EFA

Confirmatory Factor Analysis: How To Measure Something We Cannot Observe or Measure Directly

by Christos Giannoulis, PhD

Many times in science we are intrigued to measure an underlying characteristic that cannot be observed or measured directly. This measure is hypothesized to exist to explain variables, such as behavior, that can be observed.

The measurable variables are called manifest variables. The unmeasurable are called latent variables.

Latent variables are often called factors, especially in the context of factor analysis.

Four Common Misconceptions in Exploratory Factor Analysis

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by Christos Giannoulis, PhD

Today, I would like to briefly describe four misconceptions that I feel are commonly perceived by novice researchers in Exploratory Factor Analysis:

Misconception 1: The choice between component and common factor extraction procedures is not so important.

In Principal Component Analysis, a set of variables is transformed into a smaller set of linear composites known as components. This method of analysis is essentially a method for data reduction.

The Four Models You Meet in Structural Equation Modeling

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By Manolo Romero Escobar

On a previous post (Why do I need to have knowledge of multiple regression to understand SEM?) we showed how a multiple regression model could be conceptualized using Structural Equation Model path diagrams.

That’s the simplest SEM you can create, but its real power lies in expanding on that regression model. Here I will discuss 4 ways to do that.

Path Analysis

More interesting research questions could be asked and answered using Path Analysis. Path Analysis is the application of structural equation modeling without latent variables. [Read more…] about The Four Models You Meet in Structural Equation Modeling