What Is CB-SEM in SPSS and How Do You Read the Results

What Is CB-SEM in SPSS

CB-SEM stands for Covariance-Based Structural Equation Modeling. It uses observed data to estimate how closely the theoretical model you propose matches the sample's actual covariance matrix. When someone says they are running CB-SEM in SPSS, the precise meaning is usually that SPSS is being used for data cleaning, sample description, scale assessment, and preliminary regression, while the CB-SEM itself is estimated in AMOS, software that belongs to the IBM SPSS ecosystem.

In CB-SEM, you build two parts at the same time. The measurement model describes the relationship between latent variables and observed variables. The structural model describes the paths between latent variables, such as Perceived Usefulness affecting Intention to Use. You can review the background explanation in What Is SEM before working through the specific output tables.

Technically, CB-SEM searches for a set of parameters that makes the covariance matrix predicted by the model as close as possible to the observed covariance matrix. These parameters include factor loading, covariance, path coefficient, and measurement error. The software then provides a group of Model Fit indices, including Chi-square, CFI, TLI, RMSEA, and SRMR, so you can assess the model's overall fit.

Why CB-SEM Matters in Quantitative Research

CB-SEM is appropriate when your study starts from a relatively clear theory, has a model specified in advance, and mainly aims to test that theoretical model. You often see this approach in studies testing TAM, TPB, UTAUT, service quality, or models whose structure has already been examined many times.

Read the results in two layers. The first layer is the measurement model: do the observed variables measure the intended construct, are the factor loadings large enough, and do reliability and convergent validity meet the required criteria. The second layer is the structural model: are the hypothesized paths supported, are the effect coefficients statistically significant, and does the overall model fit the data. This separation follows the logic of assessing the measurement model before interpreting the structural model described by (Anderson and Gerbing, 1988).

CB-SEM lets you assess the fit of the model as a whole rather than looking only at separate regression coefficients. A good Model Fit does not automatically prove that every hypothesis is correct. A model can have acceptable CFI and TLI while one path coefficient is insignificant or one observed variable has a weak loading.

You also need to distinguish CB-SEM from PLS-SEM. PLS-SEM is often used when the main focus is prediction, the model is more exploratory, or the data and model have characteristics suited to the PLS method. CB-SEM focuses more on testing how well a theoretical model fits the data. Both methods use the term SEM, but you should not mix their thresholds or reporting practices in the same analysis.

What Values Are Acceptable for CB-SEM in SPSS

No single number determines whether CB-SEM is acceptable. Read several indices together while considering the theory, sample size, data distribution, and model specification. The thresholds below are commonly reported reference points for CB-SEM. CFI, TLI, RMSEA, and SRMR are Model Fit indices.

Do not count how many indices pass their thresholds and stop there. (Hu and Bentler, 1999) is the source used for CFI, TLI, RMSEA, and SRMR in CB-SEM. These thresholds should not be carried into a SmartPLS article to assess PLS-SEM. This mistake is common because both methods include SEM in their names.

For the measurement model, also check Corrected Item-Total Correlation if you assessed the scale in SPSS. A value of 0.3 or above is commonly used according to (Nunnally and Bernstein, 1994). Cronbach's Alpha at 0.7 or above is considered acceptable according to (Nunnally, 1978), while an exploratory scale may be considered at a lower level according to (Hair et al., 2010). State the research context clearly instead of choosing a lower threshold only because the result is less attractive.

How to Read CB-SEM Output

If you run the data in SPSS and then move to AMOS, the table names will differ from the Cronbach's Alpha or EFA tables. You will usually read the output in this order: Model Fit Summary, Regression Weights, Standardized Regression Weights, Squared Multiple Correlations, and any validity or reliability tables provided by the software.

The table below is illustrative output, not the result of a real study. The column names follow the format commonly found in AMOS output and CB-SEM reports. Replace them with the figures from your own file.

In the Regression Weights table, Estimate is the unstandardized coefficient. S.E. is the standard error, C.R. is the Critical Ratio, which is generally read like a test ratio, and P is the p-value. The *** symbol in AMOS usually indicates a very small p-value in the software's display format. When writing your thesis, reproduce the symbol shown in the output and do not replace it with a specific number if the table does not provide that number.

When comparing the size of effects between variables, use the Standardized Regression Weights table because the coefficients have been standardized. In the example above, the standardized coefficient of 0.51 from PU to BI can be compared with 0.18 from PEU to BI. You should not directly compare the Estimate column in the unstandardized table when the variables use different measurement scales.

Read the Model Fit table as a group. If CFI and TLI meet the reference points but RMSEA is high, check the model specification, data distribution, sample size, and assumed relationships. Do not add covariance between errors simply to make the indices look better. Modification Indices can point to a problem, but every adjustment needs a theoretical reason and must be recorded in the research method.

You can review the meaning of a latent variable before reading the loadings in What Is a Latent Variable. If you are building the complete model, SEM Model can help you separate the measurement model from the structural model.

What to Do When CB-SEM Does Not Meet the Criteria

Work from data checks toward theory checks. This order helps you avoid deleting variables or adding paths only because you want one number to pass a threshold.

First, check the data file in SPSS. Review missing values, reverse-coded variables, Likert scale coding, outlier cases, and unusual response rows. One incorrectly coded variable can lower a loading or make the covariance matrix unstable.

Next, inspect the measurement model. If an observed variable has a low loading, read the item wording, scale source, and theoretical meaning before removing it. Removing an item for statistical reasons without being able to explain the conceptual effect is likely to create questions at the defence. After each removal, rerun the model and save the output from every round.

Then check the structural model. Examine whether the paths point in the correct direction, whether the mediator has been specified properly, and whether any effect lacks a theoretical basis. If the model has too many paths, the results can become difficult to interpret and less stable.

Finally, treat Modification Indices as diagnostic suggestions, not as a list of commands to follow. Add a covariance or path only when the theory and the literature support it. If the model still does not fit, report the limitation, consider an alternative specification, or discuss it with your supervisor. Software cannot turn a theoretically weak model into a valid one.

Distinguishing CB-SEM from PLS-SEM

CB-SEM and PLS-SEM can both analyze relationships between latent variables, but their aims and evaluation methods differ. CB-SEM focuses on how well the theoretical model reproduces the observed covariance matrix. For that reason, Model Fit indices such as CFI, TLI, RMSEA, and SRMR commonly appear in the report.

PLS-SEM places more emphasis on explaining and predicting endogenous variables. When you run SmartPLS, you typically read Outer Loadings, CR, AVE, HTMT, VIF, R², f², Q², and Path Coefficients. R² values of 0.75, 0.50, and 0.25 are interpreted as substantial, moderate, and weak, respectively, according to (Hair et al., 2011). VIF below 5 is a reference point in PLS-SEM reporting according to (Hair et al., 2019).

In CB-SEM, do not use R² or HTMT as the sole basis for concluding that the Model Fit is acceptable. In PLS-SEM, do not transfer CFI, TLI, and RMSEA directly into the SmartPLS model assessment. The article What Is CB-SEM is useful if you want a deeper comparison of the aims, data, and software used by the two approaches.

Regarding software, SPSS is suitable for data cleaning, descriptive statistics, Cronbach's Alpha, EFA, and regression. AMOS is commonly used to draw and estimate CB-SEM models. SmartPLS is suited to PLS-SEM. JASP or R may reduce software costs, but you must learn the interface or write the code yourself. Paying for an SPSS service may reduce the time spent clicking through menus, but the risk is that you will not understand the output well enough to explain it. At the defence, the committee usually asks why you chose the model, why you removed an item, and why you accepted or rejected a hypothesis. They do not only look at the result table.

Common Errors

The first error is calling every SEM analysis performed with SPSS CB-SEM. SPSS itself is not specialized software for fully estimating CB-SEM. State which software handled each step, such as SPSS for cleaning and preliminary checks and AMOS for estimating the model.

The second error is declaring the model acceptable based on one index. A high CFI is not enough, just as a low RMSEA is not enough. Report a group of indices, give the sourced thresholds, and explain any remaining areas of poor fit.

The third error is mixing CB-SEM and PLS-SEM in one criteria table. CFI, TLI, and RMSEA from (Hu and Bentler, 1999) belong to the CB-SEM context. Indices such as HTMT, Q², and bootstrapping with 5,000 subsamples are commonly reported in PLS-SEM according to (Henseler et al., 2015) and (Hair et al., 2022).

The fourth error is deleting many observed variables to obtain attractive loadings. Every removed item changes the conceptual content and coverage of the scale. Record the reason for removal, the number of runs, and each output version, then report them consistently in Chapter 4.

How to write this in your thesis

You can adapt the following paragraph: “The results show that the model has CFI = [value], TLI = [value], RMSEA = [value], and SRMR = [value]. The indices were compared with the reference thresholds of (Hu and Bentler, 1999). The path from [independent variable] to [dependent variable] has a standardized coefficient of [β], p-value = [p], so hypothesis [H] was [accepted/not accepted] in the study sample.” This is a template for replacing the brackets with your actual figures. It is not a ready-made conclusion for your study.

Frequently asked questions

Does CB-SEM in SPSS run directly in SPSS?

SPSS usually handles preparation and data analysis steps such as file cleaning, descriptive statistics, Cronbach's Alpha, EFA, and regression. CB-SEM is commonly estimated in AMOS or specialized SEM software. In the method section, state the tool used for each step rather than writing generally that you ran CB-SEM in SPSS.

What values are acceptable for CB-SEM?

Review Model Fit and the measurement model together. Common reference points include CFI and TLI at 0.90 or above, RMSEA and SRMR at or below 0.08 according to (Hu and Bentler, 1999), factor loading at 0.50 or above according to (Hair et al., 2010), CR at 0.70 or above, and AVE at 0.50 or above according to (Fornell and Larcker, 1981).

These are reference points, not targets for adjusting the data until the output passes. You still need to assess the theory, sample size, data quality, and whether the model makes sense.

How are CB-SEM and PLS-SEM different?

CB-SEM is generally selected to test how well a theoretical model fits the data. PLS-SEM emphasizes explanation and prediction, using indices such as R², f², Q², HTMT, and VIF. Because the aims differ, the evaluation criteria and reporting practices also differ.

What should you do if RMSEA is high but CFI meets the reference point?

Check the data, sample size, measurement model, distribution, and specified relationships. Then review the Modification Indices to identify points that need attention, but make an adjustment only when there is a theoretical basis. Do not add covariance between errors only to reduce RMSEA.

Should you remove an item when its factor loading is below 0.5?

A low loading is a signal to investigate, not an automatic deletion command. Read the item content, check the coding, review reliability, and consider how removal would affect the construct's meaning. If you remove the item, record the reason and rerun the model transparently.

Open the data file again, identify which step SPSS is handling, and check each Model Fit, Regression Weights, and Standardized Regression Weights table against the registered model. If you need to run CB-SEM on .sav or .csv data and turn the results into an analysis section you can defend, DoThesis M4 analysis is the module for this step.