Mixed Models Offer No Freedom from Degrees of Freedom

Trends in Ecology and Evolution

Volumn 35, Issue 4, 2020, Pages 329-335

  Arnqvist, Göran ^a  

^a UPPSALA UNIVERSITY   (Sweden)

Author keywords

biostatistics; experimental evolution; hierarchical data; linear mixed models; pseudoreplication; type I errors

Indexed keywords

ECOLOGY; EMPIRICAL ANALYSIS; ERROR ANALYSIS; EVOLUTION; EXPERIMENTAL STUDY; HIERARCHICAL SYSTEM; STATISTICAL ANALYSIS;

ECOLOGY; METHODOLOGY; STATISTICAL MODEL;

ECOLOGY; LINEAR MODELS; MODELS, STATISTICAL; RESEARCH DESIGN;

EID: 85078182091     PISSN: 01695347     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tree.2019.12.004     Document Type: Review

References (20)

1. Hurlbert, S.H., Pseudoreplication and the design of ecological field experiments. Ecol. Monograph 54 (1984), 187–211.
2. Heffner, R.A., et al. Pseudoreplication revisited. Ecology 77 (1996), 2558–2562.
3. Ramage, B.S., et al. Pseudoreplication in tropical forests and the resulting effects on biodiversity conservation. Conserv. Biol. 27 (2013), 364–372.
4. Waller, B.M., et al. Pseudoreplication: a widespread problem in primate communication research. Anim. Behav. 86 (2013), 483–488.
5. Lazic, S.E., et al. What exactly is ‘N’ in cell culture and animal experiments?. PLoS Biol., 16, 2018, e2005282.
6. Schielzeth, H., Forstmeier, W., Conclusions beyond support: overconfident estimates in mixed models. Behav. Ecol. 20 (2008), 416–420.
7. Ramírez, C.C., et al. Pseudoreplication and its frequency in olfactometric laboratory studies. J. Chem. Ecol. 26 (2000), 1423–1431.
8. Kroodsma, D.E., et al. Pseudoreplication in playback experiments, revisited a decade later. Anim. Behav. 61 (2001), 1029–1033.
9. Lazic, S.E., The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis?. BMC Neurosci., 11, 2010, 5.
10. Millar, R.B., Anderson, M.J., Remedies for pseudoreplication. Fish. Res. 70 (2004), 397–407.
11. Paterson, S., Lello, J., Mixed models: getting the best use of parasitological data. Trends Parasitol. 19 (2003), 370–375.
12. Colegrave, N., Ruxton, G.D., Using biological insight and pragmatism when thinking about pseudoreplication. Trends Ecol. Evol. 33 (2018), 28–35.
13. Harrison, X.A., et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ, 6, 2018, e4794.
14. Van de Pol, M., Wright, J., A simple method for distinguishing within-versus between-subject effects using mixed models. Anim. Behav. 77 (2009), 753–758.
15. Barr, D.J., et al. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68 (2013), 255–278.
16. Murtaugh, P.A., Simplicity and complexity in ecological data analysis. Ecology 88 (2007), 56–62.
17. Parker, T.H., et al. Transparency in ecology and evolution: real problems, real solutions. Trends Ecol. Evol. 31 (2016), 711–719.
18. Bolker, B.M., et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24 (2009), 127–135.
19. Gelman, A., Hill, J., Data Analysis Using Regression and Multilevel/Hierarchical Models. 2006, Cambridge University Press.
20. Harrison, X.A., A comparison of observation-level random effect and beta-binomial models for modelling overdispersion in binomial data in ecology and evolution. PeerJ, 3, 2015, e1114.