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Hi, I ran an experiment with 5 manipulations (4within, 1 between), and I have two continuous dependent variables. Each Respondent gave me four responses. I want to estimate latent classes at the RESPONDENT level, not the response level. At the current analysis, I do get instances where one respondent's response 1 is in class 1 and response 3 is in class 2. I used the cluster variable to indicate the respondents. And specified a between and within level model. thanks, Mahima Code: VARIABLE: NAMES ARE Rep Needs_sim Homoph R_inten SF_fam Lcons_1 Lcons_2 Lcons_conf MAN manid; USEVARIABLES ARE Lcons_1 MAN Rep SF_fam; CLASSES = c(2); WITHIN = Rep; BETWEEN = SF_fam; CLUSTER = MAN; IDVARIABLE IS manid; ANALYSIS: TYPE=TWOLEVEL MIXTURE RANDOM; MODEL: %WITHIN% %OVERALL% Lcons_1 on Rep; %BETWEEN% %OVERALL% Lcons_1 on SF_fam; OUTPUT: TECH1 TECH11 TECH7; PLOT: TYPE=PLOT3; SAVEDATA: FILE IS 2class_bet.dat; SAVE=CPROB; 


What are the names of the four response variables in your data set? 

Mahima Hada posted on Thursday, July 08, 2010  11:56 am



Hi, The response variable I am interested in is only 1 Lcons_1. (The other two are Lcons_2, Lcons_conf.) The within subject manipulations are REP, Needs_sim Homoph R_inten. Between subject is SF_FAM. And "MAN" identifies one manager  who has 3 or 4 responses 


I would treat this as a multivariate rather than a multilevel analysis. I would arrange the data so that each each respondent is one record in the data set with four variables, one for each response. Multivariate analysis takes care of the clustering due to respondent. 

Mahima Hada posted on Thursday, July 08, 2010  7:24 pm



Hi Linda, Thanks for your response. So to be clear, my equations would be something like: Lcons_1A on RepA R_intenA Needs_simA HomophA SF_FAMA; Lcons_1B on RepB R_intenB Needs_simB HomophB SF_FAMB; Lcons_1C on RepC R_intenC Needs_simC HomophC SF_FAMC; where each set  A,B,C  corresponds to one response from a manager. Mahima 


Yes. 


Thanks. I tried your method  but as soon as I include two sets of equations together (Response 1 and 2 from Manager 1), the model is not able to get a solution. I get the error: " 1268.235 unperturbed 0 THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY. THE COMPUTATION OF THE FISHER INFORMATION MATRIX COULD NOT BE COMPLETED. THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. THIS IS OFTEN DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. CHANGE YOUR MODEL AND/OR STARTING VALUES. THE SAMPLE COVARIANCE MATRIX COULD NOT BE INVERTED FOR CLASS 2. PROBLEM INVOLVING VARIABLE REP2." If I use starting values, I still get the same error.REP2 does not give me any problems if I estimate only the model for Response 2. My feeling is that there is correlation between the two responses  which is creating problems in the estimation. But that is part of the data  and what I want to capture in clusters. 


My Code: ANALYSIS: TYPE=MIXTURE; STARTS 1500 50; !ALGORITHM=INTEGRATION; MODEL: %OVERALL% !%c#1% ![bias_av*1 Lcons_av*1 Lcons_av2*1]; !%c#2% ![bias_av2*1 Lcons_av*1 Lcons_av2*1]; Bias_av on Rep R_inten; Bias_av2 on Rep2 R_inten2; Lcons_av on Rep R_inten Needs_sim Homoph bias_av; Lcons_av2 on Rep2 R_inten2 Needs_sim2 Homoph2 bias_av2;M 


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