Decision Making

HRM115 – Resourcing the Organization

Lecture 11

MNU Business School HRM115 – Resourcing the Organizations 10/22/2020 1

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Learning Objectives
LO1 Assess which criteria to consider when deciding which assessment
method(s) to use.
LO2 Learn about determining assessment scores for each candidate using
single and multiple predictors.
LO3 Learn how to establish hiring standards and cut scores.

LO4 Evaluate various methods of making a final selection choice.

LO5 Understand the roles of various decision makers in the staffing

process.

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 Staffing organizations model
Organization
Mission
Goals and Objectives

Organization Strategy HR & Staffing Strategy

Staffing Policies and Programs

Support Activities Core Staffing Activities
Legal compliance Recruitment: external, internal
Planning Selection: measurement, external, internal
Job analysis Employment: decision making, final match

Staffing System and Retention Management

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The overall staffing organizations model depicts that the organization’s mission, along with
its goals and objectives, drives both organization strategy and HR and staffing strategy,
which interact with each other when they are being formulated. Staffing policies and
programs result from such interaction and serve as an overlay to both support activities and
core staffing activities. Employee retention and staffing system management concerns cut
across these support and core staffing activities. Staffing levels and staffing quality are the
key focal points of staffing strategy, policy, and programs. This lecture focuses on decision
making as one of the core activities of staffing.

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 LO1
Introduction
• Collecting data on applicants does not ultimately lead to a
straightforward conclusion about who should be selected.
• Should interviews take precedence over standardized ability tests?
• Should job experience be the primary focus of selection decisions?
• Will organizations make better choices if experience ratings are
supplemented with data on personality?

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Collecting data on applicants does not ultimately lead to a straightforward conclusion about
who should be selected. Should interviews take precedence over standardized ability tests?
Should job experience be the primary focus of selection decisions, or will organizations
make better choices if experience ratings are supplemented with data on personality? What
role should experience and education have in selection? This information can be used to
make decisions about who will ultimately be hired.

Subjective factors often enter into the decision process. Having methods to resolve any
disputes that arise in the process of evaluating candidates in advance can greatly facilitate
efficient decision making and reduce conflict among members of the hiring committee.

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 LO1
Choice of Assessment Method
• Validity coefficient
• Face validity
• Correlation with other predictors
• Adverse impact
• Utility - hiring success gain

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In our discussions of external and internal selection methods, we listed multiple criteria to
consider when deciding which method(s) to use. Some of these criteria require further
discussion, specifically validity, correlation with other predictors, adverse impact, hiring
success, and economic gain.

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 Validity coefficient
LO1

• Practical significance
• Extent to which predictor adds value to prediction of job success
• Assessed by examining
• Sign
• Magnitude
• Validities above 0.15 are of moderate usefulness
• Validities above 0.30 are of high usefulness

• Statistical significance
• Assessed by probability or p values
• Reasonable level of significance is p < 0.05

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Validity refers to the relationship between predictor and criterion scores. Often this
relationship is assessed using a correlation. The correlation between predictor and criterion
scores is known as a validity coefficient. The usefulness of a predictor is determined on the
basis of the practical significance and statistical significance of its validity coefficient.
Practical significance represents the extent to which the predictor adds value to the
prediction of job success. It is assessed by examining the sign and the magnitude of the
validity coefficient.

The sign of the validity coefficient refers to the direction of the relationship between the
predictor and the criterion. The magnitude of the validity coefficient refers to its size. It can
range from 0 to 1.00, where a coefficient of 0 is least desirable and a coefficient of 1.00 is
most desirable. The closer the validity coefficient is to 1.00, the more useful the predictor.
Validities above .15 are moderately useful, and validities above .30 are highly useful.

Statistical significance, as assessed by probability or p values (see Chapter 7), is another

factor that should be used to interpret the validity coefficient. If a validity coefficient has a
reasonable p value, chances are good that it would yield a similar validity coefficient if the
same predictor was used with different sets of job applicants. A reasonable level of
significance is p < .05.

Caution must be exercised in using statistical significance as a way to gauge the usefulness
of a predictor. Research has clearly shown that nonsignificant validity coefficients may
simply be due to the small samples of employees used to calculate the validity coefficients.

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 LO1
Correlation With Other Predictors
• To add value, a predictor must add to prediction of success
above and beyond forecasting powers of current predictors.
• A predictor is more useful the
• Smaller its correlation with other predictors and

• Higher its correlation with the criterion

• Predictors are likely to be highly correlated with one another

when their content domain is similar

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If a predictor is to be considered useful, it must add value to the prediction of job success,
which is to say that it must add to the prediction of success above and beyond the forecasting
powers of current predictors. In general, a predictor is more useful if it has a smaller
correlation with other predictors and a higher correlation with the criterion. If the
correlations between the new predictor and the existing predictors are higher than the
correlations between the new predictor and the criterion, the new predictor is not adding
much that is new. Predictors are likely to be highly correlated with one another when their
content domain is similar. For example, both biodata and application blanks may focus on
the applicants’ previous training. Instead, it might be more useful to supplement the
application blank with a situational interview or a cognitive ability test.

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 LO1
Adverse Impact
• Role of predictor
• Discriminates between people in terms of the likelihood of their job
success
• When it discriminates by screening out a disproportionate number of
minorities and women, adverse impact will occur

• Issues
• What if one predictor has high validity and high adverse impact?
• And another predictor has low validity and low adverse impact?

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A predictor discriminates between people in terms of the likelihood of their success on the
job. A predictor may also discriminate by screening out a disproportionate number of
minorities and women. To the extent that this happens, the predictor has adverse impact, and
it may result in legal problems. As a result, when the validity of alternative predictors is the
same and one predictor has less adverse impact than the other predictor, the one with less
adverse impact should be used. A very difficult judgment call arises when one predictor has
high validity and high adverse impact while another predictor has low validity and low
adverse impact. From the perspective of accurately predicting job performance, the former
predictor should be used. From an equal employment opportunity and affirmative action
(EEO/AA) standpoint, the latter predictor is preferable.

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 LO1
Utility - Hiring Success Gain
• Taylor-Russell Tables
• Focuses on proportion of new
hires who turn out to be
successful
• Requires information on:
• Selection ratio: Number hired /
number of applicants
• Base rate: proportion of employees
who are successful
• Validity coefficient of current and
“new” predictors

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Hiring success refers to the proportion of new hires who turn out to be successful on the job.
Hiring success gain refers to the increase in the proportion of successful new hires that is
expected to occur as a result of adding a new predictor to the selection system. The selection
ratio is simply the number of people hired divided by the number of applicants (sr = number
hired / number of applicants). When the company has a large number of applicants for an
opening, the selection ratio is low. The base rate is defined as the proportion of current
employees who are successful on some criterion or human resource (HR) outcome (br =
number of successful employees / number of employees).

Taylor-Russell tables combine information on selection ratios, base rates, and predictor
validity coefficients in an easily interpretable format. The cells show the percentages of new
hires who will be successful. The top matrix (A) shows the percentages of successful new
hires when the base rate is low (.30), the validity coefficient is low (.20) or high (.60), and
the selection ratio is low (.10) or high (.70). The bottom matrix (B) shows the percentages of
successful new hires when the base rate is high (.80), the validity coefficient is low (.20) or
high (.60), and the selection ratio is low (.10) or high (.70).

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 LO1
Utility - Economic Gain
• Economic Gain Formula
• Focuses on the monetary impact of using a predictor

• Requires a wide range of information on current employees, validity,

number of applicants, cost of testing, etc.

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Economic gain refers to the bottom-line or monetary impact of a predictor on the

organization. The greater the economic gain the predictor produces, the more useful the
predictor. This final component is crucial for enhancing the strategic relevance of HR
practices, as HR managers are continually urged to put the value of interventions, like
staffing, into terms that are well understood by those in finance, marketing, operations, and
other functional areas.

The utility approach is highly useful for communication purposes because it takes raw
validity and hiring success information and converts it into a dollar metric.

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 LO2
Determining Assessment Scores

• Single predictor • Multiple predictors

• Using one predictor • Use more than one predictor
• Simple • Combine the scores
• Validity and utility issues • Three models;
• compensatory,
• multiple hurdles, and
• combined approaches.

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Once the predictors for final decision making have been selected, it is necessary to determine
assessment scores for each candidate. This process is focused on developing a method to
assign numerical scores to each candidate.

Using a single predictor in selection decisions simplifies the process of determining scores.
In fact, scores on the single predictor are the final assessment scores. Thus, concerns over
how to combine assessment scores are not relevant when a single predictor is used in
selection decisions. In most cases, using two valid selection methods results in more
effective selection decisions than using a sole predictor.

Most organizations use multiple predictors in making selection decisions. With multiple
predictors, decisions must be made about combining the resultant scores. These decisions
can be addressed through compensatory, multiple hurdles, and combined approaches.

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 Compensatory model
LO2

• Scores are added to get a total

score
• A low score can compensate for a
high score
• Procedures;
• Clinical prediction
• Unit weighting
• Rational weighting
• Multiple regression

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When making multiple predictors to make selection decision with a compensatory model,
scores on one predictor are simply added to scores on another predictor to yield a total score.
This means that high scores on one predictor can compensate for low scores on another. The
advantage of a compensatory model is that it recognizes that people have multiple talents
and that many different constellations of talents may produce success on the job. In terms of
using the compensatory model to make decisions, four procedures may be followed: clinical
prediction, unit weighting, rational weighting, and multiple regression.

In the clinical prediction approach as shown here, note that managers use their expert
judgment to arrive at a total score for each applicant. That final score may or may not be a
simple addition of the three predictor scores
Shown. The advantage of the clinical prediction approach is that it draws on the expertise of
managers to weight and combine predictor scores. The problem with this approach is that the
reasons for the weightings are known only to the manager, and therefore decisions can be
less accurate.

With unit weighting, each predictor is weighted the same at a value of 1.00. As shown here,
the predictor scores are simply added together to generate a total score. The advantage of
unit weighting is that it is a simple and straightforward process and makes the importance of
each predictor explicit to decision makers. The problem with this approach is that it assumes
each predictor contributes equally to the prediction of job success, which is often not the
case.

With rational weighting, each predictor receives a differential rather than equal weighting.
Managers and other subject matter experts (SMEs) establish the weights for each predictor

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according to the degree to which each is believed to predict job success. The
advantage of this approach is that it considers the relative importance of each
predictor and makes this assessment explicit. The downside, however, is that it is an
elaborate procedure that requires managers and SMEs to agree on the differential
weights to be applied.

Multiple regression is similar to rational weighting in that the predictors receive

different weights. With multiple regression, however, the weights are established on
the basis of statistical procedures rather than on judgments by managers or other
SMEs. The statistical weights are developed from (1) the correlation of each predictor
with the criterion, and (2) the correlations among the predictors.

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 Selecting the Best Weighting Scheme
LO2

• For clinical weighting:

• Do decision makers have considerable experience and insight into selection
decisions? Is managerial acceptance of the selection process important?

• For unit weighting:

• Is there reason to believe each predictor contributes relatively equally to job
success?

• For rational weighting:

• Are there differences in importance across predictor areas, and can these be
better assessed through judgment rather than through statistical tools?

• For regression weighting:

• Are conditions under which multiple regression is superior satisfied?
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The choice of the best weighting scheme is consequential and likely depends on answers to
the most important questions about clinical, unit, rational, and multiple regression schemes:
answers to these questions will go a long way toward deciding which weighting scheme to
use.

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 Multiple Hurdles Model
LO2

• An applicant must earn a passing score on each predictor

before advancing in the selection process.
Job Offer

Job
Rejected
Interview

Job Knowledge
Test Rejected

Application
Rejected
blank

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With a multiple hurdles approach, an applicant must earn a passing score on each predictor
before advancing in the selection process. Such an approach is taken when each requirement
measured by a predictor is critical to job success. The decision to move a candidate forward
is based on either a top-down selection from early predictor scores (or through the use of cut
scores at some stages) or a top-down selection for later stages.

Many organizations use multiple hurdles selection systems to both reduce the cost of
selecting applicants and make the decision-making process more tractable in the final
selection stage. It would be very inefficient to process all the possible information the
organization might collect on a large number of candidates, so some candidates are screened
out relatively early in the process.

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 Combined Model
LO2

• Combining both compensatory and the multiple hurdles models

Job Offer

Interview
+ Rejected
References

Job Knowledge
Test Rejected

Application
Rejected
blank

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For jobs where some but not all requirements are critical to job success, a combined method
involving both the compensatory and the multiple hurdles models may be used. The process
starts with the multiple hurdles model and ends with the compensatory method.

An example of the combined approach for the position of recruitment manager is shown
here. The selection process starts with two hurdles that applicants must pass in succession:
the application blank and the job knowledge test. Failure to clear either hurdle results in
rejection. Applicants who pass receive an interview and have their references checked.
Information from the interview and the references is combined in a compensatory manner.
Those who pass are offered jobs, and those who do not pass are rejected.

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 LO3
Hiring Standards and Cut Scores
• Issue -- What is a passing score?
• Score may be a
• Single score from a single predictor or
• Total score from multiple predictors

• Description of process
• Cut score - Separates applicants who advance from those who are
rejected

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Once one or more predictors have been chosen for use in a multiple hurdles model, a
decision must be made as to who advances in the selection process. This decision requires
that one or more cut scores be established. A cut score is the score that separates those who
advance in the process (e.g., applicants who become candidates) from those who are
rejected. For example, even when a compensatory model is used, there might be a minimum
standard on one predictor that would completely rule out a candidate no matter how well he
or she performed on other parts of the process.

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 Consequences of Cut Scores
LO3

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Setting a cut score is a very important process, as it has consequences for the organization
and the applicant. The consequences of cut scores are shown here, which contains a
summary of a scatter diagram of predictor and criterion scores.

The consequences of setting the cut score at a particular level are shown in each of the
quadrants. Quadrants A and C represent correct decisions, which have positive consequences
for the organization. Quadrant A applicants are called true positives because they were
assessed as having a high chance of success using the predictor and would have succeeded if
hired. Quadrant C applicants are called true negatives because they were assessed as having
little chance for success and, indeed, would not be successful if hired.

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 Methods to Determine Cut Scores
LO3

• Minimum competency
• Top-down
• Banding

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Three methods may be used to determine cut scores: minimum competency, top-down, and
banding. Using the minimum competency method, the cut score is set on the basis of the
minimum qualifications deemed necessary to perform the job. SMEs usually establish the
minimum competency score. This approach is often needed in situations where the first step
in the hiring process is the demonstration of minimum skill requirements.

Another method of determining the level at which the cut score should be set is the top-down
method. It is to simply examine the distribution of predictor scores for applicants and then
determine which proportion of applicants will be hired. These demands might include the
number of vacancies to be filled and EEO/AA requirements. Once that number has been
determined, applicants are selected from the top based on the order of their scores until the
number desired is reached. The advantage of this approach is that it is easy to administer. It
also minimizes judgment required because the cut score is determined on the basis of the
demand for labour.

Banding refers to the procedure whereby applicants who score within a certain score range or
band are considered to have scored equivalently. In practice, band widths are usually
calculated on the basis of the standard error of measurement. Research suggests that banding
procedures result in substantial decreases in the adverse impact of cognitive ability tests.

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 LO4
Methods of Final Choice
• Random selection
• Each finalist has equal chance of being selected
• Ranking
• Finalists are ordered from most to least desirable based on results of
discretionary assessments
• Grouping
• Finalists are banded together into rank-ordered categories
• Ongoing hiring
• Hiring all acceptable candidates as they become available for open
positions

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The methods of final choice are the mechanisms by which discretionary assessments are
translated into job offer decisions. Methods of final choice include random selection, ranking,
and grouping. With random selection, each finalist has an equal chance of being selected. The
only rationale for selecting a person is the “luck of the draw.” With ranking, finalists are ordered
from the most desirable to the least desirable based on results of discretionary assessments.
With the grouping method, finalists are banded together into rank-ordered categories.

In some organizations, the hiring process is continuous, meaning that there is never a final list
of candidates to be selected. Instead, an organization that has ongoing needs for employees in a
variety of positions might continuously collect résumés from interested parties, and then when
positions open up, call in for interviews everyone who passes the minimum qualifications for
open jobs.

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LO4
Methods of Final Choice

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 LO5
Decision Makers
• Role of human resource professionals
• Determine process used to design and manage selection system
• Contribute to outcomes based on initial assessment methods
• Provide input regarding who receives job offers
• Role of managers
• Determine who is selected for employment
• Provide input regarding process issues
• Role of employees
• Provide input regarding selection procedures
and who gets hired, especially in team approaches
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Who should determine the process to be followed (e.g., establishing cut scores), and who
should determine the outcome (e.g., who gets the job offer)? The answer is that
organizational leaders, HR professionals, and line managers must play a role. Employees
may play certain roles as well.

Selection systems can have a huge impact on organizational capabilities and performance, so
leaders of the organization, including executives and directors, will have some input into
decision making. As a general rule, HR professionals should have a high level of
involvement in the processes used to design and manage the selection system. They should
be consulted in matters such as which predictors to use and how to best use them. In
particular, they need to orchestrate the development of policies and procedures in the staffing
areas covered.

As a general rule, a manager’s primary involvement in staffing is in determining who is

selected for employment. Managers are the SMEs for their business areas, and they are thus
held accountable for the success of the people hired. In team assessment approaches,
coworkers may have a voice in both the staffing process and its outcomes. That is, they may
have ideas about how selection procedures are established, and they may make decisions
about, or provide input into, who gets hired.

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References
Heneman, H., Judge, T. and Kammeyer-Mueller, J. (2019). Staffing
Organizations. 9th ed. New York: McGraw-Hill Education.

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