Measure features: correlations and reliability

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Jennifer Brussow

Psychometrician

Correlations

lowerCor(gcbs)
lowerCor(gcbs)
    Q1   Q2   Q3   Q4   Q5   Q6   Q7   Q8   Q9   Q10  ...
Q1  1.00                                                                      
Q2  0.53 1.00                                                                 
Q3  0.36 0.40 1.00                                                            
Q4  0.52 0.53 0.50 1.00                                                       
Q5  0.48 0.46 0.40 0.57 1.00                                                  
Q6  0.63 0.55 0.40 0.61 0.50 1.00                                             
Q7  0.47 0.67 0.42 0.57 0.45 0.54 1.00                                        
Q8  0.39 0.38 0.78 0.49 0.41 0.41 0.41 1.00                                   
Q9  0.42 0.49 0.49 0.56 0.46 0.48 0.53 0.48 1.00                              
Q10 0.44 0.38 0.32 0.40 0.43 0.41 0.39 0.36 0.37 1.00 
...
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Testing correlations' significance: p-values

corr.test(gcbs, use = "pairwise.complete.obs")$p
    Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15
Q1   0  0  0  0  0  0  0  0  0   0   0   0   0   0   0
Q2   0  0  0  0  0  0  0  0  0   0   0   0   0   0   0
Q3   0  0  0  0  0  0  0  0  0   0   0   0   0   0   0
Q4   0  0  0  0  0  0  0  0  0   0   0   0   0   0   0
Q5   0  0  0  0  0  0  0  0  0   0   0   0   0   0   0
...
Q11  0  0  0  0  0  0  0  0  0   0   0   0   0   0   0
Q12  0  0  0  0  0  0  0  0  0   0   0   0   0   0   0
Q13  0  0  0  0  0  0  0  0  0   0   0   0   0   0   0
Q14  0  0  0  0  0  0  0  0  0   0   0   0   0   0   0
Q15  0  0  0  0  0  0  0  0  0   0   0   0   0   0   0
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Testing correlations' significance: confidence intervals

corr.test(gcbs, use = "pairwise.complete.obs")$ci
            lower         r     upper p
Q1-Q2   0.4970162 0.5259992 0.5538098 0
Q1-Q3   0.3206223 0.3553928 0.3892067 0
Q1-Q4   0.4953852 0.5244323 0.5523079 0
Q1-Q5   0.4503342 0.4810747 0.5106759 0
...
Q1-Q11  0.6199265 0.6435136 0.6659388 0
Q1-Q12  0.4932727 0.5224025 0.5503620 0
Q1-Q13  0.3464313 0.3805006 0.4135673 0
Q1-Q14  0.5059498 0.5345780 0.5620298 0
Q1-Q15  0.4753633 0.5051815 0.5338405 0
...
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Coefficient alpha

alpha(gcbs)
Reliability analysis   
Call: alpha(x = gcbs)

  raw_alpha std.alpha G6(smc) average_r S/N   ase mean sd
      0.93      0.93    0.94      0.48  14 0.002  2.9  1

 lower alpha upper     95% confidence boundaries
0.93 0.93 0.94 
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Coefficient alpha

alpha(gcbs)
 Reliability if an item is dropped:
    raw_alpha std.alpha G6(smc) average_r S/N alpha se
Q1       0.93      0.93    0.94      0.48  13   0.0021
Q2       0.93      0.93    0.94      0.48  13   0.0021
Q3       0.93      0.93    0.94      0.49  13   0.0020
Q4       0.93      0.93    0.94      0.47  13   0.0022
Q5       0.93      0.93    0.94      0.48  13   0.0021
...
Q11      0.93      0.93    0.94      0.48  13   0.0021
Q12      0.93      0.93    0.94      0.47  13   0.0022
Q13      0.93      0.93    0.94      0.48  13   0.0021
Q14      0.93      0.93    0.94      0.48  13   0.0021
Q15      0.93      0.93    0.94      0.49  14   0.0020
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Split-Half reliability

splitHalf(gcbs)
Split half reliabilities  
Call: splitHalf(r = gcbs)

Maximum split half reliability (lambda 4) =  0.95
Guttman lambda 6                          =  0.94
Average split half reliability            =  0.93
Guttman lambda 3 (alpha)                  =  0.93
Minimum split half reliability  (beta)    =  0.86
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