Message/Author 

Somtawin posted on Wednesday, April 25, 2001  7:24 pm



Can I employ latent growth curve model to study the academic growth of schools taking the past mean of achievement score of each school, not using the same student for five consecutive years ? 


In principle, that is possible, although you would need to consider whether the school can be considered the same across years when the student body changes. This is a substantive issue that other more substantive researchers might have better comments on. 

Anonymous posted on Wednesday, July 07, 2004  7:32 am



I have data on children spanning two years and six waves, three in the last year of kindergarten, three in the first year of elementary school. Data are about the quality of the teacherchild relationships. I want to estimate a model that captures all data waves, despit the fact that the teacher in the first three waves (kindergarten) is someone else than the teacher in elementary school (the last three waves). So, though the same measures are used at all waves, the scores at first and last waves might not be compared directly. Nevertheless, I guess there might be a solution... How can I include all six waves of data in a single LGC model framework? Thanx. 


You could have two sequential growth processes one for each teacher. Or you could do one growth process. I would do both and then compare them. 

Anonymous posted on Wednesday, July 07, 2004  11:52 pm



OK, that's of course what we could do, do both an compare. However, what's on my mind since last night, is whether a piecewise LGC model (maybe with differential slopes also, like in Willet, Singer & Martin, 1998) also makes sense in this context? That is, when measures (scales, observers, etc.) are the same, but only target persons (teachers) differ from kindergarten to elementary school. So, we allow the intercept and slope to vary in the second part of the model, compared to the first. Does this make sense to you, looking at the transition from kindergarten to elementary school as an intervention? 


I think this is the same as having two sequential growth processes. 


I have data on children's weekly vocabulary scores from a curriculum based assessment and i would like to model their growth in vocabulary over time. However, while the number of vocabulary words is the same for each week, the actual vocabulary words are different for each week. Thus, the scores go up and down from week to week. I was wondering if i could use a running total from one week to next of number of correct vocabulary words and use that in LGM framework. Would this work? If not, do you have another suggestion? 


Growth curve modeling assumes the same outcome is measured repeatedly. I don't think a running total would work. Is there some standard test you can use each week in addition to their regular tests? See the paper, The Metric Matters, by Mike Seltzer. 


I have 25 time points from 1975 to 2000 with different cases each year. Thus, it's not the same subjects at each year. I have approximately 3000 subjects each year. Year after year, the same set of questions (on delinquency) was assigned at 3000 students. I want to see how delinquency progresses across time. Can I do LGM on these subjects like example 6.1 even if it’s not the same cases at each time? Each year represents an independent sample. If not, what would be the best strategy of analysis in MPlus? Thank you. 


You need to have the same subjects across time to estimate a growth model. I'm not sure how people use data such as yours. 


I have data on children's relationships with their teachers from kindergarten to sixth grade. I did individual growth modeling to determine if relationsihp quality declines over timeit did. Then I tried to use those growth models (with all 7 time pointsie., kindergarten to 6th graade) to predict an outcome at age 15 using latent growth curve modeling but the models would not converge. I finally dropped the first 3 time points of the growth models (i.e., k2nd grade) and I was able to get the lgcm to fit. When doing the latent growth curve models, did just the intercept and slope for each child predict the outcome or did the model also include the values of teacherchild relationship quality at each time point as well as each child's intercept and slope? 


If you want help with the nonconvergence, please send the output and your license number to support@statmodel.com. Typically a distal outcome is regressed on the growth factors. 


I use the computer lab on campus so I am not sure of the license number. I guess what I am getting at is when the outcome is regressed on the growth factors, are the growth factors based on an average intercept and an average slope for each child or do the growth parameters include data values for each child at each time point? I'm sorry. Someone keeps asking me this and I don't know the answer. 


Growth factors are random effects. They have means and variances. They include values for each child. 

RuoShui posted on Monday, November 18, 2013  4:56 pm



Dear Dr. Muthen, I am looking at emotional wellbeing over 4 waves. The subjects are the same across waves but the informants (teachers) are different at each wave. I am wondering whether I can justify using latent growth curve modeling? Thank you very much. 


This question is more appropriate for a general discussion forum like SEMNET. 


I would like to conduct LGM using Child Behavior checklist data over 3 years. Although the CBCL is used with the same participants at all waves, the items used vary due to age appropriateness of the CBCL. Is there a recommended way of adjusting for this using Mplus for LGM? Many thanks 


If you have itemlevel data and have some items in common at adjacent time points it is possible to model this so that you have a comparable scale at all time points. You hold equal parameters for the items that are the same at the different time points. 

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