How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.

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Generalized linear mixed models: a practical guide for ecology and evolution

Statistics for Spatial Data.

To better define levels of accomplishment for publishing journal articles in strategic management, a bibliometric study was performed on the publication records of 96 doctorates in the field whose first post-degree job was in academics. By examining 20 journals that are outlets for research in strategic management, publication records were developed for each individual for the first 5-10 years following receipt of the doctoral degree. Two factors influenced the publication records of these new faculty. Having publications prior to receiving the doctorate and getting a first job at an institution with a graduate program in management were associated with more frequent publishing after an academic career began. As expected, the number of papers published was related to the likelihood of receiving tenure. However, despite the fact that they had produced more papers during the first 5 years than male faculty members and had higher citation rates, female faculty members were less likely to receive tenure. The findings are discussed in terms of institutional policy for hiring and evaluating new faculty.

Publication records and tenure decisions in the field of strategic management

Unit root tests for panel data

Genetics and analysis of quantitative traits

SAS for mixed models

Mixed linear models were developed by animal breeders to evaluate genetic potential of bulls. Application of mixed models has recently spread to all areas of research, spurred by availability of advanced computer software. Previously, mixed model analyses were implemented by adapting fixed-effect methods to models with random effects. This imposed limitations on applicability because the covariance structure was not modeled. This is the case with PROC GLM in the SAS® System. Recent versions of the SAS System include PROC MIXED. This procedure implements random effects in the statistical model and permits modeling the covariance structure of the data. Thereby, PROC MIXED can compute efficient estimates of fixed effects and valid standard errors of the estimates. Modeling the covariance structure is especially important for analysis of repeated measures data because measurements taken close in time are potentially more highly correlated than those taken far apart in time.

Statistical analysis of repeated measures data using SAS procedures.

The term 'repeated measures' refers to data with multiple observations on the same sampling unit. In most cases, the multiple observations are taken over time, but they could be over space. It is usually plausible to assume that observations on the same unit are correlated. Hence, statistical analysis of repeated measures data must address the issue of covariation between measures on the same unit. Until recently, analysis techniques available in computer software only offered the user limited and inadequate choices. One choice was to ignore covariance structure and make invalid assumptions. Another was to avoid the covariance structure issue by analysing transformed data or making adjustments to otherwise inadequate analyses. Ignoring covariance structure may result in erroneous inference, and avoiding it may result in inefficient inference. Recently available mixed model methodology permits the covariance structure to be incorporated into the statistical model. The MIXED procedure of the SAS((R)) System provides a rich selection of covariance structures through the RANDOM and REPEATED statements. Modelling the covariance structure is a major hurdle in the use of PROC MIXED. However, once the covariance structure is modelled, inference about fixed effects proceeds essentially as when using PROC GLM. An example from the pharmaceutical industry is used to illustrate how to choose a covariance structure. The example also illustrates the effects of choice of covariance structure on tests and estimates of fixed effects. In many situations, estimates of linear combinations are invariant with respect to covariance structure, yet standard errors of the estimates may still depend on the covariance structure.

https://faculty.washington.edu/heagerty/Courses/VA-longitudinal/private/Littell-StatMed2000.pdf

Modelling covariance structure in the analysis of repeated measures data.

Acknowledgments. Chapter 1. Introduction. Chapter 2. Regression. Chapter 3. Analysis of Variance for Balanced Data. Chapter 4. Analyzing Data with Random Effects. Chapter 5. Unbalanced Data Analysis: Basic Methods. Chapter 6. Understanding Linear Models Concepts. Chapter 7. Analysis of Covariance. Chapter 8. Repeated-Measures Analysis. Chapter 9. Multivariate Linear Models. Chapter 10. Generalized Linear Models. Chapter 11. Examples of Special Applications. References. Index.

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SAS for Linear Models

Professionals and students with a background in two-way ANOVA and regression and a basic knowledge of linear models and matrix algebra will benefit from the topics covered.Includes a free CD-ROM with example SAS code!

Ramon C. Littell

Papers

SAS for mixed models

Statistical analysis of repeated measures data using SAS procedures.

Modelling covariance structure in the analysis of repeated measures data.

SAS for Linear Models

SAS for Mixed Models, Second Edition