Generalized structural equations improve sexual-selection analyses

Abstract

Sexual selection is an intense evolutionary force, which operates through competition for the access to breeding resources. There are many cases where male copulatory success is highly asymmetric, and few males are able to sire most females. Two main hypotheses were proposed to explain this asymmetry: “female choice” and “male dominance”. The literature reports contrasting results. This variability may reflect actual differences among studied populations, but it may also be generated by methodological differences and statistical shortcomings in data analysis. A review of the statistical methods used so far in lek studies, shows a prevalence of Linear Models (LM) and Generalized Linear Models (GLM) which may be affected by problems in inferring cause-effect relationships; multi-collinearity among explanatory variables and erroneous handling of non-normal and non-continuous distributions of the response variable. In lek breeding, selective pressure is maximal, because large numbers of males and females congregate in small arenas. We used a dataset on lekking fallow deer (Dama dama), to contrast the methods and procedures employed so far, and we propose a novel approach based on Generalized Structural Equations Models (GSEMs). GSEMs combine the power and flexibility of both SEM and GLM in a unified modeling framework. We showed that LMs fail to identify several important predictors of male copulatory success and yields very imprecise parameter estimates. Minor variations in data transformation yield wide changes in results and the method appears unreliable. GLMs improved the analysis, but GSEMs provided better results, because the use of latent variables decreases the impact of measurement errors. Using GSEMs, we were able to test contrasting hypotheses and calculate both direct and indirect effects, and we reached a high precision of the estimates, which implies a high predictive ability. In synthesis, we recommend the use of GSEMs in studies on lekking behaviour, and we provide guidelines to implement these models.

Examining the Boundaries of Ethical Leadership: The Harmful Effect of Co-worker Social Undermining on Disengagement and Employee Attitudes

“…Therefore, the model and proposed hypotheses were tested using generalised multilevel structural equation modeling (GSEM) with STATA. GSEM is a technique that combines the flexibility and power of both Generalized Linear Models and Structural Equation Models in an integrated modeling framework (Lombardi et al 2017). It simultaneously considers direct and indirect effects of several interacting factors and is ideal for addressing hypotheses with nested data (Preacher et al 2010;Lombardi et al 2017).…”

A quantitative method for measuring the relationship between an objective endpoint and patient reported outcome measures

“…However, because a negative agreement was not considered, the Rasch positive probability did not measure the probability of revealing truth from a PROM. Our approach is related to latent variable models for similar problems [ 37 , 38 ] in the sense that a iz can be regarded as a latent variable. On the other hand, we do not assume a particular distribution for a iz , which makes our approach different from most latent variable models.…”

The Role of Parent-Adolescent Relationships in the Development of (Pre)Hypertension in Young Adulthood in the U.S.

“…However, GSEM estimations using the Stata software are unable to compute goodness-of-fit indices. To address this issue, we performed case-specific residual examinations based on the principle that the residuals should have a mean 0 if the observed and predicted frequencies match well [37]. We found that residuals had a mean very close to 0; this suggests that our model was high-quality and fitted well with the data.…”