Introduction

Cronbach’s alpha (α) is the most widely used measure of internal consistency and is mainly employed to assess the reliability of a set of questionnaire items. It is a statistical index that shows whether the questions in a survey, designed to measure the same latent variable, actually manage to do so consistently. Internal consistency is essential because if the questions do not work harmoniously together, the results of the study may be unreliable. Cronbach’s alpha ranges from zero to one, with higher values indicating better reliability. Typically, values below 0.6 are considered low, while values above 0.7 indicate satisfactory reliability.

Research Example

To better understand the use of Cronbach’s alpha, let us examine a specific example. A researcher designed a nine-item questionnaire in order to investigate how safe employees feel in an industrial complex. Each question used a five-point Likert scale, ranging from “strongly disagree” to “strongly agree.” The main assumption was that all the questions would contribute to measuring the same latent variable, that is, the sense of safety. To determine whether these questions actually form a reliable scale, Cronbach’s alpha was applied to a sample of fifteen employees. If the analysis showed satisfactory internal consistency, the researcher could then compute an overall score rather than analyzing each question individually.

Data Setup in SPSS Statistics

In the SPSS Statistics software, the nine questions of the questionnaire were entered as nine data columns, named Qu1 through Qu9. Each row of the dataset represents a participant, that is, one of the fifteen employees. With this setup, SPSS is ready to run the Reliability Analysis procedure, which is necessary for calculating Cronbach’s alpha.

Testing Procedure in SPSS Statistics

The computation of Cronbach’s alpha in SPSS is performed through the Reliability Analysis procedure. To begin the analysis, from the menu bar one selects Analyze, then Scale, and finally Reliability Analysis. In the dialog box that appears, the researcher transfers all the variables Qu1 through Qu9 into the Items box. In the Model field, the option Alpha is chosen, which corresponds to Cronbach’s alpha. Additionally, through the Statistics button, extra information can be requested, such as the Item-Total Statistics, which indicate how each question correlates with the overall scale. It is also recommended to activate the Scale if item deleted option, in order to see how the overall result would change if a question were removed. Once all the options are set, the analysis is executed by clicking OK, and the results appear in the SPSS output window.

Interpretation of Results

The value of Cronbach’s alpha is the key criterion for assessing reliability. Generally, values equal to or greater than 0.9 indicate excellent reliability, values between 0.8 and 0.9 show good reliability, and values from 0.7 to 0.8 are considered acceptable. On the other hand, values between 0.6 and 0.7 are marginal and require caution, while values below 0.6 are regarded as unacceptable. Apart from the overall result, SPSS also provides tables with the Corrected Item-Total Correlation and Cronbach’s Alpha if Item Deleted. These are particularly useful, as they reveal if any question decreases overall reliability. If the removal of a question significantly increases Cronbach’s alpha, then that item may not substantially contribute to measuring the latent variable and may need to be revised or excluded. In the example of the safety perception survey, if Cronbach’s alpha is found around 0.85, it can be concluded that the questionnaire has high internal consistency and is suitable to be used as a single scale.

Conclusion

Cronbach’s alpha is one of the most important tools available to researchers for assessing the reliability of questionnaires and Likert-type scales. Its use within SPSS Statistics is simple and straightforward, while providing clear and useful results. Proper interpretation of the findings allows for the improvement of measurement instruments, enhances the quality of the research, and makes the conclusions more reliable. In this way, the analysis of Cronbach’s alpha is not merely a formal statistical procedure but a vital step in securing the validity of any research project.