Blindness, Deprivation, and IQ: A Meta-Analysis
Rionne van Rijk and Stephanie Kämper
Master’s thesis
University of Amsterdam
Work and Organizational Psychology
Supervisor: Dr. Jan te Nijenhuis
Augustus 2007
Acknowledgement
We would like to thank Dr. Jan te Nijenhuis, Dr. Riet Dekker, Prof. Dr. Paul Nater, and Prof.
Dr. Helga Weinläder for their enthusiastic support and helpful guidance on our project.
Table of contents
SUMMARY ............................................................................................................................................................... 0
INTRODUCTION .................................................................................................................................................... 1
GROUP DIFFERENCES IN MEAN IQ......................................................................................................................... 1
VISUAL IMPAIRMENT: THE BLIND AND THE PARTIALLY SIGHTED ........................................................................ 2
VISUAL IMPAIRMENT NEGATIVELY IMPACTS COGNITIVE DEVELOPMENT ............................................................ 4
VISUAL IMPAIRMENT AS A NATURAL EXPERIMENT ............................................................................................... 5
RESEARCH HYPOTHESES ........................................................................................................................................ 6
METHOD .................................................................................................................................................................. 7
LITERATURE SEARCH ............................................................................................................................................. 7
CRITERIA FOR INCLUSION....................................................................................................................................... 8
DESCRIPTIVE STATISTICS ..................................................................................................................................... 10
ADJUSTMENT OF US DATA SETS FOR THE PERCENTAGE OF NONWHITES ........................................................... 11
ADJUSTMENT FOR THE FLYNN EFFECT ................................................................................................................ 11
PSYCHOMETRIC META-ANALYSIS ........................................................................................................................ 14
RESULTS ................................................................................................................................................................ 14
DISCUSSION.......................................................................................................................................................... 21
CONCLUSION ....................................................................................................................................................... 24
REFERENCES ....................................................................................................................................................... 25
Summary
The genetic basis of individual within-group differences in intelligence among the
majority populations of the industrial nations has been established through the use of twin
studies and adoption studies. The question of the cause of the well documented difference in
mean IQ test scores between ethnic groups, however, remains one of the most hotly debated
issues in behavioral science. A major difficulty in resolving the question is that genetic
differences and environmental (cultural, social, and economic differences) between the groups
are usually confounded.
The present study examines a different group: the blind and partially sighted. One
major non-genetic hypothesis, cultural deprivation, argues that the deleterious effects of a
poor and non-stimulating environment increase over time, leading to the full standard
deviation difference in average test scores between Blacks and Whites in the US.
The present study performed a meta-analysis of studies of the IQ of visually impaired
children and adults. The results of our analysis demonstrated that visual deprivation showed
no effect on the average IQ scores of a severely handicapped group, and therefore it
disconfirmed the cumulative deficit hypothesis. Further disconfirmation of the cumulative
deficit hypothesis comes from the finding that for the blind and partially sighted, any IQ
deficit decreases with age, contrary to the predictions of cumulative deficit theory.
Of course, the environment of visually impaired is not exactly the same as the
environment of Blacks. However, it does not seem unreasonable to assume that the
environment of the visually impaired is much more deprived than the environment of Blacks.
If the severe deprivation caused by blindness does not impact IQ, it seems unlikely that the
arguably lesser deprived environment of Black children is an important source of their lower
average IQ score. Our meta-analytically based study makes environmental causes of group
differences in IQ less plausible and therefore genetic causes less implausible.
Introduction
Children raised in the same family show large individual differences in scores on IQ
tests. As genetic and environmental causes of IQ differences are fully confounded within
families, twin and adoption designs are required to determine their separate effects. There is
consensus among people who have studied the empirical findings that among the majority
population of modern industrial societies, genetic causes are more powerful than
environmental causes in explaining individual differences in IQ. However, there is no such
consensus regarding the cause(s) of between-group differences in intelligence, primarily
because genetic and environmental causes are strongly confounded in the large majority of
studies. Environmental deprivation, for instance, has often been suggested as an important
cause of group differences in mean IQ scores. It takes quasi-experimental designs, such as the
traditional twin and adoption designs to test the deprivation hypothesis. However, the
comparison of IQ data of visually impaired and sighted persons provides a potentially
important additional quasi-experimental design. The present paper, the first to employ this
methodology, provides a strong test of the deprivation hypothesis.
The IQ data of samples of visually impaired persons offer a unique insight into the
causes of between-group differences. Due to their lack of vision, visually impaired persons
grow up in a world deprived of visual stimuli. Their environment differs markedly from that
of sighted individuals. Visual impairment provides a natural experiment in deprivation on
which to test the deprivation hypothesis of group differences. If visually impaired persons
score substantially lower on IQ tests than sighted persons it would give strong support to the
environmental deprivation hypothesis. We therefore carried out a meta-analysis of IQ scores
of the visually impaired in order to determine the degree to which environmental deprivation
affected intelligence test scores.
Group Differences in Mean IQ
Finding group differences in mean IQ scores is more the rule than the exception (see
Berry, 1966; Jensen, 1980; Lynn, 1982, 1988, 1997; Lynn & Vanhanen, 2002; Ogbu, 1994;
Reynolds & Murdoch-James, 1994; Shuey, 1966; Wright, Taylor, & Ruggiero, 1996;
1
Zeidner, 1987). The best studied group difference in mean IQ is the 15 IQ point difference
between black people and white people (Jensen, 1998). Because test scores are the best
predictor of economic success in Western society (Schmidt & Hunter, 1998, 2004), these
group differences have important societal implications.
Although there is a consensus among those who have studied the empirical findings
that group differences in IQ scores exist and that they translate into real-world outcomes in
educational and work achievement, there is no consensus regarding their cause. Throughout
the history of psychology, no question has been so persistent, so resistant to resolution, or so
heatedly debated as that of the relative roles of nature and nurture in causing group
differences in cognitive ability. The numerous reviews and analyses include those by Loehlin,
Lindzey, and Spuhler (1975), P.E. Vernon (1979), Herrnstein and Murray (1994), Brody
(1992), the APA Task Force (Neisser et al., 1996), Nisbett (1998), Jensen (1998), and Lynn
(2006). The IQ data of samples of visually impaired persons offer a unique opportunity to test
the deprivation hypothesis of the causes of group differences in mean IQ scores.
Visual Impairment: The Blind and the Partially Sighted
Visual impairment entails a severe limitation of visual capability and includes both
partial sightedness and blindness (Bailey & Hall, 1990). Visual acuity is the main
characteristic used to assess visual impairment. It refers to the measurement of the ability to
discriminate clearly the fine details of objects at a given distance (Lowenfeld, 1973).
Descriptive indices are used such as having 20/70 vision, which means that a person must be
able to see at a distance of 20 feet what a normally sighted person can see at a distance of 70
feet (Bishop, 1996).
Various definitions of blindness can be found in the literature. In the present research
we use the following (see Figure 1): Visual impairment is defined as having less than 20/70
vision and it includes the categories of partially sighted and blind; Partially sighted is defined
as having visual acuity better than 20/200 up to and including 20/70 (Lowenfeld, 1973); Blind
is defined as having less than 20/200 vision in the healthier eye (Bishop, 1996). Individuals
who are blind must rely primarily or exclusively on senses other than sight, such as audition
and touch to acquire information. The category of blind includes persons classified as totally
blind, which ranges from complete lack of vision to having a severely limited residual vision
2
with which they are able to read using special aids. In the present research we focus on the
blind and the partially sighted.
Vision
Sighted
Visually impaired
Blind
Partially sighted
Figure 1
Classification of Degree of Visual Impairment
Visual impairment can be the result of either congenital or adventitious factors.
Congenital visually impaired individuals are diagnosed as blind either at birth or during the
first year of life. Their handicap is due to heredity, prenatal damage, or trauma at birth.
Adventitious visual impairment happens after the age of four years, and such blindness may
have a sudden onset, (as in the case of accidents), or it may develop progressively as a
consequence of a disease (as it does in cases of retinitis pigmentosa; Bishop, 1996). Further,
many blind individuals have additional handicaps such as mental retardation (Dekker, 1987).
However, the present study is restricted to visually impaired individuals with no additional
handicaps.
3
Age at onset does affect functioning and instructional needs of the visually impaired
individual (Sandoval, Frisby, Geisinger, Scheuneman, & Grenier, 1998). Children who lost
their sight before they were five to seven years of age do not retain useful visual or color
imagery, so they rely completely upon their nonvisual senses. However, children who lose
their sight after the age of five may retain visual and color imagery of which they make use in
their learning processes (Lowenfeld, 1973, Sandoval et al., 1998).
Visual Impairment Negatively Impacts Cognitive Development
To what extent does visual impairment affect cognitive development? Vision is the
feedback sense that helps the sighted child accumulate information by which to adapt to the
environment. When vision is reduced or absent nearly all aspects of early development can be
affected (Bishop, 1996). The development of the visually impaired individual from infancy to
adulthood will be described using Piaget’s theory of cognitive development.
Piaget’s sensorimotor period, which extends from birth to two years, is characterized
by the progressive formation of sensory motor schemes. The gross and fine motor skills of
blind children show a slower development compared to those of sighted children. This
produces a reduced ability to manipulate objects, a deficiency of body image, and an inability
of imitation, which results in sitting, crawling, and walking at a later age than sighted
children. Also, visual deprivation retards the capacity for spatial representation (Hatwell,
1985). Moreover, blind children have to rely on ear-hand coordination in contrast to the eyehand coordination of sighted children. The development of the eye-hand coordination of the
sighted starts at five months, whereas the development of the ear-hand coordination of the
blind starts as late as nine months. In sum, the motor development of young blind children is
severely delayed.
Piaget's preoperational period, which generally extends from the age of two to seven
years, is characterized by the development of language and symbolic functions. The
underdevelopment of the sensorimotor skills of blind children, which started in the
sensorimotor period, reduces their capacity to acquire speech and language, which results in a
delay of one year on mastering verbal tasks (Hatwell, 1985). Children who became visually
impaired before five to seven years of age do not retain a valuable imagery of symbols and
therefore are forced to rely completely upon their nonvisual senses (Lowenfeld, 1973;
4
Sandoval et al., 1998). Taken together the factors cause visually impaired children to have an
average delay of three years in school achievement compared to sighted children.
In Piaget’s model the period of concrete operations, which generally extends from the
age of seven to the age of twelve, is characterized by the progressive attainment of the notions
basic to the comprehension of space, time, classification, seriation, and numbers. The final
period in the model, termed formal operations, is characterized by reasoning based on verbal
statements rather than on concrete manipulations (Hatwell, 1985). Delays of up to two and a
half years on tasks of classification-seriation, volume-conservation, and spatial and formal
abstract operational tasks have been reported for blind children (Dekker, 1987, 1990; Hatwell,
1985).
In sum, visually impaired children are severely delayed in their cognitive
development. School achievement is substantially delayed as well. Therefore, loss of vision
produces a severe handicap in the development of visually impaired children.
Visual Impairment as a Natural Experiment
A fundamental problem in the study of individual differences in intelligence is that in
the large majority of studies genetic and environmental causes are strongly confounded. Only
a fraction of studies employ quasi-experimental research designs capable of distinguishing
between the effects of genetic and environmental causes. In the quasi-experimental adoption
design, for example, biological children and adopted children who grow up in the same family
are compared. Unlike siblings, these children share the same home environment, but not the
same genes. The study of visually impaired children should be regarded as a complement to
the adoption design: visually impaired and sighted children growing up in the same family
share the same genes, but not the same environment. Due to visual deprivation, the
environment of visually impaired children is fundamentally different from that of sighted
siblings. Comparing their average test scores offers a new quasi-experimental methodology to
disentangle the influences of nature and nurture. Of course other groups of visually impaired
and sighted can also be compared, as long as they are matched on important background
variables, such as age.
The present study is the first to use this new research design to test a specific
hypothesis of the causes of between-group differences in intelligence, namely the cumulative
5
deficit theory (Jensen, 1974, 1977). This theory suggests that the deleterious effect of general
environmental deprivation on IQ scores accumulate over time. It is assumed that early in life,
individuals in deprived and enriched environments have similar IQs. However, over the
course of time, the cumulative effects of the environmental conditions experienced by
deprived and enriched groups gradually separate the distributions of IQ. In other words, those
in an intellectually enriched environment have a faster, steeper growth curve of cognitive
development relative to those in impoverished environments, which in turn leads to wider
separation between groups as age increases. Simply stated, the effects of active environmental
factors add up over time.
Visual impairment provides a natural experiment in deprivation with which to test the
cumulative deficit hypothesis of group differences. It is obvious and documented that visually
impaired children grow up in a deprived environment and that their blindness is a severe
handicap: both their cognitive development and their scholastic achievement lag far behind
those of sighted children. The cumulative deficit hypothesis predicts that the IQs of visually
impaired people and sighted people, growing up in divergent environments, will gradually
diverge over time. Visually impaired persons scoring substantially lower on IQ tests than
sighted persons would provide strong support for the environmental deprivation hypothesis.
Research Hypotheses
In this paper we test the cumulative deficit theory, which predicts that environmental
deprivation negatively effects intelligence scores. This general theory leads to four specific
research hypotheses to be tested. First, the visually impaired should have substantially lower
mean IQ scores than the sighted. Second, the blind should have substantially lower mean IQ
scores than the partially sighted. Third, the congenitally visually impaired should have
substantially lower mean IQ scores than the adventitiously visually impaired. Fourth, younger
visually impaired children should have substantially higher mean scores than older children
and adults (who have experience more years of deprivation).
6
Method
Literature Search
To identify studies for inclusion in the meta-analysis, eight search strategies were
used.
1) A computer search was conducted in PsycINFO, PiCarta, PubMed, ERIC, Scirus, ABIInform, and Tesionline. A number of keyword combinations were used to conduct the
searches, for example: visually impaired/handicapped, congenital blind*, blind
person*/subject*, legally blind* AND IQ, intelligence, cognitive functioning, IQ tests for the
blind (* is a truncation symbol to represent multiple spellings or endings; AND is a Boolean
operator that combines search terms so that the search result contains all of the terms).
2) A manual article by article search was carried out in a large number of journals, such as
The International Journal for the Education of the Blind, Journal of Visual Impairment and
Blindness, British Journal of Visual Impairment, Education of Visually Handicapped,
Sehgeschädigte, Zeitschrift für das Blinden- und Sehbehindertenbildungswesen, and Research
Bulletin: American Foundation for the Blind, from 1930-2007. Additionally, an electronic
search of the journal Visual Impairment Research was conducted.
3) Manuals of tests for the assessment of visually impaired individuals were checked
searching for mean IQs.
4) Several institutions for the blind, namely Bartimèus and Visio in the Netherlands, and
Center of Learning for Visually Impaired and Blind [BBS–Nürnberg] in Germany were
contacted and asked for studies that reported the mean IQ scores of blind and partially sighted
individuals.
5) Libraries and test libraries of the Universiteit van Amsterdam, Vrije Universiteit
Amsterdam, Universiteit Groningen, Universiteit Nijmegen, Universität Dortmund,
Humboldt-Universität, and Universität Heidelberg were visited in order to search for relevant
articles.
6) Several well-known researchers were contacted in order to obtain additional articles and
supplementary information.
7) The reference lists of all currently included empirical studies were checked to identify
articles of interest.
7
8) A citation search was conducted in which the reference section from previously gathered
articles had been examined to identify any articles that may have potentially been missed by
earlier search methods.
Fourteen books, twelve test manuals, one dissertation, and more than two hundred
articles were collected, yielding 72 data points. Approximately 85% of the data points were a
result of searching manually article by article. A database was developed containing Dutch,
Flemish, German, British, and American studies, which were used for the meta-analyses.
Criteria for Inclusion
Throughout the search for studies, the following criteria were used for selecting
studies in the meta-analysis.
1) Articles had to include samples fitting the definitions of blind or partially sighted.
2) A study had to include a sample consisting of at least four research participants.
3) The study had to report sufficient data for the calculation of at least one mean IQ score.
4) The study had to describe the performance of a sample in quantitative fashion. Studies
merely reporting that intelligence tests were reported, or those reporting statistical output that
could not be converted into IQ scores were excluded from the analysis.
5) The study had to report data for persons with only a single handicap, namely visual
impairment. Visually impaired persons with additional handicaps, known to result in lowered
IQ scores, such as brain damage, were excluded.
6) The various versions of the Wechsler test batteries were the most used test battery in the
present study. The manuals of the WISC (Wechsler, 1948) and the German WISC, the
HAWIK (Hardesty & Priester, 1966) report nationally representative samples, including a
certain percentage of mentally handicapped individuals. We therefore included samples that
contained a percentage of visually impaired persons with IQs of 69 or lower, but groups
consisting of more than five percent of mentally handicapped were excluded.
7) Samples included in the meta-analysis had to be homogeneous with regard to the mean IQ
scores of their countries. Thus samples from Western Europe and the United States were
combined, as they all have IQs close to 100. Samples from countries with mean IQs that differ
substantially from the Western European and United States mean were not included in the
8
meta-analysis. Samples of minority groups from Western Europe and the US that scored
lower on the general factor of mental ability (g) were also excluded from the meta-analysis.
8) Finally, studies had to be published in English, Dutch, or German or offer an abstract in
one of these languages describing major results.
Measures Used in the Meta-analysis
The measures used in this meta-analysis can be divided into broad test batteries, verbal
scales, and performance scales. Test authors are reported where that information was
available; however, authorship of some tests remains unknown, despite thorough searches.
With regard to broad test batteries, various forms of the Wechsler test batteries were used,
such as WPPSI (Wechsler, 1967), WISC (Wechsler, 1948), WISC-R (Wechsler, 1974), WAIS
(Wechsler, 1955), and WAIS-R (Wechsler, 1988), as well as the Dutch WISC, the German
WISC, the Hamburg-Wechsler-Intelligenztest für Kinder [HAWIK] (Hardesty & Priester,
1956), and the British Ability Scale [BAS] (Elliott, 1983). Another broad test battery used in
this study is the Dutch Intelligence Test for Visually Impaired Children [ITVIC] (Dekker,
Drenth, & Zaal, 1989), which, for instance, uses various haptic subtests to measure Broad
Visual Perception.
The most commonly used tests of verbal IQ are the various versions of the Binet tests
(Interim Hayes-Binet (1942) and the Perkins-Binet (1980)) and the verbal scales of the WISC.
Two studies used only the vocabulary subscale of the WISC. The various versions of the
Binet are based on forms L and M of the Stanford-Binet (1937; Goldman, 1970). Less
commonly used tests of verbal IQ are the Intelligenztest für normalsichtige und
sehgeschädigte Kinder und Jugenliche [INS] (Dortmund, 1970), the Slosson Intelligence Test
[SIT] (Slosson, 1963), and the Williams Intelligence Test (Williams, 1956). The INS is based
on the Williams, which in its turn was based on the Stanford-Binet and the WISC (Warren,
1984).
One of the few performance tests used in this meta-analysis is Stanford-Ohwaki-Kohs
Block Design Intelligence Test for the Blind (Suinn & Dauterman, 1966). This tactile version
of the Kohs Block Design Test requires adult subjects to reproduce a stimulus design by
assembling blocks. The stimulus designs are formed by combinations of four differing fabrics:
flannel, silk, flax, and cotton, each with raised projection. Another performance test for blind
adults is the Haptic Intelligence Scale [HIS] (Shurrager & Shurrager, 1964), which is modeled
9
on the Performance Scale of the WAIS. The Blind Learning Aptitude Test [BLAT] (Newland,
1971) is a performance test for blind children. Although it is based on learning potential
theory, the correlation with total score on the Hayes-Binet and the WISC are high.
Descriptive Statistics
Mean IQs were computed for all datasets. First, when a study reported a mean IQ
score it was included as a data point in the meta-analysis. Second, when a study reported two
mean IQ scores, one of them was included as a data point in the meta-analysis. Third, when a
study reported an IQ range for one test, the middle of the range was included as a data point.
Fourth, when a study reported IQ ranges for two tests, the middle of one of the ranges was
included as a data point in the meta-analysis.
In certain datasets the research participants took two separate IQ tests. Our choice of
independent data points leads to the following procedure. First, we choose the sample with the
highest N. Second, when sample sizes were equal we chose the sample with the most recent
version of a traditional intelligence test. Third, when both samples were of equal size and took
the same test we choose the sample which took a whole test battery over the one that took
only one or a few subtests.
Data points were usually based on a test battery or a full scale, most often a verbal
scale. Two studies reported only means on the WISC vocabulary subtest, but were included as
data points in the meta-analysis because this subtest is an excellent measure of general
intelligence.
In the BLAT manual (Newland, 1971) mean scores for various groups of Whites and
Nonwhites are reported. The effect sizes of the White/Nonwhite score differences were
computed by subtracting the Nonwhite mean score from the White mean score and dividing
the result by the combined White and Nonwhite SD. However, these differences cannot be
directly compared to White/Nonwhite differences on, for instance, the Wechsler batteries,
because the BLAT is not a complete test battery. The BLAT total score correlates only .71
with the score on the Verbal scale of the WISC, while the Verbal scale and the full scale score
of the WISC correlate .91 (Wechsler, 1948). In order to put White/Nonwhite differences on
the BLAT on a scale that is comparable to White/Nonwhite group differences on the
10
Wechsler full scale IQ, all effect sizes were multiplied by 1/ (.71 x .91); we assumed there
were on other important differences between BLAT and WISC. Assuming a mean IQ of 100
for the White sample on the BLAT, the White/Nonwhite difference was expressed in IQ
points, and these IQ points were subtracted from the White mean, resulting in an estimate of
the mean IQ of blind Nonwhites in the U.S.
Adjustment of US Data Sets for the Percentage of Nonwhites
The U.S. population consists of a much higher percentage of Blacks and Hispanics than
the European population, especially at the time most data sets for the meta-analysis were
collected, namely the 1960s, the 1970s, and the early 1980s. Because the average score for
U.S. Blacks is one SD lower than that for Whites, and that of U.S. Hispanics is 91, U.S.
samples, which usually contain both Black, Hispanic, and White visually impaired, cannot be
directly compared against the homogeneous European samples from the 1960s, 1970s, and
early 1980s. Estimates of the percentage Nonwhites among the U.S. visually impaired were
based upon data from the BLAT manual, which uses a norm group representative for all blind
American children, yielding 15% Nonwhites. A percentage of 15% Nonwhites scoring 12 IQ
points lower than Whites results in a 1.8 IQ points lower mean score, so we corrected the U.S.
samples by this value.
Only four studies actually reported the percentage White and Nonwhite. They were not
corrected by using the standard value of 1.8 IQ points, but rather by using the percentage
reported in the study.
Adjustment for the Flynn Effect
James Flynn (1984, 1987, 1998) was the first to show that average scores on
intelligence tests have risen substantially and consistently, all over the world. Between 1930
and 1990 the gain on standard broad-spectrum IQ tests, such as the WISC and the WAIS,
averaged three IQ points per decade. For verbal tests, or more precisely, tests with a content
11
that most reflects the traditional classroom subject matter, the gain is two IQ points per
decade, and for non-verbal (Fluid and Visual) tests the gain is four IQ points per decade.
When calculating IQ scores for blind test takers, an adjustment needs to be made for
this increase in mean IQs in economically developed countries since the 1930s. In most of the
studies of blind test takers the raw scores were compared to the scores of norm groups of the
specific test taken, yielding estimated IQ scores for the blind. However, the various samples
of blind test takers show a large variance in the number of years elapsed between the year in
which the specific test was standardized and the year in which a specific test was taken. Tests
were taken a few years up to a few decades after the tests were taken by the persons making
up the standardization sample. For instance, the WISC-R was standardized in 1972, so
samples of blind persons taking the WISC in 1975 show only a modest, three-year Flynn
effect, whereas samples of blind persons taking the WISC in 1994 show a much larger,
twenty-two-year Flynn effect.
We adjusted verbal scales using the value of two IQ points per decade as a standard
and non-verbal or performance scales using the value of four IQ points per decade as a
standard. When studies employed test batteries for which the rate of secular increase in means
is not known, we assumed an increase of three IQ points per decade. When the date at which
standardization was carried out is not given, it was assumed to have taken place two years
before the date of publication. When the collection of the standardization sample took several
years we took the year in the middle. In cases where the subjects of a study took different tests
we chose the most recent test. When the study did not report the test battery taken we chose
the Wechsler batteries’ value of three IQ points per decade based on the fact that the various
versions of the Wechsler test batteries are the most used test battery in the present study.
Table 1 shows the value of the correction per decade, the publication date, and the date the
standardization sample was collected for the manuals of each of the various tests used in the
present study.
12
Table 1
Value of the Flynn Effect Correction per Decade, Publication Date, and the Date of the
Collection of the Standardization Sample for the Manuals of All Tests Employed
correction p.d.
date
publication stand. sample
test
WPPSI US
3
1967
1963-1966
WISC US
3
1948
1946*
2
1948
1946*
3
1974
1971-1973
2
1974
1972
3
1955
1953-1954
2
1955
1953-1954
3
1988
1986*
2
1988
1986*
3
1956
1954*
2
1956
1954*
BAS
3
1983
1981*
ITVIC
3
1989
1987*
Perkins-Binet Flanders
3
Unk.
1973
Interim Hayes-Binet
2
1942
1940*
INS
2
1970
1968*
Williams Test
2
1956
1954*
SIT
2
1971
1969*
HIS
4
1964
1962*
BLAT
4
1971
1969*
Stanford Ohwaki Kohs Block Design
4
1960
1958*
WISC US Verbal
WISC-R US
WISC-R US verbal
WAIS US
WAIS US Verbal
WAIS-R US
WAIS R US Verbal
HAWIK Germany
HAWIK Germany Verbal
Note. * = year estimated. Unk. = unknown
The modest amount of information available on the INS suggests it is a verbal test.
13
Psychometric Meta-analysis
After the studies were collated and their characteristics recorded, we performed a barebones psychometric meta-analysis (Hunter & Schmidt, 2004), using the Schmidt and Le
(2004) software package. A bare-bones psychometric meta-analysis estimates how much of
the observed variance of findings across studies is due to sample size alone.
To test the main hypothesis, we used all 72 data points based on studies that reported a
mean IQ score or an IQ range. Additionally, various study characteristics were coded and
treated as potential moderators of the relation between group membership and mean IQ
scores. We tested the following moderators: age, blind versus partially sighted, and
congenitally versus adventiously visually impaired. Age is hypothesized to play an import
role due to the fact that young visually impaired children fall three years behind in their
development when compared with sighted children. The BLAT manuals report mean IQ
scores for ages six to nineteen. These show an increase of 4 IQ points after the age of twelve.
Therefore, we separated young children up to the age of twelve from older children and
adults. Blind versus partially sighted plays an important role in the actual amount of
deprivation. Congenital versus adventitious blindness plays an important role in the amount of
visual memories, which influences the development of mental concepts and is hypothesized to
lower IQ scores.
Results
Table 2 summarizes the studies used in this research. Data are derived from 72 studies
with a total number of 6,293 participants. The Table gives the reference for the study, the
sample size, and the mean IQ scores of U.S. samples corrected for percentage Nonwhites, the
mean IQ scores of all samples corrected for the Flynn effect, and the mean IQ scores as
reported in the original studies. When the individual studies reported the specific data, the
Table also reports the IQ range, the age range, the IQ test or tests used in the study, the
category of blindness, the origin of the study (Europe or United States), and the cause of the
blindness (congenital or adventitious).
14
Table 2
Dutch, Flemish, English, German , and US studies of Mean IQs of Visually Impaired
Study
N
1) Alford, Moore, &
Simon (1979)
21
2) Baitinger & Bernd
(1970)
135
3) Baker, Koenig, &
Sowell (1995)
30
4) Bauman (1971)
Mean IQ
Corr. For FE
+ % Non-Wh
91.2
Mean IQ
Corr. for
Flynn eff.
Mean
IQ
Range
age
Test
Blindness
category
Additional
information
89.4
90.4
WISC-R
verbal
Visually
impaired
US
102.3
105.1
HAWIK
verbal
Visually
impaired
Germany
92.8
91.0
97.3
WISC-R
Blind
US
5
106
104.2
109
Unknown
Visually
impaired
US
5) Blackhurst, Marks,
& Tisdall (1969)
152
104
102.2
106.4
Unknown
Blind
US
6) Blackhurst & Marks
(1977)
152
101.9
100.1
106.7
Unknown
Visually
impaired
US
7) Blasch (1978)
6
104.4
102.6
109.5*
Unknown
Visually
impaired
US
27
(A=8,
B=19)
8 (A)
93.6
91.8
99.0
Unknown
Visually
impaired
US
8) Bonfati (1979)
93.6
91.8
99.0
Unknown
Visually
impaired
US
Congenital
19 (B)
93.6
91.8
99.0
Unknown
Visually
impaired
US
Adventiously
89.6
100
BLAT
Visually
impaired
UK
Unknown
Blind
US
WISC-R
Visually
impaired
US
10-12
16-20
9) Boyer (1997)
28
10) Brothers (1971)
40
99.0
97.2
102
11) Chin (1988)
16
97.1
95.3
99.5*
12) Cleaves & Royal
(1979)
24 (A=12,
B=12)
99.9
98.1
102.9
WAIS
verbal
Visually
impaired
US
12 (A)
97.8
96.0
100.8
WAIS
verbal
Visually
impaired
US
Congenital
12 (B)
102.0
100.2
105.0
WAIS
verbal
Visually
impaired
US
Adventiously
99.1
103.0
BAS
Visually
impaired
UK
97.0
95.2
103.0
Unknown
Visually
impaired
US
Congenital
89.7
87.9
95.7
Unknown
Visually
impaired
US
13) Corley & Pring
(1996)
11
14) Czerwinski & Tait
(1981)
12
(A=3,
B=4,
C=4)
3 (A)
6-10
7-10
5-8
( continued on next page)
15
Table 2 (continued)
15) Daugherty (1977)
4 (B)
102.5
100.7
108.5
9-12
Unknown
Visually
impaired
Visually
impaired
Visually
impaired
US
4 (C)
97.0
95.2
103.0
13-17
Unknown
29
(A=10,
B=19)
10 (A)
85.9
84.1
89.9
WISC
verbal
83.3
81.5
87.3
WISC
verbal
Blind
US
19 (B)
87.2
85.4
91.2
WISC
verbal
Partially
sighted
US
99.5
92.1
93.7
7-18
WISC-R
verbal
Visually
impaired
US
6-16
ITVIC
Visually
impaired
The
Netherlands
PerkinsBinet
Flaanders
Blind
Belgium
US
US
16) Daugherty &
Moran (1982)
51
17) Dekker (1993)
155
98.8
100.0
18) Devos (1985)
40
101.2
104.2
19
91.8
94.8
13-20
PerkinsBinet
Flaanders
Blind
Belgium
15
88.7
91.7
4-12
PerkinsBinet
Flaanders
Blind
Belgium
93.6
97.8
6.411.8
WISC-R
vocabulary
Visually
impaired
UK
102.5
110.0*
17-42
Unknown
Visually
impaired
US
110.0
111.0
INS
Visually
impaired
Germany
Unknown
Visually
impaired
US
19) Dimcovic & Tobin
(1995)
30
20) Everthart, Luzader,
& Tullos (1980)
8
21) Fischer (1975)
40
22) Franks & Nolan
(1971)
48
96.5
94.7
99.5
23) Gadbaw, Dolan, &
De L’Aune (1977)
297
108.3
106.5
113.1
Unknown
Visually
impaired
US
24) Gore (1969)
16
118.7
116.9
121.1
Unknown
Blind
US
25) Gutterman, Wart,
& Genshaft (1985)
52
83.4
81.6
83.8
WISC-R
verbal
Partially
sighted
US
26) Hammill &
Crandell (1969)
73
89.8
88.0
92.2
WISC
verbal or
SIT
Visually
impaired
US
27) Harley & Rawls
(1970)
39
80.3
78.2
82.7
Unknown
Visually
impaired
US
28) Hasselt, Hersey,
Simon, & Mastanuono
(1983)
4
78.8
77.0
78.8
14-20
WISC-R
verbal
Visually
impaired
US
Congenital
29) Herman, Herman,
& Chatman (1983)
12
86.6
84.8
93.0
12.424.6
HayesBinet
Visually
impaired
US
Congenital
104.3
12-22
6.310.7
( continued on next page)
16
Table 2 (continued)
30) Hill, Spencer, &
Baybutt (1985)
28
85.4
94.4
31) Hopkins &
McGuire (1967)
30
100.8
105.8
32) James & Gill
(1974)
21
96.4
100.0
33) Jones (1983)
56
96.2
98.6
34) Klauer (1962)
62
92.9
94.7
35) Krüger (1974)
53
81.2
86.6
36) Lambert, Griffin,
Pike, & Kurr (1980)
7
94.3
92.5
100.0*
37) MacCluskie,
Tunick, Dial, & Paul
(1998)
60
97.0
95.2
97.2
30
96.9
95.1
30
97.1
93.5
102.6
98.0
5-12
Unknown
Visually
impaired
UK
HayesBinet
Blind
US*
Congenital
7.417.7
Williams
Test
Visually
impaired
UK
16-79
SIT
Visually
impaired
US
HAWIK
Partially
sighted
Germany
HAWIK
Visually
impaired
Germany
Unknown
Visually
impaired
US
18-65
WAIS-R
verbal
Visually
impaired
US
97.1
18-65
WAIS-R
verbal
Visually
impaired
Congenital
95.3
97.3
18-65
WAIS-R
verbal
Visually
impaired
Adventiously
91.7
91.7
6-24
Unknown
Visually
impaired
US
99.5
105.1
6-11.9
Williams
Test
Visually
impaired
UK
Congenital
6-10
SIT
Visually
impaired
US
Congenital
SOKTBD
Blind
US
6.116.1
38) Merry & KieferMerry (1933)
98
39) Millar (1984)
20
40) Miller (1969)
26
96.0
94.2
94.2
41) Mills &
Adamshick (1969)
82
102.0
100.2
103.8
42) Monahan, Giddan,
& Emener (1978)
200
111.6
109.8
114.4
17-19
WAIS
verbal
Visually
impaired
US
43) Mommers & Smits
(1975)
95
99.1
98.3 **
⅔ same
sample
7-13
WISC
verbal
Blind
The
Netherlands
1970
44) Mommers & Smits
(1975)
94
104.8
104.4 **
WISC
verbal
Blind
The
Netherlands
1972
45) Mommers (1977)
106
91.0
97.6
Unknown
Visually
impaired
The Netherland
46) Myers (1978)
36
98.9
100.3
SIT
Visually
impaired
US
47) Nater (1982)
68
101.0
106.2
HAWIK
verbal
Visually
impaired
Germany
100.7
14-22
( continued on next page)
17
Table 2 (continued)
48) Nater (1984)
217
90.7
96.3*
49) Nelson, Dial, &
Joyce (2002)
292
50) Newland (1971)
482
HAWIK
verbal
Visually
impaired
Germany
87.7
85.9
100.0
18-69
Unknown
Visually
impaired
US
89.0
87.2
91.8
7-18
WISC
verbal
Blind
US
158
80.1
78.3
82.9
7-12
WISC
verbal
Blind
US
324
90.7
88.9
93.5
13-18
WISC
verbal
Blind
US
51) Parke, Shallcross,
& Anderson (1980)
21
94.3
92.5
100.0*
5-15
Unknown
Visually
impaired
US
52) Parsons (1987)
17
96.6
90.4*
100*
6-19
Unknown
Visually
impaired
US
53) Pring (1985)
10
104.0
113.0*
Unknown
Blind
UK
Congenital
54) Pring, Dewart, &
Brockbank (1998)
16
108.5
116.9
Williams
Test
Visually
impaired
UK
55) Rath (1967)
46
90.6
92.8
HAWIK
verbal
Visually
impaired
Germany
56) Roeske (1969)
26
(A=10,
B=16)
95.7
93.9
98.1
8-18
Unknown
Blind
US
10 (A)
92.6
90.8
95.0
8-11
Unknown
Blind
US
16 (B)
97.6
95.8
100.0
13-18
Unknown
Blind
US
98.5
96.7
99.4
12-18
WISC-R
N=7;
WISC
N=2;
PerkinsBinet
N=1
Visually
impaired
US
Congenital
105.8
105.8
WISC-R
verbal
Visually
impaired
Germany
12 (A)
108.0
108.0
WISC-R
verbal
Blind
Germany
12 (B)
105.0
105.0
WISC-R
verbal
Visually
impaired
Germany
12 (C)
104.4
104.4
WISC-R
verbal
Partially
sighted
German
98.6
100.4
WISC-R
vocabulary
Visually
impaired
US
57) Sanford (1983)
10
58) Schindele (1974)
36
(A=12,
B=12,
C=12)
59) Schwartz (1983)
28
100.4
9.212.7
( continued on next page)
18
Table 2 (continued)
60) Shurrager &
Shurrager (1964)
994
108.2
106.4
106.4
61) Stephens & Grube
(1982)
29
95.4
93.6
100.4
62) Suinn &
Dauterman (1966)
170
115.8
114
116.4
63) Sykes (1971)
41
101.0
99.2
104.0
64) Teare & Thompson
(1982)
28
88.2
86.4
14 (A)
87
14 (B)
16-64
HIS
Blind
US
WISC
verbal
Blind
US
Congenital
16-55
SOKTBD
Visually
impaired
US
13.320.1
Unknown
Visually
impaired
US
88.8
WISC-R or
WAIS
Visually
impaired
US
85.2
87.6
WISC-R or
WAIS
Blind
US
89.4
87.6
90.0
WISC-R or
WAIS
Partially
sighted
US
65)Thomas (1979)
61
80.1
78.3
85.5*
8-22
Unknown
Visually
impaired
US
66) Tillman & Osborne
(1969)
167
92.1
90.3
96.6
7-11
WISC
Blind
US
67) Tillman (1967)
110
90.0
88.2
92.0
7-13
WISCverbal
Blind
US
68) Tobin (1984)
21
109.1
114.3
9.1-15
BLAT
Visually
impaired
UK
69) Tobin, Clarke,
Lane, & Pittam (1970)
20
100.5
105.0
14-17
Unknown
Blind
UK
70) Vander Kolk
(1977)
597
100.7
98.9
105.5
Unknown
Visually
impaired
US
71) Wilhelm (1989)
139
90.8
89.0
92.0
6-16
WISC-R
verbal
Visually
impaired
US
72) Wormsley (1996)
20
90.0
88.2
100.5
6-12
Unknown
Visually
impaired
US
Note. *= estimated mean IQ **= studies used (partially) the same sample
Table 3 shows the results of the psychometric meta-analysis of the 72 data points, and
subsequent meta-analyses on subsets of data points. It shows (from left to right): the number
of data points (K), total sample size (N), the mean IQ score (mean), the percentage of variance
explained by sample size (% VE), and the 80% Credibility Interval (80% CI).
19
Table 3
Meta-analysis Results for Mean IQs
K
N
Mean
% VE
80% CI
72
6293
98.06
70.54
91.87-104.25
All minus age 4-12
56*
5162
99.39
68.06
92.93-105.87
Age 4-12
17
761
90.16
n.a.
n.a.
Age 13-79
16
2250
102.05
37.71
91.97-112.14
All
Note. n.a. = not applicable: % VE could not be computed
*The study by Czerwinski & Tait (1981) includes two different sets of data with the age range
4-12.
The large number of data points and the large sample size indicate that we can have
confidence in the outcomes of this meta-analysis. The estimated true IQ based on all 72 data
points has a value of 98.06 and 71% of the variance in the observed IQ values is explained by
sample size alone.
Age has a substantial impact on the mean IQ of visually impaired individuals. Table 3
shows that the mean IQ of young visually impaired children is 11.9 IQ points below the mean
scores of older children and adults. This strongly contradicts the cumulative deficit
hypothesis, which predicts exactly the opposite pattern.
The substantial differences between young children and older children and adults
result in some heterogeneity in the data points in the meta-analysis. To get a more precise
estimate of the mean IQ of older children and adults we performed an additional analysis on
the dataset excluding young children. We estimated that the 17 data points (N = 864 or 15%
of the reduced dataset with N = 5162) which contained both young children and older children
and adults contained some 64% young children. Leaving out these low-scoring young
children would increase the mean score of 99.4 with 1.1 IQ point (15% of 64% of 11.9 IQ
points) yielding an adjusted mean of 100.5. Therefore, it appears that the mean IQ score of
visually impaired older children and adults is virtually indistinguishable from the mean IQ of
sighted older children and adults. This is again, contrary to the predictions of the cumulative
deficit hypothesis.
Separate meta-analyses were not carried out for blind versus partially sighted, and
congenital versus adventitious blind, because most studies report only data for groups labeled
visually impaired. There were twenty studies reporting data on groups of blind (total N =
20
2453), but only five small studies on partially sighted (total N = 159). Similarly, there were
only ten small studies of the congenital blind (total N = 203) and only three small studies of
the adventitious blind (total N = 61). We therefore only report means for these groups.
The mean for the blind is 100.5, but partially sighted individuals have a mean IQ score
of 90.1. Congenital and adventitiously visually impaired have mean IQ scores of 97.1 and
97.0, respectively. However, half of the small total sample of 61 of the adventitiously visually
impaired is based on older children and adults, who as a group have a higher score than
younger children. A more detailed analysis showed that three studies reported mean scores of
both congenital and adventitious visually impaired. Most likely the two sub samples making
up each of the three complete samples are quite comparable with regard to background
variables, resulting in more reliable estimates of group differences. The weighted averages
were 96.6 for the congenital visually impaired and 97.0 for the adventitious visually impaired.
Both comparisons show there are virtually no differences between these two groups.
Discussion
In this research we tested the cumulative deficit theory, which predicts that
environmental deprivation has a substantial and accumulating negative effect on intelligence
test scores. A bare-bones meta-analysis of IQ scores of visually impaired individuals was
carried out to see to what degree environmental deprivation affects intelligence. The most
fundamental prediction from the theory was disconfirmed: older children and adults with a
visual impairment have a mean IQ virtually identical to the sighted IQ. None of the other
analyses provided any support for the deprivation theory.
The cumulative deficit theory makes several predictions concerning the IQs of the
total group of visually impaired and subgroups studied in this research. As to the mean IQ
score of visually impaired individuals in comparison to sighted individuals, the results show
that the visually impaired individuals have a slightly lower mean IQ than the sighted
individuals (98.06 versus 100). The percentage of variance explained by artifacts is 71%
which indicates that the large majority of the variance in the 72 data points can be explained
by sample size. Some of the heterogeneity in the data points in the meta-analysis is
attributable to the substantial difference between young children and older children and
adults. Excluding datasets which included young children and applying a correction yielded
21
an estimated mean IQ score of 100.5 for older children and adults. This value is virtually
indistinguishable from that for sighted individuals.
The cumulative deficit theory predicts that the gap between visually impaired and
sighted should increase with age. However, the data show the opposite, that is, the gap
between visually impaired and sighted shrinks with about a third of an SD as age proceeds.
Therefore, these findings clearly do not support the theory.
For young children, the scores on the Piagetian tasks are much lower than the scores
on the various IQ tests. It may be that the reliance on visual elements of many Piagetian tasks
makes them biased against visually impaired children. Therefore it may be that the score of 90
on predominantly verbal tests is a better indicator of young visually impaired children’s
capacities than the lower scores on the Piagetian tasks.
For comparisons of blind against the partially sighted, deprivation theory predicts the
blind should have lowest mean IQ score because they have the most severe deprivation.
However, the mean for the blind is 100.5 (total N = 1971 based on 19 studies), which is
similar to the overall mean IQ of 98.06. This means that the groups of visually impaired and
the blind group have comparable IQs. The mean from the five small studies on the partially
sighted (total N = 159) is substantially lower, namely 90.1. It possible that the empirical value
of 90.1 is a result of statistical artifacts, such as small sample size and biased sampling. As the
IQ score of the visually impaired and the blind are comparable it is not unreasonable to
assume that the mean IQ of partially sighted is, most likely, also close to the value of 98.4.
This would mean that blind and partially sighted have comparable mean IQ scores, again
providing no support for the deprivation hypothesis.
The deprivation hypothesis predicts that blind persons who have been handicapped
since birth or during the first year of life should have substantially lower mean IQ scores than
blind persons who have been disabled after the age of four. The mean score of the
adventiously blind is based on only three small studies and therefore does not allow a solid
comparison against the mean score of the congenitally blind. However, the studies which
report mean scores for both groups show virtually no difference in their mean scores. Once
again the data fail to support the deprivation hypothesis.
Cumulative deficit theory predicts that blind Nonwhites should have an average IQ
test score below 88, which value lies between the average of 85 for Blacks and 91 for
Hispanics. However, the Nonwhite average IQ score that emerges from our analyses is 93.55,
22
which is higher than the values for sighted Nonwhites. Therefore, the findings from samples
of Nonwhite visually impaired also do not support deprivation theory.
It has frequently been postulated that visually disabled people develop strong physical
and psychological features to compensate for their disability (see, for instance, Klauer, 1962).
It has been proposed that visually impaired children may learn techniques with which to cope
with their environment and thereby increase their IQ scores. However, there seems to be no
support for compensation theory, either because: (1) the average IQ for the visually impaired
is virtually indistinguishable from that for the sighted, and (2) most likely, blind and partially
sighted have comparable average IQ scores. An alternative, less parsimonious explanation
assumes a substantial effect of deprivation which is counteracted by an equally sized
compensatory effect, for older children and adults. However, an empirical test of the
deprivation hypothesis showed no support (Hengstler, 1975), making this alternative
explanation implausible. It appears our meta-analysis provides no support for either
environmental theory: deprivation or compensation.
Every meta-analysis is as good as the quality of the data points. It’s best to consider
every data point an estimate of the mean IQ of the group studied. For instance, we excluded
visually impaired individuals with no additional handicaps. When a blind individual with
brain damage has a low IQ score, is this caused by the brain damage or by the individual’s
genes? The quality of the assessment of the effects of brain damage on the IQ score
determines the value of the data point.
The meta-analysis of IQ scores of the visually impaired disconfirms the cumulative
deficit theory. A strongly deprived environment leads to a mean IQ score of about 100 for
older children and adults with a visual impairment. Environmental deprivation has also been
proposed to account for the difference in average IQ between Blacks and Whites in the US.
Of course, the environment of visually impaired is not exactly the same as the environment of
Blacks. However, it does not seem unreasonable to assume that the environment of the
visually impaired is much more deprived than the environment of Blacks. If the severe
deprivation caused by blindness does not impact IQ, it seems unlikely that the arguably lesser
deprived environment of Black children is an important source of their lower average IQ
score.
23
Conclusion
Visual deprivation showed no effect on the average IQ scores of a severely
handicapped group (the blind and the partially sighted). This implies the cumulative deficit
hypothesis does not have empirical support. Young visually impaired children do have lower
mean IQ scores when compared to sighted children. However, with increases in age the gap
shrinks and then disappears, rather than the increasing gap predicted by cumulative deficit
theory. The finding that the severely deprived environment of visually impaired shows no
substantial impact on their average IQ score makes it less likely that the arguably less
deprived environment of Blacks in the U.S. is a cause of their significantly lower mean IQs.
We conclude that our meta-analysis provides no evidence that environmental deprivation
causes group differences in average IQ scores. Environmental causes of group differences in
IQ become less plausible and therefore genetic causes become less implausible.
24
References
Studies included in the meta-analysis are marked with a *.
*Alford, D. W., Moore, M. W., & Simon, J. L. (1979). A preliminary assessment of the
validity and usefulness of the WRAT with visually handicapped residential school
students. Education of the Visually Handicapped, 4, 102-108.
Bailey I. L., & Hall A. (1990). Visual Impairment: An overview. New York: American
Foundation for the Blind.
*Baitinger, U. & Bernd, R. (1970). HAWIK-Untersuchungen bei Blinden und
Sehbehinderten [Research of the HAWIK on the blind and visually impaired]. Der
Blindenfreund, Zeitschrift für das Blindenwesen, 89, 134-142.
*Baker, C. P., Koenig, A. J., & Sowell, V. M. (1995). Relationship of the Blind Learning
Aptitude Test to braille reading skills. Journal of Visual Impairment and Blindness,
89,
441-447.
*Bauman, M. K. (1971). Clinical interpretation of personality inventories. Education of the
Visually Handicapped, 3, 82-87.
Berry, J.W. (1966). Temne and Eskimo perceptual skills. International Journal of
Psychology, 1, 207-222.
Bishop, V.E. (1996) Teaching visually impaired children ( 2nd Ed.). Springfield, IL:
Charles C Thomas.
*Blackhurst, A.E. & Marks, C.H. (1977). Day school and residential school settings.
Education of the Visually Handicapped, 3, 85-91.
*Blasch, B.B. (1978). Blindism: Treatment by punishment and reward in laboratory and
natural settings. Journal of Visual Impairment and Blindness.
*Bonfati, B.H. (1979). Effects of training on nonverbal and verbal behaviors of congenitally
blind adults. Journal of Visually Impairment and Blindness, 73, 1-9.
*Boyer, A.S. (1997). Identification of characters with shared representations: decoding
musical and literary braille. Journal of Visual Impairment and Blindness, 91, 77.86.
Brody, N. (1992). Intelligence (2nd Ed.). London: Academic Press.
*Brothers, R.J. (1971). Aural study for the visually handicapped effects of message length.
Education of the Visually Handicapped, 3, 65-70.
25
*Chin, D.L. (1988). Dance movement instruction: Effects on spatial awareness in visually
impaired elementary students. Journal of Visual Impairment and Blindness, 14, 188192.
*Cleaves, W. T. & Royal, R. Wm. (1979). Spatial memory for configurations by congenitally
blind, late blind, and sighted adults. Journal of Visual Impairment and Blindness, 73,
13-19.
*Corley, G. & Pring, L. (1996). The ability of children with low vision to recall pictures.
Journal of Visual Impairment and Blindness, 90, 58-72.
*Czerwinski, M. H. & Tait, P. E. (1981). Blind children's perceptions of normal, withdrawn,
and antisocial behavior. Journal of Visual Impairment and Blindness, 75, 252-257.
*Daugherty, K. M. (1977). Monterey learning systems: Improving academic achievement of
visually impaired learners. Journal of Visual Impairment and Blindness, 71, 298-301.
*Daugherty, K. H. & Moran, M. F. (1982). Neuropsychological learning and developmental
characteristics of the low vision child. Journal of Visual Impairment and Blindness,
76, 398-406.
Dekker, R. (1987). Intelligentie van visueel gehandicapte kinderen in de leeftijd van 6 tot 15
jaar [Intelligence of visually impaired children aged 6 to 15]. The Netherlands,
Amsterdam, Vrije Universiteit: Unpublished doctoral dissertation.
*Dekker, R. (1993). Cognitive development of visually handicapped children. New York:
Brooks.
*Devos, M. (1985). De Perkins-Binet Tests of Intelligence for the Blind: Vlaamse
Aanpassing [The Perkins-Binet Tests of Intelligence for the Blind: Flemish
Belgium, Leuven, Faculteit der Psychologie en Pedagogische
adaptation].
Wetenschappen,
Katholieke Universiteit Leuven.
*Dimcovic, N. & Tobin, M. J. (1995). The use of language in simple classification tasks by
children who are blind. Journal of Visual Impairment and Blindness, 89, 449-459.
*Everhart, G., Luzader, M., & Tullos, S. (1980). Assertive skills training for the blind.
Journal of Visual Impairment and Blindness, 74, 62-65.
*Fischer, H. (1975). Sprachliche Leistungen blinder und hochgradig sehbehinderter
Grundschulkinder- Ergebnisse einer empirischen Untersuchung formaler sprachlicher
Leistungen mit Hilfe verschiedener Sprachindizes [Language skills of blind and
visually impaired primary school children – Results of an empirical study into
language skills using various speech indices]. Sehgeschädigte, 5, 64-79.
*Franks, F. L. & Nolan, C. Y. (1971). Measuring geographical concept attainment in visually
26
handicapped students. Education of the Visually Handicapped, 3, 11-17.
*Gadbaw, P. D., Dolan, M. T., & De L'Aune, W. L. (1977). Optacon skill acquisition by
blinded veterans. Journal of Visual Impairment and Blindness, 71, 23-28.
*Gore, G. V. (1969). A Comparison of two methods of speeded speech. Education of the
Visually Handicapped, 1, 69-76.
*Gutterman, J. E., Ward, M., & Genshaft, J. (1985). Correlations of scores of low vision
children on the Perkins-Binet Tests of Intelligence for the Blind, the WISC-R and the
WRAT. Journal of Visual Impairment and Blindness, 79, 55-58.
*Hammill, D. & Crandell, J. M. (1969). Implications of tactile-kinesthetic ability in
visually handicapped children. Education of the Visually Handicapped, 1, 65-69.
*Harley, R. K. & Rawls, R. (1970). Comparison of several approaches for teaching braille
reading to blind children. Education of the Visually Handicapped, 2, 47-51.
*Hasselt, v. V. B., Hersey, M., Kazdin, A. E., Simon, J., & Mastanuono, A. (1983). Training
blind adolescents in social skills. Journal of Visual Impairment and Blindness, 77,
199-203.
Hatwell, Y. ( 1985). Piagetian reasoning and the blind. New York: American Foundation for
the blind.
Hengstler (1975).
*Herman, J. F., Herman, T. G., & Chatman, S. P. (1983). Constructing cognitive maps from
partial information: A demonstration study with congenitally blind subjects. Journal
of Visual Impairment and Blindness, 77, 195-198.
Herrnstein, R.J. & Murray, C. (1994). The Bell Curve: Intelligence and class structure in
American life. New York: The Free Press.
*Hill, A., Spencer, C., & Baybutt, K. (1985). Predicting efficiency of travel in young, visually
impaired children form their other spatial skills. Journal of Visual Impairment and
Blindness, 79, 297-300.
*Hopkins, K.D. & McGuire, L. (1967). IQ constancy and the blind child. International
Journal for the Education of the blind, 16, 113-114.
Hunter, J.E., & Schmidt, F.L. (2004). Methods of meta-analysis (2nd Ed.). London:
Sage.
*James, G. A. & Gill, J. M. (1974). Mobility maps for the visually handicapped: a study of
learning and retention of raised symbols. Research Bulletin / American Foundation for
the Blind, 27, 87-98.
Jensen, A.R. (1974). Cumalative deficit: A testable hypothesis? Developmental Psychology,
27
10, 996-1019.
Jensen, A. R. (1977). Cumulative deficit in IQ of blacks in the rural South. Developmental
Psychology, 13 184-191.
Jensen, A.R. (1980). Bias in mental testing. London: Methuen.
Jensen, A.R. (1998). The g factor: The science of mental ability. London: Praeger.
*Jones, P. W. (1983). Measurement of personality traits of the visually limited. Education of
the Visually Handicapped, 15, 12-19.
*Klauer, K.J. (1962) Sehschwäche und Intelligenz. HAWIK-Untersuchungen bei
sehschwachen Kindern [Visually impairment and intelligence. Research of the
HAWIK on visually impaired children]. Zeitschrift für Experimentelle und
Angewandte Psychologie, 9, 570-593.
*Krüger, H. (1974). Intelligenzdiagnostik bei Sehbehinderten [Intelligence assessment of the
visually impaired]. Der Blindenfreund, Zeitschrift für das Blindenwesen, 94, 110-127.
*Lambert, W., Griffin, T., Pike, C., & Kurr, R. (1980). Group assessment and structured
learning. Journal of Visual Impairment and Blindness, 74, 225-228.
Loehlin, J.C., Lindzey, G., & Spuhler, J.N. (1975). Race differences in intelligence. San
Francisco, CA: Freeman.
Lowenfeld, B. (1973). The visually handicapped child in school. London: Constable.
Lynn, R. (1982). IQ in Japan and the United States shows a growing disparity.
Nature, 297, 222-223.
Lynn, R. (1997). Geographical variation in intelligence. In H. Nyborg (Ed.), The
scientific study of human nature. Oxford: Pergamon.
Lynn, R. & Vanhanen, T. (2002). IQ and the wealth of nations. Westport, CT:
Praeger.
Lynn, R. (2006). Race differences in intelligence: An evolutionary analysis. Augusta, GA:
Washington Summit.
*MacCluskie, K. C., Tunick, R. H., Dial, J. G., & Paul, D. S. (1998). The role of vision in the
development of abstraction ability. Journal of Visual Impairment and Blindness, 92,
189-199.
*Merry, R. V. & Kiefer-Merry, F. (1933). Tactual recognition of embossed pictures by blind
children. Journal of Applied Psychology, 17, 148-163.
*Millar, S. (1984). Is there a 'best hand' for braille? Cortex, 20, 75-87.
*Miller, C. K. (1969). Conservation in blind children. Education of the Visually
Handicapped, 1, 101-105.
28
*Mills, R. J. & Adamshick, D. R. (1969). The effectiveness of structured sensory training
experiences prior to formal orientation and mobility instruction. Education of the
Visually Handicapped, 1, 14-21.
*Mommers, M.J.C. (1977). Lezen met de vingers The Netherlands, Den Haag:
Staatsuitgeverij. [Braille reading of blind children, relations with verbal intelligence
and haptic perception, Schindele Verlag, Germany].
*Mommers, M. J. C. & Smits, B. W. G. M. (1975). Comparison of the performance of blind
and sighted children on the verbal part of the Wechsler Intelligence Scale for Children.
Sehgeschädigte, 21-36.
*Monahan, L. H., Giddan, N. S., & Emener, W. C. (1978). Blind students: Transition from
high school to college. Journal of Visual Impairment and Blindness, 72, 85-87.
*Myers, J. A. (1978). Compressed speech increases learning efficiency. Education of the
Visually Handicapped, 2, 57-64.
*Nater, P. (1982). An electronic teaching device for blind students. Journal of Visual
Impairment and Blindness, 76, 274-278.
*Nater, P. (1984). Sehschädigung und Sehleistung- (3) Psychische Faktoren [Visual
impairment and visual faculty – (3) Psychic factors]. Zeitschrift für das
Sehgeschädigten- Bildungswesen, 104, 63-72.
Neisser, U. et al., (1996). Intelligence: Knowns and unknowns. American Psychologist, 51,
77-101.
*Nelson, P.A., Dial, J.G., Joyes, A. (2002). Validation of the Cognitive Test for the Blind as
an assessment of intellectual functioning. Rehabilitation Psychology, 47, 184-193.
Nisbett, R.E. (1998). Race, genetics, and IQ. In C. Jencks and M. Philips (Eds.), The BlackWhite test score gap. Washington, D.C.: Brookings Institution Press.
*Newland, T.E. (1971). Blind Learning Aptitude Test, Champaign, IL: University of Illinois
Press.
Ogbu, J.U. (1994). Culture and intelligence. In R.J. Sternberg (Ed.), Ecyclopedia of
human intelligence (pp. 328-338). New York: Macmillan.
*Parke, K. L., Shallcross, R., & Anderson, R. J. (1980). Differences in coverbal behavior
between blind and sighted persons during dyadic communication. Journal of
Visual
Impairment and Blindness, 74, 142-146.
*Parsons, S. (1987). Locus of control and adaptive behavior in visually impaired children.
Journal of Visual Impairment and Blindness, 81, 429-432.
*Pring, L. (1985). Processes involved in braille reading. Journal of Visual Impairment and
29
Blindness, 79, 252-255.
*Pring, L., Dewart, H., & Brockbank, M. (1998). Social cognition in children with visual
impairments. Journal of Visual Impairment and Blindness, 92, 754-768.
*Rath, W. (1967). Untersuchungen bei Blinden und hochgradig Sehbehinderten zum
Vergleich zwischen HAWIK-Verbalteil und IPT [Research of the visually impaired
and blind. A comparison between the HAWIK verbal scale and the IPT]. Der
Blindenfreund, Zeitschrift für das Blindenwesen, 87, 113-129.
Reynolds, C.R., & Murdoch-James, E. (1994). Regional differences in intelligence. In Robert
J. Sternberg (Ed.), Encyclopedia of human intelligence (pp. 944-949). New York:
MacMillan.
*Roeske, N. A. (1969). Improving blind children's scholastic and social performance with
medication. Education of the Visually Handicapped, 1, 105-113.
Sandoval, R., Frisby, C.L., Geisinger, K.F., Scheuneman, J.D. & Grenier, J.R. (1998). Test
interpretation and diversity. Washington, DC: American Psychological Association.
*Sanford, L. (1983). A formative evaluation of an instructional program designed to teach
visually impaired students to use microcomputers. Education of the Visually
Handicapped, 15.
*Schindele, R. (1974). The social adjustment of visually handicapped children in different
educational settings. Research Bulletin / American Foundation for the Blind, 27, 125144.
Schmidt, F.L. & Hunter, J.E. (1998). The validity and utility of selection methods in
personnel psychology: Practical and theoretical implications of 85 years of research
findings. Psychological Bulletin, 124, 262-274.
*Schwartz, T. (1983). Social cognition in visually impaired and sighted children. Journal of
Visual Impairment and Blindness, 77, 377-381.
Shuey, A.M. (1966). The testing of Negro intelligence (2nd Ed.). New York: Social
Science Press.
*Shurager, H.C. & Shurager, P.S. (1964). HISAB Manual: Haptic Intelligence Scale for Adult
Blind. Chicago: Psychology Research, Technology Centre.
*Stephens, B. & Grube, C. (1982). Development of Piagetian reasoning in congenitally blind
children. Journal of Visual Impairment and Blindness, 76, 133-143.
*Suinn, R.M. & Dauterman, W.L. (1966). Stanford-Ohwaki-Kohs Block Design Intelligence
Test for the Blind. Los Angeles, CA: Western Psychological Services.
*Sykes, K. C. (1971). A comparison of the effectiveness of standard print and large print in
30
facilitating the reading skills of visually impaired students. Education of the Visually
Handicapped, 3, 97-105.
*Teare, J. F. & Thompson, R. W. (1982). Concurrent validity of the Perkins-Binet Tests of
Intelligence for the Blind. Journal of Visual Impairment and Blindness, 76, 279-280.
*Thomas, J. E. (1979). Factors influencing the integration of visually impaired children.
Journal of Visual Impairment and Blindness, 73, 359-363.
*Tillman, M.H. (1967a). The performance of blind and sighted children on the WISC: Study
1. International Journal for the Education of the Blind, 16, 65-74.
*Tillman, H. M. & Osborn, R. T. (1969). The performance of blind and sighted children on
the Wechsler Intelligence Scale for children. Education of the Visually Handicapped,
1, 1-32.
*Tobin, M.J. (1984). Assessing learning aptitude in blind children. British Journal of Visual
Impairment, 2, 2, 45-47.
*Tobin, M. J., Clarke, D., Lane, I., & Pittam, V. G. (1970). Programmed learning for the
blind: Some exploratory studies. Education of the Visually Handicapped, 2, 11-23.
*Vander Kolk, C. J. (1977). Intelligence testing for visually impaired persons. Journal of
Visual Impairment and Blindness, 71, 158-163.
Vernon, P.E. (1979). Intelligence: Heredity and environment. San Francisco, CA: Freeman.
Warren, D.H. (1984). Blindness and early childhood development (2nd Ed). New York:
American Foundation for the Blind.
*Wilhelm, J. G. (1989). Fear and anxiety in low vision and totally blind children. Education
of the Visually Handicapped, 20, 163-172.
Williams, M. (1965). Williams Test for Children with Defective Vision. Great Britain,
Windsor: NFER.
*Wormsley, D. P. (1996). Reading rates of young braille-reading children. Journal of Visual
Impairment and Blindness, 90, 278-282.
Wright, S.C., Taylor, D.M., & Ruggiero, V.R. (1996). Examining the potential for
academic achievement among Inuit children: Comparisons on the Raven
coloured progressive matrices. Journal of Cross-Cultural Psychology, 27, 733-753.
Zeidner, M. (1987). Test of the cultural bias hypothesis: Some Israeli findings.
Journal of Applied Psychology, 72, 38-48.
31