Mediation with Binary Categorical Independent Variables in SEM Model

Mediation with a categorical independent variable is run very similarly to a mediation analysis with a continuous variable.The independent variable will need to be coded as a “1” or a “0” for the groups. In this example, customers who received no adaptation in the service were coded as a “0” and those customers that

Multicategorical Independent Variables in SEM

In our previous example, our independent variable had only two categories (Treatment and Control group).The dummy coding for this two group category was straightforward and easy, with one group getting labeled a “1” and the other a “0”.When you have more than two groups in your independent variable, the dummy coding for this categorical

Mediation With Multicategorical Independent Variables in SEM

To test for mediation with a multicategorical independent variable, we will initially have to dummy code all the categories of the independent variable into separate variables. This is accomplished in the same way as discussed in the previous section about multicategorical vari- ables.We are dummy coding the multiple categories because one of the categories

Moderated Mediation With a Multicategorical Independent Variable in SEM

So far, we have examined multicategorical independent variables in a simple structural model test and then a mediation test. Let’s extend our example now to assess moderated mediation with multicategorical independent variables.We are going to use the same example as before, except this time we will add a moderator. The moderator in this test

How Do I Report the Results of My Multicategorical Moderated Mediation Test in SEM?

With multiple categories, you need to make sure the reader can easily find the statistical tests for each group.The categories and reference group also need to be overtly clear to the reader. I have provided two possible examples on how to present your results. Example 8.1: Example 8.2: Source: Thakkar, J.J. (2020). “Procedural Steps

Introduction to Latent Growth Curve Models

Latent growth curve models allow us to see the “growth” or change over numerous time points for a respondent.This type of analysis works well for longitudinal data collection, espe- cially with test-retest situations. If a respondent was measured at only two time points, we could use a two group analysis to determine differences of

Including Predictors Into a Latent Growth Curve Model

In the previous example, we had two groups and we simply tracked spending behaviors across the groups. With many latent growth curve models, you will want to include a predictor of the intercept and slope; specifically, the variable or construct that would influence the intercept and growth over the time period. Using our environmentally

How Do I Present My Latent Growth Curve Results?

There are many alternative templates you can use to present your latent growth curve results, but I find it is a good idea to present the mean values of the construct captured over the time periods for both groups. I also like to present the slope and intercept. Lastly, including the regression coefficients of

Latent Growth Curves With Nonlinear Patterns

Up to this point, we have examined latent growth curves that were linear in nature. Latent growth curves can assess nonlinear patterns as well. If you think your data might have linear and quadratic components, latent growth curves can assess the growth between time points and also assess the potential curvilinear nature of the

Latent Growth Curves Across Two Domains

There are situations where you want to test two domains together in a latent growth curve model. If we believe that the growth or change of one variable is also correlated with the change or growth of another variable, we may want to test a latent growth curve model with two domains. In essence,

Using Weighted Scores in AMOS

If your data sample is skewed to the point that a certain group is underrepresented, you might want to use a weighted score where underrepresented groups will have a stronger weight in the analysis.An example of this might be where 80% of your sample is female and you need to let the males in

How to Use Bootstrapping to Account for Non-Normal Data in SEM

One of the fundamental assumptions with SEM is that your data is normally distributed. Having non-normal data can lead to chi-square values being inflated and thus can lead researchers to start making changes or alterations in a model that are not really needed. To address the issue of having non-normal data, you can look

Mixture Structural Equation Modeling

Mixture modeling is a type of modeling where data is divided into subgroups and classified. Using a Bayesian analysis, all data that is unclassified will then be assigned to a group based on the existing criteria of the data that has already been assigned. For instance, let’s say we are trying to classify the

Analyzing Censored Data in SEM

Censored data occurs when the value of a measurement is only partially known.You will often see censored data in clinical trials where there is a drug trying to prevent patient death. With some of the patients, we have a definite birth and death date, but for patients who have not died, we still do

Non-Recursive Models (Feedback Loops) in SEM

A non-recursive model is a structural model where there is a feedback loop between two con- structs. A recursive model is what we would consider a structural model where there are no reciprocal influences between constructs, or specifically, no feedback loops exist. The problem with feedback loops is you are creating an infinite loop

Post-Hoc Power Analysis in SEM

A post-hoc or observed power analysis is a test of power after the sample has been collected. Using the existing sample size and effect size, you can determine the power of a specific rela- tionship in your model. Using a post-hoc test for power assessment can often be misleading. These post-hoc power analyses can

Using Specification Search to Perform Exploratory Factor Analysis in SEM

In Chapter 5, I explained how to use the specification search tool to trim a model.The specifi- cation search tool can also be used to perform an exploratory factor analysis (EFA). An EFA, as its name suggests, is an exploratory or investigative attempt to understand the factor structure of your constructs and if your

How to Use Plugins to Create a Model Instead of Drawing a Model in AMOS

If you do not want to use the graphical interface to conceptualize your model, AMOS does have an option where you can use a syntax option to run your analysis. Numerous SEM pro- grams in the past were solely syntax based, and if you feel more comfortable typing in the rela- tionships than using

How to Use the Program Editor to Conceptualize Your Model and Run the Analysis

The dedicated program editor software is included in the suite of AMOS programs when you install it on your computer. The program editor software allows you to fully code your model and request any analysis you need without using the AMOS graphics window. It has a slightly different coding structure from that of the