Anonymous posted on Tuesday, March 12, 2002 - 5:05 am
I have a rather complex data set that I'm going to examine.
1. Survey data were collected at three distinct time periods of an event (pre-, mid-, and post-event - approximately 9 months from beginning to end) from one cohort. Each time period the sampling frame was sampled and about 700 respondents completed the survey all three times. An additional 500+/- responded to the survey, of which some may have completed surveys for two of the three time periods (I have not received the database yet so I do not know a more accurate figure).
2. The respondents are members of distinct subgroups and group level data were collected to allow for conceptually relevant multi-level analysis.
3. At time one, group level variables (3 variables) and one individual predictor variable were collected. At time two, individual predictor variables (4 variables) and outcome variables (3 variables) were collected in addition to group level variables described. At time three the same variables as time two were collected.
1. I would like to use as much of the time one thru three data as possible. Is it possible to use SEM and Mplus to model the missing values for those respondents who did not answer all three time periods. Is there a reference you are aware of that provides a model for such a procedure.
2. Each of the variables, I conjecture, are latent factors rather than manifest indicators (although, composite scores for the variables can be derived by summing the values for each variable). Do I have too many latent varibles to adequately model this or should I consider a SEM path analysis strategy using manifest variables?
3. I'm not certain how to include covariates (two) in a SEM model? please clarify
Thanks in advance
bmuthen posted on Tuesday, March 12, 2002 - 9:39 am
This type of analysis should not be problematic. It sounds like you have longitudinal data with missingness, where the interest is not in growth over time, but rather a path analysis model with variables on both individual and group levels. Missing data can be handled in SEM using standard ML estimation under MAR (see missing data references under the Reference section on this web site); here you use all available data. I am not clear on the groups you mention - have you sampled the groups so that this could be considered a random effect (such as sampling schools), or are they fixed? The former leads to multilevel modeling and the latter to multiple-group or MIMIC modeling using covariates (for basic SEM concepts, see Bollen and other ref's under References on this web site). Mplus 2.02 cannot handle multilevel SEM with missing data, but this is forthcoming in version 2.1. Latent variables typically need at least 2 manifest indicators, and preferrably more.
Anonymous posted on Wednesday, March 27, 2002 - 2:47 pm
Thank you for responding so quickly. I finally received the database and am now looking at the patterns of missingness and have questions about minimum number of observations to obtain unbiased estimates using a FIML estimation for missing values. Here is how the database is arrayed:
Completed T1 & T2 = 178 Completed T2 & T3 = 482 Completed T1 & T3 = 132 Completed all 3 = 186 Completed only 1 time = 3,789
I've done some reading on missing values and ML but have not run across acceptable missingness levels.
Finally, The groups are random & unbalanced (military units) so I plan to test the data using a multilevel path analysis. I've used Mplus for CFA and basic SEM models so i'm venturing into new territory and trying to aviod as many mistakes as possible.
Good references for acceptable missingness levels would be the Little and Rubin and Shafer books that are referenced on our website. You will have computational difficulties if you have more than 90% missing. Information about this is given in the coverage output. However, with a large percentage missing, the analysis relies very strongly on model and missing data assumptions
The current version of Mplus cannot combine multilevel and missing. Version 2.1 which is due out in a few months (a free update for Version 2 users) will allow this.
I want to estimate a multilevel model where individuals are nested within couples and the dependent variable is measured repeatedly and the main predictor of interest is measured repeatedly as well. There are no latent components to my model.
I am not interested in estimating change over time. Rather, I would like to estimate the overall relation between X and Y, but take into consideration the non-independence of my data I have looked through the manual, the handouts from the one week Mplus training and documents you provide on your website. The only examples of multilevel repeated measures modeling I can find estimate latent curves.
On page 72 of your publication “Multilevel modeling with latent variables using Mplus” There is a model estimating the intercept and slope in math scores, but data on attendance are available at all 4 time points. Using this example, what if we wanted to know the effect of attendance on math scores in any given year? How would the model be altered so that we would be estimating the association between attendance and math scores?
Here is my stab at some syntax using your example on page 7 :
VARIABLE: NAMES ARE cohort id school weight math7 math8 math9 math10 att7 att8 Att9 att10 gender mothed homres; USEOBS: (gender EQ 1 AND cohort EQ 2); MISSING = ALL (999); USEVAR = math7-math10 att7-10 mothed homers; Cluster = school; ANALYSIS: TYPE = TWOLEVEL; ESTIMATOR = MMUL;
MODEL: %WITHIN% Math7 ON att7; within individual time-varying Math8 ON att8; Math9 ON att9; Math10 ON att10; Math7 ON mothed; within individual time-invariant Math8 ON mothed; Math9 ON mothed; Math10 ON mothed; %BETWEEN%; Etc……
I can use the widelong command to change math7-10 to an across-time “math”, but I have no way to “telling” Mplus that the repeated observations are not independent. Is there a way to estimate a repeated measures TWOLEVEL model without making the latent slope the DV?
You can do this in a couple of different ways. Growth modeling is not needed; it is ok to consider only a regression of y on x. One way is to use a multivariate approach to indviduals within couples (since there are only 2), taking care of the couple correlation, and let the time dependence be handled by Type = Complex. This means that you would say
cluster = couple;
and have your data arranged as
y1 y2 x1 x2
where the subscript refers to person within couple for a given time point. So you give the data in long form wrt time - each couple has as many rows as there are time point. The number of rows a couple has is their "cluster size". In this way, Type = Complex computes SEs that take the correlatedness over time within couple into account.
Your model statement would be:
y1 on x1; y2 on x2;
where the x's are correlated by default and so are the residuals of the y's.
Just a follow-up. If I combine husbands and wives into one line of data, I will be modeling men and women seperately, so I will not be estimating any effect of gender on the DV. Because I have to estimate the regressions seperately for each time I would not get a time-independent estimate of the regression Y ON X.
What I am interested in is the effect of marital status on mental health. Because there is no invervention in this study and because the points of data collection don't have developmental significance, I'd like an overall estimate of the association between marital status and depression, irrespective of time. Because the data are nested in individuals, who are nested in couples, I feel the data require a multilevel model. Otherwise, this would simply be a single logistic regression. But I feel I cannot ignore the nestedness here.
It is looking like I can't do what I'm trying to do if I use MPLUS.
I suppose there is another option if I select only cases where no remarriage takes place after divorce and then align the data among divorced individuals such that all respondents are married at times 1 and 2 and divorced at times 3 through 5 (data from non-divorced people would be left as is). Then I could estimate a piecewise lcm at the WITHIN level and regress the intercepts and slopes on the within and between subjects variables. This would allow me to do things like see if couple-level characteristics relate to slope before divorce differently than they relate to slope after divorce. If I do this, is there also a way to compare intercept 1 to intercept 2 and slope 1 to slope 2?
Your first paragraph suggests that you misunderstood my recommendation. You don't do the regression separately for each time. My suggestion implies that you do get a time-independent estimate of the regression of y on x - you get only one intercept and one slope. You do take into account the nestedness of the data by using Type = Complex.
You are right that my suggestion estimates separate regressions for men and women, so allowing both different intercepts and slopes.
You don't need to do multilevel modeling to take nestedness into account. But you can do multilevel modeling in Mplus if that is what you want.
If this is unclear, let me know how I can help clarify further.
I am turning to Mplus because of my need for a complex analysis. I'd like to test for mediation and moderation (in separate analyses) in a longitudinal dataset consisting of three waves. I would like to do this using multilevel, in order to investigate both changes within and between subjects. I have been searching for papers that show examples of how to this and preferably perhaps even something even more hands on, but have not been successful. Do you have any reading tips or online courses where I can learn more? Perhaps also a syntax or example to practise with?
I apologize in advance for any errors in my interpretation of my current problems.
I have now read more on the suggested literature and see how example 9.3 might apply to my mediated model. I only have a couple of questions.
My mediated research model is with continuous variables (except time and person). Thus, I wonder if having u as a dependent variable does not makes sense in this case. It seems more corrects that y is the DV, x2 is the M and x1 is the IV. If I then understand Bollen & Curran (2006) correct, time should then be regressed (or multiplied with?) on all the included variables. Further, I have to admit, I am not sure if u should represent time or person, and this goes for w as well. I see that w is meant to represent the cluster level covariate, but since this is longitudinal all my variables are at the same level, thus I’m confused.
My next question pertains to a moderated research model. This is based on the same dataset, where time is within person, and the moderator variable is time-variant like the rest of the variables. Again, if I read B&C correctly, time should then be multiplied with all the variables, how to describe this in the syntax and how to interpret the results is very difficult to understand.
I see now that you don't have clustering except for time. In this case, you don't need multilevel modeling. When data are in a wide multivariate format, you have a single-level model. The multivariate analysis takes care of clustering due to repeated measures.
OK, thank you very much. I'm still not sure how to calculate the moderating variable and write syntax to test for moderation with longitudinal data, though. I've been investigating the examples in ch. 6, but I can't seem to find anything similar. Any suggestions? I apologize for my ignorance and thank you very much for you time.
Moderation can be tested for categorical moderators using multiple group analysis. If a parameter is not equal across groups, this is an interaction. It can also be tested by including an interaction in the regression:
Thank you very much. How do you then include the time variable in this? I am familiar with moderation, but have never done it with a longitudinal data set. My hypotheses is whether and how x1x2 moderates the x1-y relationship over time. It is theorethically relevant to investigate this both within and between subjects. My intial thought was to do a triple interaction in multilevel, x1x2t, however, it seems problematic as t is then a categorical variable.
Here's an idea I had, but I see that it's just a start. Can I write the syntax something like this?
I am confused about your model. The | symbol for growth is used for wide format data where you would have variables x1, x2, and x3. It seems to me you have long format data because you show only x1 three times. See Example 9.16 of the user's guide. Then you would create an interaction with time using the DEFINE statement, for example,
Thank you, I am now able to run the full model, but I get the following message:
THE ESTIMATED WITHIN COVARIANCE MATRIX IS NOT POSITIVE DEFINITE AS IT SHOULD BE. COMPUTATION COULD NOT BE COMPLETED. PROBLEM INVOLVING VARIABLE INT. THE CORRELATION BETWEEN INT AND TIME IS 0.999 THE RESIDUAL CORRELATION BETWEEN INT AND TIME IS 0.999 THE PROBLEM MAY BE RESOLVED BY SETTING ALGORITHM=EM AND MCONVERGENCE TO A LARGE VALUE.
Of course there is logic in time and int being correlated as int is defined by time*moderator. However, I do not understand why the correlation is so high, in SPSS, the correlation is -.058**. Second, I'm am unsure how I set the "ALGORITHM=EM AND MCONVERGENCE TO A LARGE VALUE."
I'd like to estimate a path analysis model (no latent variables) the same as Alicia Merline posted on Wednesday, April 12, 2006. I have three time reps (I'm not interested in time variation, but just to correct for the dependency) and two groups represented by two soil depth.
I was thinking to do the same approach but to input: CLUSTER : time and organize the data by depth, as: y1 y2 x1 x2 where the 1 and 2 represent the two depth, and model would be:
y1 on x1 y2 on x2
Does this sound correct?
Also when I will graphically represents my model, is there any way to represent a total model and not separate by the two depth? because when I get the output from Mplus the estimates are grouped on Y1 on X1 and Y2 on X2 obviously representing the two separate groups.
Thanks for your answer. However I still have doubts. As I mentioned I will use CLUSTER=time to correct for the 3 time reps. Then I want to see the effect of x on y (well the model is a little more complicated), were both x and y have data from the two depths (therefore is not 2 groups influencing 1, it's not multilevel). Let's say x corrispond to microbial biomass and y is soil carbon. Therefore the data for microbial biomass and soil carbon come from both depth. But in my thinking I believe that being the two soil depth one after each other there's some sort of correlation I should correct for? Does it make sense?
What I meant when asking for a "total model" was: the output when calculating
y1 on x1 y2 on x2
it's obviously going to give me two results(in terms of estimates, s.e., p-values etc), one for variable y1x1 and the other for y2x2. My question was: is there a way to combine them together, in order to have a graphical output that include both?
In my case it's a path analysis, with more variables than just x and y (I used only two to simplify my example). For instance I would have something like:
y1 on x1; y2 on X2; X1 on a1 b1; X2 on a2 b2; a1 with b1; a2 with b2;
And so the output gives me the estimates (and standardize estimates) and R square which I use to graphically show interaction between different soil parameters as path analysis. But if I have two estimates (and 2 R square) for each path (because of the two depths) is it possible to combine the two values in each path? in order to have only one value per path, and then graphically represent only one model.
I am wondering whether I have correctly specified a model.
We conducted a study using twice-daily diaries. Youth were asked to indicate how much they co-ruminated in person, over text, over phone, and over social media at each prompt. They were also asked to indicate their positive affect. We created time-lagged variables so that we could regress positive affect on levels of co-rumination across each type of communication at the previous time point. (So we are examining how co-rumination at time T-1 predicts positive affect at T). We have already ran the model in DSEM and now want to try it using MLR.
Is the below syntax correct? Please note that Inp_M_1, t_M_1, sm_M_1, and p_M_1 are the lagged variables for co-rumination in minutes in person, over text, over social media, and over the phone, respectively. PosAf is positive affect.
usevariables are Inp_M_1 t_M_1 sm_M_1 p_M_1 PosAf PosAf_1; within are Inp_M_1 t_M_1 sm_M_1 p_M_1 PosAf_1 PosAf; Cluster is ID;
analysis: type is twolevel random;
model: %within% s_n | PosAf ON PosAf_1; s_i | PosAf ON Inp_M_1; s_t | PosAf ON t_M_1; s_s | PosAf ON sm_M_1; s_o | PosAf ON p_M_1;
Looks fine. The correlation within subjects over time is here captured only by the cluster effects on Between. The correlation between adjacent observations close in time is not captured as it is with DSEM and its autocorrelation. This means that SEs may be too small.
We noted that our DSEM models and models run in the software HLM (which uses MLR) were producing different results. Do you know why this might be? For example, besides using different estimators, does DSEM and the HLM program apply different assumptions, or handle missing data differently?