Author: Kanda Data
How to Determine the Minimum Sample Size in Survey Research to Ensure Representativeness
When conducting survey research, the number of samples observed will naturally be one of the main considerations. In survey-based studies, using samples is often a more efficient choice compared to carrying out a census on all population members. By taking a representative sample, we can observe behaviors that reflect the larger population.
Regression Analysis for Binary Categorical Dependent Variables
When we talk about regression analysis, we often think about parametric variables measured on at least an interval or ratio scale. But what if we want to analyze the effect of independent variables on a dependent variable that happens to be categorical in nature?
How to Sort Values from Highest to Lowest in Excel
When conducting tabulation and data analysis, we sometimes need to sort values for a specific purpose. It turns out that sorting values from the highest to the lowest, or vice versa, can easily be done using Excel. We are, of course, already familiar with Excel as a tool that we use in our daily office work, one of which is its advantage in data processing.
How to Perform Descriptive Statistics in Excel in Under 1 Minute
Descriptive statistical analysis is essential to carry out for your research data. From the output of descriptive statistics, you can obtain information such as the minimum, maximum, mean, standard deviation, standard error, and more. These values are important to interpret for each variable observed in your research.
How to Tabulate Data Using Pivot Table for Your Research Results
For those of you currently conducting research, the stages of data entry and data tabulation are important parts of the process. Excel, which we already use daily for data processing, can also help us perform data tabulation quickly.
Dummy Variables: A Solution for Categorical Variables in OLS Linear Regression
If you’re analyzing data using OLS linear regression, there are certain assumptions you need to meet. The purpose of these assumption tests is to ensure that the estimation results are consistent and unbiased.
The Difference Between Residual and Error in Statistics
For those of you who are learning statistics, you’ve probably come across theories explaining the concepts of residual and error. At first glance, they seem almost identical, and many people even think they mean the same thing. However, in statistics, residual and error actually have different meanings.
Use Stratified Random Sampling When the Population Is Not Entirely Homogeneous
Sampling techniques are very important, especially when we’re observing a specific population. By taking samples, we can save on costs, time, and effort—yet still obtain results that represent the population being studied.