Stephanie R. Aarsman , Christopher J. Greenwood , Jake Linardon , Rachel F. Rodgers , Mariel Messer , Hannah K. Jarman , Matthew Fuller-Tyszkiewicz
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引用次数: 0
Abstract
Causal inference is often the goal of psychological research. However, most researchers refrain from drawing causal conclusions based on non-experimental evidence. Despite the challenges associated with producing causal evidence from non-experimental data, it is crucial to address causal questions directly rather than avoiding them. Here we provide a clear, non-technical overview of the fundamental concepts (including the counterfactual framework and related assumptions) and tools that permit causal inference in non-experimental data, intended as a starting point for readers unfamiliar with the literature. Certain tools, such as the target trial framework and causal diagrams, have been developed to assist with the identification and reduction of potential biases in study design and analysis and the interpretation of findings. We apply these concepts and tools to a motivating example from the body image field. We assert that more precise and detailed elucidation of the barriers to causal inference within one’s study is arguably a key first step in the enhancement of non-experimental research and future intervention development and evaluation.
期刊介绍:
Body Image is an international, peer-reviewed journal that publishes high-quality, scientific articles on body image and human physical appearance. Body Image is a multi-faceted concept that refers to persons perceptions and attitudes about their own body, particularly but not exclusively its appearance. The journal invites contributions from a broad range of disciplines-psychological science, other social and behavioral sciences, and medical and health sciences. The journal publishes original research articles, brief research reports, theoretical and review papers, and science-based practitioner reports of interest. Dissertation abstracts are also published online, and the journal gives an annual award for the best doctoral dissertation in this field.