Testing Your HypothesisTesting 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 Null and Alternative HypothesesHypotheses• 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

Testing Research Testing Research HypothesesHypothesesWhen 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 HypothesisTesting the HypothesisNull 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 Hypothesis Testing ProcessProcessIn 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 Hypothesis Testing for a CorrelationCorrelation• 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 CoefficientsCorrelation CoefficientsFor 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 Hypothesis Testing for a CorrelationCorrelation–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 Testing the Correlation ProcedureProcedureFor Pearson, Point Biserial,

Spearman RankTo 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 LevelAlpha LevelYou 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

Dependent Variable Independent Variable Test

Interval or ratio Interval or ratio Pearson's

Ordinal Ordinal Spearman Rank Order

Dichotomous Dichotomous Phi Coefficient

Interval Categorical Eta Coefficient

Interval Dichotomous Point Biserial

Categorical Categorical Chi Squared

Ordinal or ratio Categorical Mann-Whitney

Ordinal Categorical Gamma

ExamplesExamplesIn Module 6, you will find examples of

the various correlation proceduresYou 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 VariableRecoding 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