[R-meta] Question about MASEM with categorical predictors

Catia Oliveira c@t|@@o||ve|r@ @end|ng |rom york@@c@uk
Wed Nov 23 04:16:30 CET 2022 

Dear Mike,

Thank you again. I have to clarify that there are no latent variables in
the model I am interested in, it's just a path analysis.

Do you have resources for the regression analyses you suggested? It would
be much easier to make sure I don't end up deviating from what is the
recommended track if there's some sample code and a paper. Sorry again for
all the questions.

Best wishes,

Catia


On Wed, Nov 23, 2022, 2:43 AM Mike Cheung <mikewlcheung using gmail.com> wrote:

> Dear Catia,
>
> If I understand it correctly, you use hetcor() or other functions to
> estimate a "correlation matrix" of continuous and binary variables and use
> this correlation matrix in tssem1() and then tssem2().
>
> The part about continuous variables should be fine. But I am not sure
> about the part on binary and continuous variables. Moreover, the meaning of
> including a latent variable with a threshold on gender is questionable.
>
> The most defensible approach is (1) fitting the regression model in each
> study, (2) estimating the path coefficients and their sampling covariance
> matrix, and (3) conducting a multivariate meta-analysis on the path
> coefficients.
>
> Best,
> Mike
>
>
> On Tue, Nov 22, 2022 at 10:25 PM Catia Oliveira <catia.oliveira using york.ac.uk>
> wrote:
>
>> 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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>>>>>>> > > Today's Topics:
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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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>

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