Open Science Journal of Statistics and Application 2013; 1(1): 6-12 Published online December 10, 2013 (http://www.openscienceonline.com/journal/osjsa)
Comparison between ranking technique and profile model for job placement based on complete job analysis F. Z. Okwonu1, 2, A. R. Othman1 1 2
School of Distance Education, Universiti Sains Malaysia, 11800, Penang, Malaysia Department of Mathematics and Computer Science, Delta State University, P.M.B.1, Abraka, Nigeria
Email address
[email protected] (F. Z. Okwonu)
To cite this article F. Z. Okwonu, A. R. Othman. Comparison between Ranking Technique and Profile Model for Job Placement Based on Complete Job Analysis. Open Science Journal of Statistics and Application. Vol. 1, No. 1, 2013, pp. 6-12.
Abstract This short note is designed to provide unbiased placement practice and to address the “emamadu” syndrome. This note describes a novel job placement model that allows decision makers to select qualified applicants for job placement based on the information provided by job analyst. The model addresses the problem of ties in applicant performance and avoids bias decision during decision making. The model via the control factor allows decision makers to specify the number of qualified applicants to be engaged. The performance benchmark of this procedure is automatically generated based on the applicant’s performance. The comparative performance of this technique and the ranking procedure is investigated. The validation of both techniques revealed that the new procedure solves the problem of tie.
Keyword Applicant, Mahalanobis Distance, Rank, Profile Variable, Selection, Control Factor
1. Introduction Job placement (JP) or recruitment is based on well articulated job analysis (JA), JA is a preprocessing step for placement. It entails techniques for obtaining, documenting and analyzing content, context and job qualification in order to identify the vital functions of the positions, competencies, skills and capabilities required for the job[1]. JA helps to determine vacant positions and exact number of applicant to engage and is cost effective. Job placement has been studied and defined by various authors. Vandergoot [2] observed that job placement is a culmination of services and output. It is defined as a process which people evolve to obtain competitive work-life for the state[2-4]. Job placement is also defined as a procedure to obtain secure gainful employment [5]. It can be considered as all inclusive process that entails policies, any measures, practices and techniques applied to secure recruitment, promotional decision but not restricted to recruitment, rating and ranking applicants, interview and
final selection[1]. Job placement procedure requires certain conditions that may include age, educational level, work experience, health status and other conditions that may be stipulated by the hiring firms. In this regard, assessment of the job seeker may involve physical and psychological examination, written test and interview. Generally, human factor may hinder or influence the selection procedure due to emamadu (“Emamadu means advocating the hiring, promotion of friends (political, religious, tribal and racial consideration) and relatives at the expense of qualified personnel”). Thus, for the growth of the hiring firms the human resource department has to emphasize on competency and different procedures have been adopted to perform this task. Previously, the ranking method based on within and between group mean vectors are applied to select the best candidate but this technique may be biased when some candidates are tied. Hence other techniques have been proposed to remedy such situations. Techniques
Open Science Journal of Statistics and Application 2013; 1(1): 6-12
based on distance measures have been considered and applied in recent past. Saad[6] applied distance measures based on Hamming and Hausdorff distance to select applicants based on certain conditions and then compared their performance with the ranking procedure to select applicant for job placement. Saad[6] noted that distance measures such as Hamming, Minkwoski, Euclidean and Hausdorff are often applied to applicant selection process. Detail discussion on the use of distance techniques for applicant selection is contain in[6]. It has been observed that various job placement templates are not properly designed and often contradictory in applications [5]. They also observed that job placement was based on “personal preference” or choice. It has been suggested that the scope of research be expanded beyond client-oriented placement techniques and practice to include evaluating employer contact practices. It is the view of these authors that job placement be all inclusive to enable decision makers examine the contribution of all information provided by the applicants and their physical build rather than theoretical framework. This implies that more conditions be consider to include client, decision maker and expanded job placement methods and practices[4]. It has been stated that career development theory should be based on career selection procedures. Suzanne [4] (p. 2) noted that the Minnesota theory of work adjustment is based on job “satisfaction and satisfactoriness”. It was also observed that the above theory has not been able to successfully address job placement problem. Detail procedures for applicant selection process is described in[1]. Vandergoot[3] observed that investigation from labor market revealed that the social sciences and management sciences contribute immensely to the labor market. In this respect, the contributions of this field of study and others as may be specified indicate that for the Africa situations with special interest to Nigeria is more complex due to unemployment and government inability to address massive youth engagement and lack of basic and social amenities. This is because in Nigeria, irrespective of your field of study the government of the day with respect to the federal ministry of labor and productivity has not been able to create data bank in conjunction with the graduating institutions to determine the exact number of graduates employed and unemployed. Even if it exists such data bank is not utilitized. It may also be possible that job placement policy is not well regulated due to emamadu. The emamadu concept does not allow competency and capability because it is a product of manipulation. This study considers the Nigeria situation with regard to the emamadu syndrome. This short note is designed to address the emamadu syndrome with respect to job placement in Nigeria. This note gives brief description of known placement models and further focused on ranking technique and the profile placement model. The former is easily manipulated to suite personal interest with respect to data alteration and difficulty in handling tie among
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applicants while the latter is difficult to manipulate due to the phases in decision making and profile crosschecking. The profile model evaluates the contribution of applicant profiles and then determines the qualified applicants based on the Mahalanobis distance value using the comparative cutoff point. This technique is unique in the sense that based on the given cutoff point; the control parameter α allows the user to determine the exact number of qualified applicants as enumerated by the job analyst. It solves the problem of ties by automatically crosschecking the applicant that has the highest profile contributions. It gives the exact number of applicants qualified and requested by the recruitment firm. It solves emamadu syndrome because it automatically generate the most qualified applicant based on the number of requests by the hiring firm. It can also be used to access the performance of existing staff based on their profiles. The profile selection model helps to prevent bias decision making. This model allows the decision makers to be unbiased in applicant placement and helps to recruit the required number of applicants with the aid of the control factor. This technique is a two stage procedure that evaluates the profiles and test scores of the applicant using the Mahalanobis distance. In this regard, after the applicant has been assessed based on the profile variables and test scores the information provided by stage one are then inputted to the second phase for final decision. In this case, the control factor is fixed in order to determine the number of required applicant to be engage. This procedure allows the applicant to get instant results by visual inspection. The results of the applicants can be displayed instantly, in this regard; one signifies that the applicant is selected for the job while zero shows that the applicant is not qualified. The interesting thing about this model is that it allows for applicant reservation and solves the problem of ties. The performance of the applicant with respect to the profile variables contribution can easily be verified by displaying the output of the applicant from stage one, detail of this is contain in Section Four. To the best of our knowledge at the time of writing this short note, the existing procedures have not addressed the problem of ties for job placement and to determine the exact number of job hunters qualified for placement. The aim of this note is to address the above problems using the profile contributions of each job hunter and control parameter. It is our obvious view that the proposed statistical model will assist in developing human capital management that will energize the workforce by engaging dedicated, competent applicants and hence build a healthy workforce that provides better services to the organization. The rest of this note is organized as follows: Section Two reviews the existing placement model. Section Three describe selection procedure based on ranking. Section Four contains job profile model. Application and discussion are presented in Section Five. Conclusion is contained in Section Six.
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F. Z. Okwonu and A. R. Othman: Comparison between Ranking Technique and Profile Model for Job Placement Based on Complete Job Analysis
2. Placement Models In accordance with the various techniques used for job placement, it has been suggested that the known procedures be modified to account for applicant profiles contribution and the work environment. Other factors that hamper job satisfaction and work environment include the population of the host environment and type of job or business respectively. As observed by Suzanne [4] that job placement be vigorously organized. It is vital to state that decision makers or practitioner should inculcate the profiles of the applicants, job demands and the nature of job under consideration. Furthermore, it can be stated that operational job classification system was based on the view that “ every job is a job-worker situation” this implies that applicants talents, capacities and examination of the applicant physical, emotional and psychological conditions of the applicant contribute immensely to job satisfaction[4]. According to Suzanne [4] some authors have pointed out that specific job fit be considered desirable and though mentioned that this factor is not vital in general. It was also noted that years of working experience and planning is vital in certain job placement requirement. It was also noted that decision makers should continuously update and modify their hiring conditions to enable job seekers study these conditions before applying. Various job models have been proposed to enhance effective job placement and performance; such models include job coach model, employment training model, supported job model, enclave model, mobile crew model, benchwork model, entrepreneurial model, job readiness model and train place model[7]. Job placement can be categorized as selective and client centered placement, respectively[2]. Suzanne [4] observed that job placement strictly depends on the applicant’s profiles and the hiring firms. The former was defined by Vandergoot [3] and is described as the contribution of profiles of each applicant requirements and job compatibility. It was noted that selective job placement entails comparing the applicant capabilities with the available jobs[8]. On the other hand, the placement facilitator acts as a resource person, job advocate which eventually assist the applicant to get the job, though this is conventional. The alternative is that the applicant undergoes this process personally. Suzanne [4] (p.8-9) described selective job placement model as unsuccessful and resulting in unsatisfactory due to the control parameters and non-involvement of the applicant. Relying on the limitations of the selective job placement model [9], Suzanne [4] suggested that the peril of the selective job placement model can be mitigated by the client-centered job placement model. This procedure allows the applicant to take responsibility, obtain the job and relate with the hiring firm and hence decide on the acceptance of the job[10]. Other school of thought that have observed the altitude of job seeker to self esteem are those applicant who secured their placement personally[11]. Self job placement gives the applicant confidence to search for other jobs in
future. Suzanne [4] opined that client centered job placement model (JPM) based on job club model (JCM) is very efficient. This model provides applicant to share information, design curriculum vitae, relating with hiring firms and rehearsing interview sessions among others.
3. Selection Based on Ranking Technique The concept of ranking technique for decision makers with respect to job placement was introduced in the 1940’s and subsequently developed in 1956 by Gupta[12]. This concept was basically developed in [13] to select the population with the largest mean vector from the normal populations with unknown mean vectors and common covariance matrix with a probability of one. In general, the probabilities lies between 1/ N and 1 , where N is the sample size. This procedure revealed that for g = 5 which is the number of group or population, the population or group with the largest mean vector is selected[12]. He further explained that the population means are ranked and the selection procedure is based on the comparison between the ordered mean vectors and the cutoff point. He also adopted his selection criteria by using the population with the best t value. Another technique to select the group with the largest mean vector is based on the subset selection procedure[12]. This technique selects a non-empty subset of the group in order to choose the best group or the group with the largest mean vector. Gupta [13] noted for the subset selection method the best selection is based on the group with the largest mean vector.
4. Profile Model This note discusses a novel selection model to enable recruitment firms to select qualified candidates for job placement by relying on well defined profile variables and test scores respectively. The selection process strictly depends on the contribution of the profile variables and the value of the selection model. However, if the output of the profile variables are positive (1) and coincides with the output of the decision model, then that applicant is chosen first. On the other hand, if the output of the profile variables is zero and the output of the selection model is one, such applicant may be considered if the contribution of the profile variables account for more than 2/3 of the performance value. An applicant that scores one for both the profile variables and the decision model is considered suitable for the job placement. On the other hand, if both contributions are zero the applicant is rejected. The number of applicant to be engaged as specified using job analysis can be determine by varying the value of the control factor α . The control factor (α ) help to determine the proportion of applicant to be engaged based on the test score and the contribution of each profile variable. In a strict sense, the decision maker has to fix the control factor because it allows for applicant reservation; this procedure is
Open Science Journal of Statistics and Application 2013; 1(1): 6-12
z = y + yˆ.
advantageous if a qualified and accepted applicant rejects the job offer due to service conditions. The information of the reserved applicant can be retrieved and process for the job. In such situation, the reserved candidate may be invited as a replacement. In general, this model is designed to provide equal opportunity for qualified applicants via open competition. This procedure works effectively if complete job analysis (CJA) is performed; CJA helps to determine the exact number of applicants to be engaged. The model is described using the sample mean vectors and covariance matrices as follows; N
N
j =1
j =1
Κ=
Ξ=
xi
,
N
∑ xij
,
(6)
otherwise, the applicant is rejected if the following condition is fulfilled
Ψ = Κ < Ξ.
(7)
In general, the application of this model is based on the test scores and the profile variables of the applicant. Once the test scores is obtained, these scores are then imputed and the output based on Equations (3) and (6) are used by the decision makers to select the successful applicant for the job placement. The control factor used helps to determine the exact value of applicant to be engaged based on the conclusion of the job analyst.
(2)
j =1
The contribution of the profile variables is obtained based on the following equation
Ω = di ≥ ξ .
Κ
α
Ψ = Κ ≥ Ξ,
(1)
ξ = xiη .
(5)
where α is the control factor and is user specify respectively. Based on Equation (3) and the following relation, an applicant is selected for job placement if the following condition is satisfied
This distance measure allows us to determine the relationship between the test scores and the profile variable contributions. To obtain the contribution of each profile variable, the value of the Mahalanobis distance is compared with the observation wedge. The observation wedge is described as
η=
Σz . N
The comparative factor is given as
where N is the sample size and x is the test scores, respectively. Based on the above equations, the Mahalanobis distance is described as
di = ( xij − xi ) S ( xij − xi )′.
(4)
Equation (4) is the interaction parameter and the mean of the interaction parameter is defined as
xi = ∑ xij / N , Si = ∑ ( xij − xi )( xij − xi )′ / ( N − 1),
−1
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5. Application
(3)
OFEND General Services is a public limited company saddle with distributions, supplies and consultancy services to major oil and telecommunication companies. Due to its expansion, the management board decided on its 23rd general meeting that the number of staff should be increase by ten. Based on the board decision, the human resource department was mandated to advertise for vacancy. Due to the working condition and staff welfare of the company over three hundred applicants applied. Relying on the company policy on competency, staff recruitment history and job analysis performed, the number was prone to thirty. The conditions used are based on age, educational level, work experience and medical history. The test scores are given in Table 1 below.
If this equation is satisfied then the value one is assigned to the profile variable which shows that the applicant satisfy the conditions otherwise zero is assigned which means that the prospective applicant did not fulfilled the stated conditions. The second phase of the model is the selection process which also includes the number of applicant to be engaged and those to be place on reservation. In order for the decision makers to select prospective applicant for job placement, the following parameters are redefined y = di , and yˆ = ξ . At this point, the relationship between the Mahalanobis distance and the observation wedge is obtained and is defined as follows
Table 1. Test scores of applicants with ranking Candidate number
English
Mathematics
Business Management
IQ
Total
Mean
Rank
1
40
30
38
52
160
40.00
2
2
53
17
39
41
150
37.50
4
3
29
30
17
33
109
27.25
19
4
12
30
19
21
82
20.50
26
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F. Z. Okwonu and A. R. Othman: Comparison between Ranking Technique and Profile Model for Job Placement Based on Complete Job Analysis
Candidate number
English
Mathematics
Business Management
IQ
Total
Mean
Rank
5
43
29
30
15
117
29.25
18
6
8
32
17
9
66
16.50
27
7
35
31
19
40
125
31.25
13
8
51
33
17
31
132
33.00
9
9
20
29
39
29
117
29.25
18
10
39
18
23
20
100
25.00
23
11
29
30
43
29
131
32.75
10
12
7
39
43
18
107
26.75
21
13
33
12
17
38
100
25.00
23
14
43
22
35
19
119
29.75
16
15
18
33
35
39
125
31.25
13
16
40
44
47
50
181
45.25
1
17
23
29
60
31
143
35.75
5
18
27
41
43
40
151
37.75
3
19
33
14
17
20
84
21.00
25
20
22
18
50
31
121
30.25
14
21
27
40
23
9
99
24.75
24
22
23
43
42
29
137
34.25
6
23
16
35
19
38
108
27.00
20
24
53
21
23
37
134
33.50
7
25
35
18
42
39
134
33.50
7
26
41
32
18
29
120
30.00
15
27
33
28
42
23
126
31.50
12
28
12
35
41
39
127
31.75
11
29
29
32
43
29
133
33.25
8
30
24
43
18
19
104
26.00
22
From Table 1 above, using the ranking procedure one can easily predict the most qualified applicant suitable for the job placement. Using the ranking method and setting the aggregate as 121.4, fifteen applicants are qualified for the job. The problem here is that the company has just ten vacancies to fill. The interesting scenario is that they all satisfy the cutoff point stipulated. To decide on the candidate to be engaged, the management team has to reconvene a meeting, this has more cost implication. On the other hand, based on the published aggregate the respective applicants are hopeful of having the job since they all fulfilled the requirement. Note also that the aggregate can be stiffened to be 130 to allow eleven applicants to be qualified. This procedure often allows for ties between respective applicants which may give room for bias decision, see [14]. Using the ranking technique, the applicant score can easily be altered to enhance his/her performance. This is the type of problem we intend to remedy. The proposed model is designed to address the deficiency of the ranking procedure using the same test scores. The new technique avoids such predicament encounter when using the ranking or sorting method.
Intuitively, this model provides straight forward selection of job seekers for the recruiting firm unless the decision makers intend to influence or alter the selection process. Generally, the ranking procedure may not give the specified number of applicants to be selected for the new task even though thorough job analysis is carried out but this technique does directly. Using this technique and the corresponding number of vacancy so determine by the job analyst, the control factor is applied to determine the required number of qualified applicant for job placement which may correspond to the exact vacant position declared by the analyst. Table 2 below; contain the results of successful applicants (1) and unsuccessful applicants (0) with respective control factors. In this case, since the management decision via job analysis is to engage only ten new staff, the control factor that satisfies this condition is 1.0315. Suppose the company is interested in hiring more staff says 12, 15 or more, the control factor that satisfies these conditions are 1.125, 1.25…, respectively. To validate the performance of this model with the ranking procedure, we compare the number of successful applicants reported on both methods.
Open Science Journal of Statistics and Application 2013; 1(1): 6-12
Table 2. Successful (1) and unsuccessful (0) applicant based on the control factor Candidate number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
α =1.0315
α =1.125
α =1.25
1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0
1 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 1 1 0 0
1 1 0 0 0 0 1 1 0 0 1 0 0 0 1 1 1 1 0 0
Candidate number 21 22 23 24 25 26 27 28 29 30
11
α =1.0315
α =1.125
α =1.25
0 1 0 1 1 0 0 0 1 0
0 1 0 1 1 0 0 1 1 0
0 1 0 1 1 0 1 1 1 0
Relying on the result reported in Table 2, we generally agree that the new procedure is capable for use with respect to job placement and more efficient than the ranking technique. This procedure avoids bias decision and gives confidence to both the applicants and the decision makers and straight forward than any existing techniques. In the following table, Q represent qualified, NQ means not qualified, NQ* first reserve and NQ** second reserve.
Table 3. comparison between ranking procedure and profile technique Candidate number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Total 160 150 109 82 117 66 125 132 117 100 131 107 100 119 125 181 143 151 84 121 99 137 108 134 134 120 126 127 133 104
Mean 40.00 37.50 27.25 20.50 29.25 16.50 31.25 33.00 29.25 25.00 32.75 26.75 25.00 29.75 31.25 45.25 35.75 37.75 21.00 30.25 24.75 34.25 27.00 33.50 33.50 30.00 31.50 31.75 33.25 26.00
Rank 2 4 19 26 18 27 13 9 18 23 10 21 23 16 13 1 5 3 25 14 24 6 20 7 7 15 12 11 8 22
Profile Technique 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 1 1 0 0 0 1 0
Table 3 reveals the comparative performance of both techniques. The analysis revealed that the ranking procedure is very flexible and easy to manipulate whereas the profile model is rigid and fulfill the requirement of the job analyst. The simulation performed based on Table 2 gives detail
Decision for rank method Q Q NQ NQ NQ NQ Q Q NQ NQ Q NQ NQ NQ Q Q Q Q NQ NQ* NQ Q NQ Q Q NQ** Q Q Q NQ
Decision for profile technique Q Q NQ NQ NQ NQ NQ Q NQ NQ NQ NQ NQ NQ NQ Q Q Q NQ NQ NQ Q NQ Q Q NQ NQ NQ Q QN
contribution of each profile variables, say Equation (3). This equation allows the decision maker to make decision if tie occur by considering the profile contribution of the applicants. In this case, the contribution of the profile variables, say Equation (3) and the selection model
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F. Z. Okwonu and A. R. Othman: Comparison between Ranking Technique and Profile Model for Job Placement Based on Complete Job Analysis
(Equation (6)) are applied to take final decisions. Suppose that three applicants are tied and have 1,0,1 and 1,1,0 for the profile variables (Equation (3)) and selection/decision process (Equation (6)), based on this model, the most qualified applicant is 1,1 this is so because for the assessment of the profiles it is positive (1) and the selection/decision procedure is equally positive (1), while the second is negative (0) and positive (1) and the third is positive (1) and negative (0). The results reported based on the new model using the control factor revealed that to increase the number of applicants depends strictly on the control factor. In other words, as the control factor increases, the number of applicants to be engaged increases and hence this model is suitable for job analyst.
References
6. Conclusion The new model allows decision makers to take unbiased decisions since the basis of their decision rely strictly on the profile variable contribution of the applicants and the selection model which is automatically activated when the data is supplied. The profile assessment is based on the Mahalanobis distance values and the relations defined. The proposed technique also provides remedies to tie problem encounter when the sorting or ranking approach is used. Since the new technique is a two stage procedure it solves tie problem by using the contribution of the profile variables. If the profile variables assessment is positive and the selection phase is positive that applicant is offered the job. On the other hand, if one of these conditions is not satisfied such applicant is not qualified for the job placement otherwise, the control factor is increased to select well qualified applicant. The issue of over qualified number of applicant was internally addressed by this technique and hence allows the most qualified applicant to be engaged directly. From Table 3, we observed that the ranking procedure did not satisfy the number of vacancy declared by the job analyst. We hope that this unbiased model when applied in practice will assist hiring firms to build a competent and strong workforce and hence high quality service delivery. The authors are not in haste to summarize that both techniques are competent and applicable for different categories of placement. These procedures can be used to evaluate existing staff performance for promotions.
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