A big obstacle use of structural equation modeling(SEM) is handling categorical variables. When you want to measure the impact of one or more variables on a categorical variable such as gender, you have trouble using SEM statistical software.

Latent variables can be measured indirectly through the use of measurement variables. We need to consider how latent variables are indirectly measured.

Often, estimating one single SEM model is not enough. We are not referring to play with one model under slightly different configurations. We are talking about dealing with information from different groups in data.

In structural equation modeling, the concern of researchers arises when they want to measure the effect of one or more variables on a categorical variable. The concern becomes more serious when the categorical variable has more than two dimensions.

Other software, after calculating the latent variables, measures the relationship between them using OLS or bootstrap OLS method. Using this method becomes a serious problem, especially when the number of latent variables is large.

One of the most fundamental problems when starting a project in structural equation modeling is determining the sample size.

Many datasets are far from being a homogenous mass of data. Diversity is the rule. When we estimate the SEM-PLS model, we do it under the implicit assumption that all the observations in the data are more or less homogeneous.

The SEM-PLS results will be distorted when the assumptions of the OLS method are not confirmed. The PLS and PC methods are included in the SEM-PLS software to solve these problems.

In mediation analysis, the relationship of the independent variable(For Example Motivation) to the dependent variable(For Example Confidence) is influenced by another variable called the Mediator Variable(For Example Anxiety).

Moderating effects are evoked by variables whose variation influences the strength or the direction of a relationship between independent and dependent variables.

After fitting a model with at least one mediator variable, the question arises whether the mediator variable's role is significant?

We know that latent variables represent theoretical concepts, but sometimes we need extra constructs representing other latent variables.

These types of models have two elements: the Higher-Order Construct (HOC) captures the more abstract entity, and the Lower-Order Construct (LOC) captures sub-dimensions of the abstract entity.

We explained the "Higher-Order Construct Model" with an example.