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:

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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 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 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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