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# Testing Your Hypothesis - PowerPoint PPT Presentation

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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Presentation Transcript

• 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

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

• 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