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Archive for September 2013

Export tables to Excel

There is a new command in Stata 13, putexcel, that allows you to easily export matrices, expressions, and stored results to an Excel file. Combining putexcel with a Stata command’s stored results allows you to create the table displayed in your Stata Results window in an Excel file.

A stored result is simply a scalar, macro, or matrix stored in memory after you run a Stata command. The two main types of stored results are e-class (for estimation commands) and r-class (for general commands). You can list a command’s stored results after it has been run by typing ereturn list (for estimation commands) and return list (for general commands). Let’s try a simple example by loading the auto dataset and running correlate on the variables foreign and mpg

. sysuse auto
(1978 Automobile Data)

. correlate foreign mpg
(obs=74)

|  foreign      mpg
-------------+------------------
foreign |   1.0000
mpg |   0.3934   1.0000


Because correlate is not an estimation command, use the return list command to see its stored results.

. return list

scalars:
r(N) =  74
r(rho) =  .3933974152205484

matrices:
r(C) :  2 x 2


Now we can use putexcel to export these results to Excel. The basic syntax of putexcel is

putexcel excel_cell=(expression) … using filename [, options]

If you are working with matrices, the syntax is

putexcel excel_cell=matrix(expression) … using filename [, options]

It is easy to build the above syntax in the putexcel dialog. There is a helpful video on Youtube about the dialog here. Let’s list the matrix r(C) to see what it contains.

. matrix list r(C)

symmetric r(C)[2,2]
foreign        mpg
foreign          1
mpg  .39339742          1


To re-create the table in Excel, we need to export the matrix r(C) with the matrix row and column names. The command to type in your Stata Command window is

putexcel A1=matrix(r(C), names) using corr


Note that to export the matrix row and column names, Read more…

Categories: Programming Tags:

Measures of effect size in Stata 13

Today I want to talk about effect sizes such as Cohen’s d, Hedges’s g, Glass’s Δ, η2, and ω2. Effects sizes concern rescaling parameter estimates to make them easier to interpret, especially in terms of practical significance.

Many researchers in psychology and education advocate reporting of effect sizes, professional organizations such as the American Psychological Association (APA) and the American Educational Research Association (AERA) strongly recommend their reporting, and professional journals such as the Journal of Experimental Psychology: Applied and Educational and Psychological Measurement require that they be reported.

Anyway, today I want to show you

1. What effect sizes are.
2. How to calculate effect sizes and their confidence intervals in Stata.
3. How to calculate bootstrap confidence intervals for those effect sizes.
4. How to use Stata’s effect-size calculator.

1. What are effect sizes?

The importance of research results is often assessed by statistical significance, usually that the p-value is less than 0.05. P-values and statistical significance, however, don’t tell us anything about practical significance.

What if I told you that I had developed a new weight-loss pill and that the difference between the average weight loss for people who took the pill and the those who took a placebo was statistically significant? Would you buy my new pill? If you were overweight, you might reply, “Of course! I’ll take two bottles and a large order of french fries to go!”. Now let me add that the average difference in weight loss was only one pound over the year. Still interested? My results may be statistically significant but they are not practically significant.

Or what if I told you that the difference in weight loss was not statistically significant — the p-value was “only” 0.06 — but the average difference over the year was 20 pounds? You might very well be interested in that pill.

The size of the effect tells us about the practical significance. P-values do not assess practical significance.

All of which is to say, one should report parameter estimates along with statistical significance.

In my examples above, you knew that 1 pound over the year is small and 20 pounds is large because you are familiar with human weights.

In another context, 1 pound might be large, and in yet another, 20 pounds small.

Formal measures of effects sizes are thus usually presented in unit-free but easy-to-interpret form, such as standardized differences and proportions of variability explained.

The “d” family

Effect sizes that measure the scaled difference between means belong to the “d” family. The generic formula is

The estimators differ in terms of how sigma is calculated.

Cohen’s d, for instance, uses the pooled sample standard deviation.

Hedges’s g incorporates an adjustment which removes the bias of Cohen’s d.

Glass’s Δ was originally developed in the context of Read more…

Categories: Statistics Tags: