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Non Parametric Analysis
Welcome to our comprehensive guide on Non-Parametric Analysis with SmartstatXL. In this guide, you will find a variety of tutorials designed to help you understand and apply various types of non-parametric analyses using the Excel add-in, SmartstatXL. Each tutorial is designed to provide a clear and easy-to-understand explanation of how to perform each type of analysis, so you can confidently apply them in your own research.
Starting with How to Analyze the Friedman Test - Non-Parametric, which is a non-parametric analysis for two-way analysis of variance testing, to How to Analyze the Kolmogorov-Smirnov Test - Non-Parametric, used to determine whether two independent samples come from different populations. You will also find guides on how to perform the Cochran Q Test, Mann-Whitney U Test, McNemar Test, and various types of Wilcoxon Tests. Each of these tutorials is designed to give you a deep understanding of each method and how to apply it using SmartstatXL.
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- Written by Ade Setiawan
- Category: Non Parametric Analysis
The Friedman Test is a non-parametric method used for two-way Analysis of Variance (Two Way Anova) on ordinal data. This test can be considered an extension of the Sign Test for dependent or paired samples. If there are only two dependent sample groups, then the results of the Friedman Test will be equivalent to the Sign Test. The Friedman Test becomes a suitable alternative for two-way variance analysis (such as Two Way Anova or Randomized Block Design/RBD) when the assumptions of variance analysis are not met and an appropriate data transformation cannot be found. If the results of the Friedman Test indicate a significant treatment effect, then one can proceed with Post hoc Tests. In SmartstatXL, some available options for Post hoc Tests include the Dunn and Nemenyi tests.
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- Written by Ade Setiawan
- Category: Non Parametric Analysis
The Mann-Whitney test, also known as the Mann-Whitney U test or Wilcoxon–Mann–Whitney test, is a non-parametric analysis designed to compare the median differences between two independent groups. This test is suitable for use when the scale of the dependent variable is either ordinal or continuous. Developed by H.B. Mann and D.R. Whitney in 1947, the Mann-Whitney test serves as an alternative to the Independent Samples T-Test, particularly when the assumption of normality is not met. In the context of SmartstatXL, this test provides a reliable statistical solution for data analysis under such conditions.
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- Written by Ade Setiawan
- Category: Non Parametric Analysis
The Kruskal-Wallis test is a non-parametric analysis method used to test differences among three or more independent groups on ordinal data. This test can be considered an extension of the Mann-Whitney U test, which is specific for two independent groups. If only two groups are being tested, then the result of the Kruskal-Wallis test will be identical to the Mann-Whitney U test. The Kruskal-Wallis test serves as an appropriate alternative for One-Way Analysis of Variance (ANOVA) or Completely Randomized Design (CRD) when the assumptions of normality and homogeneity of variance for ANOVA are not met, and data transformation is either not possible or ineffective. If the results of the Kruskal-Wallis test indicate a significant difference among groups, it is recommended to proceed with Post hoc Tests to determine which groups differ. In SmartstatXL, several Post hoc Test options available include Dunn, Nemenyi, and Sach.
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- Written by Ade Setiawan
- Category: Non Parametric Analysis
The McNemar Test, developed by McNemar in 1947, is a non-parametric analysis designed to assess whether there is a significant difference between two dependent paired variables, such as conditions before and after a treatment. This analysis is particularly applied to nominal or categorical data. For example, the data may include categories such as "Agree" and "Disagree" or "True" and "False." The McNemar Test can be considered a special case of the Cochran Q test when there are only two data categories (k=2). In the analysis, these two conditions are often scored, for example, by assigning "True" as 1 and "False" as 0, where both scores are dichotomous and mutually exclusive. When using SmartstatXL, this test offers a reliable statistical solution for data analysis under these conditions.
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- Written by Ade Setiawan
- Category: Non Parametric Analysis
The Cochran Q test is a non-parametric analysis method designed to evaluate whether there are significant differences between two or more dependent samples originating from populations with identical or different distributions. This test can be considered an extension of the McNemar Test, which is specifically used for two dependent samples. When the assumption of normality is not met in paired data analysis, the Cochran Q test serves as a suitable alternative to the Paired T-test.
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- Details
- Written by Ade Setiawan
- Category: Non Parametric Analysis
The Sign Test for Paired Samples is a non-parametric analysis that uses plus and minus signs to evaluate whether there is a significant difference between two paired samples. In situations where the data do not meet the normality assumptions required for the Paired T-Test, the Sign Test offers a robust alternative. Using SmartstatXL, this analysis can be easily performed, providing valuable insights into differences between two different conditions or time periods within the same sample.
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