Common SEM Mistakes That Get Your Paper Rejected (And How to Fix Them)
Reviewers reject SEM papers for the same reasons over and over: poor model fit, ignored modification indices, missing measurement invariance, and reporting only CFI. If your RMSEA is above 0.08 and you don't know why, this article is for you.
Why Reviewers Keep Rejecting SEM Papers
Structural equation modelling is one of the most powerful tools in social science research, but it is also one of the most misused. Journals receive a flood of SEM-based manuscripts where the authors clearly ran the analysis without fully understanding the method. The result is predictable: rejection. The most common reviewer complaints fall into five categories: poor model specification, inadequate fit reporting, ignored assumptions, missing measurement invariance testing, and over-reliance on modification indices.
Mistake 1: Running SEM Without CFA First
If you jump straight to your structural model without first confirming that your measurement model works, reviewers will flag it immediately. Always run a confirmatory factor analysis (CFA) first. Check that each factor has adequate loadings (standardised loadings above 0.50 as a general guideline), that your model fits the data acceptably, and that discriminant validity holds (AVE should exceed the squared correlations between factors). Only after your measurement model is solid should you add structural paths.
Mistake 2: Reporting Only CFI and Ignoring the Rest
Many researchers report CFI above 0.90 and call it good fit. Reviewers expect a comprehensive set: Chi-square with degrees of freedom and p-value, CFI (above 0.95 preferred), TLI (above 0.95), RMSEA with 90 percent confidence interval (below 0.06 preferred), and SRMR (below 0.08). If your CFI is 0.93, your TLI is 0.89, and your RMSEA is 0.09 — that is not acceptable fit, regardless of what one cherry-picked index suggests. Report all indices and interpret them together.
Mistake 3: Blindly Following Modification Indices
AMOS and lavaan both provide modification indices that suggest adding paths to improve fit. The temptation to add error covariances until the model fits is strong — and it is the fastest way to get rejected. Every modification must have a theoretical justification. Adding a covariance between error terms of items from different factors just because the MI is high turns your confirmatory model into an exploratory one. If you need more than two or three theoretically justified modifications, your model probably needs to be reconsidered from the ground up.
Mistake 4: Skipping Measurement Invariance
If you compare groups (male versus female, pre versus post, different cultures), you must first demonstrate that your measurement model works equally across groups. This means testing configural, metric, and scalar invariance in sequence. Without scalar invariance, comparing latent means across groups is meaningless. Many researchers skip this entirely and go straight to multi-group comparison — reviewers who know SEM will catch this every time.
How We Fix These Problems
At Future House Academy, we specialise in SEM analysis. We review your model specification, check assumptions, run proper CFA before structural modelling, test measurement invariance when needed, and report results in full APA 7 format. If your paper has been rejected due to SEM issues, send us the reviewer comments — we have helped researchers revise and resubmit successfully. We work with AMOS, lavaan in R, and Mplus, and we explain every decision we make so you learn for your next project.