Inferental Statistic (Correlation , Regression, Chi-Square and T-Test "Correlation".pptx

Inferental Statistic (Correlation , Regression, Chi-Square and T-Test

1. Correlation Inferential Statistics (Correlation, regression, chi-square & t-test)

2. Inferential statistics These are statistics, such as CORRELATION, PARTIAL CORRELATION, MULTIPLE REGRESSION ANALYSIS AND t-test that allows THE RESEARCHERS to make conclusions about the population beyond our data.

3. Definition Correlation analysis is used to describe the strength and direction of the linear relationship between two variables . Used to test the presence, strength and direction of a linear relationship among variables . Correlation is a numerical expression that signifies the relationship between two variables. Correlation allows you to explore this relationship by 'measuring the association ' between the variables. Correlation is a 'measure of association' because the correlation coefficient provides the degree of the relationship between the variables.

4. Correlation (cont.) The Correlation Coefficient : Pearson's r, the correlation coefficient , is the numeric value of the relationship between variables. The correlation coefficient is a percentage and can vary between -1 and +1. If no relationship exists, then the correlation coefficient would equal 0. Pearson's r provides an (1) estimate of the strength of the relationship and (2) an estimate of the direction of the relationship.

5. Correlation (cont.) If the correlation coefficient lies between -1 and 0, it is a negative (inverse) relationship , 0 and +1, it is a positive relationship and is 0, there is no relationship The closer the coefficient lies to -1 or +1, the stronger the relationship.

6. Correlation (cont.) Coefficient of determination provides the percentage of the variance accounted for both variables (x & y). Example of research question What Is the relationship between Part time job employment and student performance? Is there relationship between faculty program and Student performance? Null hypotheses There is no relationship between part time job employment and student performance. There is no relationship between faculty program and student performance.

7. Requirement: two variables: both continuous, or one continuous and the other dichotomous . (Male or female)

8. Preliminary analysis for correlation Before performing a correlation analysis, it is a good idea to generate a scatterplot. This will enable you to check for violation of the assumption of linearity and homoscedasticity. This will give a better idea of the nature of the relationship between variables. Step 1: checking for outliers Step 2: Inspecting the distribution of data points Step 3: Determining the direction of the relationship between the variables.

9. Outliers

10. Types of Correlation

11. The Strength of the Relationship

12. Procedure for Requesting ‘r’ and ‘rho’

13. Interpretation of Output Explain information about the sample (N) Determining the direction of the relationship Determining the strength of the relationship Explain the variance (r x r) x 100 Assessing the significant level

14. Example of Writing the Result

15. Partial correlation Analysis It is useful for describing the relationship between two variables, while statistically controlling for another variable (s). Research question Is there a significant relationship between part time job employment and part time job satisfaction after controlling for student performance? Null hypothesis There is no significant relationship between part time job employment and part time job satisfaction, after controlling student performance.

16. Variables Variable of interest: Part time job employment Part time job satisfaction Control variable (covariate): Student performance

17. Output for Partial Correlation

18. Presenting Partial Correlation Result

19. Thank you