See the published version of this preprint at BMC Medical Research Methodology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Research article
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background The Multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis using latent variable framework, of which classical structural equation model is a special case. The MIMIC model provides rigorous results and becomes broadly available in multiple statistical software. The current study introduces the MIMIC model and how it can be implemented using statistical software SAS CALIS procedure, R lavaan package, and Mplus version 8.0.
Methods In this paper, we first discussed the formulation of the MIMIC model with regard to model specification and identification. We then demonstrated the empirical application of the MIMIC model with the Midlife in the United States II (MIDUS II) Study (N=4,109) using SAS CALIS procedure, R lavaan package and Mplus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software programs were also presented.
Results In terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities. Mplus is designed primarily for latent variable modeling and has far more modeling flexibility compared to SAS and R, but limited in data manipulation. Differences in model results from the three statistical programs are trivial. Overall, the results show that while men show better performance in executive function than women, women demonstrate better episodic memory than men.
Conclusions Our study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages. Results from our models align with empirical findings from previous research. We provide coding procedures and examples with detailed explanations in the hopes of providing a concise tutorial for researchers and methodologists interested in incorporating latent constructs with multiple indicators and multiple covariates in their research projects. Future researchers are encouraged to adopt this flexible and rigorous modeling approach.
Keywords
MIMIC model, MIDUS II, Statistical Software Comparison, Cognitive Functioning Performance, Structural Equation Model, R, Mplus, SAS
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
The full text of this article is available to read as a PDF.
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Research Methodology.
No community comments so far
Submission checks complete
On 18 Oct, 2020
Editorial decision: Accept
On 14 Oct, 2020
No comments provided
Editorial decision: Minor revision
On 06 Oct, 2020
Review #2 received
Received 02 Oct, 2020
Review #1 received
Received 30 Sep, 2020
Editor assigned
On 25 Sep, 2020
Reviewers invited
Invitations sent on 25 Sep, 2020
Reviewer #1 agreed
On 25 Sep, 2020
Reviewer #2 agreed
On 25 Sep, 2020
Submission checks complete
On 24 Sep, 2020
Editor invited
On 24 Sep, 2020
Version 2
Posted 11 Sep, 2020
No comments provided
Editorial decision: Revise before sending to peer reviewers
On 11 Sep, 2020
Editor assigned
On 07 Sep, 2020
Submission checks complete
On 06 Sep, 2020
Editor invited
On 06 Sep, 2020
No comments provided
Editorial decision: Major revision
On 07 Aug, 2020
Review #2 received
Received 05 Aug, 2020
Reviewer #2 agreed
On 21 Jul, 2020
Review #1 received
Received 04 Feb, 2020
Reviewers invited
Invitations sent on 13 Jan, 2020
Reviewer #1 agreed
On 13 Jan, 2020
Editor assigned
On 16 Dec, 2019
Submission checks complete
On 15 Dec, 2019
Editor invited
On 15 Dec, 2019
First submitted
On 13 Dec, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Medical Research Methodology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Research Methodology.
No community comments so far
Submission checks complete
On 18 Oct, 2020
Editorial decision: Accept
On 14 Oct, 2020
No comments provided
Editorial decision: Minor revision
On 06 Oct, 2020
Review #2 received
Received 02 Oct, 2020
Review #1 received
Received 30 Sep, 2020
Editor assigned
On 25 Sep, 2020
Reviewers invited
Invitations sent on 25 Sep, 2020
Reviewer #1 agreed
On 25 Sep, 2020
Reviewer #2 agreed
On 25 Sep, 2020
Submission checks complete
On 24 Sep, 2020
Editor invited
On 24 Sep, 2020
Version 2
Posted 11 Sep, 2020
No comments provided
Editorial decision: Revise before sending to peer reviewers
On 11 Sep, 2020
Editor assigned
On 07 Sep, 2020
Submission checks complete
On 06 Sep, 2020
Editor invited
On 06 Sep, 2020
No comments provided
Editorial decision: Major revision
On 07 Aug, 2020
Review #2 received
Received 05 Aug, 2020
Reviewer #2 agreed
On 21 Jul, 2020
Review #1 received
Received 04 Feb, 2020
Reviewers invited
Invitations sent on 13 Jan, 2020
Reviewer #1 agreed
On 13 Jan, 2020
Editor assigned
On 16 Dec, 2019
Submission checks complete
On 15 Dec, 2019
Editor invited
On 15 Dec, 2019
First submitted
On 13 Dec, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Research Methodology.
Background The Multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis using latent variable framework, of which classical structural equation model is a special case. The MIMIC model provides rigorous results and becomes broadly available in multiple statistical software. The current study introduces the MIMIC model and how it can be implemented using statistical software SAS CALIS procedure, R lavaan package, and Mplus version 8.0.
Methods In this paper, we first discussed the formulation of the MIMIC model with regard to model specification and identification. We then demonstrated the empirical application of the MIMIC model with the Midlife in the United States II (MIDUS II) Study (N=4,109) using SAS CALIS procedure, R lavaan package and Mplus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software programs were also presented.
Results In terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities. Mplus is designed primarily for latent variable modeling and has far more modeling flexibility compared to SAS and R, but limited in data manipulation. Differences in model results from the three statistical programs are trivial. Overall, the results show that while men show better performance in executive function than women, women demonstrate better episodic memory than men.
Conclusions Our study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages. Results from our models align with empirical findings from previous research. We provide coding procedures and examples with detailed explanations in the hopes of providing a concise tutorial for researchers and methodologists interested in incorporating latent constructs with multiple indicators and multiple covariates in their research projects. Future researchers are encouraged to adopt this flexible and rigorous modeling approach.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
The full text of this article is available to read as a PDF.
Loading...