A step-by-step lesson — Welcome back to our crash course on survey data analysis in R! In this article, I am going to share with you the following lessons: Task 0: Importing a .csv file from your local drive Task 1: Merging multiple rounds/phases of a survey by common columns Task 2: Creating a…

A step-by-step lesson — In this crash course, I will share the ten most common tasks for survey data analysis in R. I plan to share these tasks in two parts. This course is most suitable for beginners who want to quickly learn coding in R to begin survey data analysis for their research. …

Every year, thousands of students around the globe — some willingly, some unwillingly — enroll in introductory statistics/data analytics/consumer analytics courses. And it’s not difficult to find students who would put these courses on top of their scariest academic experiences. In this article, I try to take the perspective of…

This week, as I am wrapping up another semester in the applied social sciences, I have been revisiting some of the key challenges I faced over the last thirty months while transitioning from engineering to social science. In this article, I discuss some key differences between typical engineering and social…

A Detailed Explanation with Toy Examples — I. Correlation is “not sufficient” for causation (i.e., two “significantly” correlated variables may not have a causal relationship) In non-experimental data, despite the fact that — X and Y are strongly correlated/associated (coefficient close to/equal to 1) X and Y are (statistically) significantly associated (p-value of the coefficient less than 0.05)

A detailed explanation — In my previous article, I discussed why you should prefer reporting 95% confidence interval over p-value, especially when you are explaining the findings of your study to non-statistician readers/audiences. In this article, I continue the discussion further and try to provide a bit more clarity. Why You Should Prefer Confidence Interval over p-value Communicating Results of Your Statistical Analysistowardsdatascience.com

Communicating Results of Your Statistical Analysis — The journey of an analyst in the world of data science, fundamentally, consists of three phases: learning, doing, and presenting. These phases are not necessarily linear; most of us go back and forth. Which of these three phases do you perceive to be the most difficult one? Personally, I think…

A Detailed Explanation of the Regression Estimator — If you have taken an introductory applied econometrics/statistics/data science course, you must be familiar with the interpretation of the coefficient of a predictor in a linear model estimated using regression. …

A detailed explanation — While interpreting the coefficient of one of the predictors (say a continuous variable X1) of a linear model — with multiple explanatory variables (X1, X2, …..Xn) predicting the value of an outcome variable (Y) — you must have used statements such as the following: “Controlling for other factors/holding other factors…