abilities and aptitudespatrick c. kyllonen
drew h. gitomer
affective and conative processeslyn corno
jason duque raley
angela d. whipple
gender equity and schoolinghsiu-zu ho
heather a. tomlinson
angela d. whipple
ABILITIES AND APTITUDES
Abilities are cognitive or mental characteristics that affect one's potential to learn or to perform. Aptitudes are sometimes treated as interchangeable with abilities, particularly when they focus on prediction of performance in other settings or on other occasions. Cognitive abilities have been conceived very broadly (e.g., intelligence) and also in terms of specialized abilities such as verbal, spatial, memory, reasoning, problem solving, and psychomotor ability. Some authors have defined aptitudes more broadly than abilities, to include any number of individual-differences factors—affective, cognitive, and personality characteristics—that influence one's readiness or likelihood of learning or performing successfully.
During the twentieth century, there have been significant changes in conceptions of ability, moving from atheoretical models that have their basis in measurement and psychophysics to ones that are based largely on cognitive theories of human performance. Social issues continue to influence conceptions of ability and practices of ability testing. The scientific measurement of abilities has been viewed as either enabling or stifling social progress, and such controversy continues in the twenty-first century.
Ability testing in education began in 1905 in Paris when Alfred Binet along with his assistant Théodore Simon developed the Binet-Simon scale to solve the practical problem of reliably differentiating between educable, educable with special help, and uneducable children. Prior to the development of the scale, such classifications were made subjectively and inconsistently; thus the scale was considered a significant practical achievement. The scale consisted of thirty tests, such as "naming objects in pictures," "defining common words by function," and "retaining a memory of a picture." In historical treatments, Binet and Simon's approach is sometimes referred to as a task-sampling approach, in that the tests are essentially samples of typical educational events.
The task-sampling approach may be contrasted with another tradition in ability testing, a basic elements approach. This tradition began with Francis Galton's development of a battery of basic sensory-motor, reaction time, and memory tests. Galton believed that a general mental ability with biological underpinnings underlay performance on these tests, but to his disappointment, he found no relationship with educational or occupational levels. Charles Spearman developed a statistical method known as factor analysis, which demonstrated that Galton's hypothesis of a general ability was supported after all. Later, researchers such as Louis Thurstone and J.P. Guilford refined Spearman's notion, by adding various kinds of tests to the more basic batteries, and identifying "group factors" such as spatial and verbal ability in addition to general ability (Spearman's "g") to account for patterns of performance.
Developing conceptions of ability and aptitude have proceeded in tandem with practical applications, particularly in military, educational, and employment settings. In the military, the development of the "Army Alpha and Beta" intelligence tests during World War I, which helped efficiently sort 1.7 million World War I conscripts into job classifications (training, frontline, officer, and so forth) has been cited by Frederick McGuire as one of psychology's most influential contributions to American society. The Army Alpha, used essentially as a measure of general cognitive ability, consisted of eight subtests (oral directions, arithmetic problems, practical judgment, antonyms, disarranged sentences, number series, analogies, and information), most of which are still being used in test batteries today. During World War II and through the 1950s the number of abilities measured was expanded to include such constructs as verbal and quantitative ability, technical knowledge, and psychomotor abilities, the latter being particularly important for pilot and navigator selection. In the early twenty-first century the U.S. military services use a multidimensional test battery, the Armed Services Vocational Aptitude Battery (ASVAB), for both selecting applicants and assigning them to training and occupational specialties.
Following the success of the Army Alpha, there was general optimism about the role ability assessment could play in "social engineering," such as providing opportunities for higher education to students based on merit rather than on birthright. An example was the Scholastic Aptitude Test (now known as the SAT) during the period between World War I and World War II. The SAT was designed, in the words of the American educator James Bryant Conant, to "reorder the 'haves and have-nots' in every generation to give flux to our social order." The composition of the SAT has fluctuated minimally over the years, consistently yielding "verbal" and "mathematical" scores, relying on tasks that are arguably work samples of academic tasks (e.g., reading comprehension) to those that are more abstract from such tasks (e.g., verbal analogies).
A third major application of abilities and aptitudes is in employment testing. The General Aptitude Test Battery (GATB) was developed around the time of World War II, by a commission of industrial psychologists and measurement experts for the U.S. Department of Labor. The test measures general ability, verbal aptitude, numerical aptitude, psychomotor ability (motor coordination, finger dexterity, and manual dexterity), and general perceptual ability (spatial aptitude, form perception, and clerical perception). The GATB was designed to predict job performance. More than seven hundred validity studies have demonstrated that it does so, and consequently the test is widely used as an employment screen. A meta-analysis of these studies conducted by John Hunter and Ronda Hunter has shown that general cognitive ability is the primary determinant of job success, but that psychomotor ability is important for relatively low complexity jobs.
The major aptitude batteries–the ASVAB, GATB, and SAT, in the military, industrial, and educational sectors, respectively–were developed in the post—World War I period, and although still in use in the early twenty-first century, have not fundamentally changed over the years in what they measure or how they measure it. But advances in the knowledge of how people think, learn, and solve problems, since the 1970s, has triggered a reevaluation of what abilities are and how they ought to be measured.
Information processing. The information-processing view likens the individual to an information-processing system, suggesting that the parameters governing the performance of an information-processing system, such as speed and memory capacity, might be the abilities that govern human learning and performance. Since the 1960s, numerous studies have examined the relationship between mental speed and learning and performance. The conclusion has been that increased sophistication in the procedures for measurement are responsible for a slightly more favorable view than Galton had of the importance of mental speed in everyday life. Still, the work has primarily been of theoretical interest, and there have been few suggestions that mental speed is an important ability to begin routinely including it in large-scale aptitude batteries. In fact, to the contrary, the military services are in the process of removing their "speed" composite from the ASVAB because it has proven not to be a valid predictor of training success or job performance. Similarly, ETS routinely considers speed to be an irrelevant factor for most of its measures.
There has been considerable support, though, for the notion that another aspect of information processing, working-memory capacity, is central to human performance. Several studies have shown that working-memory capacity (as measured by tasks such as mental arithmetic) is indistinguishable from Spearman's "g" and therefore is the primary ability governing learning and performance. Ian Deary has suggested that Spearman's "g" is simply being relabeled to have a more contemporary-sounding title. But information-processing explanations of abilities have several advantages. One is that they allow for the construction of ability measures based not simply on previous measures, but on the understanding of how people learn, think, and solve problems. A second is that an information-processing scheme has potential use in task analysis. For example, as Susan Embretson has demonstrated, the requirements of a task can be characterized in information-processing terms, enabling the a priori prediction of item difficulty. A third advantage is that information-processing based concepts connect with wider areas of inquiry such as cognitive and brain sciences, and therefore allow for the uniting of what Lee Cronbach referred to as the two disciplines of scientific psychology, the correlational and the experimental.
Knowledge and expertise. A second perspective based on new conceptions in cognitive science might be called the knowledge and expertise view. In this view, characteristics such as working-memory capacity and information-processing speed are not viewed as fixed characteristics of an individual, but as dependent upon knowledge and skill that is developed over long periods of time. So, for example, expert chess players have the ability to recall actual chess positions with impressive accuracy and to a much greater extent than novice players. However, when chess pieces are arranged in random fashion, the two groups have equal recall. Similarly, many studies have demonstrated that processing speed is a direct function of repeated practice. These studies have demonstrated that efficient information processes are, in large part, the residue of well-organized knowledge structures that are developed over years of active engagement and practice within a domain. Further, well-developed knowledge structures in a domain make one a much more effective learner of other concepts in that domain, since there is an established knowledge organization within which to embed and connect new information.
This emphasis on domain specific expertise has profound implications for considering ability testing and ability development. The implication of the expertise approach is to assess and then facilitate the development of knowledge structures and processing skills that will support performance and learning within a domain, without significant attention paid to broad, domain-independent skills. Clearly, the debate over how much of ability is specific to or independent of particular domains continues, and has profound implications for the selection, education, and training of individuals.
Hierarchical model. A third new development might be called the hierarchical model of ability differences. This work continues the factor analytic tradition began by Spearman, and further refined by Thurstone and Guilford. The key idea behind the hierarchical model is that what had been thought to be rival hypotheses concerning the number and organization of human abilities can be now seen as compatible. Even into the 1980s one had to take a position on whether the evidence was more favorable towards the Spearman view of one general ability (along with number test-specific abilities); with Thurstone's view of eight to eleven primary mental abilities; or Guilford's 120 or 160 ability models. The hierarchical model, as developed by Jan-Eric Gustafsson and John Carroll, shows the fruitfulness of considering abilities as varying in their generality from fairly specific abilities (e.g., memory span, associative memory, and free recall memory), to broader ones (e.g., general memory and learning), to the most broad (general intelligence). In fact, this example is taken from Carroll's "Three-stratum structure of cognitive abilities," which posits sixty-seven or so "Stratum I" abilities, eight "Stratum II" abilities, and one "Stratum III" ability. Gustafsson's scheme is similar in spirit, but he did not consider nearly as many datasets as Carroll did, and so consequently his proposal may be seen as a subset of Carroll's.
Multiple intelligences. A final new development might be called the "multiple intelligences" view. There are actually two quite distinct notions that might be considered in this category, the "triarchic theory" of Robert Sternberg and the "multiple intelligences" theory of Howard Gardner. Sternberg's idea is that the field, the testing industry, and the application sectors themselves (education, industry, and the military) are preoccupied with one notion of abilities, which he playfully refers to as the "gocentric view." He also calls this analytic intelligence, and suggests that while important, success in school and in life is perhaps equally if not more importantly determined by other intelligences, namely creativity and practical intelligence. Although this work has not yet resulted in any widescale applications, preliminary work, particularly in the area of practical intelligence appears promising.
Gardner has proposed a different scheme, called "multiple intelligence theory," which identifies eight abilities–linguistic, logical-mathematical, musical, spatial, bodily-kinesthetic, interpersonal, intrapersonal, and naturalist intelligences. This scheme is quite popular in educational circles, perhaps attributable to its elevation of abilities other than the usual linguistic and logical-mathematical abilities, encouraging the recognition of some students for talents normally overlooked. However, the scheme is often dismissed in scientific circles because by eschewing measurement, it fails to allow for its validity to be tested. As Nathan Brody has pointed out, multiple-intelligence theory is at odds with the rest of the field in its rejection of a general cognitive ability, and with its choice of the particular eight abilities–neither empirical nor theoretical justifications for these features of the theory have been produced.
Discussions and investigations of aptitudes, abilities, and individual differences have been fraught with controversy throughout their history. Certainly one of the most contentious issues is that of the heritability of abilities. Comparisons of monozygotic and dizygotic twins, twins reared apart versus together, adoption studies, and various other behavioral genetics studies, suggest that abilities are somewhat heritable, although the issue of by what amount remains unsettled. There is ample evidence for notably high consistency in test scores over time. A study reported by Ian Deary showed that Scottish eleven-year-olds performed remarkably similarly on an identical test of mental ability administered sixty-six years later, when they were seventy-seven years old.
Some of the reason for the intense interest in heritability seems to be inappropriately due to a misconception that to the degree that abilities are inherited, there is not much one can do to improve one's abilities. Heritability, however, is not synonymous with immutability, as can be proven with a simple thought experiment. Height is highly heritable, but has steadily increased over the twentieth century due to improvements in environmental factors such as nutrition. Similarly, as documented by James Flynn, intelligence scores have been shown to have risen quite dramatically in the second half of the twentieth century in numerous parts of the world. Early childhood intervention programs, such as Head Start, have met with mixed success, but others, such as the Carolina Abecedarian project, have shown strong and persistent gains in cognitive skills, academic test scores, and language use in follow-up studies through the age of fifteen.
Another area of some controversy in the abilities and aptitudes literature concerns the role of other factors such as metacognition, attitudes, motivation, and concepts such as emotional intelligence, in performance in school and the workplace. Concepts such as test anxiety have for a long time been cited as a threat to the validity of an estimate of a student's ability. Claude Steele has suggested that additional attitudinal factors, "disidentification" and "stereotype threat," may impair the performance of minority students in certain contexts. Motivation has long been considered an important factor in governing learning and performance success and related concepts such as goal setting, self-efficacy, and optimism are being investigated for their role in learning. Emotional intelligence refers to a wide variety of factors that may mediate the relationship between abilities and performance, or may serve as abilities and aptitudes in their own right.
A third controversy centers on the extent to which ability can even be considered as an individual phenomenon. In the situated cognition view, abilities can only be considered as they are manifest and develop within social situations. The interaction of the person with the social environment is what defines ability–it is not a construct that can be defined independent of such interaction. Much of the support for this work comes from examining very sophisticated cognitive strategies that develop without the benefit of any formal academic training or ability to demonstrate the strategies in traditional academic ways.
The predominant use of individual differences in abilities and aptitudes in education has been in assigning students to special remedial programs, selecting them for admission into schools (e.g., colleges), or identifying them for receiving awards such as scholarships or fellowships. Although this use will undoubtedly continue, it is likely that an increasing emphasis will be put on new uses, opening the door to additional opportunities, in the form of diagnosis, tailored educational programs, and self-assessments to facilitate more efficacious career choices. It is likely that the noncognitive ability and aptitude variables, including interests, personality, motivation, and the like, will prove particularly important for the new uses role.
Another trend in ability and aptitude testing in education is an increased emphasis on the assessment of achievements rather than the more basic abilities. Some of this is due to what some have characterized as the pernicious effects of test coaching, and is certainly consistent with cognitive conceptions of expertise. The idea is that as long as there are high-stakes ability tests, there will be a coaching industry designed to help students improve their chances of succeeding on those tests. To the extent that the tests directly reflect the achievements that are supposed to be learned in school, coaching then becomes a positive force, a productive adjunct to the school curriculum, reinforcing lessons learned in school. Another reason for this trend is that test users and the public in general have increasingly demanded tests that look more like the learning or performance activities that the tests are designed to predict. There seems to be less patience with arguments based exclusively on predictive validity statistics. One sees this in military and industry testing as well as educational testing.
A third trend in abilities and aptitude assessment is what is sometimes called "embedded assessment" or the insertion of abilities tests within the context of instruction itself. This trend fits with the general trend toward the increased use of achievement testing, but for the specific purpose of tailoring instruction to a particular individual, based on that individual's changing knowledge and understanding of a particular topic area. Richard Snow and Valerie Shute have suggested both "macroadaptive" and "microadaptive" responding on the part of a computerized ("intelligent") tutoring system. Microadaptive instruction refers to the specific reactions a tutor makes in response to its continually updated understanding of what a student knows and is learning; macroadaptive instruction refers to more global approaches to delivering coaching and feedback the tutor makes based on more general assessments of a student's abilities.
Finally, a possible future trend is increasing attention paid to what might be called "mediators" for their role in affecting abilities and aptitudes. These include factors such as nutrition, psychopharmacology, fatigue, and circadian rhythms. Nutritional explanations have begun to appear more frequently in discussions of changes in abilities, and the role of nutrition, vitamins, and glucose has been investigated but as of yet, little is known. Similarly, there have been some investigations of "smart drugs," such as caffeine, ginseng, and ginko biloba, as well as other psychopharmacological agents, particularly cholinergic enhancers. Finally, much has been learned over the past decade about the role of circadian rhythms in affecting hormonal production and concommittant behavioral effects such as fatigue and alertness over the course of the day. There has been some work suggesting that "morningness" and "eveningness" may be characteristic dispositions that mediate both cognitive abilities and personality factors.
See also: Assessment; Intelligence; Testing.
Atkinson, Richard C. 2001. "Tests and Access to American Universities." The 2001 Robert H. At-well Distinguished Lecture Delivered at the 83rd Annual Meeting of the American Council on Education, Washington, DC.
Benton, David; Griffiths, Rebecca; and Haller, Jurg. 1997. "Thiamine Supplementation Mood and Cognitive Functioning." Psychopharmacology 129 (1):66–71.
Binet, Alfred, and Simon, ThÉ odore. 1916. The Development of Intelligence in Children (The Binet-Simon Scale), trans. Elizabeth S. Kite. Baltimore: Williams and Wilkins.
Brody, Nathan. 1992. Intelligence, 2nd edition. San Diego, CA: Academic.
Campbell, Francis A., and Ramey, Craig T. 1994. "Effects of Early Intervention on Intellectual and Academic Development: A Follow-Up Study of Children from Low-Income Families." Child Development 65:684–698.
Carroll, John B. 1993. Human Cognitive Abilities. New York: Cambridge University Press.
Coull, Jennifer T., and Sahakian, Barbara J. 2000. "Psychopharmacology of Memory." In Memory Disorders in Psychiatric Practice, ed. German E Berrios and John R Hodges. New York: Cambridge University Press.
Cronbach, Lee J. 1957. "The Two Disciplines of Scientific Psychology." American Psychologist 12:671–684.
Deary, Ian J. 2000. Looking Down on Human Intelligence: From Psychometrics to the Brain. Oxford: Oxford University Press.
Deary, Ian J., et al. 2000. "The Stability of Individual Differences in Mental Ability from Childhood to Old Age: Follow-Up of the 1932 Scottish Mental Survey." Intelligence 28:49–55.
Embretson, Susan E. 1995. "The Role of Working Memory Capacity and General Control Processes in Intelligence." Intelligence 20:169–190.
Flynn, James R. 1987. "Searching for Justice: The Discover of IQ Gains over Time." American Psychologist 54:5–20.
Furey, Maura; Pietrini, Pietro; and Haxby, James V. 2000. "Cholinergic Enhancement and Increased Selectivity of Perceptual Processing during Working Memory." Science 290 (5500):2315–2319.
Galton, Francis. 1973. Inquiries into Human Faculty and Its Development (1883). New York: AMS Press.
Guilford, Joy P. 1967. The Nature of Human Intelligence. New York: McGraw-Hill.
Gustafsson, Jan Eric. 1984. "A Unifying Model for the Structure of Intellectual Abilities." Intelligence 8:179–203.
Hunter, John E. 1994. "The General Aptitude Test Battery." In Encyclopedia of Human Intelligence, ed. Robert J. Sternberg. New York: Macmillan.
Hunter, John E., and Hunter, Ronda F. 1984. "Validity and Utility of Alternate Predictors of Job Performance." Psychological Bulletin 96:72–98.
Jensen, Arthur R. 1998. The g Factor: The Science Of Mental Ability. Westport, CT: Praeger.
Kyllonen, Patrick C. 2001. "'g': Knowledge, Speed, Strategies, or Working-Memory Capacity? A Systems Perspective." In The General Factor of Intelligence: How General Is It? ed. Robert J. Sternberg and Elena L. Grigorenko. Mahwah, NJ: Erlbaum.
Kyllonen, Patrick C., and Christal, Raymond e. 1990. "Reasoning Ability Is (Little More Than) Working-Memory Capacity?!" Intelligence 14 (4):389–433.
Lehman, Nicholas. 1999. The Big Test: The Secret American Meritocracy. New York: Farrar, Strauss and Giroux.
Lave, Jean, and Wenger, Ellen. 1991. Situated Learning: Legitimate Peripheral Participation. Cambridge, Eng.: Cambridge University Press.
Lohman, David F. 2000. "Complex Information Processing and Intelligence." In Handbook of Intelligence, ed. Robert J. Sternberg. New York: Cambridge University Press.
Lynn, Richard. 1987. "Nutrition and Intelligence." In Biological Approaches to the Study of Intelligence, ed. Phillip. A. Vernon. Norwood, NJ: Ablex.
Mayer, Jack d., Caruso, David R., and Salovey, Peter. 2000. "Selecting a Measure of Emotional Intelligence: The Case for Ability Scales." In The Handbook of Emotional Intelligence: Theory, Development, Assessment, and Application at Home, School, and in the Workplace, ed. Reuven Bar-On. San Francisco: Jossey-Bass.
McGuire, Frederick L. 1994. "Army Alpha and Beta Tests of Intelligence." In Encyclopedia of Human Intelligence, ed. Robert J. Sternberg. New York: Macmillan.
Motowidlo, Stephan j.; Dunnette, Marvin D.; and Carter, Gary W. 1990. "An Alternative Selection Procedure: The Low-Fidelity Simulation." Journal of Applied Psychology 75:640–647.
Pentland, Alex. 1998. "Wearable Intelligence." Scientific American. 9 (4):90–95.
Roberts, Richard R., and Kyllonen, Patrick C. 1999. "Morningness–Eveningness and Intelligence: Early to Bed, Early to Rise Will Likely Make You Anything but Wise!" Personality and Individual Differences 27 (6):1123–1133.
Scholey, Andrew. 2001. "Fuel for Thought." Psychologist 14 (4):196–201.
Shute, Valerie J. 1992. "Aptitude-Treatment Interactions and Cognitive Skill Diagnosis." In Cognitive Approaches to Automated Instruction, ed. J. Wesley Regian and Valerie J. Shute. Hillsdale, NJ: Erlbaum.
Snow, Richard E. 1992. "Aptitude Theory: Yesterday, Today, and Tomorrow." Educational Psychologist 27:5–32.
Spearman, Charles. 1904. "General Intelligence Objectively Determined and Measured." American Journal of Psychology 15:201–293.
Spielberger, Charles D., and Vagg, Peter R. 1995. Test Anxiety: Theory, Assessment, and Treatment. Philadelphia: Taylor and Francis.
Steele, Claude M. 1997. "A Threat in the Air: How Stereotypes Shape Intellectual Identity and Performance." American Psychologist 52 (6):613–629.
Sternberg, Robert J. 1997. Successful Intelligence. New York: Plume.
Sternberg, Robert J., et al. 2000. Practical Intelligence in Everyday Life. New York: Cambridge University Press.
Thurstone, Louis L. 1938. Primary Mental Abilities. Chicago: University of Chicago Press.
Van Dongen, Hans P. A., and Dinges, David F. 1999. "Circadian Rhythms in Fatigue, Alertness and Performance." In Principles and Practice of Sleep Medicine, 3rd edition, ed. Meir H. Kryger, Thomas Roth, and William C. Dement. Orlando, FL: Saunders.
Patrick C. Kyllonen
Drew H. Gitomer
AFFECTIVE AND CONATIVE PROCESSES
People react emotionally to their own and others' performances, often in characteristic ways. Getting a grade of "B" in a course can produce devastation in an anxious student who expected an "A." Psychologists believe that the student's temperament interacts with expectations for the course grade to produce a negative emotional response.
Characteristic emotional reactions and certain qualities of temperament are examples of psychological processes that are affective. Affective processes include all feelings and responses, positive or negative, related to emotion-laden behavior, knowledge, or beliefs. Affect can alter perceptions of situations as well as outcomes of cognitive effort; it can also fuel, block, or terminate cognition and behavior.
Affective processes intertwine with aspects of motivation and volition. In educational situations, students' motivational beliefs and judgments of their own capabilities influence their intentions and plans. Thus students who see themselves as "not good at math" will prefer other subjects and struggle in math class. Conation, an ancient psychological concept whose dictionary definition refers to purposive striving, covers the range of motivational and volitional processes that human beings display. Motivational processes underlie the decision to pursue a goal; they are the wishes and desires that lead to intentions, in turn dictated by interest and experience. Volitional processes come into play after goals and intentions are formed; these processes reflect steps to implement goals, and ways of managing resources. Modern psychology has come to see motivation and volition as category labels for distinct conative processes.
In 1980 Ernest Hilgard wrote that the main agenda for modern scientific psychology ought to be to understand the processes underlying three central human functions–cognition (perception, memory, and the processing of information), affection, and conation. Indeed, since the beginning of the twentieth century, psychology in education has been peppered with programs of research on the qualities and characteristics of people that fit into one of these three functional categories. Intelligence, for example, is cognitive, impulsivity is affective, and self-concept is conative. Within conation, some well-studied processes influence commitments and are therefore considered motivational. One example is self-efficacy, a kind of personal capability belief. Other research examines processes that people use to protect commitments already formed–for example, self-monitoring and self-rewards. When these processes occur once a commitment is made, they are volitional.
As early as the mid-twentieth century, theories began to identify the range of variables within and between categories that could be measured validly and reliably in persons. But despite much progress up to and past Hilgard's writing in 1980, more effort is needed to explain how the triad of human functions works together at the process level. Moreover, this research agenda is virtually unknown to those outside psychology, and laypeople are frequently unaware of how psychologists use these terms.
For some purposes, affective and conative processes have proven to be so interconnected that it makes little sense even to psychologists to separate them. One group of researchers, the Stanford Aptitude Seminar, invented the hybrid term, affcon, to reflect this viewpoint. Given the complicated sociocultural context in which schooling takes place today, it is hard to dismiss the importance for educators, parents, and counselors of understanding how changes in affect can influence conation and vice versa: Both play central roles in the willingness to work and quality of effort invested by students in academics.
Three examples serve to illustrate how the interplay between academic-intellectual processes and affcon processes affects objectives of educational practitioners, decision makers, and students. The examples reflect current issues as well as persistent problems.
A school classroom is a social context offering many opportunities for students to be distracted from their work. At the same time, the organization of a classroom demands behavioral self-control. This paradoxical combination of opportunities and demands suggests the need for creative curricular experiences that engage students fully in classroom work, making them want to succeed. The concept of cognitive engagement is a prominent goal of classroom teaching, at virtually all levels of education.
Part of what new paradigms for instruction are about is providing teachers with a repertoire of strategies for promoting cognitive engagement. Modern education reform emphasizes success as its own reward, but encourages teachers to use other incentives to move students along. Activities and experiences grounded in students' own interests, and assignments that require meaningful discussion of topics and material, help to promote cognitive engagement. Also supported by empirical research are inquiry teaching methods that make thinking explicit, and uncover hidden assumptions in content ranging from narratives to persuasive arguments. Research on teaching conducted between 1970 and the early twenty-first century makes it clear that students tune out in tired models of conventional teaching, where students listen and teachers talk.
Knowledge about which reforms and strategies work in different situations has been informed by evaluations of educational programs. Frequently, these demonstrate the insufficiency of seeking direct impact on learning outcomes such as achievement scores. Rather, achievement improves as a result of tapping into the affcon responses of students as they engage in and with schoolwork.
A student who takes responsibility for learning is self-regulated and self-motivated, intentionally directing energy toward learning tasks. Self-starters have long reaped academic rewards. Research conducted between 1980 and the early twenty-first century has uncovered the attitudes, skills, and behavior that characterize self-regulated learners, and emphasized the important role played by self-regulation skills in schooling outcomes. Researchers have also designed programs to help weaker students acquire self-regulation knowledge and skills. Counselors and teachers can use such programs to teach students responsibility. Likewise, parents can model self-regulation and strategies for doing homework.
Self-regulation and personal responsibility involve affcon processes. Careful self-management is necessary when follow-through is in jeopardy; for example, when a student experiences boredom or perceives that a task will be difficult to perform. The effortful processes that mark volition come forward then, with the sense to "buckle down." Not all academic situations demand volitional control, however. In some activities in the curriculum, learning can seem to occur with no effort, almost automatically. When a person is in the affective state that Mihalyi Csikszentmihalyi called "flow," there is no need to drain volitional reserves.
Astute teachers and parents will listen and watch closely for evidence of emotional stress in school children, and encourage the highly motivated to enjoy their time between work and play. Negative physiological as well as emotional changes can result from too much pressure on children in school. Although the ability to monitor and control emotions increases developmentally, some variation remains even among adults.
An increasingly diverse population of students leads teachers to differentiate curriculum and instruction. Some common differentiation strategies have proven ineffective from an academic standpoint; for example, teaching to the bottom third of the class. There are also well-documented negative social consequences to other differentiation strategies, most notably the common practices of tracking and retention in grades. To accommodate individual differences in students, a teacher must do only good.
Some types of cooperative work groups, formed to reflect the heterogeneity of students within classes, produce positive affcon outcomes: Students give and receive help from peers; they learn how to organize and manage due dates; the whole group incurs rewards when individuals contribute best efforts. But even effective grouping arrangements work best in short bursts of time; motivation and affect are buoyed by flexibility. In forming cooperative groups teachers should take into account students' affect and self-regulatory styles as well as their status characteristics and levels of achievement. The importance of considering the affcon profile of students has historically been left out of adaptive teaching discussions and decisions.
Another important point to make about adaptive teaching is that good teachers have always addressed students according to attitudinal and work styles. For any given task, what a student presents in the way of interest and attitude, as well as prior knowledge, often dictates a particular explanation, example, or suggestion for improvement. Different explanations, examples, and suggestions will reach students with other interests or markedly opposite styles of behavior. Moreover, teaching adaptively means shifting with the student's own development. Hence, the teacher's dictum of "If you can't reach them one way, then try another" comes into play. Adaptive teaching not only requires a teacher to circumvent observed student weaknesses, but also to capitalize on strengths. When a student is removed from conventional instruction for compensatory purposes, there ought to be simultaneous efforts to develop an aptitude for conventional instruction directly. It is, after all, to the regular classroom that many special program students eventually will return.
Despite the growing body of research on affective and conative processes in education, many programs remain disjointed, even when constructs overlap considerably. There are important theoretical issues still contested, including the need for distinctions between concepts as interrelated as motivation and volition, and the precise nature of the connections in Hilgard's trilogy of mental functions. Some highly regarded theorists, such as Richard Snow and Julius Kuhl, offer sophisticated and situated (context-dependent) models, models that suggest the need for new language as well as new methods of practical assessment and clinical treatment. As the twenty-first century continues, work will advance in these directions, ultimately producing entirely different ways of thinking about affect and conation in education.
See also: Affect and Emotional Development; Instructional Strategies.
Berliner, David C., and Calfee, Robert C., eds. 1996. Handbook of Educational Psychology. New York: Macmillan.
Cohen, Elizabeth. 2001. "Equity in Schools and Classrooms." In Education across a Century: The Centennial Volume, ed. Lyn Corno. Chicago: National Society for the Study of Education.
Corno, Lyn. 1993. "The Best-Laid Plans: Modern Conceptions of Volition and Educational Research." Educational Researcher 22 (2):14–22.
Corno, Lyn. 2000. "Looking At Homework Differently." Elementary School Journal 100:529–548.
Csikszentmihalyi, Mihalyi. 1975. Beyond Boredom and Anxiety. San Francisco: Jossey-Bass.
Goleman, Daniel. 1997. Emotional Intelligence. New York: Bantam.
Hidi, Suzanne; Renninger, K. Ann; and Krapp, Andreas. l992. "The Present State of Interest Research." In The Role of Interest in Learning and Development, ed. Suzanne Hidi, K. Ann Renninger, and Andreas Krapp. Hillsdale, NJ: Erlbaum.
Hilgard, Ernest R. 1980. "The Trilogy of Mind: Cognition, Affection, and Conation." Journal of The History of Behavioral Sciences 16:107–117.
Kuhl, Julius. 2000. "The Volitional Basis of Personality Systems Interaction Theory: Applications." In Learning and Treatment Contexts. International Journal of Educational Research 33:665–703.
Oakes, Jeanne. 1985. Keeping Track: How Schools Structure Inequality. New Haven, CT: Yale University Press.
Pintrich, Paul R., and Schunk, Dale H. 1996. Motivation in Education: Theory, Research, and Applications. Englewood Cliffs, NJ: Prentice-Hall.
Randi, Judi, and Corno, Lyn. 1997. "Teachers as Innovators." In International Handbook of Teachers and Teaching, Vol. 2, ed. Bruce J. Biddle, Thomas L. Good, and Ivor F. Goodson. Dordrecht, the Netherlands: Kluwer.
Reigeluth, Charles M., ed. 1999. Instructional-Design Theories and Models: A New Paradigm of Instructional Theory, Vol. 2. Mahwah, NJ: Erlbaum.
Stanford Aptitude Seminar. 2001. Remaking the Concept of Aptitude: Extending the Legacy Of Richard E. Snow. Mahwah, NJ: Erlbaum.
Webb, Noreen M., and Palincsar, Annmarie S. 1996. "Group Processes in the Classroom." In Handbook of Educational Psychology, ed. David C. Berliner and Robert C. Calfee. New York: Macmillan.
Zimmerman, Barry J. 1990. "Self-Regulated Learning and Academic Achievement: An Overview." Educational Psychologist 25:3–17.
As the student population in the United States continues to become more ethnically diverse, the central challenge facing education is how to provide schooling experiences that maximize the participation and academic success of all students. The representation of ethnic minority students rose from 22 percent in 1972 to 38 percent in 2000, and is expected to increase dramatically through the year 2020, when more than two-thirds of the total public-school student population will be African American, Asian American, Hispanic, or Native American. Meanwhile, comparison studies continue to show a consistent gap in school achievement for various ethnic school populations.
Derived from the Greek term ethnos, meaning people, ethnicity refers to a sense of membership in and identification with a distinct group in which members perceive themselves, and are perceived by outside observers, to be bound together by a common origin, history, and culture. Cultural features that define an ethnic group include shared expectations for behavior, such as family roles, health practices, and work and recreational activities; shared values, such as religion, politics, and concepts of achievement, beauty, time and space; and shared symbols, such as language, art, music, and modes of dress. Although broad ethnic categories such as African American, Asian American, Hispanic, Native American, and white are used conventionally in the United States, there are, in fact, important national, linguistic, religious, tribal, regional, and generational differences within each of these broad categories.
Empirical Approaches: Cross-Cultural and Cultural Process
In the social sciences, two main approaches, distinct in their assumptions, foci, and methodologies, are used to investigate the role of ethnicity and culture in education: a cross-cultural approach and a cultural-process approach. In a cross-cultural approach, ethnicity and culture are viewed as separate from human behaviors. Cross-cultural researchers focus on the influence that ethnicity and culture have on human behavior. Alternatively, the cultural-process approach treats ethnicity and culture as interdependent with social processes; in other words, ethnicity and culture influence human interactions, and, at the same time, are constructed within those interactions.
Cross-cultural researchers tend to view ethnicity as relatively stable and fixed, while cultural-process researchers tend to view ethnicity as more dynamic with its content and boundaries continually under revision and redefinition. Cross-cultural researchers usually employ quantitative methodologies, such as survey questionnaires and experiments, with a focus on the attributes of individuals. Cultural-process researchers almost exclusively utilize qualitative methodologies, such as observations and interviews, with a focus on actions and interactions in context. For example, in studying parent involvement, a researcher using a cross-cultural approach might conduct a survey on a large sample of families from different ethnic groups to assess group differences in the amount of time parents spend on a variety of activities related to their child's schooling. A researcher using a cultural-process approach might interview parents from several ethnic groups on what it means to be involved with their child's school, while also engaging in an observational study of the interactions between students and parents, in order to detail the processes through which the parents engage with their children in their schoolwork. Ultimately, the combination of both a cross-cultural approach and a cultural-process approach is beneficial for a more in-depth understanding of the role of ethnicity and culture in education.
The Achievement Gap
The results of numerous cross-cultural studies indicate that many ethnic minority students are not faring well in U.S. schools. Ethnic group differences are found in school grades, standardized achievement tests, course enrollment, grade retention rates, high school graduation, and level of educational attainment. The achievement gap appears in the early school years, increases during the elementary school years, and persists through the secondary school years. While achievement gaps narrowed between 1971 and 1999, the average scores of African-American and Hispanic students have remained significantly below those of non-Hispanic white students.
Rates of grade retention tend to be higher for African-American and Hispanic students (particularly males), when compared to other groups. High school dropout rates tend to be highest for Hispanic students, followed by African-American students. African-American males tend to be disproportionately represented in special education classes. With the exception of Asian Americans, ethnic minority students are not adequately represented within programs for gifted and talented education (GATE).
In general, Asian-American students fare well academically, displaying high levels of performance on standard achievement indicators. With respect to the educational attainment level of students, high school graduation rates since 1971 have greatly increased overall for ethnic minority youth, however the rates in 2000 were lower for Hispanic and African-American students than for Asian-American and white students. Asian-American students, in general, show higher college graduation rates, as well as higher graduate degree attainment, than white students. It may be noted that for some of the cross-cultural studies, comparisons were not made across all of the major ethnic groups due to the relatively small sample sizes of Native American students and, in some cases, Asian-American students.
A number of issues require consideration in light of these general findings of ethnic group differences in school achievement. First, many studies do not account for socioeconomic differences, such as family income, parental employment and education, when examining ethnic groups. Given that the average socioeconomic status (SES) differs substantially among ethnic groups, the failure to disentangle ethnicity and SES can lead to erroneous interpretations. SES is a significant predictor of academic success, and ethnic minority families are disproportionately represented in the lower SES bracket. Moreover, schools with the highest proportion of low-income students are more likely to have fewer qualified teachers, have substantially fewer resources (computers, enrichment materials), and be located in a neighborhood with fewer informal educational resources (such as museums and libraries). Economic pressures at home, compounded by poor neighborhoods and poor schools, makes the separation of socioeconomic factors from ethnic cultural factors even more difficult. Thus, it is imperative for future studies to examine comparability as well as account for the disparities among ethnic groups with respect to SES levels.
Many educators and policymakers often perceive technology as a promising tool for leveling inequities in educational achievement. However, studies show that children who come from lower-income families (which are disproportionately ethnic minority families) have fewer computers in the home. The provision of updated educational technology within schools is uneven at best, and this, combined with unequal access within the home, limits such computer-based activities as homework completion, research, word processing of reports, and presentations.
Second, while many comparative studies typically focus on the major ethnic groups as broad groups, it is important to acknowledge that individual differences within each ethnic group are substantial. Such categorization may give the illusion of overall cultural similarity and obscure substantial national, tribal, or other subgroup differences. For example, the broad category of Asian Americans is comprised of diverse national backgrounds, including Cambodian, Chinese, Filipino, Korean, Japanese, Vietnamese, Thai, Khmer, and Asian Indian. Additionally, much variation within an ethnic group is likely to exist with respect to variables such as gender, social class, generation/immigrant status, and level of assimilation.
Studies that examine differences within an ethnic group are useful for a variety of reasons. First, within-group studies may serve to weaken the uniform, and often stereotyped, views associated with particular ethnic groups. Second, studies of within-group differences in which the subgroups differ on various demographic variables would be helpful in understanding the role that variables such as generation/immigration status, level of assimilation, language, and socioeconomic status may play. Third, within-group studies may provide a further understanding of the cultural processes underlying achievement-related outcomes.
Theoretical Models Explaining the Achievement Gap
Researchers have attempted to explain the consistent achievement gap among ethnic groups from a number of different perspectives. The explanations offered may be grouped into three theoretical models. The first, a cultural deprivation, or deficit, model, explains the poor performance of ethnic minority students as the result of an impoverished and restricted home life. The underlying theory is that "culturally deprived" or "socially disadvantaged" students do not achieve because they lack a cognitively stimulating environment. Research may identify, for example, a lack of parental support, a low value placed on education, a language-poor environment, or even low intellectual capacity. The use of whites as the norm against which other ethnic minority groups are compared may perpetuate a deficit model in which ethnic minority groups are perceived as second-rate to the majority group.
The second theoretical model, the cultural difference model, points to differences in values, expectations, languages, and communication patterns between teachers and students–or between schools and families–as a source of difficulty for ethnic minority students. The underlying theory is that the social organization, learning formats and expectations, communication patterns, and sociolinguistic environment of schools are incongruent with the cultural patterns of different ethnic groups, and therefore limit the opportunities for student success. For some researchers in this area, the important differences exist at the level of interpersonal communication, where teachers and students are unable to fully understand each other. Important communicative differences may be identified at many levels, including formal language (e.g., English versus Spanish), conventions for interacting (e.g. distance between speakers, acceptable physical contact, and turn-taking rules), preferences for rhetorical style (e.g., the use of emotion in persuasion), and storytelling patterns.
A number of studies have also suggested that differences between social worlds, such as home and school, can be difficult for ethnic minority students to negotiate. For example, where U. S. public schooling tends to encourage independence, with competition and rewards for individual achievement, some ethnic groups may tend to encourage interdependence among members, with rewards for collaborative effort. Socialization practices also vary across ethnic groups, so that, for example, the parenting styles acceptable within one ethnic group may vary significantly from the parenting styles valued by schools and educators. Likewise, expectations for the role of parents in education may differ across ethnic groups, so that while some teachers expect active parent involvement at school, parents' conceptions of involvement may be altogether different. Additionally, some parenting practices may focus on social and observational learning and apprenticeship examples, and thus favor visual rather than auditory information processing. Insofar as early learning experiences may vary systematically by ethnic group, ethnicity can have important consequences for learning in (and out) of school.
Some researchers argue that the cultural difference model presumes ethnic differences to be inherently problematic when, in fact, it is the perception of differences and how people act on such perceived differences that is an important source of difficulty for minority students. These researchers typically utilize a cultural-process approach and focus on social interactions. Barriers to school success are identified by examining how students, teachers, and parents understand patterns of language use and socialization. In any case, the cultural difference model has made important contributions to understanding the relationship between ethnicity and school achievement by pointing out that children from different ethnic groups may vary in culturally patterned ways, some of which are relevant for educators.
A third theoretical model, which can be termed sociosystemic, moves outside the classroom in an effort to identify the social, economic, and political forces that contribute to the achievement gap. Researchers have come to recognize how differences in perceived economic opportunity affect the level of school engagement for ethnic minority students. For example, when students' families, peers, and community members hold beliefs that economic and social opportunities are limited, regardless of school achievement, students are far less likely to engage in meaningful ways with formal educational activities. Such beliefs may lead to a youth's active resistance to school or a "disidentification" with schooling overall (i.e., when students are apathetic or disaffected toward schooling). Some studies have identified patterns of differences within ethnic minority groups, where school achievement varies according to the conditions of one's minority status. Specifically, members of voluntary minority groups, including those who immigrated for improved economic opportunity, often do better in school than members of involuntary minority groups, such as those who were colonized or whose residency was forced (e.g., through slavery). Research utilizing a sociosystemic model has also identified schools as the places where societal pressure to assimilate is most keenly perceived–and often resisted. Many researchers and practitioners, for example, have noted that student peer groups link school achievement to the acceptance or rejection of various identities.
Each of the theoretical models on the achievement gap–cultural deficit, cultural difference, and sociosystemic–corresponds with specific policies (federal, state, regional, district, school), curricula, and teaching practices seeking to narrow the gap. Within the cultural deficit model, educators are encouraged to intervene as early as possible in children's development. Many federal and state programs, such as Head Start, focus on the compensation of deficits. From the cultural difference model, schools and teachers are encouraged to make better use of the knowledge and practices of diverse cultures and to form home—school connections. The various forms of multicultural education also derive from a basic cultural difference model, as do some bilingual education programs. Also included in this model are recent efforts to develop theories and practices of culturally-relevant pedagogy, an approach to teaching that modifies both curriculum and communication to reflect the diverse cultural practices of students. The cultural difference model appears to decrease the pressure on children to conform to mainstream culture standards, yet increase the pressure on teachers and schools to transform their practices to better reflect the diversity that is present around them.
For those with a sociosystemic perspective, repairing the achievement gap demands a commitment to an ongoing examination of the social and political systems, along with direct action to counter systemic bias. Few formal policies or programs with this model exist, although critical theory and criticalpedagogy are actively promoted within this perspective. Critical theory seeks to make systemic injustice visible and critical pedagogy encourages teachers and students to understand and contend with stereotyping, racism, sexism, and other forms of prejudice.
Because of the tremendous variation within any ethnic group, it would be inappropriate to make generalizations about the needs and abilities of any individual student based solely on his or her membership in a given ethnic group. That said, there is no doubt that variation does exist along several lines, and educators should be aware of this. Perhaps most important of all, members of the teaching and learning community should be reflective of their own perceptions and actions with respect to all learners. It must be recognized that just as schools themselves vary, so do the students within them. Schools are the spaces where a great deal of a youth's development occurs, and on multiple levels, including academic achievement, identity, and social competence. In the end, ethnicity and culture must become part of the face of education in order to reflect and better serve our youth as they encounter an increasingly diverse world.
See also: Literacy and Culture; Multicultural Education; Poverty and Education; Race, Ethnicity, and Culture.
Banks, James A. 1999. An Introduction to Multicultural Education. Needham Heights, MA: Allyn and Bacon.
Barth, Frederick. 1969. Ethnic Groups and Boundaries. Boston: Little, Brown.
Bennett, Christine. 1997. "Teaching Students as They Would Be Taught: The Importance of Cultural Perspective." In Culture, Style, and the Educative Process, ed. Barbara J. Shade. Springfield, IL: Charles C. Thomas.
Erickson, Frederick. 1987. "Transformation and School Success: The Politics and Culture of Educational Achievement." Anthropology and Education Quarterly 18 (4):335–356.
Fan, Xitao. 2001. "Parental Involvement and Students' Academic Achievement: A Growth Modeling Analysis." Journal of Experimental Education 70 (1):27.
Greenfield, Patricia M. 1997. "Culture as Process: Empirical Methods for Cultural Psychology." In Handbook of Cross-Cultural Psychology, Vol. 1, ed. John W. Berry, Ype H. Poortinga, and Janek Pandey. Needham Heights, MA: Allyn and Bacon.
McDermott, Raymond P. 1997. "Achieving School Failure, 1972–1997." In Education and Cultural Process: Anthropological Approaches, 3rd edition, ed. George D. Spindler. Prospect Heights, IL: Waveland Press.
Meece, Judith L., and Kurt-Costes, Beth. 2001. "The Schooling of Ethnic Minority Children and Youth." Educational Psychologist 36 (1):1–7.
National Center for Education Statistics. 2000. National Assessment of Educational Progress (NAEP), 1999 Long-Term Trend Assessment. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement.
National Center for Education Statistics. 2001a. The Condition of Education. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement.
National Center for Education Statistics. 2001b. Dropout Rates in the United States: 2000. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement.
Ogbu, John, and Simons, Herbert. 1998. "Voluntary and Involuntary Minorities: A Cultural-Ecological Theory of School Performance With Some Implications For Education." Anthropology and Education Quarterly 29 (2):155–188.
Okagaki, Lynn. 2001. "Triarchic Model of Minority Children's School Achievement" Educational Psychologist 36 (1):9–20.
Phelan, Patricia; Davidson, Ann L.; and Yu, Han C. 1991. "Students' Multiple Worlds: Navigating the Borders of Family, Peer, and School Cultures." Anthropology and Education Quarterly 22:224–250.
Shields, Margie K., and Behrman, Richard E. 2000. "Children and Computer Technology: Analysis and Recommendations." The Future of Children 10 (2):4–30.
Steele, Claude. 1992. "Race and the Schooling of African-American Americans." Atlantic Monthly 269 (4):68–78.
Jason Duque Raley
Angela D. Whipple
GENDER EQUITY AND SCHOOLING
The 1992 publication of the landmark report How Schools Shortchange Girls, by the American Association of University Women (AAUW) Educational Foundation, brought gender equity to the forefront of educational reform. Since then, the focus of discussions about quality education for all students has shifted from equality to equity. In the context of gender, equitable education appropriately addresses the needs of both girls and boys rather than assuming that those needs are identical. Thus, equity in education provides equal opportunities for reaching a shared standard of excellence. Simply defined, gender equity in education is the absence of gender differences in educational outcomes.
Researchers struggling to identify the origins of gender differences have examined a range of theories, including biological, psychoanalytic, social learning, and cognitive developmental approaches to gender differences. While there has been ongoing debate about the role of biology as a source of cognitive differences, educators agree that changes in educational outcomes must focus on the psychosocial aspects of behavior. Regardless of the specific causes of gender gaps, schools have a mission to ensure that all students can fully participate in and experience educational success. While acknowledging that individual differences within each gender are substantial, this analysis will focus on girls and boys as aggregate groups in an examination of similarities and differences in schooling experiences and outcomes. These differences will be reviewed in relation to mathematics, science, humanities, technology, and extracurricular activities, including differences in both attitudes and outcomes. Except where noted, the information presented here is based on data from public schools in the United States, for kindergarten through twelfth grade. Statistical findings reported are based primarily upon results from a number of large-scaled studies and reports, such as those of the American Association of University Women, the U.S. Department of Education, and the National Center for Education Statistics.
Gender Equity Pertains to Boys and Girls
Because much of the literature regarding gender in education has focused on areas where girls are underserved, some have argued that gender equity appeared to pertain to girls only. Near the turn of the new millennium, however, a few authors brought attention to the education gender gap for boys, showing that the national phenomenon of male underachievement has been nearly invisible in the gender-equity literature. Gender equity is not "for girls only," and improvement for one gender should not imply a disadvantage for the other.
Early Behavioral Outcomes
Girls and boys appear to have similar types and amounts of opportunities to help them prepare for elementary school. For example, equal numbers of girls and boys are enrolled in center-based preschool programs and receive equivalent amounts of literacy activities at home. Preschool girls perform higher on tests of small motor skills than boys, and they show fewer signs of developmental difficulties in areas such as physical activity, attention, and speech. Boys in the early grades are more likely to be identified as learning disabled, to be tracked into remedial and special education classes, to be diagnosed with attention-deficit disorder, to be involved with crimes and violence on school property, to repeat a grade, and to be suspended from school. Subsequently, boys are more likely to drop out of school altogether. Girls, on average, receive higher grades than boys in all subjects beginning in the early grades–a trend that continues throughout middle and secondary school.
Aspects of the classroom environment have been found to foster and/or reinforce gender biases. Often unintentionally, many teachers exhibit gender biases when interacting with students. For example, teachers generally give more attention to boys than to girls. Ironically, this is partly caused by the fact that girls tend to be better behaved in the classroom and more attentive to assigned tasks. Teachers' attention is often consumed by boisterous or aggressive behaviors more typical of boys. Teachers also tend to give less feedback (positive or negative) to female students. Without such feedback, girls may be deprived of valuable opportunities to evaluate their own behaviors and ideas and to learn to cope with constructive criticism. Boys, on the other hand, when reinforced for their boisterous behaviors, may fail to learn self-control, listening skills, and respect for others.
Classroom materials may also serve to reinforce gender biases in the schools. Although editors of textbooks and other instructional materials have made greater efforts to include women since 1992, female characters continue to play a smaller role than male characters in classroom materials. Moreover, when female characters are represented, they are often shown in stereotypical roles that reinforce gender biases.
Among the core academic subjects, some are considered typically "male," and others "female." In spite of increased female enrollment in mathematics and science courses, ideas persist that these subjects are for boys, while the humanities and social sciences are for girls. Students often act on these stereotypes in class activities by self-selecting into groups and roles according to gender norms. In science classes, for example, boys often dominate laboratory equipment, controlling hands-on experiments while girls observe and take notes. Similarly, when boys and girls work together on computers, boys tend to sit where they can more easily view the monitor and take control of the mouse. Also, computer usage is typically dominated by boys during after-school activities.
Differences remain in boys' and girls' attitudes toward academic subjects. On average, girls report liking mathematics and science less than boys, and having less confidence in their ability to succeed at these subjects. Girls also rate themselves lower in computer abilities than boys, and are far more likely to suffer from math anxiety or tech anxiety. They tend to perceive these subjects as being less useful in their lives, which may diminish their achievement motivation in these areas. Hence, fewer girls plan to choose careers in mathematics, science, or technology. Additionally, whereas boys tend to believe their success in academics is the result of ability, girls tend to attribute academic successes and failures to luck and other external factors. This attribution of success to factors other than effort may lead some girls to feel "helpless," particularly in subjects they perceive as male domains.
Mathematics and Science
Encouragingly, the gender gap in mathematics and science course enrollment is closing. At least as many girls as boys are now enrolling in algebra, geometry, precalculus, calculus, trigonometry, and statistics/probability courses. Moreover, girls receive higher grades than boys, on average, in math classes (as in all academic subjects). Nevertheless, girls consistently lag behind boys in scores on standardized mathematics assessments, including the mathematics sections of the Preliminary SAT (PSAT), the SAT, the National Assessment of Educational Progress (NAEP), and mathematics Advanced Placement (AP) exams. Such "high-stakes" exams are critical factors in determining college admission and scholarship awards, and lower scores can therefore limit career opportunities.
Gender differences found in science are similar to those in mathematics. In high school, girls are more likely than boys to take biology and chemistry, while about equal numbers of girls and boys enroll in engineering and geology. Physics, however, continues to be a male-dominated subject. Girls also take fewer AP science exams than boys, and they receive proportionately fewer top grades.
Humanities, an area in which female students typically excel, receives little attention in the research about gender in education. With the shrinking of gender gaps in math and science, however, the humanities are quickly becoming an area in which the most substantial gender differences can be found. More girls than boys enroll in English, sociology, psychology, foreign languages, and fine arts. Particularly notable because of its role in standardized testing is the subject of English. Girls consistently outnumber boys in English classes, and significantly outperform them in most reading and writing assessments, with the notable exception of the AP English exam.
Many educators, administrators, and policymakers advocate technology as a tool for empowering otherwise disadvantaged groups, thus leveling inequities in educational achievement. Nevertheless, a number of disturbing gender inequities have already been observed in educational technology use. For example, female enrollment in computer science courses lags significantly behind that of males. Moreover, only a small percentage of the students taking the AP exam in computer science (17% in 1995 and 1996) is female. Many more men than women are computer educators, and women remain greatly underrepresented in computer technology careers. Such disparities are of particular concern because technology skills are increasingly crucial for high-skill, well-paying jobs.
Several hypotheses have been suggested to explain the gender gaps in technology. Social and parental expectations, along with teacher biases, are commonly suggested reasons. Differential access to computers is also thought to play a significant role in creating gender disparities. It is often reported that parents are more likely to buy computers for boys than girls. Moreover, even when given equal access, boys use computers at home more frequently than girls. Additionally, computer software may appeal more to boys than to girls. Computer-related toys and games, designed mostly by males, are marketed primarily to boys and are typically found in the "boys' aisles" in toy stores. Games, which play a significant role in computer use, are dominated by images of competition, sports, and violence, which typically appeal more to males. These games, along with other technology-based materials, may help to perpetuate gender stereotypes.
Extracurricular School Activities
Girls and boys tend to participate in different types of extracurricular activities, representing traditional areas of gender dominance. Females, for example, are more likely than males to participate in performing arts, belong to academic clubs, work on the school newspaper or yearbook, or participate in the student council or government. Females are also more active than males in community service. Males, on the other hand, are more likely than females to play on athletic teams. While girls' rates of participation in team sports have increased since 1972, equity has not been achieved.
The differences in boys' and girls' choices of extracurricular activities may have important consequences. For example, sports participation has been linked to higher academic achievement as well as greater leadership capacity, better overall health, higher self-esteem, and more positive attitudes toward school. On the other hand, participation in non-sports-based extracurricular activities has also been found to build self-esteem, leadership, and social skills; to improve general health; and is associated with higher mathematics and reading test scores.
It is important to acknowledge that the gender differences described above are based on average group scores. Girls (and boys) are not a uniform group and their needs are certainly not singular. In fact, large differences exist within each gender across different racial, ethnic, and socioeconomic groups. For example, African-American girls, despite the existence of both racial and gender discrimination, have a higher self-esteem, healthier body image, and greater social assertiveness than their white female counterparts. These girls also perform better on many academic indicators than their black male counterparts. Similarly, Latino girls score higher than Latino boys in mathematics by the eighth grade, and in science by the twelfth grade–contradicting patterns for girls on the whole. However, Latino females also have the highest dropout rate of all groups of girls, with one in five leaving school by the age of seventeen.
Race, ethnicity, and socioeconomic status (SES) appear to play a larger role than gender in determining enrollment in remedial and special education classes; and participation rates are lower in all extracurricular activities for low-SES students. Furthermore, students in ethnically diverse and low socioeconomic schools have less access to technology. Finally, regional differences may also contribute to gaps in educational outcomes. Girls in rural southern regions of the country consistently perform below girls from rural and nonrural areas in other regions. Greater understanding of gender, racial, ethnic, regional, and socioeconomic class differences and needs can only improve U.S. schooling. Building on all students' cultures, interests, and ways of knowing can make schooling experiences meaningful to their lives and useful for addressing social problems. At its core, educational equity seeks to enrich classrooms, expand choices, and widen opportunities for reaching a shared standard of excellence for all students.
See also: Developmental Theory, subentry on Cognitive and Information Processing; Gender Issues, International; Moral Development; Motivation, subentry on Instruction; Single-Sex Institutions; Title IX.
American Association of University Women. 1992. "How Schools Shortchange Girls: The AAUW Report, A Study of the Major Findings on Girls and Education." Washington, DC: American Association of University Women Educational Foundation.
American Association of University Women. 1998. "Gender Gaps: Where Schools Still Fail Our Children." Washington, DC: American Association of University Women Educational Foundation.
Bae, Yupin; Choy, Susan; Geddes, Claire; Sable, Jennifer; and Snyder, Thomas. 2000. "Educational Equity for Girls and Women." NCES publication 2000-030. Washington, DC: National Center for Education Statistics.
Fox, Lynn H., and Soller, Janet F. 2001. "Psychosocial Dimensions of Gender Differences in Mathematics." In Changing the Faces of Mathematics: Perspectives on Gender, ed. Walter G. Secada, Joanne R. Becker, and Gloria F. Gilmer. Reston, VA: National Council of Teachers of Mathematics.
Greenfield, Teresa A. 1996. "Gender and Grade Level Differences in Science Interest and Participation." Science Education 81:259–276.
Harrell, William, Jr. 1998. "Gender and Equity Issues Affecting Educational Computer Use." Equity and Excellence in Education 31 (3):46–53.
National Center for Education Statistics. 2000. National Assessment of Educational Progress (NAEP), 1999 Long-Term Trend Assessment. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement.
Orenstein, Peggy. 1994. Schoolgirls: Young Women, Self-Esteem, and the Confidence Gap. New York: Bantam Doubleday.
Pollack, William. 1998. Real Boys: Rescuing Our Sons From the Myths of Boyhood. New York: Random House.
Sadker, Myra, and Sadker, David. 1994. Failing at Fairness: How America's Schools Cheat Girls. New York: Scribner's.
Sommers, Christina H. 2000. The War against Boys: How Misguided Feminism Is Harming Our Young Men. New York: Simon and Schuster.
Valentine, Elizabeth. 1998. "Gender Differences in Learning and Achievement in Mathematics, Science and Technology Strategies for Equity: A Literature Review." ERIC Document Reproduction Service ED 446915.
Heather A. Tomlinson
Angela D. Whipple
"Individual Differences." Encyclopedia of Education. . Encyclopedia.com. (August 22, 2017). http://www.encyclopedia.com/education/encyclopedias-almanacs-transcripts-and-maps/individual-differences
"Individual Differences." Encyclopedia of Education. . Retrieved August 22, 2017 from Encyclopedia.com: http://www.encyclopedia.com/education/encyclopedias-almanacs-transcripts-and-maps/individual-differences
The study of individual and group differences in psychological traits is the domain of differential psychology. The measurement of such differences has led to the accumulation of a vast array of descriptive data that is of direct scientific and practical interest. A more fundamental aim of differential psychology, however, is to provide one approach to an understanding of behavior. Differential psychology approaches this task through a comparative analysis of behavior under varying environmental and biological conditions. By relating observed behavioral differences to known concomitant circumstances, we are able to identify the relative contributions of different factors to behavior development. If we can discover why one individual reacts differently from another, we shall thereby advance our knowledge of what makes each behave as he does.
Individual differences in behavior are not limited to the human species; they occur throughout the animal scale. Investigations of animal behavior, from unicellular organisms to anthropoid apes, reveal wide individual differences in learning, motivation, emotionality, and other measurable traits. So large are these differences that the distributions of individual performance overlap, even when widely separated species are compared. When tested with the same learning problem, for example, the brightest rat in a given sample may excel the dullest monkey (see Anastasi 1958, pp. 48-53).
Interaction of heredity and environment
All traits are the result of innumerable and complex interactions between the individual’s heredity and his environment. An individual’s heredity consists of the genes he receives from each parent at conception. Genes are units of complex chemical substances that affect the course of the individual’s development from the one-cell stage to the mature organism. If there is a chemical deficiency or imbalance in one of these genes, a seriously defective organism may result, with bodily anomalies as well as severely retarded intelligence. Certain rare forms of mental deficiency, such as that associated with phenylketonuria, have been traced to defective genes. In these cases, some of the minimum physical prerequisites for normal intellectual growth are lacking. Except for such pathological deviates, however, heredity sets very broad limits to behavior development. Within these limits, what the individual actually becomes depends upon his environment.
Environment includes the sum total of stimuli to which the individual responds from conception to death. It comprises a vast multiplicity of factors, ranging from air and food to the social and emotional climate of home and community and the beliefs and attitudes of one’s associates. Environmental influences begin to operate before birth. Nutritional deficiencies, toxins, and other chemical or physical conditions of the prenatal environment may exert a profound and lasting effect upon both physical and mental development. Several varieties of mental deficiency, for example, result from abnormalities of prenatal environment. Such terms as “inborn,” “innate,” and “congenital” may be misleading because they suggest that all characteristics present at birth are hereditary, which is not the case. Another common confusion is that between organic and hereditary conditions. Mental deficiency resulting from early brain injury, for instance, may be properly said to have an organic but not a hereditary origin.
The research techniques employed to investigate the operation of hereditary and environmental factors in behavioral development may be classified under three major approaches, namely, selective breeding, experimental variation, and statistical studies of familial resemblance. [SeeGenetics, article onGenetics and Behavior.]
Selective breeding for behavioral characteristics has been successfully applied to several species. From a single initial group of rats, for example, it proved possible to breed two strains comprising good and poor maze-learners, respectively (Tryon 1940). After seven generations during which good performers were mated only with good performers and poor performers only with poor performers, virtually no overlapping remained between the distributions of maze scores of the two strains. Later cross-breeding of good and poor performers yielded results indicating that a large number of genes influence maze performance in rats. That the two strains did not differ in general learning capacity, however, was demonstrated by the finding that both strains performed equally well in certain other learning problems (Searle 1949). Other data suggested that emotional and motivational factors may have played an important part in the maze performance of the two strains. There is also evidence that the two strains differed significantly in certain biochemical factors that affected the efficiency of brain action (Rosenzweig et al. 1960).
More recent selective breeding experiments have extended these procedures to other behavior functions and other types of organisms (see Fuller & Thompson 1960). Of particular significance was the development of techniques for measuring individual differences in behavior among such organisms as the fruit fly Drosophila (Hirsch 1962). It thus became possible to capitalize on the mass of available genetic knowledge regarding the morphology of Drosophila, as well as on such other advantages as the short time span between generations and the abundance of progeny. By these procedures, a strain of fruit flies has been bred that will fly toward a source of light and another that will fly away from it. Similarly, one strain has been developed that tends to fly upward, and another downward, when released in a vertical maze. [SeeEugenics.]
A second approach to the heredity-environment problem is concerned with the behavioral effects of systematic variations in experience. This method has frequently been used with animals to study a wide variety of activities, ranging from the swimming of tadpoles and the singing of birds to sexual behavior and care of the young (see, for example, Beach & Jaynes 1954). Many experiments have utilized artificial devices to reduce or cut off sensory stimulation or to eliminate opportunity for the exercise of specific motor functions, in order to discover how far a function will develop in the absence of appropriate experience. Others have followed the opposite approach, providing intensive controlled training in various activities. Significant effects of such prior experiences have been reported for nearly all aspects of behavior, including perceptual, motor, learning, emotional, and social reactions. Through such experiments, many activities formerly regarded as completely unlearned or “instinctive,” such as nest building and care of the young by rats, have been shown to depend upon the animal’s prior experiences (Beach & Jaynes 1954; Birch 1956). Even when the animal has no opportunity to learn the specific activity in question, his behavior may be influenced by the exercise of other, related functions. [Seeethologyandinstinct.]
Of particular interest to differential psychology is a group of experiments on monkeys demonstrating the effect of prior experience upon learning ability itself (Harlow 1949). Through the formation of learning sets, the animals were able to learn the solution of complex problems because of their prior experience in solving simpler problems of a similar nature. By means of this problem-solving experience, the animal thus “learns how to learn.” Other research has shown that animals exposed to a rich variety of perceptual experience during early life are better subsequent learners than those deprived of such experience.
In studies of infants and young children, one group of experiments has utilized the method of co-twin control. In these experiments, one member of a pair of identical twins is given intensive training in some activity, such as climbing stairs or manipulating objects, while the other is retained as a control subject and temporarily prevented from exercising the function under investigation. The results generally show that, if training is introduced when the child is physically ready for it, progress will be faster than if training is given earlier. Another method is the comparative study of children reared in culturally deprived or psychologically limited environments, such as isolated mountain communities, gypsy caravans, houseboats, and orphanages. Considerable intellectual retardation has been found in all these situations, the retardation becoming more severe with increasing age. Some studies have demonstrated significant improvement in intellectual functioning as a result of preschool education on the part of orphanage children and other culturally deprived groups.
Among adults, it is well established that intelligence test scores correlate highly with the amount of schooling completed. Follow-up studies of groups retested after 10 to 30 years have revealed that individuals who continue their education longer show larger mean gains in intelligence test scores than do those with less intervening education (see Anastasi 1958, chapters 4 and 7).
The third major approach is based upon statistical analyses of familial resemblances and differences. Similarity of performance on both aptitude and personality tests has been investigated for parents and children, siblings, and twins (including both fraternal and identical pairs). In general, the closer the hereditary relation, the more similar the test scores will be. On most intelligence tests, for example, identical twin correlations are close to .90, being nearly as high as the correlations between test and retest scores of the same persons. Fraternal twin correlations cluster around .70; those between siblings cluster around .50, as do those between parents and children. It should be noted, however, that a family is a cultural as well as a biological unit and that a hierarchy of environmental similarity and mutual influence parallels the hierarchy of hereditary relationships. Special studies of foster children and of identical twins reared apart permit some isolation of hereditary and environmental influences, but various uncontrolled factors preclude definitive interpretation of results (see Anastasi 1958, chapter 9).
Physique and behavior
The term “physique” is herein used to refer collectively to all organic characteristics, including anatomical, physiological, and biochemical properties of the organism. The relationship between physique and behavior concerns the differential psychologist for both theoretical and practical reasons. Investigation of the role of physical factors in the development of psychological traits advances our understanding of the causes of individual differences in these traits. Insofar as heredity contributes to behavioral differences, moreover, identification of the physical bases of such differences is an important step in tracing the long and intricate path from gene to behavior. From a practical standpoint, interest in the relationship between physical and psychological traits stems from the possibility of assessing people and predicting behavior on the basis of physical characteristics.
In evaluating the results of any study of the relationship between physique and behavior, we must bear in mind that a significant correlation may mean that the physical condition influences the behavior in question, or that the behavior influences the physical condition, or that both result independently from the common influence of some third factor. [SeePsychology, article onconstitutional psychology.] The many ways in which physical factors influence behavior may be arranged along a continuum, ranging from relatively direct and rigidly limiting control to highly indirect and flexible relationships (see Anastasi 1958, chapter 5). The former extreme is illustrated by neurological, glandular, and metabolic disorders (of either hereditary or environmental origin) that lead to severe behavioral pathology. The abnormally small brain of the microcephalic, the underactive thyroid of the cretin, and the metabolic disorder of the phenylketonuric are examples of this mechanism. In all these instances, the individual lacks the minimum organic prerequisites for normal intellectual development. Unless the physical disorder can be corrected at an early developmental stage, behavioral deficiencies will result.
A more indirect influence of physique on behavior is illustrated by severe sensory or motor handicaps, such as blindness, deafness, or cerebral palsy, which reduce normal social intercourse and interfere with education. Unless special instructional techniques are employed, these physical handicaps may seriously retard intellectual development and affect personality in various ways. Depending upon concomitant circumstances, however, these physical conditions may lead to very dissimilar effects in different individuals.
At the other extreme of the continuum of indirectness is the operation of social stereotypes. Through this mechanism, the individual’s visible physical characteristics serve as social stimuli, which elicit differential responses from his associates. They may thus affect the attitudes he encounters, the opportunities he receives, and the shaping of his own self concept. As a result, his behavior may gradually come to approximate that associated with the stereotype.
The reverse relation, in which behavior influences physique, is illustrated by the powerful shoulder muscles of the swimmer and by the scholar’s stoop. Smiles and frowns, too, eventually leave their marks upon the human countenance. Of particular interest in this connection are psychosomatic disorders, that is, physical diseases in whose development psychological factors play at least a contributing part. From another angle, research on immigrant groups has demonstrated that such characteristics as stature and skull shape may be influenced by dietary habits, child-rearing practices, and other culturally determined behavior. Still another example is provided by comparative studies of schizophrenics and normals, in which organic differences may result from differences in emotional stress, degree of activity, nutritional state, and other behavioral variables associated either with the psychotic condition itself or with institutionalization.
The third type of causal relation between physique and behavior is that in which the correlation results from the common influence of a third factor, such as socioeconomic level. The child reared in a superior home, for example, has richer opportunities for intellectual development, as well as better diet, hygiene, and medical care, than does the child who grows up in a city slum or poor rural area. Consequently, some positive correlation will be found between intelligence and a number of physical conditions within a culturally heterogeneous population. The correlation usually disappears, however, when socioeconomic level is held constant (see Anastasi 1958, chapter 5).
Nature and distribution of intelligence
Intelligence has been commonly identified with the intelligence quotient (IQ) obtained on an intelligence test. Such tests do reflect, at least partly, the concept of intelligence current in the culture in which they were developed. Nevertheless, once an intelligence test has undergone the years of preparation and standardization required for its construction, it may tend to freeze a particular concept of intelligence and thereby retard change. Moreover, intelligence tests are designed to meet practical demands within specific settings. Hence they often represent a compromise between practical testing needs and the concept of intelligence that might have developed in the less restricted context of basic research. [SeeIntelligence andintelligence testing.]
Most intelligence tests measure chiefly scholastic aptitude, or that combination of abilities required for school achievement. Modern intelligence testing originated with Alfred Binet’s development of a test to assess intellectual retardation among school children. Current intelligence tests have frequently been validated against such academic criteria as school grades, teachers’ ratings of intelligence, promotion and graduation data, and amount of schooling completed. High positive correlations between test scores and these criterion measures are regarded as evidence that the test is a valid measure of intelligence.
With regard to content, most intelligence tests are heavily weighted with verbal aptitudes. To a lesser extent, they may also sample arithmetic skills, quantitative reasoning, and memory. Different intelligence tests, moreover, may cover somewhat different combinations of abilities. Nonlan-guage and performance tests, for instance, often make much heavier demands upon spatial visualization, perceptual speed and accuracy, and abstract reasoning than do the usual verbal-type tests. It is largely for this reason that an IQ should always be accompanied by the name of the test from which it was derived. [SeeAchievement testing.]
Following the widespread application of intelligence tests, psychologists soon recognized that certain aptitudes remained largely unmeasured by these tests. The increasing participation of psychologists in vocational counseling and in the screening and classification of industrial and military personnel highlighted the need for tests of other aptitudes. As a result, so-called special aptitude tests were developed to supplement general intelligence tests in mechanical, clerical, musical, artistic, and other aptitude areas. [SeeAptitude Testingandvocational interest testing.]
At the same time, clinical psychologists working intensively with individual cases were impressed with the large intraindividual differences often found from one intelligence test to another, or among different parts of the same intelligence test. Thus an individual might score consistently better on performance than on verbal tests; or within the same intelligence test, he might do well on numerical and poorly on verbal tasks.
Concurrently, basic research on the nature of intelligence was being conducted by the techniques of factor analysis. Essentially, these techniques involve statistical analysis of the intercorrelations among test scores in the effort to discover the smallest number of independent factors that can account for their interrelations. Among the aptitudes or “factors” thus identified are verbal comprehension, word fluency, arithmetic skills, quantitative reasoning, perceptual speed, spatial visualization, and mechanical comprehension. Through factor analysis, what had formerly been called intelligence could itself be subdivided into relatively independent aptitudes, and these aptitudes could be recombined with some of those underlying special aptitude tests to provide a more comprehensive picture of intelligence.
Later factor-analytic research has been extending the concept of intelligence in our culture to include creativity, originality, and divergent thinking, in contrast to the comprehension and retention skills emphasized in traditional intelligence tests (Guilford 1959). Several research projects concerned with creativity have been exploring new testing areas and developing new types of tests suitable for measuring divergent thinking in both children and adults. Although these new test materials have not been incorporated to any appreciable extent in commercially available intelligence tests, it is likely that they will be utilized increasingly in the intelligence tests of the future. [SeeFactor analysis.]
Recognizing that the IQ is a composite measure and that its nature shifts with changing concepts of intelligence, we may nevertheless inquire into its distribution in the population. In intelligence, as in all psychological traits, individuals do not fall into sharply separated categories or types. Instead they vary by degree along a continuous scale. Most psychological tests are constructed so as to conform with the bell-shaped normal probability curve, with the greatest clustering of persons near the center of the range and a gradual decrease in numbers as the extremes are approached. First derived by mathematicians in their study of probability, the normal curve is obtained whenever the variable measured is the result of a very large number of independent and equally weighted factors. In view of the extremely large number of genes and of environmental factors that contribute to the development of intelligence in the general population, the normal curve is generally accepted as the most appropriate model for the distribution of intelligence.
The mentally deficient and the gifted represent the lower and upper extremes of the distribution of intelligence. Because the distribution is continuous, there is no clearly deined separation between these groups and the normal. In terms of intelligence test performance, mental deficiency is customarily identified with IQ’s below 70. About 2 to 3 per cent of the general population fall in this range. Decisions regarding the disposition and treatment of individual cases ought, of course, to take much more than an IQ in account. They should be based upon a comprehensive study of the individual’s intellectual level, social competence, educational history, physical condition, familial situation, and other pertinent data. [SeeMental Retardation.]
With regard to etiology, one distinction is that between unifactor and multifactor mental deficiency. Unifactor cases are extreme deviates; they manifest both organic pathology and intellectual defect, which are traceable to a single defective gene or to a major environmental disturbance. Multifactor defectives, on the other hand, represent merely the lower end of the normal distribution of intelligence. They exhibit varying degrees of deficiency, depending upon the particular combination of adverse hereditary and environmental factors in each case. Since the unifactor defectives are added to the multifactor defectives at the lower end of the distribution, the frequency of low IQ’s should exceed that expected in a mathematically derived normal probability curve. Large-scale surveys of the distribution of IQ’s in various populations do, in fact, reveal such a deviation from normality at the low end of the scale (Dingman & Tarjan 1960; Roberts 1952).
The intellectually gifted have been investigated by many techniques and from many points of view. In the monumental study conducted by Terman and his associates at Stanford University, approximately one thousand California school children with Stanford-Binet IQ’s of 140 or higher were intensively examined and followed up through adulthood (Terman & Oden 1959). Slightly more than 1 per cent of the general population obtain IQ’s at this level. The results of the Stanford study, which have been corroborated in other studies conducted elsewhere, revealed the gifted child as typically successful in school, healthy, emotionally well-adjusted, having wide interests, and excelling his average classmates in nearly every trait measured. Although there were individual exceptions, the group as a whole clearly dispelled the early stereotype of the intellectually gifted child as weak, sickly, timid, and narrowly specialized. As they grew into maturity, the California gifted children amply fulfilled their youthful promise in outstanding adult achievements. [SeeTerman; creativity, article ongenius and ability.]
Since the middle of the twentieth century, the most conspicuous development in the investigation of superior deviates is to be found in research on creativity with both adults and children (Golann 1963). Although such research is yielding a wealth of data with important practical and theoretical implications, we must guard against exaggerating the distinction between creativity and intelligence as traditionally measured. To be sure, neither a high IQ on current intelligence tests nor high academic achievement is identical with creativity. These qualifications do not ensure that an individual will make outstanding contributions. On the other hand, they do not preclude creativity, nor are they completely unrelated to it. Traditional intelligence tests show a moderate but significant positive correlation with measures of creativity. Rather than differentiating between intelligence and creativity, moreover, we need to broaden the concept of intelligence to include newly identified creative traits (Guilford 1959). [SeeCreativity, article onpsychological aspects.]
An important fact about all comparisons among human groups is that individual differences within each group are far greater than average differences between groups. As a result, the distributions of the groups overlap to a marked degree. Even when the means of two groups differ by a large and statistically significant amount, individuals can be found in the low-scoring group who surpass individuals in the high-scoring group. Hence an individual’s membership in a particular group is a very unreliable indicator of his standing in most psychological traits. Group averages need to be evaluated with reference to some measure of overlap of total distributions, such as the proportion of one group that reaches or exceeds the median of the other.
Another methodological problem in group comparisons arises from the use of unrepresentative samples, in which selective factors may have operated differentially for the two populations. Insofar as more boys than girls drop out of school, for example, a comparison of the intelligence test performance of high school boys and girls will yield a mean sex difference in favor of boys. This difference would disappear, however, if we were to include drop outs, who tend to score near the low end of the distribution. A similar error in the opposite direction is illustrated by surveys of institutions for mental defectives, which generally show an excess of males. Although once regarded as evidence of the greater incidence of mental deficiency among males, these findings were later traced to selective admission policies. For a variety of social and economic reasons, mentally defective women are more likely to remain in the community than are males of the same intellectual levels.
The use of global scores on intelligence tests may also be misleading in the study of sex differences. In the construction of several intelligence tests, such as the Stanford-Binet, sex differences have been ruled out by omitting items that favored either sex. Even when this is not done, a composite score on a heterogeneous test may obscure genuine group differences in specific abilities.
Psychological test surveys in contemporary Western cultures have demonstrated significant mean differences between the sexes in a number of aptitudes and personality traits (see Anastasi 1958, chapter 14). Males as a group excel in speed and coordination of gross bodily movements, spatial orientation, mechanical comprehension, and arithmetic reasoning. Females excel in manual dexterity, perceptual speed and accuracy, memory, numerical computation, verbal fluency, and other tasks involving the mechanics of language. Among the principal personality differences found between the sexes are the greater aggressiveness, achievement drive, and emotional stability of the male and the stronger social orientation of the female.
Most investigations of sex differences yield only descriptive data about existing differences between men and women within a given culture. The origins of such differences must be sought in the complex interactions of cultural and biological factors. Although living in the same homes, boys and girls in most societies are reared in different subcultures. In countless ways, they receive differential treatment from parents, other adults, and age peers. They are dressed differently, given different toys, taught different games, and expected to behave differently in many situations. The personalities of mother and father are themselves important factors in the child’s developing concept of sex roles, providing models of what is expected of each sex in the particular culture.
From a biological viewpoint, the different parts men and women play in the reproductive function undoubtedly contribute to sex differentiation in psychological development. Thus the long period of child bearing and child rearing, which falls biologically upon the female, has far-reaching implications for sex differences in interests, attitudes, emotional traits, vocational goals, and achievement. Sex differences in aggressiveness and dominance are associated with the greater body size, strength, and physical endurance of the male, as well as with the presence of male sex hormones.
Another significant sex difference is to be found in the developmental acceleration of girls. Not only do girls reach puberty earlier than boys, but throughout childhood they are further advanced toward their own adult status in all physical traits. The psychological effects of this sex difference in developmental rate probably vary widely from trait to trait. In infancy, the developmental acceleration of girls may be an important factor in their more rapid acquisition of language and may give them a head start in verbal development as a whole. These few examples suffice to illustrate the varied and intricate mechanisms whereby biological and cultural differences between the sexes interact to produce differences in aptitudes, interests, and other psychological traits.
Race and culture
The biological concept of race refers to populations that differ in the relative frequency of certain genes. Races are formed when a group becomes relatively isolated, for either geographic or social reasons, so that marriage among its members is more common than marriage with outsiders (see Dobzhansky 1962, chapter 10). The very isolation that leads biologically to race formation also fosters cultural differentiation. Hence the populations that have been compared in research on race differences have usually differed in their cultural environments as well. Under these conditions, any differences in aptitudes or personality traits found between racial groups cannot be unequivocally attributed to racial or genetic factors.
In an effort to circumvent cultural differences among groups, some investigators have utilized so-called culture-free tests (see Anastasi 1958, pp. 561-569; 1961, chapter 10). These tests have been especially developed for comparative studies of persons reared in different cultures. They not only undertake to eliminate language barriers, but they also employ content presumably common to all cultures. Parenthetically, it should be noted that the term “culture-free test” is a misnomer. Since a psychological test is only a standardized measure of a behavior sample, any condition that influences behavior will be reflected in test scores. It is theoretically possible to construct a test that presupposes only experiences common to different cultures, but not one that is free from cultural influences.
Even this theoretical goal, however, has not been attained by any available test; each test still favors certain cultural groups and handicaps others. Every psychological test inevitably draws most heavily upon the information, skills, work habits, and attitudes fostered by the culture in which it was developed. The difference between “culture-free” and other tests is merely one of degree. Even the use of paper and pencil or the presentation of abstract tasks having no immediate practical significance will tend to discriminate against some cultures or subcultures. Other cultural differences include intrinsic interest of test content, degree of familiarity with pictorial or diagrammatic representation, rapport with the examiner (especially one of a different race), motivation to perform well on a test, competitive desire to excel others, and previously developed problem-solving attitudes.
The analysis of population differences has also been approached through experimental designs that permit some isolation of biological and cultural influences. Test performance of hybrid, or racially mixed, groups, has been investigated for this purpose. It has been argued that, if one race is intellectually superior to another because of genetic factors, the hybrid offspring of both races should be intermediate in intelligence. Genetically, this hypothesis is questionable, since it assumes complete linkage between the genes determining skin color or other racial indices and the genes determining intelligence. With incomplete linkage, the correlation between racial characteristics and intelligence would disappear within a few generations of crossbreeding. The results are further complicated by the fact that race mixture is usually selective within either or both races, as well as by the tendency toward greater cultural assimilation of hybrids. In groups that are fairly homogeneous in their assimilation of the dominant culture, the correlation between test score and extent of race mixture is negligible (e.g., Rohrer 1942).
Another group of studies concerns changes in the comparative test performance of racial groups with age. Several investigations of American Negro infants and preschool children, for example, revealed little or no retardation in terms of white norms (Anastasi & D’ Angelo 1952; Brown 1944; Gilliland 1951; Pasamanick 1946). Tests of school-age children conducted in the same areas and periods, on the other hand, showed significant mean retardation that increased with age. These findings are similar to those obtained with other culturally deprived groups. The age decrement has been ascribed to the cumulative effects of deficient environments and to the increasing inadequacy of such environments to meet the expanding intellectual needs of the growing child. From a broader viewpoint, such an age decrement in relation to test norms may be said to occur when a test demands intellectual functions not fostered in a particular culture or subculture (Levinson 1961).
A third approach is based upon a comparison of samples of the same race reared in different environments. In general, such studies have yielded larger differences in test performance among subgroups of a given race living in different milieus than among different racial groups living under more nearly similar conditions (see Anastasi 1958, pp. 584-592). That the regional differences found within a racial population are associated with cultural differences rather than with selective migration has been demonstrated in several studies. Of particular relevance are the results of a longitudinal investigation of American Negro children who had moved from an area with poorer school facilities to one with better school facilities (Lee 1951). Mean intelligence test scores of these children improved significantly with increasing length of residence in the educationally more favorable area.
Despite a mass of descriptive data on psychological differences among races, research on the origins of such differences is meager and beset with methodological difficulties. In the light of available knowledge, only a few conclusions can be drawn with confidence. First, no biological basis has as yet been identified for any existing psychological differences among races. Second, there is considerable evidence, both from racial studies and from other types of investigations in differential psychology, showing the part played by cultural factors in producing the sort of behavioral differences commonly found among racial groups. Finally, in all psychological traits, the range of individual differences within each race is far greater than mean differences between races.
Anastasi, Anne 1958 Differential Psychology: Individual and Group Differences in Behavior. 3d ed. New York: Macmillan. → First published in 1937.
Anastasi, Anne 1961 Psychological Testing. 2d ed. New York: Macmillan. → First published in 1954.
Anastasi, Anne; and D’Angelo, Rita Y. 1952 A Comparison of Negro and White Preschool Children in Language Development and Goodenough Draw-a-Man Iq. Journal of Genetic Psychology 81: 147-165.
Beach, Frank A.; and Jaynes, Julian 1954 Effects of Early Experience Upon the Behavior of Animals. Psychological Bulletin 51:239-263.
Birch, Herbert G. 1956 Sources of Order in the Maternal Behavior of Animals. American Journal of Orthopsychiatry 26: 279-284.
Brown, Fred 1944 An Experimental and Critical Study of the Intelligence of Negro and White Kindergarten Children. Journal of Genetic Psychology 65: 161-175.
Dingman, Harvey F.; and Tarjan, George 1960 Mental Retardation and the Normal Distribution Curve. American Journal of Mental Deficiency 64: 991-994.
Dobzhansky, Theodosius 1962 Mankind Evolving: The Evolution of the Human Species. New Haven: Yale Univ. Press.
Fuller, John L.; and Thompson, W. Robert 1960 Behavior Genetics. New York: Wiley.
Gilliland, Adam R. 1951 Socioeconomic Status and Race as Factors in Infant Intelligence Test Scores. Child Development 22:271-273.
Golann, Stuart E. 1963 Psychological Study of Creativity. Psychological Bulletin 60:548-565.
Guilford, J. P. 1959 Three Faces of Intellect. American Psychologist 14:469-479.
Harlow, Harry F. 1949 The Formation of Learning Sets. Psychological Review 56:51-65.
Hirsch, Jerry 1962 Individual Differences in Behavior and Their Genetic Bases. Pages 3-23 in Eugene L. Bliss (editor), Roots of Behavior: Genetics, Instinct, and Socialization in Animal Behavior. New York: Harper.
Lee, Everett S. 1951 Negro Intelligence and Selective Migration: A Philadelphia Test of the Klineberg Hypothesis. American Sociological Review 16:227-233.
Levinson, Boris M. 1961 Subcultural Values and IQ Stability. Journal of Genetic Psychology 98:69-82.
Pasamanick, Benjamin 1946 A Comparative Study of the Behavioral Development of Negro Infants. Journal of Genetic Psychology 69:3-44.
Roberts, John A. Fraser 1952 The Genetics of Mental Deficiency. Eugenics Review 44:71-83.
Rohrer, John H. 1942 The Test Intelligence of Osage Indians. Journal of Social Psychology 16:99-105.
Rosenzweig, Mark R.; Krech, David; and Bennett, Edward L. 1960 A Search for Relations Between Brain Chemistry and Behavior. Psychological Bulletin 57:476-492.
Searle, Lloyd V. 1949 The Organization of Hereditary Maze-brightness and Maze-dullness. Genetic Psychology Monographs 39:279-325.
Terman, Lewis M.; and Oden, Melita 1959 Genetic Studies of Genius. Volume 5: The Gifted Group at Mid-life: Thirty-five Years’ Follow-up of the Superior Child. Stanford Univ. Press.
Tryon, Robert C. 1940 Genetic Differences in Maze-learning Ability in Rats. Volume 1, part 1, pages 111-119 in National Society for the Study of Education, 39th Yearbook. Bloomington, III.: Public School Publishing Company.
Since the beginning of the twentieth century, psychological sex differences have been the subject of continuous research. The objectives and orientation of this research, however, have shifted several times. At the beginning, the feminist movement generated interest in the question of whether the intelligence of women was or was not equal to that of men. What most investigators hoped to find was scientific evidence for the equality of the sexes.
As techniques for measuring aspects of personality as well as mental abilities were developed in the 1920s and 1930s, more and more comparisons of male and female groups on nonintellectual characteristics were reported. Psychoanalysis was becoming increasingly influential during this period, and personality theorists drew from the writings of Freud and his followers hypotheses about what sex differences should be explored through research. The social objectives were to prevent neurosis and improve relationships between the sexes through understanding the differing emotional needs and ways of expression in men and women. Some investigators also hoped during this period—roughly the second quarter of the century—to construct a scale for measuring general masculinity or femininity that would be accurate enough to differentiate between persons of the same sex. They thought that such a scale would have many practical uses, such as the assignment of individuals to suitable occupations and the diagnosis of homosexual trends.
During the 1950s the emphasis shifted once more. Investigators became very much aware of sex roles. Questions about when and how a young child learns what these roles are and develops preferences and patterns of behavior in accordance with them began to seem very important. The concept of identification, as interpreted in psychoanalytic writings and in other types of personality theory, became the focus of much research effort. The objectives of the research of this period were not just to understand sex differences for their own sake, but to utilize information about this highly visible aspect of personality development as a source of clues about the developmental processes through which many other aspects of personality may have come into existence.
One persistent question has stimulated research interest throughout all these periods: why have women’s achievements failed to match those of men? Why are there so few outstanding female artists, scientists, or statesmen? As the processes of rapid social change have produced an occupational situation in which highly trained professional persons are in demand and unskilled workers are needed in fewer and fewer numbers, this problem has taken on a new urgency. Findings from all the main types of research on differences in abilities, personality traits, roles, and development have been brought to bear on this issue.
Sex differences in abilities
As sophistication in mental-testing procedures increased over the years, it became apparent that the question investigators first asked about the differences in intelligence between the sexes is unanswerable, at least by present methods of measurement. Hundreds of studies were made, most of them using students as subjects. Some reported male superiority, some female superiority, and some no significant differences. Analyzing these conflicting results, one could see that some of the discrepancies arose from selective factors in the groups tested. But, more important, discrepancies were related to the type of intelligence test the investigators happened to use. Some tests consistently gave girls a slight advantage; others favored boys.
From many sources, evidence was accumulating that intelligence tests of all varieties do not and cannot measure pure native capacity. Although genetic differences in intellectual potential undoubtedly exist, the only way we have to test a person is to ask him questions or give him problems to solve. His performance in such a situation always reflects the experience he has undergone as well as his native capacity. It is, therefore, only natural that males show superiority in dealing with some kinds of questions and tasks and that females show superiority in dealing with others. In order to produce intelligence tests that will be equally fair to members of both sexes, psychologists try to include approximately the same number of both kinds of items. McNemar (1942) has explained how this was done in developing the Terman-Merrill revision of the Stanford-Binet test. When this kind of test is given to samples of the population that are really representative, sex differences in over-all score or IQ turn out to be negligible. The two major surveys undertaken by the Scottish Council for Research in Education in 1939 and 1949, in which all children in the country who had been born on certain dates were tested, revealed almost identical mean IQ’s the first time and a 4-point difference in favor of boys the second time. This 4-point difference, although statistically significant for this large number of cases, appears not to have much practical significance in view of the fact that the group test given in the same survey produced a 2-point difference in favor of girls. [SeeAchievement TESTING; Intelligence
and intelligence testing.]
Verbal, mathematical, and spatial abilities
More meaningful than the question of whether one sex is more intelligent than the other is the question of what special abilities are related to sex. Much of the research on this problem is summarized by Terman and Tyler (1954). Females appear to excel in verbal ability, when this is defined as fluency—the use of words rather than the comprehension of verbal meanings. Females tend to talk more, to read faster, and to be less susceptible to reading and speech difficulties. They do not typically score higher than boys on vocabulary tests, and one study of a large and representative sample of English children from 5 to 15 years of age showed consistent although small differences in favor of boys on four oral vocabulary tests from leading intelligence scales (Dunsdon & Roberts 1957).
Males consistently score higher than females on mathematical ability, when this is defined as reasoning or problem solving rather than computational skill. There also is a large and significant difference in favor of males on tests of spatial visualization. These differences, especially the one in mathematics, partially explain why males tend to do better in science. In the annual science talent search carried on each year in the United States, high school male applicants score significantly higher on the achievement test than female applicants, despite the fact that the girls who apply are much more highly selected than the boys who apply (Edgerton & Britt 1947).
Vocational psychologists have found that on tests of aptitude for particular kinds of work, females score significantly higher for clerical aptitude and dexterity, males significantly higher for mechanical aptitude. Aptitude tests for art and music tend to give higher scores for females, but this may be because more girls than boys are exposed to art and music lessons as they grow up.
Two general findings must be kept in mind if misinterpretations of these differences in ability are to be avoided. One is that in all such comparisons there is a large amount of overlap between the distributions for the two sexes. In making any practical decision about job placement, admission to professional schools and training programs, and the like, one must judge on the basis of the individual’s own ability rather than on the basis of sex. Individual differences far outweigh sex differences. The other general finding from comparisons of sex groups at different ages is that most of these varieties of differences in ability do not show up until the elementary school years or later. With regard to mathematical ability, for example, girls at the kindergarten and preschool levels do as well as boys in counting and identifying numbers. Sex differences become more apparent with increasing education, even when it is coeducation. [SeeAptitude testing.]
One promising line of research has identified what may be important sex differences in cognitive style. Witkin and others (1954) discovered that females are less able than males to disregard the visual field in which a perceptual pattern they are trying to grasp is embedded. The difference was apparent on tests in which the subject was required to straighten a tilted chair in a tilted room and thus to separate kinesthetic cues from visual distractions; the difference was also apparent on the purely visual embedded-figures test. This tendency toward what Witkin labeled field-dependence was related to personality characteristics having to do with passive acceptance rather than active coping with one’s environment. Sandström (1953) reported a somewhat similar phenomenon. Women are less accurate than men in pointing to a luminous spot of light in a completely darkened room and are more likely than men to show disoriented behavior. In problem-solving experiments, the difficulty females have been shown to experience in restructuring the problem situation would appear to involve this same factor of field-dependence.
Interests, motives, and personality
All of the evidence seems to point to a conclusion that the sexes differ far more in their general orientation to life than they do in abilities.
Interests and values
Strong (1943) has presented the most comprehensive findings with regard to interest differences, findings that are corroborated by other research on the topic. Males respond with a higher degree of preference than do females to interest items of the following kinds: (1)mechanical and scientific activities; (2) physically strenuous, adventuresome activities; (3) legal, political, and military occupations; (4) selling; (5) certain forms of entertainment, such as smokers, “roughhouse” initiations, and chess; (6) certain miscellaneous aspects of work, such as outdoor activity rather than indoor and self-employment rather than working for others. The distinctly feminine interests are seen in responses that indicate greater preference for the following kinds of items: (1) musical, artistic activities; (2)literary activities; (3) certain kinds of persons, especially the unfortunate and disagreeable; (4) certain forms of entertainment, such as for-tunetelling, full-dress affairs, and social-problem movies; (5) clerical work; (6) teaching; (7) social work; (8) merchandise, that is, looking at shop windows, displaying merchandise, etc. Strong constructed a masculinity-femininity scale for the Vocational Interest Blank made up of these discriminating items.
Although representative groups of men and women reveal different patterns of scores on the occupational scales of the Strong and other interest tests, there is some evidence that highly selected groups of women in predominantly male occupations, such as medicine and life insurance selling, obtain interest scores very similar to those of their male colleagues. Women, in general, show a standard pattern of occupational interest scores. The large majority of them receive their only high scores on the office worker, stenographer-secretary, and housewife scales of the Strong Vocational Interest Blank for women. [SeeVocational Interest testing.]
When groups of men and women are compared on the Allport-Vernon (now Allport-Vernon-Lindzey) test of values, men obtain significantly higher scores on theoretical, economic, and political values; this indicates that they are more oriented than women toward abstract ideas, practical success, and power. Women receive higher scores on aesthetic, social, and religious values; this indicates that they are oriented more toward art, religion, and social welfare.
Studies of children. Numerous studies of children’s interests using many observational and assessment techniques reveal marked interest differences even at any early age. Boys engage more in active games and vigorous physical activity and prefer tales of adventure and violent action in books and in radio and television programs. Girls are more likely to enjoy dolls, paper activities, and games calling for skillful movements and to prefer sentimental and domestic stories. Tyler’s comparison of the responses of English and American children to questions on an interest inventory (1956) and Gaier and Collier’s comparison of the reading interests of American and Finnish children (1960) both suggest that sex differences are greater than nationality differences, at least within Western culture.
Another large-scale research program that has repeatedly revealed sex differences is the work of McClelland, Atkinson, and their associates on achievement motivation (McClelland et al. 1953). The projective method employed in these studies, the evaluation of stories written about achievement-oriented pictures for various indicators of achievement motivation, typically shows males to be much more oriented toward competitive effort than females are.
Males and females also differ in their propensity to take risks (Kogan & Wallach 1964). Generally speaking, women seem to be less given to taking risks than men, but the ingenious experiments these authors report show that the sex difference interacts in a complex way with variation in the nature of the task or situation and with the personality traits of anxiety and defensiveness.
There is considerable evidence from many sources that males and females differ in the strength of some of their emotional needs and the manner in which these are expressed. Some kinds of free responses in standardized situations suggest that these differences are related to body images and the differing requirements of sexual intercourse. Erikson (1951), for example, in the California guidance study asked the subjects to “construct an exciting movie scene” from materials provided. Boys typically produced high structures, ruins, and scenes suggesting sudden arrest of motion, whereas girls produced static, open enclosures, such as rooms. It is difficult to see how all of the differences reported in studies of this sort can be explained simply on the basis of differential treatment of boys and girls in home and community.
The emotional characteristic about which the largest amount of evidence has accumulated is aggression. Again and again, whether the subjects are preschool children, adult men and women, or any age group in between and whether the comparison is made on the basis of personality test scores, observations and ratings, or projective techniques, male groups score significantly higher on aggression than female groups. [SeeAggression.]
An equally consistent type of finding, although not resting on quite as substantial a body of research findings, is that females are more oriented toward other persons than males are. Sometimes this orientation toward people shows up as sensitivity and responsiveness, sometimes as passivity and dependence, sometimes as needs for succor-ance, nurturance, and affiliation. A finding that is perhaps related to these is that neuroticism seems to be more prevalent among females than among males, just as delinquency seems to be more prevalent among males than among females.
Summary of differences
Bennett and Cohen (1959), on the basis of a comprehensive study of their own, summarized the differences between the masculine and feminine approaches under five general principles. These fit in with most of the reported research on personality differences.
(1)Masculine thinking is of less intensity than feminine thinking.
(2)Masculine thinking is oriented more in terms of the self, whereas feminine thinking is oriented more in terms of the environment.
(3)Masculine thinking anticipates rewards and punishments determined more as a result of the adequacy or inadequacy of the self, whereas feminine thinking anticipates rewards and punishments determined more as a result of the friendship or hostility of the environment.
(4)Masculine thinking is associated more with desire for personal achievement, feminine thinking more with desire for social love or friendship.
(5)Masculine thinking finds value more in malevolent and hostile actions against a competitive society, whereas feminine thinking finds value more in freedom from restraint in a friendly and pleasant environment.
It must always be remembered that these sex differences are only group trends and the distributions for males and females on measures of any of them overlap to a considerable extent. As in the ability domain, there are marked individual differences within each sex. But the evidence suggests that the average difference between sex groups is somewhat greater for temperamental qualities than for abilities.
Because consistent sex differences show up on many kinds of items used in personality tests and because there are differences within each sex group in the responses individuals give to these items, many psychologists found it feasible to construct empirical masculinity-femininity (M-F) scales made up entirely of items that had been shown to differentiate the sexes. It was hoped that such scales would be useful for diagnostic and counseling purposes, as masculinity-femininity would seem to be a fundamental aspect of an individual’s personality.
The first and most comprehensive of these M-F scales was the Terman and Miles Attitude-Interest Analysis Blank (Terman & Miles 1936). The authors carried out an extensive research program using this test to determine how various special groups in the population distribute themselves with regard to the M-F variable. Among occupational groups, men who are athletes and engineers obtained the most masculine scores. Men who are journalists, artists, and clergymen were least masculine. Women who are domestic employees were most feminine; women who are athletes and doctors were least feminine. The age trends differed for the two sexes. Eighth-grade girls were the most feminine of all the groups tested, eleventh-grade boys the most masculine. With advancing age, males tended to become a little more feminine, females a little more masculine.
Since Terman and Miles began on this line of research, several other M-F scales have been constructed in the same manner. It is common practice for a multiscore test, like the Strong Vocational Interest Blank or the Minnesota Multiphasic Personality Inventory, to include an M-F key among the other scoring keys.
Critique of M-F scales
Although all of these scales have served a useful research purpose, they have not had as much practical value as had been expected. In general, they have not been of much use in diagnosing homosexuality. Terman and Miles in their original series of investigations showed that it was only the passive homosexuals (those who customarily played female roles in homosexual relationships) who made unusually feminine scores on the test, and since this effect was based to a large extent on the Interests sub-test, on which persons like artists and clergymen also tend to make feminine scores, one must be very cautious about drawing diagnostic conclusions from test performance. The same difficulty arises in connection with the later M-F scales. There are various factors that tend to produce deviant scores, so that one cannot assume they point to homosexuality.
The hope has been largely abandoned that M-F scales might play a useful part in counseling situations to help individuals find work and living situations that would fit in with their basic temperamental qualities. The difficulty is that masculinity-femininity is not unidimensional. Two persons with identical M-F scores may have obtained them from a very different combination of item responses. The emotional qualities that characteristically differentiate males from females show little or no correlation with masculine or feminine interests. It is quite possible for either a man or a woman to deviate in the direction of the opposite sex on one type of item and not on another. Several studies have produced unambiguous evidence of this lack of unidimensionality. One of the most recent (Barrows & Zuckerman 1960) showed that the correlations between three commonly used M-F scales were only about .3.
The organization of personality
A kind of research evidence that is being increasingly emphasized as psychologists attempt to incorporate what they know about sex differences into some coherent theoretical system is the repeated finding that variables are related differently to one another in male and female samples. Often the difference in the pattern of correlations between variables appears more impressive than the mean differences on any one variable. Such differences in correlational pattern have been identified in many areas of personality research, and in most cases the reasons for their existence have not yet been made clear. It is possible only to summarize these areas and in some cases suggest tentative explanations.
Discrepant patterns for the two sexes have repeatedly turned up in factor-analytic research on abilities and on personality traits. The only general conclusion one might draw from a miscellany of such studies is that perhaps females are less differentiated than males. There is a tendency for fewer factors to emerge from the data of females than from those of males.
The predictive validity of scholastic aptitude and achievement tests seems to be greater for females than for males (Seashore 1962). However, in longitudinal studies in which personality status at adulthood is to be predicted from childhood measurements of a particular trait (Kagan & Moss 1962), females are more predictable in some areas, males in others. The fact that the stability coefficients are higher on aggression for males and on passivity-dependence for females suggests the reasonable hypothesis that traits characterizing either sex as a whole are more stable within that sex group than are traits characterizing the other sex as a whole.
Different personality variables show significant relationships to general adjustment and popularity in males and females. Correlations often even turn out to be of opposite sign. The only general summary one might hazard of a number of studies in this area is that characteristics related to conformity of behavior and attitude bring more social rewards for females and that those related to initiative and independence bring more social rewards for males. Many specific findings, however, cannot be classified under this one statement.
The developmental antecedents of some traits, especially aggression and underachievement in school, are different for the two sexes. The growth curves differ in shape for these and several other characteristics that have been studied.
It is this last type of finding that has generated what appears to be the most fruitful research ideas for dealing with the whole mass of diverse material. What they suggest is that the development of male and female individuals follows a different course. Thus developmental concepts would seem to offer the most promising theoretical formulations.
Sex role concepts and behavior
Much of the developmental research of the 1950s and 1960s has focused on the learning of sex roles. Sociologists are primarily responsible for the formulation of the role concept, but it fits in well with several varieties of psychological personality theory. Historical as well as biological factors enter into the formulation of what the sex roles are in any given culture, but once these roles exist, the shaping of masculine and feminine personalities in growing children requires that each person learn in detail what these roles are and at the same time learn to prefer for himself what is considered appropriate for his sex. The purpose of much developmental research is to determine when and how such learning occurs and what factors influence it, both positively and negatively.
Various experimental techniques for finding out what young children consider the proper sex roles to be and how they relate themselves to them have been devised. These techniques include standardized doll-play situations, the telling of stories about pictures, and the choice of toys or activities for a hypothetical boy or girl. Investigators find with considerable consistency that children as young as five understand what is considered appropriate behavior for each sex in much the same way as adults do. Some researchers have worked with children as young as three and have shown that the same sex role concepts, not quite so precisely formulated, can be identified even at this early age. When children are asked to make choices or state preferences, interesting age trends appear. In general, increasing age, from the preschool years on, brings an increasing degree of preference for the characteristics and activities of one’s own sex, but there are irregularities and exceptions. Particularly at the early ages, girls show more preference for the activities of boys than boys show for those of girls (Brown 1956).
In trying to find out how such learning occurs, psychologists have made use of the concept of identification. It is difficult, if not impossible, to specify a precise meaning for this term, but most of the particular ways in which it has been used in research on sex differences have something to do with the way that an individual makes the standards and attitudes of his parents a part of himself. Some studies in this field have compared boys from families that are intact with boys from families in which the father is temporarily or permanently absent. Sex-typed behavior, such as aggression, develops at an earlier age in boys with fathers in the home than in boys from “father-absent” families; this suggests that sex roles are acquired through identification. The fact that most boys from “father-absent” homes eventually develop the sex-typed behavior, however, suggests that identification with the parent of the same sex is not the only way in which such roles can be acquired. Other studies have shown that preschool children are aware of what their parents expect of boys and girls and are influenced by these expectations. Father-son and mother-daughter similarities on tests of interests and values have been demonstrated.
In all of these types of investigation, many individual irregularities and exceptions to the prevailing trend of the evidence occur. The process through which a son learns from his father and a girl learns from her mother may not be identical. Most aspects of the mother’s sex role can be directly observed and imitated by her daughter, whereas the son does not have the same opportunity to observe and imitate his father’s actual behavior. Boys must thus proceed more on the basis of inference. Rapid social change complicates the problem of sex roles. What a child learns in his own home may not be completely appropriate to the situation that exists when he becomes an adult. These and many more considerations point to the conclusion that the learning of sex-appropriate behavior is an enormously complex process, as yet only incompletely understood.
General significance of research
As indicated in the introduction, the study of sex differences has been undertaken in order to answer important practical questions and in order to contribute to our knowledge of the ways in which many sorts of individual differences in personality come into existence. If the complicated interweaving of biological and social determiners can be unraveled from the fabric of sex differences in our culture, perhaps the pattern of other designs of still greater complexity can be traced. Both practical and theoretical purposes continue to influence the planning and conduct of research. In the middle 1960s, the theoretical purpose predominates. Research on sex differences is a strategic meeting ground for biologists, psychologists, anthropologists, and sociologists. An adequate theory, when one is achieved, must represent a synthesis of ideas from many sources.
Leona E. Tyler
Barrows, Gordon A.; and Zuckerman, Marvin 1960 Construct Validity of Three Masculinity-Femininity Tests. Journal of Consulting Psychology 24:441-445.
Bennett, Edward M.; and Cohen, Larry R. 1959 Men and Women: Personality Patterns and Contrasts. Genetic Psychology Monographs 59:101-155.
Brown, Daniel G. 1956 Sex-role Preference in Young Children. Psychological Monographs 70, no. 14.
Dunsdon, M. I.; and Roberts, J. A. F. 1957 A Study of the Performance of 2,000 Children on Four Vocabulary Tests. British Journal of Statistical Psychology 10:1-16.
Edgerton, Harold A.; and Britt, Steuart H. 1947 Technical Aspects of the Fourth Annual Science Talent Search. Educational and Psychological Measurement 7:3-21.
Erikson, E. H. 1951 Sex Differences in the Play Configurations of Preadolescents. American Journal of Orthopsychiatry 21:667-692.
Gaier, Eugene L.; and Collier, Mary J. 1960 The Latency-stage Story Preferences of American and Finnish Children. Child Development 31:431-451.
Kagan, Jerome; and Moss, Howard A. 1962 Birth to Maturity: A Study in Psychological Development. New York: Wiley.
Kogan, Nathan; and Wallach, Michael A. 1964 Risk Taking: A Study in Cognition and Personality. New York: Holt.
Mcclelland, David C. et al. 1953 The Achievement Motive. New York: Appleton.
Mcnemar, Quinn 1942 The Revision of the Stanford-Binet Scale. New York: Houghton.
Sandstrom, Carl I. 1953 Sex Differences in Localization and Orientation. Acta Psychologica 9:82-96.
Seashore, Harold G. 1962 Women Are More Predictable Than Men. Journal of Counseling Psychology 9: 261-270.
Strong, Edward K. Jr. 1943 Vocational Interests of Men and Women. Stanford (Calif.) Univ. Press.
Terman, Lewis M.; and Miles, Catherine C. 1936 Sex and Personality: Studies in Masculinity and Femininity. New York: McGraw-Hill.
Terman, Lewis M.; and Tyler, Leona E. 1954 Psychological Sex Differences. Pages 1064-1114 in Leonard Carmichael (editor), Manual of Child Psychology. 2d ed. New York: Wiley.
Tyler, Leona E. 1956 A Comparison of the Interests of English and American School Children. Journal of Genetic Psychology 88:175-181.
Witkin, Herman A. et al. 1954 Personality Through Perception. New York: Harper.
"Individual Differences." International Encyclopedia of the Social Sciences. . Encyclopedia.com. (August 22, 2017). http://www.encyclopedia.com/social-sciences/applied-and-social-sciences-magazines/individual-differences
"Individual Differences." International Encyclopedia of the Social Sciences. . Retrieved August 22, 2017 from Encyclopedia.com: http://www.encyclopedia.com/social-sciences/applied-and-social-sciences-magazines/individual-differences