Analysis of multiplicative combination rules when the causal variables are measured with error
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Linear models are generally found to account quite well for psychological data, even when the data set is known to have been generated by a multiplicative model.
“It is not clear why significant interactions failed to emerge in these studies, but it is worth noting that linear models are generally found to account quite well for psychological data, even when the data set is known to have been generated by a multiplicative model (Bimbaum, 1972;Busemeyer & Jones, 1983).”
From The theory of planned behavior (1991) · cites this paper as Busemeyer & Jones, 1983
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“Voting choice, of course, poses no problems in terms of volitional control, and perceptions”
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Statistical difficulties of detecting interactions and moderator effects.
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Issues and Challenges in Studying Parental Control: Toward a New Conceptualization
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Relating alternative forms of contingency fit to the appropriate methods to test them
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- Authors
- Busemeyer, J. R., & Jones, L. E.
- Affiliations
- Indiana University – Purdue University Indianapolis (US)
- ORCID
- Busemeyer, Jerome R.
- Venue
- Psychological Bulletin
- Published
- 1983-05
- Volume
- 93
- Issue
- 3
- Pages
- 549-562
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W2013267907
- Cited by
- 448
- References
- 0
- Citations by year
- 2026: 16 · 2025: 9 · 2024: 14 · 2023: 10 · 2022: 12 · 2021: 12 · 2020: 14 · 2019: 18 · 2018: 17 · 2017: 13 · 2016: 14 · 2015: 11 · 2014: 14 · 2013: 17 · 2012: 11
- FWCI
- 5.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Bulletin (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- causal factors, measurement error, error propagation, causal models
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