Author: Kanda Data
How to Differentiate between Nominal, Ordinal, Interval, and Ratio Data Measurement Scales in Research
In statistics, data measurement scales can be divided into four types: nominal, ordinal, interval, and ratio scales. Understanding the differences among these four measurement scales is crucial for researchers to grasp. This is because the choice of data analysis in research is heavily influenced by the measurement scale of the variables, whether they are nominal, ordinal, interval, or ratio scales.
Descriptive Statistical Analysis of Non-Parametric Variables (Nominal and Ordinal Scales)
Based on its methods, statistics can be divided into descriptive statistics and inferential statistics. Researchers can choose to use either of these methods or even combine both methods of data analysis.
Wilcoxon Test | Different test of two paired samples for non-parametric variables
Differences test is one of the most commonly used associative tests by researchers. Differences test can be conducted on both parametric and non-parametric variables. For parametric variables, the differences test can utilize the t-test assuming normally distributed data.
Mann-Whitney Test | Different test of two independent samples for non-parametric variables
In statistics, the association tests commonly conducted by researchers consist of tests of influence, relationship, and difference. Researchers often use the t-test to examine the mean difference between two sample groups. Typically, the measurement scale used in the t-test is the interval and ratio scales, which are normally distributed.
How to Test Normality of Residuals in Linear Regression and Interpretation in R (Part 4)
The normality test of residuals is one of the assumptions required in the multiple linear regression analysis using the ordinary least square (OLS) method. The normality test of residuals is aimed to ensure that the residuals are normally distributed.
How to Test Heteroscedasticity in Linear Regression and Interpretation in R (Part 3)
One of the assumptions required in Ordinary Least Squares (OLS) linear regression is that the variance of the residuals is constant. This assumption is often referred to as the homoscedasticity assumption. Some researchers are more familiar with the term heteroscedasticity test.
How to Analyze Multicollinearity in Linear Regression and its Interpretation in R (Part 2)
Non-multicollinearity is one of the assumptions required in the ordinary least square (OLS) method of linear regression analysis. Non-multicollinearity assumption implies that there is no strong correlation among the independent variables in the equation.
How to Analyze Multiple Linear Regression and Interpretation in R (Part 1)
Multiple linear regression analysis has been widely used by researchers to analyze the influence of independent variables on dependent variables. There are many tools that researchers can use to analyze multiple linear regression.