# Kanda Data

## 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 …

## 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. Whatâ€™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.

## Regression Analysis on Non-Parametric Dependent Variables: Is It Possible?

In multiple linear regression analysis, the measurement scale of the dependent variable is typically parametric. However, can multiple linear regression analysis be applied to a dependent variable measured on a nominal (non-parametric) scale?