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Conceptualizing and quantifying body condition using structural equation modelling: A user guide.

ABSTRACT: Body condition is an important concept in behaviour, evolution and conservation, commonly used as a proxy of an individual's performance, for example in the assessment of environmental impacts. Although body condition potentially encompasses a wide range of health state dimensions (nutritional, immune or hormonal status), in practice most studies operationalize body condition using a single (univariate) measure, such as fat storage. One reason for excluding additional axes of variation may be that multivariate descriptors of body condition impose statistical and analytical challenges. Structural equation modelling (SEM) is used in many fields to study questions relating multidimensional concepts, and we here explain how SEM is a useful analytical tool to describe the multivariate nature of body condition. In this 'Research Methods Guide' paper, we show how SEM can be used to resolve different challenges in analysing the multivariate nature of body condition, such as (a) variable reduction and conceptualization, (b) specifying the relationship of condition to performance metrics, (c) comparing competing causal hypothesis and (d) including many pathways in a single model to avoid stepwise modelling approaches. We illustrated the use of SEM on a real-world case study and provided R-code of worked examples as a learning tool. We compared the predictive power of SEM with conventional statistical approaches that integrate multiple variables into one condition variable: multiple regression and principal component analyses. We show that model performance on our dataset is higher when using SEM and led to more accurate and precise estimates compared to conventional approaches. We encourage researchers to consider SEM as a flexible framework to describe the multivariate nature of body condition and thus understand how it affects biological processes, thereby improving the value of body condition proxies for predicting organismal performance. Finally, we highlight that it can be useful for other multidimensional ecological concepts as well, such as immunocompetence, oxidative stress and environmental conditions.

SUBMITTER: Frauendorf M

PROVIDER: S-EPMC9291099 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe literature has identified various factors that promote or hinder people's intentions towards COVID-19 vaccination, and structural equation modelling (SEM) is a common approach to validate these associations. We propose a conceptual framework called social media infodemic listening (SoMeIL) for public health behaviours. Hypothesizing parameters retrieved from social media platforms can be used to infer people's intentions towards vaccination behaviours. This study preliminarily validates several components of the SoMeIL conceptual framework using SEM and Twitter data and examines the feasibility of using Twitter data in SEM research.MethodsA total of 2420 English tweets in Toronto or Ottawa, Ontario, Canada, were collected from March 8 to June 30, 2021. Confirmatory factor analysis and SEM were applied to validate the SoMeIL conceptual framework in this cross-sectional study.FindingsThe results showed that sentiment scores, the log-numbers of favourites and retweets of a tweet, and the log-numbers of a user's favourites, followers, and public lists had significant direct associations with COVID-19 vaccination intention. The sentiment score of a tweet had the strongest relationship, whereas a user's number of followers had the weakest relationship with the intention of COVID-19 vaccine uptake.InterpretationThe findings preliminarily validate several components of the SoMeIL conceptual framework by testing associations between self-reported COVID-19 vaccination intention and sentiment scores and the log-numbers of a tweet's favourites and retweets as well as users' favourites, followers, and public lists. This study also demonstrates the feasibility of using Twitter data in SEM research. Importantly, this study preliminarily validates the use of these six components as online reaction behaviours in the SoMeIL framework to infer the self-reported COVID-19 vaccination intentions of Canadian Twitter users in two cities.FundingThis study was supported by the 2023-24 Ontario Graduate Scholarship.

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Conceptualizing and quantifying body condition using structural equation modelling: A user guide.

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