Johnson

If you have ever run a Rasch analysis and stared at a column of fit statistics wondering whether your items are performing well, you are not alone. Researchers, graduate students, and psychometricians routinely ask the same question: what exactly do infit and outfit mean in Rasch measurement, and how should I interpret them? These two … Read more

Differential Item Functioning (DIF) is one of the most important concepts in educational measurement and test fairness. When I first encountered the ETS DIF classification categories during a psychometric review, the A, B, and C labels felt like a foreign language. If you are in the same boat, this guide breaks down each category in … Read more

Learning how to run exploratory factor analysis step by step can feel overwhelming, especially if you are a graduate student staring at your first dataset with 30 survey items and no idea where to begin. I remember spending three weeks on a single EFA during my thesis, making every mistake in the book, and wishing … Read more

Deciding how many factors to retain in exploratory factor analysis (EFA) is one of the most consequential and frequently debated decisions in psychometric research. Get it right and your measurement model reflects genuine latent constructs that hold up across samples and studies. Get it wrong and you either force artificial dimensions onto your data or … Read more

A scree plot is a line graph that displays the eigenvalues of factors or principal components, and it tells you how many factors to retain in factor analysis or principal component analysis. The plot helps you separate meaningful structure from random noise by showing where the decline in eigenvalues flattens out. Understanding what a scree … Read more

Deciding how many factors to retain in exploratory factor analysis is one of the most consequential choices a researcher makes. Retain too few and you collapse distinct constructs together. Retain too many and you model noise as if it were meaningful structure. Parallel analysis has emerged as the most reliable, empirically grounded method for making … Read more

You run a PCA on your dataset, and the software tells you to keep every component with an eigenvalue greater than 1. But what does that number actually represent? Why 1 and not 0.8 or 1.5? And is that rule always safe to follow? Our team has spent years working through principal component analysis across … Read more

Principal axis factoring (PAF) and principal components analysis (PCA) are two of the most widely used multivariate techniques for reducing a large set of variables into a smaller set of components or factors. Despite their surface similarities, they answer fundamentally different research questions. PCA is a data reduction technique that creates linear combinations of your … Read more

If you have ever stared at an SPSS communalities table and wondered whether your factor analysis actually worked, you are in the right place. Learning how to interpret communalities in factor analysis is one of the most common stumbling blocks for researchers running exploratory factor analysis for the first time. I have spent years working … Read more

If you have ever run a structural equation model and watched your RMSEA come back at 0.087, you already know the panic that follows. Is that acceptable? Should you aim for 0.05, 0.06, or 0.08? Your committee wants one number, your reviewer wants another, and the software just prints a value with no apology. You … Read more