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Testing Your HypothesisPowerPoint Presentation

Testing Your Hypothesis

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Testing Your Hypothesis

- 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.

Null and Alternative Hypotheses The Alternative Hypothesis states the opposite or “There is significant relationship between….”

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

- Represented by H1

Testing Research Hypotheses

- 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.

Testing the Hypothesis

- 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.

Hypothesis Testing Process

- 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.

Hypothesis Testing for a Correlation

- 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.

Correlation Coefficients

- 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

Hypothesis Testing for a Correlation

- 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)

Testing the Correlation Procedure

- 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

Alpha Level

- 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

Examples

- 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

Recoding a Variable

- 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

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