Creating Likert scale score categories is essential to answer one of the research objectives, particularly in descriptive statistical analysis. We can categorize non-parametric variables that use the Likert scale into high, medium, and low categories. This information will significantly enrich the research findings.
Statistical hypotheses, consisting of the null hypothesis and the alternative hypothesis, play a crucial role in the process of testing and analyzing statistical data. Understanding the concept of a hypothesis is a critical first step in ensuring that research results are valid and scientifically accountable.
When we want to compare the means of two data groups, we often use a difference test. The most commonly used difference test is the t-test. In a t-test, the samples being compared can be either paired or independent.
In the field of statistics, the terms “residual” and “error” are often used interchangeably. Many researchers and practitioners consider these terms to have the same meaning, but in reality, they represent significantly different concepts.
Linear regression is one of the most commonly used statistical analysis techniques to understand the impact of independent variables on a dependent variable. In regression analysis, the estimated coefficients indicate the extent to which each independent variable affects the dependent variable.
