John Breidert
&
James Hellrung
GMA Haiku
General Mental Tests
One Concept With Many Parts
Test the “g factor”
Overview
 Introduction into GMA and Supporting Theories
 GMA on the job and in tests
Introduction into GMA and
Supporting Theories
 Introduction to General Mental Ability
 Spearman’s Two-factor Theory of Intelligence
 Vernon’s Hierarchical Theory of Intelligence
 Carroll’s Three-Stratum Factor Analytic Theory of
Cognitive Abilities
Introduction to General Mental
Ability
 General Mental Ability is the sum of many parts of
intelligence
 Building Example
Spearman’s Two-factor Theory of
Intelligence
 Spearman (1863- 1945) Proposed the theory in 1927
 General Factor (g) in addition to one or more specific
factors accounted for people’s performance on
intelligence tests
 Spearman saw the g factor as a mental energy that was
expended on different mental tasks
 Spearman saw the g factor as more of the inventive
aspect of mental ability
Spearman’s Two-factor Theory of
Intelligence
Vernon’s Hierarchical Theory of
Intelligence
 Philip E. Vernon (1950)
 Hierarchical theory of intelligence
 g at highest level, must consider it in order to
understand or measure intelligence
 At next level are the major group factors:
 Verbal-Educational
 Spatial-Mechanical
Vernon’s Hierarchical Theory of
Intelligence
 Next level is minor group factors:
 Lowest level contains specialized factors that are
unique to specific tests
 Therefore, the lower on the hierarchy, the most
specific the behavior
 Vernon’s theory is supported by numerous studies
finding positive intercorrelations among different tests
Vernon’s Hierarchical Theory of
Intelligence
Carroll’s Three-Stratum Factor
Analytic Theory of Cognitive
Abilities
 John B. Carroll (1993) proposed a three stratum factor
analytic theory of cognitive abilities
 There are many distinct differences in cognitive ability
Carroll’s Three-Stratum Factor
Analytic Theory of Cognitive
Abilities
 Narrow (stratum 1)
 65 narrow abilities
 Level factors
 Speed factors
 Rate factors
 Broad (stratum 2)
 8 broad factors
 General (stratum 3)
 Consists of only g
Carroll’s Three-Stratum Factor Analytic
Theory of Cognitive Abilities
GMA on the Job and in Tests
 GMA and Occupational Level
 GMA and Job Performance
 GMA and Training Performance
 Other Traits and Variables Affecting Job Performance
 Group Differences for GMA
 General Reactions to GMA
 New Methods of Testing GMA
GMA and Occupational Level
 Cross-sectional & Longitudinal Studies relate GMA to
occupational level
 Cross-sectional Studies – mean GMA increases with
occupational level
 Longitudinal Studies – GMA measured earlier in life
predicts later occupational level.
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Job mobility predicted by congruence between peoples’ GMA
scores and complexity of their job
Childhood GMA predicts adult occupation level (r = .51) and
income (r = .53)
 GMA predicts attained job level, but not which
occupation within that level
GMA and Job Performance
 GMA used for predicting Job Performance since WWI
 Situational Specificity theory says GMA predicts job
performance sporadically
 Validity coefficients varied across studies
 Some statistically significant, some not
 Truth – variability in validity findings due to statistical
and measurement artifacts.
 After correcting for effects of artifacts, there was little
variability in validity, and GMA measures were
predictive of job performance for all jobs.
GMA and Job Performance
 Validity ranges
 .58 for most complex jobs
 .23 for least complex jobs
 Validity for job performance shown in many studies:
 Clerical jobs - .52 (Pearlman, Schmidt, & Hunter, 1980)
 Law Enforcement - .38 ( Hirsh, Northrup, & Schmidt (1986)
 Military “Core Technical Proficiency” - .63 (McHenry et al.,
1990)
 Military “General Soldiering Proficiency” - .65 (McHenry et
al., 1990)
 Air Force jobs – mean of .45 (Ree, Earles & Teachout, 1994)
GMA and Training Performance
 Validity for training performance also:
 Meta-analysis of 90 studies - > .50 (Hunter & Hunter, 1984)
 Military meta-analysis of over 82,000 trainees - > .63 (Hunter,
1986)
 Air Force meta-analysis of over 77,958 trainees - > .60 (Ree &
Earles, 1991)
 Clerical workers – mean of .71 (Pearlman et al., 1980)
 Law enforcement – mean value of .76 (Hirsh et al., 1986)
 Across meta analyses, unweighted average validity:
 .55 for job performance
 .63 for training performance
Other Traits and Variables Affecting
Job Performance
 Specific Aptitudes
 Cognitive abilities narrower than GMA
 Regression equations optimize prediction of job and training
performance
 Disconfirmed - Causal analysis modeling failed to fit the data, but a
hierarchical model fit well (Hunter, 1983b)
 Use of specific aptitudes may reduce group differences
 Job Experience
 More job experience, not GMA should predict job performance
 As experience increases, predictive validity of GMA does not
decrease.
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Actually goes from .36 for 0-6 years to .44 for 6-12 years, up to .59 for
more than 12 years.
If anything, as experience increases, so does validity of GMA
Other Traits and Variables Affecting
Job Performance
 Personality Traits
 Predicted occupational level and income (Judge et al., 1999)
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Conscientiousness : .49 and .41
Openness to experience: .32 and .26
Neuroticism: -.26 and -.34
GMA: .51 and .53
 When placed career success in regression equation:
 Multiple r = .63
 Neuroticism: β = -.05
 Openness: β = -.03
 Conscientiousness: β = .27
 GMA: β = .43
 When only Conscientiousness and GMA in equation:
 Multiple r = .63
 Conscientiousness is only personality trait contributing to career
success
Group Differences for GMA
 Specific aptitudes have smaller group differences
 May be due to unreliability and range restriction
 However GMA tests are more reliable than other predictors
 GMA produces racial differences
 3-5 times more difference than produced by interviews, biodata,
and work sample tests.

Could be due to measurement error in the above
 Four-fifths rule
 Infers adverse impact when selection rate for the low-scoring group
< 4/5 the selection rate for the high-scoring group
 Because job complexity increases the likelihood of adverse impact,
Viswesvaran & Ones (2002) suggest a sliding adverse impact rule
(e.g., .50 for complex jobs and .80 for simple ones)
 GMA is a best predictor of job performance, but also predictor with
most adverse impact
General Reactions to GMA
 Even students who are not aware of group differences have
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negative reactions
In homogenous societies, there are also negative reactions
to GMA (Viswesvaran & Ones, 2002)
Past abuses of testing for GMA still haunt us
Research on applicant reactions to GMA needs to continue,
but still at its infancy
Laypeople maybe convince that cognitive ability is not
important in determining intelligent behavior.
Although research suggests validity of GMA increases with
increased job complexity, organizations are less likely to
use GMA for high-level jobs than lower-level jobs. (Face
validity?)
New Methods of Testing GMA
 Low cost of paper & pencil
 Killed demand for other testing media
 To reduce group differences
 One strategy is to change test medium

Computerized and video-based assessments
 Must be careful not to change construct being measured

Format changes may induce differences in GMA and
individual differences in responding to the new medium
New Methods of Testing GMA
 In the future:
 May see tools based on physiological, biological, and
genetic markers identified for GMA

Whether they are accepted depends on societal views on
privacy rights versus organizational needs
 Bottom line – If the use of different mediums reduces
adverse impact without reducing validity for a
criterion, then the new method is preferred
Questions?