# What is a factor in SEM?

## What is a factor in SEM?

The Measurement Model A latent construct (also known as a factor or scale) is a variable that cannot directly be measured. It is measured by a set of observable variables (indicators) that are weighted based on their variance/covariance structure.

**What is a latent factor in SEM?**

SEM uses latent variables to account for measurement error. Latent Variables. A latent variable is a hypothetical construct that is invoked to explain observed covariation in behavior. Examples in psychology include intelligence (a.k.a. cognitive ability), Type A personality, and depression.

### What is the difference between factor analysis and SEM?

As discussed in Chapter 1, the key difference between path analysis and SEM is that the former analyzes relationships among observed variables, while the latter focuses on relationships among latent variables (latent constructs or factors).

**What is the SEM method?**

Structural equation modeling (SEM) is a set of statistical techniques used to measure and analyze the relationships of observed and latent variables. Similar but more powerful than regression analyses, it examines linear causal relationships among variables, while simultaneously accounting for measurement error.

## What is a factor score?

Factor scores are standard scores with a Mean =0, Variance = squared multiple correlation (SMC) between items and factor. Procedure maximizes validity of estimates. Factor scores are neither univocal nor unbiased. The scores may be correlated even when factors are orthogonal.

**What are latent factors in research?**

A latent variable is a variable that cannot be observed. The presence of latent variables, however, can be detected by their effects on variables that are observable. Most constructs in research are latent variables.

### What is indicator and latent variable?

Latent variables are variables that are unobserved, but whose influence can be summarized through one or more indicator variables. They are useful for capturing complex or conceptual properties of a system that are difficult to quantify or measure directly.

**Why do we use SEM?**

SEM is used to show the causal relationships between variables. The relationships shown in SEM represent the hypotheses of the researchers. Typically, these relationships can’t be statistically tested for directionality.