What is reliability Analysis in Statistics
Reliability analysis allows you to study the properties of measurement scales and the items that compose the scales. The Reliability Analysis procedure calculates a number of commonly used measures of scale reliability and also provides information about the relationships between individual items in the scale. Intraclass correlation coefficients can be used to compute inter-rater reliability estimates.
There are a few reliability test, and the most commonly used in social science is the Cronbach Alpha test.
What does Cronbach's alpha mean?
Cronbach's alpha is a measure of internal consistency, that is, how closely related a set of items are as a group. A "high" value of alpha is often used (along with substantive arguments and possibly other statistical measures) as evidence that the items measure an underlying (or latent) construct. However, a high 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. For conceptual purposes, the formula for the standardized Cronbach's alpha:
Reliability analysis allows you to study the properties of measurement scales and the items that compose the scales. The Reliability Analysis procedure calculates a number of commonly used measures of scale reliability and also provides information about the relationships between individual items in the scale. Intraclass correlation coefficients can be used to compute inter-rater reliability estimates.
There are a few reliability test, and the most commonly used in social science is the Cronbach Alpha test.
What does Cronbach's alpha mean?
Cronbach's alpha is a measure of internal consistency, that is, how closely related a set of items are as a group. A "high" value of alpha is often used (along with substantive arguments and possibly other statistical measures) as evidence that the items measure an underlying (or latent) construct. However, a high 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. For conceptual purposes, the formula for the standardized Cronbach's alpha:
wHere 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.
If the number of items is increased, Cronbach's alpha will also increase. 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).
To get the Cronbach Alpha value in SPSS, from the QOL questions, let us ru some reliability test.
- Open the QOL dataset.
- Assuming the dataset is opened,
click => Analyse
=>Scale
=> Reliability Analysis
3. In the reliability Analysis window, bring F4.1, F4.2 and F4.3 into the items box.You can do this by drag-and-dropping the variables into their respective boxes or by using the 
button.
button.
4. Leave the Model: set as "Alpha", which represents Cronbach's alpha in SPSS. If you want to provide a name for the scale, enter it in the Scale label: box.
5. Click => Statistics..and the Reliability Analysis : Statistics box appears.
6. Click,=>" Item" and "Scale if item deleted" and also "Correlations".
7. Click the 
button. This will return you to the Reliability Analysis dialogue box.
button. This will return you to the Reliability Analysis dialogue box.
8. Click the 
button to generate the output.
button to generate the output.
6. The output will appear as follows:
- SPSS produces many different tables. The first important table is the Reliability Statistics table that provides the actual value for Cronbach's alpha,
- Cronbach Alpha score for this variable with three items considered is 0.937,which indicates a high level of internal consistency for our scale with this specific sample.
- The Item-Total Statistics table presents the "Cronbach's Alpha if Item Deleted" in the final column,
- This column presents the value that Cronbach's alpha would be if that particular item was deleted from the scale. We can see that removal of any question, would result in a lower Cronbach's alpha. Therefore, we would not want to remove these questions.
- If Removal of any question would lead to a small improvement in Cronbach's alpha, and we can also see that the "Corrected Item-Total Correlation" value will also be low for the item. This might lead us to consider whether we should remove this item.
- Cronbach's alpha simply provides you with an overall reliability coefficient for a set of variables (e.g., questions). If your questions reflect different underlying personal qualities (or other dimensions), for example, employee motivation and employee commitment, Cronbach's alpha will not be able to distinguish between these. In order to do this and then check their reliability (using Cronbach's alpha), you will first need to run a test such as a principal components analysis (PCA).
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