Performance Management: Factors Influencing Rating Distortion Analysis

This report delves into the complexities of performance management, specifically focusing on the intentional rating distortion factors that can undermine the accuracy and fairness of employee evaluations. The analysis identifies both conscious errors, such as similarity error, leniency factor, halo error, and central tendency, and unconscious errors, including negativity bias and recency bias, which can significantly skew the rating process. The report uses a case study to illustrate how these biases might manifest in a real-world scenario, particularly when a rater has pre-existing connections to the candidates being evaluated. Furthermore, the report evaluates various interventions to minimize these distortions, including the use of analytical measurement systems, SWOT analysis, and clear articulation of organizational goals and expectations. The author recommends a panel-based rating system to mitigate bias, especially when the primary rater has a conflict of interest, thereby ensuring a more objective and equitable performance management process.

Running head: PERFORMANCE MANAGEMENT
PERFORMANCE MANAGEMENT
Name of the Student:
Name of the University:
Author Note:

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Provide a detailed discussion of the intentional rating distortion factors that may come into
play in this situation.
The case study has shown that a number of factor can impact upon the rating system,
especially for the new hiring. In this notion, some of the conscious and unconscious factors of
rating system have been mentioned that lead to the distortion of the rating system in a firm. First,
the conscious errors have been documented.
Similarity Error:
It is observed that the raters often try to find a link or similarities between them and the
employees or the candidates that they are rating. Therefore, in case of finding such a similarity, it
is likely that the raters or the supervisors will rate them in a convenient manner. In case of new
hiring the raters will try to include or hire the candidates who share the similar characters with
them (Bianchi, & Williams, 2015).
Leniency Factor:
The factor of leniency refers to the biasness practiced by the raters or the supervisors of a
firm. The supervisors practice the factors of leniency especially when they identify that there is a
similarity in the value system, they often tend to develop a biasness towards the candidates
(Park, 2014).
Halo Error:
The Halo error refers to the distortion in the rating system that take space because of the
impressions made by the candidates on the supervisor or the rater. The aim of the candidates to
be interviewed is to create an impression upon the supervisors or the interviewers or the raters,

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and their success in doing so, can lead to biasness in the rating system (Breuer, Nieken, &
Sliwka, 2013). This is known as the Halo Error.
Central Tendency:
Central Tendency refers to the distortion in the rating system due to the characteristic
default of the raters or the supervisors. Austin (2013), had mentioned that there are certain raters
or supervisors who mark all the candidates as average performers. They rate everyone an average
marks, which therefore, affect their actual performance and in certain case decreases the chances
for selection in case of a potent employee.
Low Appraiser Motivation:
Sutton et al., (2013), have mentioned that the raters often tend to perceive the future of
the employees and therefore, they are the employees as according to that perception. This
essentially impacts upon the rating system and the position of the candidates.
Apart from these conscious rating errors, there are certain other unconscious rating errors
as performed by the raters and the supervisors.
First is, the negativity bias. It is often noticed that the reviews generated upon the
candidates often influence upon the rating system, and therefore, the negative reviews regarding
a candidate is likely to be included while rating the person (Ayub, Gan, & Kadir, 2014).
Second is the Recency Bias, which refers to the impact or the influence of the memory
that the raters or the supervisors have of a candidate. Now, if the memory is a positive one, then
the candidates are going to get a good rating, and if the memory is a negative one, then it is likely

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that the performance of the candidates will not be appreciated as much as needed (Singh, &
Twalo, 2015).
Now, considering the case study, it is obvious that the current principle of the
organization is likely to encourage a distortion in the rating system. Since the principle had a
long relationship with the organization, where the candidates belong to, it is likely that the
principle can involve both the conscious and the unconscious biasness in the rating system.
Considering the conscious errors it is likely that the principle can take apart into any one of the
errors from Leniency Factor, Similarity Factor, or the Halo Error. According to the case study it
is likely that the Principle will essentially show the Leniency Error. However, considering the
unconscious biasness factors, both the recency factor and the factor of negative bias are common,
and can take place. However, the chances for negativity bias to take place is higher since all the
organizations include a background check process before hiring any of the employee.
Evaluate the kinds of interventions you could implement to minimize intentional rating
distortion, and its reasons, that you have described. What do you recommend and why?
Biasness in the rating system is essentially refers to as an attitudinal problem among the
raters as claimed by the researchers and it is essentially an offense. Now, the researchers to the
performance mangers have opined that both the conscious and the unconscious biasness cam be
curbed and reduced. The conscious biasness can be curbed by an effective management system,
but the unconscious biasness can only be handled by the person in the act himself. Therefore,
some of the recommendations have been made in the following which can be very helpful to
reduce such a practice.

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First, the researchers are of the opinion that the supervisors or the raters can use the
analytics in rating someone. The researchers are of the opinion that a measurement system
should be practiced by the raters where they performances of the candidates will be quantified
and measured ad upon that the ratings must be done (Honig, 2019). This will help the raters to
curb the biasness.
Another bunch of researchers have stated to make use of the SWOT system. Hereby, the
researchers are to develop an understanding of the candidates by evaluating their strengths,
weaknesses, opportunities and threats. The analysis of the potency of the candidates will help
and guide the raters to evaluate their performance and thereupon will help them to follow a non-
based rating system (Imoniana, De Feitas, & Jacob Perera, 2016).
The team of researchers of MIT have suggested otherwise, and according to them, the
raters or the supervisors must write down the organizational goal and the expectations of the
managers from the candidates, and therefore, the raters or the supervisors will quantify the
performances of the candidates as according to that. Along with this the raters have to reason
their marks or their opinions (Chiu et al., 2014). For example, if the rater thinks that the
characters of one of the candidates rightly justify the organizational goals, then the rater has to
justify his point of view.
However, in my opinion, the principle of the organizations must not be asked to rate the
candidates since he has a strong connection with the institution where the candidates belong to.
Therefore, it is likely that he will have a special place for the candidates. Therefore, it is better to
ask someone else to rate the candidates. However, if the principle has to be included in the rating

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system, then there must be a panel where the principle will be assisted with others for the rating
system. This can assuredly curb the probable biasness.
References:
Austin, R. D. (2013). Measuring and managing performance in organizations. Addison-Wesley.
Ayub, M., Gan, C. K., & Kadir, A. F. A. (2014, May). The impact of grid-connected PV systems
on Harmonic Distortion. In 2014 IEEE Innovative Smart Grid Technologies-Asia (ISGT
ASIA) (pp. 669-674). IEEE.
Bianchi, C., & Williams, D. W. (2015). Applying system dynamics modeling to foster a cause-
and-effect perspective in dealing with behavioral distortions associated with a city’s
performance measurement programs. Public Performance & Management Review, 38(3),
395-425.
Breuer, K., Nieken, P., & Sliwka, D. (2013). Social ties and subjective performance evaluations:
an empirical investigation. Review of managerial Science, 7(2), 141-157.
Chiu, R. H., Lin, L. H., & Ting, S. C. (2014). Evaluation of green port factors and performance:
a fuzzy AHP analysis. Mathematical problems in engineering, 2014.
Honig, D. (2019). When reporting undermines performance: The costs of politically constrained
organizational autonomy in foreign aid implementation. International
Organization, 73(1), 171-201.
Imoniana, J. O., De Feitas, E. C., & Jacob Perera, L. C. (2016). Assessment of internal control
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1-24.

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Park, S. (2014). Motivation of public managers as raters in performance appraisal: Developing a
model of rater motivation. Public Personnel Management, 43(4), 387-414.
Singh, P., & Twalo, T. (2015). Effects of poorly implemented performance management systems
on the job behavior and performance of employees. International Business & Economics
Research Journal (IBER), 14(1), 79-94.
Sutton, A. W., Baldwin, S. P., Wood, L., & Hoffman, B. J. (2013). A meta-analysis of the
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409-429.