In this section we explore the concept of correlation (especially using Pearson’s correlation coefficient) and how to perform one and two sample hypothesis testing, especially to determine whether the correlation between populations is zero (in which case the populations are independent) or equal. We briefly explore alternative measures of correlation, namely Spearman’s rho and Kendall’s tau, as well as the relationship between the t-test and chi-square test for independence and the correlation between dichotomous variables.

Topics:

- Basic Concepts
- Scatter Diagrams
- One Sample Hypothesis Testing
- Two Sample Hypothesis Testing
- Multiple Correlation
- Spearman’s Rank Correlation
- Kendall’s Tau Correlation
- Relationship with t-test
- Relationship with Chi-square Test for Independence

hello sir,

i really hope u will help me with this problem

i have 19 questions that use likert scale 1-4 (1 never, 2 rarely, 3sometime,4 always)

between this 19 questions i only choose 6 questions that i can say positive (e.g question 1: do you use seat belt?) to indicate positive practice in driving so do the rest of the question. Moreover this questionnaire doesn’t have total score.

so now, how can i analyze this data?

my research question is

1) there is significant different between good practice and gender

2) there is significant different between good practice and year of driving(1: 1-2 years, 2: 3-4 years, 3: 5-6 years, 4: 7 years above)

Farah,

For the first research question, I understand that you want to determine whether there is a significant difference between the good practice scores for males and females. The typical test used in this case is two sample t test for independence or the Mann-Whitney test if the data is not normally distributed. See the webpages http://www.real-statistics.com/students-t-distribution/two-sample-t-test-equal-variances/, http://www.real-statistics.com/students-t-distribution/two-sample-t-test-uequal-variances/ and http://www.real-statistics.com/non-parametric-tests/mann-whitney-test/.

This test can also be accomplished using the correlation coefficient as described in the webpage http://www.real-statistics.com/correlation/dichotomous-variables-t-test/

For the second question you could use one-way ANOVA or chi-square testing of independence. See the webpage http://www.real-statistics.com/chi-square-and-f-distributions/independence-testing/ for information about independence testing.

This test can also be accomplished using the correlation coefficient as described in the webpage http://www.real-statistics.com/correlation/dichotomous-variables-chi-square-independence-testing/.

Charles

Thank you very much…

You help me alot…:)