[R-meta] Question about MASEM with categorical predictors

[Mike Cheung]

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
>> >
>> > On 06.11.2022 12:00, r-sig-meta-analysis-request using r-project.org wrote:
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>> > >     1. Question about MASEM with categorical predictors (Catia
>> Oliveira)
>> > >
>> > > ----------------------------------------------------------------------
>> > >
>> > > 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
>> > > Message-ID:
>> > >       <CACw+TfdnTK=kq4vLaJMQtS4rf9Ag=
>> > gRbHhcBV1T2bPbp1zPZTg using mail.gmail.com>
>> > > 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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