Statistics Non-Parametric for Single Sample

About this course

Nonparametric statistics are essential for research in the social, behavioral, and health sciences. Non-parametric statistical methods using single sample are explained in this course. Numerous studies in these fields use data that is categorized using an ordinal or nominal scale. Interval data from these fields sometimes doesn’t have enough parameters to be considered normal. For the analysis of such data, nonparametric statistical tests are helpful instruments. There are ten module topics in this course, including:

1.       Definition of Statistic Non-Parametric

2.       Testing Data Normality

3.       Test of Randomness and Data Scale

4.       Shape and Empirical Rules

5.       Skewness and Kurtosis

6.       Rank data and Rank data with tied values

7.       One sample – Signed test - one sided

8.       One sample – Signed test - two sided

9.       One sample - Wilcoxon signed rank test- one sided

10.   One sample - Wilcoxon signed rank test- two sided

The videos in this course consist of 10-15 minutes of watching to the material in each module video and require 20-30 minutes for each module to be directly practiced with case examples that can be done manually. So, the total duration to complete this course is 200 to 300 minutes. After completing this course, participants are expected to understand the definition of non-parametric statistics, test data normality, understand data scale types, and test data randomness. After completing this course, participants are expected to understand the definition of non-parametric statistics, test data normality, understand data scale types, and test data randomness. Participants are also able to understand the types of data shapes by calculating the skewness of the data, followed by understanding how to sort the data, using the sign test, and then the Wilcoxon test both for one-way side and two-way side. In each module, there are manual tests that can be tried. As for the sign and Wilcoxon tests in this course, they are still applied to single data sets. It is recommended that participants prepare their own notes to maximize understanding.

What you will learn