Posted-Date: Thu, 16 Apr 92 11:26:47 EDT
Newsgroups: bit.listserv.edstat-l
Date:         Thu, 16 Apr 1992 11:26:47 EDT
Reply-To:     Tim Arnold <arnold@STAT.NCSU.EDU>
From:         Tim Arnold <arnold@STAT.NCSU.EDU>
Subject:      SAS techniques summary
X-To:         edstat-l@ncsuvm.cc.ncsu.edu

I asked members some time ago for their experiences and any
suggestions in teaching SAS to beginners.  This message is a
summary of the messages which were sent to me and not seen before
on the list.
I'd like to thank everyone who responded (and to those of you who asked
for a summary: here it is).

I edited some responses for length before posting here--I hope the
original authors do not mind.
==========================================================
Some replies were SAS macros intended to do
a specific job.  Because of the length, I have not included
them here, but I want to thank the authors, especially
Ping-Wu Li from SUNY. (ping@ccvm.sunysb.edu)

Some other techniques:
------------
James Semakula (james@ucrac1.ucr.edu) writes:
1 tell students sas has exactly and only 2 steps: data step & proc step of
which the data step is by far the most important. in fact 90% of the problems
with sas  are here. so we concentrate on this step.

2. sas stmt almost always begin with a keyword and end with a ;. there
are approx 150 key words of which a student MUST master the following
in order to use saseffectively: INFILE/INPUT (FILE/PUT), IF-THEN-ELSE,
DO, ARRAY,BY, SET/MERGE/UP- DATE . . . . . .

. . .i do have a dataset i got from a journal which i use to challenge
students to apply what we've learned. i give a sas manual to the
winner (only 3 so far).

--------------------
From: Ray Liedka <RJOY@CORNELLC.cit.cornell.edu>
. . .  graduate level econometrics class.  This class is the second in
a three-course sequence of stat methods and focuses on GLM, models for
count data, and models for binary data.  The text is William Greene's
book "Econometric Analysis" and students use Bill Greene's LIMDEP
program for analysis, and SAS for data handling.  Real data is used (I
have put together two data sets from the General Social Survey and the
Current Population Survey).  Students can use other data if they so
desire.  The students are expected to complete three papers during the
course of the class, each focused on a different part of the material.
Students have told me that smaller projects, more frequently...say,
weekly projects on small aspects of the data analysis
proceedure...would be preferable.  I agree.


Finally, in the first stat methods course, all projects are weekly, and the
data used is simulated.  I recommend the use of simulated data for initial
introduction to various concepts of data analysis, since it eases the
connection between stat theory and stat analysis.  For example, the effect of
sample size on the standard errors of OLS can be easily demonstrated by
simulating data samples of different size for the same model equation.  Then
the student can simply graph the effect of sample size on standard error.
Likewise the effect of error variance.  Students can gain an intuitive
understanding of the ability of OLS to estimate regression coefficients,
because the data is generated by a known function--students can just compare
the "true" coefficients with the estimated ones.  Likewise with such issues
as collinearity and heteroskadasticity, GLS and specification errors.  Such
simulated data is quite useful for illustrating sample selection bias.
The list continues.

I think that I would want initial experience with simulated data, then give
the students real data to play with.

Hope this raises questions or offers helpful (possible) solutions.
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Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU