US20030191680A1

US20030191680A1 - Computer-implemented system for human resources management

Info

Publication number: US20030191680A1
Application number: US10/410,307
Authority: US
Inventors: Katrina Dewar
Original assignee: Epredix Inc
Current assignee: Epredix Inc
Priority date: 2000-06-12
Filing date: 2003-04-10
Publication date: 2003-10-09
Also published as: EP1299820A1; EP1299820A4; US20090187446A1; ZA200208987B; US20100042574A1; US8086558B2; AU6831801A; CA2412526A1; WO2001097083A1; US20030200136A1; US7606778B2; US20020055866A1; JP2004503877A; US20030195786A1; NZ522509A

Abstract

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.

Description

This application claims the benefit of U.S. Provisional Patent Application No. 60/211,044, filed Jun. 12, 2000.[0001]

BRIEF DESCRIPTION OF THE DRAWINGS

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.

DETAILED DESCRIPTION

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. [0015]
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. [0016]
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. [0017]
FIG. 1 provides a block diagram of an exemplary system in accordance with the present invention. A job applicant can use [0018] 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.
[0019] 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.
[0020] 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, [0021] 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.
[0022] 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.
[0023] 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 [0024] 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 [0025] 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: [0026]
1. dependability [0027]
2. agreeableness [0028]
3. critical thinking [0029]
4. problem solving ability [0030]
5. talkativeness [0031]
6. assertiveness [0032]
7. gregariousness [0033]
8. persuasiveness [0034]
9. achievement [0035]
10. education [0036]
11. experience [0037]
12. customer service orientation [0038]
13. customer focus [0039]
14. conscientiousness [0040]
15. self-confidence [0041]
16. motivation [0042]
17. revenue focus [0043]
18. cognitive ability [0044]
19. leadership [0045]
20. decision making [0046]
21. flexibility [0047]
22. commitment [0048]
23. learning ability [0049]
24. dedication [0050]
25. tenacity [0051]
26. number of jobs held [0052]
27. length of time in job(s) [0053]
28. working with information [0054]
29. supervisory potential [0055]
30. judgment [0056]
31. leadership [0057]
32. coaching skills [0058]
33. teamwork [0059]
34. interpersonal skills [0060]
35. business leadership [0061]
36. leadership motivation [0062]
37. self-leadership [0063]
38. interpersonal leadership [0064]
39. communication skills [0065]
40. management potential [0066]
41. likelihood of retention [0067]
42. self-control [0068]
43. energy [0069]
44. executive potential [0070]
45. listening orientation [0071]
46. language skills (English, etc.) [0072]
47. verbal reasoning [0073]
48. spatial ability [0074]
49. interest [0075]
50. motivation [0076]
Typically, [0077] 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 [0078]
2. Identifying number (e.g. social security number). [0079]
3. Score achieved at various stages for various competencies. [0080]
4. Recommendation (or not) to continue the hiring process beyond each stage [0081]
5. Link to application information (e.g. address, resume details) [0082]
6. Contact information (phone number, e-mail address, mailing address, etc.) [0083]
7. Date of application [0084]
8. Success or failure in complying with knockout requirements for the job [0085]
9. Screening solution scores, presented as percentiles [0086]
10. A calculated recommendation to proceed or not to proceed with the applicant [0087]
11. Results (by competency) of the selection solution [0088]
12. Link to allow manual entry of the test answers if not done on computer directly by the applicant [0089]
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) [0090]
14. Additional columns for storage of data from a structured behavioral interview [0091]
15. Additional columns for storage of data from other decision-making processes such as drug testing, reference checks, or medical exams. [0092]
A process for testing and evaluating job applicants may be described with reference to FIG. 2. Generally, applicant testing [0093] 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. [0094] 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 [0095] 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 [0096] 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.
[0097] 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 [0098] 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.
[0099] 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.
[0100] 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. [0101] 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 [0102] 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, [0103] 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 [0104] 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 [0105] 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.
[0106] 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, [0107] 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 [0108] 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 [0109] 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 [0110] 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 [0111] 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.
[0112] 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.
[0113] 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 [0114] 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 [0115] 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 [0116] 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 [0117] box 550, performance data for successful applicants are collected at a later date, and sent to data warehouse 534.
Data collected at [0118] 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. [0119]
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. [0120]
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. [0121]
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). [0122]
e. Design of solutions other than recruitment related solutions. [0123]
f. Reporting of usage volumes for billing and financing accounting purposes. [0124]
Because system [0125] 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 ([0126] 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. [0127]
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. [0128]
FIG. 7 shows an example of a stack ranked table. Computer screen shot [0129] 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 [0130] 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 [0131] 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.
[0132] 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. [0133]
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 [0134] 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 [0135] 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. [0136]
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. [0137]
FIG. 12 illustrates procedural steps that may be followed by an employer according to an embodiment of the present invention. [0138]
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. [0139]

Table One may be used for entry level and general skill jobs:

TABLE ONE


Entry/General Skilled Solutions

Solution
Component	Definition	Items

Screening 7-10 Minutes

Required	Educational and	Measures potential for success in	15
	Work-Related	entry-level jobs across industry
	Experience	type and functional area. Scores
		on Education and Work-Related
		Experience are derived from
		candidates' responses to
		questions regarding
		developmental influences, self-
		esteem, work history and work-
		related values and attitudes.
	Self-Confidence	This component references: be-	7
		lief in one's own abilities and
		skills and a tendency to feel
		competent in several areas.
Optional	Decision Making/	Measures potential for success in	8
	Flexibility	entry level positions. Scores on
		Decision Making and Flexibility
		are derived from candidates'
		responses to questions regarding
		developmental influences, self-
		esteem, work history and work-
		related values and attitudes.

Selection 23-35 Minutes

Required	Conscientiousness	This component is designed to	65
		predict the likelihood that
		candidates will follow company
		policies exactly, work in an
		organized manner, return from
		meals and breaks in the allotted
		time, and keep working, even
		when coworkers are not
		working.
	Retention	Measures commitment,	44
	Predictor	impulsiveness, responsibility,
		and motivation. It predicts the
		likelihood that a new hire will
		remain on the job for at least
		three months.
Optional	Learning Ability	This component measures the	54
		tendency to efficiently and	(12
		effectively use numerical and	minute
		analytical reasoning. This	timer)
		competency is characterized by
		the ability to learn work-related
		tasks, processes, and policies.

Table Two may be used for customer service jobs:

TABLE TWO


Customer Service Solution

Solution
Component	Definition	Items

Screening 8-10 Minutes

Required	Educational and	Measures potential for success in	15
	Work-Related	customer service jobs. Scores on
	Experience	Education and Work-Related
		Experience are derived from
		candidates responses to
		questions regarding develop-
		mental influences, self-esteem,
		work history and work-related
		values and attitudes.
	Customer Service	Designed to predict the likeli-	20
	Orientation	hood that candidates will show
		persistent enthusiasm in
		customer interaction, apologize
		sincerely for inconveniences
		to customers, be patient with
		customers, tolerate rude
		customers calmly, and search
		for information or products for
		customers.
Optional	Self-Confidence	This component references: be-	7
		lief in one's own abilities and
		skills and a tendency to feel
		competent in several areas.

Selection 17-29-37 Minutes

Required	Customer Focus	Designed to predict the likeli-	32
		hood that candidates will show
		persistent enthusiasm in
		customer interaction, apologize
		sincerely for inconveniences
		to customers, be patient with
		customers, tolerate rude
		customers calmly, and search for
		information or products for
		customers.
	Conscientiousness	This component is designed to	65
		predict the likelihood that
		candidates will follow company
		policies exactly, work in an or-
		ganized manner, return from
		meals and breaks in the allotted
		time, and keep working, even
		when coworkers are not
		working.
Optional	Learning Ability	This component measures the	54
		tendency to efficiently and ef-	(12
		fectively use numerical and	minute
		analytical reasoning. This com-	timer)
		petency is characterized by the
		ability to learn work-related
		tasks, processes, and policies.
Optional	Retention	Measures commitment, im-	44
	Predictor	pulsiveness, responsibility, and
		motivation. It predicts the
		likelihood that a new hire will
		remain on the job for at least
		three months.

Table Three may be used for customer service jobs involving sales:

TABLE THREE


Customer Service Solution: Sales Positions

Solution
Component	Definition	Items

Screening 9-15 Minutes

Required	Educational and	Measures potential for success in	15
	Work-Related	customer service jobs. Scores on
	Experience	Education and Work-Related
		Experience are derived from
		candidates responses to
		questions regarding develop-
		mental influences, self-esteem,
		work history and work-related
		values and attitudes.
	Customer	This component is designed to	20
	Service	predict the likelihood that
	Orientation	candidates will show persistent
		enthusiasm in customer inter-
		action, apologize sincerely for
		inconveniences to customers, be
		patient with customers, tolerate
		rude customers calmly, and
		search for information or
		products for customers.
Optional	Sales Potential	Designed to predict the likeli-	23
		hood that candidates will suggest
		or show alternative solutions
		based on customer needs, direct
		conversation toward a
		commitment/order/sale, show
		confidence even after a hard
		refusal/rejection, and strive to
		close a transaction every time.

Selection 15-27 Minutes

Required	Sales Potential	Designed to predict the likeli-	60
		hood that candidates will suggest
		or show alternative solutions
		based on customer needs, direct
		conversation toward a
		commitment/order/sale, show
		confidence even after a hard
		refusal/rejection, and strive to
		close a transaction every time.
	Customer Focus	Designed to predict the likeli-	32
		hood that candidates will show
		persistent enthusiasm in
		customer interaction, apologize
		sincerely for inconveniences
		to customers, be patient with
		customers, tolerate rude
		customers calmly, and search for
		information or products for
		customers.
Optional	Learning Ability	This component measures the	54
		tendency to efficiently and ef-	(12
		fectively use numerical and	minute
		analytical reasoning. This com-	timer)
		petency is characterized by the
		ability to learn work-related
		tasks, processes, and policies.

Table Four may be used for customer service jobs in a call center:

TABLE FOUR


Customer Service Solution: Call Center Positions

Solution
Component	Definition	Items

Screening 9-11 minutes

Selection 16-31-39 Minutes

Required	Customer Focus	This component is designed to	32
		predict the likelihood that
		candidates will show persistent
		enthusiasm in customer inter-
		action, apologize sincerely for
		inconveniences to customers, be
		patient with customers, tolerate
		rude customers calmly, and
		search for information or
		products for customers.
	Conscientiousness	This component is designed to	65
		predict the likelihood that
		candidates will follow company
		policies exactly, work in an
		organized manner, return from
		meals and breaks in the allotted
		time, and keep working, even
		when coworkers are not
		working.
	Working with	This component is designed to	30
	Information	predict success in customer	(15
		service call-center jobs by	minute
		assessing a candidate's ability	timer)
		to retrieve information and use it
		in order to solve problems.
Optional	Retention	Measures commitment, impul-	44
	Predictor	siveness, responsibility, and
		motivation. It predicts the
		likelihood that a new hire will
		remain on the job for at least
		three months.

Table Five may be used for customer service jobs in a call center involving sales:

TABLE FIVE


Customer Service Solution: Call Center Sales Positions

Solution
Component	Definition	Items

Screening 9-15 Minutes

Required	Educational and	Measures potential for success in	15
	Work-Related	customer service jobs. Scores on
	Experience	Education and Work-Related
		Experience are derived from
		candidates' responses to
		questions regarding develop-
		mental influences, self-esteem,
		work history and work-related
		values and attitudes.
	Customer	Designed to predict the likeli-	20
	Service	hood that candidates will show
	Orientation	persistent enthusiasm in
		customer interaction, apologize
		sincerely for inconveniences
		to customers, be patient with
		customers, tolerate rude
		customers calmly, and search for
		information or products for
		customers.
Optional	Sales Potential	Designed to predict the likeli-	23
		hood that candidates will suggest
		or show alternative solutions
		based on customer needs, direct
		conversation toward a
		commitment/order/sale, show
		confidence even after a hard
		refusal/rejection, and strive to
		close a transaction every time.

Selection 30 Minutes

Required	Sales Focus	Designed to predict the likeli-	60
		hood that candidates will suggest
		or show alternative solutions
		based on customer needs, direct
		conversation toward a
		commitment/order/sale, show
		confidence even after a hard
		refusal/rejection, and strive to
		close a transaction every time.
	Customer Focus	Designed to predict the likeli-	32
		hood that candidates will show
		persistent enthusiasm in
		customer interaction, apologize
		sincerely for inconveniences
		to customers, be patient with
		customers, tolerate rude
		customers calmly, and search for
		information or products for
		customers.
	Working with	This component is designed to	30
	Information	predict success in customer	(15
		service call-center jobs by	minute
		assessing a candidate's ability	timer)
		to retrieve information and use it
		in order to solve problems.

Table Six may be used for jobs in sales:

TABLE SIX


Sales Solutions

Solution
Component	Definition	Items

Screening 10-14 minutes

Required	Educational	Measures potential for success in	15
	and Work-	customer service jobs. Scores on
	Related	Education and Work-Related
	Experience	Experience are derived from
		candidates responses to questions
		regarding developmental influences,
		self-esteem, work history and work-
		related values and attitudes.
	Sales Potential	Designed to predict the likelihood	23
		that candidates will suggest or show
		alternative solutions based on
		customer needs, direct conversation
		toward a commitment/order/sale,
		show confidence even after a hard
		refusal/rejection, and strive to close a
		transaction every time.
Optional	Customer	Designed to predict the likelihood	20
	Service	that candidates will show persistent
	Orientation	enthusiasm in customer interaction,
		apologize sincerely for incon-
		veniences to customers, be patient
		with customers, tolerate rude
		customers calmly, and search for in-
		formation or products for customers.

Selection 10-25-40 Minutes

Required	Sales Focus	Designed to predict the likelihood	60
		that candidates will suggest or show
		alternative solutions based on
		customer needs, direct conversation
		toward a commitment/order/sale,
		show confidence even after a hard
		refusal/rejection, and strive to close a
		transaction every time.
Optional	Problem	Measures the tendency to efficiently	10
	Solving	and effectively use numerical and
		analytical reasoning. This com-
		petency is characterized by the ability
		to solve complex problems, and make
		reasoned decisions.
Optional	Communi-	Measures the tendency to efficiently	10
	cation	and effectively use verbal reasoning.
		This competency is characterized by
		the ability to verbally explain
		complex information to others.

Table Seven may be used for supervisory jobs:

TABLE SEVEN


Supervisory Solutions

Solution
Component	Definition	Items

Screening 10-20 Minutes

Required	Supervisory	Measures potential for supervisory	10
	Potential	success across industry type and
		functional area. Scores on
		Supervisory Potential are derived
		from candidates' responses to
		questions regarding academic and
		social background, and aspirations
		concerning work.
	Judgment	Measures potential for making good	10
		judgments about how to effectively
		respond to work situations. Scores
		on Judgment are derived from
		candidates' responses to questions
		regarding situations one would likely
		encounter as a manager/
		supervisor.
Optional	Leadership/	Measures potential for success as a	19
	Coaching	supervisor. This is done by having
	Teamwork/	applicants' make judgments about
	Interpersonal	the most effective teamwork and
	Skills	leadership behaviors in specific
		work situations. Scores are
		determined by comparing their
		response profiles to the profiles of
		supervisors who are known to be
		successful.

Selection 22-37-52 Mins

Required	Business	Measures the candidate's thinking	28
	Leadership	styles. High scorers are likely to
		have or learn good planning and
		organizing skills, be innovative,
		consider issues from multiple
		perspectives, and create strategies
		to build their business.
Required	Leadership	Measures the candidate's desire for	23
	Motivation	achievement, drive, initiative, energy
		level, willingness to take charge,
		and persistence. High scorers are
		likely to be highly motivated to
		succeed and to set challenging
		goals for themselves and others.
	Self-	Measures the candidate's ability to	32
	Leadership	control emotions, act with integrity,
		take responsibility for actions, and
		tolerate stress. High scorers are
		also likely to have a positive attitude,
		be optimistic about the future, and
		demonstrate high levels of
		professionalism.
	Interpersonal	Measures the candidate's	30
	Leadership	interpersonal characteristics. High
		scorers are likely to persuade and
		influence others, gain commitment,
		and build effective interpersonal
		relationships. They also have
		potential to develop skills in the
		areas of employee relations,
		coaching, motivating, and leading a
		team.
Optional	Decision	Measures the tendency to efficiently	10
	Making/	and effectively use numerical and
	Problem	analytical reasoning. This
	Solving	competency is characterized by the
		ability to solve complex problems,
		and make reasoned decisions.
Optional	Communi-	Measures the tendency to efficiently	10
	cation	and effectively use verbal reasoning.
		This competency is characterized by
		the ability to verbally explain
		complex information to others.

Table Eight may be used for professional jobs:

TABLE EIGHT


Professional Solutions

Solution
Component	Definition	Items

Screening 7 - Minutes

Required	Dependa-	This competency is characterized by: a	40
	bility	willingness to behave in expected and
		agree upon ways; following through on
		assignments and commitments; keep
		promises; and accept the
		consequences of one's own actions.
	Interpersonal	This competency is indexed by a
	Skills	tendency to be pleasant, cooperative,
		and helpful when working with others,
		as well as flexible in conflict resolution
		situations.
	Self-Control	This competency is characterized by
		the ability to: stay calm and collected
		when confronted with adversity,
		frustration, or other difficult situations;
		and avoid defensive reactions or hurt
		feelings as a result of others'
		comments.
	Energy	This competency is characterized by a
		preference to stay busy, active, and
		avoid inactive events or situations.

Selection 35-50 Minutes

Required	Business	Measures the candidate's thinking	32
	Leadership	styles. High scorers are likely to have
		or learn good planning and organizing
		skills, be innovative, consider issues
		from multiple perspectives, and create
		strategies to build their business.
	Leadership	Measures the candidate's desire for	35
	Motivation	achievement, drive, initiative, energy
		level, willingness to take charge, and
		persistence. High scorers are likely to
		be highly motivated to succeed and to
		set challenging goals for themselves
		and others.
	Self-	Measures the candidate's ability to	34
	Leadership	control emotions, act with integrity,
		take responsibility for actions, and
		tolerate stress. High scorers are also
		likely to have a positive attitude, be
		optimistic about the future, and
		demonstrate high levels of
		professionalism.
	Interpersonal	Measures the candidate's	41
	Leadership	interpersonal characteristics. High
		scorers are likely to persuade and
		influence others, gain commitment,
		and build effective interpersonal
		relationships. They also have
		potential to develop skills in the areas
		of employee relations, coaching,
		motivating, and leading a team.
	Decision	Measures the tendency to efficiently	10
	Making/	and effectively use numerical and
	Problem	analytical reasoning. This competency
	Solving	is characterized by the ability to solve
		complex problems, and make
		reasoned decisions.
Optional	Communi-	Measures the tendency to efficiently	10
	cation	and effectively use verbal reasoning.
		This competency is characterized by
		the ability to verbally explain complex
		information to others.

Table Nine may be used for managerial jobs:

TABLE NINE


Managerial Solutions

Solution
Component	Definition	Items

Screening 10-20 Minutes

Required	Management	Measures potential for managerial	10
	Potential	success across industry type and
		functional area. Scores on Management
		Potential are derived from candidates'
		responses to questions regarding
		academic and social background, and
		aspirations concerning work.
	Judgment	Measures potential for making good	10
		judgments about how to effectively
		respond to work situations. Scores on
		Judgment are derived from candidates'
		responses to questions regarding
		situations one would likely encounter as
		a manager/supervisor.
Optional	Self-	This component references: belief in	10
	Confidence	one's own abilities and skills and a
		tendency to feel competent in several
		areas.
	Decision	Measures potential for success as a
	Making	manager. This is done by having
		applicants' make judgments about the
		most effective decisions in specific
		work situations. Their potential is de-
		termined by comparing their response
		profiles to the profiles of successful
		managers.

Selection 20-35-50 Mins

Required	Business	Measures the candidate's thinking	32
	Leadership	styles. High scorers are likely to have
		or learn good planning and organizing
		skills, be innovative, consider issues
		from multiple perspectives, and create
		strategies to build their business.
	Leadership	Measures the candidate's desire for	35
	Motivation	achievement, drive, initiative, energy
		level, willingness to take charge, and
		persistence. High scorers are likely to
		be highly motivated to succeed and to
		set challenging goals for themselves
		and others.
	Self-	Measures the candidate's ability to	34
	Leadership	control emotions, act with integrity,
		take responsibility for actions, and toler-
		ate stress. High scorers are also likely to
		have a positive attitude, be optimistic
		about the future, and demonstrate high
		levels of professionalism.
	Interpersonal	Measures the candidate's	41
	Leadership	interpersonal characteristics. High
		scorers are likely to persuade and
		influence others, gain commitment,
		and build effective interpersonal
		relationships. They also have potential
		to develop skills in the areas of
		employee relations, coaching,
		motivating, and leading a team.
Optional	Decision	Measures the tendency to efficiently	10
	Making/	and effectively use numerical and
	Problem	analytical reasoning. This competency
	Solving	is characterized by the ability to solve
		complex problems, and make
		reasoned decisions.
Optional	Communi-	Measures the tendency to efficiently	10
	cation	and effectively use verbal reasoning.
		This competency is characterized by
		the ability to verbally explain complex
		information to others.

Table Ten may be used for technical/professional jobs:

TABLE TEN


Technical-Professional Solutions

Solution
Component	Definition	Items

Screening 8 Minutes

Required	Dependa-	This competency is characterized by: a	40
	bility	willingness to behave in expected and
		agree upon ways; following through on
		assignments and commitments; keeping
		promises; and accepting the
		consequences of one's own actions.
	Interpersonal	This competency is indexed by a
	Skills	tendency to be pleasant, cooperative,
		and helpful when working with others,
		as well as flexible in conflict resolution
		situations.
	Self-Control	This competency is characterized by the
		ability to: stay calm and collected when
		confronted with adversity, frustration,
		or other difficult situations; and avoid
		defensive reactions or hurt feelings as a
		result of others' comments.
	Energy	This competency is characterized by a
		preference to stay busy, active, and
		avoid inactive events or situations.

Selection 35-50 Minutes

Required	Business	Measures the candidate's thinking	32
	Leadership	styles. High scorers are likely to have
		or learn good planning and organizing
		skills, be innovative, consider issues
		from multiple perspectives, and create
		strategies to build their business.
	Leadership	Measures the candidate's desire for	35
	Motivation	achievement, drive, initiative, energy
		level, willingness to take charge, and
		persistence. High scorers are likely to
		be highly motivated to succeed and to
		set challenging goals for themselves
		and others.
	Self-	Measures the candidate's ability to	34
	Leadership	control emotions, act with integrity,
		take responsibility for actions, and
		tolerate stress. High scorers are also
		likely to have a positive attitude, be
		optimistic about the future, and
		demonstrate high levels of
		professionalism,
	Interpersonal	Measures the candidate's	41
	Leadership	interpersonal characteristics. High
		scorers are likely to persuade and
		influence others, gain commitment,
		and build effective interpersonal
		relationships. They also have
		potential to develop skills in the areas
		of employee relations, coaching,
		motivating, and leading a team.
	Decision	Measures the tendency to efficiently	10
	Making/	and effectively use numerical and
	Problem	analytical reasoning. This competency
	Solving	is characterized by the ability to solve
		complex problems, and make
		reasoned decisions.
Optional	Communi-	Measures the tendency to efficiently	10
	cation	and effectively use verbal reasoning.
		This competency is characterized by
		the ability to verbally explain complex
		information to others.

Table Eleven may be used for executive positions:

TABLE ELEVEN


Executive Solutions

Solution
Component	Definition	Items

Screening 20 Minutes

Required	Executive	Measures potential for success in	53
	Potential	high-level organizational positions
		across industry type and functional
		area. Scores on Executive Potential
		are derived from candidates'
		responses to questions regarding work
		background, accomplishments, and
		career aspirations.

Selection 35-50 Minutes

Table Twelve may be used for jobs involving campus recruiting:

TABLE TWELVE


Campus Recruiting Solutions

Solution
Component	Definition	Items

Screening 12 Minutes

Required	Supervisory	Measures potential for supervisory	26
	Potential	success across industry type and
		functional area. Scores on Supervisory
		Potential are derived from candidates'
		responses to questions regarding
		academic and social background, and
		aspirations concerning work.
	Judgment	Measures potential for making good
		judgments about how to effectively
		respond to work situations. Scores on
		Judgment are derived from candidates'
		responses to questions regarding
		situations one would likely encounter
		as a manager/supervisor.
	Management	Measures potential for managerial
	Potential	success across industry type and
		functional area. Scores on
		Management Potential are derived
		from candidates' responses to
		questions regarding academic and
		social background, and aspirations
		concerning work.

Selection 20-35-50 Mins

Required	Business	Measures the candidate's thinking	32
	Leadership	styles. High scorers are likely to
		have or learn good planning and
		organizing skills, be innovative,
		consider issues from multiple
		perspectives, and create strategies
		to build their business.
	Leadership	Measures the candidate's desire for	35
	Motivation	achievement, drive, initiative,
		energy level, willingness to take
		charge, and persistence. High
		scorers are likely to be highly
		motivated to succeed and to set
		challenging goals for themselves
		and others.
	Self-	Measures the candidate's ability to	34
	Leadership	control emotions, act with integrity,
		take responsibility for actions, and
		tolerate stress. High scorers are
		also likely to have a positive
		attitude, be optimistic about the
		future, and demonstrate high levels
		of professionalism.
	Interpersonal	Measures the candidate's	41
	Leadership	interpersonal characteristics. High
		scorers are likely to persuade and
		influence others, gain commitment,
		and build effective interpersonal
		relationships. They also have
		potential to develop skills in the
		areas of employee relations,
		coaching, motivating, and leading a
		team.
Optional	Decision	Measures the tendency to efficiently	10
	Making/	and effectively use numerical and
	Problem	analytical reasoning. This
	Solving	competency is characterized by the
		ability to solve complex problems,
		and make reasoned decisions.
Optional	Communi-	Measures the tendency to efficiently	10
	cation	and effectively use verbal reasoning.
		This competency is characterized by
		the ability to verbally explain complex
		information to others.

Table Thirteen may be used for a selection solution for a job involving communication:

TABLE THIRTEEN


Communication Solution

Solution
Component	Definition	Items

Selection 37 Minutes

Required	Listening	Measure of the tendency to listen to	73
	Orientation	and understand others' perspectives,
		to care for others, to accept and
		respect the individual differences of
		people, and to be open both to multiple
		ideas and to using alternative modes
		of thinking.
	English	Measures usage of verb tense and
	Language	sentence construction. Scores on
	Skills	English Language Skills are derived
		from candidates responses to
		grammar questions.
	Verbal	Measures verbal reasoning skills and
	Reasoning/	critical thinking/reasoning skills.
	Critical	Scores on Verbal Reasoning Ability
	Thinking	are derived from candidates'
		responses to analogies and questions
		about information provided in brief
		reading passages.

Table Fourteen may be used for a selection solution for a job involving financial services jobs referred to series six/seven:

TABLE FOURTEEN


Series Six/Seven Success Solution

Solution
Component	Definition	Items

Selection 36 Minutes

Required	Problem	Measures the ability to analyze and	20
	Solving	evaluate information. Scores on
		Problem Solving are derived from
		candidates' responses to mathematical
		and analytical reasoning items,
		requiring candidates to respond to
		facts and figures presented in various
		formats.
	Verbal	Measures verbal reasoning skills and
	Reasoning/	critical thinking/reasoning skills.
	Critical	Scores on Verbal Reasoning Ability
	Thinking	are derived from candidates'
		responses to analogies and involves
		making inferences from information
		provided in the form of brief passages

Table Fifteen may be used for a selection solution for a job requiring information technology aptitude:

TABLE FIFTEEN


Information Technology Aptitude Solution

Solution
Component	Definition	Items

Selection

18 Minutes

Required	Critical	Measure reasoning and critical thinking	58
	Thinking	skills. Scores on Critical Thinking are
		derived from candidates' responses to
		information provided in the form of
		brief passages.
	Problem	Measure the ability to analyze and
	Solving	evaluate information. Scores on
		Problem Solving are derived from
		candidates' responses to mathematical
		and analytical reasoning items,
		requiring candidates to respond to
		facts and figures presented in various
		scenarios.
	Communi-	Measures the ability to efficiently use
	cation	verbal information. Scores on
		Communication are derived from
		candidates' ability to identify
		synonyms.
	Spatial	Measure the ability to visually
	Ability	manipulate objects. Scores on Spatial
		Ability are derived from candidates'
		ability to correctly identify the number
		of blocks in progressively difficult
		figures.

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 [0155] 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 [0156] 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 [0157] 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 [0158] 1305, the system can be used to enhance the validity of employee performance evaluation.
For training and development [0159] 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 [0160] 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. [0161]

Claims

14. An electronic assessment system for assessing an individual applicant for employment by an employer, the system comprising:

an electronic applicant terminal operable to present a plurality of questions to the applicant and to receive electronically the applicant's responses to the questions;

an applicant screening computer configured to provide applicant results automatically in response to receiving the electronically received responses; and

an electronic report viewer operable to present to the employer a viewable report containing the applicant results,

characterized in that the computer is configured to compare automatically the electronically received responses with electronically stored rating correlation data, the rating correlation data being indicative of calculated correlations between actual job duty performance ratings of a plurality of hired individuals and previous responses given by the hired individuals.

15. The system of claim 14, the previous responses characterized by having been collected from the individuals before the individuals were hired into the job for which the actual job duty performance ratings were collected.

16. The system of claim 14, the previous responses characterized by having been collected from the individuals in response to the plurality of questions.

17. An electronic prediction system for assessing an individual applicant for employment by an employer, the system comprising:

an applicant screening computer responsive to the electronically received responses and operable to predict expected performance for a candidate if the candidate were to be employed by the employer, the computer providing applicant results indicative of expected performance based upon correlations of the electronically received answers with answers to questions by other individuals for which job duty performance information has been collected; and

an electronic report viewer operable to present to the employer a viewable report containing the applicant results.

18. The apparatus of claim 17 wherein the job duty performance information has been collected electronically.

19. The apparatus of claim 17 wherein the job duty performance information has been stored electronically.

20. The apparatus of claim 17 characterized in that the applicant terminal is configured to communicate with the applicant screening computer over the Internet.

21. An electronic assessment system for assessing an individual for a potential human resources action by an employer, the system comprising:

an electronic terminal operable to present a plurality of questions to the individual and to receive electronically the individual's responses to the questions;

a computer configured to provide results automatically in response to receiving the electronically received responses; and

an electronic report viewer operable to present to the employer a viewable report containing the results,

22. The electronic assessment system of claim 21, characterized in that the potential human resources action is hiring the individual for a job.

23. The electronic assessment system of claim 21, characterized in that the potential human resources action is promoting the individual.

24. An apparatus for assisting in determining the suitability of an individual for employment by an employer, the apparatus comprising: an electronic data interrogator operable to present a first set of a plurality of questions to the individual; an electronic answer capturer operable to electronically store the individual's responses to at least a selected plurality of the first set of questions presented to the individual; an electronic predictor responsive to the stored answers and operable to predict at least one post-hire outcome if the individual were to be employed by the employer, the predictor providing a prediction of the outcome based upon correlations of the stored answers with answers to sets of questions by other individuals for which post-hire information has been collected; and an electronic results provider providing an output indicative of the outcome to assist in determining the suitability of the individual for employment by the employer.

25. An apparatus according to claim 24 wherein the post-hire outcome indicates whether the individual is predicted to be eligible for re-hire after termination.

26. An apparatus according to claim 24 wherein the post-hire outcomes indicate whether the individual is predicted to be involuntarily terminated and whether the individual is predicted to be eligible for re-hire after termination.

27. An apparatus according to claim 24 wherein at least one of the predicted outcomes is a predicted value for a continuous variable.

28. An apparatus according to claim 24 wherein the predicted outcome indicates whether the individual will belong to a particular group.

29. An apparatus according to claim 24 wherein at least one of the predicted outcomes is a predicted ranking of the individual for the outcome.

30. An apparatus according to claim 24 wherein at least one of the predicted outcomes indicates a predicted employment tenure for the individual.

31. An apparatus according to claim 24 wherein at least one of the predicted outcomes indicates a predicted number of accidents for the individual.

32. An apparatus according to claim 24 wherein at least one of the predicted outcomes indicates a predicted sales level for the individual.

33. An apparatus according to claim 24 wherein the predictor comprises an artificial intelligence-based prediction system.

34. An apparatus according to claim 24 wherein the data interrogator is located at a first location and the predictor is located at a second location which is remote from the first location.

35. An apparatus according to claim 34 wherein the data interrogator and the predictor are selectively electronically interconnected through a network.

36. An apparatus according to claim 35 wherein the network is the worldwide web.

37. An apparatus according to claim 35 wherein the network is a telephone network.

38. An apparatus according to claim 35 wherein the network is an electronic network.

39. An apparatus according to claim 24 wherein the first set of questions may be varied.

40. An apparatus according to claim 39 wherein the predictor is operable to determine and indicate a lack of a correlation between one or more questions of the first set of questions and at least one of the predicted outcomes, whereby questions which lack the correlation may be discarded or modified.

41. An apparatus according to claim 24 wherein at least one of the predicted outcomes is longevity with an employer and the answers to sets of questions by other individuals comprise answers by employees of the employer for whom longevity has been determined.

42. An apparatus according to claim 24 in which the predictor comprises at least one model which provides a predictor of the probability of the individual exhibiting at least one of the predicted outcomes, the model being based on correlations between the at least one of the predicted outcomes and the answers to questions by the other individuals, including answers by at least some employees of the employer, the model taking at least selected answers of the stored answers as inputs to the model, a probability of the individual exhibiting the at least one of the predicted outcomes being provided as an output of the model.

43. An apparatus according to claim 33 wherein the model comprises at least one expert system.

44. An apparatus according to claim 24 wherein the predictor is responsive to the stored answers and operable to predict plural outcomes if the individual were to be employed by the employer.

45. A method for assessing suitability of persons for employment based on information for hired employees, the method comprising: collecting pre-hire applicant information for hired employees before they are hired; collecting post-hire measures of the job effectiveness of hired employees; constructing an artificial intelligence model identifying associations of patterns within the pre-hire data associated with patterns of job effectiveness in the post-hire data; collecting pre-hire information for a new applicant; and applying the artificial intelligence model to the pre-hire information for the new applicant to provide a prediction of the new applicant's suitability for employment.

46. The method of claim 45 further comprising: collecting post-hire information for the new applicant; and using at least the pre-hire and post-hire information for the new applicant to refine the artificial intelligence model.

47. The method of claim 45 further comprising: constructing at least one other artificial intelligence model of a different type; and assessing the relative effectiveness of the artificial intelligence models at predicting suitability of employees for employment based on actual employment effectiveness of employees hired based on the models.

48. An apparatus for assisting in determining the suitability of an individual for employment by an employer, the apparatus comprising: means for electronically presenting a first set of a plurality of questions to the individual; means for electronically storing the individual's responses to at least a selected plurality of the first set of questions presented to the individual; responsive to the stored answers, means for predicting at least one post-hire outcome if the individual were to be employed by the employer, the means for predicting providing a prediction of the outcome based upon correlations of the at least one characteristic with answers to sets of questions by other individuals and the closeness of the stored answers to such correlations; and means for providing an output indicative of the outcome to assist in determining the suitability of the individual for employment by the employer.

49. An artificial intelligence-based system for predicting employee behaviors based on pre-hire information collected for the employee, the system comprising: an electronic device for presenting an employment application comprising a set of questions to an employment candidate, wherein the electronic device is operable to transmit answers of the employment candidate to a central store of employee information, wherein the central store of employee information comprises information collected for a plurality of candidate employees and a plurality of hired employees; an artificial intelligence-based model constructed from information collected from the hired employees based on answers provided by the hired employees and employment behaviors observed for the hired employees; a software system for supplying the answers of the employment candidate to the artificial intelligence-based model to produce predicted employment behaviors for the employment candidate; and a report generator to produce a hiring recommendation report for the employment candidate based on the predicted employment behaviors of the employment candidate.

50. A computer-implemented method of predicting employment performance characteristics for a candidate employee based on pre-hire information collected for hired employees, the method comprising: collecting data indicating pre-hire information for a plurality of the hired employees; collecting data indicating post-hire outcomes for the hired employees; constructing an artificial intelligence-based model from the pre-hire information and the post-hire outcomes for the employees; from the candidate employee, electronically collecting data indicating pre-hire information of the candidate employee; and applying the model to the collected pre-hire information of the candidate employee to generate one or more predicted post-hire outcomes for the candidate employee.

51. The method of claim 50 wherein collecting data from the candidate employee comprises electronically presenting a set of questions at an electronic device and electronically collecting answers to the questions at the electronic device.

52. The method of claim 50 wherein the pre-hire information comprises one or more pre-hire characteristics and constructing the model comprises: identifying one or more pre-hire characteristics as ineffective predictors; and responsive to identifying the pre-hire characteristics as ineffective predictors, omitting the ineffective predictors from the model.

53. The method of claim 50 further comprising: providing a report indicating applicant flow.

54. The method of claim 50 wherein constructing the model comprises: constructing a plurality of proposed models, wherein at least two of the models are of different types; and selecting a superior proposed model as the model to be used.

55. The method of claim 54 wherein at least two of the proposed models are different expert models.

56. The method of claim 50 further comprising using the one or more predicted post-hire outcomes to influence a hiring decision.

57. The method of claim 50 further comprising using the one or more predicted post-hire outcomes to influence a promotion decision.

58. The method of claim 50 wherein at least one of the predicted post-hire outcomes is denoted as a probability that a particular value range of a job effective measure will be observed for a candidate employee.

59. The method of claim 50 wherein at least one of the predicted post-hire outcomes is denoted as a value for a continuous variable.

60. The method of claim 50 wherein at least one of the predicted post-hire outcomes is denoted as a relative ranking for an outcome.

61. The method of claim 60 wherein the ranking is relative to other employment candidates.

62. The method of claim 60 wherein the ranking is relative to the hired employees.

63. The method of claim 50 further comprising: storing a relative importance of one or more particular post-hire outcomes; and generating automated hiring recommendations based on the predicted post-hire outcomes for the candidate employees and the importance of the post-hire outcomes.

64. The method of claim 50 further comprising: refining the model based on newly-observed post-hire outcomes.

65. The method of claim 50 wherein the pre-hire information comprises answers to questions on a job application, the method further comprising: identifying one or more questions as ineffective predictors; responsive to identifying the questions as ineffective predictors, modifying the job application by removing the questions; collecting new pre-hire information for additional candidate employees based on the modified job application; collecting new post-hire information for the additional candidate employees; and constructing a refined artificial-intelligence model based on the additional pre-hire and post-hire information for the additional candidate employees.

66. The method of claim 65 further comprising: responsive to determining pre-hire and post-hire information has been collected for a sufficient number of additional employees, providing an indication that a refined model can be constructed.

67. The method of claim 65 further comprising: providing a report indicating the identified questions are ineffective predictors.

68. The method of claim 65 further comprising: adding one or more new questions to the modified job application before collecting additional pre-hire information.

69. The method of claim 68 wherein the new questions are composed based on job skills appropriate for a particular job related to the job application.

70. The method of claim 68 further comprising: evaluating the effectiveness of the new questions.

71. An artificial intelligence-based employee performance prediction system comprising: a set of pre-hire characteristic identifiers; a set of post-hire outcome identifiers; a collection of data for employees, wherein the data includes values associated with the pre-hire identifiers and the post-hire identifiers; and an artificial intelligence-based model chosen from a set of candidate models, the artificial intelligence-based model exhibiting superior ability at predicting values associated with the post-hire outcome identifiers based on values associated with the pre-hire characteristic identifiers in comparison to the other candidate models.

72. A computer-readable medium having a collection of employment-related data, the data comprising: pre-hire information for a plurality of employees, wherein the pre-hire information comprises information electronically-collected from an applicant, wherein the information comprises a plurality of pre-hire characteristics; post-hire information for at least some of the plurality of employees, wherein the information comprises a plurality of post-hire outcomes; and a data structure identifying which of the pre-hire characteristics are effective in predicting a set of one or more of the post-hire outcomes for a job applicant.

73. A method for providing an automated hiring recommendation for a new potential employee, the method comprising: collecting pre-hire information for potential employees; storing the pre-hire information for the potential employees in a database; after hiring a plurality of the potential employees, collecting employment performance information for at least some of the hired employees; storing the employment performance information collected from the hired employees; constructing an artificial intelligence-based model based on correlations between the pre-hire information and the employment performance information collected from one or more of the hired employees; collecting pre-hire information for a new potential employee; based on the artificial intelligence-based model, providing an automated hiring recommendation for the new potential employee; after hiring the new potential employee, collecting employment performance information for the new potential employee; adding the employment performance information for the new potential employee to the database; and modifying the artificial intelligence-based model based on the pre-hire and employment performance information for the new potential employee.

74. A method for providing an automated hiring recommendation service for an employer, the method comprising: stationing a plurality of electronic devices at a plurality of employer sites, wherein the electronic devices are operable to accept directly from one or more job applicants answers to questions presented at the electronic devices; sending the answers of at least one of the job applicants to a remote site for analysis; applying an artificial intelligence-based predictive model to the answers of the least one of the job applicant to generate an automated hiring recommendation; and automatically sending the hiring recommendation to the employer.

75. A method of constructing a model generating one or more job performance criteria predictors based on input pre-hire information, the method comprising:

from a plurality of applicants, electronically 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 input pre-hire information from new applicants.

76. A computer-readable medium comprising computer-executable instructions for performing the method of claim 75.

77. The method of claim 75 further comprising: limiting the applicants for the model to those providing a certain answer to a knock-out question.

78. The method of claim 75 further comprising: limiting the applicants for the model to those not providing a certain answer to a knock-out question.

79. The method of claim 75 further comprising: limiting the applicants for the model to those with a particular occupation; and constructing the model as an occupationally-specialized model.

80. The method of claim 75 wherein the model accepts one or more inputs, the method further comprising: identifying in the pre-hire information one or more characteristics that are ineffective predictors; and omitting the ineffective predictors as inputs to the model.

81. The method of claim 75 wherein the pre-hire information comprises one or more characteristics, the method further comprising: identifying in the pre-hire information one or more characteristics that are ineffective predictors; and providing an indication that the characteristics no longer need to be collected.

82. The method of claim 75 wherein job performance criteria predictors comprise a predictor indicating whether a job candidate will be voluntarily terminated.

83. The method of claim 75 wherein job performance criteria predictors comprise a predictor indicating whether a job candidate will be eligible for rehire after termination.

84. The method of claim 75 wherein the pre-hire information comprises one or more characteristics, the method further comprising: identifying in the pre-hire information one or more characteristics that are ineffective predictors; and responsive to identifying the ineffective predictors, collecting new pre-hire information not including the ineffective predictors; and building a refined model based on the new pre-hire information.

85. The method of claim 84 further comprising: adding one or more new characteristics to be collected when collecting the new pre-hire information.

86. The method of claim 85 further comprising: evaluating the effectiveness of the new characteristics.

87. An electronic assessment system for assessing an individual for a potential human resources action by an employer, the system comprising:

characterized in that the computer is configured to rate automatically the electronically received responses to provide a rating for the individual and to rank automatically the individual in order against other individuals based on the rating.

88. An electronic assessment system for assessing an individual for a potential human resources action by an employer, the system comprising:

a computer configured to rate automatically the electronically received responses to provide a rating for the individual and to rank automatically the individual in order against other individuals based on the rating; and

an electronic report viewer operable to present to the employer a viewable report containing the individual's rating and the individual's rank order.

89. An electronic assessment system for assessing an individual for a potential human resources action by an employer comprising:

an electronic terminal operable to present an abbreviated set of questions to the individual and to receive electronically the individual's responses to the questions;

characterized in that the computer is configured to compare automatically the electronically received responses with electronically stored rating correlation data, the rating correlation data being indicative of calculated correlations between actual job duty performance ratings of a plurality of hired individuals and previous responses given by the hired individuals to a full set of questions, the abbreviated set of questions being selected from the full set of questions.

90. The system of claim 89, further characterized in that the abbreviated set of questions are selected from the full set of questions based on validated correlations between the abbreviated set and the actual job duty performance ratings.

91. A computer-readable medium substantially as shown and described.

92. A method substantially as shown and described.

93. An apparatus substantially as shown and described.

94. A method for constructing an artificial intelligence-based employment selection process based on pre-hire information comprising personal employee characteristics and post-hire information comprising employee job performance observation information, the method comprising:

generating a plurality of predictive artificial intelligence models based on the pre-hire and post-hire information, wherein at least two of the artificial intelligence models are of different types;

testing effectiveness of the models to select an effective model; and

applying the effective model to predict post-hire information not yet observed.

95. The method of claim 94 characterized in that at least one of the models is an expert system.

96. The method of claim 94 further comprising: identifying at least one of the models as exhibiting impermissible bias; and avoiding use of the models exhibiting impermissible bias.

97. The method of claim 96 wherein the impermissible bias is against a protected group of persons.

98. A computer-implemented method of refining an artificial-intelligence based employee performance selection system, the method comprising: collecting information via an electronic device presenting a set of questions to employment candidates, wherein the questions are stored in a computer-readable medium; testing effectiveness of at least one of the questions in predicting the post-hire information; and responsive to determining the question is ineffective, deleting the question from the computer-readable medium.

99. A computer-readable medium comprising a predictive model, the model comprising: inputs for accepting one or more characteristics based on pre-hire information for a job applicant; one or more predictive outputs indicating one or more predicted job effectiveness criteria based on the inputs, wherein the predictive model is an artificial intelligence-based model constructed from pre-hire data electronically collected from a plurality of employees and post-hire data, and the model generates its predictive outputs based on the similarity of the inputs to pre-hire data collected for the plurality of employees and their respective post-hire data.

100. The computer-readable medium of claim 99 wherein the predictive model comprises a predictive output indicating a rank for the job applicant.

101. The computer-readable medium of claim 100 wherein the rank is relative to other applicants.

102. The computer-readable medium of claim 100 wherein the rank is relative to the plurality of employees.

103. The computer-readable medium of claim 99 wherein the predictive model comprises a predictive output indicating probability of group membership for the job applicant.

104. The computer-readable medium of claim 99 wherein the predictive model comprises a predictive output indicating predicted tenure for the job applicant.

105. The computer-readable medium of claim 99 wherein the predictive model comprises a predictive output indicating predicted tenure for the job applicant.

106. The computer-readable medium of claim 99 wherein the predictive model comprises a predictive output indicating predicted number of accidents for the job applicant.

107. The computer-readable medium of claim 99 wherein the predictive model comprises a predictive output indicating whether the applicant will be involuntarily terminated.

108. The computer-readable medium of claim 99 wherein the predictive model comprises a predictive output indicating whether the applicant will be eligible for rehire after termination.

109. A computer-readable medium comprising a refined predictive model, the model comprising: inputs for accepting one or more characteristics based on pre-hire information for a job applicant; one or more predictive outputs indicating one or more predicted job effectiveness criteria based on the inputs, wherein the predictive model is constructed from pre-hire data electronically collected from a plurality of employees and post-hire data, wherein the pre-hire data is based on a question set refined by having identified and removed one or more questions as ineffective.

110. The computer-readable medium of claim 109 wherein the ineffective questions are identified via an information transfer technique.

111. The computer-readable medium of claim 109 wherein the model is an artificial intelligence-based model.

112. A system for assessing an individual applicant for employment by an employer, the system comprising:

an electronic applicant terminal logged on to a website and operable to present a plurality of questions to the applicant and to receive electronically the applicant's responses to the questions;

an applicant screening computer associated with the website and configured to provide applicant results automatically in response to receiving the electronically received responses;

an electronic network connecting the electronic applicant terminal to the applicant screening computer in accordance with a uniform resource locator associated with website, the uniform resource locator having been entered by the applicant into the electronic applicant terminal; and

wherein the applicant screening computer is configured to compare automatically the electronically received responses with electronically stored rating correlation data, the rating correlation data being indicative of calculated correlations between actual job duty performance ratings of a plurality of hired individuals and previous responses given by the hired individuals.

113. A method for assessing an individual applicant for employment by an employer, the method comprising:

hosting an employer website identified by a uniform resource locator;

providing via the uniform resource locator access to the website by the applicant at an applicant terminal;

transmitting questions from an applicant screening computer to the applicant terminal;

receiving at the applicant screening computer responses from the applicant to the questions transmitted over the Internet;

comparing automatically the received responses with electronically stored rating correlation data, the rating correlation data being indicative of calculated correlations between actual job duty performance ratings of a plurality of hired individuals and previous responses given by the hired individuals.

114. A method for assessing an individual applicant for employment by an employer, the method comprising:

making a connection over a network between a job applicant telephone and a computer;

transmitting questions from the computer to the job applicant telephone;

receiving at the computer responses from the applicant to the questions transmitted, the responses having been transmitted over the network from the job applicant telephone;

115. A method of assessing the suitability of an individual for a job action, the method comprising:

making a connection between a computer and a terminal;

receiving at the computer responses entered at the terminal by the individual in response to questions;

scoring the received responses according to correlations between job duty performance ratings of a plurality of workers and previous responses given by the workers.

116. The method of claim 115, wherein making the connection comprises logging the individual on to a website.

117. The method of claim 115, wherein the scoring is performed automatically.

118. The method of claim 115, wherein the scoring is performed in real time.

119. The method of claim 115, further comprising displaying a rank order of the individual.

120. The method of claim 115, wherein the previous responses were given by the workers in response to said questions.

121. The method of claim 115, wherein the scoring predicts the turnover potential.

122. The method of claim 115, wherein the scoring provides information on a probability of not terminating early.

123. The method of claim 115, wherein the scoring is indicative of a probability of successful job duty performance.

124. The method of claim 115, wherein the individual is a job applicant and the job action is employment.

125. The method of claim 115, wherein the correlations are made before the responses are received.

126. An apparatus for assessing the suitability of an individual for a job action, the apparatus comprising:

means for making a connection between a computer and a terminal;

means for receiving at the computer responses entered at the terminal by the individual in response to questions;

means for scoring the received responses according to correlations between job duty performance ratings of a plurality of workers and previous responses given by the workers.

127. The apparatus of claim 126, wherein the means for making a connection comprises a website.

128. The apparatus of claim 126, wherein the previous responses were given by the workers in response to said questions.

129. The apparatus of claim 126, wherein the terminal comprises a telephone.

130. The apparatus of claim 126, wherein the means for making a connection comprises an Internet and the responses are entered at the terminal by pointing and clicking.

131. The apparatus of claim 126, wherein the computer comprises a testing program.

132. The apparatus of claim 126, further comprising a scoring database.

133. The apparatus of claim 126, wherein the correlations are made before the responses are received.

134. A computer program capable of causing a computer to perform the functions of:

making a connection between a terminal and a computer;

receiving at the computer responses entered at the terminal by an individual in response to questions;

135. A method of constructing a computer model useful for deciding whether a new job applicant would be suitable for employment, the method comprising:

collecting complete pre-hire information from a plurality of original applicants in response to a complete set of pre-hire information items; hiring the original applicants;

collecting post-hire information for the original applicants based on job performance of the original applicants after hire;

comparing the complete pre-hire information to the post-hire information;

responsive to the comparing, selecting a sub-set of the complete pre-hire information items, the sub-set being selected for a high correlation to the post-hire information;

generating from the pre-hire information and the post-hire information a computerized predictive model operable to generate an applicant suitability indication based on newly input pre-hire information electronically collected from a new applicant,

the suitability indication indicating whether the new applicant would be suitable for employment and the newly input pre-hire information being limited to the new applicant's responses to the selected subset of the complete pre-hire information items.

136. The method of claim 135 wherein the computerized predictive model reflects input from job incumbent experts.

137. The method of claim 135 further comprising:

designing and testing the pre-hire information to comply with EEOC guidelines and to not be dependent on any group membership.

138. The method of claim 94 wherein the different types of artificial intelligence models are at least two of the following four different types of artificial intelligence models: application questions; customer service inventory; working with information test; and sales potential inventory.

139. An electronic prediction system for assessing the suitability of job applicants for an employer, the electronic prediction system comprising:

a plurality of applicant terminals connected to the Internet;

an applicant screening server connected through the Internet to the applicant terminals, the applicant screening server having a testing computer program and storing test meta-data;

an employer website configured to present questions to the applicants at the applicant terminals and to receive applicant responses entered at the applicant terminals in response to presentation of the questions, the questions having been validated by correlating job duty performance ratings of a plurality of hired workers with previous responses given by the workers to the questions;

a scoring system for automatically scoring the applicant responses, the scoring system being validated to predict both performance and turnover potential;

a scoring database connected to the applicant screening server;

an applicant input system located on the employer's premises and configured electronically to receive input from an applicant at the employer's premises after the candidate has come to the employer's premises and logged on; and

a viewing system for permitting the employer to view applicant results from the electronic prediction system and the applicant's rank order.

140. A method of constructing a computer model generating one or more job performance criteria predictors based on input pre-hire information, the method comprising:

from a plurality of applicants, collecting pre-hire information from the applicants;

responsive to the pre-hire information and the post-hire information, forming a computer model operable to generate a plurality of job performance criteria predictors based on input pre-hire information electronically collected from new applicants.

141. An electronic prediction system for assessing the suitability of job applicants for an employer, the electronic prediction system comprising:

an applicant screening server storing a testing computer program and test data, the testing computer program configured to present questions to the applicants over a network, the questions having been validated by correlating job duty performance ratings of a plurality of hired workers with responses given by the workers; and

means for receiving input from an applicant at the employer's premises after the candidate has come to the employer's premises and logged on.

142. A method of deciding whether a new job applicant would be suitable for employment, the method comprising:

collecting complete pre-hire information from a plurality of original applicants in response to a complete set of pre-hire information items;

hiring the original applicants;

comparing the complete pre-hire information to the post-hire information;

responsive to the comparing, selecting a sub-set of the complete pre-hire information items, the sub-set being selected for a correlation to the post-hire information;

preparing from the pre-hire information and the post-hire information a computerized predictive model operable to generate automatically an applicant suitability indication based on newly input pre-hire information electronically collected from a new applicant,