MIMIC models, formative indicators and the joys of research

AMS Review ◽  
2013 ◽  
Vol 3 (3) ◽  
pp. 160-170 ◽  
Author(s):  
Adamantios Diamantopoulos ◽  
Dirk Temme
Keyword(s):  
Mimic Models ◽  
Formative Indicators

Assessing Tourism Supply Quality Using Formative Indicators: Implications for Destination Management

2010 ◽  
Vol 16 (2) ◽  
pp. 405-425 ◽  
Author(s):  
Tanja Dmitrović ◽  
Vesna Žabkar
Keyword(s):  
Formative Indicators

scholarly journals Currency demand and MIMIC models: towards a structured hybrid method of measuring the shadow economy

2018 ◽  
Vol 26 (1) ◽  
pp. 4-40 ◽  
Author(s):  
Piotr Dybka ◽  
Michał Kowalczuk ◽  
Bartosz Olesiński ◽  
Andrzej Torój ◽  
Marek Rozkrut
Keyword(s):  
Hybrid Method ◽  
Shadow Economy ◽  
Mimic Models ◽  
Currency Demand

scholarly journals Exploring the Test of Covariate Moderation Effects in Multilevel MIMIC Models

2018 ◽  
Vol 79 (3) ◽  
pp. 512-544
Author(s):  
Chunhua Cao ◽  
Eun Sook Kim ◽  
Yi-Hsin Chen ◽  
John Ferron ◽  
Stephen Stark
Keyword(s):  
Interaction Effect ◽  
Type I Error ◽  
Intraclass Correlation ◽  
Type I ◽  
Cluster Number ◽  
Structural Part ◽  
Mimic Models ◽  
Level Measurement ◽  
Multiple Indicator ◽  
Empirical Demonstration

In multilevel multiple-indicator multiple-cause (MIMIC) models, covariates can interact at the within level, at the between level, or across levels. This study examines the performance of multilevel MIMIC models in estimating and detecting the interaction effect of two covariates through a simulation and provides an empirical demonstration of modeling the interaction in multilevel MIMIC models. The design factors include the location of the interaction effect (i.e., between, within, or across levels), cluster number, cluster size, intraclass correlation (ICC) level, magnitude of the interaction effect, and cross-level measurement invariance status. Type I error, power, relative bias, and root mean square of error of the interaction effects are examined. The results showed that multilevel MIMIC models performed well in detecting the interaction effect at the within or across levels. However, when the interaction effect was at the between level, the performance of multilevel MIMIC models depended on the magnitude of the interaction effect, ICC, and sample size, especially cluster number. Overall, cross-level measurement noninvariance did not make a notable impact on the estimation of interaction in the structural part of multilevel MIMIC models when factor loadings were allowed to be different across levels.

Sex differences in latent general and broad cognitive abilities for children and youth: Evidence from higher-order MG-MACS and MIMIC models

Intelligence ◽  
2008 ◽  
Vol 36 (3) ◽  
pp. 236-260 ◽  
Author(s):  
Matthew R. Reynolds ◽  
Timothy Z. Keith ◽  
Kristen P. Ridley ◽  
Puja G. Patel
Keyword(s):  
Sex Differences ◽  
Cognitive Abilities ◽  
Higher Order ◽  
Children And Youth ◽  
Mimic Models

scholarly journals In defense of causal-formative indicators: A minority report.

2017 ◽  
Vol 22 (3) ◽  
pp. 581-596 ◽  
Author(s):  
Kenneth A. Bollen ◽  
Adamantios Diamantopoulos
Keyword(s):  
Formative Indicators ◽  
Minority Report

Rethinking balanced scorecard (BSC) measures: formative versus reflective measurement models

2017 ◽  
Vol 66 (1) ◽  
pp. 92-110 ◽  
Author(s):  
Sungbum Park ◽  
Heeseok Lee ◽  
Seong Wook Chae
Keyword(s):  
Balanced Scorecard ◽  
Latent Variables ◽  
Structural Equation ◽  
Model Comparison ◽  
Empirical Studies ◽  
Equation Modeling ◽  
Content Type ◽  
Formative Measurement ◽  
Measurement Models ◽  
Formative Indicators

Purpose Most empirical balanced scorecard (BSC) studies have shown a tendency to wrongly employ reflective indicators instead of the more theoretically suitable formative indicators. However, formative indicators are difficult to apply due to the lack of statistical software support and a standardized model testing method. The paper aims to discuss these issues. Design/methodology/approach This study empirically compares the reflective and formative measurement method with standardized model comparison criteria. After collecting 217 valid questionnaires from companies in South Korea, the authors applied a structural equation modeling technique to analyze the data. Findings The result shows that the formative measure provides greater validity for the corporate performance measurement using BSC. Further, this study shows the indicators’ relative influence on each BSC perspectives using the formative measure. Practical implications This study proved the usefulness of the formative measure analysis method and suggested its practical use, focusing on the indicators most useful in developing corporate strategies. In addition, the authors showed that formative indicators could be used in the corporate environment by overcoming the limitations of conventional studies that were confined to causal relationships with latent variables. Originality/value This study may be the pioneering work that compares formative and reflective indicators simultaneously, addressing the usefulness of formative measurement and its application validity in the existing empirical studies using reflective measurements.

Synthesis and Studies of a Super-Structured Porphyrin Derivative - A Potential Building Block for CcO Mimic Models

2009 ◽  
Vol 2009 (8) ◽  
pp. 1263-1268 ◽  
Author(s):  
Georgios Charalambidis ◽  
Kalliopi Ladomenou ◽  
Bernard Boitrel ◽  
Athanassios G. Coutsolelos
Keyword(s):  
Building Block ◽  
Porphyrin Derivative ◽  
Mimic Models

MIMIC models and formative measurement: some thoughts on Lee, Cadogan & Chamberlain

AMS Review ◽  
2013 ◽  
Vol 3 (1) ◽  
pp. 30-37 ◽  
Author(s):  
Adamantios Diamantopoulos
Keyword(s):  
Formative Measurement ◽  
Mimic Models

Applications and a Generalization of Mimic Models to Criminological Research

1984 ◽  
Vol 21 (4) ◽  
pp. 333-352 ◽  
Author(s):  
DOUGLAS A. SMITH ◽  
E. BRITT PATTERSON
Keyword(s):  
Mimic Models ◽  
Criminological Research

Viewpoint – Export performance measurement: reflective versus formative indicators

1999 ◽  
Vol 16 (6) ◽  
pp. 444-457 ◽  
Author(s):  
Adamantios Diamantopoulos
Keyword(s):  
Performance Measurement ◽  
Export Performance ◽  
Formative Indicators
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