Structural Equation Modeling (SEM) is a statistical technique used for modeling complex relationships between observed and latent variables. The range of acceptable values in SEM depends on the specific parameters and indicators being estimated. Here are some considerations regarding the acceptable values in SEM:

1. Model fit indices: SEM models are typically evaluated using various fit indices that assess how well the model fits the observed data. Common fit indices include the chi-square test, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). Acceptable values for these indices vary across disciplines and depend on the context and complexity of the model. In general, lower values for the chi-square test, RMSEA, and SRMR, and higher values for CFI and TLI indicate better model fit. However, specific thresholds for acceptability can differ based on established guidelines or the researcher's judgment.

2. Standardized parameter estimates: In SEM, parameter estimates represent the strength and direction of relationships between variables. These estimates should ideally be statistically significant and consistent with theoretical expectations. The acceptable range for parameter estimates depends on the specific research context and the effect sizes being examined. It is common to interpret estimates with absolute values greater than 0.1 or 0.2 as indicating moderate to strong relationships.

3. Reliability and validity indicators: In SEM, researchers often assess the reliability and validity of latent variables using indicators such as Cronbach's alpha, composite reliability, Average Variance Extracted (AVE), and factor loadings. Acceptable values for these indicators depend on the field of study and established guidelines. For example, Cronbach's alpha values above 0.7 are often considered acceptable for reliability, and factor loadings above 0.3 or 0.4 are typically deemed acceptable.

4. Residuals and error terms: Residuals and error terms in SEM should ideally follow a normal distribution and exhibit homoscedasticity (constant variance). Assessments such as examining the skewness and kurtosis of residuals, examining scatterplots of standardized residuals against predicted values, or conducting tests of normality can help determine if the assumptions are met. Departures from normality or heteroscedasticity may suggest issues with the model's fit.

It is important to note that the range of acceptable values in SEM can vary depending on the specific research field, the complexity of the model, the data characteristics, and the research question being addressed. Additionally, different guidelines or recommendations may exist within specific disciplines or domains. Researchers should consider established guidelines, consult relevant literature, and exercise their judgment when evaluating the acceptability of values in SEM.

The range of acceptable values of structural equation modeling (SEM) depends on the specific fit index being used. However, in general, the following values are considered to be acceptable:

• Root Mean Square Error of Approximation (RMSEA): 0.05 or less
• Comparative Fit Index (CFI): 0.95 or more
• Tucker-Lewis Index (TLI): 0.90 or more
• Goodness-of-Fit Index (GFI): 0.90 or more
• Normed Fit Index (NFI): 0.90 or more

Please bear in mind that although these values can provide general guidance, the suitability of a model's fit depends on several factors, including the specific research question, sample size, and model complexity. Furthermore, certain journals may enforce specific criteria or requirements concerning acceptable values for fit indices in SEM. Therefore, it is crucial to take into account these contextual factors as well as any pertinent guidelines or standards when assessing the fit of an SEM model.

Here are some journal papers that discuss the range of acceptable values for SEM fit indices:

• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.
• Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). New York, NY: Guilford Press.
• Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York, NY: Routledge.

I hope this helps!