Proc freq: Five secrets* *Okay, well, lesser known facts

They said I wasn’t that interesting

1.Different and similar chi-squares 2.Fisher’s Exact Test. How to get one. Why you want one 3.Odds ratios 4.When NOT to compare chi-square values directly 5.Tests of binomial proportions

Proc freq getting the chi-square values & more

Enterprise Guide Method

The Syntax PROC FREQ DATA = mydata.oldpeople ; TABLES dthflag*nursehome / NOROW NOPERCENT NOCUM CHISQ MEASURES ;

Nursing home placement by death Conditional probabilities

Being able to find SPSS in the start menu does not qualify you to perform a multinomial logistic regression

1. Chi-square values

Chi-square results

Chi-square results Pearson ∑ (f o – f e ) 2 f e Pearson

Chi-square results

2. What is Fisher’s exact test & when do I get one?

Fisher’s Exact Test: probability of a table as unusual as the one that you have obtained under the null hypothesis of no relationship.

With 2 x 2 Tables it’s automatic

Recap: Fisher’s Exact Test Small sample size OR Need exact probability

3. Odds ratios

Computing odds ratios Divide frequency row 1, column 1 by frequency in row 1 column 2 2,846/184 = odds of a person who lived not being in a nursing home versus being in a home. Divide frequency in row 2, column 1 by frequency row 2, column 2 2,239/ 1,077 = 2.08 Divide first result by the second 13.51/ 2.08 = 6.49

Measures

4. Mantel-Haeszel chi-square Tests ordinal relationship Same as Pearson if only two categories

Ordinal relationship ?

Don’t just compare values

ER visits versus nursing home

Take-away 1.Different types of chi-square values, different types of correlations and other tests like odds ratios do exist. 2.These statistics are very easy to obtain using SAS. 3.While most times, all of these measures will point you in the direction of the same general conclusion, there are times when one is preferable to the others.

Testing hypothesis π = ? PROC FREQ DATA = dsname ; TABLES varname / BINOMIAL (EXACT EQUIV P =.333) ALPHA =.05 ;

BINOMIAL (EXACT EQUIV P =.333) ALPHA =.05 ; The binomial (equiv p =.333) will produce a test that the population proportion is.333 for the first category. That is “No” for death. A Z- value will be produced and probabilities for one-tail and two-tailed tests. The exact keyword will produce confidence intervals and, since I have specified alpha =.05, these will be the 95% confidence intervals.

Different data I had lying around

Hmmm…. This is interesting

Null rejected !