Questions and Answers
What are the problems associated with the Pearson Product Moment Correlation Coefficient?
Does not reflect agreement. Can only be measured with 2 groups. Cannot separate variance components due to error or true differences.
What is an Intraclass Correlation Coefficient (ICC)?
A measure of agreement based on repeated measures designs for ANOVA, usually from the same subject but different raters.
What are the ranges for a Correlation Coefficient (ICC) and how is this different from Pearson Coefficients?
ICC ranges from .00 to 1.00, while Pearson Correlation Coefficients range from -1.00 to +1.00.
How is ICC written out?
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What is Model 1?
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What is interrater reliability?
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What is intrarater reliability?
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What is Model 2?
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What is Model 3?
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What are the details of Models 1, 2, and 3 of ICC?
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What is used to see if the ICC is significant?
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How is the Null Hypothesis and Alternate Hypothesis shown?
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What does a Test Statistic: 95% Confidence Interval (95% CI) mean?
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Is there agreement when ICC = .57 (95% CI = .33 to .80)?
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Is there agreement when ICC = .32 (95% CI = -0.5 to .61)?
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What is Shrout's interpretation?
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What are the general guidelines for good and poor values of reliability according to Portney & Wadkins?
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For clinical reliability, what should reliability exceed to ensure reasonable validity?
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What are the values for ICC, 1 - ICC, SEM, and MDC?
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What represents the values for ICC, 1 - ICC, SEM, and MDC?
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What type of data does Percent Agreement require?
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What are the nonparametric equivalents for inter-rater and intra-rater reliability?
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What does Kappa (K) use?
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What is weighted Kappas used for?
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What are Slight, Fair, Moderate, Substantial, Almost Perfect grades for Kappas?
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Study Notes
Intraclass Correlation Coefficient (ICC) Overview
- Pearson Product Moment Correlation Coefficient does not reflect agreement and is limited to only two groups.
- Variance components cannot be isolated using Pearson's coefficient; repeated-measures ANOVA is often necessary for ICC values.
- ICC is derived from repeated measures designs in ANOVA, typically involving the same subjects measured by different raters.
ICC Characteristics
- ICC ranges from 0.00 to 1.00, indicating the level of agreement; differs from Pearson Coefficients, which range from -1.00 to +1.00.
- Written as ICC (model, form), where the first number is the model (1 to 3) and the second is the form (1 or k, the number of raters or measures).
ICC Models
- Model 1: Basic ICC form, least used for reliability studies; typically analyzed using one-way ANOVA.
- Model 2: Focuses on inter-rater reliability where multiple raters assess the same subjects; used for criterion concurrent validity.
- Model 3: Pertains to intra-rater reliability, involving multiple measures by a single rater; often analyzed in contexts like Goniometry assignments.
Significance Testing
- Analysis of significance of ICC involves null and alternate hypotheses: the null states that ICC equals zero, while the alternate states it does not equal zero.
- A Test Statistic with a 95% Confidence Interval indicates an alpha level of 0.05.
Agreement Determination
- Agreement is established if the confidence interval does not include zero; for example, an ICC of 0.57 (95% CI = 0.33 to 0.80) indicates agreement, while 0.32 (95% CI = -0.5 to 0.61) indicates lack of agreement.
Reliability Metrics
- Portney & Wadkins suggest values above 0.75 indicate good reliability; below this, reliability is poor to moderate.
- For clinical measurements, reliability should exceed 0.90 for reasonable validity.
Key Values and Formulas
- ICC: Measures agreement.
- 1 - ICC: Measures lack of agreement.
- Standard Error of Measurement (SEM): Calculated as SD * √(1 - ICC), representing noise.
- Minimal Detectable Change (MDC): Determined using the formula MDC = 1.96 * √(2 * SEM).
Data Types and Reliability Measures
- Percent Agreement is used for nominal data and utilizes an agreement matrix.
- Nonparametric equivalents for inter-rater and intra-rater reliability include percent agreement, Kappa (k), and Weighted Kappas.
- Kappa (k) is used for nominal data and adjusts for chance agreement, particularly in dichotomous data.
- Weighted Kappas may assign weights to cells using three methods: incremental, asymmetrical, and symmetrical.
Kappa Interpretation Grades
- Kappa values can be interpreted as follows:
- Slight: 0.00 - 0.20
- Fair: 0.21 - 0.40
- Moderate: 0.41 - 0.60
- Substantial: 0.61 - 0.80
- Almost Perfect: 0.81 - 1.00
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Description
Explore the intricacies of the Intraclass Correlation Coefficient (ICC) with these flashcards. Understand the limitations of the Pearson Product Moment Correlation Coefficient and its relevance in statistical analysis. This quiz will help reinforce your knowledge about evaluating agreement and variance in data sets.
