Reliability and Validity
In data analysis and research, measuring the quality of data is just as important as analyzing it.
Two key concepts ensure measurement quality: Reliability and Validity.
Without reliable and valid measurements, statistical results can be misleading.
What Is Reliability?
Reliability refers to the consistency of a measurement.
A measurement is reliable if it produces similar results under consistent conditions.
In simple terms:
- Reliable = consistent
- Unreliable = random or unstable
Example:
If a survey measures job satisfaction, respondents should give similar responses when the survey is repeated under similar conditions.
What Is Validity?
Validity refers to whether a measurement actually measures what it is intended to measure.
A measure can be reliable but still not valid.
Example:
A scale may consistently measure typing speed, but it is not a valid measure of intelligence.
Reliability vs Validity
| Aspect | Reliability | Validity |
|---|---|---|
| Meaning | Consistency of measurement | Accuracy of measurement |
| Focus | Stability | Correctness |
| Required for | Trustworthy data | Meaningful conclusions |
Types of Reliability
Common types include:
- Test–retest reliability
- Inter-rater reliability
- Internal consistency reliability
SPSS mainly focuses on internal consistency.
Cronbach’s Alpha
Cronbach’s Alpha is the most common statistic used to measure internal consistency.
It evaluates how closely related a set of items are as a group.
Interpretation guidelines:
- α ≥ 0.9 → Excellent
- α ≥ 0.8 → Good
- α ≥ 0.7 → Acceptable
- α < 0.7 → Needs improvement
Example Scenario
A company designs a questionnaire to measure employee engagement using five statements.
Each statement is rated from 1 to 5.
Cronbach’s Alpha checks whether these questions consistently measure the same concept.
Running Reliability Analysis (Menu)
To compute Cronbach’s Alpha in SPSS:
- Go to Analyze → Scale → Reliability Analysis
- Select all questionnaire items
- Choose Model: Alpha
- Click OK
SPSS produces reliability statistics and item-level diagnostics.
SPSS Syntax Example
RELIABILITY
/VARIABLES Q1 Q2 Q3 Q4 Q5
/SCALE('Engagement') ALL
/MODEL=ALPHA.
Improving Reliability
If reliability is low:
- Remove poorly performing items
- Rewrite ambiguous questions
- Add more relevant items
SPSS shows “Cronbach’s Alpha if Item Deleted” to help with decisions.
Validity Considerations
SPSS does not directly compute validity, but it can be evaluated using:
- Factor analysis
- Content review by experts
- Correlation with related measures
Reliability is necessary, but not sufficient for validity.
Common Mistakes
Frequent errors include:
- Ignoring low reliability values
- Assuming reliability guarantees validity
- Using too few items
Measurement quality must always be evaluated first.
Quiz 1
What does reliability measure?
Consistency of measurement.
Quiz 2
What does validity measure?
Accuracy of measurement.
Quiz 3
Which statistic measures internal consistency?
Cronbach’s Alpha.
Quiz 4
Is a reliable measure always valid?
No.
Quiz 5
What alpha value is considered acceptable?
0.7 or higher.
Mini Practice
Create a short survey with at least five questions measuring the same concept.
Compute Cronbach’s Alpha and decide whether the scale is reliable.
Use Reliability Analysis and check alpha value and item diagnostics.
What’s Next
In the next lesson, you will learn about Cluster Analysis, used to group observations based on similarity.