[R-meta] Question about MASEM with categorical predictors

Catia Oliveira c@t|@@o||ve|r@ @end|ng |rom york@@c@uk
Tue Nov 22 15:25:16 CET 2022 

Just to make sure I understand, I would start by getting the correlations
per study using the hetcor() (maybe the polychoric() from the psych package
would be better since it computes a polychoric correlation matrix). I would
then use  tssem1() to pool the correlation matrices across studies and then
fit the model (Outcome ~ categorical variable + numerical variable +
numerical variable) using sem(). Is that what you mean? I am sorry for
taking so much out of your time but I have not been able to find any
resources about this.

Thank you,

Catia

On Tue, 22 Nov 2022 at 02:32, Mike Cheung <mikewlcheung using gmail.com> wrote:

> Dear Catia,
>
> You may use the wls() function to conduct the stage two analysis in the
> metaSEM package.
>
> However, It appears that hetcor does not compute the asymptotic sampling
> covariance matrix of the estimated correlations. Thus, the statistical
> inferences may be incorrect.
>
> If you want to use this approach, it is better to use the sem() function
> in the lavaan package. I believe that it can generate the asymptotic
> sampling covariance matrix of the estimated correlations.
>
> Best,
> Mike
>
> On Mon, Nov 21, 2022 at 11:40 PM Catia Oliveira <catia.oliveira using york.ac.uk>
> wrote:
>
>> 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
>>>>> >
>>>>> > On 06.11.2022 12:00, r-sig-meta-analysis-request using r-project.org
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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=
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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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