Addressing Intentional Biases in Performance Evaluation: A Report

This report delves into the concept of intentional biases in performance appraisal, particularly within the context of Expert Engineering Incorporation. It identifies various biases, including the halo effect, horns effect, central tendency bias, leniency bias, strictness bias, and the similar-to-me effect, and explains how these can distort performance ratings. The report provides a detailed background of the company's situation, highlighting concerns about potential favoritism and unfair evaluations. It recommends interventions such as raising rater awareness, establishing clear performance standards, promoting open dialogue, and utilizing pre-mortem analysis to mitigate these biases. The conclusion emphasizes the impact of these biases on decision-making and underscores the importance of implementing strategies to ensure fair and accurate performance evaluations.

Running head: INTENTIONAL BIASES
Intentional Biases
Name of the Student
Name of the University
Author Note

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Table of Contents
Introduction................................................................................................................................3
Discussion..................................................................................................................................3
Background Information related to the present scenario of Expert Engineering
Incorporation..........................................................................................................................3
Intentional rating distortion factors that may come into play in the situation of the company
................................................................................................................................................4
Halo effect..........................................................................................................................4
Horns Effect.......................................................................................................................4
Central Tendency Bias.......................................................................................................5
Leniency Bias.....................................................................................................................5
Strictness bias.....................................................................................................................5
Similar to me effect............................................................................................................5
Recommendations of interventions for minimizing the intentional rating distortion................5
Conclusion..................................................................................................................................6
References..................................................................................................................................7

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Introduction
Intentional biases refer to a situation wherein a group or a person alters the data or is
influenced by his own perceptions for changing the results of a study or experimentation.
This bias directs the information in a predetermined particular direction. The only difference
between an intentional and an unintentional bias is that in case of an intentional bias, the data
or information is altered deliberately (Can, 2018). There can be a number of intentional
biases such as horns effect, halo effect, similar to me effect, strictness effect, Leniency effect
and others. The main aim of the paper is to identify the intentional biases and recommend
different interventions for overcoming the same. The paper will discuss about the intentional
biases and the interventions for overcoming the same.
Discussion
Background Information related to the present scenario of Expert
Engineering Incorporation
Demetri a veteran engineer who had been working with the Expert Engineering
Incorporation for the last 15 years has recently been promoted to the position of principal in
the engineering firm. The company has been known for its unique performance evaluation
process that includes- evaluation of engineers by the principals as the founders believed in
evaluation based on multiple sources and feedback to prevent any kind of favouritism.
Moreover the company has also been engaged unique performance appraisal for the purpose
of ensuring accurate performance evaluations. However recently, the company initiated a big
hiring initiative for doze new engineers, nine out of whom are the graduates of the same
university to which the principal belongs that is Demetri University. It was Demetri who was
active in moving the initiave forward (Jha & Singh, 2017). Therefore the other principals are
afraid that unchecked favouritism, unfair promotion and biased performance rating nay

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happen due to the recruitment of 9 people from Demetri’s University. Therefore there is a
need for checking is any such biases are happening and taking actions for overcoming the
same. Moreover it is necessary to see that there are no biases in performance appraisal
otherwise the company’s image may be negatively affected as it is known for its Unique
performance appraisal.
Intentional rating distortion factors that may come into play in the
situation of the company
Some of the major intentional rating distortion factors that may come into play in the
above mentioned situation includes- Halo effect, Horns effect, central tendency bias, leniency
bias, strictness bias and similar to me effect.
Halo effect
It includes focusing on a single positive attribute of an individual and that affects the
ratings of other attributes and helps in creating a positive image on the mind of the decision
maker. Halo effect causes one positive rating to inflate all other ratings because the rater
starts thinking that if a person is good at a particular thing they may be good in other things
as well. Therefore a halo effect may be created on the principal at the time of performance
appraisal of the engineers from his university because he may be knowing them personally
(Larsen, 2019).
Horns Effect
The horns effect includes- focusing on one negative attribute of an individual and that
affects the ratings of other attributes and therefore has a negative impact on the rater, For
instance, in case of the Expert Engineering Company, if the principal identifies one negative
attribute in other employees (other than the ones from his college) then he may rate them

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negatively and give them lower rates thereby discriminating against them at time of
performance appraisal (Marchegiani, Reggiani & Rizzolli, 2016).
Central Tendency Bias
The central tendency bias includes- a situation in which the raters give an average
rating to all employees (Tziner & Rabenu, 2018). The principal may give an average rating to
all the engineers from his college because he may not want to discriminate against them due
to a particular liking towards all of them.
Leniency Bias
The leniency bias includes a situation where the rater goes too easy on the person his
is rating. For instance, the principal may give good rates to the engineers from his university
because of the leniency bias (Schaerer et al., 2018).
Strictness bias
A strictness bias includes- a top hard attitude of the rater toward particular people and
that causes the rater to give a low rating to all those people. For instance- the principal may
become too strict with engineers other than the ones from his college.
Similar to me effect
The similar to me effect includes- favouring people who are similar to the rater. In
case of the company, the principal may have a similar to me effect towards the engineers
from his college (Murphy, Cleveland & Hanscom, 2018).
Recommendations of interventions for minimizing the intentional rating
distortion
In order to overcome the biases it is necessary for the raters to become aware of the
impact of their decision on others (Rosen et al., 2017). It is necessary for the raters to develop

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certain standards for performance appraisal that will remain the same for all and whoever
meets the same should be given a better rating than others. The raters need to practice an
open dialogue. The rater needs to be open towards others comments as people often become
overconfident with their estimates. Moreover it is also necessary for the raters to think about
the future before taking any decisions and they also need to think twice before making any
decisions. The decision makers can also use pre-mortems to visualise a future scenario and
then understand the cause of failures. These techniques can be applied by the principal to
ensure that he is able to overcome the intentional rating distortion.
Conclusion
Therefore from the above discussion it can be concluded that intentional biases take
place when there is a deliberate action on part of one party to place the other on an
advantageous position. From the above discussion, it has also been understood that there can
be different types of intentional biases and some of these includes- horns effect, halo effect,
central tendency bias, leniency bias, strictness bias and the similar to me effect. These biases
have a major impact on the decision making of the decision maker. From the paper, the
different interventions that can be used for the purpose of overcoming the biases have also
been understood.

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References
Can, J. (2018). A Review of the Influence Factors of the Leniency Bias of Performance
Evaluation. DEStech Transactions on Economics, Business and Management,
(icssed).
Jha, J. K., & Singh, M. (2017). Human Resource Planning as a Strategic Function: Biases in
Forecasting Judgement. International Journal of Strategic Decision Sciences
(IJSDS), 8(3), 120-131.
Larsen, D. A. (2019). Using the Elaboration Likelihood Model to Explain Performance
Appraisal Inaccuracies. Journal of Management Policy and Practice, 20(4).
Marchegiani, L., Reggiani, T., & Rizzolli, M. (2016). Loss averse agents and lenient
supervisors in performance appraisal. Journal of Economic Behavior &
Organization, 131, 183-197.
Murphy, K. R., Cleveland, J. N., & Hanscom, M. E. (2018). Performance appraisal and
management. SAGE Publications.
Rosen, C. C., Kacmar, K. M., Harris, K. J., Gavin, M. B., & Hochwarter, W. A. (2017).
Workplace politics and performance appraisal: A two-study, multilevel field
investigation. Journal of Leadership & Organizational Studies, 24(1), 20-38.
Schaerer, M., Kern, M., Berger, G., Medvec, V., & Swaab, R. I. (2018). The illusion of
transparency in performance appraisals: When and why accuracy motivation explains
unintentional feedback inflation. Organizational Behavior and Human Decision
Processes, 144, 171-186.
Tziner, A., & Rabenu, E. (2018). The performance appraisal system (PAS). In Improving
Performance Appraisal at Work. Edward Elgar Publishing.

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