JP2004334834A - Human resource decision method and program to be used for executing such human resource decision method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a human resource decision method for deciding the optimum human resources by every company by utilizing a decision result of an aptitude test which is usually performed. <P>SOLUTION: The human resource decision method has a plus or minus diagnostic item element acquisition process for obtaining a group of plus diagnostic item elements held by plus persons and a group of minus diagnostic item elements held by minus persons in a first population constituted of many human resources from plus person or minus person information sorted for items to be demand measures to each person and the judgment results regarding the aptitude test which each person performs in the first population and an assessment information generation process for generating comparative assessment in a second population constituting many human resources to newly take the aptitude test by every person who takes the aptitude test regarding the plus diagnostic item elements and/or the minus diagnostic item elements in the second population. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to, for example, a human resource judgment suitable for obtaining evaluation information on human resources when recruiting human resources, relocating human resources, or strengthening the sales power and management power of the business in a company. The present invention relates to a method and a program used to execute such a method for determining personnel.

  2. Description of the Related Art In the past, when hiring human resources, for example, it has become common practice for companies to perform aptitude tests (tests) on applicants for employment. Various tests have been proposed by each company for such tests. I have. Usually, such a test is based on a result of an answer to a predetermined question, and based on a certain judgment logic, characteristics of the person, for example, basic characteristics (related to aggressiveness, extroversion, flexibility, etc.) and occupational suitability (general occupation) Aptitude, profession aptitude, etc.) or contact information after joining the company, guidance information on educational methods, etc., and the company obtains the test questions from the company that creates the test. In addition to conducting aptitude tests, it obtains test results (judgment results) for each job applicant and uses them as criteria for recruitment and rejection.

  However, the judgment results obtained by the above-mentioned suitability test are unambiguous and universal, and do not always provide meaningful information for the company that performed the test. In other words, since the company culture, qualified personnel, required levels, and scales for measuring excellence differ from company to company, relying only on test results obtained from uniform judgment logic is truly necessary for the company. There is a possibility that you will lose the opportunity to secure human resources, or human resources that will be needed for the future.

  In addition, in order to improve the quality of human resources, based on the results of such examinations, when reassigning in-house personnel, conducting personnel training, training, etc., the above-mentioned unifying and universal If only the determination result is relied on, the desired effect may not be obtained for the same reason.

  The present invention has been made in view of the above-described problem, and employs a determination result of a normal aptitude test to determine an optimal human resource for each company. And a program for executing such a personnel determination method.

  In order to solve the above-described problems, the human resource determination method according to the present invention provides a first population including a large number of human resources. From the human information and the results of the aptitude tests performed by each of these individuals, a group of positive diagnostic item elements of the positive population and a group of negative diagnostic item elements of the negative person are included in the first population. Obtaining a plus or minus diagnosis item element, and receiving a suitability test for the plus diagnosis item element and / or the minus diagnosis item element in a second population constituting a large number of human resources newly undergoing the suitability test. An evaluation information generating step of generating a relative evaluation in the second population for each person.

  In the method described above, the first population is a sample (employee) serving as basic data for creating a judgment standard for judging a human resource. The same aptitude test as the aptitude test to be performed will be conducted, and each person will be assigned a requirement scale. In this case, the requirement scale is related to the qualities required of the human resources in determining the human resources in another second population, and for example, the positive side (such as excellent / non-excellent) It is classified into a plus person) and a minus side (called a minus person). That is, plus person information and minus person information are assigned to each person of the sample (employee), whereby the plus person has information based on the determination result regarding the suitability test performed by each person of the first population. It is possible to acquire a group of plus diagnosis item elements and a group of minus diagnosis item elements possessed by a minus person.

  Therefore, based on the determination results of the aptitude test performed on each person in the second population, the second population for each person who has undergone the aptitude test with respect to the plus diagnostic item element and / or the minus diagnostic item element By generating the relative evaluation within the above, it is possible to obtain a powerful judgment criterion when making a human resource judgment. For example, if a judgment result with a high deviation value is obtained for the positive diagnosis item element, it can be predicted that the human resource has the same qualities as the positive person in the first population. With respect to the element, if a determination result with a low deviation value is obtained, it is possible to predict that the human resource does not have a negative characteristic such as a minus person in the first population, and the like, and the first population Can be determined based on the successful case of the above.

  In the method as described above, in the plus / minus diagnostic item element acquiring step, a deviation value or a percentile of each person constituting the first population is derived for each diagnostic item element, and the derived deviation value or percentile is used. It is preferable to obtain a correlation coefficient for each diagnostic item element. As described above, by acquiring the correlation coefficient (effect coefficient) for each diagnostic item element, the importance between the diagnostic item elements is clarified, and only the diagnostic item elements with high importance can be determined. It becomes possible. As the correlation coefficient, for example, Pearson's product moment correlation coefficient, Spearman's rank correlation coefficient, Kendall's rank correlation coefficient, Cramer's association number, correlation ratio eta, etc. are applied. Is possible.

  In the above-described evaluation information generation step, an average deviation value or an average percentile in a second population is derived for each of the plus diagnostic item element and the minus diagnostic item element, and the derived result and the diagnostic item It is preferable to determine the reference value of the plus diagnostic item element and the reference value of the minus diagnostic item element using the correlation coefficient obtained for each element. By obtaining such a reference value, the criterion for determining the human resources in the second population is clarified.

Then, using the result obtained in the evaluation information generation step and the correlation coefficient obtained for each of the diagnostic items, the average value of the positive diagnostic item elements of each person constituting the second population, and the negative value By deriving the average value of the diagnostic item elements, the actual position of each person in the second population with respect to the reference value is clarified, and it is possible to easily perform human resource determination. In this case, the reference value of the diagnosis item element and the reference value of the minus diagnosis item element are set as reference axes (X ₀ , Y ₀ ) on the two-dimensional map, and the reference values are displayed on the two-dimensional map based on the reference axes. By displaying the average value of the positive diagnostic item elements and the average value of the negative diagnostic item elements for each person constituting the second population, it is easy to rank the position of each person in the second population. I can do it.

  In the evaluation information generating step described above, an average deviation value or an average percentile in the first population is derived for each of the positive diagnosis item element and the negative diagnosis item element, and the derivation result and the diagnosis item The reference value of the positive diagnosis item element and the reference value of the negative diagnosis item element may be determined using the correlation coefficient obtained for each element. Since such a reference value is determined based on the first population, it is possible to compare the judgment result of each person in the second population with the reference value of the first population, and to obtain higher quality. This makes it possible to make an appropriate human resource determination.

  The human resource determination method described above can be executed using an information processing device such as a computer by being programmed.

  According to the human resource determination method of the present invention and the program for executing such a human resource determination, an optimal human resource determination can be performed by using a response result of an ordinary aptitude test.

Hereinafter, embodiments of the present invention will be specifically described.
Note that, in the present embodiment, a description will be given of an example of a method of determining a human resource from a large number of applicants based on the following determination logic when hiring a human resource in a certain company.

  In this case, the applicant is subjected to a prescribed suitability test (test) that is generally available on the market, and a certain number of sample employees are extracted within the company (the number of extracted employees is assumed to be n). Prerequisites are that the same test as that performed for applicants is being performed. In addition, for each sample employee, items that are required scales are sorted out when hiring human resources in the company. In this embodiment, the requirement scale is defined as a currently excellent employee (excellent; defined as a plus person) and a non-excellent employee who is not (excellent; defined as a minus person) at that time. Shall be kept.

  By examining the above-mentioned excellent / non-excellent sample employees and examining the diagnostic item elements shown in the results of the aptitude test performed by the sample employees, the diagnostic item elements that lead the excellence and the non- It becomes possible to burn out the diagnostic item elements that lead to excellence. Therefore, the group of diagnostic item elements that lead excellently and the group of diagnostic item elements that lead non-excellently are classified, and the judgment result of the appropriate test performed by the applicant is determined in the judgment result of each applicant. By obtaining the deviation value or percentile of each diagnosis item element, recruit applicants with the same qualities as the excellent employee who has a successful pattern, or use the same Qualified applicants can be eliminated.

  In other words, by associating a requirement scale that differs for each company with the determination result of the suitability test of the sample employees existing in the company, in order to secure new human resources, the company's unique recruitment / non-recruitment It is possible to obtain a criterion. Moreover, such criteria can be obtained by using the results of general aptitude tests, and in particular, the time and effort required to prepare different test questions for each company for new criteria. Is not always required.

  In the present embodiment, with respect to the above-described method, by using the following determination logic, the criterion for determining the personnel is made more precise.

First, a deviation value (or percentile) is determined for each diagnostic item in the diagnostic results of n sample employees who have undergone the aptitude test. In this case, the deviation value of each diagnostic item is defined by x _i (i = 1, 2, 3,... N). The requirement scale (excellent / non-excellent) assigned to each sample employee is defined as _yi = 2 (excellent) and 1 (non-excellent) as High / Low performer classification data (excellent = 1, non-excellent). Excellent = 0).

  Then, based on the above definition, a correlation coefficient (effect coefficient) is obtained for each diagnostic item in order to quantify the degree of influence of each diagnostic item. In this case, in the present embodiment, the correlation coefficient r is obtained by the following equation (Pearson product-moment correlation coefficient).

上記した数式によって得られる相関係数ｒにおいて、ｒ≧０であれば、それは優秀度を正に牽引する診断項目であり、ｒ＜０であれば、それは優秀度を負に牽引する診断項目である。図１及び図２に示す表は、ある適性検査によって得られる判定結果の各診断項目について算出された相関係数の一例を示しており、優秀度を正に牽引する診断項目の群と、優秀度を負に牽引する診断項目の群を、それぞれ相関係数が大きい順に分類したものである。なお、表において、診断結果項目名は、適性検査の判定結果に記載のものをそのまま援用したものであり、寄与率は、その診断結果項目が全体の項目に対してどの程度影響を及ぼすかの割合を示したものである。

In the correlation coefficient r obtained by the above equation, if r ≧ 0, it is a diagnostic item that positively drives the degree of excellence, and if r <0, it is a diagnostic item that pulls the degree of excellence negatively. is there. The tables shown in FIGS. 1 and 2 show an example of the correlation coefficient calculated for each diagnostic item of the determination result obtained by a certain aptitude test. The group of diagnostic items that lead the degree to a negative degree is classified in descending order of the correlation coefficient. In the table, the diagnosis result item names are the same as those described in the determination results of the aptitude test, and the contribution ratio indicates how much the diagnosis result item affects the entire item. It shows the ratio.

Relates correlation coefficients obtained in this way, each influence coefficient r of diagnostic items positively pulling excellent degree, and _{R 1, R 2, R 3} ... R m, pulling the excellent degree of negative diagnostic item _Are defined as R ₁ ′, R ₂ ′, R ₃ ′... R _l ′. In this case, since a diagnostic item having a larger absolute value of the correlation coefficient has a greater effect on the determination criterion, in the following procedure, a diagnostic item near 0 (a diagnostic item with a small degree of influence) is a determination target. May be excluded. In the tables shown in FIGS. 1 and 2, only the diagnostic items having an absolute value of the correlation coefficient of 0.1 or more are to be determined.

Next, N applicants of the company were asked to undergo the same aptitude test, and from each judgment result, the deviation value (or percentile) was calculated for each diagnostic item for which the above-mentioned correlation coefficient was calculated. calculate. Here, the deviation values (or percentiles) of the diagnostic items that drive the excellence positively are A _i1 , A _i2 , A _i3 ... A _im (i = 1, 2, 3,... N), and the excellence is negative. , B _i1 , B _i2 , B _i3 ... B _il (i = 1, 2, 3,... N).

  This makes it possible to calculate the average deviation value (or the average percentile) for each diagnosis item of the applicant. In this case, the average deviation value (or the average percentile) of each diagnosis item is obtained by the following formula.

すなわち、優秀度を正に牽引する各診断項目、優秀度を負に牽引する各診断項目に関し、応募者の平均偏差値（又は平均パーセンタイル）が得られる。この平均偏差値（又は平均パーセンタイル）に関しては、場合によっては、上述したサンプル社員のものを取得しておいても良い。

That is, an average deviation value (or an average percentile) of applicants is obtained for each diagnostic item that positively drives the degree of excellence and for each diagnostic item that negatively drives the degree of excellence. Regarding the average deviation value (or the average percentile), the above-mentioned sample employee may be obtained in some cases.

From the average deviation value and the correlation coefficient of each diagnostic item, it is possible to obtain a reference value of a diagnostic item that positively drives the degree of excellence and a reference value of a diagnostic item that pulls the degree of excellence negatively. Here, each reference value is expressed on two axes (X axis, Y axis) on a two-dimensional map so that the position information of the applicant can be easily grasped visually. The reference value is defined as the Y axis (Y ₀ ), and the reference value of the diagnostic item to be negatively pulled is defined as the X axis (X ₀ ). Each reference value is obtained by the following equation.

図３は、上記した基準値を２次元マップ上に示したものであり、Ｙ軸におけるＹ₀、及びＸ軸におけるＸ₀は、それぞれ上記した数式によって求められた各軸の平均値である。上記した表示方法によれば、２次元マップを図に示すように４つの領域Ａ〜Ｄに分類することができる。Ａランクは、応募者が受けた適性検査に関し、優秀度が多く（プラス診断項目要素の平均値が高い）、かつ非優秀度が少ない（マイナス診断項目要素の平均値が低く、マイナス要素を多く持たない）結果を有する人が該当する領域、Ｂランクは、優秀度は多いが、非優秀度も多い結果を有する人が該当する領域、Ｃランクは、優秀度は少ないが、非優秀度が少ない結果を有する人が該当する領域、そして、Ｄランクは、優秀度が少なく、非優秀度が多い結果を有する人が該当する領域である。

Figure 3 is shows the reference value described above on the two-dimensional map, X ₀ in the Y _0, and X-axis in the Y-axis is the average value of each axis obtained by the equation described above, respectively. According to the display method described above, the two-dimensional map can be classified into four regions A to D as shown in the figure. Rank A has a high degree of excellence (high average value of positive diagnosis item elements) and a low degree of non-excellence (low average value of negative diagnosis item elements and many negative elements) (Not possessed) area where the person with the result is applicable, B rank is the area where the person with the result with many excellence but many non-excellent degree is applicable, and C rank is the area with little excellence but the non-excellent degree The area corresponding to a person having a small result and the D rank are areas corresponding to a person having a result having a low degree of excellence and a high degree of non-excellence.

  That is, when the entire applicant who has performed the aptitude test is relatively evaluated, the human resources corresponding to the A rank portion satisfy many of the required scales of the company, and the human resources corresponding to the D rank portion satisfy the required scale of the company. In recruiting human resources, there are generally no aptitude tests that are generally conducted, such as actively recruiting A-ranked applicants and actively excluding D-ranked applicants. , It is possible to obtain a clear judgment standard customized for each company.

  In this case, for each applicant, by applying the following formula, the position on the map shown in FIG. 3 can be specified, and for each applicant, it is clear which position of which rank is in which position. Can be displayed.

上述したサンプル社員のHigh/Lowパフォーマー区分に際しては、各企業にとって最も適切な要求尺度を適用することができる。例えば、その企業にとって、将来あるべき資質を有するか有しないか、或いは、営業力があるかないか、或いは、サービス精神があるかないか等、その企業に存在する人的資源を効果的に活用して、その企業における成功パターンを有する人材と同様な資質を有する人材を多数の応募者の中から容易に判断することが可能になる。

For the high / low performer classification of the sample employees described above, the most appropriate requirement scale for each company can be applied. For example, make effective use of the human resources that exist in the company, such as whether or not the company has the qualities that should be in the future, whether it has the sales force, or the service spirit. Therefore, it is possible to easily determine a human resource having the same qualities as a human resource having a success pattern in the company from a large number of applicants.

  In the above-described embodiment, the reference values shown on the two-dimensional map shown in FIG. 3 are obtained based on the suitability test received by the applicant. May be specified by the average value obtained based on the suitability test of the above. By setting the reference value as the average value of the sample employee's aptitude test, the applicant can easily make a comparison judgment with the employee who has a successful case in the company.

  In addition, for each diagnostic item, the average deviation value (or the average percentile) is calculated, and based on this, the reference value of the diagnostic item that drives the excellence positively and the reference value of the diagnostic item that drives the excellence negatively are calculated. In obtaining the data, it is preferable to use the correlation coefficients whose absolute values are equal to or larger than a predetermined value in the tables shown in FIGS. do not do). This is because if the correlation coefficient is small even if it is included in the calculation target, when the coordinate calculation on the map is performed for the excellent performer who is the High performer, for example, as shown in the excellent person determination result analysis table in FIG. This is because, while it is possible to fall into the category of rank D, a non-excellent person who is a low performer may fall into the category of rank A, and the accuracy of the determination may be reduced.

  For this reason, when obtaining the reference value of the diagnostic item that drives the degree of excellence positively and the reference value of the diagnostic item that drives the degree of excellence negatively, for example, the analysis of the judgment result of the excellent performer as the High performer is performed in the D rank. Only diagnostic item elements with a high absolute value of the correlation coefficient are used as judgment targets so that the judgment result analysis of non-excellents who are not low performers does not fall into the category of rank A. For example, it is preferable to adopt only the diagnostic item elements having the absolute value of the correlation coefficient equal to or larger than a predetermined value. That is, it is preferable to select a diagnostic item element having a large correlation coefficient so that the ratio of excellent persons increases in the A rank and the ratio of non-excellent persons increases in the D rank.

  In addition, when actually recruiting applicants, if only four ranks are used, the number of persons included in the category of each rank is too large, and it may be necessary to make a more detailed determination. For example, as shown in FIG. 5, by the primary determination according to the above-described procedure, applicants corresponding to rank A are to be adopted, applicants corresponding to ranks B and D are not to be adopted, and rank C is applicable. If it is desired to judge some of the applicants to be employed, as a secondary judgment, the reference value may be obtained again in the C rank using a method similar to the above-described method in the C rank. .

Specifically, as shown in FIG. 4, when the criteria for determining the sample employees are analyzed, excellent persons and non-excellent persons are mixed in the C rank. Here, in the same procedure as the above procedure, the correlation coefficient of each diagnosis item is obtained in the C rank (FIG. 6 shows the result of the correlation coefficient obtained by using the above-described formula 1 in the C rank). shown), the using larger correlation coefficients, in the same rank, again, the reference value of diagnostic items positively pulling (Y ₀ '), the reference value of the diagnostic items to be towed to the negative (X _0' ).

  The new reference axis is defined in the quadrant of the C rank, whereby the a rank (Ca rank), the b rank (Cb rank), the c rank (Cc rank), and the d rank (Cd) are newly set in the C rank. Rank) is formed. Therefore, it is possible to provide more detailed judgment criteria such as, for example, an applicant corresponding to the Ca rank is preferentially adopted from among the applicants corresponding to the C rank. This is obtained by dividing the 4 divisions obtained by the initial reference value into 4 divisions and dividing them into 16 divisions. When there are many sample employees and many applicants, the same method is used to divide the division into 64 divisions. .. Can be appropriately modified.

  According to the method as described above, each applicant can easily grasp which position corresponds to the required scale of the company, and in determining human resources, a unique test of aptitude test. And, regardless of the universal judgment result, the company's unique judgment criteria are clarified, and the results of the conventional aptitude test are not recruited even though they are originally meaningful human resources for the company. It is possible to make even human resources appear.

  Further, the above-described method of determining human resources can be executed using an information processing device such as a computer and a workstation, and a program for executing the above-described method on a CPU of the information processing device is a CD-ROM. It can be stored in a recording medium such as a ROM or distributed using a communication line. In this case, the program only needs to be configured to execute the above-described processing procedure.

  For example, in a certain company, a certain number of sample employees are extracted, the results of aptitude tests received by the sample employees are acquired, and a deviation value (or percentile) is derived for each diagnostic item element. For each sample employee, enter information on positive and negative information on items that will be required scales (excellent / non-excellent, etc., as described above). A correlation coefficient is obtained (see FIGS. 1 and 2). Then, among the applicants who newly undergo the same aptitude test, the average deviation value (or the average percentile) of the diagnostic item elements having the positive correlation coefficient and the diagnostic item elements having the negative correlation coefficient is respectively obtained. The reference value of the positive diagnosis item element and the reference value of the negative diagnosis item element are determined using the derivation result and the correlation coefficient obtained for each diagnosis item element (see FIG. 3). On the other hand, by using the correlation coefficient obtained for each diagnosis item to derive the average value of the positive diagnosis item element and the average value of the negative diagnosis item element of each applicant, this applicant can obtain The position corresponding to the set reference value is displayed on a two-dimensional map shown in FIG.

  Alternatively, the reference value of the above-mentioned plus diagnosis item element and the reference value of the minus diagnosis item element may be derived from the diagnosis item element of the suitability test received by the sample employee. With this configuration, it is possible for the sample employee to easily determine which position corresponds to the reference value, and also to narrow down the diagnostic item elements on which the determination is based. Will be able to

  The embodiments of the present invention have been described above. However, the present invention is not limited to the creation of a criterion when a company hires human resources, and includes, for example, basic data and a training It can be applied as basic data when doing it.

  In addition, if the number of in-house judges (assessors) who judge the high / low performer classification of the sample employees is plural, the diagnostic item elements in the tables shown in FIGS. 1 and 2 may vary, but a plurality of assessors may be used. By comparing the results of the diagnostic item element group described above, it is possible to judge the suitability as an assessor, or to clarify and standardize the main evaluation axis in the company.

  In this case, the correlation coefficient obtained for each diagnostic item element for each assessor is subjected to, for example, a correspondence analysis included in SPSS, which is a program product of SPSS, or a statistical analysis equivalent thereto, thereby performing an assessor. It is possible to determine the similarity between the diagnostic items and the similarity between the diagnostic items, and the strength of the relative association between the assessor and the diagnostic items.

  Specifically, for example, correspondence analysis is applied to the contingency table of the correlation coefficients obtained for the combination of the assessor and the diagnostic item element. At this time, a constant (1 is appropriate in many cases) is appropriately added in order to use the correlation coefficient as a non-negative weighting coefficient. As shown in FIG. 7, for any two-dimensional analysis result, the scores of the assessor and the diagnostic item elements are shown on a two-dimensional map, so that the mutual relationship is replaced with the distance on the map and clearly displayed. be able to. In the figure, a short distance between two assessors indicates that the two are evaluating human resources in a similar diagnostic item group, and a short distance between two diagnostic item elements indicates that both are similar from the assessor. Indicates that it is captured as an element. The fact that the distance between the assessor and the diagnostic item element is short indicates that the assessor places more importance on the diagnostic item than others. If there is an Assessor whose position is determined to be significantly different from the other Assessors on the map and is relatively far apart, there is room for reviewing or examining the Assessor's suitability as an Assessor. It can be roasted as something.

4 is a table showing a group of diagnostic items that shows an example of a correlation coefficient calculated for a diagnostic item of a determination result obtained by a certain aptitude test and positively drives the degree of excellence. 9 is a table showing a group of diagnostic items that show an example of a correlation coefficient calculated for a diagnostic item of a determination result obtained by a certain aptitude test and that negatively drives excellence. The figure which showed the reference value of the diagnostic item which pulls excellence positively, and the reference value of the diagnostic item which pulls excellence negatively on a two-dimensional map. Analysis table of judgment results of excellent and non-excellent sample employees. The figure which shows an example of adoption and non-employment on a two-dimensional map. 9 is a table showing an example of a correlation coefficient calculated for a diagnostic item of a determination result obtained in the C rank. The figure which showed the relevance of the assessor and the diagnostic item element on the two-dimensional map.

Claims (11)

In the first population composed of a large number of human resources, information on the plus and minus persons assigned to each of the items serving as the required scale for each person, and the judgment results on the suitability test performed by each of the persons, A plus / minus diagnosis item element obtaining step of obtaining a group of plus diagnosis item elements belonging to the plus person and a group of minus diagnosis item elements belonging to the minus person in the first population;
In the second population, which constitutes a large number of human resources newly undergoing the aptitude test, the second diagnosis is performed on the positive diagnosis element element and / or the negative diagnosis item element for each person who has received the aptitude test. An evaluation information generating step of generating a relative evaluation within the population;
A human resource determination method characterized by having:   In the plus / minus diagnostic item element obtaining step, a deviation value or a percentile of each person constituting the first population is derived for each diagnostic item element, and the derived deviation value or percentile is used for each diagnostic item element. 2. The method according to claim 1, wherein a correlation coefficient is obtained. In the evaluation information generating step, for each of the plus diagnosis item element, and the minus diagnosis item element, to derive an average deviation value or a percentile in the second population,
3. The reference value of a plus diagnosis item element and the reference value of a minus diagnosis item element are determined using the derivation result and the correlation coefficient obtained for each diagnosis item element. Human resources judgment method.   Using the result obtained in the evaluation information generating step and the correlation coefficient obtained for each of the diagnostic items, the average value of the positive diagnostic item elements of each of the individuals constituting the second population, and the negative diagnosis The human resource determination method according to claim 3, wherein an average value of the item elements is derived. The reference value of the plus diagnosis item element and the reference value of the minus diagnosis item element are set as reference axes (X ₀ , Y ₀ ) on a two-dimensional map,
5. The average value of the positive diagnostic item elements and the average value of the negative diagnostic item elements for each of the individuals constituting the second population are displayed on the two-dimensional map based on the reference axis. The method for determining human resources described in 1. In the evaluation information generating step, for each of the plus diagnosis item element and the minus diagnosis item element, derive an average deviation value or a percentile in the first population,
3. The reference value of a plus diagnosis item element and the reference value of a minus diagnosis item element are determined using the derivation result and the correlation coefficient obtained for each diagnosis item element. Human resources judgment method. The reference value of the plus diagnosis item element and the reference value of the minus diagnosis item element are set as reference axes (X ₀ , Y ₀ ) on a two-dimensional map,
7. The average value of the positive diagnostic item elements and the average value of the negative diagnostic item elements for each person constituting the second population are displayed on the two-dimensional map based on the reference axis. The method for determining human resources described in 1.   In determining the reference value of the positive diagnosis item element and the reference value of the negative diagnosis item element, only the diagnosis item element having an absolute value equal to or more than a predetermined value among the correlation coefficients obtained for each diagnosis item element is adopted. The human resource determination method according to claim 3 or 6, wherein: Computers are used to determine the required personnel from a large number of personnel who have undergone the same aptitude test.
A means for encouraging each person to enter positive and negative information regarding a required measure in the first population comprising a large number of human resources;
From the determination results regarding the aptitude test performed by each of these individuals, for each diagnostic item element, a deviation value or a percentile of each of the individuals constituting the first population is derived, and using the derived deviation value or percentile, Means for obtaining a correlation coefficient for each diagnostic item element,
In a second population constituting a large number of human resources newly undergoing the aptitude test, a diagnostic item element having a positive correlation coefficient and a diagnostic item element having a negative correlation coefficient are respectively averaged. Means for deriving the deviation value or percentile,
Means for determining a reference value of a plus diagnosis item element and a reference value of a minus diagnosis item element by using the derivation result and the correlation coefficient obtained for each diagnosis item element;
Means for deriving an average value of the positive diagnostic item elements and an average value of the negative diagnostic item elements of each of the individuals constituting the second population using the correlation coefficient obtained for each diagnostic item,
Human resources judgment program to make it function. Computers are used to determine the required personnel from a large number of personnel who have undergone the same aptitude test.
A means for encouraging each person to enter positive and negative information regarding a required measure in the first population comprising a large number of human resources;
From the determination results regarding the aptitude test performed by each of these individuals, for each diagnostic item element, a deviation value or percentile of each person constituting the first population is derived, and using the derived deviation value or percentile, Means for obtaining a correlation coefficient for each diagnostic item element,
In the first population, a means for deriving an average deviation value or a percentile for each of the diagnostic item elements having a positive correlation coefficient and the diagnostic item elements having a negative correlation coefficient,
Means for determining a reference value of a plus diagnosis item element and a reference value of a minus diagnosis item element by using the derivation result and the correlation coefficient obtained for each diagnosis item element;
In a second population constituting a large number of human resources newly undergoing the aptitude test, a diagnosis item element having a positive correlation coefficient, and a diagnosis item element having a negative correlation coefficient, Means for deriving an average value of the positive diagnostic item elements and an average value of the negative diagnostic item elements of each of the individuals constituting the second population, using the correlation coefficient obtained for each item;
Human resources judgment program to make it function. The human resource determination program according to claim 9 or 10, further comprises:
The reference value of the plus diagnosis item element and the reference value of the minus diagnosis item element are displayed on a reference axis (X ₀ , Y ₀ ) on a two-dimensional map, and the reference value is displayed on the two-dimensional map based on the reference axis. Means for displaying the average value of the positive diagnostic item elements and the average value of the negative diagnostic item elements for each person constituting the population of 2,
It is characterized by functioning as

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