Data analysis in contemporary social science, business and behavioral research tends to go beyond correlations and regressions. The tools that are required by researchers to test complex theoretical models are robust. Structural Equation Modeling (SEM) comes in here.
Now, should the words latent variables, path analysis, or model fit indices, sound threatening to you, you are not alone. SEM is confusing to many researchers, particularly non-statisticians. We think that in Mars Publications, every researcher should be in a position to know the tools that authenticate his work. We simplify complicated procedures and get your manuscripts wizened through meticulous Scientific and Technical Editing.
This manual breaks down the mystery of SEM, and concentrates on what it is, the reasons why you need to use it, and how it is different from the traditional approaches.
What exactly is SEM?
Structural Equation Modeling (SEM) is not a single statistical test, but a set of multivariate methods, which are very powerful. Consider it as a holistic method of blending two classical statistical approaches:
- Factor Analysis (Measurement Model): This component measures the extent to which your survey questions or indicators (also known as observed variables) are able to measure the concepts that are unobservable (also known as latent variables). Indicatively, to what extent do some particular questions assess the latent variable Job Satisfaction?
- Multiple Regression (Structural Model): This element evaluates the causal relationships between those latent variables that have been hypothesized. To illustrate, is there any significant predictive value of Job Satisfaction on Organization commitment?
The Big Picture: SEM provides the ability to test an entire web of relationships at a time, rather than a single relationship at a time. It allows you to test the quality of what you are doing and to test your theory within the one beautiful structure.
Why Do Researchers Use SEM?
The main characteristic of SEM used by the researchers is its capacity to deal with complexity and the ability to offer a more realistic test of theory.
The treatment of Latent Variables: SEM treats measurement error unlike regression where variables are assumed to be measured perfectly. It statistically isolates the error and the true score providing you with a better estimate on the association between conceptual variables. This is vital where the disciplines have a dependence on psychological scales or the use of complex constructs.
Testing Mediators and Moderators: SEM is useful in studying the indirect effects. It gives you the opportunity to model whether variable A affects variable C by a third variable, B (mediation). It is also able to test whether the correlation between A and C is modified depending on the degree of a fourth variable, D (moderation). These relationship tests are very complex in nature offering a rich theoretical understanding which a simple regression cannot easily get.
Measuring Model Fit: SEM provides you with a set of statistics (such as CFI, TLI, and RMSEA) that indicate how your entire hypothetical model, the entire network of relationships, fits the real data that you have gathered. It is not only that you check whether individual paths are significant; you check whether the theoretical map corresponds to the real-world territory.
The Major Ideas You Have to Know
In order to understand a concept of SEM, non-statisticians are to pay attention to the following three terms:
- Variables (or Indicators): These are those things you actually measure. They are those on your survey (e.g., I feel good about my job). You are their squares or rectangles on an SEM diagram.
- Latent Variables (or Constructs): these are the things you can think of that your observed variables are attempting to measure (e.g., “Job Satisfaction”). They cannot be directly measured. You are notated in an SEM diagram as circles or ovals.
- Paths (or Arrows): These are theoretical associations. A single-headed arrow represents a causal relationship (e.g., A leads to B). A double-headed arrow represents a simple correlation (e.g. A is related to B).
You start your SEM analysis with literally drawing your theory- connecting your circles and squares through arrows.
The key difference between SEM and Traditional Regression
The conventional statistical techniques, such as Multiple Regression, examine the correlation between observable and individual variables. Regression gives all the variance in your independent variables as true variance.
SEM essentially is a different method since it is a confirmatory method. You define a full theoretical model a priori (in advance) and the analysis would verify how the model is functioning well or not.
The traditional Regression utilizes mainly observed (measured) variables. SEM, on the contrary, involves observed and latent (unobservable) variables. Regression makes the assumption that measurement error is zero or just residual. SEM directly models and isolates measurement error and the true score. Moreover, regression tests one to one relationship. SEM checks the whole network of relations at the same time. Regression typically seeks prediction or even mere description of one outcome, whereas SEM seeks to test a complex, holistic theoretical framework.
Preparing Your SEM Manuscript to Publication
The introduction of SEM involves complex programs and effective interpretation, and the presentation of the results involves straightforward and succinct communication. It is at this point that the important step of polishing your manuscript comes in.
With a perfect analysis of the data, one can still get rejected with a poorly written methods or results section. Your complicated SEM procedure will need to be clarified to a layperson critic.
- Mars Publications Solution: You take your research to a higher level. Our team also offers some specialized services in Scientific and Technical Editing which is specialized in quantitative research. We ensure:
- Transparency of Process: We audit your SEM process and make sure that you report model specification, estimation methods (e.g. Maximum Likelihood), and software accurately.
- Reporting Accuracy: We check the correct presentation and explanation of your model fit indices, standardized path coefficients and significance levels.
- Compliance and Flow: We simplify your language to become active, persuasive and compliant to all journal requirements, so that there is a smooth and logical flow between your theoretical context and your SEM results.
Keep your breakthrough results in the limelight and not statistical complexity. Use the strength of SEM and leave your manuscript to our skill.
Get in touch with Mars Publications. The Scientific and Technical Editing of complex data presents it to the world as understandable science.