In our previous article, we embarked on a journey through categorical variables. From foundational principles, we highlighted their role in regression models and emphasized techniques to adapt these variables for quantitative studies. In this fourth article, we will discuss three-way interactions, enriching our understanding and decoding their significance. The dataset we use in this study …
In our previous articles, we began understanding categorical variables, starting with the basics. We unraveled the importance of these variables in regression models and laid the groundwork for transforming categorical variables into a format suitable for quantitative analysis. We already looked into the regression with single categorical and multiple categorical variables. This article will shed …
Two-way Interaction and Its Interpretation in Stata Part3 Read More »
In our previous article, we delved into the fundamentals of creating dummy variables for binary categories, using gender—with its two categories of male and female—as a prime example. However, the landscape of categorical variables is far more expansive, often featuring more than two categories. In this follow-up article, we’ll broaden our focus to tackle categorical …
In R, we can merge different data frames for the data analysis purpose. There are two ways to merge data frame, either by using the merge() function in base R or by using tidyverse package, we will discuss both in this article . The merge() function merges the data based on a common variable or …
Merge command in Stata serves the purpose of merging data or combining different datasets. Within the Merge command, there are several merge types, including one to one merge, many to one merge, one to many merge, and many to many merge, each with its unique applications. In their respective sections, we will explore these merge …
