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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.12
- 수록면
- 71 - 84 (14page)
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Over the past decade, we have witnessed Serious Debates in MISQ and Other Journals Between Two Camps that have Differing Views on the use of Causal Indicators to Measure Constructs. There is the Camp that advocates Causal Indicators (ADVOCATE) and the Camp that opposes Their Usage (OPPONENT). The Debates have been primarily centered on the OPPONENT’s Argument that the Meaning of a Latent Variable is determined by its Outcome Variables. However, Little Effort has been made to Validate the ADVOCATE’s Dispute (Against the OPPONENT’s Arguments) that the Meaning of a Latent Variable is decided by its Causal Indicators if there is no Misspecification. Our Study precisely examines the Integrity of the Argument. For this, we empirically examine how the two Primary Psychometric Properties-Comprehensiveness and Interrelationship-of Causal Indicators Influence Theory Testing between Latent Variables through Three Different Tests (i.e., Comprehensive Test, Interrelationship Test, and Mixed Test). Conducted on Two Different Datasets, Our Analysis Consistently Reveals that Structural Path Coefficients are Hardly Sensitive to the Changes (i.e., Misspecification) in the Properties of Causal Indicators. The Discovery offers Important Evidence that the Sound Theoretical Logic of a Causal Model is not in Sync with the Empirical Mechanism of Parameter Estimation. This Underscores that a Latent Variable Formed by Causal Indicators is empirically an elusive notion that is Difficult to Operationalize. As Our Results have Significant Implications on the Integrity of Numerous IS studies which have conducted Theory or Hypothesis Testing Using Causal Indicators, we strongly advocate Open Discussions among Methodologists regarding Our Findings and Their Implications for Both Published IS Research and Future Practices.
목차
Abstract
1. Introduction
2. Heated Debates
3. Comprehensiveness and Interrelationship
4. Empirical Testing
5. Comprehensiveness Test
6. Interrelationship Test
7. Mixed Test
8. Discussions
9. Conclusion
References
참고문헌 (32)
참고문헌 신청
[학술지(정기간행물)] - Lee, N. / 2013 / The MIMIC Model and Formative Variables : Problems and Solutions / AMS Review 3 (1) : 3 ~ 17
[학술지(정기간행물)] - Hardin, A. M. / 2013 / Does Existing Measurement Theory Support the Use of Composite and Causal Indicators in Information Systems Research? / DATA BASE for Advances in Information Systems 44 (4) : 56 ~ 65
[학술지(정기간행물)] - Bainter, S. A. / 2014 / Interpretational Confounding or Confounded Interpretations of Causal Indicators? / Measurement : Interdisciplinary Research and Perspectives 12 (4) : 125 ~ 140
[학술지(정기간행물)] - Diamantopoulos, A. / 2011 / Incorporating Formative Measures into Covariance-based Structural Equation Models / MIS Quarterly 35 (2) : 335 ~ 358
[학술지(정기간행물)] - Howell, R. D. / 2013 / Formative Measurement: A Critical Perspective / DATA BASE for Advances in Information Systems 44 (4) : 44 ~ 54
[학술지(정기간행물)] - Hardin, A. M. / 2013 / Does Existing Measurement Theory Support the Use of Composite and Causal Indicators in Information Systems Research? / DATA BASE for Advances in Information Systems 44 (4) : 56 ~ 65
[학술지(정기간행물)] - Bainter, S. A. / 2014 / Interpretational Confounding or Confounded Interpretations of Causal Indicators? / Measurement : Interdisciplinary Research and Perspectives 12 (4) : 125 ~ 140
[학술지(정기간행물)] - Diamantopoulos, A. / 2011 / Incorporating Formative Measures into Covariance-based Structural Equation Models / MIS Quarterly 35 (2) : 335 ~ 358
[학술지(정기간행물)] - Howell, R. D. / 2013 / Formative Measurement: A Critical Perspective / DATA BASE for Advances in Information Systems 44 (4) : 44 ~ 54
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UCI(KEPA) : I410-ECN-0101-2018-530-001676277