File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Multiple indicators and multiple causes (MIMIC) models as a mixed-modeling technique: A tutorial and an annotated example

TitleMultiple indicators and multiple causes (MIMIC) models as a mixed-modeling technique: A tutorial and an annotated example
Authors
KeywordsCovariance-based SEM
Protection-motivated behaviors
MIMIC modeling
Methodology
Formative construct validation
Issue Date2015
Citation
Communications of the Association for Information Systems, 2015, v. 36, p. 179-204 How to Cite?
Abstract© 2014 by the Association for Information Systems.Formative modeling of latent constructs has produced great interest and discussion among scholars in recent years. However, confusion exists surrounding researchers’ ability to validate these models, especially with covariancebased structural equation modeling (CB-SEM) techniques. With this paper, we help to clarify these issues and explain how formatively modeled constructs can be assessed rigorously by researchers using CB-SEM capabilities. In particular, we explain and provide an applied example of a mixed-modeling technique termed multiple indicators and multiple causes (MIMIC) models. Using this approach, researchers can assess formatively modeled constructs as the final, distal dependent variable in CB-SEM structural models—something previously impossible because of CB-SEM’s mathematical identification rules. Moreover, we assert that researchers can use MIMIC models to assess the content validity of a set of formative indicators quantitatively—something considered conventionally only from a qualitative standpoint. The research example we use in this manuscript involving protection-motivated behaviors (PMBs) details the entire process of MIMIC modeling and provides a set of detailed guidelines for researchers to follow when developing new constructs modeled as MIMIC structures.
Persistent Identifierhttp://hdl.handle.net/10722/233742
ISSN
2015 SCImago Journal Rankings: 0.483

 

DC FieldValueLanguage
dc.contributor.authorPosey, Clay-
dc.contributor.authorRoberts, Tom L.-
dc.contributor.authorLowry, Paul Benjamin-
dc.contributor.authorBennett, Rebecca J.-
dc.date.accessioned2016-09-27T07:21:31Z-
dc.date.available2016-09-27T07:21:31Z-
dc.date.issued2015-
dc.identifier.citationCommunications of the Association for Information Systems, 2015, v. 36, p. 179-204-
dc.identifier.issn1529-3181-
dc.identifier.urihttp://hdl.handle.net/10722/233742-
dc.description.abstract© 2014 by the Association for Information Systems.Formative modeling of latent constructs has produced great interest and discussion among scholars in recent years. However, confusion exists surrounding researchers’ ability to validate these models, especially with covariancebased structural equation modeling (CB-SEM) techniques. With this paper, we help to clarify these issues and explain how formatively modeled constructs can be assessed rigorously by researchers using CB-SEM capabilities. In particular, we explain and provide an applied example of a mixed-modeling technique termed multiple indicators and multiple causes (MIMIC) models. Using this approach, researchers can assess formatively modeled constructs as the final, distal dependent variable in CB-SEM structural models—something previously impossible because of CB-SEM’s mathematical identification rules. Moreover, we assert that researchers can use MIMIC models to assess the content validity of a set of formative indicators quantitatively—something considered conventionally only from a qualitative standpoint. The research example we use in this manuscript involving protection-motivated behaviors (PMBs) details the entire process of MIMIC modeling and provides a set of detailed guidelines for researchers to follow when developing new constructs modeled as MIMIC structures.-
dc.languageeng-
dc.relation.ispartofCommunications of the Association for Information Systems-
dc.subjectCovariance-based SEM-
dc.subjectProtection-motivated behaviors-
dc.subjectMIMIC modeling-
dc.subjectMethodology-
dc.subjectFormative construct validation-
dc.titleMultiple indicators and multiple causes (MIMIC) models as a mixed-modeling technique: A tutorial and an annotated example-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-84923011086-
dc.identifier.volume36-
dc.identifier.spage179-
dc.identifier.epage204-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats