There are at least 15 different meta-analytic syntheses on the validity of job interviews published in academic research journals. These studies show that structured interviews are very useful to predict future job performance. In comparison, unstructured interviews, which do not have a set of predefined rules for scoring or classifying answers and observations in a reliable and standardized manner, are considerably less accurate.

Psychological testing or behavioral analysis can be classified under two different tests such as projective tests and objective tests. Objective tests through self-report questionnaire assessments assume that a correspondence exists between what participants say about themselves and what is real. It also assumes that individuals are aware of their thoughts and emotions and are willing to share them openly. However, it is highly susceptible to faking, deliberate misrepresentation, and socially desirable responses. On the contrary objective tests consist of unambiguous test items containing highly structured and clear. McClelland (1980) has summarized evidence that implicit motives predict spontaneous behavioral trends over time, whereas self-attributed motives predict immediate, specific responses to specific situations or choice behavior. The story-based measures of motives have been demonstrated to have greater validity for predicting long-term trends in behavior than have self-reported desires as recorded in questionnaires. Self-report measures are likely to assess self-attributed traits that are subject to self-presentation motives. Implicit measures aim to assess non-declarative motives that affect behavior directly.

Thus projective tests are highly desirable, which makes the participant inability to respond in a socially desirable way. According to Henry Murray, people reveal who they are when they make up stories, and one’s self stories are the organizing structure of the personality. However, there is a high need for developing the framework for projective testing anHRM perspective rather than clinical conceptions of abnormality. Additionally, the scoring of projective tests has historically needed the expertise of trained clinical psychologists, which is not feasible for all organizations. Thus it is evident that straightforward scoring schemes are required for HR-centric projective tests. Additionally, normative data for these techniques would eliminate any reliance on clinical interpretation. Though computer-based tests eliminate administrative issues, only advanced algorithms will solve the exact problems. Thus algorithms developed based on Artificial intelligence technologies such as deep learning are less biased and more accurate than they are replacing .