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Testing Your Hypothesis

Testing Your Hypothesis. In your previous assignments you were supposed to develop two hypotheses that examine a relationship between two variables. For example:

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Testing Your Hypothesis

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  1. 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.

  2. Null and Alternative Hypotheses • The Null Hypothesis states “There is no significant relationship between …..” • Represented by H0 • The Alternative Hypothesis states the opposite or “There is significant relationship between….” • Represented by H1

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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)

  9. 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

  10. 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

  11. 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

  12. 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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