Crossed and nested factors in experimental designs: Are there any flowcharts for decision making?
On 14-11-14 05:21 AM, Mehdi Abedi wrote:
Dear all, I am following your nice discussion in this group. Considering your high level discussion in this group i was not sure to ask basic problems in the mailing list!, therefore, i asked this question in researchgate. I think several researchers have this kind of question which your guidance can also help them using mixed models in their researches. I can imagine that teaching advanced statistic with simple language is not easy. However in addition to advanced statistic books and codes, introducing experimental design and their link to mixed models using simple codes would be valuable as well. It would be my pleasure to have your suggestion here or in this link: Warm regards Mehdi: https://www.researchgate.net/post/With_regards_to_crossed_and_nested_factors_in_experimental_designs_Are_there_any_flowcharts_for_decision_making Ecological researches mainly have complicated statistical design. Mixed models can test complicated designs with different crossed or nested factors. For instance lme4 and nlme could be used in R. However, still decision about statistical design is complicated for most researchers. Mixed models are quit an advanced method for most researchers and recent publications still use simple statistical designs. For instance most publications in high ranked journals related to plant ecophysiology and seed researches still apply only factorial design for the statistical analysis and not mixed models including random and fixed factors. Could you recommend some references or lectures introducing nested, crossed design with simple examples and simple codes in R?. I mean some simple explanation without details which start from simple design to complicated design like http://conjugateprior.org/2013/01/formulae-in-r-anova/ <https://www.researchgate.net/go.Deref.html?url=http%3A%2F%2Fconjugateprior.org%2F2013%2F01%2Fformulae-in-r-anova%2F> Graphical designs showing these crossed and nested would also be useful. This information would be very helpful for both teachers and researchers for application correct statistical analysis.
It's pretty basic, but I like the section in Gotelli and Ellison's _Primer of Ecological Statistics_. They don't really get as far as crossed random effects, but they do discuss nested, randomized block, split-plot, and factorial (i.e. _crossed fixed effect_) designs. Ben Bolker