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Yan Li posted on Friday, December 08, 2006 - 9:47 am
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I was trying to use both "estimator=MLM" and "type=missing H1" at the same time and I got this error message: " Estimator MLM is not allowed with TYPE = GENERAL MISSING.Default will be used." Can you let me know what's going on? Thank you! Below is my syntax: ... Usevar are tr1-tr7 tr16 tr17 pn9 pn10 pn12 tr8-tr11 pn6-pn8; MISSING IS BLANK; analysis: type=missing H1; estimator=mlm; model: SA by tr1-tr7 tr16 tr17 pn9 pn10 pn12; OA by tr8-tr11 pn6-pn8; SA with OA; output: stand;sampstat; mod (20); |
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We no longer allow MLM and MISSING. We recommend MLR and MISSING instead if you want a robust estimator. |
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Yan Li posted on Monday, December 11, 2006 - 8:38 pm
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Thank you Dr. Muthen. Here is a follow up question. I want to do a model comparison for nested and comparison models. I read your guidance about "Difference Testing Using the Loglikelihood" on the website. One thing confuses me is that I got L0 and L1 for both models (2 L0 and 2 L1 values) and I don't know which L0/L1 to plug into the formula (TRd = -2*(L0 - L1)/cd). (2) Also does the parameters p0/p1 refer to the # of free parameters in the output? (3) At last, when we have the TRd, shall I look at the chi-square table using df difference (nested df-comparison df) and check out the p value? I appreciate any help you can give. Thank you! |
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1. L0 is the more restrictive model. L1 is the less restrictive model. You want the H0 loglikelihood from L0 and L1. The H1 loglikelihoods are not needed. 2. Yes. 3. Yes. |
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RuoShui posted on Sunday, September 29, 2013 - 10:25 pm
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What estimator should I use if I have data that has high skewness and kurtosis? I heard that MLM is the one to choose. Is this correct? Thank you! |
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Both MLM and MLR are robust to non-normality. With MLM, listwise deletion of any observation with a missing value on one or more analysis variables is used. MLR uses all available information. |
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RuoShui posted on Monday, September 30, 2013 - 7:02 am
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I see. Thank you so much. I wonder, if I have categorical indicators as well as continuous indicators with skewness and kurtosis issues, should I still use MLR? Or do I need to use WLSMV? Thank you. |
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WLSMV is not robust to non-normality for continuous variables. You can use MLR with a combination of continuous and categorical indicators. Factors with categorical indicators require numerical integration so we recommend not too many of these factors. |
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Under similar circumstances listed by RuoShui above (a model with continuous predictor variables, one of which exhibits kurtosis, and a categorical outcome variable), is MLR still appropriate if the categorical variable is in fact binary? |
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In regression, the model is estimated conditioned on the observed exogenous variables. No distributional assumptions are made about them. You can use WLSMV or MLR with a binary dependent variable. Distributional assumptions are made about only continuous dependent variables. |
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