System and methodologies for candidate analysis utilizing psychometric data and benchmarking

Abstract

A system and various methodologies provide the ability to analyze qualifications and capabilities of one or more candidates for employment.

1 - 26 . (canceled) 
     
     
         27 . A method for more holistically analyzing multiple qualifications and capabilities of one or more employment candidates for hiring by a hiring entity, the method comprising:
 automatically analyzing, via at least one processor connected to a user interface and/or computer readable memory, the qualifications and capabilities of the one or more employment candidates in a manner to match the one or more employment candidates to specific job roles by utilizing a benchmarking process,   wherein the benchmarking process includes creation of at least one benchmark which includes obtaining feedback from subject matter experts in order to validate the at least one benchmark,   wherein the benchmarking process includes creating the at least one benchmark utilizing a reverse psychometric model, and   wherein the reverse psychometric model includes:
 initially providing meta inputs including at least one of stakeholder input and job description data; 
 subsequently generating modeling inputs based on the meta inputs, the modeling inputs including strawman initial parameters in response to the meta input being stakeholder input, the modeling inputs further including psychometric data and raw performance data in response to the meta input being job description data; and 
 subsequently model training machine learning classification algorithms based on at least one of the strawman initial parameters, the psychometric data, and the raw performance data. 
   
     
     
         28 . The method of  claim 27 , further comprising:
 automatically determining, via the at least one processor, one or more personality traits of the one or more employment candidates using one or more psychometric tools to identify and measure presence of the one or more personality traits in the one or more employment candidates.   
     
     
         29 . The method of  claim 28 , wherein the one or more psychometric tools is configured to generate at least one objective quantitative representation of the one or more personality traits, wherein the at least one objective quantitative representation is recorded in a trait database, wherein the determining via the at least one processor is configured to utilize the trait database. 
     
     
         30 . The method of  claim 29 , wherein the automatic analyzing of the qualifications and capabilities of the one or more employment candidates further utilizes psychometric data generated by the one or more psychometric tools. 
     
     
         31 . The method of  claim 30 , wherein the benchmarking process includes the creation of the at least one benchmark which further includes the psychometric data. 
     
     
         32 . The method of  claim 31 , wherein the analysis of the qualifications and capabilities of the one or more employment candidates includes comparing the psychometric data generated by the one or more psychometric tools to the at least one benchmark generated by the benchmarking process to arrive at an overall comparative fit score. 
     
     
         33 . The method of  claim 27 , wherein the obtaining of feedback from subject matter experts is provided by performing at least one of a reactive external build and a reactive internal build and performing statistical comparison. 
     
     
         34 . The method of  claim 33 , wherein the performance of the reactive external build includes at least one of (i) utilizing a psychometric benchmark instrument to receive input from stakeholders regarding at least one personality trait of the one or more personality traits in the specific job role in order to create at least one provisional benchmark score for the at least one personality trait, and (ii) at least one of trained business consultants and client employees providing input on at least one personality trait of the one or more personality traits that determines candidate success in the specific job roles in order to create at least one provisional benchmark score for the at least one personality trait of the one or more personality traits. 
     
     
         35 . A method for more holistically analyzing multiple qualifications and capabilities of one or more employment candidates for hiring by a hiring entity by performing analysis and hiring decisions based on comprehensive, objective data pertaining to candidates' interests, skills, experience, qualifications and capabilities as well as identification of candidate psychometric data, the method comprising:
 automatically determining, via at least one processor connected to a user interface and/or computer readable memory, one or more personality traits of the one or more employment candidates using one or more psychometric tools to identify and measure presence of the one or more personality traits in the one or more employment candidates; and   automatically analyzing, via the at least one processor, the qualifications and capabilities of the one or more employment candidates in a manner to match the one or more employment candidates to specific job roles by utilizing psychometric data generated by the one or more psychometric tools and a benchmarking process,   wherein the benchmarking process includes creation of at least one benchmark which includes the psychometric data and obtaining feedback from subject matter experts in order to validate the at least one benchmark, wherein the feedback from subject matter experts includes input data obtained regarding ideal personality traits from sources including at least one source external to the one or more employment candidates and external to the hiring entity for comparison with the one or more personality traits of the one or more employment candidates, wherein the feedback and the outcome data are recorded in a feedback and outcome data database, and wherein the analyzing via the at least one processor is configured to utilize the feedback and outcome data database prior to hiring the one or more employment candidates, and   wherein the analysis of the qualifications and capabilities of the one or more employment candidates includes comparing the psychometric data generated by the one or more psychometric tools to the at least one benchmark generated by the benchmarking process to arrive at an overall comparative fit score.   
     
     
         36 . The method of  claim 35 , wherein the at least one source external to the one or more employment candidates and external to the hiring entity is unassociated with the one or more employment candidates and the hiring entity. 
     
     
         37 . The method of  claim 36 , wherein the at least one source external to the one or more employment candidates and external to the hiring entity is an individual trained and experienced in an industry related to the hiring entity. 
     
     
         38 . The method of  claim 37 , wherein the individual includes at least one of trained business consultants and client employees providing input on at least one personality trait of the one or more personality traits that determines candidate success in the specific job roles in order to create at least one provisional benchmark score for the at least one personality trait of the one or more personality traits. 
     
     
         39 . The method of  claim 38 , wherein the obtaining of feedback from subject matter experts is provided by performing at least one of a reactive external build which includes the input data obtained regarding ideal personality traits from sources including at least one source external to the one or more employment candidates and external to the hiring entity for comparison with the one or more personality traits of the one or more employment candidates, and performing statistical comparison, and wherein, after the obtaining of feedback from subject matter experts and the statistical comparison, the creation of the at least one benchmark further includes validating the at least one provisional benchmark score based on a predetermined minimum amount of iterations of the obtaining of feedback from subject matter experts and the statistical comparison in order to determine at least one validated benchmark score. 
     
     
         40 . The method of  claim 35 , wherein the one or more psychometric tools is configured to generate at least one objective quantitative representation of the one or more personality traits, wherein the at least one objective quantitative representation is recorded in a trait database, wherein the determining via the at least one processor is configured to utilize the trait database. 
     
     
         41 . A multi-stage cumulative candidate aggregator system for more holistically analyzing qualifications and capabilities of one or more employment candidates, the system comprising:
 a non-transitory computer-readable storage medium with instructions which, when executed by a computer, include:
 assigning a first fit score to the one or more employment candidates at a first hiring stage of a plurality of hiring stages, the first fit score being associated with a first set of qualifications and capabilities of the one or more employment candidates, the assigning of the first fit score including:
 automatically determining one or more personality traits of the one or more employment candidates using one or more psychometric tools to identify and measure presence of the one or more personality traits in the one or more employment candidates; 
 automatically analyzing the qualifications and capabilities of the one or more employment candidates in a manner to match the one or more employment candidates to specific job roles by utilizing psychometric data generated by the one or more psychometric tools and a benchmarking process, wherein the benchmarking process includes creation of at least one benchmark which includes the psychometric data and obtaining feedback from subject matter experts in order to validate the at least one benchmark; and 
 comparing the psychometric data generated by the one or more psychometric tools to the at least one benchmark generated by the benchmarking process to arrive at the first fit score; 
 
 assigning a second fit score to the one or more employment candidates at a second hiring stage of the plurality of hiring stages, the second fit score being associated with a second set of qualifications and capabilities of the one or more employment candidates including at least one qualification or capability different from the first set of qualifications and capabilities; and 
 aggregating the first fit score and the second fit score to arrive at an overall comparative fit score of the one or more employment candidates. 
   
     
     
         42 . The multi-stage cumulative candidate aggregator system of  claim 41 , further comprising:
 a user dashboard operably connected to the non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including further instructions which, when executed by the computer, include displaying, on the user dashboard, a first hiring stage section including the first hiring stage, the first hiring stage section including a first ranking of the first fit score of the one or more employment candidates for the first set of qualifications and capabilities of the one or more employment candidates, and displaying a second hiring stage section separate from the first hiring stage section and including the second hiring stage, the second hiring stage section including a second ranking of the second fit score of the one or more employment candidates for the second set of qualifications and capabilities of the one or more employment candidates.   
     
     
         43 . The multi-stage cumulative candidate aggregator system of  claim 42 , wherein the further instructions further include receiving user input for adding hiring stages, the user input for adding hiring stages including instructions to add additional hiring stages of the plurality of hiring stages to the user dashboard in addition to the first and second hiring stages, and further include displaying the additional hiring stages on the user dashboard in additional hiring stage sections separate from the first and second hiring stage sections. 
     
     
         44 . The multi-stage cumulative candidate aggregator system of  claim 43 , wherein the further instructions further include receiving, for each employment candidate of the one or more employment candidates at each additional hiring stage of the plurality of hiring stages added to the user dashboard, at least one hiring individual input including an additional fit score associated with the respective additional hiring stage, and further include ranking the additional fit scores of the one or more employment candidates in each additional hiring stage and displaying the ranking on the respective additional hiring stage section. 
     
     
         45 . The multi-stage cumulative candidate aggregator system of  claim 44 , wherein the instructions further include aggregating the first fit score, the second fit score, and the additional fit scores to arrive at the overall comparative fit score of the one or more employment candidates. 
     
     
         46 . The multi-stage cumulative candidate aggregator system of  claim 41 , wherein the further instructions further include surveying at least one hiring individual to verify performance of the one or more employment candidates after the one or more employment candidates has been placed in a hired stage of the plurality of hiring stages at which the one or more employment candidates have been hired by the at least one hiring individual so as to produce performance data of the one or more employment candidates, and wherein the at least one benchmark further includes the performance data via a neural network so as to improve the accuracy of the at least one benchmark.