Table of Contents

## What is Pclose in Amos?

PCLOSE provides the p-value of the null hypothesis that the estimate (0.033) is below 0.05. This is clearly not approaching significance – you can therefore not reject the null hypothesis that your RMSEA is below 0.05 – which is a good thing. (Note: I don’t know what value AMOS uses -I’m guessing 0.05).

### What is acceptable RMSEA?

It has been suggested that RMSEA values less than 0.05 are good, values between 0.05 and 0.08 are acceptable, values between 0.08 and 0.1 are marginal, and values greater than 0.1 are poor [8].

#### What is a good RMSEA in SEM?

RMSEA: The Root Mean Square Error of Approximation is a parsimony-adjusted index. Values closer to 0 represent a good fit. It should be < . 08 or < .

**What is Pclose in statistics?**

p of Close Fit (PCLOSE) This measure is a one-sided test of the null hypothesis is that the RMSEA equals . 05, what is called a close-fitting model. Such a model has specification error, but “not very much” specification error. The alternative, one-sided hypothesis is that the RMSEA is greater than 0.05.

**What is Pclose?**

DESCRIPTION. The pclose() function closes a stream that was opened by popen(), waits for the command to terminate, and returns the termination status of the process that was running the command language interpreter.

## What is the default model in Amos?

The AMOS output will report results for three models: the model you designed (also known as the default or proposed model); the independence (or null) model, which says that each measured variable is correlated exactly 0.0 with each other measured variable (with no latent constructs) and thus usually produces results …

### What is squared multiple correlation in SEM?

Squared multiple correlation (R) is called the coefficient of determination which is defined as the proportion of the total variation explained by the model. If R squared is 3.98 then R is 2 which is impossible, R being a correlation. The computation cannot be correct and should be revisited.

#### What does CMIN DF mean?

CMIN/DF = chi-square fit statistics/degree of freedom; GFI = goodness-of-fit index; AGFI = adjusted goodness of fit index; NFI = normed fix index; RFI = relative fit index; IFI = incremental fix index; TLI = Tucker-Lewis index; CFI = comparative fix index; RMSEA = root mean square error of approximation; RMR = root …

**What is normed chi-square?**

The relative chi-square is also called the normed chi-square. This value equals the chi-square index divided by the degrees of freedom. This index might be less sensitive to sample size.

**How do you read RMSEA?**

RMSEA is the root mean square error of approximation (values of 0.01, 0.05 and 0.08 indicate excellent, good and mediocre fit respectively, some go up to 0.10 for mediocre). In Mplus, you also obtain a p-value of close fit, that the RMSEA < 0.05.

## What is a good CFI in SEM?

06 and a CFI and TLI larger than . 95 indicate relatively good model–data fit in general. Hu and Bentler’s study has become highly influential, and their recommended cutoffs have been adopted in many SEM practices.

### How to test RMSEA with pclose?

For instance, if you want to test the one-sided that that RMSEA is greater than 0.05 (what is tested with pClose) with .05 alpha, you would examine the 90 percent confidence interval of the RMSEA and note whether the obtained RMSEA exceeds the lower bound.

#### Is RMSEA a close-fitting model?

This measure is a one-sided test of the null hypothesis is that the RMSEA equals .05, what is called a close-fitting model. Such a model has specification error, but “not very much” specification error.

**Does the RMSEA have specification error?**

Such a model has specification error, but “not very much” specification error. The alternative, one-sided hypothesis is that the RMSEA is greater than 0.05. So if the pis greater than .05 (i.e., not statistically significant), then it is concluded that the fit of the model is “close.”

**When does Amos fail to fit a model?**

For example, Amos will very likely fail to fit the saturated model if there is a single measured variable with very few measurements, even if the dataset as a whole is mostly complete. Below is a list of fit measures, with explanations of when Amos reports each one.