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| Anonymous posted on Monday, August 15, 2005 - 3:13 am
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Hi there,
I'm running a CFA using the WLSMV-estimator, because some of my indicators are censored, the others are continous.
Now my problems: Using ONLY the censored indicators, the model is estimated normally, but there are 999.00's as standardized output (StdXY). How can I interprete the size of the factor loadings?
Using the censored and the continous indicators in the same analysis, the estimation doesn's converge. I've tried it very often with several datasets, but I get no result.
How can I resolve that problems?
(I'm using MPlus version 3.11)
Thank you! |
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| bmuthen posted on Monday, August 15, 2005 - 9:40 am
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999's indicate that the standardized value could not be computed, typically because of a non-positive variance (see your output). If that is not the case, please email support@statmodel.com.
With a censored indicator, a factor loading can be interpreted in terms of how strongly a factor influences the (uncensored) latent response variable underlying the censored indicator. The relationship between the factor and the indicator itself is a non-linear function - see the literature on censored outcomes and "Tobit" regression.
For non-convergence problems, please contact support@statmodel.com. |
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| Dustin posted on Tuesday, October 04, 2005 - 10:41 am
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We are running a CFA across nine waves of longitudinal data to test for metric invariance using a WLSMV estimator (n=500). In conducting the two-step chi-square difference test in Mplus 3, we are finding that the model with factor loadings constrained to equivalence across the nine time points produces a worse fitting model in comparison to the model with freed loading; however the magnitude of the difference seems relatively small, chi square =61.413, df = 41, p=.021. In addition, the CFI, TLI, and RMSEA all improve for the constrained model, in comparison to the unconstrained model (although not by much).
Questions:
1) Is it possible to have a worse fitting model in terms of chi-square difference testing with WLSMV, but "better" fit in terms of CFI, TLI, RMSEA. It seems counter intuitive and I am worried about publishing the finding (reference in this regard?).
2) In terms of interpretation, with a large sample it seems that the chi-square difference test for metric invariance over time may be overly sensitive. Has anyone written on the meaningfulness of using a chi-squared difference test to as an indicator of the invariance of a construct over time? Should other factors be taken into account with large samples (e.g., change in other fit statistics).
Thanks
Dustin |
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| Anonymous posted on Thursday, October 06, 2005 - 3:04 pm
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Hi,
I have a related question. I have data on several hundred respondents on several questionnaires at multiple time points. I would like to examine the longitudinal invariance across time. For multiple group CFA, I would check configural invariance, then constrain factor loadings, etc. For the longitudinal data I would first look at the structure at each time point separately. Then would I just constrain factor loadings across the time points? Or should I be doing something else due to the data being dependent (same respondents across all time points)? |
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| bmuthen posted on Saturday, October 08, 2005 - 12:03 pm
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Regarding Dustin's question,
1)This is possible. For example, CFI tests against an uncorrelated variables model whereas chi square tests against an unrestricted model. I have no references on this.
2) Yes, chi-square difference testing can point out significant differences that are not of practical importance. I think one just has to decide what is practically important. |
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| bmuthen posted on Saturday, October 08, 2005 - 12:06 pm
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| After having examined each time point separately, I would analyze all time points in 2 ways: with measurement invariance across all time points and without it. We discuss this topic in our Nov courses. |
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Hi
Sorry, if this is a ridiculously silly question. I need to run a completely unconstrained MGCFA (so without the default factor loadings being specified as equal). What code do I put in the model command to specify this.
Many thanks |
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This is shown in Chapter 13 of the Mplus User's Guide. Basically, it is as follows:
MODEL: f BY y1-y4;
MODEL male: f BY y2-y4;
You don't want to free the first factor loading if you are using that to set the metric. |
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We are using Mplus 3.0 to run a CFA on ordinal data using the WLSMV estimator with an approximate sample size of 600. Our variables are 4-category Likert scale items and some have a very small number of affirmative responses (anything other than “no”). We are concerned that these low base-rate items may be causing problems and/or assumption violations within our analysis. So...
a. Is there a rule of thumb for the % of affirmative responses (across all categories) to retain an item in the analysis? For example, would you keep or remove an item if 96% of the sample says “no” and the remaining 4% all choose some form of affirmative response?
b. Similarly, is there a minimum percentage (i.e, 1%, 5%, etc) of response per category for item retention? We have some variables where about 1% or less falls into a particular category, but each of the remaining categories seem to have a sufficient number of responses.
c. In any of these cases, would it make more sense to dichotomize or truncate categories to get a higher response level?
Thanks for your help.
Mark |
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The major issue here is the bivariate tables. If there are zero cells, this can be problematic. So you should look at your bivariate tables and collapse categories if needed to avoid zero cells.
By the way, you should download Version 3.13. |
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| Mark posted on Friday, December 16, 2005 - 5:57 am
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Linda,
Thanks for you reply. Regarding your suggestion to look for zero cells in the bivariate tables, we are conducting this analysis on approximately 40 variables (some with different scales). Clearly we will encounter some (I dare guess a lot) zero cells in these tables. If these are encountered, how should they be handeled. I am assuming that categories need to be truncated to eliminate all zero cells.
I am particularly concerned given that some of our items are very low base rate (conduct disorder symptoms), while others have a full range across categories (ADHD symptoms). As a result, I am worried we will have to truncate more evenly distributed items causing a significant lose of information.
Lastly, how bad is the problem of zeros cells. Are a few o.K. or is the presence of any completely unacceptable, producing significant bias in parameter estimation?
Sorry for the longwindedness of the reply. |
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Some cells can be zero. The problematic situations are where the zeroes are clustered in the upper or lower corners of the bivariate tables.
You can start off your analysis with the original variables. You will receive a message if there are problems in estimation due to zero cells. You can then collapse accordingly. |
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Linda,
Thanks for your help. I just have one final round of questions (I hope)... We have not received any errors about zero cells and are finding that our low base-rate items (i.e., less than 5% anything other than "never") either have counter-intuitive loadings, no loading, or cross-loading. This seems somewhat logical since the items have little or no variability. Based on this, would there be any justification in removing such items since these items seem to produce unstable results? We are somewhat concerned that these patterns are an artifact of the low base-rate items and not a "true" representation of the factor structure.
Thanks again,
Mark |
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Rather than looking at the percent, you should be looking at the number of observations that percent corresponds to. If you have a small sample, 5% could be a problem. With a large sample, it may not be. You may find it useful to read the following article which discusses these issues:
Muthén, B. (1989). Dichotomous factor analysis of symptom data. In Eaton & Bohrnstedt (Eds.), Latent Variable Models for Dichotomous Outcomes: Analysis of Data from the Epidemiological Catchment Area Program (pp. 19-65), a special issue of Sociological Methods & Research, 18, 19-65. (#21)
If you can't find the paper, you can request it from bmuthen@ucla.edu. It is paper 21. |
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| Derek Kosty posted on Monday, October 06, 2008 - 10:34 am
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Hi,
It is my understanding that the WLSMV estimator simultaneously uses the tetrachoric correlation and asymptotic covariance matrices (ACM). Examining the tetrachoric correlations alone, I was struck by the pattern of associations that appear to support one model over the other which wasn't consistent with the generated fit statistics for the models. I was hoping to clear this up by looking at the ACM. Is it possible to request the ACM to be produced in the output?
-Derek |
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| No, this is not possible. |
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Greetings,
I would like to know which of the Mplus estimators corresponds to the DWLS estimator described (and studied) in the recent Forero, Maydeu-Olivares & Gallardo-Pujol paper (that just came out in Structural Equation Modeling (2009, 16, 4, 625-641).
From what I get, it should be either the WLSMV or the WLSM
Thank you in advance |
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| We have not yet received that journal. When we do, we will look at it and get back to you. |
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Greetings,
Please, dont forget this question in the huge amount you are receiving...
"I would like to know which of the Mplus estimators corresponds to the DWLS estimator described (and studied) in the recent Forero, Maydeu-Olivares & Gallardo-Pujol paper (that just came out in Structural Equation Modeling (2009, 16, 4, 625-641).
From what I get, it should be either the WLSMV or the WLSM"
Thanks |
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| WLSM and WLSMV have the same parameter estimates and standard errors. It is chi-square that differs. In the article, only parameter estimates and chi-square are examined so it could be either WLSM or WLSMV. |
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Hi Linda,
Not sure I follow you. You say that the chi-square differs from WLSM to WLSMV. Then you say that the article examine parameters estimates AND chi square so it could apply to both...
In fact, what you are saying is that the paper conclusions apply to both estimators ? Is that it?
In a related way, what is the "practical" difference between WLSM (mean-adjusted) and WLSMV (mean and variance adjusted). In other words, are there context in which it would be preferable to use WLSM rather than WLSMV ?
Thanks again. |
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I misspoke. The article examines parameter estimates and standard errors. So it could be WLSM or WLSMV.
I don't think it is clear when WLSM is preferable to WLSMV. |
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| Erika Wolf posted on Wednesday, March 03, 2010 - 7:24 am
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As I understand it, when using WLSMV with both continuous and dichotomous variables, the regression coefficients in the model involving dichotomous dependent variables are probit estimates and the coefficients with continuous dependent variables are linear regression coefficients--is this correct?
So then, when I look at the sample statistics reported on the output, are the sample correlations involving a dichotomous variable tetrachoric correlations while those involving 2 continuous variables are simple Pearson correlations?
Thanks for clarifying. |
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| Yes to both questions. One other fact, on the diagonal are variances of the continuous variables. |
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Hi,
I guess the run of questions for version 6 is on.
The WLSMV estimator has been changed in version 6, yielding (as it is noted on the website) df that are more "traditional". Which is good.
However, I tried running some models (for wich I already have outputs from previous versions) and found out that the fit indices also changed with the new version. So I am wondering:
1) What to make of this ?
2) What changed ?
3) How will this affect the suggested cut off scores for invariance testing with WLSMV (Yu 2002) ?
In an unrelated way: how do I make Mplus the default "opener" for .out and .inp files ?
Thaks a lot. |
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A new chi-2 approximation is used. A technical description will be posted shortly under Technical Appendices (several others will also appear shortly). Because the other fit indices are based on chi-2, they are also affected. Our simulation studies with the new chi-2 indicate, however, that the chi-2 results are very similar between this new approach and the earlier one. I would therefore use the same cutoffs as before.
I will pass on your last question. |
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| The editor can't be changed to have out show up instead of inp. |
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| Jon Elhai posted on Tuesday, April 27, 2010 - 6:33 pm
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| Alexandre: Are you asking how to arrange for a .inp file to automatically open in Mplus (as opposed to Windows not knowing what application to use)? Locate a .inp file, right click on it, select "open with" and then "choose program". Then select "Mplus" and click "Always use selected program" in the checkbox at the bottom. |
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Hi all, thanks for the answers,
Jon: Yes and thanks (I needed to restart).
Bengt: when you say that "chi-2 results are very similar between this new approach and the earlier one", you mean that cut off scores on the "fit indices" (RMSEA, CFI) when doing invariance testing are similar with the new aproach ? Or you are just assuming that since chi-2 works the same way fit indices also should ? In the first case, this is perfect, in the second one, hopefully these posts will motivate someone to test it :-) |
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| My prior is that fit indices won't be affected much because chi-2 isn't affected much. Yes, there are many simulation studies that would be of interest and made possible by this new release. |
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Dear Dr. Muthén,
I am running a CFA analysis on items. The response option is 1-5. However, I have noticed the serious deviation from normal distribution in items (floor effect), and decided to treat them as ordinal indicators. Is that correct?
Therefore I have performed WLSMV estimation in two independent samples. However the df value is different in both analyses although the model is the same. I know that calculation of df is not straightforward in WLSMV, however can the same model have different df value in different samples? If yes, can you give me a reference for it?
Thanks,
Robert |
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Yes, it is correct to treat 5-category variables with a floor effect as categorical rather than continuous.
You must not be using Version 6. Prior to Version 6 the same model could have different degrees of freedom. I suggest downloading Version 6. See Simple second order chi-square correction under Technical Appendices on the website for further information. |
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| Thank you very much. It works! Version 6 solved the problem. |
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| Just to clarify, it was not a problem. We just use a different method in Version 6 than in earlier versions. In Version 6, the chi-square value and degrees of freedom are interpretable in relation to the p-value. In earlier versions, the chi-square and degrees of freedom were adjusted to obtain a correct p-value. In both cases, difference testing must be done using the DIFFTEST option. |
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| Jon Elhai posted on Monday, June 21, 2010 - 12:35 pm
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| Linda - you said in your previous email "In Version 6, the chi-square value and degrees of freedom are interpretable in relation to the p-value." Is this the case for difference testing using DIFFTEST in Version 6? |
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| I'm not sure I understand your question. The DIFFTEST option must still be used to test nested models when using WLSMV. |
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| Jon Elhai posted on Monday, June 21, 2010 - 4:14 pm
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| To clarify my question... Prior to Version 6, only the p value was interpretable in the difference test printed in DIFFTEST's output. Now, we can report the chi-square and df that is in DIFFTEST's output? |
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| Yes. See the Technical Appendix entitled Simple second order chi-square correction which is available on the website. |
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