Here's the thing: this isn't a research project but the results of a homework 
problem. More specifically, it is the result of a mistake in data entry (since 
no stats text author would likely ever produce such a result on purpose when 
the text says that a significant omnibus F-test is a prerequisite for HSD). So 
there is nothing meaningful to be gained by trying to determine what might be 
logically expected in this case. I assume the data is constructed. My interest 
was more in the fact that it was theoretically possible to have a significant F 
test and no significant HSD comparisons.
 
 
Rick
 
 
Dr. Rick Froman
Psychology Department
Box 3055
John Brown University
Siloam Springs, AR 72761
(479) 524-7295
[EMAIL PROTECTED]
"Pete, it's a fool that looks for logic in the chambers of the human heart"
- Ulysses Everett McGill

________________________________

From: Jim Clark [mailto:[EMAIL PROTECTED]
Sent: Tue 4/3/2007 7:42 PM
To: Teaching in the Psychological Sciences (TIPS)
Subject: [tips] Re: ANOVA interpretation



Hi

As shown in following example, significant omnibus and nonsignificant Tukeys is 
not strictly speaking a simple product of small sample size (the 4 groups below 
each have 90 subjects).  It also depends on magnitude of difference relative to 
variation within groups (MSE) and the specific pattern of the difference.  
Below, groups 1 and 2 are different than groups 3 and 4 IN THE POPULATION.  
Although maximum difference is almost significant by Tukey (p = .055) that 
really does not capture the pattern in the data, as shown by the subsequent 
contrast analysis.  The contrast between 1&2 vs 3&4 is highly significant (p = 
.008).  The lesson, analyses for predicted patterns in data are more sensitive 
than omnibus or post hoc analyses (as long as the predicted pattern is in fact 
observed in the data, of course).

Rick should post a description of the conditions for the factor (WITHOUT MEANS) 
to see if we could agree on a predicted pattern that could be tested by a 
single df contrast.

Take care
Jim

set seed = 435678234.
input program.
loop o = 1 to 360.
end case.
end loop.
end file.
end input program.
comp group = trunc((o-1)/90)+1.
comp dep = rnd(rv.norm(50,10.5)).
if group > 2 dep = dep + 5.
glm dep by group /posthoc = group(tukey).

Tests of Between-Subjects Effects
Dependent Variable: dep
 Source          Type III Sum of df  Mean Square F        Sig.
                 Squares                                      
 Corrected Model 1027.744(a)     3   342.581     2.687    .046
 Intercept       990360.900      1   990360.900  7767.647 .000
 group           1027.744        3   342.581     2.687    .046
 Error           45389.356       356 127.498                  
 Total           1036778.000     360                          
 Corrected Total 46417.100       359                          

a R Squared = .022 (Adjusted R Squared = .014)

Post Hoc Tests
 
group
 
Multiple Comparisons
Dependent Variable: dep
Tukey HSD
 (I)   (J)   Mean Difference Std.     Sig. 95% Confidence Interval    
 group group (I-J)           Error         Lower Bound     Upper Bound
 1.000 2.000 -1.43333        1.683239 .830 -5.77812        2.91145    
       3.000 -4.27778        1.683239 .055 -8.62256        .06701     
       4.000 -3.51111        1.683239 .160 -7.85590        .83368     
 2.000 3.000 -2.84444        1.683239 .331 -7.18923        1.50034    
       4.000 -2.07778        1.683239 .605 -6.42256        2.26701    
 3.000 4.000 .76667          1.683239 .969 -3.57812        5.11145    

Homogeneous Subsets
Tukey HSD
 group N  Subset  
          1       
 1.000 90 50.14444
 2.000 90 51.57778
 4.000 90 53.65556
 3.000 90 54.42222

 Sig.     .055    

glm dep by group /contr(group) = spec(-1 -1 1 1  -1 1 0 0  0 0 -1 1).

 Source          Type III Sum of df  Mean Square F        Sig.
                 Squares                                      
 Corrected Model 1027.744(a)     3   342.581     2.687    .046
 Intercept       990360.900      1   990360.900  7767.647 .000
 group           1027.744        3   342.581     2.687    .046
 Error           45389.356       356 127.498                  
 Total           1036778.000     360                          

 Corrected Total 46417.100       359                          

Custom Hypothesis Tests
 group Special                             Dependent      
 Contrast                                  Variable       
                                           dep            
 L1            Contrast Estimate           6.356          
               Std. Error                  2.380          
               Sig.                        .008           
 L2            Contrast Estimate           1.433          
               Std. Error                  1.683          
               Sig.                        .395           
 L3            Contrast Estimate           -.767          
               Std. Error                  1.683          
               Sig.                        .649           



James M. Clark
Professor of Psychology
204-786-9757
204-774-4134 Fax
[EMAIL PROTECTED]

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