What Is an SEM Model? How to Read and Evaluate SEM in SmartPLS

What Is an SEM Model

An SEM model, short for Structural Equation Modeling, is a group of methods used to test relationships among research concepts and the way those concepts are measured by observed variables at the same time. When your model includes several independent variables, dependent variables, mediators, or moderators, SEM lets you test the full structure instead of running separate regressions one by one.

An SEM model usually has two parts. The measurement model answers whether the observed variables measure the latent variable correctly. The structural model tests the paths between latent variables, such as Service Quality affecting Satisfaction and Satisfaction affecting Repurchase Intention.

You can review the foundation in What is SEM, then decide whether you are using PLS-SEM or CB-SEM. Both belong to SEM, but they have different objectives, software, and reporting indicators.

In SmartPLS, the model is shown as circles or rectangles connected by arrows. A circle usually represents a latent variable, while measurement indicators are connected to that variable. The results you need to read include outer loadings, Cronbach's Alpha, composite reliability, AVE, HTMT, VIF, path coefficients, R², f², and Q², depending on your research design.

Why SEM Models Matter in Quantitative Research

An SEM model lets you test a chain of hypotheses within one analytical framework. Instead of asking only whether Reliability affects Usage Intention, for example, you can test hypotheses involving Reliability, Perceived Usefulness, Satisfaction, and Usage Intention at the same time.

The important point is that SEM separates two questions that are often mixed together. First, is the scale reliable and valid? Second, does the data support the relationships between the variables in the model? A path coefficient with a small p-value is not enough to conclude that the model is good when the scale has problems with outer loading, AVE, or HTMT.

With PLS-SEM, you typically choose SmartPLS when the objective is prediction, the model contains many latent variables, the data does not fit distributional assumptions well, or the study is exploratory. The current procedure assesses the measurement model first and the structural model second, consistent with the recommendation of (Anderson and Gerbing, 1988) and the PLS-SEM guidance in (Hair et al., 2022).

With CB-SEM, AMOS, LISREL, or similar software is commonly used to test the overall fit of a theoretical model. CFI, TLI, RMSEA, and SRMR belong to the CB-SEM group of indicators. You can read more about What is CB-SEM if your proposal requires covariance-based model fit testing.

SEM also helps you interpret latent variables, which are concepts that cannot be observed directly, such as Trust, Perceived Value, or Purchase Intention. If you are still unclear about how one latent variable is measured by several observed variables, see What is a latent variable.

What Counts as an Acceptable SEM Model

No single number determines whether the entire SEM model is acceptable. You need to assess it in order: scale quality, discriminant validity, multicollinearity, explanatory power, and the significance of the paths. The table below focuses on PLS-SEM in SmartPLS.

These thresholds are interpretation points, not permission to delete indicators mechanically. For example, a slightly low outer loading can be considered together with CR, AVE, theoretical significance, and the content of the indicator. When you remove an indicator, record the reason and check the model again after removal instead of keeping only the most attractive result.

How to Read an SEM Model in the Output

In SmartPLS 4, you usually run PLS-SEM to assess the measurement model and then run Bootstrapping to test the paths. Read the output in two layers. The first layer contains indicators for latent variables and observed variables. The second layer contains the arrows between variables.

The table below is illustrative output, not the result of a real study. The column names follow the output groups commonly shown in SmartPLS.

When you read Path Coefficients, the sign of the coefficient shows the direction of the effect. A positive coefficient of 0.428 indicates that as CLDV increases, SATISFACTION tends to increase in the illustrative model. T Statistics and P Values show the statistical evidence for the path, but your final conclusion still needs to be compared with the hypothesis and the significance level registered for the study.

Read R² for the dependent variable. If the R² of INTENTION is 0.487, the predictor variables in the model explain the corresponding share of INTENTION's variance in the illustrative dataset. Avoid writing that the model explains an absolute cause, because observational SEM generally reflects relationships within the model and the collected data.

For a mediator, inspect the indirect effect in the Bootstrapping results. An indirect effect is considered significant when the bootstrap confidence interval does not contain 0, according to (Preacher and Hayes, 2008). Do not look only at the p-values of the two component paths and then conclude that mediation exists.

What to Do When the SEM Model Does Not Meet the Thresholds

Work from the data toward the model. Do not delete an indicator immediately because one number is low.

First, check the .csv file or the imported data. Column names must match the indicators in SmartPLS, missing cells must be handled for a stated reason, and reverse-coded variables must be recoded before you run the model. An observed variable entered in the opposite direction can produce a low loading and reduce Alpha or AVE.

Next, inspect Outer Loadings and the Construct Reliability and Validity table. If an indicator is low, read its wording, its correlation with indicators in the same construct, and the change in CR and AVE if the indicator is removed. Remove it only when you have methodological and theoretical grounds. Removing many indicators just to meet a threshold can leave the original concept under-measured.

Then check HTMT between constructs. A high HTMT indicates that two concepts may not be clearly distinct in the data or that the indicators have overlapping content. You can review the questionnaire wording and theoretical basis, but do not combine two constructs merely because HTMT is high when the research topic provides no clear argument for doing so.

If VIF is high, check the predictors of the same endogenous construct. Two concepts that are almost duplicates can create multicollinearity. Review the model, the measurement approach, and the data source instead of reporting only that the software still runs.

Finally, if a path is not significant, keep the result truthful and compare it with the hypothesis. You can discuss that the data did not support the hypothesis in the research context. Reviewers generally accept a well-documented rejected hypothesis more readily than a model modified through several undocumented rounds.

If the scale fails from the beginning, you may need to return to the questionnaire or the original data. SmartPLS cannot repair an indicator written around the wrong concept, an unsuitable response sample, or incorrectly entered data.

Distinguishing SEM, PLS-SEM, and CB-SEM

SEM is the name of the method family. PLS-SEM and CB-SEM are two common implementation approaches. PLS-SEM focuses on predictive relationships and is commonly run in SmartPLS. CB-SEM focuses more on reproducing the covariance matrix and assessing overall model fit, and is commonly used in AMOS.

Read PLS-SEM when your model uses SmartPLS. In PLS-SEM, outer loading, CR, AVE, HTMT, R², f², and Q² are the main groups of indicators to review. CFI, TLI, and RMSEA belong to CB-SEM, according to (Hu and Bentler, 1999), so do not place them in a SmartPLS report as if the two procedures were identical.

SEM also differs from ordinary linear regression because it can model latent variables through multiple indicators and test the measurement model together with the structural model. SEM does not turn observational data into causal evidence automatically. If you use causal language, it must be supported by the research design, measurement sequence, and theoretical argument.

Common Errors

The first error is drawing the model before clearly defining the constructs and indicators. Prepare a variable codebook, scale sources, direction of effects, and hypotheses before opening SmartPLS. An arrow on the screen cannot replace the argument in Chapters 2 and 3.

The second error is mixing thresholds from SPSS, AMOS, and SmartPLS. Cronbach's Alpha can appear in several programs, but CFI, TLI, and RMSEA are not core indicators for evaluating PLS-SEM. State clearly which procedure you are using.

The third error is running Bootstrapping with the default settings without reporting the number of subsamples, test type, and confidence interval. The reference PLS-SEM procedure uses 5,000 bootstrap subsamples (Hair et al., 2022). In your methods chapter, record the settings you actually used.

The fourth error is deleting indicators until every p-value is small. Model adjustments must be based on theory, measurement quality, and the research objective. Save each file version, the table of removed indicators, and the reason for removal so you can explain the decisions.

The final error is writing that the model is acceptable without saying in what respect. Separate your comments on reliability, convergent validity, discriminant validity, multicollinearity, explanatory power, and the hypotheses.

Frequently asked questions

What is an SEM model and when should you use one?

An SEM model tests the measurement model and the structural model at the same time. Use SEM when your study has several latent variables, each measured by multiple observed variables, and a network of hypotheses that needs to be tested together.

What values make an SEM model acceptable?

There is no single threshold for the whole model. For PLS-SEM, you usually review outer loading, CR, AVE, HTMT, VIF, R², f², Q², and the significance of the path coefficient. Each indicator must be compared with its source and the research context, such as CR at 0.7 and AVE at 0.5 according to (Fornell and Larcker, 1981).

Should you run an SEM model in SPSS or SmartPLS?

SPSS is suitable for data cleaning, descriptive statistics, Cronbach's Alpha, EFA, and regression. SmartPLS is suitable for PLS-SEM, including assessment of the measurement model and structural model. If the study requires CFI, TLI, RMSEA, and CB-SEM model fit testing, consider AMOS or suitable CB-SEM software.

What should you do when SmartPLS shows an error or the model does not converge?

First check column names, missing data, reverse-coded variables, data types, and the links between constructs and indicators. Then check outer loading, VIF, and the paths. If the problem comes from the scale or the data, changing the software settings will not resolve the cause. Record and correct it in the original data.

Should you delete an observed variable when its loading is below 0.7?

You can consider removal, but do not delete it automatically. Review the indicator content, CR, AVE, HTMT, construct coverage, and theoretical argument. After removal, rerun the full relevant assessment and keep the reason so you can present it to your supervisor.

How to write this in your thesis

Open your SmartPLS file, check the measurement model and then the structural model, save the output tables, and record every change before writing Chapter 4. If you need to run this procedure on your own .sav or .csv dataset, see M4 data analysis.