I would like to fit an SEM model to a set of ordered categorical variables that have L or J shaped distribution. That is, the assumption of the latent variable be normally distributed is not valid and there are some missing (up to 2 percent) values.
1. Can the Mplus handle it properly 2. Can it accommodate formative construct.
Factors with categorical factor indicators are not necessarily non-normal.
1. Yes. 2. Yes.
hai hong li posted on Tuesday, August 01, 2006 - 2:03 pm
You have written that" Factors with categorical factor indicators are not necessarily non-normal". So if I have five point Likert scales which are extremely skewed (L or J shape), for analysis of the data, you do not make the assumption that the underlying latent variable is normally distributed. That is, you do not substitute polychoric correlations instead of the covariance to analyze the data.
There are 2 things here. First, the fact that your Likert scale items are skewed does not mean that the factor must be skewed. Take the example of very extreme attitude items where most people disagree. This gives skewed items, but the factor may still be normal. The observed non-normality may simply be due to extremeness of the item wording.
Second, the default assumption in Mplus is that the factor is normal. It does not have to be normal, however, if you work with mixture modeling. As for your last sentence, you may be interested in the following statement from our short courses:
Note that by assuming normal factors and using probit links, ML uses the same model as WLSMV. This is because normal factors and probit links result in multivariate normal u* variables. For model estimation, WLSMV uses the limited information of first- and second-order moments, thresholds and sample correlations of the multivariate normal u* variables (tetrachoric, polychoric, and polyserial correlations), whereas ML uses full information from all moments of the data.
Xu, Man posted on Thursday, July 12, 2012 - 11:23 am
Could I please follow up on this lead? If one's latent variable is not normally distributed, regardless of continuous or ordinal items. For example, most psychiatric screening instruments aren't normally distributed and binary cutoff is often applied on sum scores. It is now quite often to apply CFA for these ordinal item level data (either as continuous under RML or as ordinal under WLSMV), and use these latent factor as predictor or outcome in SEM. I was wondering what the implication is for the results based on this kind of measurement model and is there a way to deal with this?
Not sure what your major concern is - perhaps it is that you don't believe your latent variable is normally distributed.
You seem to take a non-normal distribution of a psychiatric screening instrument as an argument that the corresponding latent variable is not normal, but per my discussion above, I don't think that necessarily follows.
For a related, recent article, see
Wall, M. M., Guo, J., & Amemiya, Y. (2012). Mixture factor analysis for approximating a nonnormally distributed continuous latent factor with continuous and dichotomous observed variables. Multivariate Behavioral Research, 47:2, 276-313.
where a non-normal latent variable is obtained using the Mplus mixture approach.
yes, I am worried that the factor is not normally distributed. I checked the data I have (3 instruments). One of them has factor scores normally distributed (as you say). The other two are not. I declared all items to be categorical with the default estimator.
On the item level, I found the instrument has normal distribution has more items that are normally distributed. The two that have got skewed factor score distributions have mostly highly skewed items.
Does it mean that the latent variable approach is not suitable for the other two intruments.
Take a look at slide 117 of out Topic 2 handout and you can see why an observed score can be non-normal at the same time as a latent score being normal.
A latent variable can be normal and still give a non-normal estimated factor score distribution. This is because of items that don't capture the tails of the factor distribution well, for instance too easy or too hard items.
I would guess that the issue you are concerned with is likely of less importance than other aspects of your modeling.
In my ESEM model I try to estimate relationship between two variables: explanatory variable is positevely skewed (likert scale on preference) and dependent variable is negatively skewed (consumption data). I wonder if I need to use log-transfromation to normilze the data or it is enough to use WLSMV - estimator and skip transformations? Another reason for choosing WLSMV-estimator is that I have some other categorical variables in the model. Thanks, Jazgul
I would not transform variables unless that makes the linearity specification more realistic. The MLR estimator handles non-normality, or if you don't want to assume continuous variables, then WLSMV takes care of it.
I was hoping you can help me. I am new to Mplus but I have read many discussions here and also the Mplus manual. However, I am failing to find a way how I can improve my CFA model fit (incremental stats used already).
I am trying to fit 2 factor CFA ( 2 latent variables). I am using factor indicators that were calculated as the means of specific items measured on a likert-scale. Thus the factor indicators are not normally distributed (histogram and normality tests support my assumption).
On the top of the non-normality,my data possesses quite a large number of missing data and imputation method nor transformation did not improve the normality.
I have tried to use WLSMV as well as WLS and getting errors stating that I have no categorical variables present (I can not select the factor indicators as categorical as they are not integers). When I use MLM or MLR I get an error message that I have to use listwise deletion which is impossible due to the amount of missing values.
I have used FIML so far but the model fit is poor when I evaluate all fit indices other than chi-square (TLI= 0.8).
If I have data that is not normally distributed -- reaction time, so likely exGaussian, would it be appropriate to use Mplus 7.2's features for skew and kurtosis to model this? Or are there properties of the distribution that limit the feasibility of applying the new features to such a distribution? Is it possible to predict the amount of skew with an exogenous variable? If so, how?
If you have a continuous DV that is non-normal and without floor or ceiling effects, why don't you try out the new 7.2 features. If you have an exogenous variable, the non-normal specification is on the residual of the DV. This means that the DV can be non-normal due to both a non-normal exogenous variable and a non-normal residual.
Daniel Lee posted on Monday, June 30, 2014 - 9:08 pm
Hi Dr. Muthen,
Thank you for responding to my questions. I appreciate you, and your team, very much! While conducting factor analysis, I had a few questions in mind:
1) Items in my latent variable are scaled differently (ordinal & dichotomous). Could I use WLSMV in this scenario? If so, is there anything else I need to be aware of in terms of conducting my analyses the right way?
2) If I elect to use WLSMV estimation, do I need to conduct a test (Tech13?) for multivariate normality? I would guess no since WLSMV uses a probit function...but I just wanted to make sure!
3) Lastly, are there any diagnostics I should be aware of when conducting a CFA using WLSMV?
Thank you so much Dr. Muthen!!!
Daniel Lee posted on Monday, June 30, 2014 - 9:11 pm
Hi Dr. Muthen,
I'm so sorry, I had one more question that I forgot to include in the previous post!
4) If I conduct a WLSMV, can I still use FIML to treat missingness in data?
4. No. FIML is full-information maximum likelihood. With WLSMV, missing data are handled using pairwise present. If you want FIML, use the ML estimator.
Daniel Lee posted on Tuesday, July 01, 2014 - 11:17 am
Hi Dr. Muthen,
Thank you for the response! Just one question for clarification purposes:
With regards to my first question, if you have categorical and dischotomous manifest variables, wouldn't ML estimation generate biased standard errors? I always thought (I don't remember where I saw this) WLSMV was the way to go when indicators were categorical. Therefore, I'm curious as to why ML might be appropriate in this situation?
Thank you so much! Your responses are always so helpful!