hi team,

please bring an USB drive to class this week and the following.

if you prefer to use your own machine, do standard r-install or r-studio.

**questions**

1. Please provide a brief summary describing how to critically interpret/read your advanced statistical topic when it is used in a scientific paper. Feel free to include a table or figure illustrating the guidelines, assumptions, or approaches to interpreting the reporting of this test in a scientific paper.

I really like this short article as an example: http://www.bmj.com/content/315/7109/672

2. Please repeat the above process but for a second topic that you did not present.

**marking key /50 (but worth 25%) – please use as your outline**

Q1. Worth 25 points.

Introduction to statistical test/10

Basic intro to purpose of statistical test & why you use it.

List assumptions and scope of inference for this test.

List advantages/disadvantages to this test relative to other options

Interpretation (or guidelines for readers)/10

Explain how to interpret the test-statistic(s)

Explain how to interpret the visualization

Explain what high, medium, and low values mean for the estimates and explain the accepted alpha for this test

List the key citations the reader would need to best understand this topic at a beginner level

Implications & context /5 (just a few sentences only)

Conclude your short note on this advanced statistical topic with a comment on its importance or changes over time. For instance, it is a relatively new technique and we can expect to see it more etc.

Implication or context could also include how it relates to our understanding of the underlying processes we are applying the statistical test to, i.e remind the reader the ‘why’ you do this test but in a bigger picture way here, for instance, this is an exploratory stat, or one for model building, or for assessing casualty, etc.

Q2. Worth 25 points.

Same marking key.

**sample papers**

http://onlinelibrary.wiley.com/doi/10.1002/jrsm.1109/abstract

http://opr.sagepub.com/content/1/2/99.refs

http://www.bmj.com/content/315/7109/672

Here is a link to a whole course on SEM by one of the world’s leaders on the topic

http://byrneslab.net/teaching/sem/

My fav two readings:

Grace et al. 2010 SEM in ecology

We need to do one of these for learning SEM.

**1) what is the difference between a GLM and a GLMM?
**

GLM: only has fixed effects

GLMM: has fixed and random effects

both have: a set distribution and associated link function, response variable not assumed to be linear or normal, response variable should be continuous, explanatory variables are categorical and can have continuous covariates

**2)What is the difference between fixed and random effects? **

Fixed: this is the treatment in your experiment, should have an effect on your response (slope changes)

Ex: soil characteristics are the fixed effect when testing the influence of soil on plant growth of different species

Random: intercept changes but should not have an effect on your response (slope does not change)

Ex: species is a random effect when testing the influence of soil characteristics on plant growth of different species

**3) Checklist of steps for running a GLM/GLMM (Box 4 Bolker)**

- specify model: define – fixed effects, random effects, covariates, response variable,
*a priori*decide on the number of factors to include in the model - choose distribution and appropriate link function
- check assumptions: homogeniety of variances, outliers, does your distribution match the assumed distribution? (goodness of fit statistics). If variances are not homogeneous or the distribution does not match then change your model, adding effects or covariates, interactions etc
- recheck assumptions of final model (see previous step)
- check overdistribution (especially for poisson distributions)
- can choose/compare models using AIC values (lower AIC = better)

**4) Common errors that are made when performing a GLM/GLMM**

Selecting the wrong distribution and/or the wrong link function

not checking the fit of the model to the distribution

Hey everyone!

Here is the list of possible analyses to do for our systematic review that we discussed in class yesterday

1) How statistical tests changed by year

2) Impact factor of journals

3) How stats influence citation rate of appers

4) Diversity of tests used in your field

5) The classification (central tendency, randomized, algorithm) of tests your field uses

6) How often stats are used in your field

7) Sample sizes used in your field

8) Stats tests used by country

9) What factors are used in your field – subdominant search terms

edited version

Here is is on slideshare:

http://bit.ly/zenthesis