This is a comprehensive and detailed summary on Chapter 10; Intelligence for the book Psychology by Saundra K. Ciccarelli.
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Intelligence:the ability to acquire knowledge, to think and reason effectively, and to deal
adaptively with the environment.
INTELLIGENCE IN HISTORICAL PERSPECTIVE
2 scientists played seminal roles in study and measurement of mental skills
Contributions of Sir Francis Galton and Alfred Binet set stage for later attempts to
measure intelligence and discover its causes
Sir Francis Galton: Quantifying Mental Ability
Cousin of Charles Darwin strongly influenced by Theory of Evolution
Book, Hereditary Genius, Galton showed through study of family trees that eminence and
genius seemed to occur within certain families
o Eminent people had ‘inherited mental constitutions’ that made them more fit for
thinking that their less successful counterparts
o Dismissed fact that the more successful people he studied almost invariably came
from privileged environments
Attempted to demonstrate biological basis for eminence by showing that people who were
more socially and occupationally successful would also perform better on variety of
laboratory tasks thought to measure ‘efficiency on the nervous system.’
Developed measure on reaction speed, hand strength and sensory acuity
o Measured size of people’s skulls, believing that skull size reflected brain volume
intelligence
Galton’s approach to mental skills disfavoured because measures of nervous-system
efficiency proved unrelated to socially relevant measures of mental ability, such as
academic and occupational success
Alfred Binet’s Mental Tests
Modern intelligence-testing movement began in 20th century
o Started by Binet as he was interested in solving practical problem rather than
supporting a theory
o Noticed that certain children were unable to benefit from normal public school –
educators wanted an objective way to identify these children as early as possible
so that some form of special education could be arranged
Binet made 2 assumptions about intelligence:
, 1. Mental abilities develop with age
2. Rate at which people gain mental competence (e.g. a child that is less competent at
age 5 should also be lagging at age 10)
Binet tests to measure mental skills:
1. Asked experiences teachers what sorts of problems children could solve at age 3,4,5,
etc.
2. Used their answers to develop standardized interview in which adult examiner posed
series of questions to child to determine whether child was performing at correct
mental level for his/her age
3. Result of testing was score called, mental age
o Example: if an 8-year old could solve problems at level of average 10-year old,
the child would be said to have mental age of 10
Concept of mental age further developed by William Stern to provide relative score
(common yardstick of intellectual attainment) for people of different chronological ages
o Developed intelligence quotient (IQ): ratio of mental age to chronological age,
multiplied by 100 IQ = (mental age/chronological age) x 100
o Example: child with mental age of 10 and a chronological age of 8 would have an
IQ of (10/8 x 100 = 125) BUT a 16-year old with mental age of 20 would also
have 1Q of 125, so 2 would be comparable in intelligence even though ages differ
Flawed as many of intelligence tests are acquired by age of 16 through normal life
experiences and schooling, so Stern’s quotient is less useful for adults
Moreover, some intellectual tests show decline at advanced ages
o Example: a 20-year old, who performed at typical age of an 80-year old, would
have an IQ of 400
Today’s intelligence tests provide an ‘IQ’ score that is not quotient but rather based on
person’s performance relative to scores of other people the same age (with score of 100
corresponding to average performance of that age group)
Refer to table 10.1 (pg. 368)
Binet’s Legacy: An Intelligence-Testing Industry Emerges
Lewis Truman (prof. at Standford University) revised Binet’s work became known as
Standford-Binettest
o Contains mainly verbal items, and it yielded single IQ score
One of Truman’s students then developed 2 tests:
o Army Alpha: verbally oriented test that was used to screen large numbers of US
army recruits for intellectual fitness
, o Army Beta: non-verbal instrument using maze, picture completion problems and
digit-symbol tasks
o Tests measured intelligence of large numbers of people in group setting
o Resulted in educators wanting tests that measured groups of school children
Lorge-Thorndike Test and Otis-Lennon School Ability Test
2-decades after Standford-Binet test, David Wechsler developed own theory
o Believed Standford-Binet relied to much on verbal skills
o Believed intelligence should be measured as group of distinct but related verbal
and non-verbal abilities
o Developed Wechsler Adult Intelligence Scale (WAIS), children’s version of scale
(WISC), Wechsler Pre-School and Primary Scale of Intelligence (WPPSI)
o Today, most popular scales are WAIS-III and WISC-IV
NATURE OF INTELLIGENCE
2 approaches to study of intelligence
o Psychometric approach: maps structure of intellect and discovers kind of mental
competencies that underlie test performance
o Cognitive process approach: studies specific though processes that underlies
those mental competencies
Psychometric Approach: Structure of Intellect
Identifies and measures the abilities that underlie individual differences in performance;
tries to provide measurement-based map of mind
Factor Analysis
Researches administer diverse measures of mental abilities and then correlate them with
one another – if certain tests are correlated highly with one another, then performance on
tests probably reflects same underlying mental skills
If tests within cluster correlate highly with one another but much less with tests in other
clusters, then these various test clusters probably reflect different mental abilities
When large numbers of tests are correlated with one another, many correlation
coefficients result, and it is difficult to determine by visual examination the actual
patterning of test scores factor analysis is used
Factor analysis: reduces large number of measures to smaller number of clusters, or
factors, with each cluster containing variables that correlate highly with one another but
less highly with variables in other clusters
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