HRMT300 lectures 5 and 6 Recruitment and Selection 2016

Recruitment and

Selection 2

(Usefulness of a selection procedure)

Usefulness of a Selection Procedure/Predictor

•

_{Concerned with the accuracy of prediction.}

•

_{It is about how a predictor is good in enhancing selection}

decisions.

•

_{Thus how well a selection tool predicts a job behavior}

•

_{Some factors used to assess the usefulness of a selection}

technique

•

_{Reliability/Validity}

•

_{Base Rate}

• _{The main reason why a selection procedure is developed is to show}

that that procedure will bring out successful performers on the job.

• _{Selection system/procedures/techniques must be reliable and valid.}

Terms to note

• _{Performance predictors / Predictors(x)- selection techniques, e.g.}

employment tests, interviews, work experience, recommendation letters.

• _{Criterion (y)- standard for assessing /determining successful}

performance on the job

• _{The required standard for accepting that performance is satisfactory}

or not

Reliability and Validity Issues in Selection

• _Reliability

• _{The extent to which the measurement tool produces a consistent score}

or a set of scores.

Illustrations on reliability – Class Activity

• _{Test retest- administration of same test to same sample twice at}

different times

• _{Split half - administration of test to sample once but items are split}

into two halves

• _{Parallel forms – administration of two different equivalent tests to}

same sample

• _{There are several computer softwares that can be used for this}

Reliability and Validity Issues in

Selection

Reliability and Validity Issues in Selection

Validation(Cont.)

Validity

Tells whether the measure is right for the purpose.

Measurement tools or selection procedures must be valid.

Validation is the process of establishing validity of the selection techniques/procedures.

Four Ways of assessing validity

•

_{Construct validity}

•

_{Content validity}

•

_{Criterion validity}

Reliability and Validity Issues in

Selection Validation(Cont.)

Construct validity:

• _{Constructs are inferences about psychological phenomena that}

cannot be directly measured so they have to be translated into attitude or behavior in operational terms that can be measured.

• _{The test of construct validity is to determine whether the indirect}

Reliability and Validity Issues in

Selection Validation(Cont.)

Content validity:

•

_{The selection procedure should cover all the indicators of}

Reliability and Validity Issues in

Selection Validation (Cont.)

Face validity

•

_{Whether a measure appears relevant to the test domain.}

Some tests are more obviously job relevant (eg., work

samples involving concrete tasks) and others are not (eg.,

personality tests/inventories).

•

_{The importance of face validity is mainly for how applicants}

Reliability and Validity Issues in

Selection Validation (Cont.)

Criterion validity:

• _{Pertains to whether the measurement tool or the predictor can}

account for significant and concomitant variation in the criterion.

• _{Extent to which a selection device makes accurate forecast/prediction}

about future job performance.

• _{A predictor has criterion validity if it yields scores that predict job}

success.

• _{Criterion validation is the process of establishing relationship between}

Estimating the Validity of Personnel Selection Procedures (Cont.)

•

_{In establishing the relationship between the predictor (x) and}

the criterion (y), the ‘r’ calculated (Pearson’s moment

correlation coefficient) is the validity coefficient.

•

_{The stronger the magnitude of the ‘r’, the stronger the}

predictor in predicting/forecasting future job performance.

•

_{Two ways of criterion validation}

•

_{Predictive Validation}

Predictive

validity/validation

•

_{Involves testing job applicants and then correlating their test}

scores (x) with their criterion or on-the-job performance

scores (y) later when they are employed, to obtain the validity

coefficient (r).

•

_{I.e. potential selection technique (eg. an aptitude/ cognitive}

ability test) is administered to all job applicants. When the

Predictive validity/validation

(Cont.)

•

_{If a high degree of correlation or relationship exists between}

the scores, i.e. (predictor) and later job performance,

Concurrent

validity/validation

•

_{By contrast, involves testing current job holders and correlating}

their test scores (x) with their performance scores (y) to obtain

the validity coefficient (r).

•

_{I.e. current job holders instead of applicants, are tested and}

Concurrent validity/validation

(Cont.)

•

_{If the predictor scores are highly correlated to the job}

performance, i.e. criteria scores, (obtained from performance

appraisal results) the technique may be useful for selecting

Class Discussion

•

_{Does HR practitioners have to go through all these}

steps before selecting worker?

•

_{To what extent do you agree that if the performance}

Prediction of Criterion Scores from Selection Test Scores

• _{Validity Co-efficient- Correlation between predictor scores and criterion}

scores

The accuracy of the selection methods is obtained by assessing the criterion-related validity of the predictors

The extent to which variations in x scores lead to concomitant variations in y scores determine the criterion related validity

When the correlation between two variables ( x & y) is high, it is possible to predict the score of one (y) given the other (x).

When a selection test score (x) for an applicant is known , the criterion (y), i.e. predicted performance can be obtained.

Prediction of Criterion Scores from

Selection Test Scores(Cont.)

Two ways of predicting criterion scores The graphical approach

Generic regression equations approach

• _{1) Graphical Approach}

• _{The criterion-related validity of a selection procedure is}

normally indicated by the magnitude of the validity coefficient (the correlation between the predictor (X) and criterion (Y)

Hypothetical Predictor and criterion Data on 16 Employees obtained using Concurrent/Predictive Validation in an Organisation

Predictor (X) Criterion (Y)

Graphical Approach

50 80 100 150 200

Prediction of Criterion Scores from

Selection Test Scores(Cont.)

2) Regression Approach A. Single predictor

• _{A Predictor can be used for selection purposes by developing prediction}

or regression equation

• _{The prediction equation is used for selection among future applicants on}

whom we have no job performance data. It enables us to predict applicant’s job performance before selection

To make the predictions, the correlation or validity coefficient is

Simple Regression Approach

Regression (Prediction Line) Y=a +bx

Simple Regression

Approach

Predicting Performance/ Criterion

Score (Example)

• _{Y= a +bx}

• Given that a = 25 (intercept on Y, see graph)

• b = 0.7 (validity coefficient of Predictor X computed from x and y values)

• _{Predictor Equation}

• Y = 25 + 0.7X (Substituting a and b values)

• _{Assuming a candidate scored 100 marks on a test (predictor X)} • Then the predicted criterion score, Y?

• Y = 25 + 0.7x100

• = 25 + 70 = 95

Limitations in the use of single predictor

• _{In predicting job success the greater the absolute value of r, (validity}

coefficient) the better the prediction of a criterion performance ,given a knowledge of predictor performance.

• _{The square of r (r²) gives variance in the criterion accounted for a given}

predictor.

• _{Assuming a predictor (selection test) – criterion correlation i.e., the validity}

coefficient r of a single predictor is 0.70, r² = .49 That is, 49% of the variance in the criterion may be determined /explained by the predictor (test).

• _{The statistic r² =coefficient of determination. The remaining 51% could be}

due to other factors (predictors) ; the need for the use of multiple predictors, e.g., interviews, biographical data ,recommendation letters, etc. in the

B. Multiple predictors- Multiple Regression Approach

• _{If we generate two or more sets of scores based on two or more}

predictors (for example, selection test, interview and recommendation/ reference letter), we can derive a ‘multiple’ regression equation

• _{An example of a multiple regression equation:} • _{Y = a + b1X1 + b2X2 + b3X3 + … bnXn}

Where

• Y = the predicted criterion or job performance.

• _{a = the constant, “Y–intercept”.}

• _{b1, b2 and b3 = the constant regression coefficients (validity coefficients)}

Multiple predictors- Multiple

Regression Approach

• _{This approach is a compensatory process/model.}

• _{designed to recognize that applicants’ limitations on some}

qualification can be counter – balanced by strengths in others.

• _{In addition to minimizing errors in prediction, the model combines}

Multiple Cut – off Approach / Multiple

Hurdles Process

• _{assumes that an applicant’s strengths and weaknesses do not}

balance each other

• _{Selection is then made from the group of applicants who meet}

Multiple Cut – off Approach /

Multiple Hurdles Process (Cont.)

Appropriate when :

• _{Stringent procedures are required}

• _{You have a large applicant with low selection ratio}

• _{Compensation is claimed inappropriate, for example, visual acuity,}

auditory acuity, manual dexterity, high degree of eye – hand co-ordination are needed for aircraft pilot success.

• _{It is essential that every single hurdle must be job – oriented and free from}

Hybrid Approach / Process

• _{In this process, multiple hurdles and compensatory logic are both}

used.

• _{For most jobs, certain minimum qualifications are required for}

successful performance, e.g. college degree, typing speed, two year experience etc.

• _{Usually the hybrid process begins with hurdles to screen out those}

Usefulness Utility of a Selection

Procedure

(Cont.)

Validity

• _{This is the relationship between the selection and procedure}

(predictor) and the job behavior (criterion).

• _{The correlation between the predictor and the criterion}

variable- the validity coefficient (r) in selection design computed using Pearson r.

• _{The stronger or higher the validity coefficient of the predictor}

100

75

50

25

50 80 100 150 200

low high Selection Procedure

Usefulness Utility of a Selection Procedure

Base Rate

• _{Base Rate (BR)}

• -a measure of the quality of the applicant pool. It is the proportion of applicants who will be satisfactory performers on the job. (See figure)

BR = No. of applicants who will be satisfactory Performers on the job/ Total no. of applicants

= A + D

A + B + C + D

• _{Significance of the Base Rate}

If all the applicants are satisfactory, (base rate = 1) then there is no need for a selection technique no matter how valid. Anyone that the recruiters turn up will be satisfactory.

Usefulness Utility of a Selection Procedure

(Selection Ratios)

• _{Selection Ratio (SR)}

• The proportion of applicants hired/selected for a job. The ratio of the number of available job openings /vacancies/ number of applicants offered jobs to the total of available applicants. (See figure )

• _{SR = Number of applicants offered jobs}

Total number of applicants

• = A+B A+B+C+D

• _{A very high selection ratio means that more of the applicants are required to fill job}

vacancies. As the selection ratio increases, the selection procedure has less

Correct Selection

Decisions and Errors

• Correct Selection Decisions and Errors

D = False negatives (error)

Rejection of persons who turn out to be successful

B = False positives (error)

Selection of persons who turn out to be unsuccessful

C = True negatives (correct decision) Rejecting poor performers

Usefulness /Utility of a Selection Procedure

(Cont.)

• _{There are two objectives of the selection procedure}

• _{To increase the proportions of correct decisions (A and C)}

• _{To minimize the chance of making the two types of errors (D}

and B)

• _{Main concern is to avoid the last error B}

• _{The cost of hiring a poor performer would have enormous}

consequences to the organization

• _{Designers of the selection procedure attempt to minimize the}