Author: Kanda Data
How to Detect Normally Distributed Data in Linear Regression Analysis
When you conduct data analysis using linear regression, there are several assumptions that must be met. We need to fulfill these assumptions to ensure that the estimation results are consistent and unbiased.
If the Data Is Not Normally Distributed, Can We Still Use the Paired Sample t-Test?
In parametric statistical analysis, there are generally several assumptions that must be met to ensure the estimation results are unbiased. One of the key assumptions is that the data must be normally distributed. Now, if my aim is to determine the mean difference between two paired sample groups but the data is not normally distributed, can the paired sample t-test still be used? In this article, I will discuss this further.
Understanding Cross-Section, Time Series, and Panel Data Structures in Research
For those of you currently conducting research, I believe it’s important to have a solid understanding of data structure before starting. This is crucial because the structure of your data will determine the appropriate analytical tools to use when analyzing your research results.
Tutorial on Tabulating Likert Scale Data for Social Research
In social research, many researchers measure variables using the Likert scale. Have you ever conducted research involving variables measured with the Likert scale? In this article, Kanda Data will discuss variables measured using the Likert scale and provide a tutorial on how to tabulate Likert scale variables.
Tutorial on One-Way ANOVA Test for Non-Laboratory Research
The one-way ANOVA test is a parametric statistical test used to examine the differences in means across more than two sample groups. It is important to emphasize that the one-way ANOVA is only applicable when comparing three or more groups. If you are comparing the means of only two groups, then a t-test should be used instead.
Natural Logarithm Data Transformation to Improve Data Normality, Is It True?
In parametric statistical analysis, several assumptions must be met, one of which is the assumption that data should be normally distributed. However, in practice, the data obtained from research does not always follow a normal distribution based on statistical tests. Therefore, some researchers attempt to adjust the distribution of data to make it more closely resemble a normal distribution. One common method is data transformation. Among various types of data transformations, the natural logarithm transformation is one of the most commonly used.
Can Outliers Make Your Data Look Non-Normal? Here’s a Simulation and How to Handle It
In many parametric statistical tests, it’s assumed that the data must follow a normal distribution. That’s why, when we’ve gathered research data and are planning to use parametric statistical analysis, checking for normality is crucial. We need to make sure that the data follows a normal distribution before proceeding with further analysis.
Alternative to One-Way ANOVA When Data Are Not Normally Distributed
If you’re conducting research to compare the means of more than two sample groups, one-way ANOVA is a commonly used statistical test. However, using this test comes with certain assumptions that must be met, specifically, that the data are normally distributed and homogenous.