Methods or approaches are given in the Compensatory

Methods or approaches are given in the Compensatory
Methods or approaches are given in the Compensatory

Methods or approaches are given in the Compensatory model

In your own words? Describe each of the below four methods or approaches are given in the Compensatory model

Methods or approaches are given in the Compensatory

– In your own words describe each of the below four methods or approaches are given in the Compensatory model used for final selection decisions. Then, in your own words, describe the pros and cons of each.

– Also, Find and discuss an article that offers current ideas about the topic of our discussion BE SURE TO PROVIDE AN ACTIVE LINK TO YOUR ARTICLE.

Methods or approaches are given in the Compensatory Clinical Prediction

With this approach, the final decision is based on the managers’ expert judgment. With this, the weights arenít always unambiguous. Managers evaluate the candidate based on their experience and if they are good judges of people’s abilities they can easily select efficient candidates with the help of this method. However, usually many managers cannot judge others accurately and this will lead to detrimental results.

Pros:

Cons:

Unit Weighting: This method is simple and straightforward. It involves simply addicting the various weights. Simplicity is the advantage of this method. The disadvantage of this method is that all the predictors may not be equally important.

Pros:

Cons:

Methods or approaches are given in the Compensatory Rational Weighting

In this method, each predictor is given a differential weighting based on its importance. This is much more efficient than the unit weighting method. However, a manager must be capable of clearly identifying which predictor is more important and how much weight to be given to it. Slight errors in giving weights can lead to undesirable results.

Pros:

Cons:

Multiple Regressions: In these methods, weights are based on multiple regression. This method gives higher validity and utility when compared to weighting methods. It is a better choice when the number of predictors is small, the sample size is large and when there is a low correlation among the predictors. However, it is somewhat complex to apply.

Pros:

Cons:

model

In your own words? Describe each of the below four methods or approaches are given in the Compensatory model

– In your own words describe each of the below four methods or approaches are given in the Compensatory model used for final selection decisions. Then, in your own words, describe the pros and cons of each.

– Also, Find and discuss an article that offers current ideas about the topic of our discussion BE SURE TO PROVIDE AN ACTIVE LINK TO YOUR ARTICLE.

Methods or approaches are given in the Compensatory Clinical Prediction

With this approach, the final decision is based on the managers expert judgment. With this, the weights arenít always unambiguous. Managers evaluate the candidate based on their experience and if they are good judges of people’s abilities they can easily select efficient candidates with the help of this method. However, usually many managers cannot judge others accurately and this will lead to detrimental results.

Pros:

Cons:

Unit Weighting: This method is simple and straightforward. It involves simply addicting the various weights. Simplicity is the advantage of this method. The disadvantage of this method is that all the predictors may not be equally important.

Pros:

Cons:

Rational Weighting: In this method, each predictor is given a differential weighting based on its importance. This is much more efficient than the unit weighting method. However, a manager must be capable of clearly identifying which predictor is more important and how much weight to be given to it. Slight errors in giving weights can lead to undesirable results.

Pros:

Cons:

Multiple Regressions: In these methods, weights are based on multiple regression. This method gives higher validity and utility when compared to weighting methods. It is a better choice when the number of predictors is small, the sample size is large and when there is a low correlation among the predictors. However, it is somewhat complex to apply.

Pros:

Cons:

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