need to recode your continuous variable into a categorical variable and use Chi-Squared

- Let’s say you collected your dependent variable as a ratio format variable and you need to recode it into a categorical variable
- You asked the subjects “How many days have you missed from work over the past year?” and they wrote in the number of days.
- Set up categories such as:
- 0-2 days
- 3-5 days
- 6-8 days
- 9 or more days

- For those that wrote in 0, 1, or 2 days, they will be assigned to the first category
- For those that wrote in 3, 4, or 5 days, they will be assigned to the second category
- And so on