# HOW TO RUN RELIABILITY ANALYSIS TEST IN SPSS

## What is the Reliability Analysis Test?

### 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.

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## How to report a Chi-square test for independence results: Explanation Step by Step

### How to Report Case Processing Summary Table in SPSS Output?

The first table shows Case Processing Summary (valid, excluded cases, and total)

### How to Report Reliability Statistic Table in SPSS Output?

The second table shows the Reliability Statistics. To clarify, it shows Cronbach’s alpha coefficient and the number of items.

Cronbach’s alpha coefficient should be greater than 0.70 for good reliability of the scale. In our example, Cronbach’s alpha coefficient = 0.768 > 0.70, so our scale Empathy had good internal consistency and reliability.

### How to Report Item StatisticsTable SPSS Output?

The next table shows Item Statistics (mean, standard deviation, and number of observations for every item).

### How to Report Item Correlation Matrix in SPSS output?

Next table Inter-Item Correlation Matrix shows the correlation between items.

### How to report Summary Item statistics in SPSS Output?

The next table shows Summary Item statistics, mean, minimum, maximum, range, variance, and number of items.

### How to Report Item Correlation Matrix in SPSS output?

Next table Item-Total Statistics is very important for Reliability analysis.

Column Corrected Item-Total correlation shows the correlation of each item with the total value. That’s to say, values lower than 0.30 indicates that item does not measure the same thing as a scale, so we must remove that item from the scale.

### How to Report Scale statistics in SPSS output?

Finally, the last table shows Scale statistics, mean, variance, standard deviation, and number of items.

## How to Interpret a Reliability Analysis Results in APA Style?

Cronbach’s alpha coefficient was used to determine the reliability and internal consistency of the 4-item Empathy scale. Therefore, the results indicate that scale Empathy has good reliability and internal consistency (Cronbach’s alpha coefficient = 0.768).

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