Participating in a 360 Degree Assessment presents a unique opportunity for receiving frank, constructive feedback. The comprehensive results serve to enhance self-awareness and reveal any blind spots that might be hindering your growth. However, carefully choosing the right people to rate you is the key to gaining accurate insights. Below are some pointers to guide your 360 Degree Assessment raters selection process.
- Select those who interact with you frequently: Choose those who have significant interaction with you in a work context. This ensures they have substantial information about your work habits and performance.
- Prioritise familiarity over favourability: Nominate individuals who understand your work ethic and professional conduct best, regardless of whether their feedback is positive or negative. Unbiased input improves the validity of the assessment results.
- Consider all rater categories: Raters can be classified into different categories: Self, Manager, Colleagues, Subordinates and Customers. While you typically only have one manager, there should be several possible raters in the other categories.
- Maintain an optimum number of raters: Aim for a balance between 3 and 6 raters in each category. An excess or deficiency of raters might distort the results.
- Have a balanced total number of raters: The total number of all raters should ideally fall between 9 and 14 to ensure a diverse perspective.
- Preserve the anonymity of raters: Except for the manager’s rating (since everyone knows their manager), all other ratings are combined and anonymised to protect individual confidentiality.
Decoding the Roles of Rater Categories
- Self: Research, like that conducted by Yammarino & Atwater (1993), shows that self-ratings are typically higher than those from other rater groups. This could be due to inflated self-perception, self-serving bias, or differing understandings of performance standards. Furthermore, a meta-analysis by Heidemeier and Moser (2009) found that self-other agreement is usually moderate to low, suggesting self-ratings might not be as accurate as those from others.
- Manager: Harris and Schaubroeck (1988) found that supervisor ratings are closely related to job performance, often more so than ratings from other sources. This is possibly because managers have a comprehensive perspective on an individual’s role and responsibilities. They also tend to focus more on output and results rather than behaviours, according to a study by Conway and Huffcutt (1997).
- Peers/Colleagues: A study by Facteau and Craig (2001) concluded that peer ratings have a strong relationship with team performance. Peer feedback is often valued due to peers’ frequent interactions and shared experiences with the self, which provide a unique insight into their day-to-day performance. However, Borman (1997) cautioned that peer reviews might be affected by factors like competition and personal friendships.
- Subordinates: Research has suggested that subordinate ratings are particularly relevant for assessing leadership effectiveness. A meta-analysis by Lowe, Kroeck, and Sivasubramaniam (1996) revealed that subordinates’ leadership ratings are strongly correlated with unit performance and satisfaction measures. However, fear of retaliation or personal bias can sometimes affect the accuracy of these ratings.
- Customers: Research in this area is less extensive, as external raters are not used in all 360-degree feedback processes. However, some studies, such as one by Campion, Pursell, and Brown (1988), found that external feedback, particularly from customers, can be beneficial for roles with high customer interaction. It provides a perspective on the individual’s ability to meet customers’ needs and service skills.
In conclusion, while each rater type brings unique insights and biases, using a balanced mix of all types can help create a well-rounded view of an individual’s performance. Incorporating feedback from various perspectives can help to balance out potential biases and provides a holistic overview of an individual’s skills, behaviours, and performance.
Photo credit Google DeepMind