How can organizations mitigate the risk of 'faking good' in pre-employment personality assessments?

Question

Tyler Smith · Accepted Answer

A frequently asked question on verified product pages and by buyers considering personality tests is the concern about candidates 'faking good' or trying to present an overly positive image. This social desirability bias is a valid challenge, but organizations can employ several strategies to mitigate its impact and obtain more authentic results.

1.  **Integrate Validity Scales:** Many well-designed personality tests include embedded validity scales (sometimes called 'lie scales' or 'social desirability scales'). These scales are designed to detect inconsistent responses or exaggerated positive self-descriptions, indicating that the candidate might be attempting to manipulate their answers. While not foolproof, they provide a strong indicator for further scrutiny.
2.  **Forced-Choice Formats:** Some modern assessments utilize a *forced-choice format*, where candidates must select between two or more equally desirable (or undesirable) statements. This makes it much harder to consistently choose only socially desirable options, thereby reducing the ability to fake good.
3.  **Situational Judgment Tests (SJTs):** While not pure personality tests, SJTs often present candidates with hypothetical work scenarios and ask how they would respond. These can be harder to game because there isn't always an obvious 'right' answer, and they reveal behavioral tendencies more organically than direct self-reports.
4.  **Emphasize Honesty:** Instructions can remind candidates that there are no 'right' or 'wrong' answers and that honesty leads to the best fit for both parties. While this alone isn't a solution, it can set a tone.
5.  **Cross-Validation with Other Methods:** The most robust approach is to *never rely solely on a personality test*. Combine results with structured interviews, behavioral questions, reference checks, and potentially work samples. Inconsistencies across different assessment methods can flag potential 'faking good.'

Based on thousands of implemented solutions, a multi-method approach combined with sophisticated test design significantly reduces the impact of social desirability, leading to more reliable hiring decisions.