Experimental Design and Data Analysis for Biologists

by Gerry P. Quinn, Michael J. Keough

Overview

Applying statistical concepts to biological scenarios, this established textbook continues to be the go-to tool for advanced undergraduates and postgraduates studying biostatistics or experimental design in biology-related areas. Chapters cover linear models, common regression and ANOVA methods, mixed effects models, model selection, and multivariate methods used by biologists, requiring only introductory statistics and basic mathematics. Demystifying statistical concepts with clear, jargon-free explanations, this new edition takes a holistic approach to help students understand the relationship between statistics and experimental design. Each chapter contains further-reading recommendations, and worked examples from today's biological literature. All examples reflect modern settings, methodology and equipment, representing a wide range of biological research areas. These are supported by hands-on online resources including real-world data sets, full R code to help repeat analyses for all worked examples, and additional review questions and exercises for each chapter.

Product Details

ISBN-13:	9781107085664
Publisher:	Cambridge University Press
Publication date:	03/21/2002
Sold by:	Barnes & Noble
Format:	eBook
File size:	39 MB
Note:	This product may take a few minutes to download.

About the Author

Gerry Quinn is an Honorary Professor in the School of Life and Environmental Sciences at Deakin University, having served as Chair in Marine Biology and Head of Warrnambool Campus during his academic career. He has extensive experience in teaching biostatistics at Deakin University and the University of Gothenburg.

Michael J. Keough is an ecologist, environmental scientist, and honorary Professor in the School of Biosciences at University of Melbourne.

Table of Contents

	Preface	xv
1	Introduction	1
1.1	Scientific method	1
1.2	Experiments and other tests	5
1.3	Data, observations and variables	7
1.4	Probability	7
1.5	Probability distributions	9
2	Estimation	14
2.1	Samples and populations	14
2.2	Common parameters and statistics	15
2.3	Standard errors and confidence intervals for the mean	17
2.4	Methods for estimating parameters	23
2.5	Resampling methods for estimation	25
2.6	Bayesian inference - estimation	27
3	Hypothesis testing	32
3.1	Statistical hypothesis testing	32
3.2	Decision errors	42
3.3	Other testing methods	45
3.4	Multiple testing	48
3.5	Combining results from statistical tests	50
3.6	Critique of statistical hypothesis testing	51
3.7	Bayesian hypothesis testing	54
4	Graphical exploration of data	58
4.1	Exploratory data analysis	58
4.2	Analysis with graphs	62
4.3	Transforming data	64
4.4	Standardizations	67
4.5	Outliers	68
4.6	Censored and missing data	68
4.7	General issues and hints for analysis	71
5	Correlation and regression	72
5.1	Correlation analysis	72
5.2	Linear models	77
5.3	Linear regression analysis	78
5.4	Relationship between regression and correlation	106
5.5	Smoothing	107
5.6	Power of tests in correlation and regression	109
5.7	General issues and hints for analysis	110
6	Multiple and complex regression	111
6.1	Multiple linear regression analysis	111
6.2	Regression trees	143
6.3	Path analysis and structural equation modeling	145
6.4	Nonlinear models	150
6.5	Smoothing and response surfaces	152
6.6	General issues and hints for analysis	153
7	Design and power analysis	155
7.1	Sampling	155
7.2	Experimental design	157
7.3	Power analysis	164
7.4	General issues and hints for analysis	171
8	Comparing groups or treatments - analysis of variance	173
8.1	Single factor (one way) designs	173
8.2	Factor effects	188
8.3	Assumptions	191
8.4	ANOVA diagnostics	194
8.5	Robust ANOVA	195
8.6	Specific comparisons of means	196
8.7	Tests for trends	202
8.8	Testing equality of group variances	203
8.9	Power of single factor ANOVA	204
8.10	General issues and hints for analysis	206
9	Multifactor analysis of variance	208
9.1	Nested (hierarchical) designs	208
9.2	Factorial designs	221
9.3	Pooling in multifactor designs	260
9.4	Relationship between factorial and nested designs	261
9.5	General issues and hints for analysis	261
10	Randomized blocks and simple repeated measures: unreplicated two factor designs	262
10.1	Unreplicated two factor experimental designs	262
10.2	Analyzing RCB and RM designs	268
10.3	Interactions in RCB and RM models	274
10.4	Assumptions	280
10.5	Robust RCB and RM analyses	284
10.6	Specific comparisons	285
10.7	Efficiency of blocking (to block or not to block?)	285
10.8	Time as a blocking factor	287
10.9	Analysis of unbalanced RCB designs	287
10.10	Power of RCB or simple RM designs	289
10.11	More complex block designs	290
10.12	Generalized randomized block designs	298
10.13	RCB and RM designs and statistical software	298
10.14	General issues and hints for analysis	299
11	Split-plot and repeated measures designs: partly nested analyses of variance	301
11.1	Partly nested designs	301
11.2	Analyzing partly nested designs	309
11.3	Assumptions	318
11.4	Robust partly nested analyses	320
11.5	Specific comparisons	320
11.6	Analysis of unbalanced partly nested designs	322
11.7	Power for partly nested designs	323
11.8	More complex designs	323
11.9	Partly nested designs and statistical software	335
11.10	General issues and hints for analysis	337
12	Analyses of covariance	339
12.1	Single factor analysis of covariance (ANCOVA)	339
12.2	Assumptions of ANCOVA	348
12.3	Homogeneous slopes	349
12.4	Robust ANCOVA	352
12.5	Unequal sample sizes (unbalanced designs)	353
12.6	Specific comparisons of adjusted means	353
12.7	More complex designs	353
12.8	General issues and hints for analysis	357
13	Generalized linear models and logistic regression	359
13.1	Generalized linear models	359
13.2	Logistic regression	360
13.3	Poisson regression	371
13.4	Generalized additive models	372
13.5	Models for correlated data	375
13.6	General issues and hints for analysis	378
14	Analyzing frequencies	380
14.1	Single variable goodness-of-fit tests	381
14.2	Contingency tables	381
14.3	Log-linear models	393
14.4	General issues and hints for analysis	400
15	Introduction to multivariate analyses	401
15.1	Multivariate data	401
15.2	Distributions and associations	402
15.3	Linear combinations, eigenvectors and eigenvalues	405
15.4	Multivariate distance and dissimilarity measures	409
15.5	Comparing distance and/or dissimilarity matrices	414
15.6	Data standardization	415
15.7	Standardization, association and dissimilarity	417
15.8	Multivariate graphics	417
15.9	Screening multivariate data sets	418
15.10	General issues and hints for analysis	423
16	Multivariate analysis of variance and discriminant analysis	425
16.1	Multivariate analysis of variance (MANOVA)	425
16.2	Discriminant function analysis	435
16.3	MANOVA vs discriminant function analysis	441
16.4	General issues and hints for analysis	441
17	Principal components and correspondence analysis	443
17.1	Principal components analysis	443
17.2	Factor analysis	458
17.3	Correspondence analysis	459
17.4	Canonical correlation analysis	463
17.5	Redundancy analysis	466
17.6	Canonical correspondence analysis	467
17.7	Constrained and partial "ordination"	468
17.8	General issues and hints for analysis	471
18	Multidimensional scaling and cluster analysis	473
18.1	Multidimensional scaling	473
18.2	Classification	488
18.3	Scaling (ordination) and clustering for biological data	491
18.4	General issues and hints for analysis	493
19	Presentation of results	494
19.1	Presentation of analyses	494
19.2	Layout of tables	497
19.3	Displaying summaries of the data	498
19.4	Error bars	504
19.5	Oral presentations	507
19.6	General issues and hints	510
	References	511
	Index	527

Editorial Reviews

‘The new edition of this ‘go-to’ text, has been revised to take a more holistic, informative, and up-to-date approach to the subject. This remarkably accessible, practical and - at times - entertaining guide to how we can best translate our questions and ideas into informative experiments and analyses is highly recommended for everyone wanting to investigate, visualise, and analyse biological phenomena.’ Jonathon Havenhand, University of Gothenburg

‘The new book is an excellent resource for researchers, analysts and teachers. The text clearly outlines the important concepts of the tests and models. The examples are all based on published data which makes it easy to source the full manuscript, datasets and replicate the analyses which is incredibly important and assists with interpretation.’ Victoria Goodall, VLG Statistical Services

‘At last, a book for undergraduates and graduates that distills the complexities of biological data analysis into an easy-to-understand generalized linear model approach - with examples in R! The authors challenge the reader to think critically about data by providing important details on design, summary statistics, power analysis/effect size, model fit, and data visualization. This book will be used in my classes for years to come.’ Greg Moyer, Mansfield University

‘I have been using Experimental Design and Data Analysis for teaching and research for 20 years and have been hoping for a second edition for 10. It was worth the wait. The new edition shares many attributes with the original. It is quite easy to read, the examples are varied and interesting, there are ample and revealing box examples and there remains an attitude of Statistics as a tool that will be used by many. There are a number of changes that reflect these attributes - two key ones are the emphasis on Generalized Models as a framework for a broad array of approaches and the conversion of examples to R, with code and additional material available online.’ Peter Raimondi, University of California, Santa Cruz

‘I have taught in and coordinated a third-year design/statistics paper for zoology and ecology students for ten years - an enjoyable and sometimes challenging task. The first edition of Quinn and Keough has been immensely helpful for me in teaching this course. The second edition has been updated and expanded considerably. I’m sure it will continue to inspire my future teaching.’ Christoph Matthaei, University of Otago

‘I was excited to see Quinn and Keough have updated their classic guide to experimental design and data analysis. I read the earlier edition of this book as a graduate student, and the advice it provides on experimental design is the foundation of my own studies, as well as my approach to training graduate students … This book is foundational reading for aspiring scientists. Not only does it teach you how to analyse your data, it also provides invaluable advice on how to communicate analyses and write up scientific studies. The book’s advice will help give early career scientists the confidence they need to write-up and publish their first studies.’ Chris Brown, University of Tasmania

From the Publisher