Developing an accurate and yet simple (and interpretable) model in machine learning can be a very challenging task. Depending on the modeling approach (e.g., neural networks vs. logistic regression), having too many features (i.e., predictors) in the model could either increase model complexity or lead to other problems such as multicollinearity and overfitting¹. Furthermore, with a highly complex model, it could be harder to acquire (or maintain) a large set of features for future predictions. Thus, it is important to select optimal features.

Sometimes, less is more. —William Shakespeare

Recursive Feature Elimination², or shortly RFE, is a widely used algorithm…

Artificial Intelligence, Fairness

Last September, I came across an article on The Verge that talks about how a group of seventh-grade students figured out an easy way to cheat on a history exam graded by artificial intelligence (AI). The article focuses on the story of a young student who receives a low score on a history assignment at Edgenuity but later on figures out how to trick the AI grading system with the help of her mother (who happens to be a history professor) and gets 100% in the rest of his assignments. A very intriguing and eye-catching story, right?

There is always…

Are there are too many questions on your survey? Are you worried that your participants may get tired of responding to the questions in the middle of the survey? In this article, I describe how to shorten surveys using ant colony optimization (ACO) in R.

In the social and behavioral sciences, researchers often use online surveys and questionnaires to collect data from a sample of participants. Such instruments provide an efficient and effective way to collect information about a large group of individuals.

Surveys are used to collect information from or about people to describe, compare, or explain their knowledge…

Researchers conduct measurement invariance analysis to ensure that the interpretations of the latent construct(s) being measured with their measurement instruments (e.g., scales, surveys, and questionnaires) are valid across subgroups of a target population (e.g., gender, ethnic/racial groups) or multiple time points (e.g., results from 2019 vs. results from 2020). In this post, I demonstrate how to test for measurement invariance (i.e., configural, metric, scalar, and strict invariance) of an instrument using R.

Measurement Invariance

In the social sciences, researchers often use self-reported measurement instruments (e.g., scales, surveys, and questionnaires) to assess different latent constructs (e.g., emotions, attitudes, and preferences). Data collected through…