Matthew Schell

Logistic Regression in R: Logits, Odds, and Odds Ratios

There are a few industries that seem to perennially have the reputation of terrible customer service: internet providers, phone companies…and airlines. How often have you head someone gush about the amazing customer care and support they received from an airline, or raved about how enjoyable their flight was (especially if not business or first class)?Continue reading “Logistic Regression in R: Logits, Odds, and Odds Ratios”

Finite Mixture Modeling – Latent Profile Analysis, Part 2

Welcome to Part 2 of this three part series! If you have not read Part 1, and are unfamiliar with latent profile analysis, I recommend taking a few minutes to do that before you dive in here. Additionally, check out Spurk et al. (2020) for a succinct overview of the profile enumeration process. In thisContinue reading “Finite Mixture Modeling – Latent Profile Analysis, Part 2”

Telling Compelling Stories: Creating Engaging Power BI Dashboards

Choosing the appropriate type of visualization and creating an engaging dashboard are oftentimes not as straightforward is it might seem. The powerful visualization tools that are readily available and relatively easy to use has given anyone with the time and desire the ability to create professional looking visualizations and dashboards. However, creating pretty graphs andContinue reading “Telling Compelling Stories: Creating Engaging Power BI Dashboards”

Finite Mixture Modeling – Latent Profile Analysis, Part 1

This is the first post in a three part series about latent profile analysis. Three posts you say?! Yes. This is the statistical method I used in my dissertation, so needless to say I have lots of thoughts about it. This first post will provide some theoretical background on mixture models more generally, and latentContinue reading “Finite Mixture Modeling – Latent Profile Analysis, Part 1”

Visualizing Retail Sales: A PowerBI Introduction

I recently decided to learn PowerBI. In my search for a job I have noticed that many employers prefer their candidates have some sort of experience using either PowerBI or Tableau. So, I thought what better time to learn than now, and what better way to learn than making some sample dashboards (along with takingContinue reading “Visualizing Retail Sales: A PowerBI Introduction”

A Deeper Dive into CFA and Measurement Invariance Fit Issues

Confirmatory Factor Analysis In a previous post I did an exploratory and confirmation factor analysis, and measurement invariance work with data generated from the depression, anxiety, and stress scale (DASS). In that post there were no fit issues with the CFA or the measurement invariance models. However, this is often not the case when weContinue reading “A Deeper Dive into CFA and Measurement Invariance Fit Issues”

Confirmatory Factor Analysis and Measurement Invariance

Factor Analysis Many constructs of interest related to human behavior, attitude, identity, and motivation are not directly observable. For example, the Depression, Anxiety, and Stress scale (DASS) measures individuals’ perceptions of their depression, anxiety, and stress. We can not directly measure any of these constructs, so we refer to them as “latent variables”. We haveContinue reading “Confirmatory Factor Analysis and Measurement Invariance”

The Importance of Within and Between-Subject Correlations for Multilevel Data

Multilevel data are those data which are ordered in a hierarchy, data that has a “nested” structure. A common nesting structure is having multiple measurements within one subject. For example, if we measure students’ engagement in science class several times over the course of an entire semester the data we get has a specific hierarchy.Continue reading “The Importance of Within and Between-Subject Correlations for Multilevel Data”