US 20030200136 A1
A system and method for testing and/or evaluating employees or potential employees is disclosed. A computer arranges a plurality of applicants in a stack ranked table. The table may rank or re-rank applicants against each other, from best to worst, after successive screening, selecting, and/or interviewing stages for a particular job. Performance evaluations of hired workers may be fed back to the computer for adjusting the system and method. Competencies shown to be predictive of successful performance of a given type of job are tested for at various stages in an online testing system.
146. A method of constructing a model generating one or more job performance criteria predictors based on input pre-hire information collected over the Internet, the method comprising:
from a plurality of applicants, collecting pre-hire information from the applicants, the collecting comprising administering application questions to the applicants, the application questions comprising:
validated questions designed to minimize adverse impact on minority groups and validated by correlating job performance ratings of a plurality of hired workers with previous responses given by the workers to the application questions before the workers were hired; and
requirements questions eliciting information on whether the applicants meet employment requirements;
collecting post-hire information for the applicants based on job performance ratings of the applicants after hire; and
from the pre-hire information and the post-hire information, generating a computer-based predictive model operable to generate one or more job performance criteria predictors and a turnover predictor based on newly input pre-hire information from new applicants, the newly input pre-hire information comprising:
responses from the new applicants to a short subset of the validated questions, the short subset being selected to serve as a fast job-related pre-screen; and
later responses collected from the new applicants at the employer's premises after the applicants have come to the employer's
premises and logged on, the later responses collected in response to a more in-depth assessment;
wherein the computer-based predictive model is operable in real time to generate from the job performance criteria predictors, the turnover predictor, and responses of the new applicants to the requirements questions a rank order of all the new applicants.
147. A method of constructing a model generating one or more job performance criteria predictors based on input pre-hire information collected electronically, the method comprising:
from a plurality of applicants, collecting pre-hire information from the applicants, the collecting comprising administering validated questions to the applicants, the validated questions designed to minimize adverse impact on minority groups and validated by correlating job performance ratings of a plurality of hired workers with previous responses given by the workers to the application questions before the workers were hired;
collecting post-hire information for the applicants based on performance of the applicants after hire; and
from the pre-hire information and the post-hire information, generating a computer-based predictive model operable to generate one or more job performance criteria predictors based on newly input pre-hire information from new applicants, the newly input pre-hire information comprising responses from the new applicants to a short subset of a large assessment, the short subset being selected to serve as a fast job-related pre-screen;
wherein the computer-based predictive model is operable to generate a rank order of the new applicants.
148. The method of
149. The method of
150. A method of constructing a model generating one or more job performance criteria predictors based on input pre-hire information collected electronically, the method comprising:
from a plurality of applicants, collecting pre-hire information from the applicants;
collecting post-hire information for the applicants based on job performance of the applicants after hire; and
from the pre-hire information and the post-hire information, generating an artificial intelligence-based predictive model operable to generate one or more job performance criteria predictors based on newly input pre-hire information from new applicants, the newly input pre-hire information comprising responses from the new applicants to a short set of validated questions numbering less than about sixty;
wherein the artificial intelligence-based predictive model is operable in real time and generates a rank order of the new applicants.
 This application claims the benefit of U.S. Provisional Patent Application No. 60/211,044, filed Jun. 12, 2000.
FIG. 1 provides a block diagram of an exemplary system in accordance with the present invention.
FIG. 2 illustrates a process for testing and evaluating job applicants in accordance with an embodiment of the present invention.
FIG. 3 depicts a hiring procedure in accordance with one embodiment of the invention.
FIG. 4 is a block diagram of a process employing feedback.
FIG. 5 diagrams an online system in accordance with one embodiment of the invention.
FIG. 6 shows an example of a web-based presentation for a screening solution.
FIG. 7 shows an example of a stack ranked table.
FIG. 8 shows an example of a screening solution question presented to an applicant taking a screening solution test over the Internet.
FIG. 9 shows an example of a structured interview guide for use in an interview solution.
FIG. 10 illustrates procedural steps that may be followed in a web-based applicant system according to an embodiment of the present invention.
FIG. 11 illustrates procedural steps that may be followed in a web-based selection solution according to an embodiment of the present invention.
FIG. 12 illustrates procedural steps that may be followed by an employer according to an embodiment of the present invention.
FIG. 13 illustrates a human capital management life-cycle.
 A system for testing a job applicant provides a computerized stack ranking of multiple applicants, predictive of the comparative levels of successful job performance. The predictive stack ranking may be used as a dynamic interactive filter with a pool of applicants over the course of the evaluation or employment process. The system may utilize a communications network to communicate between an applicant terminal and a system server.
 The system may be used for example for screening, selecting, retaining, assigning, or analyzing the job applicant. The job applicant can for example be a new job applicant, an employee seeking to retain a job, an employee seeking a different job in the same organization, or an employee being evaluated for retention, re-assignment, or promotion. Applicants may or may not know they are being evaluated.
 Once an applicant becomes an employee, the system may collect data regarding the employee for use in a feedback loop informing the online hiring process and improving the accuracy of the predictive stack ranking. For example, the data may indicate the employer's rating of the employee's actual job performance. Such a rating can be cross-checked against the answers that the employee gave during the application process. The cross-checking can be used as feedback to refine the questions and evaluation criteria used at each stage of the hiring process. For example, the cross-checking may be analyzed to select from among many questions a small subset having high predictive value. The small subset can then be used in a quick initial screening stage. Or, the small subset can be given greater weight than other questions in a computerized stack ranking of candidates.
FIG. 1 provides a block diagram of an exemplary system in accordance with the present invention. A job applicant can use applicant terminal 102 to communicate over network 104 with system server 106. Applicant terminal 102 may for example be a telephone handset, a personal computer, a workstation, a handheld wireless device such as those marketed under the trademarks PALM or HANDSPRING, or a Wireless Application Protocol enabled device such as a mobile phone. Network 104 may for example be the Internet, the World Wide Web, a wide area network, a local area network, a telephone network, a wireless communication network, a combination thereof, or any other link capable of carrying communications between an applicant terminal and a server.
 System server 106 employs a testing computer program 108 and has access to a scoring database 110. System server 106 communicates with applicant terminal 102 in accordance with instructions from testing computer program 108.
 System server 106 may communicate with employer server 112 over network 104 or over direct link 114. System server 106 is shown as a unitary server, but may be a distributed computing platform.
 An applicant terminal may be remote from, or co-located with, system server 106 and/or employer server 112. For example, applicant terminal 102 may be located at a job applicant's home, applicant terminal 116 may be located at a job fair or employment office, and applicant terminal 120 may be located at an employer's location.
 Partner server 121 may be linked to network 104 and system server 106 to facilitate integration of a business partner seeking to participate in the system of FIG. 1.
 System server 106 may pose questions to a job applicant located at an applicant terminal, receive responses from the job applicant, and score the answers in accordance with scoring database 110. The scoring may take place in real time, i.e., while the applicant is still online, and may be reported in the form of a comparative stack ranking of multiple applicants. The stack ranking may be delivered from system server 106, over either network 104 or direct link 114, to employer server 112.
 Scoring of each answer by system server 106 may be instant, i.e., before the next question is answered. Thus, adaptive testing techniques may be implemented over network 104. For example, the answers given by an applicant at applicant terminal 102 to questions propounded early in a test may determine which questions are propounded by system server 106 to the applicant later in the same test. In addition, if an applicant at terminal 102 provides an unacceptable answer to a disqualifying “knock-out” question, server 106 may immediately terminate the test.
 These same adaptive testing principles may be applied to a software program used to support a real time interview, either in person or over a communications network. For example, an employer conducting an oral interview in person or over a telephone can enter a candidate's oral answer into employer terminal 124, which then communicates the answer to system server 106, which in turn suggests via employer terminal 124 the next question for the employer to ask the interviewee.
 The system may test an online applicant for any competency desired, in any sequence. The tested competencies may be abilities, traits, knowledge, skills, etc., that have been proven relevant to and predictive of successful job performance. By way of example and not limitation, the following competencies may be tested:
 1. dependability
 2. agreeableness
 3. critical thinking
 4. problem solving ability
 5. talkativeness
 6. assertiveness
 7. gregariousness
 8. persuasiveness
 9. achievement
 10. education
 11. experience
 12. customer service orientation
 13. customer focus
 14. conscientiousness
 15. self-confidence
 16. motivation
 17. revenue focus
 18. cognitive ability
 19. leadership
 20. decision making
 21. flexibility
 22. commitment
 23. learning ability
 24. dedication
 25. tenacity
 26. number of jobs held
 27. length of time in job(s)
 28. working with information
 29. supervisory potential
 30. judgment
 31. leadership
 32. coaching skills
 33. teamwork
 34. interpersonal skills
 35. business leadership
 36. leadership motivation
 37. self-leadership
 38. interpersonal leadership
 39. communication skills
 40. management potential
 41. likelihood of retention
 42. self-control
 43. energy
 44. executive potential
 45. listening orientation
 46. language skills (English, etc.)
 47. verbal reasoning
 48. spatial ability
 49. interest
 50. motivation
 Typically, system server 106 tests for certain ones of the competencies that have been proven to be predictive of successful performance of the type of job for which the applicant is being considered. The results of the testing are tabulated in a stack ranked table. The stack ranked table may rank a number of applicants against each other and list them in order, from first to last. The table may also present other information for each applicant. The other information may include, by way of example and not limitation:
 1. Name
 2. Identifying number (e.g. social security number).
 3. Score achieved at various stages for various competencies.
 4. Recommendation (or not) to continue the hiring process beyond each stage
 5. Link to application information (e.g. address, resume details)
 6. Contact information (phone number, e-mail address, mailing address, etc.)
 7. Date of application
 8. Success or failure in complying with knockout requirements for the job
 9. Screening solution scores, presented as percentiles
 10. A calculated recommendation to proceed or not to proceed with the applicant
 11. Results (by competency) of the selection solution
 12. Link to allow manual entry of the test answers if not done on computer directly by the applicant
 13. A calculated recommendation to hire or not hire based on a weighted overall score of selection competencies (or other factors the hiring company wishes to use and that are approved as statistically valid and legally defensible)
 14. Additional columns for storage of data from a structured behavioral interview
 15. Additional columns for storage of data from other decision-making processes such as drug testing, reference checks, or medical exams.
 A process for testing and evaluating job applicants may be described with reference to FIG. 2. Generally, applicant testing 201 includes providing a test to a job applicant and scoring the applicant's answers. The test may be administered online or it may be administered manually off-line. Scores are entered into a system for calculating a stack ranked table. Predictive stack ranking 202 generally includes ranking a job applicant against other job applicants in order from first to last or other comparative ranking. The other job applicants may be current job applicants, past job applicants, or fictional job applicants.
FIG. 3 depicts a hiring procedure in accordance with one embodiment of the invention. Announcement 302 may be an online job announcement such as a web page with an “apply now” hyperlink icon. The web page may reside on an employer's website or an employment agency website, for example. Or, an online job announcement may be a recorded announcement on a menu-driven telephone voice processing system. Alternatively, announcement 302 may be an offline job announcement such as a newspaper advertisement.
 In response to announcement 302, an interested job applicant requests administration of screening test 304. Screening test 304 may be remotely administered and scored online, with the scores being automatically provided to predictive stack ranking 306. Alternatively, screening test 304 may be administered manually with paper and pencil, and then graded by hand or machine, with the scores being provided to predictive stack ranking 306. The predictive stack ranking may for example be constructed by system server 106 or employer server 112.
 Predictive stack ranking 306 totals the graded answers according to particular competencies known to be relevant to successful job performance. Predictive stack ranking 306 may be administered by a computer processor located at system server 106, for example. Predictive stack ranking 306 may give different weight to different questions, and may at any stage immediately disqualify an applicant providing an unacceptable answer to a “knock-out” question. Predictive stack ranking 306 may rank the applicant in order against other job applicants in a table. Predictive stack ranking 306 may be used to decide which applicants to invite for the next stage, selection test 308.
 Selection test 308 is preferably conducted under supervised conditions. For example, selection test 308 may be administered in person. An in-person test may take place at a job fair, an employer's location, a job site, or an employment agency. An in-person test may include verification of the job applicant's identity, such as by examination of a photo identification document produced by a test-taker. Selection test 308 may be administered online or manually. Supervised conditions typically include observation of the test-taker during administration of the test. The answers to selection test 308 are graded and the results are incorporated in predictive stack ranking 306.
 Predictive stack ranking 306 may then update a previously created entry for the applicant and rank or re-rank the applicant in order against other job applicants. After this is accomplished, the highest ranking applicants may be invited for interview 310.
 Interview 310 may be structured or unstructured, online or in person. If interview 310 is structured, a program leads the interviewer through the interview by suggesting questions one at a time. The program may be a list of questions written on paper or it may be a computer program resident for example in system server 106. The program suggests questions that are predetermined to be valid, i.e., proven to be associated with successful job performance and legally permitted. The interviewer can input the answers and/or a score for the answers, either after each answer or at the conclusion of the interview. This can be done via employer terminal 124, for example.
 Interview 310 results in an interview score being provided to predictive stack ranking 306. Predictive stack ranking 306 is revised to reflect the interview score. In particular, the relative rank of the job applicants is reassessed.
FIG. 4 is a block diagram of a process employing feedback. Test design 402 is initially performed using industry-accepted standards. Test administration 404 tests and scores job applicants and/or incumbents. Employee performance evaluation 406 measures actual job performance of the applicant or incumbent after holding the job for a period of time. This information is fed back to test design 402 and/or test administration 404. Test design 402 may be revised to delete questions which were not predictive of successful job performance. This can be done for example by deleting questions whose answers bore no relation to performance evaluation 406 for a statistically valid sample. Test administration 404 may be revised by adjusting the weight given to certain questions or answers that showed an especially strong correlation to employee performance evaluation 406. For example, if test administration 404 is associated with predictive stack ranking 306, feedback from employee performance evaluation 406 may help determine how various job applicants are comparatively ranked against each other.
FIG. 5 diagrams an online computer based system 500 in accordance with one embodiment of the invention. Box 502 represents a job vacancy with a requirement for an online screening and selection solution. The vacancy can come to the attention of a potential job applicant in a number of ways.
 For example, box 504 represents an online application via a hiring company's own website. A company offering a job may post a vacancy announcement on the company's website and invite job seekers to apply by clicking on an icon labeled “apply here” or the like. Box 506 represents a similar posting on an online job board. Box 508 represents candidates given a Uniform Resource Locator (URL) directly by the company. This may occur when the company offering a job identifies a potential candidate. Box 510 represents a media advertisement including a URL for a job. Thus, job seekers observing the advertisement can direct their browsers to the indicated URL.
 At job fair 512, job seekers may be provided a URL associated with the company or the particular vacancy. Paper-and-pencil measures could also be used at job fairs and entered into the system. A computer terminal may be provided for use of job seekers at job fair 512, enabling job seekers to participate in the online system. Box 514 represents an executive search via a recruiter network. Job seekers relevant to the search are identified in recruitment firm applicant database 516. Database 516 can link to a URL associated with the job.
 Preferably, no matter how a potential applicant becomes aware of or identified for a job opening in system 500, the potential applicant is considered at decision 520. Decision 520 asks whether applicant has completed the required screening solution 524. If not, the applicant at box 522 is given via e-mail, mail, or in person, a URL for assessment. For example, system 500 may send an e-mail message to a potential applicant, the e-mail message inviting the potential applicant to apply for vacancy 502 by directing a browser to a screening solution URL provided in the e-mail message. Alternatively, when a potential applicant is visiting a website at which decision 520 determines that the required screening solution has not been completed, the website host can provide a link to a web page identified by the screening solution URL. Decision 520 may be based on a potential applicant's name, e-mail address, and/or other identifying information.
 Screening solution 524 is administered via the Internet and is hosted at the screening solution URL mentioned above. Screening solution 524 asks screening questions to ascertain if the applicant has the basic qualifications to do the job. These are based on questions typically asked by recruiters but which are statistically validated over time to ensure they are legally defensible and predictive. The questions may include a combination of biodata and personality measures. They may include self-assessments of skill levels appropriate to the job requirements. Screening solution 524 requires applicants to transmit elicited information over the Internet. A possible example of a web-based presentation for screening solution 524 is illustrated in FIG. 6. Screen shot 600 shows a portion of the presentation.
 Once completed, screening solution 524 provides applicant feedback 540 and conveys applicant details and screening scores to stack ranked table of applicants 530. Applicant feedback 540 may provide a message to the online applicant indicating that the screening solution is complete, that the applicant has passed or failed the screening stage, and that the applicant may or may not be contacted in due course. Other information may also be provided to the applicant in the feedback pages, like a realistic job preview, recruiter phone number, scheduling information, etc.
 Once an applicant has completed the screening solution, system 500 ranks the applicant in comparative order against other applicants in stack ranked table of applicants 530. A certain number or percentage of applicants in table 530 will be chosen for further consideration. For example, the applicants ranking among the top five of all applicants ranked in table 530 may be chosen for advancement in the system at this juncture. Information identifying the chosen applicants will be included on a “short list” as indicated by box 536.
 The short list chosen at box 536 is transmitted to selection solution 538, at which the advancing applicants are invited to answer selection questions. Selection solution 538 asks additional questions and requires an advancing applicant to input answers. Preferably, the applicant completes selection solution 538 while sitting at a terminal located at one of the company's locations. The terminal communicates over the Internet with a website set up to administer the selection solution.
 At the conclusion of selection solution 538, applicant feedback 540 is provided from the website to the applicant, and applicant details and scores 541 are incorporated in stack ranked table 530. Feedback 540 may optionally include a sophisticated report on the applicant's strengths and weakness. The applicant may then be directed to an appropriate web page chosen by the hiring company. One page may indicated successful completion and a second page may indicate failure. The appropriate web page may suggest other openings appropriate to the applicant's test responses and may provide hyperlinks the applicant can use to initiate the application process for these other openings.
 Once stack ranked table 530 re-ranks the applicants as a result of selection solution 538, some applicants are invited to participate in interview solution 542. For example, the top three applicants as ranked by table 530 after selection solution 538 may be invited for an in-person interview. Because the selection solution is preferably in instant communication with stack ranked table 530, the interview invitation may be extended immediately at the conclusion of the selection solution.
 Interview solution 542 is preferably a structured interview, with questions provided via the Internet to the interviewer at the company's location. The interviewer reads the provided questions and reports a score over the Internet from the company's location for incorporation in stack ranked table 530. Benchmark performance anchors may assist the interviewer in grading the applicant's responses.
 Interview solution 542 can be designed according two exemplary models. In a first model, an employer is provided with standard interview guides for several job types as well as the competency templates for these types so that the employer can build variations to meet specific needs. In a second model, an employer can build new interview guides and new competency templates. In the second model, the employer has access to the full array of work-related competencies and associated questions in a comprehensive question bank.
 In ranking applicants, stack ranked table 530 may consider a combination of different biographical, personality, behavioral, and other appropriate information and competencies. In addition to the comparative ranking, table 530 may indicate for each applicant a yes/no recommendation, a percentage likelihood of successful job performance, biographical information not used for evaluative purposes, and so forth.
 Stack ranked table 530 may be developed by grading the various solution stages with a computer implementing the following algorithm. First, search for disqualifying answers to “knock-out” questions. Second, give points for answers matching those of the previously hired candidates who achieved a successful performance evaluation. Third, deduct points for answers matching those of the previously hired candidates who received an unsuccessful performance rating. Fourth, multiply the added or subtracted points by any weighting assigned each question. Fifth, sum the points for all questions related to a given competency. Sixth, compare the summed points for each competency to norms of either the job-holders in the company or a wider population. Seventh, predict performance of the applicant as a worker in the job, based on the business outcomes identified by the hiring company and the competencies that contribute to those outcomes.
 A final selection is made based on stack ranked table 530. Preferably, the selection is transmitted over the Internet to the company, enabling the company to make an offer to the selected applicant(s). For example, if there is only one opening, an offer may be extended to the applicant ranked highest by stack ranked table 530. If the applicant accepts the offer, the applicant is employed by the company. If the applicant declines, the next highest ranked applicant in stack ranked table 530 is offered the job. If a plural number of openings exist, that number of applicants may be selected off the top of stack ranked table 530 and offered the job. If one of the applicants declines, the next highest ranked applicant in stack ranked table 530 is offered the job. Data from stack ranked table 530 is forwarded to data warehouse 534.
 The performance of successful applicants is monitored during their employment. At box 550, performance data for successful applicants are collected at a later date, and sent to data warehouse 534.
 Data collected at data warehouse 534 are used for research and development and for reporting purposes. For example, functions enabled by storing comprehensive data generated by system 500 may include:
 a. Storage of question level responses from applicants for jobs. This can be used for re-checking of applicant information (auditing etc.) and for research to develop new solutions and questions.
 b. Reporting on Equal Employment Opportunity Commission requirements. Data on ethnicity etc. can be stored to enable an employer to comply with reporting requirements to government agencies.
 c. Source of data for designing new solutions including data on the nature of the job and the competencies that are required by the role (job analysis). This data is collected using online assessments.
 d. Source of data for statistical research on correlation between the solutions and their predicted outcomes for applicants, and the actual outcomes for employees who were hired (validation studies).
 e. Design of solutions other than recruitment related solutions.
 f. Reporting of usage volumes for billing and financing accounting purposes.
 Because system 500 preferably uses instant communications, adaptive testing techniques may be implemented online. An applicant's failure to overcome hurdles in a given solution will deliver a different path through the solution than that of a successful applicant. The degree of advancement of a given applicant through system 500 may result in different charges to the company from a solutions provider. For example, a solutions provider that hosts a website supporting screening solution 524, selection solution 538, and interview solution 542 may charge the hiring company the following amounts: one dollar for every applicant completing only the screening solution, five dollars for every applicant advancing only to the end of the selection solution, ten dollars for every applicant rejected after the interview solution, twenty dollars for every applicant offered a job, and fifty dollars for every applicant accepting an offer.
 In practice, any of the various stages (screening solution 524, selection solution 538, and interview solution 542) may be skipped, re-ordered, combined with other stages, or eliminated. Or, a short telephone interview may be structured early in the process to quickly screen applicants.
 In a preferred embodiment, the questions to be asked at the various stages are selected for a particular type of job being offered in accordance with a proven relationship with desired business outcomes. Business outcomes can for example include: level of sales, customer satisfaction, quality measures such as fault rates, retention and tenure of employment, time keeping, learning ability, progression to more senior roles over time, and supervisor ratings of behavioral success. The particular type of job is defined in conjunction with the U.S. Department of Labor “O*NET” classification system. Some types of jobs might include customer service, technical, professional, or managerial. Various competencies are determined to be associated with desired business outcomes for a given type of job. These competencies are tested for at various solution stages with appropriate questions.
 The appropriate competencies, questions, scoring, weighting, and ranking factors for a new job can be designed from historical tests for existing jobs, by applying statistical techniques and using the gathering of data on the Internet to ensure rapid validation of the new assessment solution. Confirmatory job analysis is used to determine the appropriateness of solutions for a particular job.
FIG. 7 shows an example of a stack ranked table. Computer screen shot 700 illustrates a sample stack ranked table 730 for a customer service job. Various tabs permit viewing of data generated by each solution stage. Tab 702 reveals data 703 from a screening solution, tab 704 reveals data 705 from a selection solution, tab 706 reveals data 707 from an interview solution, and tab 708 reveals all results. In screen shot 700, tab 708 is selected.
 Section 709 of screen shot 700 shows general information about each applicant, including current rank 710, a link 712 to application information (not shown), last name 714, first name 716, and application date 718.
 Screening solution data 703 includes an indication 720 of whether each applicant successfully passed the knockout requirements for the job. Data 703 also includes scores on certain competencies such as educational and work related experience 722, customer service orientation 724, and self-confidence 726. Column 728 indicates whether each applicant is recommended to advance beyond the screening stage.
 Selection solution data 705 includes scores on certain competencies such as customer focus 732, conscientiousness 734, and problem solving 736. Column 738 indicates whether each applicant is recommended to advance beyond the selection stage.
 Additional information (not shown) may include columns for storage of data from other decision-making processes such as drug testing, reference checks, or medical exams.
FIG. 8 shows an example of a screening solution question presented to an applicant taking a screening solution test over the Internet. In screen shot 800, simulated customer contact record 802 is presented to the applicant. The applicant is asked question 804, and is required to click on a circle next to one of the answers. Question 804 may test for a competency in working with information, for example.
FIG. 9 shows an example of a structured interview guide for use in an interview solution. As illustrated, the interview guide is being presented online on a computer screen to an interviewer conducting an interview with an applicant. Screen shot 900 shows interview item 902 for a sample customer service job. The customer service job opening is for a call center position, and revenue focus has been identified as a relevant and predictive competency. Item 902 elicits from the applicant a situation 904, the applicant's behavior 906 in the situation, and the outcome 908 reported by the applicant. The interviewer can grade the applicant's responses to item 902 by marking a score 910 from 1 to 10.
FIG. 10 illustrates procedural steps that may be followed in a web-based applicant system according to an embodiment of the present invention.
FIG. 11 illustrates procedural steps that may be followed in a web-based selection solution according to an embodiment of the present invention. For example, these steps may follow those illustrated in FIG. 10.
FIG. 12 illustrates procedural steps that may be followed by an employer according to an embodiment of the present invention.
 The following tables provide examples of screening solutions and selection solutions designed for different types of jobs. The tables show components (competencies) shown to be relevant to successful performance of each job type. In the tables, some components are considered required, and others are considered optional.
 Table One may be used for entry level and general skill jobs:
 Table Two may be used for customer service jobs:
 Table Three may be used for customer service jobs involving sales:
 Table Four may be used for customer service jobs in a call center:
 Table Five may be used for customer service jobs in a call center involving sales:
 Table Six may be used for jobs in sales:
 Table Seven may be used for supervisory jobs:
 Table Eight may be used for professional jobs:
 Table Nine may be used for managerial jobs:
 Table Ten may be used for technical/professional jobs:
 Table Eleven may be used for executive positions:
 Table Twelve may be used for jobs involving campus recruiting:
 Table Thirteen may be used for a selection solution for a job involving communication:
 Table Fourteen may be used for a selection solution for a job involving financial services jobs referred to series six/seven:
 Table Fifteen may be used for a selection solution for a job requiring information technology aptitude:
 Although the above disclosure has focused on recruiting applications, the generated data may be used in other human capital applications. FIG. 13 illustrates a human capital management life-cycle. Measurement and data 1301 is initially used in the context of recruiting 1302. For recruiting 1302, screening, selection, and interview solutions measure applicants' competencies and predict on-the-job performance and thus contribution to business outcomes.
 For compensation 1303, data about potential can be weighed against performance data to ensure that high potential employees who are on difficult assignments where they are structurally constrained from succeeding are not underpaid by pure focus on performance. For example, structural constraints may include business environment, poor staff, unreliable equipment, etc.
 For retention 1304, business with jobs that have high turnover use the system to ensure that applicants have qualities that contribute to longer tenure in roles.
 For performance management 1305, the system can be used to enhance the validity of employee performance evaluation.
 For training and development 1306, a company may test current employees in order to design executive training programs addressing each individual's strengths and weaknesses. Or, for employees that took a test and were hired despite weaknesses, the data can be used to structure appropriate training.
 For succession 1307, data on employees may be collected in the process of organization mergers to assist planning for retrenchment or change. Also, by measuring competencies and mapping them between roles, it is possible to assess the potential that an individual may have for a role other than the job they are currently holding, such as for a promotion or a transfer to another area.
 The foregoing description is to be considered as illustrative only. The skilled artisan will recognize many variations and permutations within the spirit of the disclosure.