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Experimental Design and Data Analysis for Biologists
Abstract
An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.
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“VIFs did not reveal collinearity problems, as indicated by the largest value being <4 (51) (event model 2.59; anticipation model 2.43; defection model 1.98; intergroup conflict type model 2.74; persistence model 2.03; reduced event model 2.72).”
From Oxytocin reactivity during intergroup conflict in wild chimpanzees (2017) · cites this paper as [51]
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Standard methods for research onApis melliferagut symbionts
Engel, P., James, R. R., Koga, R., Kwong, W. K., McFrederick, Q. S., & Moran, N. A. (2013) · Journal of Apicultural Research · doi:10.3896/ibra.1.52.4.07 · Cited by 138
“NMDS is recommended, as NMDS is non-parametric, free of assumptions, and can reduce the data into fewer axes than PCoA (Quinn and Keough, 2002; Ramette, 2007).”
BackgroundMethodInfluentialEldridge, D. J., & Soliveres, S. (2015) · Australian Journal of Botany · doi:10.1071/bt14137 · Cited by 136
Lyons, C. L., Coetzee, M., & Chown, S. L. (2013) · Parasites & Vectors · doi:10.1186/1756-3305-6-104 · Cited by 132
“Deviations from normality occurred in some instances, but model assumptions were generally met [47,48] (Additional files 3 and 4).”
Global Demand for Natural Resources Eliminated More Than 100,000 Bornean Orangutans
Voigt, M., Wich, S. A., Ancrenaz, M., Meijaard, E., Abram, N. K., Banes, G. L., Campbell‐Smith, G., D'Arcy, L. J., Delgado, R. A., Erman, A., et al. (2018) · Current Biology · doi:10.1016/j.cub.2018.01.053 · Cited by 150
“We tested for collinearity, which was not an issue (largest Variance Inflation Factor = 4.429, see also Table S1) and leverage values as well as DFBeta values did not indicate obviously influential cases [63,64].”
InfluentialTemporal shifts in activity of prey following large predator reintroductions
Tambling, C. J., Minnie, L., Meyer, J. M., Freeman, E. W., Santymire, R. M., Adendorff, J., & Kerley, G. I. H. (2015) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-015-1929-6 · Cited by 138
“05 (Quinn and Keough 2002), and excluding variables that failed to meet the criteria.”
BackgroundMethodInfluentialEffects of tree height on branch hydraulics, leaf structure and gas exchange in California redwoods
Ambrose, A. R., Sillett, S. C., & Dawson, T. E. (2009) · Plant Cell & Environment · doi:10.1111/j.1365-3040.2009.01950.x · Cited by 134
Fundamentals of Experimental Design: Guidelines for Designing Successful Experiments
Casler, M. D. (2013) · Agronomy Journal · doi:10.2134/agronj2013.0114 · Cited by 138
“Replicate observations must occur at a spatial and temporal scale that matches the application of the treatments (Quinn and Keough, 2002).”
BackgroundChanging philosophies and tools for statistical inferences in behavioral ecology
Garamszegi, L. Z., Calhim, S., Dochtermann, N. A., Hegyi, G., Hurd, P. L., Jørgensen, C., Kutsukake, N., Lajeunesse, M. J., Pollard, K. A., Schielzeth, H., et al. (2009) · Behavioral Ecology · doi:10.1093/beheco/arp137 · Cited by 133
“Behavioral Ecology doi:10.1093/beheco/arp137 Advance Access publication 2 November 2009 Review Changing philosophies and tools for statistical inferences in behavioral ecology”
BackgroundInfluentialLong-Term Effects of the Cleaner Fish Labroides dimidiatus on Coral Reef Fish Communities
Waldie, P., Blomberg, S. P., Cheney, K. L., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021201 · Cited by 133
“25) following Quinn and Keough [51].”
Forest tree growth response to hydroclimate variability in the southern Appalachians
Elliott, K. J., Miniat, C. F., Pederson, N., & Laseter, S. H. (2015) · Global Change Biology · doi:10.1111/gcb.13045 · Cited by 133
“Only a single variable from the same family of variables was included in the analysis (Quinn & Keough, 2002).”
BackgroundMcKie, B. G., Woodward, G., Hladyz, S., Nistorescu, M., Preda, E., Popescu, C., Giller, P. S., & Malmqvist, B. (2008) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2008.01357.x · Cited by 136
Protected‐Area Boundaries as Filters of Plant Invasions
FOXCROFT, L. C., JAROŠÍK, V., PYŠEK, P., RICHARDSON, D. M., & ROUGET, M. (2011) · Conservation Biology · doi:10.1111/j.1523-1739.2010.01617.x · Cited by 133
“Thus, we used them as explanatory variables in a logistic regression with binomial errors and logit link function (Quinn & Keough 2002) in which the presence or absence of non-native species in each of the 638 segments was the response variable.”
MethodConservation and the botanist effect
Ahrends, A., Rahbek, C., Bulling, M. T., et al. (2010) · Biological Conservation · doi:10.1016/j.biocon.2010.08.008 · Cited by 133
“7) according to the predictive power of variables in univariate tests (Quinn and Keough, 2002).”
InfluentialVine, S. J., Crowther, M. S., Lapidge, S. J., Dickman, C. R., Mooney, N., Piggott, M. P., & English, A. W. (2009) · Wildlife Research · doi:10.1071/wr08069 · Cited by 133
“We used log-linear models with expected frequencies modelled against variables by using log-link and Poisson error terms (Agresti 1996; Quinn and Keough 2002), and maximum likelihood methods to fit the models and estimate their parameters.”
The effect of coral morphology on shelter selection by coral reef fishes
Kerry, J. T., & Bellwood, D. R. (2011) · Coral Reefs · doi:10.1007/s00338-011-0859-7 · Cited by 143
Hawaiian albatrosses track interannual variability of marine habitats in the North Pacific
Kappes, M. A., Shaffer, S. A., Tremblay, Y., Foley, D. G., Palacios, D. M., Robinson, P. W., Bograd, S. J., & da Costa, D. P. (2010) · Progress In Oceanography · doi:10.1016/j.pocean.2010.04.012 · Cited by 132
Koiter, A. J., Owens, P. N., Petticrew, E. L., et al. (2017) · CATENA · doi:10.1016/j.catena.2017.01.024 · Cited by 132
“All models were checked for multiple collinearity (all variation inflation factors (VIF) were b5 and condition numbers (i.e., kappa) were b20) (Quinn and Keough, 2002).”
MethodBond, N. R., & Downes, B. J. (2003) · Freshwater Biology · doi:10.1046/j.1365-2427.2003.01016.x · Cited by 132
Glen, A. S., & Dickman, C. R. (2008) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2007.01449.x · Cited by 133
Werden, L., Barker, I. K., Bowman, J., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085640 · Cited by 131
“To allow comparison of the relative effects of the coefficients, continuous fixed effects variables were standardized to have a mean of zero and standard deviation of one [65].”
MethodAbed, R. M. M. (2009) · International Biodeterioration & Biodegradation · doi:10.1016/j.ibiod.2009.10.008 · Cited by 131
Sheriff, M. J., Kenagy, G. J., Richter, M. M., Lee, T., Tøien, Ø., Kohl, F., Buck, C. L., & Barnes, B. M. (2010) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2010.2482 · Cited by 135
Kikuchi, T., Fujii, M., Terao, K., et al. (2016) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2016.10.068 · Cited by 131
The area‐independent effects of habitat complexity on biodiversity vary between regions
Johnson, M. P., Frost, N. J., Mosley, M. W. J., Roberts, M. F., & Hawkins, S. J. (2003) · Ecology Letters · doi:10.1046/j.1461-0248.2003.00404.x · Cited by 130
Maguire, G. S., Miller, K. K., Weston, M. A., et al. (2011) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2011.07.012 · Cited by 131
“Statistical procedures follow Quinn and Keough (2002) and readers are referred to that text for detailed explanations of analyses.”
BackgroundEye shape and the nocturnal bottleneck of mammals
Hall, M., Kamilar, J. M., & Kirk, E. C. (2012) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2012.2258 · Cited by 132
Pesticide Risk Mitigation by Vegetated Treatment Systems: A Meta-Analysis
Stehle, S., Elsaesser, D., Grégoire, C., Imfeld, G., Niehaus, E., Passeport, E., Payraudeau, S., Schäfer, R. B., Tournebize, J., & Schulz, R. (2011) · Journal of Environmental Quality · doi:10.2134/jeq2010.0510 · Cited by 137
Determinants of the biogeographical distribution of butterflies in boreal regions
Luoto, M., Heikkinen, R. K., Pöyry, J., & Saarinen, K. (2006) · Journal of Biogeography · doi:10.1111/j.1365-2699.2005.01395.x · Cited by 133
“To improve the normality of the predictors (Quinn & Keough, 2002), the land-cover variables were log10transformed, except for the cover of coniferous forest, which was normally distributed.”
MethodSaproxylic beetles in natural and man-made deciduous high stumps retained for conservation
Jonsell, M., Nittérus, K., & Stighäll, K. (2003) · Biological Conservation · doi:10.1016/j.biocon.2003.08.017 · Cited by 130
Hamer, P. A., & Jenkins, G. P. (2007) · Journal of Fish Biology · doi:10.1111/j.1095-8649.2007.01570.x · Cited by 130
Díaz, L. (2005) · Forest Ecology and Management · doi:10.1016/j.foreco.2005.10.061 · Cited by 130
“Both backward and forward procedures were used for each regression analysis, due to the influence that the order of introduction or exclusion of variables in the stepwise procedures may have on the results (chiefly if colinearity problems exist; see Sen and Srivastava, 1990; Quinn and Keough, 2002).”
MethodBarnacle reproductive hotspots linked to nearshore ocean conditions
Leslie, H. M., Breck, E. N., Chan, F., Lubchenco, J., & Menge, B. A. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0503874102 · Cited by 132
“When missing data led to biased estimates of the cape effect in nested models (34), linear contrasts or t tests were used instead to test for cape effects.”
MethodUthicke, S., Logan, M., Liddy, M., Francis, D. S., Hardy, N. A., & Lamare, M. D. (2015) · Scientific Reports · doi:10.1038/srep08402 · Cited by 134
Analysis of Variance and Covariance: How to Choose and Construct Models for the Life Sciences
Doncaster, C. P., & Davey, A. J. H. (2007) · ePrints Soton (University of Southampton) · Cited by 129
Herbivore damage along a latitudinal gradient: relative impacts of different feeding guilds
Andrew, N. R., & Hughes, L. (2004) · Oikos · doi:10.1111/j.0030-1299.2005.13457.x · Cited by 129
“Pillai’s trace was the test statistic used (Tabachnick and Fidell 2001, Quinn and Keough 2002).”
MethodBoyett, H. V., Bourne, D. G., & Willis, B. L. (2007) · Marine Biology · doi:10.1007/s00227-006-0603-y · Cited by 129
“compensate for missing values (Zar 1999; Scheiner and Gurevitch 2001; Quinn and Keough 2002).”
BackgroundBishop, C. D., Huggett, M. J., Heyland, A., Hodin, J., & Brandhorst, B. P. (2006) · Integrative and Comparative Biology · doi:10.1093/icb/icl043 · Cited by 137
“Data compilation, tree construction, and character correlation analyses For a 2 · 2 · 2 contingency table, there are 8 cells, and therefore at least 40 data points are required (5 · the number of cells) (Keough and Quinn 2002), and our data met this requirement.”
Adams, M. D., Law, B., & French, K. (2009) · Forest Ecology and Management · doi:10.1016/j.foreco.2009.08.002 · Cited by 128
Personality-dependent dispersal cancelled under predation risk
Côté, J., Fogarty, S., Tymen, B., Sih, A., & Brodin, T. (2013) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2013.2349 · Cited by 126
Gallagher, R. V., Hughes, L., & Leishman, M. R. (2012) · Ecography · doi:10.1111/j.1600-0587.2012.07514.x · Cited by 124
The Association of Antarctic Krill Euphausia superba with the Under-Ice Habitat
Flores, H., van Franeker, J. A., Siegel, V., Haraldsson, M., Strass, V. H., Meesters, E. H., Bathmann, U. V., & Wolff, W. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031775 · Cited by 130
“VIF values above 10 are generally considered indicative of high collinearity [43].”
BackgroundWild boar mapping using population-density statistics: From polygons to high resolution raster maps
Pittiglio, C., Khomenko, S., & Beltrán‐Alcrudo, D. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193295 · Cited by 128
Gauthier-Clerc, S., Pellerin, J., & Amiard, J. C. (2005) · General and Comparative Endocrinology · doi:10.1016/j.ygcen.2005.08.004 · Cited by 128
“Data were then subjected to a Tukey’s HDS test or a Student–Newman–Keuls non-parametric test to conWrm critical diVerences between groups as suggested in Quinn and Keough (2002). Linear correlations and Pearson’s moment correlation were also performed.”
MethodPederson, H. G., & Johnson, C. R. (2006) · Journal of Experimental Marine Biology and Ecology · doi:10.1016/j.jembe.2006.04.010 · Cited by 128
SOURCES OF GENETIC AND PHENOTYPIC VARIANCE IN FERTILIZATION RATES AND LARVAL TRAITS IN A SEA URCHIN
Evans, J. P., García–González, F., & Marshall, D. J. (2007) · Evolution · doi:10.1111/j.1558-5646.2007.00227.x · Cited by 128
Gamble, S., Lindholm, A. K., Endler, J. A., & Brooks, R. C. (2003) · Ecology Letters · doi:10.1046/j.1461-0248.2003.00449.x · Cited by 126
Blana, V. A., Γρούντα, Α., Tassou, C. C., Nychas, G. E., & Panagou, E. Ζ. (2013) · Food Microbiology · doi:10.1016/j.fm.2013.09.007 · Cited by 129
“Sensory data were subjected to KruskaleWallis non parametric analysis of variance (Quinn and Keough, 2002) using Minitab release 14.”
MethodPettersen, A. K., Turchini, G. M., Jahangard, S., et al. (2010) · Aquaculture · doi:10.1016/j.aquaculture.2010.09.024 · Cited by 127
“Statistical analysis of lipid ranked data (visual lipid score) was carried out using a nonparametric Kruskal–Wallis test for both experiments and its equivalent parametric test, a one-way ANOVA (when assumptions were met) as suggested by Quinn and Keough (2002) to determine differences betweenmeans for days 20 to 30.”
MethodMore than Mere Numbers: The Impact of Lethal Control on the Social Stability of a Top-Order Predator
Wallach, A. D., Ritchie, E. G., Read, J., & O’Neill, A. J. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006861 · Cited by 129
“Howling frequency was arcsine-transformed prior to analyses in accordance with recommendations in Quinn and Keough [36].”
MethodSoudzilovskaia, N. A., van Bodegom, P. M., & Cornelissen, J. H. C. (2013) · Functional Ecology · doi:10.1111/1365-2435.12127 · Cited by 126
Effects of landscape heterogeneity on the emerging forest disease sudden oak death
Condeso, T. E., & Meentemeyer, R. K. (2007) · Journal of Ecology · doi:10.1111/j.1365-2745.2006.01206.x · Cited by 125
“Microclimate variables were analysed separately to avoid problems due to multicollinearity with the landscape pattern metrics (Quinn & Keough 2002; Graham 2003).”
BackgroundMethodInfluentialDickman, C. R., & Newsome, T. M. (2014) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2014.09.021 · Cited by 135
“We used two dimensions to improve interpretability of the ordination, and used the lowest Behaviour Science 173 (2015) 76–87 stress value from 20 random starts (Quinn and Keough, 2002).”
BackgroundMethodInfluentialWitharana, C., & Civco, D. L. (2013) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2013.11.006 · Cited by 124
Quantitative hydrologic performance of extensive green roof under humid-tropical rainfall regime
Wong, G. K. L., & Jim, C. Y. (2014) · Ecological Engineering · doi:10.1016/j.ecoleng.2014.06.025 · Cited by 125
Azhar, B., Saadun, N., Puan, C. L., Kamarudin, N., Aziz, N., Nurhidayu, S., & Fischer, J. (2015) · Global Ecology and Conservation · doi:10.1016/j.gecco.2015.02.009 · Cited by 129
“This inclusion of covariates was undertaken to remove any potential bias of confounding variables and to reduce within-group error variance (Quinn and Keough, 2002).”
MethodUrquiza‐Haas, T., Dolman, P. M., & Peres, C. A. (2007) · Forest Ecology and Management · doi:10.1016/j.foreco.2007.04.015 · Cited by 125
“Robust ANOVA (Welch test) was used when variances were heterogeneous (Quinn and Keough, 2002).”
MethodSwearer, S. E., Forrester, G. E., Steele, M. A., et al. (2003) · Estuarine Coastal and Shelf Science · doi:10.1016/s0272-7714(02)00317-7 · Cited by 125
Engemann, K., Enquist, B. J., Sandel, B., Boyle, B. L., Jørgensen, P. M., Morueta‐Holme, N., Peet, R. K., Violle, C., & Svenning, J. (2015) · Ecology and Evolution · doi:10.1002/ece3.1405 · Cited by 131
“Ecology and Evolution published by John Wiley & Sons Ltd. data were downloaded from the WorldClim Web site (12 November 2011, http://www.worldclim.org/bioclim) at 1 9 1 km resolution (Hijmans et al. 2005).”
MethodCaselle, J. E., Rassweiler, A., Hamilton, S. L., et al. (2015) · Scientific Reports · doi:10.1038/srep14102 · Cited by 125
Bennett, L. T., Bruce, M. J., MacHunter, J., Kohout, M., Tanase, M. A., & Aponte, C. (2016) · Forest Ecology and Management · doi:10.1016/j.foreco.2016.08.047 · Cited by 124
“Assumptions of normality and variance homogeneity were checked, and dependent variables transformed as necessary (fourth-root for densities, arcsine for percentages; Quinn & Keough, 2002).”
McKie, B. G., & Malmqvist, B. (2008) · Freshwater Biology · doi:10.1111/j.1365-2427.2008.02150.x · Cited by 125
Lusk, C. H., & Warton, D. I. (2007) · New Phytologist · doi:10.1111/j.1469-8137.2007.02264.x · Cited by 125
Avoidance of aposematic prey in European tits (Paridae): learned or innate?
Exnerová, A., Štys, P., Fučíková, E., et al. (2006) · Behavioral Ecology · doi:10.1093/beheco/arl061 · Cited by 125
“Significance of the overall difference among species was evaluated by G2 test (e.g., Quinn and Keough 2002, p. 363–365) and differences among the means of individual species by a priori least significant difference (LSD) test (e.g., Sokal and Rohlf 1995, p. 243).”
MethodComputational procedures for optimal experimental design in biological systems
Balsa-Canto, E., Alonso, A. A., & Banga, J. R. (2008) · IET Systems Biology · doi:10.1049/iet-syb:20070069 · Cited by 124
Williams, N. M., & Winfree, R. (2013) · Biological Conservation · doi:10.1016/j.biocon.2012.12.035 · Cited by 123
“Although this indicates that some imprecision may exist in path coefficients, it is not unreasonably large; for example, Quinn and Keough (2002) suggest values of 10 or more are a problem.”
BackgroundA glass full of optimism: Enrichment effects on cognitive bias in a rat model of depression
Richter, S. H., Schick, A., Hoyer, C., Lankisch, K., Gass, P., & Vollmayr, B. (2012) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-012-0101-2 · Cited by 123
“We chose such a repeated measures design with stable groups to precisely measure individual reactions to enrichment, making the experiment more efficient and allowing for smaller sample sizes (Quinn & Keough, 2002).”
MethodBohlman, S., & O’Brien, S. T. (2006) · Journal of Tropical Ecology · doi:10.1017/s0266467405003019 · Cited by 124
“To determine whether each factor was significant for all estimated diameters simultaneously, a sequential Bonferroni correction based on P = 0.05 was applied (Quinn & Keough 2002, Rice 1989).”
MethodInfluentialWadt, L. H. D. O., Kainer, K. A., Staudhammer, C. L., & Serrano, R. O. P. (2007) · Biological Conservation · doi:10.1016/j.biocon.2007.10.007 · Cited by 124
Competitor density cues for habitat quality facilitating habitat selection and investment decisions
Forsman, J. T., Hjernquist, M. B., Taipale, J., et al. (2008) · Behavioral Ecology · doi:10.1093/beheco/arn005 · Cited by 124
Modelling corrosion propagation in reinforced concrete structures – A critical review
Otieno, M. B., Beushausen, H. D., & Alexander, M. G. (2010) · Cement and Concrete Composites · doi:10.1016/j.cemconcomp.2010.11.002 · Cited by 123
Brudvig, L. A., Grman, E., Habeck, C. W., et al. (2013) · Forest Ecology and Management · doi:10.1016/j.foreco.2013.09.053 · Cited by 123
“There was no evidence for co-linearity between predictor variables in remnant (maximum Variance Inflation Factor (VIF) = 3.1; values >10 indicate co-linearity among variables (Quinn and Keough, 2002)) or post-agricultural models (maximum VIF = 3.8).”
BackgroundInfluentialFinch, T., Pearce‐Higgins, J. W., Leech, D. I., et al. (2014) · Biodiversity and Conservation · doi:10.1007/s10531-014-0731-5 · Cited by 123
Strategies for fitting nonlinear ecological models in R , AD M odel B uilder, and BUGS
Bolker, B. M., Gardner, B., Maunder, M. N., Berg, C. W., Brooks, M., Comita, L. S., Crone, E. E., Cubaynes, S., Davies, T. D., de Valpine, P., et al. (2013) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12044 · Cited by 124
“…old-school procedure of designing a well-controlled field experiment with response variables that are normally distributed (or transformable: O’Hara & Kotze 2010; Warton & Hui 2011) and analysing them according to simple ANOVA frameworks (Underwood 1996; Quinn & Keough 2002; Gotelli & Ellison 2004).”
MethodOccurrence and fate of pesticides in the Argentine stretch of the Paraguay-Paraná basin
Etchegoyen, M. A., Ronco, A., Almada, P., et al. (2017) · Environmental Monitoring and Assessment · doi:10.1007/s10661-017-5773-1 · Cited by 123
Responses of butterfly and moth species to restored cattle grazing in semi-natural grasslands
Pöyry, J., Lindgren, S., Salminen, J., & Kuussaari, M. (2004) · Biological Conservation · doi:10.1016/j.biocon.2004.09.007 · Cited by 124
“In our case, 32 parallel GLM tests with p 0.05 would create a family-wise error rate of 81% of making one Type I error (Quinn and Keough, 2002).”
BackgroundMethodInfluentialModeling interannual variability of global soil respiration from climate and soil properties
Chen, S., Huang, Y., Zou, J., Shen, Q., Hu, Z., Qin, Y., Chen, H., & Pan, G. (2010) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2010.02.004 · Cited by 123
“The parameter estimation of the nonlinear regression models in this study was based on the least-sum-of residual-squares criterion (Quinn and Keough, 2004).”
MethodHannes Rakoczy, B., & Tomasello, M. (2009) · Cognition · doi:10.1016/j.cognition.2009.07.013 · Cited by 121
“This method combines a number of p-values into a single chi-square-distributed variable with degrees of freedom equaling twice the number of p-values (Haccou & Meelis, 1994; Quinn & Keough, 2002).”
Insight into c-di-GMP Regulation in Anammox Aggregation in Response to Alternating Feed Loadings
Guo, Y., Liu, S., Tang, X., Wang, C., Niu, Z., & Feng, Y. (2017) · Environmental Science & Technology · doi:10.1021/acs.est.6b06396 · Cited by 118
Shade-induced response and recovery of the seagrass Posidonia sinuosa
Collier, C. J., Lavery, P. S., Ralph, P. J., & Masini, R. J. (2009) · Journal of Experimental Marine Biology and Ecology · doi:10.1016/j.jembe.2008.12.003 · Cited by 122
Ambriz, G. J., Petit, C., Bourrié, I., Dubois, S., Olivieri, I., & Ronce, O. (2006) · New Phytologist · doi:10.1111/j.1469-8137.2006.01923.x · Cited by 121
Dijkstra, F. A., Augustine, D. J., Brewer, P. E., & von Fischer, J. C. (2012) · Oecologia · doi:10.1007/s00442-012-2336-6 · Cited by 127
“We used a partly nested design with the water treatment as the between-plots factor, the 15N form and date treatments as within-plots factors, and block as a random factor nested within the water treatment (Quinn and Keough 2002).”
Belmar, Ó., Bruno, D., Martínez‐Capel, F., et al. (2013) · Ecological Indicators · doi:10.1016/j.ecolind.2013.01.042 · Cited by 121
“It can be used to 15 analyze complex experimental designs, and therefore, is considered to be a robust 16 analysis (Anderson et al., 2008; Quinn and Keough, 2002).”
MethodAn integrative index of Ecosystem Services provision based on remotely sensed data
Paruelo, J. M., Texeira, M. A., Staiano, L., Mastrángelo, M. E., Amdan, L., & Gallego, F. (2016) · Ecological Indicators · doi:10.1016/j.ecolind.2016.06.054 · Cited by 127
Immune challenge reduces reproductive output and growth in a lizard
Uller, T., Isaksson, C., & Olsson, M. M. (2006) · Functional Ecology · doi:10.1111/j.1365-2435.2006.01163.x · Cited by 121
Gill, P., Morrice, M., Page, B., Pirzl, R., Levings, A. H., & Coyne, M. (2010) · Marine Ecology Progress Series · doi:10.3354/meps08914 · Cited by 126
“Table 3 also shows results for hierarchical partitioning, which quantifies the contribution of each habitat variable in explaining blue whale occurrence (Quinn & Keough 2002).”
BackgroundMethodBioinformatics tools for cancer metabolomics
Blekherman, G., Laubenbacher, R., Cortes, D. F., Mendes, P., Torti, F. M., Akman, S. A., Torti, S. V., & Shulaev, V. (2011) · Metabolomics · doi:10.1007/s11306-010-0270-3 · Cited by 123
“Several excellent textbooks (Hastie et al. 2001; Johnson and Wichern, 2007; Quinn and Keough 2002; Tettamanzi and Tomassini 2001; Wilcox 2005) provide extensive discussion on both the mathematical and practical aspects of the different statistical and machine learning algorithms for data analysis,…”
BackgroundInfluence of habitat type and surrounding landscape on spider diversity in Swedish agroecosystems
Öberg, S., Ekbom, B., & Bommarco, R. (2007) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2006.12.034 · Cited by 121
Walsh, C. J., Waller, K. A., Gehling, J., et al. (2007) · Freshwater Biology · doi:10.1111/j.1365-2427.2006.01706.x · Cited by 121
Torrente‐Vilara, G., Zuanon, J. A. S., Leprieur, F., et al. (2011) · Ecology Of Freshwater Fish · doi:10.1111/j.1600-0633.2011.00508.x · Cited by 121
“The ANOSIM procedure uses the R statistic that ranges from 1 to )1 (Quinn & Keough 2002).”
Bailey, H., & Thompson, P. M. (2009) · Marine Ecology Progress Series · doi:10.3354/meps07887 · Cited by 121
“A low tolerance indicates that the variable is correlated with 1 or more of the other explanatory variables, with a value below 0.1 being of concern (Quinn & Keough 2002).”
BackgroundInfluentialJohnson, M. D., & Carpenter, R. C. (2012) · Journal of Experimental Marine Biology and Ecology · doi:10.1016/j.jembe.2012.08.005 · Cited by 116
“25, tanks were pooled within treatments (Quinn and Keough, 2002), and the remaining effects were analyzed as fixed main effects with a two-way ANOVA.”
MethodIps typographus population development after a severe storm in a nature reserve in southern Sweden
Komonen, A., Schroeder, L. M., & Weslien, J. (2010) · Journal of Applied Entomology · doi:10.1111/j.1439-0418.2010.01520.x · Cited by 120
“…exhibited overdispersion, as indicated by deviance/ d.f. > >1, we re-fitted the data with quasi-Poisson models in which the dispersion parameter was estimated from the data using Pearson’s Chi-squared method or, alternatively, we used the negative binomial distribution (Quinn and Keough 2002).”
MethodMurray, B. R., Hardstaff, L. K., & Phillips, M. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079205 · Cited by 119
“For all leaf traits except % water content of leaves, the averaged data were log-transformed to meet (general) linear modelling assumptions [39].”
MethodSocial organization in a flatworm: trematode parasites form soldier and reproductive castes
Hechinger, R. F., Wood, A. C., & Kuris, A. M. (2010) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2010.1753 · Cited by 120
Experimental tests of light-pollution impacts on nocturnal insect courtship and dispersal
Firebaugh, A., & Haynes, K. J. (2016) · Oecologia · doi:10.1007/s00442-016-3723-1 · Cited by 120
“Following the finding of a significant multivariate response, univariate ANOVAs were also performed using each of the response variables (Quinn and Keough 2002).”
MethodCasas‐Ruiz, J. P., Catalán, N., Gómez‐Gener, L., von Schiller, D., Obrador, B., Kothawala, D. N., López, P., Sabater, S., & Marcé, R. (2017) · Limnology and Oceanography · doi:10.1002/lno.10471 · Cited by 119
“A Sequential Bonferroni correction was used to reduce the probability of Type I errors in multiple pairwise correlations (Quinn and Keough 2002).”
Carvajal‐Endara, S., Hendry, A. P., Emery, N. C., et al. (2017) · Ecology Letters · doi:10.1111/ele.12753 · Cited by 120
“We did not detect significant multicollinearity among predictors, as assessed by variance inflation factors (all VIFs < 10; Quinn & Keough 2002).”
Jane, S. F., Mincer, J. L., Lau, M. P., Lewis, A. S. L., Stetler, J. T., & Rose, K. C. (2022) · Global Change Biology · doi:10.1111/gcb.16525 · Cited by 123
“We transformed the data using log10(days + 1) as the response, where days is the change in the end day of stratification, which mitigated these potential concerns (Quinn & Keough, 2002).”
MethodEcological Indicator Values for Europe (EIVE) 1.0
Dengler, J., Jansen, F., Chusova, O., Hüllbusch, E., Nobis, M. P., Meerbeek, K. V., Axmanová, I., Bruun, H. H., Chytrý, M., Guarino, R., et al. (2023) · Vegetation Classification and Survey · doi:10.3897/vcs.98324 · Cited by 134
Encalada, A. C., Calles, J. A., Ferreira, V., et al. (2010) · Freshwater Biology · doi:10.1111/j.1365-2427.2010.02406.x · Cited by 120
Metadata
- Authors
- Quinn, G. P., & Keough, M. J.
- Affiliations
- Monash University (AU)
- The University of Melbourne (AU)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2002-03-21
- OpenAlex
- W1489838709
- Cited by
- 10,674
- References
- 0
- Citations by year
- 2026: 200 · 2025: 218 · 2024: 239 · 2023: 250 · 2022: 275 · 2021: 394 · 2020: 448 · 2019: 430 · 2018: 446 · 2017: 473 · 2016: 528 · 2015: 625 · 2014: 763 · 2013: 719 · 2012: 675
- FWCI
- 50.52
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- experimental design, statistical analysis, biology, hypothesis testing, Bayesian analysis, linear models, generalized linear models, logistic regression, analysis of variance, repeated measures, log-linear models, multivariate analysis, classification, ordination, exploratory data analysis, assumption checking
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