Using Cronbach’s Alpha Statistic in Research
This easy tutorial will show you how to run the Reliability Analysis test in SPSS, and how to interpret the result.
Reliability analysis allows you to study the properties of measurement scales and the items that compose the scales. That’s to say, 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. In addition, Intraclass correlation coefficients can be used to compute inter-rater reliability estimates. (Source)
Reliability analysis is the degree to which the values that make up the scale measure the same attribute. In addition, the most used measure of reliability is Cronbach’s alpha coefficient.
It is the average correlation between all values on a scale. In other words, the value of Cronbach’s alpha coefficient is between 0 and 1, with a higher number indicating better reliability.
Finally, Cronbach’s alpha coefficient should be higher than 0.70; that scale has good internal validity and reliability.
Assumptions of the Reliability Analysis
Observations should be independent, and errors should be uncorrelated between items. Firstly, each pair of items should have a bivariate normal distribution. Secondly, scales should be additive and each item is linearly related to the total score.
An Example: Reliability Analysis Test
This guide will explain, step by step, how to run the reliability Analysis test in SPSS statistical software by using an example.
We developed a 5-question questionnaire and then each question measured empathy on a Likert scale from 1 to 5 (strongly disagree to strongly agree).
Above all, we wanted to know whether all items are a reliable measure of the same variable (empathy). Therefore, we used Cronbach’s alpha coefficient as a measure of reliability.
Finally, This easy tutorial will show you how to run the reliability analysis test in SPSS, and how to interpret the result.