Validating scale measurement credibility. Buros Center for Testing.



Validating scale measurement credibility

Validating scale measurement credibility

By contrast, situations involving unambiguous measurement, such as simple counting tasks e. Measurement involving ambiguity in characteristics of interest in the rating target are generally improved with multiple trained raters.

Such measurement tasks often involve subjective judgment of quality examples include ratings of physician 'bedside manner', evaluation of witness credibility by a jury, and presentation skill of a speaker. Variation across raters in the measurement procedures and variability in interpretation of measurement results are two examples of sources of error variance in rating measurements.

Clearly stated guidelines for rendering ratings are necessary for reliability in ambiguous or challenging measurement scenarios. Without scoring guidelines, ratings are increasingly affected by experimenter's bias , that is, a tendency of rating values to drift towards what is expected by the rater. During processes involving repeated measurements, correction of rater drift can be addressed through periodic retraining to ensure that raters understand guidelines and measurement goals.

The philosophy of inter-rater agreement[ edit ] There are several operational definitions [1] of "inter-rater reliability" in use by Examination Boards, reflecting different viewpoints about what is reliable agreement between raters. There are three operational definitions of agreement: Reliable raters agree with the "official" rating of a performance.

Reliable raters agree with each other about the exact ratings to be awarded. Reliable raters agree about which performance is better and which is worse. These combine with two operational definitions of behavior: Reliable raters are automatons, behaving like "rating machines". This category includes rating of essays by computer [2]. This behavior can be evaluated by Generalizability theory.

Reliable raters behave like independent witnesses. They demonstrate their independence by disagreeing slightly.

This behavior can be evaluated by the Rasch model. Statistics[ edit ] Joint probability of agreement[ edit ] The joint-probability of agreement is the simplest and least robust measure. It is estimated as the percentage of the time the raters agree in a nominal or categorical rating system.

It does not take into account the fact that agreement may happen solely based on chance. There is some question whether or not there is a need to 'correct' for chance agreement; some suggest that, in any case, any such adjustment should be based on an explicit model of how chance and error affect raters' decisions. This is because both raters must confine themselves to the limited number of options available, which impacts the overall agreement rate, and not necessarily their propensity for "intrinsic" agreement an agreement is considered "intrinsic" if it is not due to chance.

Therefore, the joint probability of agreement will remain high even in the absence of any "intrinsic" agreement among raters [4]. A useful inter-rater reliability coefficient is expected a to be close to 0, when there is no "intrinsic" agreement, and b to increase as the "intrinsic" agreement rate improves.

Most chance-corrected agreement coefficients achieve the first objective [5]. However, the second objective is not achieved by many known chance-corrected measures [6]. Cohen's kappa and Fleiss' kappa Cohen's kappa [7] , which works for two raters, and Fleiss' kappa [8] , an adaptation that works for any fixed number of raters, improve upon the joint probability in that they take into account the amount of agreement that could be expected to occur through chance.

They suffer from the same problem as the joint-probability in that they treat the data as nominal and assume the ratings have no natural ordering. If the data do have an order, the information in the measurements is not fully taken advantage of.

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Validating scale measurement credibility

By contrast, situations involving unambiguous measurement, such as simple counting tasks e. Measurement involving ambiguity in characteristics of interest in the rating target are generally improved with multiple trained raters. Such measurement tasks often involve subjective judgment of quality examples include ratings of physician 'bedside manner', evaluation of witness credibility by a jury, and presentation skill of a speaker. Variation across raters in the measurement procedures and variability in interpretation of measurement results are two examples of sources of error variance in rating measurements.

Clearly stated guidelines for rendering ratings are necessary for reliability in ambiguous or challenging measurement scenarios. Without scoring guidelines, ratings are increasingly affected by experimenter's bias , that is, a tendency of rating values to drift towards what is expected by the rater. During processes involving repeated measurements, correction of rater drift can be addressed through periodic retraining to ensure that raters understand guidelines and measurement goals.

The philosophy of inter-rater agreement[ edit ] There are several operational definitions [1] of "inter-rater reliability" in use by Examination Boards, reflecting different viewpoints about what is reliable agreement between raters.

There are three operational definitions of agreement: Reliable raters agree with the "official" rating of a performance. Reliable raters agree with each other about the exact ratings to be awarded. Reliable raters agree about which performance is better and which is worse. These combine with two operational definitions of behavior: Reliable raters are automatons, behaving like "rating machines".

This category includes rating of essays by computer [2]. This behavior can be evaluated by Generalizability theory.

Reliable raters behave like independent witnesses. They demonstrate their independence by disagreeing slightly. This behavior can be evaluated by the Rasch model. Statistics[ edit ] Joint probability of agreement[ edit ] The joint-probability of agreement is the simplest and least robust measure. It is estimated as the percentage of the time the raters agree in a nominal or categorical rating system.

It does not take into account the fact that agreement may happen solely based on chance. There is some question whether or not there is a need to 'correct' for chance agreement; some suggest that, in any case, any such adjustment should be based on an explicit model of how chance and error affect raters' decisions. This is because both raters must confine themselves to the limited number of options available, which impacts the overall agreement rate, and not necessarily their propensity for "intrinsic" agreement an agreement is considered "intrinsic" if it is not due to chance.

Therefore, the joint probability of agreement will remain high even in the absence of any "intrinsic" agreement among raters [4]. A useful inter-rater reliability coefficient is expected a to be close to 0, when there is no "intrinsic" agreement, and b to increase as the "intrinsic" agreement rate improves. Most chance-corrected agreement coefficients achieve the first objective [5].

However, the second objective is not achieved by many known chance-corrected measures [6]. Cohen's kappa and Fleiss' kappa Cohen's kappa [7] , which works for two raters, and Fleiss' kappa [8] , an adaptation that works for any fixed number of raters, improve upon the joint probability in that they take into account the amount of agreement that could be expected to occur through chance. They suffer from the same problem as the joint-probability in that they treat the data as nominal and assume the ratings have no natural ordering.

If the data do have an order, the information in the measurements is not fully taken advantage of.

Validating scale measurement credibility

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