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Topic: URGENT Help needed with LCA model selction
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Posts: 1
Registered: 6/29/06
URGENT Help needed with LCA model selction
Posted: Jun 29, 2006 7:31 PM
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I am working on my MS thesis, and a committee member and I are
disagreeing about how I have selected the best model. Based on my
readings and the LatentGold LCA manual, I think this is reasonable. I
would appreciate anyone agreeing or disagreeing with this method and
letting me know why (time is of the essence for me to complete my
thesis). Thanks!

Quick overview of project: I sought to determine if the symptoms of
individuals experiencing a major depressive episode (MDE), with a
diagnosis of recurrent unipolar or bipolar I disorder, would cluster
into meaningful and clinically interpretable subgroups of depression.
Outpatients (N = 293; Unipolar n = 195, Bipolar n = 98) who met DSM-IV
diagnostic criteria for a MDE were assessed at intake to treatment with

the 25-item Hamilton Rating Scale for Depression 25-item (HRS-D-25). I

applied latent class analysis (LCA) to the 25 HRS-D items in the
unipolar depressed sample and then the unipolar sample combined with
the bipolar I depressed sample.

Data analysis plan: I entered the HRS-D-25 items as coded, ordinal
variables (rather than dichotomized absent/present). I applied LCA to a

195row x 25-column data matrix and 293row x 25-column data matrix with
Latent Gold 4.0. For each model, gender was entered as an active

I first fit a one-class solution followed by a two-class solution, and
so on, until I reached a seven-class solution, or the "best"
solution. I stopped the analyses at seven classes because I felt that
more than seven classes would provide little clinical or practical
significance. I defined the best solution by the following criteria:
(1) the solution fit the data significantly better than the previous
solution and (2) the estimated parsimony of the model was associated
with a significant p-value.

First, I estimated the parsimony of each possible model solution, using

the bootstrap p-value test statistic. Since I have a small sample size

and the chi-squared estimation may be problematic, I used the bootstrap

p-value. Then, I sought to determine if each level of a restricted
model (a solution with a greater number of classes) was a significant
improvement of the less restricted model (a solution with a smaller
number of classes). I thought that if the bootstrap -2LL Diff test
statistic has a significant p-value, then the less restricted model is
more parsimonious than the more restricted model. For instance, if the

bootstrap -2LL Diff statistic is not significant, then a 3-class
solution may be accepted as a good fit and as a more parsimonious model

than a 2-class solution. Conversely, if the bootstrap -2LL Diff
p-value is significant, then a 2-class solution should be accepted as
the more parsimonious model. Next, I evaluated how well the selected
model permitted predictions of class membership based on the observed
indicator variables with entropy R-squared.

Results: Based on the Bootstrap -2LL Diff statistic, a six-class
solution was the most parsimonious solution and was able to adequately
predict class membership (entropy R2 = 0.85). The Bootstrap -2LL
Diff statistic between a 6- and 7-class solution was significant. This

suggests that the less restricted 6-class model was preferrable to the
more restricted 8-class model. In the combined unipolar-bipolar
sample, a 5-class solution was the most parsimonious solution and was
able to adequately predict class membership based on the HRS-D-25 items

(entropy R-squared = 0.87).

L2 Bootstrap p Entropy R2 BIC (L2)
-2LL Diff
1 3457.31 .33 1.0 2592.54
2 3262.55 .25 .91 2534.87
3 3174.07 .20 .83 2583.49
4 3098.52 .17 .84 2645.04
5 3027.51 .07 .86 2711.13
6 2978.08 .03 .85 2798.80 6v5, p = .10
7 2930.77 .01 .87 2888.59 7v6, p = .04

1 5310.86 .32 1.0 3834.01
2 4976.12 .27 .93 3681.04
3 4835.15 .18 .86 3721.83
4 4742.17 .10 .84 3810.62
5 4635.77 .05 .89 3885.99
6 4568.08 .04 .87 4000.07 6v5, p = .01
7 4507.73 .01 .86 4121.48 7v6, p = .00

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