Statistical Non-Parametric for Single-Sample

Explore the power of nonparametric statistics for research in the social sciences, behavioral sciences, and health sciences. This course provides a comprehensive understanding of non-parametric statistical methods, focusing on single sample analysis.

Many studies in these fields utilize ordinal or nominal scale data, which often deviates from the assumptions of normal data required for traditional parametric tests. Learn how nonparametric statistical tests offer valuable tools for analyzing such data.

The course is structured into ten modules covering key concepts:

• Definition of Statistics: A foundational understanding of statistical principles.
• Non-Parametric Testing: A deep dive into the core concepts of nonparametric analysis.
• Testing Data Normality: Evaluating whether your data meets the requirements for parametric tests.
• Test of Randomness: Assessing the randomness of your data.
• Data Scale: Understanding different data scale types (nominal, ordinal, interval, ratio).
• Shape and Empirical Rules: Analyzing data distribution.
• Skewness and Kurtosis: Calculating and interpreting data shape.
• Rank Data: Working with ranked data and rank data with tied values.
• One Sample – Signed Test - One Sided: Performing a one-sample signed-rank test with a directional hypothesis.
• One Sample – Signed Test - Two Sided: Performing a one-sample signed-rank test with a non-directional hypothesis.
• One Sample - Wilcoxon Signed Rank Test - One Sided: Applying the Wilcoxon signed-rank test for single samples with a directional hypothesis.
• One Sample - Wilcoxon Signed Rank Test - Two Sided: Applying the Wilcoxon signed-rank test for single samples with a non-directional hypothesis.

Each module features engaging video content (10-15 minutes) complemented by hands-on practice exercises (20-30 minutes) using case examples. The total course duration is estimated at 200-300 minutes.

Upon completion, you will be able to:

• Define nonparametric statistics and its applications.
• Test data normality and determine appropriate statistical methods.
• Understand different data scale types and their implications.
• Assess data randomness.
• Analyze data shapes using skewness calculations.
• Apply rank data techniques.
• Perform signed tests and Wilcoxon tests for single samples (both one-sided and two-sided).

Manual tests are included in each module to reinforce learning. The sign and Wilcoxon tests focus on single datasets. We recommend preparing your own notes to maximize comprehension.