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What is Reliability?
Reliability measures consistency of measurement. A reliable instrument produces similar results under consistent conditions - the same person taking a test twice should get similar scores, different raters should agree, and items measuring the same construct should correlate. Reliability is prerequisite for validity; unreliable measures cannot validly assess what they claim to measure.
Types of Reliability
- Internal consistency - Do scale items measure the same construct?
- Test-retest reliability - Are scores stable over time?
- Inter-rater reliability - Do different raters agree?
- Parallel forms - Do alternate versions produce similar scores?
- Split-half reliability - Do two halves of a test correlate?
Cronbach's Alpha
What It Measures
Cronbach's Alpha (α) assesses internal consistency - the extent to which scale items intercorrelate. High alpha indicates items measure a unified construct. Alpha ranges from 0 to 1, with higher values indicating greater internal consistency.
Interpretation Guidelines
- α ≥ 0.90 - Excellent (may indicate redundancy if too high)
- α = 0.80-0.89 - Good
- α = 0.70-0.79 - Acceptable
- α = 0.60-0.69 - Questionable
- α < 0.60 - Unacceptable
When to Use
Calculate alpha for:
- Likert scale questionnaires
- Multi-item psychological measures
- Attitude scales
- Summated rating scales
Alpha assumes unidimensionality - items measure one construct. For multidimensional scales, calculate alpha for each subscale separately.
Factors Affecting Alpha
Number of items: More items increase alpha, even with lower inter-item correlations. Don't rely solely on alpha for long scales.
Item intercorrelations: Higher correlations produce higher alpha. Items should correlate moderately (0.30-0.70).
Sample size: Alpha stabilizes with 200+ participants. Small samples produce unreliable estimates.
Test-Retest Reliability
What It Measures
Test-retest reliability assesses temporal stability. Participants complete the same measure twice, separated by time interval. Correlation between administrations indicates score stability.
Calculating Test-Retest
Compute Pearson correlation between Time 1 and Time 2 scores:
- r ≥ 0.80 - Excellent stability
- r = 0.70-0.79 - Good
- r = 0.60-0.69 - Acceptable
- r < 0.60 - Poor
Optimal Time Interval
Balance two concerns:
- Too short (hours/days): Participants remember responses, inflating reliability
- Too long (months): True change occurs, lowering reliability
Typical intervals: 2-4 weeks for stable traits, 1 week for state measures.
When to Use
Assess test-retest for:
- Personality measures (expected stability)
- Cognitive ability tests
- Trait measures (vs. state measures)
- Diagnostic assessments
Don't use for measures expected to change (mood states, treatment outcomes).
Inter-Rater Reliability
What It Measures
Inter-rater reliability quantifies agreement between independent raters scoring the same observations, performances, or materials. Essential for subjective coding, performance ratings, or behavioral observations.
Cohen's Kappa
For categorical ratings by two raters:
- κ ≥ 0.80 - Excellent agreement
- κ = 0.60-0.79 - Good
- κ = 0.40-0.59 - Moderate
- κ < 0.40 - Poor
Kappa accounts for chance agreement, unlike simple percent agreement. Two raters agreeing 80% of time may have low kappa if ratings are skewed toward one category.
Intraclass Correlation (ICC)
For continuous ratings or more than two raters:
- ICC(1,1): Single rater, each participant rated by different random rater
- ICC(2,1): Single rater, same raters for all participants
- ICC(3,1): Single rater, raters are only ones of interest
Choose ICC model matching your study design. Interpretation similar to correlation coefficients.
Improving Inter-Rater Reliability
Rater training: Extensive training with practice coding and feedback increases agreement.
Clear coding schemes: Precise definitions and decision rules reduce ambiguity.
Consensus meetings: Raters discuss disagreements, refining shared understanding.
Calibration sessions: Periodic rechecks prevent rater drift over time.
Split-Half Reliability
What It Measures
Split-half reliability divides scale into two halves and correlates them. Assesses internal consistency like Cronbach's alpha but for two arbitrary groups of items.
Calculation Methods
Odd-even split: Odd-numbered items vs. even-numbered items First-half/second-half: Beginning items vs. ending items Random split: Randomly assign items to halves
Correlation between halves estimates reliability of half-length test. Apply Spearman-Brown formula to estimate full-length reliability.
Spearman-Brown Formula
Adjusted reliability = (2 × r) / (1 + r)
Where r = correlation between halves
Interpretation
Similar to alpha coefficients:
- ≥ 0.80 - Good
- 0.70-0.79 - Acceptable
- < 0.70 - Questionable
KR-20 (Kuder-Richardson Formula 20)
What It Measures
KR-20 assesses internal consistency for dichotomous items (correct/incorrect, yes/no, true/false). Equivalent to Cronbach's alpha for binary data.
When to Use
Calculate KR-20 for:
- Multiple-choice tests
- True/false questionnaires
- Binary response scales
- Achievement tests
Interpretation
Same thresholds as Cronbach's alpha:
- ≥ 0.80 - Good reliability
- 0.70-0.79 - Acceptable
- < 0.70 - Poor
KR-20 assumes equal item difficulty. Very easy or very hard items reduce KR-20 even if test is reliable.
Improving Low Reliability
Item Analysis
Examine item-total correlations:
- High correlations (> 0.30): Good items, keep
- Low correlations (< 0.20): Poor items, revise or remove
- Negative correlations: Reverse-scored or problematic items
Remove items that don't correlate with scale total to increase alpha.
Increase Items
Add items measuring the same construct. More items generally increase reliability, but only if new items are good quality. Adding poor items can decrease reliability.
Clarify Wording
Ambiguous items reduce reliability. Participants interpret unclear questions differently across administrations or between individuals. Revise confusing wording.
Improve Response Options
Vague response scales (somewhat, kind of, pretty much) introduce measurement error. Use specific, well-defined response options with appropriate number of points (5-7 works well for most Likert scales).
Homogenize Content
Mixing different content domains in one scale reduces internal consistency. If alpha is low despite high inter-item correlations, you may have multiple subscales requiring separate analysis.
Reporting Reliability
In Methods Sections
Report appropriate reliability for your instrument:
- Published scales: Cite original reliability plus your sample's reliability
- New scales: Report all relevant reliability types
- Modified scales: Explain modifications and report reliability
Example: "The 10-item Perceived Stress Scale (Cohen et al., 1983) assesses stress perceptions. Original reliability was α = 0.78. In our sample (n = 245), internal consistency was excellent (α = 0.87)."
In Results
If reliability is primary focus:
- Report detailed statistics (item-total correlations, alpha if item deleted)
- Include confidence intervals
- Describe procedures for improving reliability
- Document final scale composition
Minimum Standards
Most journals require:
- Cronbach's alpha for multi-item scales
- Inter-rater reliability for coded data
- Test-retest for stable trait measures
Check target journal requirements before data collection.
Common Mistakes
Alpha as Only Criterion
High alpha alone doesn't ensure good measurement. Also assess:
- Content validity (items cover construct domain)
- Unidimensionality (items measure one thing)
- Appropriate item difficulty
- Convergent and discriminant validity
Ignoring Low Reliability
Don't proceed with unreliable measures hoping for good results. Unreliable measures attenuate correlations, reduce statistical power, and produce misleading findings. Fix reliability before data collection or interpret findings cautiously.
Over-Reliance on Cutoffs
Reliability thresholds are guidelines, not rules. A measure with α = 0.69 isn't necessarily worse than one with α = 0.70. Consider reliability in context of measurement precision needs and previous research in your area.
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