ECO 173: Research Paper

“How statistics helps to identify talents in

the recruitment process of AIESEC”

Prepared for:

Dr. Gour Gobinda Goswami

Chairman, Economics Department

North South University, Dhaka

Prepared by:

S. M. Tanveer Saad

ID no. 041 154 030

ECO 173, Section 2

Wednesday, April 18, 2007

Abstract

The following research has been done on candidates who applied for

membership in AIESEC at a certain period of time. Basically recruitment in

AIESEC is quite lengthy process. Firstly, candidate has to fill-up the

application form properly. The application form is a brief description of the

candidate. It reflects their personal information, gender, CGPA, total earned

credits, hours spending internet, affiliations with other clubs, issues interested,

skills gained, expected hours spending per weeks for AIESEC etc. After

screening the forms if the candidate is suitable, he/she gets call for interview.

If the candidate passes the interview, AIESEC selects him/her as a

probationary member for two months. During his/her probationary period, the

applicant has to face several tasks, and complete those tasks within deadline.

After two months the probationary member’s overall performance during

his/her probation period determines he/she will be given membership or not.

What I will try to explain here is that, the overall performance during the

probation period is actually dependent on the quality of the candidate’s

application form.

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Table of Contents

Content Page

o Introduction 4

o Data Source 4

o About the Model 4

o Hypothesis 6

o Assumptions 6

o Limitations 6

o Model Estimations 7

o Interpretations 8

o Model Verification 9

o Analysis of graphs 9

o Model Testing 10

o Conclusion 13

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Introduction:

In this project I will try to explain, the overall performance during the probation period of an

AIESEC applicant is actually dependent on the quality of the candidate’s application form.

This means, how the probationary member will perform in his/her first two month in AIESEC

will depend on his/her filled-out application form which actually means his/her total earned

credits, CGPA, hours spending internet, affiliations with other clubs, skills gained, expected

hours spending per weeks etc.

I will do multiple regression analysis to predict the outcome. Because in statistics, it examines

the relation of a dependent variable (response variable) to specified independent variables

(predictors). My focus is regarding the relationship between applicant’s performance and their

quality. So, through regression analysis I’ll try to explain the relationship.

Data Source:

The entire project will be based on AIESEC’s primary data. That means through the filled-out

application forms (Appendix 1) of 50 probationary members (Appendix 2) who applied in June

2004 recruitment and final evaluation sheets (Appendix 3) after the end of their probation

period provided by the HR Head of AIESEC. Though the data is extremely confidential and

nearly impossible to collect by externals, I didn’t have any problem since I’m the current HR

Head of AIESEC. So, I hope you will also keep it confidential.

About the Model:

As I mentioned earlier in this multiple regression model, I will try to explain the relationship

between AIESEC applicants’ probation-period performance (based on 2 months evaluation)

and their quality (based on their application form filled by themselves). Probation-period

performance depends on the primary task performance evaluation, assessing team works,

and measuring consistency of the candidate. Where as application form’s quality depends on

so many things but just to avoid complications we will only count his/her gender, total earned

credits, CGPA, hours spending internet, affiliations with other clubs, skills gained, expected

hours spending per weeks for AIESEC, and number of issues interested. So, here dependent

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variable is over all performance of probation-period, and independent variables are other

factors that represent the application form. The following is the listing of the dependent and

the explanatory variables along with the regression coefficients.

Y = Overall performance

X1 = Total academic credits earned by the candidate.

X2 = Current CGPA of the candidate.

X3 = Total hours spending on internet per day.

X4 = Number of affiliations with other clubs.

X5 = Candidate’s gender specification.

X6 = Number of skills gained by the candidate.

X7 = Candidate’s expected hours spending per weeks for AIESEC.

X8 = Number of world issues the candidate is interested in.

b1 = Coefficient of academic credits earned by the candidate.

b2 = Coefficient of current CGPA of the candidate.

b3 = Coefficient of total hours spending on internet per day.

b4 = Coefficient of number of affiliations with other clubs.

b5 = Coefficient of the candidate’s gender specification.

b6 = Coefficient of number of skills gained by the candidate.

b7 = Coefficient of the candidate’s expected hours spending per weeks for AIESEC.

b8 = Coefficient of the number of world issues the candidate is interested in.

b0 = Interception of the regression line.

Hypothesis:

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From this existing regression model, I’m expecting to get a positive relationship between the

dependent and the explanatory variables. My assumption is that the quality of application

form has a great positive impact on the overall performance. Therefore, I would state my null

hypothesis for the model that there is a negative relation between application form’s quality

and overall performance during probationary period.

Assumptions:

For this model we are considering the assumptions that are imposed on the random error є in

the classical model:

o All X’s are fixed or if they are random they are uncorrelated with the error term.

o Errors terms are zero expectations; E (єi) = 0 for all observations.

o Errors terms have same variance for all observations. This condition is called

homoscedasticity. E (єi2) = σ2Є for all observations.

o The error terms for any two different observations are not correlated with one another.

E (єi єj) = 0 for i ≠ j.

o For multiple regression models we need another additional assumption. That is, all

the repressors are linearly independent there is no question of multicollinearity.

Limitations:

The major conceptual limitation of all regression methods is that one can only establish

relationships, but never be sure about underlying mechanism. For example, we would find a

strong positive relationship (correlation) between quality of forms and post probation

performance. We cannot conclude this as the ultimate decision as my research has not

looked into other internal or external factors or variables, such as personal IQ, competencies,

adaptations, smartness etc.

Model Estimations:

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————— 15/04/2007 15:38:04 ————————————————————

Welcome to Minitab, press F1 for help.

Regression Analysis: Overall Performa versus Credits Passed, CGPA, ...

The regression equation isOverall Performance = 431 - 2.40 Credits Passed - 30.3 CGPA + 42.8 Hours spending on internet - 38.6 Affiliations with other clubs + 33.7 Gender + 5.90 Number of skills gained + 4.82 Expected hrs spending per week + 21.7 Number of issues interested

Predictor Coef SE Coef T PConstant 430.5 107.3 4.01 0.000Credits Passed -2.3973 0.9031 -2.65 0.011CGPA -30.32 19.54 -1.55 0.128Hours spending on internet 42.770 6.567 6.51 0.000Affiliations with other clubs -38.63 13.81 -2.80 0.008Gender 33.67 18.23 1.85 0.072Number of skills gained 5.904 5.902 1.00 0.323Expected hrs spending per week 4.816 2.842 1.69 0.098Number of issues interested 21.736 7.347 2.96 0.005

S = 57.2981 R-Sq = 89.3% R-Sq(adj) = 87.2%

Analysis of Variance

Source DF SS MS F PRegression 8 1126594 140824 42.89 0.000Residual Error 41 134606 3283Total 49 1261200

Source DF Seq SSCredits Passed 1 411476CGPA 1 10958Hours spending on internet 1 534175Affiliations with other clubs 1 111251Gender 1 11690Number of skills gained 1 7519Expected hrs spending per week 1 10791Number of issues interested 1 28734

Unusual Observations

Credits OverallObs Passed Performance Fit SE Fit Residual St Resid 11 42.0 450.00 557.82 28.06 -107.82 -2.16R 12 36.0 550.00 654.19 30.32 -104.19 -2.14R

R denotes an observation with a large standardized residual.

Interpretations:

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b0 = 431: The intercept gives us an interesting result. It says that when an applicant, no matter

male or female, who applied in AIESEC and selected as a probationary member but did not

mention anything in the application form or put “0” to all answers will end up having 431 points

after two months of probation period. One reason for that, perhaps the candidate is very

potential but did not fill-up the form properly

b1 = -2.40: This indicates, when an applicant passes one more credit, his/her overall

performance during probationary period decreases by -2.40 points while other factors remain

constant. It’s because, when a person’s credit is increasing, he/she is actually taking more

higher level courses. As a result, s/he gets less time to concentrate in AIESEC activities. So,

his/her performance decreases.

b2 = -30.3: It shows, when a candidate’s CGPA increases by one more point, his/her AIESEC

performance decreases by -30.3 points while other factors remain constant. Because, only

then a person’s CGPA increases when he/she gives more time on studies, less time on other

things.

b3 = 42.8: It means, when a person spend one extra hour on internet, his/her overall

performance increases by 42.8 points, while other things remain constant. It’s because,

AIESEC is a global organization, and most of its work is done virtually, over the internet. So, if

a person spends more time on internet, more tasks are completed. So, performance

increases.

b4 = -38.6: So, when an applicant joins one more club, while other things remain constant;

his/her overall performance decreases by -38.6 points. This means, affiliating with more clubs

hampers his/her AIESEC performance, probably because he/she becomes defocused form

his work or he/she gets less time for AIESEC activities.

b5 = 33.7: Dummy variables have been used in this case, where “0” denotes female, and “1”

denotes male. So, if every thing remains constant then male is getting 33.7 points more than

the female after their 2 months probationary period. It’s probably because male can give more

time to AIESEC than that of female or male can finish a hard task more effectively or

efficiently than female.

b6 = 5.90: It signifies, when an applicant adds one more skill, his/her performance rating

increases by 5.90 points after probationary period, while other factors remain constant.

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b7 = 4.82: This specifies, when a candidate expected to spend one more hour in AIESEC,

his/her overall rating increases by 4.82 points, while other things remain unchanged.

b8 = 21.7: It denotes that, when a person is interested about one more world issue, his/her

evaluation marks after probation period increases by 21.7 points, whereas other factors

remain constant.

Model Verification:

The model totally supports my assumptions. Definitely candidate’s application form’s quality

has a strong positive impact on the evaluation marks of the probationary period. It’s because,

5/8 of the explanatory variables have positive relationship with the performance.

Here in this model R2 = 89.3% it means 89.3% of the variation in the overall performance

during probation period is explained by the regression model. Moreover, Adjusted R2 =

87.2% So, the difference between R2 and Adjusted R2 is very low (2.1%), which is a good

sign. Therefore, reviewing the whole thing we can say that the model is good.

Analysis of graphs:

Individual graphs for performance rating with each independent variable are provided in next

page. If we can carefully analysis the graph we will find that, other than the total credit

passed, CGPA, and affiliation with other clubs, all the other graphs show very steep positive

slopes. That means those variables have direct or indirect positive impact on the performance

assessment rating after probation period. That means, If those increases, rating will also

increase.

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Model Testing:

Test of significance for the whole regression (F-test):

H0: β1 = β2 = β3 = β4 = β5 = β6= β7 = β8 = 0

Ha: At least one pair ≠ 0

We reject the null hypothesis at any significant level above the p-value.

Here, p-value is 0.000. As a result, H0 is rejected.

Hypothesis test for coefficients (T-test):

Each of the coefficients are going to be tested to see if they individually have any influence on

the dependent variable, keeping the other variable constant.

Hypothesis test for β0:

H0 : β0 = 0, Ha : β0 ≠ 0

We reject the null hypothesis at any significant level above the p-value.

Here, p-value is 0.000. As a result, H0 is rejected.

Therefore, the rejection of the null hypothesis tells us that the regression coefficient, b0 is

related with the dependent variable Y.

Hypothesis test for β1:

H0 : β1 = 0, Ha : β1 ≠ 0

We will reject the null hypothesis if Calculated t > Critical t (n-k-1, α/2)

Here, Calculated t = 2.65, and Critical t (n-k-1, α/2) = t (41, 0.025) = 2.0

2.65 > 2.0 So, we will reject the null hypothesis. The rejection of the null hypothesis tells us

that the coefficient of academic credits earned by the candidate (b1) is related with the overall

performance rating during probationary period (Y).

Confidence Interval for β1

b1 - Sb1 × t (n-k-1, α/2) < β1 < b1 + Sb1 × t (n-k-1, α/2)

-2.3973 – 0.9031 × 2.0 < β1 < -2.3973 + 0.9031 × 2.0

-6.6008 < β1 < -2.9884

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Therefore, the 95% confidence interval for the expected decrease in overall performance

rating during probation period resulting from an academic credit increase lies in the range -

6.6008 to -2.9884

Hypothesis test for β2:

H0 : β2 = 0, Ha : β2 ≠ 0

We reject the null hypothesis if Calculated t > Critical t (n-k-1, α/2)

Here, Calculated t = 1.55, and Critical t (n-k-1, α/2) = t (41, 0.025) = 2.0

1.55 < 2.0 So, we will not reject the null hypothesis. It means that, the coefficient of current

CGPA of the candidate (b2) is not related with the overall performance rating during

probationary period (Y).

Confidence Interval for β2

b2 - Sb2 × t (n-k-1, α/2) < β2 < b2 + Sb2 × t (n-k-1, α/2)

-30.32 - 19.54 × 2.0 < β2 < -30.32 + 19.54 × 2.0

-99.72 < β2 < -21.56

Therefore, the 95% confidence interval for the expected decrease in overall performance

rating during probation period resulting from CGPA increase lies in the range -99.72 to -21.56

Hypothesis test for β3:

H0 : β3 = 0, Ha : β3 ≠ 0

We reject the null hypothesis if Calculated t > Critical t (n-k-1, α/2)

Here, Calculated t = 6.51, and Critical t (n-k-1, α/2) = t (41, 0.025) = 2.0

6.51 > 2.0 So, we will reject the null hypothesis. The rejection of the null hypothesis tells us

that the coefficient of total hours spending on internet per day (b3) is related with the overall

performance rating during probationary period (Y).

Confidence Interval for β3

b3 - Sb3 × t (n-k-1, α/2) < β3 < b3 + Sb3 × t (n-k-1, α/2)

29.636 < β3 < 55.904

Therefore, the 95% confidence interval for the expected increase in overall performance

rating during probation period resulting from hours spending on internet per day lies in the

range 29.636 to 55.904

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Hypothesis test for β4:

H0 : β4 = 0, Ha : β4 ≠ 0

We will reject the null hypothesis at any significant level above the p-value.

Here, p-value is 0.008 So, 0.05 > 0.008 As a result, H0 is rejected at 5% level of significant.

Therefore, the rejection of the null hypothesis tells us that, the coefficient of number of

affiliations with other clubs (b4) is related with the overall performance rating during

probationary period (Y).

Confidence Interval for β4

b4 - Sb4 × t (n-k-1, α/2) < β4 < b4 + Sb4 × t (n-k-1, α/2)

-66.25 < β4 < -11.01

Therefore, the 95% confidence interval for the expected decrease in overall performance

rating during probation period resulting from affiliations with other clubs lies in the range -

66.25 to -11.01

Hypothesis test for β5:

H0 : β5 = 0, Ha : β5 ≠ 0

We will reject the null hypothesis at any significant level above the p-value.

Here, p-value is 0.072 So, 0.05 < 0.072 Therefore, H0 is not rejected at 5% level of significant.

It implies that, the coefficient of candidate’s gender specification (b5) is not related with the

overall performance rating during probationary period (Y).

Hypothesis test for β6:

H0 : β6 = 0, Ha : β6 ≠ 0

We will reject the null hypothesis at any significant level above the p-value.

Here, p-value is 0.323 So, 0.05 < 0.323 Therefore, H0 is not rejected at 5% level of significant.

It implies that, the coefficient of number of skills gained by the candidate (b6) is not related

with the overall performance rating during probationary period (Y).

Hypothesis test for β7:

H0 : β7 = 0, Ha : β7 ≠ 0

We will reject the null hypothesis at any significant level above the p-value.

Here, p-value is 0.098 So, 0.05 < 0.098 Therefore, H0 is not rejected at 5% level of significant.

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It implies that, the coefficient of the candidate’s expected hours spending per weeks for

AIESEC (b7) is not related with the overall performance rating during probationary period (Y).

Hypothesis test for β8:

H0 : β8 = 0, Ha : β8 ≠ 0

We will reject the null hypothesis if Calculated t > Critical t (n-k-1, α/2)

Here, Calculated t = 2.96, and Critical t (n-k-1, α/2) = t (41, 0.025) = 2.0

2.96 > 2.0 So, we will reject the null hypothesis. The rejection of the null hypothesis tells us

that the coefficient of the number of world issues the candidate is interested in (b8) is related

with the overall performance rating during probationary period (Y).

Confidence Interval for β8

b8 - Sb8 × t (n-k-1, α/2) < β8 < b8 + Sb8 × t (n-k-1, α/2)

7.042 < β8 < 36.43

Therefore, the 95% confidence interval for the expected increase in overall performance

rating during probation period resulting from number of world issues the candidate is

interested in, lies in the range 7.042 to 36.43

Conclusion:

If I can closely look into my paper, I will find the relationship between the candidates’ filled-out

application forms’ quality and their overall performance rating after their probation period.

These types of analysis can greatly help a Human Resources professional to recruit right

types of talents in the organization.

The End

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Appendix 1(Blank Application Form)

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Appendix 2(Probationary Member List)

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Appendix 3(Blank Final Evaluation Sheet)

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Appendix 4(The Research Proposal)

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