Cronbach's alpha
Command:  Statistics Agreement & responsiveness Cronbach's alpha 
Description
Cronbach's alpha is a widely used measure of internal consistency reliability (Cronbach, 1951; Bland & Altman, 1997). It calculates the average correlation among all items in a scale or questionnaire. It ranges from 0 to 1, with higher values indicating greater internal consistency. Cronbachâ€™s alpha is based on the assumption that the items are measuring the same underlying construct.
How to enter data
Each question of the questionnaire results in one variable and the answers (numerically coded) are entered in the respective columns of the spreadsheet. The answers of one subject are entered on one row of the spreadsheet.
Required input
 Variables: the variables that contain the answers to the different questions of the questionnaire.
 Select: an optional filter to include only a selected subgroup of subjects (rows).
 Options
 Correct for scale reversal: Some variables may be inversely related to other variables. When you select the option "Correct for scale reversal", MedCalc will detect these variables automatically (based on the correlation matrix) and reverse the values of those variables before analysis.
Results
Cases in spreadsheet  5 

Cases with missing values  0 
Cases included in the analysis  5 
The following variable was reversed prior to analysis: 
Q3 
1. Cronbach's alpha with raw variables
Cronbach's alpha  0.8966 

95% lower confidence limit  0.6628 
InterItem correlation
 Q2  Q3  Q4 

Q1  0.8663  0.7456  0.9363 
Q2 
 0.9435  0.7715 
Q3 

 0.6508 
Average InterItem correlation  0.8190 

ItemTotal correlation
Variable  Correlation coefficient 

Q1  0.9492 
Q2  0.7346 
Q3  0.5885 
Q4  0.9279 
Effect of dropping variables
Variable dropped  Alpha  Change 

Q1  0.7914  0.1051 
Q2  0.8608  0.03579 
Q3  0.9121  0.01554 
Q4  0.8759  0.02064 
2. Cronbach's alpha with standardized variables
Cronbach's alpha  0.9476 

95% lower confidence limit  0.8294 
ItemTotal correlation
Variable  Correlation coefficient 

Q1  0.9165 
Q2  0.9323 
Q3  0.8197 
Q4  0.8282 
Effect of dropping variables
Variable dropped  Alpha  Change 

Q1  0.9180  0.02968 
Q2  0.9130  0.03468 
Q3  0.9477  0.00008852 
Q4  0.9452  0.002456 
In the example, the results of Question 3 were found to be inversely related to the results of the other questions. Therefore the results of Question 3 were reversed prior to analysis.
MedCalc performs the calculations on (1) the raw data and (2) on the standardized variables (a transformation so that their mean is 0 and variance is 1).
Using the "raw" data, questions that have more variability contribute more to the variability of the resulting scale; in the "standardized" form, each question gets equal weight.
For both the raw data, and the standardized data, MedCalc reports the following.
Cronbach's Alpha
Cronbach's alpha is given with its lower confidence limit (Feldt, 1965).
For research purposes alpha should be more than 0.7 to 0.8, but for clinical purposes alpha should at least be 0.90 (Bland & Altman, 1997).
InterItem correlation
The InterItem correlation table reports the correlation between each variable and every other variable. The average of these is the InterItem correlation.
This provides an estimate of the overall internal consistency of the items. The higher the average InterItem correlation, the greater the internal consistency.
Note: the InterItem correlation table is only reported for the raw data. These correlations are the same for the standardized data.
ItemTotal correlation
ItemTotal correlation examines the correlation between each variable i and the totals obtained by summing all other variables.
Because the variable i is excluded from the total, this ItemTotal correlation is sometimes called the "ItemRest" or "ItemRemainder" correlation.
ItemTotal correlation measures how well an individual item relates to the overall scale. Variables with low ItemTotal correlations may indicate poor internal consistency and may need to be revised or removed.
Note: use of the ItemTotal correlation table on the standardized variables may be preferred over that on the raw data.
Effect of dropping variables
Finaly, MedCalc calculates the alpha obtained with each question in turn dropped. If the deletion of a question causes a considerable increase in alpha then you should consider dropping that question from the questionnaire.
Literature
 Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16:297334.
 Bland JM, Altman DG (1997) Statistics notes: Cronbach's alpha. British Medical Journal 314:572.
 Feldt LS (1965) The approximate sampling distribution of KuderRichardson reliability coefficient twenty. Psychometrika 30:357371.