Examples for Spss

Cronbach alpha

Cronbach's alpha is the most common measure of internal consistency ("reliability"). It is most commonly used when you have multiple Likert questions in a survey/questionnaire that form a scale and you wish to determine if the scale is reliable.

Cronbach's alpha is a measure of internal consistency, that is, how closely related a set of items are as a group. It is considered to be a measure of scale reliability. A "high" value for alpha does not imply that the measure is unidimensional. If, in addition to measuring internal consistency, you wish to provide evidence that the scale in question is unidimensional, additional analyses can be performed. Exploratory factor analysis is one method of checking dimensionality. Technically speaking, Cronbach's alpha is not a statistical test - it is a coefficient of reliability (or consistency). 

Cronbach's alpha can be written as a function of the number of test items and the average inter-correlation among the items.  Below, for conceptual purposes, the formula for the standardized Cronbach's alpha:

Here N is equal to the number of items, c-bar is the average inter-item covariance among the items and v-bar equals the average variance. 

One can see from this formula that if you increase the number of items, you increase Cronbach's alpha.  Additionally, if the average inter-item correlation is low, alpha will be low.  As the average inter-item correlation increases, Cronbach's alpha increases as well (holding the number of items constant).

Example:

For this example, dataset that contains four test items is used - q1, q2, q3 and q4. To compute Cronbach's alpha for all four items - q1, q2, q3, q4 - use the reliability command:

RELIABILITY

  /VARIABLES=q1 q2 q3 q4.

Here is the resulting output from the above syntax:

The alpha coefficient for the four items is .839, suggesting that the items have relatively high internal consistency.  (Note that a reliability coefficient of .70 or higher is...