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Revisiting Personality: Can you have too much of a good thing?


Apr 25, 2017 | by Eleni Lobene & Nicholas Martin

Among the very first analyses one learns as a graduate student in Industrial-Organizational Psychology is linear regression. In its most basic form, regression assumes a linear relationship between two variables.  In other words, as one variable increases (like hours spent exercising) another may as well (total pounds lost.) Most images representing regression even include a perfectly straight line stretching from bottom left to top right indicating the line of best fit (see Figure 1 for a caricature example of linear regression).  However, the assumption that relationships among variables are always linear has long been questioned (Ones, Viswesvaran, Dilchert, & Judge, 2007). Reality is never that simple! More sophisticated models have been developed to better support causal inferences among psychological phenomena. One of these models is the concept of curvilinearity, also called the “Too-Much-of-a-Good-Thing Effect.” This is defined as “when ordinarily beneficial antecedents (i.e., predictor variables) reach inflection points after which their relations with desired outcomes (i.e., criterion variables) cease to be linear and positive” (Pierce & Aguinis, 2013, p. 315). See Figure 2 for a similarly caricature example of a curvilinear relationship between variables.


Figure 1. A quintessential example of linear regression

Figure 2. A quintessential example of curvilinear regression

What do we know about curvilinearity already?
Many constructs have been explored in a curvilinear context, such as cognitive ability (Ferriman-Robertson, Smeets, Lubinski, & Benbow, 2010) and personality (see Carter, Dalal, Boyce, O’Connell, & Kung., 2014 for review). Most studies find a linear relationship between cognitive ability and job performance, even at extremely high levels of cognitive ability (Ferriman-Robertson et al., 2010; Wai, Lubinski, & Benbow, 2005). For personality, however, findings have been mixed.  Meta-analytic studies find evidence that a linear relationship exists and persists with certain traits (e.g., Conscientiousness) and job performance across any job.  However, results from primary studies, which can vary more so than meta-analyses on the specific assessment used, criterion definitions, and organizational context, provide evidence that curvilinear relationships with work outcomes exist (Benson & Campbell, 2007; Grijalva, Harms, Newman, Gaddis, & Fraley, 2015; LaHuis, Martin, & Avis, 2005; Le et al., 2011; Carter et al., 2014). Thus, there is a need to conduct research that further explores the extent to which curvilinear relationships between personality variables and performance outcomes exist and the variables that could influence non-linear findings. 

 A research team at Aon Hewitt conducted a study  to contribute to the discussion of curvilinearity in personality by investigating multiple personality traits using an assessment leveraging an ideal point model, while also investigating relationships across job types, with large samples representing multiple industries, and with various performance criteria.

What does this study tell me that we didn’t already know?
Our team explored curvilinear relationships using large datasets from a variety of industries and jobs. We argued that inconsistent results found previously in the literature may be partly due to differences in the jobs examined as well as differences in the performance domain measured. To address this, we examined curvilinearity using more consistent criteria where possible within specific industries and jobs to more precisely estimate whether these relationships exist. Our results suggest inconsistency for some curvilinear relationships, but certain clear patterns of curvilinearity also appear to emerge. That is, most of the curvilinear relationships we found (over 75%) were negative and a number of curvilinear relationships found explained a substantial amount of the total variance in job performance (across 20 jobs, nearly 300 relationships with around 200 criteria; average R2 = .07 including linear effect; linear effect was non-significant after including the curvilinear effect in 88% of relationships tested). This finding suggests that too much or too little of specific traits may be harmful for performance, that curvilinear relationships are found in the workplace, and without measuring curvilinear relationships, our predictions of job performance may be misspecified and unable to provide the best estimates of job performance.

Two aspects stood out as most commonly featuring negative curvilinear relationships across industries and jobs. The first was Awareness (average R2 = .10), especially in financial services jobs. Extremely high Awareness may result in being overly concerned with the opinions of others or being overly impacted by harsh criticism. Alternatively, extremely low Awareness indicates individuals may not seek feedback and may not consider how their behaviors impact others. The second most prominent trait was Drive (average R2 = .09). Extremely high Drive could be characterized by being unable to shift between tasks, while extremely low Drive could indicate likelihood to procrastinate and be less concerned with deadlines. These results suggest a moderate level of Awareness and Drive may be optimal to successfully perform these jobs.

What do these new findings mean?
These results have important implications for both research and practice. For research, the results we found may explain why results from previous studies have been inconsistent. Researchers need to look across a variety of jobs, use larger samples, and consider the use of assessments leveraging an ideal point model as this will provide greater power for detecting nonlinear effects (McClelland & Judd, 1993; we assert similar problems with detecting interaction effects occur for detecting curvilinear effects). One should not expect the same job/industry to show the same curvilinear relationships, so multi-job studies are necessary. For practice, organizations should be aware that curvilinear relationships do exist in a variety of jobs and may be worth examining when validating personality assessments. Understanding the optimal level of personality for job performance in a given job will increase the utility of personality tests, leading to greater direct, tangible performance benefits for organizations.

Want to know more? Come visit us!

“An Ideal Point for Performance: Revisiting Curvilinear Relationships of Personality”

Johnathan Cottrell, Michael McKenna, John Capman, Eleni Lobene, & Anthony Boyce

2017 Conference of the Society for Industrial and Organizational Psychology
Walt Disney World Swan and Dolphin
April 27, 2017
4:30 PM
Room Atlantic BC



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2Pierce, J. R., & Aguinis, H. (2013). The too-much-of-a-good-thing effect in management. Journal Of Management, 39(2), 313-338.

3Ferriman-Robertson, K., Smeets, S., Lubinski, D. & Benbow, C.P. (2010). Beyond the threshold hypothesis: Even among the gifted and top math/science graduate students, cognitive abilities, vocational interests, and lifestyle preferences matter for career choice, performance, and persistence. Current Directions in Psychological Science, 19(6), 346-351.

4Carter, N.T., Dalal, D.K., Boyce, A.S., O’Connell, M.S., Kung, M-C., & Delgado, K. (2014). Uncovering curvilinear relationships between conscientiousness and job performance: How theoretically appropriate measurement makes an empirical difference. Journal of Applied Psychology, 99, 564-586.

5Wai, Lubinski, & Benbow (2005). Creativity and occupational accomplishments among intellectually precocious youths: An age 13 to age 33 longitudinal study. Journal of Educational Psychology, 97(3), 484-492.

6Benson, M.J., & Campbell, J.P. (2007). To be, or not to be, linear: An expanded representation of personality and its relationship to leadership performance. International Journal of Selection and Assessment, 15(2), 232-249.

7Grijalva, E., Harms, P. D., Newman, D. A., Gaddis, B. H., & Fraley, R. C. (2015). Narcissism and leadership: A meta‐analytic review of linear and nonlinear relationships. Personnel Psychology, 68(1), 1-47.

8LaHuis, D. M., Martin, N. R., & Avis, J. M. (2005). Investigating Nonlinear Conscientiousness-Job Performance Relations for Clerical Employees. Human Performance, 18(3), 199-212.

9Le, H., Oh, I., Robbins, S. B., Ilies, R., Holland, E., & Westrick, P. (2011). Too much of a good thing: Curvilinear relationships between personality traits and job performance. Journal Of Applied Psychology, 96(1), 113-133.

10McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological bulletin, 114(2), 376-390.

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