We get many questions from clients who use the terms mediator and moderator interchangeably.
They are easy to confuse, yet mediation and moderation are two distinct terms that require distinct statistical approaches.
The key difference between the concepts can be compared to a case where a moderator lets you know when an association will occur while a mediator will inform you how or why it occurs.
Structural Equation Modelling (SEM) increasingly is a ‘must’ for researchers in the social sciences and business analytics. However, the issue of how consistent the theoretical model is with the 
data, known as model fit, is by no means agreed upon: There is an abundance of fit indices available – and wide disparity in agreement on which indices to report and what the cut-offs for various indices actually are. (more…)
There are two main types of factor analysis: exploratory and confirmatory. Exploratory factor analysis (EFA) is data driven, such that the collected data determines the resulting factors. Confirmatory factor analysis (CFA) is used to test factors that have been developed a priori.
Think of CFA as a process for testing what you already think you know.
CFA is an integral part of structural equation modeling (SEM) and path analysis. The hypothesized factors should always be validated with CFA in a measurement model prior to incorporating them into a path or structural model. Because… garbage in, garbage out.
CFA is also a useful tool in checking the reliability of a measurement tool with a new population of subjects, or to further refine an instrument which is already in use.
Elaine will provide an overview of CFA. She will also (more…)
By Manolo Romero Escobar
If you already know the principles of general linear modeling (GLM) you are on the right path to understand Structural Equation Modeling (SEM).
As you could see from my previous post, SEM offers the flexibility of adding paths between predictors in a way that would take you several GLM models and still leave you with unanswered questions.
It also helps you use latent variables (as you will see in future posts).
GLM is just one of the pieces of the puzzle to fit SEM to your data. You also need to have an understanding of:
(more…)
By Manolo Romero Escobar
What is a latent variable?
“The many, as we say, are seen but not known, and the ideas are known but not seen” (Plato, The Republic)
My favourite image to explain the relationship between latent and observed variables comes from the “Myth of the Cave” from Plato’s The Republic. In this myth a group of people are constrained to face a wall. The only things they see are shadows of objects that pass in front of a fire (more…)
by Manolo Romero Escobar
General Linear Model (GLM) is a tool used to understand and analyse linear relationships among variables. It is an umbrella term for many techniques that are taught in most statistics courses: ANOVA, multiple regression, etc.
In its simplest form it describes the relationship between two variables, “y” (dependent variable, outcome, and so on) and “x” (independent variable, predictor, etc). These variables could be both categorical (how many?), both continuous (how much?) or one of each.
Moreover, there can be more than one variable on each side of the relationship. One convention is to use capital letters to refer to multiple variables. Thus Y would mean multiple dependent variables and X would mean multiple independent variables. The most known equation that represents a GLM is: (more…)