Gatewood
HUMAN RESOURCE
SELECTION, 8E
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classroom use.
Using Selection Data
In this last section of the text, Part 4, will
help you take advantage of the job-related
predictor information you collect
 We offer you practical advice on how you
can use that information to interpret the
scores you have gathered
 Hopefully, our guidelines and suggestions
will increase your chances of making the
best personnel selection decisions

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Strategies for
Selection Decision Making
Chapter 15
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certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Strategies for
Selection Decision Making

Even when an organization collects reliable
and valid selection procedure data, poor
implementation can undermine the entire
selection system
 Do not combine “gut instincts” with the
selection data
 Keep a record of your decision-making process,
and audit it occasionally
 Which employees performed well?
 Which did not?
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Strategies for
Selection Decision Making (2)

Answering these questions will improve
employment decision making:
 1. For a specific selection situation, what are the
best methods for collecting predictor
information on job applicants?
 2. Because we often collet applicant information
using more than one predictor, how should we
combine scores?
 3. Once we have a total score, how should this
score be sued to make selection decisions?
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Strategies for Selection…
Modes for Collecting Predictor Information

We all make selection decisions in our daily
lives, but we sometimes make errors
 This is measurement error
In personnel selection, decision makers
collect information from job applicants
using predictors that reflect one of two
different measurement philosophies
 Table 15.1 shows two examples of modes
used to collect predictor information

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Table 15.1 – Modes of Collecting
Predictor Information …
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Strategies for Selection…
Modes for Combining Predictor Information
Once data is collected, the information
must be combined to reach a selection
decision
 Table 15.2 shows two modes (judgmental
and mechanical) for combining predictor
information

 When predictor data are combined with gut
instincts, predictor data have been combined
judgmentally
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Table 15.2 – Modes of Combining
Predictor Information …
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Strategies for Selection…
Methods for Collecting & Combining…

We describe 6 methods for collecting and
combining predictor information
 These describe how selectin decision makers
collect & combine information mechanically,
judgmentally, or both
 Table 15.3 lists these methods and provides an
example of each
 Some methods are more effective than others
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Table 15.3 – Methods of Collecting &
Combining Predictor Information …
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Strategies for Selection…
Which Method is Best?

Meehl’s work concluded that clinical
experts’ intuitive predictions of human
behavior were significantly less accurate
than predictions made using more formal,
mechanical means
 A later review found that pure statistical and
mechanical composite methods for collecting
and combining data were always either equal or
superior to all other methods
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Strategies for Selection…
Which Method is Best? (2)

A recent review concluded that the major
issue is how data are combined, rather than
how data are collected
 Conclusion: mechanical methods of combining
predictor data improved the ability to predict
work performance by more than 50%
 The rate of identifying acceptable hires was
reduced by more than 25% when judgmental
data combination methods were used
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Strategies for Selection…
Implications for Selection Decision Makers
1. Be careful about relying too heavily on
resumes and other initial information
 2. Use standardized selection procedures
that are reliable, valid and suitable for the
specific selection purpose
 3. When feasible, use selection procedures
that minimize the role of decision maker
judgment; use structured interviews,
objectively scored inventories, etc.

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Strategies for Selection…
Implications for Selection Decision Makers (2)
4. Avoid using judgment in combining data
collected from two or more selection
procedures used for determining
applicants’ overall scores
 5. In some settings, managers having
position authority might insist on
interjecting their clinical intuition. However,
that would lead to a significant loss of
information and reduced decision quality

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Methods for
Combining Predictor Scores
There are many different ways of
mechanically combining predictor scores
 We describe and give an example of two
methods, each with a long history of use in
human resource selection and other fields:

 (a) multiple regression
 (b) unit weighting

Table 15.4 presents the data for the job of
patient account representative
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Table 15.4 – Patient Account Rep
Selection Procedure & …Score Data
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Methods …Combining …Scores
Method One: Multiple Regression
Multiple regression shows the maximum
linear association between two or more
predictors and a criterion
 Applicant’s scores can be entered into an
equation:

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Methods …Combining …Scores
Method One: Multiple Regression (2)
Because it is possible to compensate fro
low scores on one predictor by high scores
on another, multiple regression may be
referred to as a compensatory method
 Multiple regression makes 2 basic
assumptions:

 (a) predictors are linearly related to the
criterion
 (b) the predictors are additive and can
compensate for one another
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Methods …Combining …Scores
Method One: Multiple Regression (3)

Multiple regression strategy has several
advantages:
 It minimizes errors in prediction and combines
predictors to yield best estimate of applicant’s
future performance
 It is very flexible, and can be modified to handle
nominal data, nonlinear relationships, or both
 Regression equations can be constructed using
the same predictors weighted differently, or
different predictors
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Methods …Combining …Scores
Method One: Multiple Regression (4)

Multiple regression strategy also has
disadvantages:
 Some statistical issues difficult to resolve
 If a small sample size is used to determine regression
weights or predictors are correlated with one
another, weights will not be able from one sample to
the next and standard errors of weights increase;
cross validation essential
 If no preliminary screening, multiple regression
requires assessing all applicants on all predictors,
which can be costly with a large applicant pool
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Methods …Combining …Scores
Method Two: Unit Weighting
 Multiple-regression model called a proper linear
model because predictors are identified to
maximize the relationship with the criterion
 An improper linear model is chosen by some
non-optimal means (based on a theory, job
analysis results, opinions of SMEs, etc.)
 With sample sizes pf up to 100, unit weights
and regression weights perform about equally
 When sample sizes are less than 75, unit
weights perform better
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Methods …Combining …Scores
Method Two: Unit Weighting (2)
How is unit weighting of predictors
calculated?
 See the sample in Table 15.5

 For each applicant, raw math and interview
scores converted into z scores to standardize
them
 One predictor can be weighted more than
other predictors
 Always standardize the predictor scores before
assigning different weights
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Table 15.5 – Combining Predictor Scores
through Multiple Regression & Composite z
Scores
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Strategies for Making Employment
Decisions
Numerous strategies have been reported
on the human resource selection literature
 We will devote our attention to these:

 (a) top-down selection
 (b) cutoff scores
 (c) multiple cutoff scores
 (d) multiple hurdles
 (e) a combination method, and
 (f) banding (including fixed & sliding bands
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Strategies.. Employment Decisions
Strategy One: Top-Down Selection

Applicants’ scores are rank-ordered from
highest to lowest
 Assumes person with higher score will perform
better on job

Beginning with applicant at top of list, with
highest score, job offers extended until all
jobs filled
 Maximum utility is gained from a predictor
when top-down hiring used
 But likely results in adverse impact
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Strategies.. Employment Decisions
Strategy Two: Cutoff Scores

Applicants scoring below the cutoff score
are rejected
 Judgment plays a role in choosing the method
for setting the cutoff score
 Rationale and specific procedures for setting
that score should be carefully documented

Cutoff scores can be set in two ways:
 (a) on how applicants performed on a selection
procedure
 (b) judgments of SMEs re content used to set
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Table 15.6 –The Ebel Method for
Setting Cutoff Scores
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Table 15.7 –The Angoff Method for
Setting Cutoff Scores
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Strategies.. Employment Decisions
Strategy Two: Cutoff Scores (2)
Legal and Psychometric Issues
 The Uniform Guidelines:

 adverse impact should be considered

The Principles for the Validation & Use of
Personnel Selection Procedures:
 if based on valid predictors demonstrating
linearity…cutoff scores may be set as high or
low as needed to meet the firm’s requirements

Table 15.8 contains guidelines
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Table 15.8 –Selected Guidelines for
Using Cutoff Scores in Selection…
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Strategies.. Employment Decisions
Strategy Three: Multiple Cutoff Scores
 Each applicant is assessed on each predictor
 All predictors are scored on a pass-fail basis
 Applicants are rejected if any one of their
predictor scores falls below a minimum cutoff
score

Method assumes:
 (a) A nonlinear relationship exists among the
predictors and the criterion
 (b) predictors are not compensatory
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Strategies.. Employment Decisions
Strategy Three: Multiple Cutoff Scores (2)

Advantages:
 Narrows the applicant pool to a smaller subset
or candidates who are all minimally qualified
 Conceptually simple and easy to explain to
managers

Disadvantages:
 Requires assessing all applicants using all
predictors; if large pool, costly
 Approach identifies only applicants minimally
qualified; no way to rank-order them
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Strategies.. Employment Decisions
Strategy Four: Multiple Hurdles
 Each applicant must meet minimum cutoff or
hurdle for each predictor before going to next
 Failure to pass a cutoff at any stage means
applicant dropped from further consideration

In double-stage strategy, two cutoff scores
are set, C1 and C2 (See Figure 15.1)
 Those whose scores above C2 are accepted
 Those whose scores below C1 are rejected
 Those whose scores between C1 and C2
accepted provisionally
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Figure 15.1 –Double-Stage Multiple
Hurdle Strategy
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Strategies.. Employment Decisions
Strategy Five: Combination Method

A combination of a hybrid of the multiplecutoff and multiple-regression approaches
 Each applicant measured on each predictor
 Any applicant with score below a minimum
cutoff is rejected
 Next, multiple regression is used to calculate
overall scores for all applicants who pass
 Applicants who remain are rank-ordered based
on overall scores calculated by regression
equation
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Strategies.. Employment Decisions
Strategy Five: Combination Method (2)
Method has the advantages of the multiplecutoff strategy, but additionally provides a
way to rank-order acceptable applicants
 Disadvantage is that it is more costly than
the multiple-hurdle approach
 Combination method most appropriate
when multiple cutoffs are reasonable and
predictors compensatory and size of
applicant pool not too large

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Strategies.. Employment Decisions
Strategy Six: Banding

Banding can address adverse impact
 Establish ranges of scores where scores within
that range are treated as the same
 For those scores within a band, other means or
attributes are used to choose among them
 Rationale: scores do not have perfect reliability
 Small differences in scores are due to
measurement error
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Strategies.. Employment Decisions
Strategy Six: Banding (2)

Establishing Bands
 Two methods used to determine whether
scores differ, each requiring calculation:
 (a) standard error of measurement method
 (b) standard error of differences

Fixed and Sliding Bands
 In fixed bands (top-score referenced bands) use
the top applicant score as the starting point
 See Figure 15.2
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Figure 15.2 –Illustration of Fixed &
Sliding Bands for Comparing…Scores
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Strategies.. Employment Decisions
Strategy Six: Banding (3)

Fixed and Sliding Bands (cont.)
 Sliding bands also based on top applicant’s score
 Then the band is recalculated using the next
highest score; sequential

Selecting Within a Band
 Once bands created, other specifications used
to choose people within the same band:
 Interpersonal skills
 Job experience
 Work performance, etc.
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Strategies.. Employment Decisions
Strategy Six: Banding (4)

Advantages of Banding
 Employer has more flexibility in hiring decisions
 Banding helps employer take into account
factors not measured by traditional selection
methods
 The very narrow perspective of maximum
economic gain from than top-down selection
ignore the social and financial impacts of not
having a diverse workforce
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Strategies.. Employment Decisions
Strategy Six: Banding (5)

Concerns Regarding Banding
 Banding makes it possible to choose a larger
number of minorities, but debate re whether
using banding to identify equally qualified
people, then choosing on the basis of minority
status will withstand legal challenge
 But when minority status used with other
specifications when selecting
 Problem if banding cannot be used to reduce
adverse impact, but banding leads to loss of
economic utility
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certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
A Practical Approach
to Making Selection Decisions

When choosing selection procedures, ask:
 (a) should procedure be sequential or not?
 (b) should the decision be compensatory or not?
 (c)should the decision be based on ranking
applicants or on banding?
Assess job and nature of work performance;
what determines success?
 Implement the strategy systematically using a
mechanical strategy

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A Practical Approach
to Making Selection Decisions (2)

Often not possible for small businesses to
build objective strategies
 Must build a systematic way of combining
selection procedure data
 Bootstrapping is based on the assumption that tho
people can make sound judgments, they are not
able to articulate how they did it
 Through regression analysis infer the weights used by
the decision maker to arrive at ranking
 Whatever method you choose, it is critical
procedures be perceived as fair
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Auditing Selection Decisions:
Learning From Your Successes …

The only way to improve your selection
decision making is to learn from what you
have done in the past
 Validation studies identify those factors that will
predict job success and those that do not
 Most managers do not think about their success
and failure rate when it comes to making
selection decisions, but should
 Even a simple box-score tally of success and
failures helps
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Recommendations for Enhancing
Selection Decision Making
 1. Use standardized, reliable, and valid selection
procedures for collection information on job
applicants whenever possible
 2. Encourage decision makers to participate in
the data-collection process, but discourage them
from combining scores with their gut feelings
 3. When combining selection procedure scores,
use a mechanical means for doing so, for
example, multiple regression, unit weighting
 4. Train managers & others making selectin
decisions to make systematic decisions, p
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certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Recommendations for Enhancing
Selection Decision Making
 4. Train managers & others making selectin
decisions to make systematic decisions,
preferably using one of the objective
(mechanical) strategies described
 5. Although difficult, small businesses can specify,
in advance, (a) the weight of standards to be used
in evaluating candidates, (b) the procedures used
for judging whether applicants meet those
standards, (c) the procedures for combining the
standardized weights multiplied by the ratings
given in order to arrive at an overall score
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Recommendations for Enhancing
Selection Decision Making
 6. Decide whether a compensatory method for
selecting job applicants will be used. The relative
value an organization places on minority hiring
versus performance will likely affect the choice of
selection method
 7. For organizations using cutoff scores, the
modified Angoff procedure is acceptable
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certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use.
Recommendations for Enhancing
Selection Decision Making
 8. Assuming a selection procedure predicts work
performance, and that all applicants who are
offered employment accept, maximum work
performance will occur when top-down
selection is used. But, disparate impact against
racial minorities is likely if the procedure is highly
correlated with general mental ability
 9. Banding of selection procedure scores has
been support in the courts. However, using
minority status alone as a basis for selecting
within bands probably not legal
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Recommendations for Enhancing
Selection Decision Making
 10. For jobs in which selectin has taken place,
decide on a standard for defining a successful
hire (ex: on the job 6 months or more), and an
unsuccessful hire (ex: persons’ dollar sales fall
below minimum standard, or supervisor ratings
low). Then have managers keep track of hits and
misses
 11. Periodically audit selectin decisions
throughout the organization to identify areas or
individuals needing improvement
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