Transcript The Dangers and Wonders of Statistics Using SAS

The Dangers and Wonders of
Statistics Using SAS
or
You Can Have it Right
and
You Can Have it Right Away
AnnMaria De Mars, Ph.D.
The Julia Group
http://www.thejuliagroup.com
July, 2008
Presentation at Los Angeles Basin SAS Users Group Meeting
Statistics are Wonderful
Three common statistical plots
1. Analyzing a large-scale
dataset for population
estimates
2. Small datasets for market
information
3. Comparison of two groups
to determine effectiveness
How much time do people spend
alone?
1. National Survey Example
Very quick, wrong answer
First problem: Data are wrong
Know your data.
Know your data.
I said it twice because
it was important.
Get to intimately know
your data before
you do ANYTHING.
American Time Use Survey
• Conducted by U.S. Census Bureau
• Study of how a nationally representative
sample of Americans spend their time
Common Survey Issue
Samples are not simple random but often multistage stratified, meaning that …
“Users need to apply weights when computing
estimates with the ATUS data because simple
tabulations of unweighted ATUS data produce
misleading results. “
Equation provided by ATUS
Ti = ∑ fwgt Tij
-----------∑fwgt
In other words ….
The average amount of time the population spends
in activity j Tj is equal to
• The sum of the weight for each individual
multiplied by the individual responses of how
much time they spend on activity j
• Divided by the sum of the weights.
Really easy answer
PROC SORT ;
BY sex child ;
PROC MEANS DATA= in.atus ;
BY sex child ;
WEIGHT tufinlwgt ;
Right procedure,
wrong answer
Data are coded with negative numbers, e.g.,
-1 = blank
-2 = don’t know
-3 = refused to answer
With the result that for some procedures the means shown
are actually negative time spent in an activity
Fixing the data
DATA atus ;
SET mylib.atus ;
ARRAY rec{*} _numeric_ ;
DO i = 1 TO DIM(rec) ;
IF rec{i} < 0 THEN rec{i} = . ;
END ;
How this impacts output
# of minutes per day alone
With and without weights
Child at
Home
Female
Male
NO
Unweighted Weighted
Mean
Mean
404
347
YES
199
207
NO
372
324
YES
201
205
Generalizing to the Population
The Problem
I would like to get an estimate of
the population values.
•How many children are in the
average household?
•How many hours does the average
employed person work?
A bigger problem
It is not acceptable to just calculate
means and frequencies, not even
weighted for percent of the population,
because I do not have a random
sample. My sample was stratified by
gender and education.
Some common “messy data”
examples
• Small, medium and large
hospitals in rural and urban
areas
• Students selected within
classroms in high- , low- and
average- performing schools
Data requiring special handling
• Cluster samples - subjects are not
sampled individually, e.g., classrooms
or hospitals are selected and then every
person within that group is sampled.
• Non-proportional stratified samples - a
fixed number is selected from, e.g.,
each ethnic group
SAS PROCEDURE FOR A STRATIFIED SAMPLE
PROC SURVEYMEANS DATA=in.atus40
TOTAL = strata_count ;
WEIGHT samplingweight ;
STRATA sex educ ;
VAR hrsworked numchildren ;
PROC SURVEYMEANS DATA=in.atus40
TOTAL = strata_count ;
Gives a dataset with the population totals
for each strata
WEIGHT samplingweight ;
STRATA sex educ ;
VAR hrsworked numchildren ;
SURVEY MEANS OUTPUT
Data Summary
Number of Strata
30
Number of Observations
1200
Sum of Weights
13038
Stratum Information
Stratum Edited:
Population Sampling
Index
sex educ
Total
Rate N Obs Variable
1
1
0
41
97.6%
40 TRCHILDNUM #children <18
TEERNHRO
2
1
1
119
33.6%
Label
N
40
hours
worked/week
15
40 TRCHILDNUM #children <18
40
Surveymean Output
Statistics
Variable
Label
TRCHILDNUM
#children <18
TEERNHRO
hours worked/week
N
1200
Std
Error of
Mean Mean
95% CL for Mean
0.988551 0.056994 0.8767287
1.1003729
247 34.630187 0.867781 32.9198281 36.3405450
Dataset with total counts
Answers Price List
Answers
$1
Answers, Correct $100
Answers, Requiring Thought -- $1,000
Survey Procedures
• Surveymeans - can provide estimates of
means, standard errors, confidence
intervals
• Surveyfreq - provides estimates of
population totals, standard errors,
confidence limits
And now for something
completely different …
2. Using SAS Enterprise Guide
to analyze target market survey
data in the hour before your
meeting
It’s not always rocket science
There may be a
tendency to use the
most sophisticated
statistical
techniques we can
find when what the
customer really
wants is a bar chart
Customer Need
Our target market is Native
Americans with chronic illness in
the Great Plains region. We want
to know how people get most of
their information so that we can
develop a marketing strategy.
Questions
1. How often do people read the
newspaper versus use the Internet?
2. Is it the same people who are using a
lot of media, e.g. email, radio, Internet,
or do different people use different
sources of information?
Creating Enterprise Graphs
1.
2.
3.
4.
5.
Double-click on SAS dataset to open
Select Graph > Bar Chart > Colored Bars
Select Task Roles
Click on Internet_Use
Select Analysis Variable
Repeat steps for second chart for newspaper
readership
Correlations
1. Select Analyze > Correlations
2. Select variables from list
3. Click RUN
Frequency Distribution
Select Describe > One-Way Frequencies
Recommendations
• Create a website and an email list to
contact potential customers on the
reservations
• Advertise on the radio and in the
newspaper
That will be $4,000, please.
Nice theory, but does it work?
3. Evaluating program effectiveness
We changed something.
Did it work ?
A two-day staff training program was offered.
A pre-test was given before training occurred
and at the conclusion of training.
The test consisted of multiple choice questions
and case studies.
Just for fun ….
I decided to do the whole project using
only two procedures,
PROC CORR and PROC GLM
Wonder of SAS: One step produces
multiple steps in psychometric analysis
PROC CORR DATA = tests ALPHA ;
WHERE test_type = “pre” ;
VAR q1 – - q40 ;
Descriptive statistics output from PROC CORR
Simple Statistics
Variable
N
Mean
Std Dev
Sum
Minimum
Maximum Label
q1
56
0.48214
0.50420
27.00000
0
1.00000 q1
q2
56
0.73214
0.44685
41.00000
0
1.00000 q2
q3
56
0.80357
0.40089
45.00000
0
1.00000 q3
Check for data entry errors, restriction in range,
low variance
My alpha is not
very good and
I am sad
Cronbach Coefficient Alpha
Variables
Alpha
Raw
0.670499
Standardized
0.715271
Item Analysis (continued)
Are two different factors being measured?
Cronbach Coefficient Alpha with Deleted Variable
Raw Variables
Deleted
Variable
Correlation
with Total
Standardized Variables
Alpha
Correlation
with Total
Alpha Label
q1
0.165402
0.666968
0.255112
0.707299 q1
q2
-.308810
0.680713
-.243679
0.733907 q2
q3
-.128138
0.675074
-.104595
0.726719 q3
q4
-.020201
0.671974
0.055058
0.718250 q4
Inspect the correlation matrix
Pearson Correlation Coefficients, N = 56
Prob > |r| under H0: Rho=0
q1
q1
q1
q2
q2
q3
q3
q2
q3
q4
q5
q6
q7
q8
q9
q10
q11
1.00000 0.18013 0.02731 0.06754 0.18570 0.04052 0.22696 0.18570 0.26395 0.04443 -0.10316
0.1840 0.8417 0.6209 0.1706 0.7668 0.0925 0.1706 0.0493 0.7451 0.4493
0.18013 1.00000 0.00544 -0.01524 0.10088 -0.10669 0.11641 0.10088 0.09141 -0.23885 -0.06984
0.1840
0.9683 0.9112 0.4594 0.4339 0.3929 0.4594 0.5028 0.0763 0.6090
0.02731 0.00544 1.00000 -0.22085 0.14705 -0.05096 0.02595 0.14705 -0.12161 -0.02958 -0.28545
0.8417 0.9683
0.1019 0.2795 0.7091 0.8494 0.2795 0.3719 0.8287 0.0330
Items with negative item-total correlations
are not intercorrelated
The General Linear Model
It really is general.
You may now jump
for joy at this
obvious revelation.
REGRESSION
PROC GLM DATA=in.test2 ;
MODEL score = age years_of_ed ;
WHERE test_type = "pre" ;
PROC GLM OUTPUT
Source
DF
Model
2
3499.27032
Error
40
9775.66508
Corrected Total
42
13274.93540
Parameter
Sum of Squares
R-Square
Coeff Var
0.263600
33.52720
Estimate
Mean Square
F Value
1749.63516
7.16
Pr > F
0.0022
244.39163
Root MSE
score Mean
15.63303
Standard Error
46.62791
t Value
Pr > |t|
Intercept
4.114947869
12.74413685
0.32
0.7485
Age
0.199948936
0.21070443
0.95
0.3483
Years_of_Ed
2.779100645
1.18441794
2.35
0.0240
2 x 2 Analysis of Variance
PROC GLM DATA=in.test2 ;
CLASS disability job;
MODEL score = disability job
disability*job ;
WHERE test_type = "pre" ;
R-Square
Coeff Var
0.146002
35.66606
Source
DF
Type I SS
Root MSE
score Mean
18.10823
Mean Square
50.77160
F Value
Pr > F
Self_fam_disability
1
259.979424
259.979424
0.79
0.3775
Disability_job_servi
1
2393.234665
2393.234665
7.30
0.0094
Self_fam_*Disability
1
149.785278
149.785278
0.46
0.5022
Source
DF
Type III SS
Mean Square
F Value
Pr > F
Self_fam_disability
1
220.3043773
220.3043773
0.67
0.4163
Disability_job_servi
1
917.6599111
917.6599111
2.80
0.1006
Self_fam_*Disability
1
149.7852784
149.7852784
0.46
0.5022
Repeated Measures ANOVA
Uh - maybe someone should have
mentioned this …..
One record per subject
Data Preparation for Repeated Measures ANOVA
DATA step
OUTPUT pre
OUTPUT post
(RENAME )
PROC SORT
Data = pre
DATA merge
pre post
PROC SORT
Data = post
REPEATED MEASURES ANOVA
PROC GLM DATA = in.mrgfiles ;
CLASS test_group ;
MODEL score score2 = test_group ;
REPEATED test 2 ;
LSMEANS test_group ;
Repeated Measures ANOVA
Repeated Mea sures Level
Information
Dependent Variable score score2
Level of test
1
2
MANOVA Test Criteria and Ex act F Statistics for the Hypothesis of no test Effect
H = Type III SSCP Matrix for test
E = Error SSCP Matrix
S=1 M=-0.5
Statistic
Value
N=23
F Value
Num DF
Den DF
Pr > F
Wilks' Lambda
0.61294702
30.31
1
48
<.0001
Pillai's Trace
0.38705298
30.31
1
48
<.0001
Hotelling-Lawley Trace
0.63146237
30.31
1
48
<.0001
Roy's Greatest Root
0.63146237
30.31
1
48
<.0001
Repeated Measures ANOVA
Source
DF
Type III SS
Mean Square
F Value
Pr > F
test
1
3716.740741
3716.740741
30.31
<.0001
test*test_group
1
2787.851852
2787.851852
22.74
<.0001
48
5885.925926
122.623457
Error(test)
LSMEANS OUTPUT
test_group
score LSMEA N
score2 LSMEA N
COMPA RISON
55.2222437
57.6700577
TRAINED
44.6864949
67.4265126
CONCLUSIONS

SAS has made it possible to obtain output of
statistical procedures without ever needing to
understand the underlying assumptions. This is a
mixed blessing.

Enterprise Guide makes statistics accessible to
a wider audience . This is a good thing.
The best statistical analysis is not the one that the
fewest people can understand but that the most
people can understand.