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PNG0098
Introduction to Meta-analysis in Ecology
Unit 3: Meta-analysis Teacher: Julia Koricheva (Julia.Koricheva@rhul.ac.uk ) Royal Holloway University of London,UK. 12-13 April 2021
1.Formulating the research question for a meta-analysis
2.Data extraction from primary studies
3.Calculation of effect sizes
4.Combining effect sizes across studies using fixed and random effect models
5.Exploring causes of heterogeneity across studies (meta-regression)
6.Sensitivity analysis and testing for publication bias
The course activites includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for two half days, equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students could use their own datasets as well.
1.Formulating the research question for a meta-analysis
2.Data extraction from primary studies
3.Calculation of effect sizes
4.Combining effect sizes across studies using fixed and random effect models
5.Exploring causes of heterogeneity across studies (meta-regression)
6.Sensitivity analysis and testing for publication bias
The course activites includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for two half days, equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students could use their own datasets as well.
Syllabus and other information
Syllabus
PNG0098 Introduction to Meta-analysis in Ecology, 1.0 Credits
Subjects
Mathematical StatisticsEducation cycle
Postgraduate levelGrading scale
Pass / Failed
Prior knowledge
Admitted to PhD-studies. Basic knowledge of statistics and RObjectives
This course is a separate unit of the course theme Advanced Statistics in Practice that aims to fill potential knowledge gaps by preparing students to analyze, interpret and report their data using the most up-to-date methods in R. Special attention will be given to discussion of questions springing from the students’ own work and the biological interpretation of data. The course is intended to deepen the students’ understanding of all aspects of ecological inferences, not as a cookbook of "how to". Learning outcomes 1-Demonstrate the ability to identify relevant functions and packages in R for analyzing their own datasets. 2-Analyze data using R, including reporting advanced statistics and generation of graphs. 3-Interpret, think critically and draw conclusions on data analysis results.Content
Unit 3: Meta-analysis Teacher: Julia Koricheva (Julia.Koricheva@rhul.ac.uk ) Royal Holloway University of London,UK. 12-13 April 2021 1.Formulating the research question for a meta-analysis 2.Data extraction from primary studies 3.Calculation of effect sizes 4.Combining effect sizes across studies using fixed and random effect models 5.Exploring causes of heterogeneity across studies (meta-regression) 6.Sensitivity analysis and testing for publication bias The course activites includes initial readings, classroom discussions, hands-on activities, followed by home exercises. This unit corresponds to one theme/workshop and is planned for two half days, equaling one credit (ca 27 hours of work for the PhD student). Example datasets will be provided but students could use their own datasets as well.Additional information
This course is the third independent course unit of five in the course theme Advanced Statistics in Practice organized by Mohammad Bahram in collaboration with the NJ-faculty research schools ’Ecology-basics and applications’ and ’Focus on Soils and Water’. The course plan for the theme and this course unit was accepted 20200922 by the steering group of the research school Ecology-basics and application.Course units of the theme Advanced Statistics in Practice 2021
•Time series analysis: Jonas Knape and Örjan Östman, February 1
•GIS and spatial analysis: Alistair Auffret and Mohammad Bahram, Feb. 15 and 18
•Meta-analysis: Julia Koricheva, April 12-13
•Getting more out of community data, Tomas Roslin and Giovanni Strona, May 3 & May 6
•Dealing with complexities of GLMs: Matt Low, May 31 & June 1
Responsible department
Department of Ecology