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Hello, I am doing multilevel modeling on a complex survey data with following variables bmi age sex education setting psu strata pweight bmi is a continuous outcome variable; age is a continuous explanatory variable at level 1; sex is a categorical explanatory variable at level 1; education is a ordered explanatory variable at level 1; setting (urban or rural) is a categorical explanatory variable at level 2 (PSU level); psu is clustering in survey design ( I am also using psu for grouping that means psu is level 2); strata is stratification; pweight is weights TITLE: Multilevel modeling with sampling characteristics of complex survey data. DATA: FILE = met.dat; VARIABLE: NAMES = bmi age sex education setting psu strata pweight; CLUSTER = psu; STRATIFICATION= strata; WEIGHT=pweights; WITHIN= age sex education; BETWEEN= setting; ANALYSIS: TYPE = COMPLEX TWOLEVEL; MODEL: %WITHIN% bmi ON age sex education; %BETWEEN% bmi ON setting; Are the input commends correct? will this model correctly take account of sampling characteristics and multilevel modeling with psu as level 2. Finally outcome I am looking for is estimates, SE and p value for fixed and random effect. I am also looking for variance by psu and residual variance. how can i get these outcomes. regards masood |
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It looks correct. Any sampling characteristics you have mentioned are taken into account. You should get the variance for bmi on the between level as the default. |
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Thanks Linda for your prompt reply. |
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Thanks again Linda for prompt reply on my last post,here is one more question I am doing MLM with following variables bmi age sex education setting psu. individual is Level 1 PSU is level 2 or grouping variable bmi is a continuous outcome variable; age is a continuous explanatory variable at level 1; sex is a categorical explanatory variable at level 1; education is a ordered explanatory variable at level 1; setting (urban or rural) is a categorical explanatory variable at level 2 (PSU level); TITLE: Multilevel modeling DATA: FILE = bmi.dat; VARIABLE: NAMES = bmi age sex education setting psu; CLUSTER = psu; WITHIN= age sex education; BETWEEN= setting; ANALYSIS: TYPE = TWOLEVEL; MODEL: %WITHIN% bmi ON age sex education; %BETWEEN% bmi ON setting; Are the input commends correct to see the effect of level 1 and level 2 explanatory variable on outcome variable at level 1? regards masood |
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BMI is not on either the BETWEEN or WITHIN list. It is decomposed into a between and within part. See Slide 48 of the Topic 7 course handout on the website to see exactly how this works. |
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It means these commands are correct? If this model and commends are correct why R and Mplus giving different results. They should give similar results. |
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Your commands look correct. I don't believe that R can do latent variable decomposition of an individual-level variable. The only comparison to R would be the first part of the example where there is an observed variable on within and an observed variable on between. |
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Hi Linda I am trying to generate some simulation data fitted with multilevel complex survey model (COMPLEX TWOLEVEL). That means I am looking for data as it coming from complex survey (sampling weights and clustering). I couldn't find in Mplus how to incorporate weights in it. Please let me know how can I generate this data for my Monte Carlo study. Regards Masood |
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Weights cannot be included in a Monte Carlo study using Mplus. |
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Therefore, I can only generate data for multilevel Monte Carlo study without weights? Any Suggestion which software can generate weighted Monte Carlo data. Can I bring that data to Mplus for COMPLEX TWOLEVEL analysis. I appreciate your help Thanks Masood |
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I do not know of any software that generates weighted data. |
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Mohd Masood posted on Wednesday, November 07, 2012 - 6:58 pm
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I am planning for a simulation analysis for multilevel complex data. As we have discussed that Mplus can not generate data with weights. Now I am planning to generate a population and doing a complex sampling from that. Please guide me how can I generate a population fitting a multilevel Model. Here are the parameters and variables for my population Total population= 100,000 Number of PSU= 200 Mean PSU size= 500 Variance in outcome variable due to PSU= 20% Outcome variable (continuous)eg BMI Mean=A, SD=B Level 1 Predictor 1 (dichotomous) eg Gender Proportion= Male:Female Level 1 Predictor 2 (categorical) eg education Proportion=Primary:Secondery:University Level 1 Predictor 3 (Continuous) Eg income Mean=G, SD=H Level 1 Predictor 4 (Continuous) eg Age Mean=I, SD=J Level 2 Predictor 1 (categorical) eg Rural PSU/urban PSU Proportion=Rural:Urnan Level 2 Predictor 2 (continuous) eg number of Gymnasium in that PSU. Mean=M,SD=N Please guide me how can I generate this population. Kindest Regards Masood |
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See Example 12.4 and the Monte Carlo counterpart examples to the examples in Chapter 9. The CSIZES and NCSIZES options are what are used to generate the clustered data. |
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Mohd Masood posted on Monday, December 03, 2012 - 8:52 pm
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I am generating the data using example 12.4 but I am not sure how can I save this data. Please let me know how can I save this data in a excel or spss or stata file as i want to analyze this data in R. Thanks |
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See the SAVE option in the MONTECARLO command. |
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Mohd Masood posted on Wednesday, December 12, 2012 - 12:42 am
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I am working on the example 12.4. I do get the explanation for the following codes MONTECARLO: NAMES ARE y1-y4 x w; NOBSERVATIONS = 1000; NREPS = 500; SEED = 58459; CUTPOINTS = x (1) w (0); MISSING = y1-y4; NCSIZES = 3; CSIZES = 40 (5) 50 (10) 20 (15); WITHIN = x; BETWEEN = w; But don't get the explanations for the following codes. Please let me know where i can get the explanations for them. MODEL POPULATION: %WITHIN% x@1; iw sw | y1@0 y2@1 y3@2 y4@3; y1-y4*.5; iw ON x*1; sw ON x*.25; iw*1; sw*.2; %BETWEEN% w@1; ib sb | y1@0 y2@1 y3@2 y4@3; y1-y4@0; ib ON w*.5; sb ON w*.25; [ib*1 sb*.5]; ib*.2; sb*.1; MODEL MISSING: [y1-y4@-1]; y1 ON x*.4; y2 ON x*.8; y3 ON x*1.6; y4 ON x*3.2; Thanks |
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MODEL POPULATION is described in Example 12.1. MODEL MISSING is described in Example 12.8. |
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Mohd Masood posted on Sunday, January 06, 2013 - 6:10 am
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Dear Linda Thanks for your prompt replies Please let me know how can i set ICC in outcome variable in example 12.4. I mean i want my intercept model give me cluster variance/ residual variance=0.2 Thanks |
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I don't know what you mean by your "intercept" model. You have a growth model with an intercept growth factor and a slope growth factor. The ICC for the outcome variable changes over the time points. As always ICC = (between variance)/(between var+ within var). |
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Mohd Masood posted on Sunday, January 06, 2013 - 9:03 pm
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In example 12.4 in MODEL POPULATION: %WITHIN% x@1; iw sw | y1@0 y2@1 y3@2 y4@3; y1-y4*.5; iw ON x*1; sw ON x*.25; iw*1; sw*.2; %BETWEEN% w@1; ib sb | y1@0 y2@1 y3@2 y4@3; y1-y4@0; ib ON w*.5; sb ON w*.25; [ib*1 sb*.5]; ib*.2; sb*.1; My questions are what y1-y4*1 is stands for in %WITHIN% and what y1-y4@0 is stands for in %BETWEEN%. |
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y1-y4*.5; and y1-y4@0; refer to residual variances. |
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Mohd Masood posted on Wednesday, May 08, 2013 - 4:35 am
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Dear Muthans I want to do bootstrapping for complex survey data but I want to analysis the data generated by bootstrap as multilevel model. Please advice how can I run that. |
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Bootstrap is not available in Mplus for TYPE=COMPLEX or TWOLEVEL. |
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