SEM for impact evaluation, with or without survey weights

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Joseph Watson

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Jan 31, 2019, 3:11:29 PM1/31/19
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Dear all,

 

I am a first-time poster. Apologies for any errors, and please let me know if I need to change the format of this message including code images. 

 

I am attempting to mimic a cross-section regression model in lavaan, but with a latent dependant variable formed of three manifest variables. (No more manifest variables are available for the latent.) The key purpose of the initial regression model/lavaan model is to investigate the impact on maths outcomes/ability of an intervention (represented by the variable h1708_binary) amongst approx. 25,000 6-11 year olds in Tanzania. The regression model is produced as follows, with h1705_binary (a binary maths outcome variable) being predicted by binary outcome measures for Kiswahili (h1701_binary), English (h1703_binary) and other independent variables:


logisticmodel <- glm(h1705_binary ~ h1708_binary + h1701_binary + h1703_binary + age + asset_wealth_index + mother_attended_school + enr_binary, family = quasibinomial(), data = Uwezo6plus)


Results are below if helpful.

log unweighted.PNG

 

In the lavaan model, I essentially expand my dependent variable to become a latent variable formed of one 5-level (1-6) (h1705) and two binary (0-1) (h1706_add_binary and h1706_sub_binary) ordered variables.  

 

This maths latent on its own functions well (with a CLI and TLI of 1 and an RMSEA and SRMR of 0), in spite of a high level of correlation between the two h1706 variables. (I do not believe that I can make changes in the model as it is just specified, due to the lack of available manifest variables.)

 

Incorporating this maths latent in a regression model mirroring my glm model is done as follows.

 

maths.lbmodel <- 'maths =~ h1706_add_binary + h1706_sub_binary + h1705

maths ~ h1708_binary + h1701_binary + h1703_binary + age + asset_wealth_index + mother_attended_school + enr_binary'

 

maths.lbfit <- cfa(model = maths.lbmodel, data = Uwezo6plus, ordered = c("h1705", "h1706_add_binary", "h1706_sub_binary")) #fit model

 

Results and an image of the model are below if helpful.

RplotUnweightedmaths.png


semunweightoutput.PNG



Results appear logical, although fit measures are less good for SRMR, RMSEA, CFI and TLI, which are 0.063 0.081 0.996 and 0.999 respectively. The R2 for maths is 0.56


My questions are:

 

1. Is using SEM in this manner to establish impact viable (and are there any working examples of SEM being used to ‘mimic’ regression in this manner, within education or any other field)?


2. Am I correct in thinking that responses to previous posts remain correct: a model with a latent variable of ordered manifest variables cannot function through lavaan.survey? Applying survey weights to the model through lavaan.survey shifts the estimator to ML. And, specifying the DWLS estimator returns multiple errors (in spite of this model functioning without survey weights). (If helpful, code is provided below.)

 

maths.w_lbfit <- lavaan.survey::lavaan.survey(lavaan.fit=maths.lbfit, survey.design = Uwezo6plus.design) #uses ML estimator (which ignores the ordered nature of manifest variables for 'maths')

 

maths.w_lbfit <- lavaan.survey::lavaan.survey(lavaan.fit=maths.lbfit, survey.design = Uwezo6plus.design, estimator = "DWLS") #specifying additional argument to alter estimator from default (ML) returns errors only

 

Any responses to this post would be hugely appreciated.


Joe


Post tags: ordered, DWLS, lavaan.survey, impact (could not select when posting, sorry.)

Terrence Jorgensen

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Feb 2, 2019, 6:04:34 AM2/2/19
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1. Is using SEM in this manner to establish impact viable (and are there any working examples of SEM being used to ‘mimic’ regression in this manner, within education or any other field)?


That is one of the most popular applications of SEM: structural regressions among latent variables.

2. Am I correct in thinking that responses to previous posts remain correct: a model with a latent variable of ordered manifest variables cannot function through lavaan.survey? 


Correct, the survey package does not accommodate discrete data, so there is nothing lavaan.survey can ask it to do.  FYI, you can apply sampling weights in the latest version of lavaan, although it is also limited to continuous data

?lavaan


Terrence D. Jorgensen
Assistant Professor, Methods and Statistics
Research Institute for Child Development and Education, the University of Amsterdam

Joseph Watson

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Feb 3, 2019, 7:12:57 AM2/3/19
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Dear Dr. Jorgensen,

Thank you so much for the reply. This is hugely appreciated. I look forward to working more with lavaan in my work.

Joe
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