It's the effect size, stupid: what effect size is and why it is important

It's the Effect Size, StupidWhat effect size is and why it is important Robert CoeSchool of Education, University of Durham, email r.j.coe@dur.ac.uk Paper presented at the Annual Conference of the British Educational Research Association, University of Exeter, England, 12-14 September 2002 Abstract Effect size is a simple way of quantifying the difference between two groups that has many advantages over the use of tests of statistical significance alone. 'Effect size' is simply a way of quantifying the size of the difference between two groups. The routine use of effect sizes, however, has generally been limited to meta-analysis - for combining and comparing estimates from different studies - and is all too rare in original reports of educational research (Keselman et al., 1998). The following guide is written for non-statisticians, though inevitably some equations and technical language have been used. 1. (a) (b) Figure 1 2. Equation 1 3. Table I: Interpretations of effect sizes 4. 5. 6. 7.

Togaware: Rattle: A Graphical User Interface for Data Mining using R Code School - Try R Matlab Matlab is a tool for doing numerical computations with matrices and vectors. It can also display information graphically. The best way to learn what Matlab can do is to work through some examples at the computer. After reading the " getting started " section, you can use the tutorial below for this. Getting started Here is a sample session with Matlab. % matlab >> a = [ 1 2; 2 1 ] a = 1 2 2 1 >> a*a ans = 5 4 4 5 >> quit 16 flops In this example you started Matlab by (you guessed it) typing matlab. The tutorial below gives more examples of how to use Matlab. Matrices To enter the matrix and store it in a variable a, do this: >> a = [ 1 2; 3 4 ] Do this now: define the matrix a. To redisplay the matrix, just type its name: >> a Once you know how to enter and display matrices, it is easy to compute with them. >> a * a Wasn't that easy? >> b = [ 1 2; 0 1 ] Then we compute the product ab: >> a*b Finally, we compute the product in the other order: >> b*a Of course, we can also add matrices: >> a + b x y

HyperStat Online: An Introductory Statistics Textbook and Online Tutorial for Help in Statistics Courses Click here for more cartoons by Ben Shabad. Other Sources NIST/SEMATECH e-Handbook of Statistical Methods Stat Primer by Bud Gerstman of San Jose State University Statistical forecasting notes by Robert Nau of Duke University related: RegressIt Excel add-in by Robert Nau CADDIS Volume 4: Data Analysis (EPA) The little handbook of statistical practice by Gerard E. Stat Trek Tutorial Statistics at square 1 by T. Concepts and applications of inferential statistics by Richard Lowry of Vassar College CAST by W. SticiGui by P. StatPrimer © B. Gerstman 2003 StatPrimer (Version 6.4) B. Burt Gerstman (email) Part A (Introductory) (1) Measurement and sampling [Exercises] (2) Frequency distributions [Exercises] (3) Summary statistics [Exercises] (4) Probability [Exercises Part A] [Exercises Part B] (5) Introduction to estimation [Exercises] (6) Introduction to hypothesis testing [Exercises] (7) Paired samples [Exercises] (8) Comparing Independent means [Exercises] (9) Inference about a proportion [Exercises] (9.5) Comparing two proportion (*.ppt) [Exercises] (10) Cross-tabulated counts [Exercises] Part B (Intermediate) (11) Variances and means [Exercises] (12) ANOVA [Exercises] (13) ANOVA topics (post hoc comparisons, Levene's test, Non-parametric tests) [Exercises] (14) Correlation [Exercises] (15) Regression [Exercises] (16) Risk ratios and prevalence ratios [Exercises] (17) Case-control odds ratios [Exercises] Additional notes Power and sample size [Exercises] How To Know What to Use [Exercises]Approaches Toward Data Analysis Data Files

Trefethen numerical ODE/PDE textbook Finite Difference and Spectral Methods for Ordinary and Partial Differential Equations Lloyd N. Trefethen Available online -- see below This 325-page textbook was written during 1985-1994 and used in graduate courses at MIT and Cornell on the numerical solution of partial differential equations. The book has not been completed, though half of it got expanded into Spectral Methods in MATLAB. The files currently online are missing some tables, figures, and text that are not available in PostScript or are in copyright; a list of some of these omissions is available. Thanks to Darryl Yong of Harvey Mudd College for converting these PostScript files into searchable pdf files. Front matter. Perhaps a reasonable way to cite this book would be: Lloyd N. Return to L.