Biographies of a scientific subject: The intelligence test

Intelligence testing is the process of measuring cognitive ability using standardized measures and scales. The use of intelligence testing for education purposes, including curriculum differentiation, is controversial. This entry discusses the history and criticisms of intelligence testing, along with the role of intelligence testing in curriculum differentiation.

The history of David Wechsler's intelligence scales is reviewed by tracing the origins of the subtests in the 1939 Wechsler±Bellevue Intelligence Scale. The subtests originated from tests developed between 1880 and World War I, and was based on approaches to mental testing including anthropometrics, association psychology, the Binet±Simon scales, language-free performance testing of immigrants and school children, and group testing of military recruits. Wechsler's subtest selection can be understood partly from his clinical experiences during World War I. The structure of the Wechsler±Bellevue Scale, which introduced major innovations in intelligence testing, has remained almost unchanged through later revisions.

The previous article ended with the Cattell-Horn-Carroll (CHC) consensus model of a transcendent third order factor g and, an array of nine second order factors. This emerged near to the culmination of a series which has mainly focussed on the structure of human intelligence.

Due to the prevalence of IQ tests in Western cultures as a method of categorization (McClelland, 1973), intelligence testing has thus accumulated a large volume of literature. An objective definition of intelligence seems to have escaped the grasp of academics, with many conceptualisations of intelligence owing their origins to socio-cultural influences (Sternberg, 2000). The aim of this experiment was to examine intelligence testing measures and understand how to transform z scores into standardised IQ scores. Digit Span, a subset of the Working Memory Index of the WAIS-III was administered to 12 undergraduate university students. Participants were asked to perform two tasks independently of each other, Digit Span Forward (DSF), and Digit Span Backward (DSB). To ensure validity and objectivity in the administration of the test, the examiner was required to adhere to certain rules concerning voice inflection on the final number of a sequence, and speaking the numbers at a rate of one number per second. A more detailed procedure concerning examiner guidelines can be found in the method section of this report. Z scores were calculated from the participant’s raw data scores, which were then transformed into standardised IQ scores. The standardisation of z scores allows for a broader interpretation of an individual’s raw data scores in comparison to others of a similar age and demographic. Furthermore, in this experiment the validity of intelligence testing measures as accurate measures of intelligence was investigated.

The Annual Convention of the American …, 2004

Journal of Psychoeducational Assessment, 2015

It is well established that Canadians produce higher raw scores than their U.S. counterparts on intellectual assessments. As a result of these differences in ability along with smaller variability in the population’s intellectual performance, Canadian normative data will yield lower standard scores for most raw score points compared to U.S. norms. Two recent studies have questioned the utility of the WAIS–IV Canadian norms based on the performance of a mixed clinical sample of post-secondary students. These studies suggest that a greater proportion of cases from their mixed clinical samples fall below a full-scale IQ of 85 using the WAIS–IV Canadian norms than should be “expected.” The purpose of the current study is threefold: First, to summarize the consistent finding of Canada–U.S. differences on measures of ability and present new empirical analyses to demonstrate these results are not due to a smaller sample size for Canadian norms. Second, and most importantly, matched sample ...

Intelligence, 1979

Intelligence testing is the practice of administering a series of exercises and tasks to measure cognitive and mental functions like comprehension, reasoning, critical thinking and abstraction. This practice had it beginnings in the United States of America almost two centuries ago, to help ensure equitable and effective education for all, at a time when the public education system was on the verge of a sudden spurt of expansion. In its early days, intelligence testing was merely viewed as a diagnostic tool to facilitate timely intervention and support for individuals with learning difficulties. However, it soon assumed the unintended role of classifying and segregating individuals, resulting in ‘intellectual casteism’ and meritocracy in society. This paper contends that a skewed and narrow perspective of intelligence – one that overlooks the larger socioeconomic and cultural context of human existence – is to be held responsible for these undesirable fallouts. With a renewed understanding of the sociocultural and gender-based factors that underlie the expression of human intelligence, it becomes possible to devise culturally inclusive and student-friendly models of testing that facilitate growth and development of human potential. Keywords: Intelligence, Intelligence testing, Stanford-Binet Intelligence scale, Ecological systems theory, Four-tiered Bio-ecological assessment

Applied Measurement in Education, 2019

This brief article introduces the topic of intelligence as highly appropriate for educational measurement professionals. It describes some of the uses of intelligence tests both historically and currently. It argues why knowledge of intelligence theory and intelligence testing is important for educational measurement professionals. The articles that follow in this special issue will provide readers with considerable information about the history of intelligence theory and testing, and especially of the Cattell-Horn-Carroll (CHC) model of testing and its implementation. The following articles will also provide a well-reasoned approach to the way science should work in evaluating tests and the models on which they are based.