[R-meta] Question about MASEM with categorical predictors

Catia Oliveira c@t|@@o||ve|r@ @end|ng |rom york@@c@uk
Mon Nov 21 16:39:47 CET 2022 

Dear professor Mike,

Thank you for your response. Wouldn't it be possible to run the path model
I described if I used the hetcor() function in R on the raw data per study,
where I would gather all the correlations between variables, including
between gender and the numeric variables? Wouldn't this allow me to use the
two staged approach you describe in "metaSEM: an R package for
meta-analysis using structural equation modeling"?

Thank you!

Best wishes,

Catia



On Sun, 13 Nov 2022 at 04:02, Mike Cheung <mikewlcheung using gmail.com> wrote:

> Dear Catia,
>
> If you have the raw data, you may use either a multiple-group SEM or
> multilevel SEM (assuming all variables are in comparable scales across
> studies). However, you may need to standardize or harmonize the variables
> before the analyses if the variables are not directly comparable across
> studies.
>
> It may be tricky to pool correlation matrices when there are categorical
> variables.
>
> An alternative is to fit the regression model in each group and
> meta-analyze the regression coefficients. There was some discussion in the
> following paper.
>
> Cheung, M. W.-L., & Cheung, S. F. (2016). Random-effects models for
> meta-analytic structural equation modeling: Review, issues, and
> illustrations. Research Synthesis Methods, 7(2), 140–155.
> https://doi.org/10.1002/jrsm.1166
>
> I hope it helps.
>
> Best,
> Mike
>
> On Sun, Nov 13, 2022 at 11:00 AM Catia Oliveira <catia.oliveira using york.ac.uk>
> wrote:
>
>> Thank you both for replying to my question.
>>
>> @Mike Cheung <mikewlcheung using nus.edu.sg> The categorical variable
>> represents sex, so only female and male. We aim to include only studies
>> that have reported all the predictors of interest in the meta-analysis, so
>> we are not anticipating missing data. It is unclear whether we will have
>> access to the raw data for all studies, as we will not be able to know
>> until we start going through the literature, but first, we need to
>> preregister the meta-analysis. However, we are trying to anticipate all
>> situations. I have read about the work you have done with Susanne Jak, but
>> it is not clear whether I could include categorical variables using that
>> approach. It also seems to require a lot of data, which may not be our case.
>> If we have access to the raw data and are able to fit the model on each
>> dataset, do you have any suggestions for how to better analyse it? From
>> what I've read it seems that we could analyse each factor loading on its
>> own and run a meta-regression, would that be reasonable?
>>
>> Thank you.
>>
>> Best wishes,
>>
>> Catia
>>
>> On Sun, 13 Nov 2022 at 01:26, Mike Cheung <mikewlcheung using gmail.com> wrote:
>>
>>> Dear Catia,
>>>
>>> Could you be more specific about how the data look like? For example,
>>> do you have the raw data? If not, what types of summary statistics do you
>>> have?
>>>
>>> How many levels are in V1? How many groups are? Are there incomplete
>>> data?
>>>
>>> --
>>> ---------------------------------------------------------------------
>>>  Mike W.L. Cheung               Phone: (65) 6516-3702
>>>  Department of Psychology       Fax:   (65) 6773-1843
>>>  National University of Singapore
>>>  http://mikewlcheung.github.io/
>>> <http://courses.nus.edu.sg/course/psycwlm/internet/>
>>> ---------------------------------------------------------------------
>>>
>>> On Sat, Nov 12, 2022 at 9:17 PM Lukasz Stasielowicz <
>>> lukasz.stasielowicz using uni-osnabrueck.de> wrote:
>>>
>>> > Dear Catia,
>>> >
>>> > Disclaimer: I am not up-to-date with MASEM advances so perhaps there is
>>> > a more user-friendly solution. Consider checking recent work by Mike
>>> > Cheung and Suzanne Jak for references about cutting-edge methods.
>>> >
>>> > If you have access to raw data then one could use the parameter-based
>>> > MASEM approach. Since it is possible to use categorical predictors in
>>> > lavaan/blavaan, one could fit the model separately for each sample and
>>> > then pool the estimates. This approach has a clear practical
>>> limitation:
>>> > all variables/categories need to be assessed in all studies, which is
>>> > not always the case.
>>> >
>>> > Alternatively, one could fit separate models for each category (e.g.,
>>> > women, men). After all, stratification is just another kind of
>>> adjusting
>>> > for variables.
>>> >
>>> > Some references about the parameter-based approach can be found in this
>>> > article:
>>> > Cheung, M. W.-L. (2021). Meta-analytic structural equation modeling.
>>> > Oxford Research Encyclopedia of Business and Management, Oxford
>>> > University Press.
>>> https://doi.org/10.1093/acrefore/9780190224851.013.225
>>> >
>>> >
>>> > Best,
>>> > Lukasz
>>> > --
>>> > Lukasz Stasielowicz
>>> > Osnabrück University
>>> > Institute for Psychology
>>> > Research methods, psychological assessment, and evaluation
>>> > Lise-Meitner-Straße 3
>>> > 49076 Osnabrück (Germany)
>>> > Twitter: https://twitter.com/l_stasielowicz
>>> >
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>>> > >     1. Question about MASEM with categorical predictors (Catia
>>> Oliveira)
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>>> ----------------------------------------------------------------------
>>> > >
>>> > > Message: 1
>>> > > Date: Sat, 5 Nov 2022 23:21:10 +0000
>>> > > From: Catia Oliveira <catia.oliveira using york.ac.uk>
>>> > > To: R meta <r-sig-meta-analysis using r-project.org>
>>> > > Subject: [R-meta] Question about MASEM with categorical predictors
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>>> > > Content-Type: text/plain; charset="utf-8"
>>> > >
>>> > > Dear all,
>>> > >
>>> > > Has any of you ever used MASEM with categorical predictors, where the
>>> > path
>>> > > model is "X1 ~ V1 + V2 + V3", with X1 as the outcome variable and V1
>>> as a
>>> > > categorical variable whilst V2 and V3 are continuous. If so, could
>>> you
>>> > > please point me to the paper/code? I have only found examples of how
>>> to
>>> > do
>>> > > it with continuous predictors but never using categorical variables.
>>> > >
>>> > > Best wishes,
>>> > >
>>> > > Catia
>>> > >
>>> > >       [[alternative HTML version deleted]]
>>> > >
>>> > >
>>> > >
>>> > >
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>>
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