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| Multilevel Time Series Question |
 
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| Anonymous posted on Tuesday, March 12, 2002 - 5:05 am
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I have a rather complex data set that I'm going to examine.
Background:
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.
Questions:
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
Steve Lewis |
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| bmuthen posted on Tuesday, March 12, 2002 - 9:39 am
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| 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. |
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| Anonymous posted on Wednesday, March 27, 2002 - 2:47 pm
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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.
Thanks in advance,
Steve Lewis |
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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. |
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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? |
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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. |
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Just to add a clarification, your variable list would be:
Names = couple y1 y2 x1 x2;
with
Usev = y1 y2 x1 x2;
and your data set would then have the same couple value for the rows of that couple (the repeated measures on y and x). |
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Thanks, your response cleared things very well for me.
Alicia |
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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?
Thanks again,
Alicia |
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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. |
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