Inter-rater and intra-rater Reliability

In Facets Table 7, there are "Reliability" indexes reported for every facet. This reliability is the Spearman Reliability. Cronbach Alpha is also an estimate of Spearman Reliability. This reliability "distinguishes between different levels of severity among" the elements of the facet. Higher reliability = more levels.

 

Inter-rater reliability is not the same as Spearman Reliability. For inter-rater reliability, higher reliability = more similarity. There are three families of inter-rater reliability statistics. (i) Do the raters agree with each other about the examinee's rating? (ii) Do the raters agree with each other about which examinees are better and which examinees are worse? (iii) Do the raters give the correct rating to the performance?

 

Inter-rater reliability (i)is used for pass-fail decisions about the examinees. Inter-rater reliability (ii) is used when the rank-order of the examinees is crucial. Inter-rater reliability (iii) is used when certifying raters.

 

Intra-rater reliability can be deduced from the rater's fit statistics. The lower the mean-square fit, the higher the intra-rater reliability. This is because high intra-rater reliability implies that the ratings given by the rater can be accurately predicted from each other.

 


 

There is no generally-agreed index of inter-rater reliability (IRR). The choice of IRR depends on the purpose for which the ratings are being collected, and the philosophy underlying the rating process.
 
For raters, there are a number of steps in deciding what quality-indexes to report:
1. Are the raters intended to act as independent experts or as "rating machines"?
2. Are the ratings reflective of criterion-levels or of relative performance?
3. How are differences in rater leniency to be managed?
4. How are rater disagreements to be managed?

 

First, you have to decide what type of rater agreement you want.

 

Do you want the raters to agree exactly with each other on the ratings awarded? The "rater agreement %".

 

Do you want the raters to agree about which performances are better and which are worse? Correlations

 

Do you want the raters to have the same leniency/severity? "1 - Separation Reliability" or "Fixed Chi-square"

 

Do you want the raters to behave like independent experts? Rasch fit statistics
 
Typical indexes include: proportion of exact agreements (Cohen's kappa), correlations, variances (G-Theory).

 

In the literature there is no clear definition of this, so you must decide what the term means for your situation.

A. It can mean "to what extent to do pairs of raters agree on the same rating?". This is the "exact observed agreement" statistic. If you want your raters to act like "rating machines" (human optical scanners), then you expect to see agreement of 90%+. Raters are often trained to act like this.

B. It can mean "are the ratings of pairs of raters highly correlated?". Facets does not report this directly.

C. Are pairs of raters acting like independent experts (the ideal for Facets)? If so the "observed agreements" will be close to the "expected agreements".

D. Do raters have same level of leniency/severity? This is reported by the "Reliability (not inter-rater)" statistic. For raters we like to see this close to 0, so that the rater measures are not reliably different. We also like to see the "Fixed all-same" chi-square test not be rejected.

 

Rasch Agreement        Interpretation

Observed < Expected        Indicates disagreement, normally happens with untrained raters.

Observed ≈ Expected        Raters act independently. Need verification with fit statistics.

Observed somewhat >expected        Normal for trained raters. Training emphasizes agreement with others but rating requires raters to rate independently.

Observed >> expected        Raters do not rate independently. There may be pressure to agree with other raters.

Observed > 90%        Raters behave like a rating machine. Seriously consider excluding from the measurement model  facet.

 

There is not a generally agreed definition of "inter-rater reliability". Do you intend your raters to act like "rating machines" or as "independent experts"? "Rating machines" are expected to give identical ratings under identical conditions. "Independent experts" are expected to show variation under identical conditions. Facets models raters to be "independent experts". An interrater reliability coefficient, IRR, is not computed. But, from one perspective, it is the reverse of the Separation Reliability, i.e., 1 - Separation Reliability.

 

For "rating machines", there are several inter-rater approaches. For these you need to use other software:

 

1. Raters must agree on the exact value of the ratings: use a Cohen's-Kappa type of inter-rater reliability index. Cohen's Kappa is (Observed Agreement% - Chance Agreement%)/(100-Chance Agreement%) where chance is determined by the marginal category frequencies. A Rasch version of this would use the "Expected Agreement%" for an adjustment based on "chance + rater leniency + rating scale structure". Then the Rasch-Cohen's Kappa would be: (Observed%-Expected%)/(100-Expected%). Under Rasch-model conditions this would be close to 0.

 

2. Raters must agree on higher and lower performance ratings: use a correlation-based inter-rater reliability index.

 

3. Inter-rater variance must be much less than inter-examinee variance: compare the Rater facet S.D. with the Examinee facet S.D.

 

When the raters are behaving like rating machines, alternative analytical approaches should be considered, such as Wilson M. & Hoskens M. (2001) The Rater Bundle Model, Journal of Educational and Behavioral Statistics, 26, 3, 283-306, or consider omitting the rater facet from your analysis.

 

The computation

How many ratings are made under identical conditions (usually by different raters) and how often are those ratings in exact agreement? This investigation is done pairwise across all raters. All facets except the Inter-rater= facet participate in the matching. If the inter-rater facet is Entered= more than once, only the first entry is active for this comparison.

 

To exclude dummy facets and irrelevant ones, do a special run with those marked by X in the model statements. For example, facet 1 is persons, facet 2 is gender (sex) (dummy, anchored at zero), facet 3 is rater, facet 4 is item, facet 5 is rating day (dummy, anchored at 0). Then Gender and Rating Day are irrelevant to the pairing of raters:

Inter-rater=3

Model = ?,X,?,?,X, R6

 

Raters:

Senior scientists

Junior Scientists

Traits

Observation

Inter-Rater

Agreement Opportunities

Observed

Exact

Agreement

Avogadro

Anne

Attack

5

1

0.5 (agrees with Cavendish but not Brahe)

Cavendish

Anne

Attack

5

1

0.5 (agrees with Avogadro but not Brahe)

Brahe

Anne

Attack

6

1

0 (disagrees)

 

 

 

 

 

 

Avogadro

Anne

Basis

5

1

1 (agrees)

Brahe

Anne

Basis

5

1

1

Cavendish

Anne

Basis

5

1

1




 

 

 

Avogadro

Anne

Clarity

3

1

0 (disagrees)

Brahe

Anne

Clarity

4

1

0

Cavendish

Anne

Clarity

5

1

0

 

In the Table above, "Inter-Rater Agreement Opportunities" are computed for each rater. There is one opportunity for each observation awarded by a rater under the same circumstances (i.e., same person, same item, same task, ....) as another observation. In the Guilford.txt example, there are 105 observations, all in situations where there are multiple raters, so there are 105 agreement opportunities.

"Observed Exact Agreement" is the proportion of times one observation is exactly the same as one of the other observations for which there are the same circumstances. If, under the same circumstances, the raters all agree, then the Exact Agreement is 1 for each observation. If the raters all disagree, then the Exact Agreement is 0 for each observation. If some raters agree, then the Exact agreement for each observation is the fraction of opportunities to agree with other raters. In the Guilford data, there are 35 sets of 3 ratings: 5 sets of complete agreement = 5 *3 =15. There are 18 sets of partial agreement = 18 * 2 * 0.5 = 18. There are 12 sets of no agreement = 12 * 0 = 0. The agreements sum to 33.

 

By contrast, Fleiss' kappa has the formula: Kappa = (Pobserved - Pchance) / (1 - Pchance)

 

Proportion of observations in category j  is reported in Table 8 as the "Count %".

 

+-----------------------

|      DATA            |

| Category Counts  Cum.|

|Score   Used   %    % |

|----------------------+

|  1        4   4%   4%|

|  2        4   4%   8%|

|  3       25  24%  31%|

|  4        8   8%  39%|

|  5       31  30%  69%|

|  6        6   6%  74%|

|  7       21  20%  94%|

|  8        3   3%  97%|

|  9        3   3% 100%|

+-----------------------

 

Pchance = Σ(Count %/100)² = .04² + .04² + .24² + .08² + .30² + .06² + .20² + .03² + .03² = 0.20

 

Considering "7 Junior scientists + 5 Traits" as 35 "subjects", so that there are three raters, and n = 3 observations for each subject.

 

Pi = extent of agreement for subject i = (Σ(count of observations for subject i in category j)² - n)/(n(n-1))

 

For 15 sets of complete agreement with 3 observations, Pi = (3² - 3) / (3*2) = 1

For 18 sets of partial agreement with 3 observations, Pi = (2² + 1 - 3)/(3*2) = 0.33

For 12 sets of disagreement with 3 observations, Pi = (1 + 1 + 1 - 3)/(3*2) = 0

 

Pobserved = Mean (Pi) = (1*15 + 18*0.33 + 12*0)/35 = 0.60

 

Fleiss kappa = (0.60 - 0.20) / (1 - 0.20) = 0.40 / 0.80 = 0.5, which is considered "moderate agreement".

 

Here are the results from Guilford's data. Note that Avogadro and Cavendish show much higher agreement rates than the model predicts. It seems that they share something which contrasts with Brahe:

-------------------------  -------------------------------------

| Obsvd   Obsvd  Obsvd     | Exact Agree. |                    |

| Score   Count Average    | Obs %  Exp % | N Senior scientists|

-------------------------  -------------------------------------

|    156     35     4.5    |  21.4   25.2 | 2 Brahe            |

|    171     35     4.9    |  35.7   25.8 | 1 Avogadro         |

|    181     35     5.2    |  37.1   25.3 | 3 Cavendish        |

----------------------------------------------------------------------------------------

Rater agreement opportunities: 105  Exact agreements: 33 = 31.4%  Expected: 26.7 = 25.4%

----------------------------------------------------------------------------------------

 

When an anchorfile= is produced, and used for a subsequent analysis with Brahe commented out, then the agreement between Avogadro and Cavendish is twice what is expected!

-------------------------  -------------------------------------

| Obsvd   Obsvd  Obsvd     | Exact Agree. |                    |

| Score   Count Average    | Obs %  Exp % | N Senior scientists|

-------------------------  -------------------------------------

|    171     35     4.9    |  51.4   25.9 | 1 Avogadro         |

|    181     35     5.2    |  51.4   25.9 | 3 Cavendish        |

-------------------------  -------------------------------------


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