Individual Differences In Patterns Research Articles

Midlife Influences Upon Intellectual Functioning in Old Age

Evidence is presented from the Seattle Longitudinal Study to show that cognitive style and other life style variables in midlife can predict part of the individual differences in pattern and rate of ability decline in old age. Differential importance of certain predictor variables are examined as a function of the extent of actul ability decline at various ages. During early middle age attitudinal flexibility maintains high level of function on the fluid abilities and on motor-cognitive flexibility. High performance on the latter variables at midlife as well as an engaged life style and the absence of family dissolution appear to be critical for the maintenance of high levels of ability performance from middle into old age.

The discrepant repressor: differentiation between low anxiety, high anxiety, and repression of anxiety by autonomic-facial-verbal patterns of behavior.

This study examined the notion that personality questionnaires can be used to predict different styles of coping with anxiety as expressed by individual differences in patterns of autonomic, verbal, and nonverbal reactions. In line with earlier modifications of the repression-sensitization concept, the Taylor Manifest Anxiety Scale (MAS) and the Marlowe-Crowne Social Desirability Scale (SDS) were used to select four groups of 12 subjects each from a pool of 206 male students in Germany: low-anxious subjects (low MAS, low SDS), repressors (low MAS, high SDS), high-anxious subjects (high MAS, low SDS), and defensive high-anxious subjects (high MAS, high SDS). Several measures of autonomic arousal, facial activity, and self-reported affect were obtained during a potentially anxiety-arousing free-association task and during a number of control conditions, including a funny film. Significant differences in baseline-corrected heart rate and self-reported anxiety as well as rated facial anxiety all indicated that repressors exhibited a discrepancy between low self-reported anxiety and high heart rate and facial anxiety; low-anxious subjects reported an intermediate level of anxiety, although they showed low heart rate and facial anxiety; high-anxious subjects had consistently high values on all three variables; and the defensive high-anxious group showed an intermediate level of anxious responding. These group differences were specific to the task of freely associating to phrases of mixed (sexual, aggressive, neutral) content (but not to other experimental situations) and to self-reported anxiety (but not to other self-rated emotions or task difficult), indicating that they reflect individual differences in coping with anxiety.

The discrepant repressor: Differentiation between low anxiety, high anxiety, and repression of anxiety by autonomic–facial–verbal patterns of behavior.

This study examined the notion that personality questionnaires can be used to predict different styles of coping with anxiety as expressed by individual differences in patterns of autonomic, verbal, and nonverbal reactions. In line with earlier modifications of the repression-sensitization concept, the Taylor Manifest Anxiety Scale (MAS) and the Marlowe-Crowne Social Desirability Scale (SDS) were used to select four groups of 12 subjects each from a pool of 206 male students in Germany: low-anxious subjects (low MAS, low SDS), repressors (low MAS, high SDS), high-anxious subjects (high MAS, low SDS), and defensive high-anxious subjects (high MAS, high SDS). Several measures of autonomic arousal, facial activity, and self-reported affect were obtained during a potentially anxiety-arousing free-association task and during a number of control conditions, including a funny film. Significant differences in baseline-corrected heart rate and self-reported anxiety as well as rated facial anxiety all indicated that repressors exhibited a discrepancy between low self-reported anxiety and high heart rate and facial anxiety; low-anxious subjects reported an intermediate level of anxiety, although they showed low heart rate and facial anxiety; high-anxious subjects had consistently high values on all three variables; and the defensive high-anxious group showed an intermediate level of anxious responding. These group differences were specific to the task of freely associating to phrases of mixed (sexual, aggressive, neutral) content (but not to other experimental situations) and to self-reported anxiety (but not to other self-rated emotions or task difficult), indicating that they reflect individual differences in coping with anxiety.

Individual differences in eye-movement patterns.

The reliability of individual differences in patterns of eye fixation during a size-estimation task was studied in normal adults, each tested twice. The most stable measures not specifically related to the stimulus configuration appeared to be the number of fixations per unit time; measures related to dispersal of looking were unreliable. Data on where S looked, the duration of each fixation and the distance between fixation points appeared to follow first-order Markov processes and the transitional probabilities appeared to be individual characteristics stable over time.

Seeking Styles and Individual Differences in Patterns of Inquiry

Previous articleNext article No AccessSeeking Styles and Individual Differences in Patterns of InquiryLee S. ShulmanLee S. Shulman Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Volume 73, Number 3Autumn, 1965 Article DOIhttps://doi.org/10.1086/442743 Views: 3Total views on this site Citations: 16Citations are reported from Crossref Journal History This article was published in The School Review (1893-1979), which is continued by the American Journal of Education (1979-present). Copyright 1965 University of ChicagoPDF download Crossref reports the following articles citing this article:Muhui Li, Binyan Xu The Development of Problem-Posing in Chinese Mathematics Curriculum, (May 2021): 53–66.https://doi.org/10.1007/978-3-030-68157-9_4Marten Scheffer, Frances Westley, William Brock Slow Response of Societies to New Problems: Causes and Costs, Ecosystems 6, no.55 (Jul 2003): 493–502.https://doi.org/10.1007/PL00021504Linda M.H. Lai, Kjell Grønhaug Managerial problem finding: Conceptual issues and research findings, Scandinavian Journal of Management 10, no.11 (Mar 1994): 1–15.https://doi.org/10.1016/0956-5221(94)90033-7Michael T. Moore, Mary C. Murdock On problems in problem‐finding research, Creativity Research Journal 4, no.33 (Jan 1991): 290–293.https://doi.org/10.1080/10400419109534403MICHAEL T. MOORE Problem Finding and Teacher Experience*, The Journal of Creative Behavior 24, no.11 (Dec 2011): 39–58.https://doi.org/10.1002/j.2162-6057.1990.tb00527.xArthur S. Elstein, Lee S. Shulman, Sarah A. Sprafka Medical Problem Solving, Evaluation & the Health Professions 13, no.11 (Jun 2016): 5–36.https://doi.org/10.1177/016327879001300102JONATHAN SMILANSKY, NAFTALI HALBERSTADT Inventors Versus Problem Solvers: An Empirical Investigation, The Journal of Creative Behavior 20, no.33 (Dec 2011): 183–201.https://doi.org/10.1002/j.2162-6057.1986.tb00436.xBernice Y. L. Wong Self-Questioning Instructional Research: A Review, Review of Educational Research 55, no.22 (Jul 2016): 227–268.https://doi.org/10.3102/00346543055002227RICHARD J. HAZLER, GERRY ROBERTS Decision Making in Vocational Theory: Evolution and Implications, The Personnel and Guidance Journal 62, no.77 (Dec 2011): 408–410.https://doi.org/10.1111/j.2164-4918.1984.tb00238.xJ. T. DILLON Problem Finding and Solving*, The Journal of Creative Behavior 16, no.22 (Dec 2011): 97–111.https://doi.org/10.1002/j.2162-6057.1982.tb00326.xRichard Lesh Applied mathematical problem solving, Educational Studies in Mathematics 12, no.22 (May 1981): 235–264.https://doi.org/10.1007/BF00305624R. Nancy Greer, Stanley S. Blank Cognitive Style, Conceptual Tempo and Problem Solving: Modification through Programmed Instruction, American Educational Research Journal 14, no.33 (Nov 2016): 295–315.https://doi.org/10.3102/00028312014003295ROBERT L. HOHN Competency-Based Formats in Educational Psychology, (Jan 1977): 187–214.https://doi.org/10.1016/B978-0-12-697750-9.50014-8Lee S. Shulman, Arthur S. Elstein 1: Studies of Problem Solving, Judgment, and Decision Making: Implications for Educational Research, Review of Research in Education 3, no.11 (Jun 2016): 3–42.https://doi.org/10.3102/0091732X003001003Jerome S. Allender The teaching of inquiry skills using a learning center, AV communication review 17, no.44 (Dec 1969): 399–409.https://doi.org/10.1007/BF02767733Jerome S. Allender A Study of Inquiry Activity in Elementary School Children, American Educational Research Journal 6, no.44 (Jun 2016): 543–558.https://doi.org/10.3102/00028312006004543

DIFFERENTIAL IMPACT OF WEIGHT AND HEIGHT ON WETZEL DEVELOPMENTAL AGE

The developmental concept has been used extensively in the study of child development in order to facilitate the direct comparison of disparate measures of growth and maturation. Applied to both psychological and physical data, the technique of converting achievement scores or absolute measures of development into such constructs as mental age, age, and skeletal age has been demonstrated to have practical utility in depicting and emphasizing individual differences in pattern and symmetry of development (5). In the area of physical growth, height and weight have been used to provide a comparative picture of developmental status in total body mass and total body length. Traditionally, developmental ages for height and weight have been calculated from separate tables or graphs contained in published studies reporting normative data for these measures. Plotted graphically, relative progress in height and weight could be compared visually and further comparison could then be made with other aspects of development. The popularity of the traditional method of treating height and weight data separately has resulted, in part, from the theoretical difficulties inherent in combining growth in total body mass comprising, as it does, an increase in fat, muscle, bone, neural, and visceral tissue, on one hand, with stature which represents linear increase of the skeleton, on the other. The complexity of various mathematical formulations for relating height and weight has also discouraged their use by the nonspecialist. Further, most heightweight indices, correlational procedures, and regression equations have been designed to determine normal weight for height and/or and are not