General Mental Ability aka (GMA) aka (g factor) aka (g) John Breidert & James Hellrung

General Mental Tests One Concept With Many Parts Test the “g factor” 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 Building example: Many rooms in Tate Page, some take up larger parts of the building than others but all of them together make up the building Likewise Many objects have measurements of length width and height, but all of the measurements determine the size of the object

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 Key aspects of g are the ability to determine the relationship between two or more ideas and to find a second idea associated with the previous one

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 Verbal educational – creative, verbal fluency, and numerical factors Spatial Mechanical- psychomotor, mechanical information

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 65 Narrow: Level factors: individual’s level of mastery along a difficulty scale Speed factors: Speed in performing tasks or in learning material Rate factors: amount of info an individual learns in a set amount of time 8 Broad factors: 1 Fluid intelligence: basic process of reasoning that depends minimally on learning 2 Crystallized intelligence: mental processes that reflect not only fluid but also effects of experience, and learning 3 General Memory and Learning: Call for learning and memory of new content or responses Broad Visual perception: require perception or discrimination of visual forms Broad Auditory Perception: discrimination between auditory patterns of speech (musical ear) Broad Retrieval Ability: Tasks or performances that require retrieval from long term memory (multiplication tables) Broad cognitive speediness: rapid cognitive processing of information Processing speed: reaction time or decision speed

Carroll’s Three-Stratum Factor Analytic Theory of Cognitive Abilities Level factors: ( Lightface ) individual’s level of mastery along a difficulty scale Speed factors: ( Bold ) Speed in performing tasks or in learning material Rate factors: ( Bold Italic type ) amount of info an individual learns in a set amount of time

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. 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 Cross Sectional – Military Data using the Army General Classification Test showed that Mean GMA scores clearly increase with occupational level. It was also seen that standard deviations and score ranges decrease with increasing occupational level. I think this is due to a wider range of GMA qualified and able to do simpler jobs than the higher and smaller range of GMA qualified and able to do more complex jobs. Longitudinal studies – in a sample of almost 4,000, Wilk et al. found that people with higher GMA scores moved up over a five year period in the job hierarchy, and that people with lower GMA scores moved down. Wilk and Sackett found that job mobility was predicted by the congruence between individuals’ GMA scores and the objectively measured complexity of their jobs. If GMA is higher than the complexity level of their job, they are likely to move up. If the complexity level of the job is higher than their GMA, then they are likely to move down to a less complex job. Occupations within job levels more related to interests.

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. Wonderlic Personnel Test is most widely used today, and takes ten minutes to complete 50 free-response items. Statistical and measurement artifacts such as sampling error variance, measurement error in job performance measures, restriction in range on GMA scores, and other artifacts. They reduced statistical power and biased validity estimates downward.

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) Complex jobs include professional, scientific, and upper management jobs Lowest complexity jobs include feeding and off-bearing jobs Even .23 is predictive of performance

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. 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 Examples of specific aptitudes: verbal aptitude, spatial aptitude, numerical aptitude Causal analysis modeling - Four large samples of military personnel. Pitted specific aptitude theory against GMA in prediction of performance. All four samples, models with causal arrows from specific aptitudes to training performance failed to fit the data. In all samples, a hierarchical model showing a single causal path from GMA to performance (.62), and no paths from specific aptitudes to performance, fit the data well. (show figure 1)

Other Traits and Variables Affecting Job Performance Personality Traits Predicted occupational level and income (Judge et al., 1999) 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: Conscientiousness is only personality trait contributing to career success Career success is combined variable of occupational level and income From the equation, it shows that prediction comes almost entirely from GMA and Conscientiousness, with GMA being 59% more important that conscientiousness. Best meta-analytic estimate for validity of conscientiousness for predicting job performance is .31 (Mount & Barrick, 1995). GMA is 60-80% larger in validity. But it is contended that conscientiousness contributes to validity over and above GMA because the two are not correlated.

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 Specific aptitudes may have less unreliability and range restriction because of more select samples than used in GMA studies

General Reactions to GMA Even students who are not aware of group differences have 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?) The relation between GMA assessment and social movements such as eugenics--the self-direction of human evolution—has stigmatized the societal view of GMA.

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 If changes in responding to the new medium is unrelated to the criterion of interest (GMA), then changing the medium may be good. Many of the most recent attempts to reduce group differences by changes to GMA test format have been due to changes in the level of measurement error in GMA assessments.

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?