ArticleEpidemiology (Cambridge, Mass.)2022
Constructed Measures and Causal Inference: Towards a New Model of Measurement for Psychosocial Constructs.
Article in Epidemiology (Cambridge, Mass.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Assessing the Potential Cumulative Effects of Longitudinal Interventions for Depression and Deviant Peer Association on Academic Achievement: a Causal Inference Approach.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Negatives about positivity and consistency as conditions for causal inference.American journal of epidemiology · 2026Article
- Development of a baseline battery to optimize depression treatment assignment in primary care: Balancing breadth and brevity.Journal of affective disorders · 2026Article
- Novel Psychometric Indicator Assessments: The Relative Excess Correlation and Associated Matrices.Epidemiology (Cambridge, Mass.) · 2026Article
- A Bayesian Causal Model for Matrix-Valued Exposures With Applications to Radiotherapy Planning.Statistics in medicine · 2026Article
- Reflective, formative, hybrid, and composite measurement models in social science research: a diagnostic construct-measurement alignment framework.Frontiers in research metrics and analytics · 2026Review
- Effect of Early Life Income and Sugars Intake on Child Oral Health: Marginal Structural Modelling Using a Birth Cohort Study.Caries research · 2026Article
- Depicting patient-reported outcome measures within directed acyclic graphs: practice and implications for causal reasoning.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Multidimensional versus unidimensional approaches to well-being.Nature human behaviour · 2025Review
- How to develop causal directed acyclic graphs for observational health research: a scoping review.Health psychology review · 2025Article
- The quantiles of extreme differences matrix for evaluating discriminant validity.Epidemiologic methods · 2025Article
- Sexual harassment, sexual violence and subsequent depression and anxiety symptoms among Swedish university students: a cohort study.Social psychiatry and psychiatric epidemiology · 2024Article
- Causal inference on human behaviour.Nature human behaviour · 2024Review
- Leisure Engagement and Self-Perceptions of Aging: Longitudinal Analysis of Concurrent and Lagged Relationships.The journals of gerontology. Series B, Psychological sciences and social sciences · 2024Article
- Methods in causal inference. Part 1: causal diagrams and confounding.Evolutionary human sciences · 2024Article
- Methods in causal inference. Part 3: measurement error and external validity threats.Evolutionary human sciences · 2024Article
- Tyler J. VanderWeele's contribution to the Discussion of 'Vintage Factor Analysis with Varimax Performs Statistical Inference' by Rohe & Zeng.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2023Article
- Exploring the meaning of unresolved loss and trauma in more than 1,000 Adult Attachment Interviews.Development and psychopathology · 2023Article
- Identifying causal subsequent memory effects.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Niche diversity effects on personality measurement - evidence from large national samples during the COVID-19 pandemic.Current research in ecological and social psychology · 2023Article
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Abstract
Psychosocial constructs can only be assessed indirectly, and measures are typically formed by a combination of indicators that are thought to relate to the construct. Reflective and formative measurement models offer different conceptualizations of the relation between the indicators and what is sometimes conceived of as a univariate latent variable supposed to correspond in some way to the construct. I argue that the empirical implications of reflective and formative models will often be violated by data since the causally relevant constituents will generally be multivariate, not univariate. These empirical implications can be formally tested but factor analysis is not adequate to do so. I argue that formative models misconstrue the relation between the constructed measures and the underlying reality by which causal processes operate, but that reflective models misconstrue the nature of the underlying reality itself by typically presuming that the constituents of it that are causally efficacious are unidimensional. I discuss the ensuing problems arising from these misconstruals. I propose a causal interpretation of associations between constructed measures and various outcomes that is applicable to both reflective and formative models and is applicable even if the usual assumptions of these models are violated. I put forward an outline for a new model of the process of measure construction and discuss the practical implications of these observations and proposals for the provision of definitions, the selection of items, item-by-item analyses, the construction of measures, and the interpretation of the associations of these measures with subsequent outcomes.
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