Slide 1, Speaker:
This video addresses the question, “What is Structural Equation Modeling?”
Slide 2, Speaker: First, equations that are “structural”
are those we intend to represent cause-effect connections in quantitative models. Typically,
we are working with network hypotheses in structural equation modeling, such as the
one shown here. Slide 3, Speaker:
Structural equation modeling, or “SEM”, can be thought of in a number of ways. The
first I would like to mention is as a framework for quantitative analysis.
The graphic here is meant to make the point that we use statistical and mathematical tools
within an SEM framework to seek a causal understanding about the multiple processes operating in
systems. Slide 4, Speaker:
SEM also provides us with a means for translating abstract ideas into testable expectations.
Here we show a theoretical proposition. SEM methods allow us to develop a so-called causal
diagram that translates the general ideas into a network of expectations. Using the
causal diagram and data, we can test the hypothesized network, and therefore the theoretical proposition,
arrive at a new and quantitative understanding. Slide 5, Speaker:
SEM unfolds as a process, moving from theory to model to results and eventually back to
theory. This process supports generating a clarity
of meaning, repeatability, learning, and building on prior knowledge.
Slide 6, Speaker: SEM is also a method for learning.
Here, we start with an observation, that older forest stands that burn show poorer recovery
after fire. We can propose and test ideas about the mechanisms
behind this observation by using SEM. In this case, we explicitly test the idea that fire
severity might explain all or part of the relationship between forest age and plant
recovery. Stated in a different way, that older stands burn hotter and that the stands
that burn hotter have weaker recovery. This is called the “test of mediation.”
Through the use of this kind of test, we can dig deeper and deeper into understanding systems
by bringing in more variables. Slide 7, Speaker:
Structural equation models also have a particular form.
In these models, responses (y-variables) can depend on other responses (other y-variables)
as well as external predictors (x-variables). This equational form allows networks of relationships
to be represented. Slide 8, Speaker:
SEM is also a body of knowledge. It is important to realize that there is a
great deal of information about SEM, but it is derived from many different scientific
disciplines. Slide 9, Speaker:
History has played an important role in shaping the literature on SEM.
This diagram represents a citation map showing the historical flow of knowledge among disciplines.
From this perspective, we can realize how the flow of information, and especially the
lack of flow of information , shapes peoples’ perceptions of quantitative methods, as well
as the language people are familiar with. Slide 10, Speaker:
SEM is also a community of practice. There are active online discussion groups, both
general and also ones specifically related to software packages. There also exist a number
of working groups focused on particular scientific problems that use SEM for the solution.
Slide 11, Speaker: I hope this overview has been useful. More
detail can be found in the written document summarized by this video presentation. Also,
you can search for examples that relate to your subject of interest.