Message/Author 

Zen Goh posted on Wednesday, June 25, 2014  1:59 am



Hi, I'm running a multilevel moderated mediation (1, (1,1), 1) and am checking my sample statistics with SPSS descriptives to ensure my data is read right. My output says the following: ESTIMATED SAMPLE STATISTICS FOR WITHIN Means all variables = 0 ESTIMATED SAMPLE STATISTICS FOR BETWEEN Means W = 0.004 M1 = 2.483 M2 = 2.321 Y = 3.370 X = 0.000 Covariances and Correlations  0 for X with all variables  other values are close to SPSS's output Question 1. Is it normal to get mean = 0 for within? 2. Why am I getting mean = 0 for between for only W and X  these values do not correspond to SPSS's output. Thank you! 


Means are reported on between in multilevel modeling. I doubt that SPSS gives both within and between sample statistics. To compare the programs use TYPE=GENERAL. 

Zen Goh posted on Thursday, June 26, 2014  7:57 pm



Hi, thanks for the quick reply! Yes, SPSS doesn't give within sample statistics I compared only for between sample statistics. I wasn't clear. I also used your suggestion. it says that: TITLE: Workload and Life Satisfaction Multilevel moderated mediation analysis L1: X (wload), Y (lsat), M1 (wfctimestrain), M2 (wfcemo3) L2: W (wfc specificmgsup)  L2 DATA: FILE IS IC_Aggregate_WFCemo and WFCtsb.csv; VARIABLE: NAMES = clust x m1 m2 y w; MISSING ARE ALL (1); USEVARIABLES ARE x m1 m2 y w; ANALYSIS: TYPE = GENERAL; *** WARNING in MODEL command All variables are uncorrelated with all other variables in the model. Check that this is what is intended. 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS 2 problems I found: > The variables are correlated in SPSS but not in Mplus > my Mplus estimates for W are way too high: mean = 6.2 (it's a 5 point scale)and variance is 312. Could it be some problem with the way I saved/formatted the data? I simply saved SPSS as a .csv file. Thank you. 


You may be reading your data incorrectly. I would do a TYPE=BASIC with no MODEL command and compare that to the same in SPSS. I would not complicate things with the multilevel component until I was sure that data are being read correctly. Check the means and sample size. You may have blanks in the data set. SPSS uses blanks as certain types of missing data and this is not allowed with free format data. 

Zen Goh posted on Monday, June 30, 2014  8:25 pm



It seems to have read it right  the output with what you suggested is very close to SPSS output. thanks! 


Then your multilevel results should be correct. I don't believe SPSS does multilevel analysis so the comparison should not be with multilevel results. 

Tyler Burch posted on Friday, October 30, 2015  9:08 am



I have a related issue. I run ANALYSIS: TYPE=BASIC and compare it to the descriptives produced in SPSS. The ESTIMATED SAMPLE STATISTICS and the UNIVARIATE HIGHERORDER MOMENT DESCRIPTIVE STATISTICS means do not match within the MPLUS output. I am not sure why this is. The UNIVARIATE HIGHERORDER MOMENT DESCRIPTIVE STATISTICS descriptive statistics match the SPSS descriptives output perfectly, but I want to make sure that I am not missing something being that the means within the output do not match. 


The univariate in Mplus use only cases without missing data one variable at a time like SPSS does. The other sample statistics are estimated in a multivariate fashion using all available data. 

Ashley Hum posted on Monday, October 21, 2019  10:27 am



Hello, I am using type=complex and weighting, stratification, and cluster procedures. Is there a way to get quartiles for analysis variables when weighting is being used? Thank you. Ashley 


You can use some code like that to get the estimated quartiles. MODEL: [y] (m); y (v); model constraints: new(q1); q1= m0.6745*v; new(q2); q2= m+0.6745*v; If you want the sample values you can get these in excel by ordering the data and adding up the weights  form a column that has sum(w1:wi) and then find the values in the summed weights column that matches 0.25*N, 0.5*N, 0.75*N You can use the standardized weights that Mplus produces with the savedata command. 

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