Reliability Analysis (Cronbach’s Alpha)
In many studies, data is collected using questionnaires or scales that measure abstract concepts such as satisfaction, attitude, motivation, or stress.
Before analyzing such data, it is essential to verify whether the items in a scale are consistent with each other. This consistency is measured using reliability analysis.
What Is Reliability?
Reliability refers to the degree to which a measurement instrument produces consistent results. If the same concept is measured repeatedly, a reliable instrument will produce similar outcomes.
In survey-based research, reliability answers a simple question:
Do all the questions measure the same underlying concept?
Cronbach’s Alpha is the most widely used statistic to measure internal consistency.
Understanding Cronbach’s Alpha
Cronbach’s Alpha (α) ranges between 0 and 1. Higher values indicate better internal consistency.
General interpretation guidelines:
- α ≥ 0.90 → Excellent reliability
- 0.80 ≤ α < 0.90 → Good reliability
- 0.70 ≤ α < 0.80 → Acceptable reliability
- α < 0.70 → Questionable or poor reliability
These thresholds are guidelines, not strict rules. Interpretation depends on context and research field.
Example Dataset
Consider a customer satisfaction survey with five statements rated on a scale from 1 to 5:
| Respondent | Q1 | Q2 | Q3 | Q4 | Q5 |
|---|---|---|---|---|---|
| 1 | 4 | 5 | 4 | 4 | 5 |
| 2 | 3 | 4 | 3 | 4 | 4 |
| 3 | 5 | 5 | 4 | 5 | 5 |
All five questions aim to measure the same construct: customer satisfaction.
Running Reliability Analysis in SPSS (Menu)
To calculate Cronbach’s Alpha using the SPSS menu:
- Go to Analyze → Scale → Reliability Analysis
- Select all scale items (Q1–Q5)
- Choose Model: Alpha
- Click OK
SPSS generates a reliability table showing Cronbach’s Alpha value.
Running Reliability Analysis Using Syntax
RELIABILITY
/VARIABLES=Q1 Q2 Q3 Q4 Q5
/SCALE('Customer Satisfaction') ALL
/MODEL=ALPHA.
This syntax computes Cronbach’s Alpha for the selected items.
Interpreting the Output
The most important output value is Cronbach’s Alpha.
SPSS also provides:
- Item-Total Statistics
- Alpha if Item Deleted
These help identify items that reduce overall reliability.
If removing an item increases alpha significantly, that item may not fit the scale well.
Common Mistakes
Common errors in reliability analysis include:
- Including items measuring different concepts
- Running alpha on too few items
- Blindly accepting high alpha without theory
Reliability must always be supported by conceptual reasoning.
Quiz 1
What does reliability measure?
Consistency of measurement.
Quiz 2
What does Cronbach’s Alpha evaluate?
Internal consistency of scale items.
Quiz 3
What value of alpha is generally acceptable?
0.70 or higher.
Quiz 4
Which SPSS menu contains reliability analysis?
Analyze → Scale → Reliability Analysis.
Quiz 5
What does “Alpha if Item Deleted” indicate?
How reliability changes if an item is removed.
Mini Practice
Create a survey dataset with at least 5 Likert-scale questions measuring the same concept.
Run reliability analysis and:
- Report Cronbach’s Alpha
- Identify any weak items
Use Reliability Analysis → Model Alpha, then inspect Item-Total Statistics.
What’s Next
In the next lesson, you will learn about correlation analysis, which examines relationships between numerical variables.