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Cronbach's Alpha (α) using SPSS

Introduction

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. If you are concerned with inter-rater reliability, we also have a guide on using Cohen's (κ) kappa that you might find useful.

SPSS

Example

A researcher has devised a nine-question questionnaire to measure how safe people feel at work at an industrial complex. Each question was a 5-point Likert item from "strongly disagree" to "strongly agree". In order to understand whether the questions in this questionnaire all reliably measure the same latent variable (feeling of safety) (so a Likert scale could be constructed), a Cronbach's alpha was run on a sample size of 15 workers.

SPSS

Setup in SPSS

In SPSS, the nine questions have been labelled Qu1 through to Qu9. To know how to correctly enter your data into SPSS in order to run a Cronbach's alpha test, see our Entering Data into SPSS tutorial. Alternately, you can learn about our enhanced data setup content here.

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SPSS

Test Procedure in SPSS

The eight steps below show you how to check for internal consistency using Cronbach's alpha in SPSS. At the end of these eight steps, we show you how to interpret the results from your Cronbach's alpha.

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SPSS

SPSS Output for Cronbach's Alpha

SPSS produces many different tables. The first important table is the Reliability Statistics table that provides the actual value for Cronbach's alpha, as shown below:

Cronbach's Alpha Output

Published with written permission from SPSS, IBM Corporation.

From our example, we can see that Cronbach's alpha is 0.805, which indicates a high level of internal consistency for our scale with this specific sample.

SPSS

Item-Total Statistics

The Item-Total Statistics table presents the "Cronbach's Alpha if Item Deleted" in the final column, as shown below:

Cronbach's Alpha Output

Published with written permission from SPSS, IBM Corporation.

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, except question 8, would result in a lower Cronbach's alpha. Therefore, we would not want to remove these questions. Removal of question 8 would lead to a small improvement in Cronbach's alpha, and we can also see that the "Corrected Item-Total Correlation" value was low (0.128) for this 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). You can learn how to carry out principal components analysis (PCA) using SPSS, as well as interpret and write up your results, in our enhanced content. You can learn more here. It is also possible to run Cronbach's alpha in Minitab.

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