Mplus modification indices syntax

OUTPUT: MOD provided me with the modification indicies for BY statement only.

I am trying to fit a model with one latent factor that was measured by 10 items.
First, I did a CFA with these 10 items being indicators of my latent factor (without parcels). However, the fit of the model was not good. I then looked at modification indices and ended up adding 9 WITH-statements which did result in a good fit(item2 with item 6, . ), but I'm asking myself if this makes any sense? Is this a common finding?
I also tried to remedy the bad fit of my model by using item parcels, with result in very good fit indices, but then my structural paths were no longer significant, whereas these paths were significant in the adjusted model with the WITH-statements.
Any ideas on how to solve this problem? Or is my model just not good enough?

I am trying to run measurement model.

Is there any commend to view all modication indices?
I ran my measurement model with several latent variables.
I wrote "mod(all)", then output did not show modification indices across indicators.
In other words, my latent variables, A, B, & C, have three indicators, a1 a2 a3, b1 b2 b3, c1 c2 c3.
But the modification indices showed a1&b1, a1&b2, a1 & c1 and so on.

However, I'd like to see correlated errors within one latent variable such as a1 & a2, a1 & a3, a2 & a3.

What commands can I use instead of "mod(all)"?

However, even though I tried to use that, it does not seem to give what I wanted.

Are there any other reasons for this problem?

If you don't mind, could you have a look at my input commands?

pwb by pposaf pnegaf; pposaf @3;
PLE by psoact ppasset@.5; psoact@.5;
Health by HealPro poars18 poars19 ;
PES by MNEEDS FINEMRG PEXTRAS;
PHY by piadlsum ppadlsum; ppadlsum@3.5;
SS by PSRES02 PSRES01 PSRES03;
pwb with PLE @0; pwb with health @0; pwb with PES @0; pwb with PHY @0; pwb with SS @0; PLE with health@0; PLE with PES @0; PLE with PHY@0; PLE with SS@0;health with PES@0; health with PHY@0; health with SS@0; PES with PHY@0; PES with SS@0; PHY with SS@0;

I am fitting a factor model on 5 items. Instead of adding a residual covariance between item 2 and 4, I want to add an uncorrelated common factor with its variance fixed to 1 and equality constraints on the factor loadings (gives identical fit).

The covariance is negative, so this is the syntax I used:

f2 by v2* (l1) ;
f2 by v4 (l2);

MODEL CONSTRAINT:
l1 = -1 * l2;

Using this syntax however, modification indices are not given, because

"MODINDICES option is not available in conjunction with nonlinear constraints
through the use of MODEL CONSTRAINT. "

Is there a way to still obtain the modification indices in my situation?

In your example syntax, if p is negative, the model implied covariance between the items is still positive (p*p).

Because I want the extra factor instead of a negative residual covariance, lambda21*psi11*lambda41 should be negative. And therefore one of the factorloadings should be negative and the other positive, but of equal size.

Is there an other way to do this then via the MODEL CONSTRAINT command so that i still obtain modification incidices?

This will give a loading fixed at 1 for y1 and a free loading lambda_2 for y2. The residual covariance is then parameterized as lambda_2:

The lambda_2 loading can be negative or positive, so you get full flexibility.

But if you insist on the y1 loading being free as well and holding them equal but with opposite signs, you can use Model Constraint with

Is there a way to get these indices with WLSM estimation or should ML estimation be used instead to get them?

I am new to the Mplus software, and was wondering how the MODINDICES command is scaled. Does the output reflect Modification Indices by fixing the factor variance or by fixing a reference indicator?

In our current project we would like to be able to run multiple models using each possible reference indicator and generate their respective resulting modification indices, following the strategy suggested by Cheung & Rensvold (1999) and Nye, Roberts, Saucier, & Zhou (2008).

Does the modindices already correct for the MLR-estimation? Or does the reported chi-square correspond to the ML-Chi-Square?

And is it correct, that the p-value of the chi�-difference is equivalent to the p-value of the relevant estimate (for ML)?

And if this is true, does it also apply to MLR-estimation?

is it possible, that this (see above) is the result of multicollinearity.

Is the Chi�-difference test to the same extent affected by multicoll. like the SE of the estimate?

What is considered to be an EPC high enough to modify the parameter?

I get the following and am not sure which to consider for modification:

M.I. E.P.C. Std E.P.C. StdYX E.P.C.

25.818 -0.123 -0.106 -0.093

43.591 0.174 0.140 0.146

11.818 0.171 0.171 0.306

41.095 0.109 0.109 0.201

15.937 0.037 0.037 0.123

10.939 -0.046 -0.046 -0.090

Thank you very much

My CFA model turns out nicely I think, with an RMSEA of 0.035 and CFI/TLI of 0.985/0.980. But some MI's turn out to be 999.999. When I exclude an item, this effect is gone and the model is still very similar in other respets. Could you tell be why these very similar model suddenly have not calculable MI's? And how should I judge my model?

Thank you in advance,

I think I have read the MIs are related to chi squared values and hence are affected by sample size? I was just wondering because I have a sample of over 6,500 people. I am testing a CFA (3 factors, each with 6-9 observed indicators) for a new theoretical concept (first explored with an EFA). My chi squared value is understandably large at 5,192 with 227 degrees of freedom, but my model fit indices are very good (CFI/TFI=0.97, RMSEA= 0.55 with tight 90%CI). My observed indicators are ordinal and very skewed so I am using WLSMV estimator.

My questions:
1) I have very large MI values (in just the BY statements I have 15 with values over 100, 5 over 400) but could the size of these values be affected by my sample size? Only 5 of them have StdYX E.P.C greater than 0.3, so potentially not all of them affect the model parameters that much? Or is this saying something counter to my good model fit indices?

2) My WRMR value is 3.707. This seems oddly large? What does that mean?

Typically, if you free a parameter that has a large MI, convergence problems don't occur. If they do, you can request SVALUES in the Output command of the first run which gives the estimates of that run to be used as starting values in the second run where you free the parameter.

If i want to reduce my chi squared because in my modification indices two with statement variables are correlated, how do i do this in the codes??

lets say the MI for
x with Y, and
Y with Z
are larger than 3.84 and correlated theoretically

What is the code to free the parameters in my CFA ?

I am currently using the MODEL CONSTRAINT command to set parameters equal across groups (all variables are observed), but this means I cannot get any modindices to see which parameters "ask to" be set free. is there an alternate way to do this that would allow me to access modindices?

We conducted a CFA, and although the model fit is good, there are some misspecifications in the model as indicated by 'the detection of misspecification�-procedure (Saris, Satorra, & van der Veld, 2009). We can deal with these misspecifications by adding three additional parameters to the model, but this is not preferable as these parameters needs to be added to items that are not in the same factor. Therefore, although the model scores good with respect to the traditional fit indices, the model fit is not good (enough).

We are now thinking about a solution to test whether the model we tested may have a few misspecifications, but regardless of these misspecifications, the tested model is the best possible model. We want to use a permutation test for this (in which we test whether the dividing of the items in the factors in our tested model is significantly better than the dividing of items in any other model), but I'm not sure whether this is possible in Mplus. And if so, how can I use it in mplus?

My model fit is poor and MI's are numerous

What I do not understand is (A) why the following MI's are given
MSE_1 ON RSE_1 /
RSE_1 BY MSE_1 49.837 -1.321 -1.581 -1.581

when RSE_1 on MSE_1 is in the model.

(B) what change(modification) do I need to make in the model for

CP_1 ON AP_1 /
AP_1 BY CP_1 44.125 1.587 1.576 1.576

(c) which modification do I choose first

RSE_1 WITH INTEX1 49.626 0.155 0.867 0.867
RSE_1 WITH MSE_1 49.837 -0.221 -1.285 -1.285

[please note the model had RSE_1 on MSE_1: so why is this indicating a change]

thank you for the prompt reply. Sorry, can I ask a clarification here. In the case of RSE_1 on MSE_1 , a parameter was estimated in the model "RSE_1 on MSE_1;"

It is a substantive question. If it is already in the model what modification can I ask for as model2 ?

Your last sentence clarifies what I need to do

Why does Mplus sometimes give me modification indices for things I already have in the model?

For example, if I have specified:

Why will it sometimes give me a mod index that suggests that adding
F1 BY A
will improve the fit?

Does this especially happen in invariance models?

I am ran a CFA for the same measure between two groups (males & femaleS). Both CFAs did not fit the data well (RMSEA = .10, CFI = .90), and the fit improved after covarying two residuals.

My question is, when proceeding to test measurement invariance, what should we do with the modifications we applied to the measurement models? If we are to proceed with the modifications in the tests of measurement invariance, should we constrain the residual covariances to be equal between both groups?

What is being regressed on what in this case?