Hello, I am novice in using Mplus. I'm trying to analyse my data from a longitudinal study with three phases using Latent Growth Curve. I want to analyse the course of a dependent variable included in the three moments and also want that the x-axis of the graph will contain the ages of subjects (variables for age were 3: subjects’ age on first phase, on second phase and on third phase = edat1 edat2 edat3). In addition we want to include a control variable throughout all analyses (a quantitative variable) and not really know how to add this control variable in the analyses (concretely the nse variable) We also want to analyse the trajectory of the subjects (on dependent variable) by sex; what will be the correct way to add this function? Is the Grouping function? Syntax is included in another message; can you tell me which parts are incorrect, considering our goal?
I think what you want is Example 6.12. You don't need a multilevel model unless you have clustering other than the repeated measures. The edat variables can be time-varying covariates as shown in the example.
Núria V M posted on Friday, May 16, 2014 - 7:59 am
Thank you for your response. I followed the example 6.12 but the syntax fails. What happens?
variable: names are sexe edat1 edat2 edat3 nse; names are f11 f12 f13;
Núria V M posted on Wednesday, May 28, 2014 - 8:00 am
Finally we have performed a LINEAR GROWTH MODEL FOR A CONTINUOUS OUTCOME with our data. We indicate in the model that in the first phase the average of participants’ age is 10, in the second phase is 11 and in the third phase is 13 (from the first to the second phase a year passed and from the second to the third phase two years passed). The output is correct, but when we create the graph we only observed the possibility to obtain a curve for each subject. How could we create a single curve considering the dependent variable average score of all subjects ? (We want that the average of the dependent variable appear at the y-axis) We are interested in graphs with the trajectory of our dependent variable along three phases and on the other hand we want to observe the differences between the group of boys and the group of girls . We know that it is necessary to use the grouping function, but the graph does not represent at the same time the trajectory of the two groups.
Use the PLOT2 setting and look at Estimated Means.
Núria V M posted on Friday, May 30, 2014 - 7:48 am
When I perform my syntax (see below), two messages appear and I don't understand it properly variable: names = sexe nse f11 f12 f13; Grouping is sexe (1=male 2=female); analysis: type = general; estimator = mlr; model: i s | f11@10f12@11f13@13; i s ON nse; plot: type=plot3; series=f11(10) f12(11) f13(13); output: TECH4;
1)WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO LATENT VARIABLES. CHECK THE TECH4 OUTPUT FOR MORE INFORMATION. PROBLEM INVOLVING VARIABLE I. - There is something that does not work? Is correct the data that appear in this output?
2)The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used for chi-square difference testing in the regular way. MLM, MLR and WLSM chi-square difference testing is described on the Mplus website. MLMV, WLSMV, and ULSMV difference testing is done using the DIFFTEST option. - Whether we have a longitudinal study with three measurement times, should we apply the Satorra-Bentler scaled chi-square difference test? How to apply?
hello i have aproblem would you please guide me? i want to do a simple LGM, I have 86 individuals that half of them are men and the other half are wemon, but when i write my program in this way and run it, the program run truly but only 50 of my observation is read!!! can you help me in this matter?
TITLE: this is an example of a linear growth model for a continuous outcome DATA: FILE= lgm.dat; VARIABLE: NAMES ARE id y1-y5 sex country; USEVARIABLES ARE y1-y5; IDVARIABLE IS id; missing=.; Analysis: ESTIMATOR=MLR MODEL: i s | y1@0y2@1y3@2y4@3y5@4;