Month: September 2024
How to Perform Multiple Linear Regression Analysis Using R Studio: A Complete Guide
Multiple linear regression analysis requires commands to be executed in R Studio. Given the importance of understanding how to analyze and interpret multiple linear regression using R Studio, Kanda Data will write an article discussing this topic.
How to Analyze Likert Scale Variables | Non-Parametric Ordinal Scale Variables
Non-parametric variables measured using the Likert scale (ordinal scale) have a slightly different analysis approach compared to parametric variables. This Likert scale approach is often used in social and management research. In such studies, we are often confronted with variables that cannot be directly quantified.
Assumption Tests for Multiple Linear Regression on Cross-Sectional Data
In multiple linear regression analysis using cross-sectional data, there are several assumption tests that must be conducted to obtain the best linear unbiased estimator. It is crucial to understand which assumption tests are required for research utilizing cross-sectional data. This is important because the assumption tests for cross-sectional, time series, and panel data differ in some respects.
How to Choose a 5% or 10% Margin of Error in Slovin’s Formula | Calculating the Minimum Sample Size
In calculating the minimum sample size using Slovin’s formula, researchers can choose a 5% or 10% margin of error. What’s the difference, and how do you choose the right one? In survey research, when observing a population, we are often faced with the challenge of a large population size that needs to be observed.
How to Create a Likert Scale Score Category (Ordinal Scale)
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.
Differences Between the Null Hypothesis and the Alternative Hypothesis in Statistical Analysis
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.