The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

31 32The use of linear mixed effects models (LMMs) is increasingly common in the analysis 33 of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of 34 data types, ecological data are often complex and require complex model structures, 35 and the fitting and interpretation of such models is not always straightforward. The 36 ability to achieve robust biological inference requires that practitioners know how and 37 when to apply these tools. Here, we provide a general overview of current methods for 38 the application of LMMs to biological data, and highlight the typical pitfalls that can be 39 encountered in the statistical modelling process. We tackle several issues relating to the 40 use of information theory and multi-model inference in ecology, and demonstrate the 41 tendency for data dredging to lead to greatly inflated Type I error rate (false positives) 42 and impaired inference. We offer practical solutions and direct the reader to key

Maternal attachment representations were assessed using the George, Kaplan, and Main (1985) Adult Attachment Interview (AAI), and emotional availability during observed mother-child interactions was assessed using the third edition of the Emotional Availability (EA) Scales (Biringen, Robinson, & Emde, 1998). This edition of EA included four parental scales and two child scales (Maternal Sensitivity, Structuring, Nonintrusiveness and Nonhostility; and Child Responsiveness and Child Involvement). Separate Hierarchical Multiple Regressions (HMRs) were computed to examine the prediction of the separate EA dimensions from demographic information, the AAI classification, and AAI scales. These analyses indicated that each of the EA dimensions (with the exception of maternal nonintrusiveness and nonhostility) was predicted by the AAI classification and/or AAI scales. Using three-step HMRs, the strongest prediction was for maternal sensitivity where 54% of the total variance in maternal sensitivity was explained by maternal education, AAI classification, and AAI 'state of mind' scales. Maternal nonhostility was predicted by maternal education and gender of the child, with lower-income mothers and mothers of girls demonstrating greater hostility.

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues relating to the use of information theory and multi-model inference in ecology, and demonstrate the tendency for data dredging to lead to greatly inflated Type I error rate (false positives) and impaired inference. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.