The RMSEA  table can be found under: View → Text Output → Model fit → RMSEA







LO 90

HI 90


Default model





Independence model







RMSEA = Root Mean Square Error of Approximation is a goodness-of-fit index in SEM that assesses the discrepancy between the observed covariance matrix and the model-implied covariance matrix, taking into account model complexity.




The smaller the RMSEA value, the better the model fit.


RMSEA values higher than 0,1 are considered poor,

RMSEA values between 0,08 and 0,1 are considered borderline, values ranging from 0,05 to 0,08 are considered acceptable,

and RMSEA values ≤ 0,05 are considered excellent

MacCallum et al, 1996

an RMSEA value below 0,05 indicates good fit,

a RMSEA value between 0,05 and 0,08 indicates acceptable fit,

and a RMSEA value above 0,10 indicates poor fit

Browne & Cudeck, 1993;

Hu & Bentler, 1999;

Kline, 2016

0,05 ≤ RMSEA ≤ 0,08 are considered acceptable,

Browne, Cudeck, 1993, S.144

RMSEA < 0,1 are considered acceptable,

RMSEA < 0,06 are considered excellent

Hu and Bentler, 1999




LO 90 = 0,010 Lower boundary (RmseaLo) of a 90% confidence interval of the RMSEA.

HI 90 = 0,050 Higher boundary (RmseaHi) of a 90% confidence interval of the RMSEA.



PCLOSE is a test of close fit for the model in structural equation modeling (SEM). It is typically used in conjunction with other fit indices, such as the root mean square error of approximation (RMSEA) and comparative fit index (CFI), to evaluate model fit.

There is no single agreed-upon reference value for PCLOSE. However, some researchers have suggested using a cutoff value of 0,05, meaning that if PCLOSE is greater than 0,05, the model is considered to have close fit. Others have suggested using a more stringent cutoff of 0,01 or 0,001.

It is worth noting that the appropriateness of using PCLOSE as a test of close fit has been called into question by some researchers, as it is sensitive to sample size and model complexity. Instead, some researchers recommend using multiple fit indices, along with other model evaluation techniques such as modification indices and residual plots, to thoroughly evaluate model fit.

PCLOSE  = 0,556 P-value of the null hypothesis

The obtained RMSEA values for this model is RMSEA = 0,020 and show that this model is adequate. From the table above, we see that the root mean square error of approximation RMSEA for the given model is between 0,010 and 0,050 with a confidence level of approximately 90 percent, which represents an excellent fit of the model. PCLOSE = 0,556 so it is greater than 0.05 and the model is considered to have close fit.

Cite this article in your research paper:

Statistische Beratung Leonardo Miljko (datum) How to interpret SEM model fit results in AMOS. Retrieved from https://www.StatistischeDatenAnalyse.de/images/services/How_to_interpret_SEM_model_fit_results_in_AMOS.pdf .


Statistische Beratung Leonardo Miljko  January 10, 2020 How to interpret SEM model fit results in AMOS. viewed datum < https://www.StatistischeDatenAnalyse.de/images/services/How_to_interpret_SEM_model_fit_results_in_AMOS.pdf >


Wichtiger Hinweis: Der Originalinhalt ist auf Kroatisch. Die Übersetzung ins Deutsche und Englische erfolgte über einen Web-Übersetzer. Wir entschuldigen uns für die Fehler.


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