OUR TECHNOLOGY BREAKTHROUGH
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Lawshe introduced the concept of synthetic validity as a form of validity generalization in 1952, in which jobs are characterized by a unique set of common components. By determining the validity of predictor tests for each of these components, it is possible to estimate the validity of a set of predictor tests for any particular job. The challenges to implementing synthetic validity are first in defining the job component taxonomy that can model any job and second in establishing the validities of predictor tests of each of these job components. In recent years a scalable solution to the first challenge has been offered through the U.S. Department of Labor’s O*NET database which can be used to model any job as a combination of its 27 generalized work activity (GWA) and 11 Work Context components. The second challenge has been more difficult to solve because to estimate the validity of a predictor test for each component requires gathering validity data between the predictor test and each of the more than 950 O*NET occupations. As a result, commercial implementation of a scalable synthetic validity solution has not been traditionally feasible.
In collaboration with the world’s foremost experts in synthetic validity, RightHire has implemented a novel solution to this second challenge without the need to collect validation data for each O*NET occupation. This method involves using I/O psychologists as subject matter experts (SMEs) to estimate the validities of the 27 O*NET GWAs and 11 Work Context items for RightHire’s battery of 17 cognitive and 16 work style assessments. This patented approach greatly improves the practicality, ease, and speed of building a viable and scalable employee selection system.
The industry-standard Principles for the Validation and Use of Personnel Selection Procedures produced by the Society of Industrial-Organizational Psychology (SIOP) explicitly identifies synthetic validity as a viable solution. Two court cases to date involving selection procedures based on synthetic validity were decided in favor of the synthetic validity approach, though these were summary judgments with bounded legal precedent. Although future legal challenges will further clarify the issue of defensibility, peer-reviewed expert opinion is that synthetic validity will be considered a sound foundation for employee selection systems.
In order to build a custom assessment for a job using RightHire’s synthetic validity system, one or more employee subject matter experts (SMEs) need to rate the job on 27 O*NET Generalized Work Activities and 11 O*NET Work Context items. Once this job-analysis is completed, RightHire’s platform immediately builds the custom assessment for the job. There is no need to conduct a criterion validation study.
RightHire also comes out-of-the-box with assessments for 954 O*NET occupations. To build these assessments, we used the ratings already provided by the O*NET database on the 27 O*NET Generalized Work Activities and 11 O*NET Work Context items for each occupation. When employers feel that their job is similar to one of these O*NET occupations, they can simply use the pre-build assessment for that occupation rather than build a custom assessment.
The assessments built using RightHire’s synthetic validity assessment platform, whether they are custom built or pre-built, are job-specific and valid. Therefore, they should be legally defensible and also do not cause poor candidate experience.
RightHire's assessments include job-specific cognitive abilities, work personality characteristics, and soft skills, allowing you to predict the future job performance of your applicants with higher accuracy than most other assessment solutions in the market
Using RightHire's synthetic validity platform, you will be able to build job-specific assessments in hours compared with months it takes today. Your job SMEs need to rate the job on 27 O*NET Generalized Work Activities and 11 O*NET Work Context Items. There is no need to collect criterion-related data.
RightHire's assessment system is especially useful when you don't have large sample sizes (100+) to perform a local validity study. In such cases, employers typically use validity transportation and meta-analytic validity generalization, but synthetic validity is superior to both these methods for generalizing validity evidence.