Multiple intelligences: Can they be measured?

AbstractThis paper is about issues relating to the assessment of multiple intelligences. The first section introduces the authors' work on building measures of multiple intelligences and moral sensitivities. It also provides a conceptual definition of multiple intelligences based on Multiple Intelligences theory by Howard Gardner (1983). The second section discusses the context specificity of intelligences and alternative approaches to measuring multiple intelligences. The third section analyses the validity of self-evaluation instruments and provides a case example of building such an instrument. The paper ends with concluding remarks.Key words: Giftedness, multiple intelligences theory, MIPQ, CFA, Bayesian modeling(ProQuest: ... denotes formula omitted.)IntroductionIn this paper, we introduce our work on building measures of multiple intelligences and moral sensitivities based on the Multiple Intelligences theory of Howard Gardner (1983, 1993). We have developed several instruments for self-assessment that can be used in educational settings (Tirri & Nokelainen, 2011). Gardner's theory of Multiple Intelli- gences (MI) focuses on the concept of an 'intelligence', which he defines as "the ability to solve problems, or to create products, that are valued within one or more cultural settings" (Gardner, 1993, p. x). Gardner lists seven intelligences that meet his criteria for an intelligence, namely linguistic, logical-mathematical, musical, spatial, bodily kines- thetic, interpersonal, and intrapersonal (Gardner, 1993, p. xi).In a broad sense, Gardner views his theory as a contribution to the tradition advocated by Thurstone (1960) and Guilford (1967) because all these theories argue for the existence of a number of factors, or components, of intelligence. All these theories also view intel- ligence as being broader and multidimensional rather than a single, general capacity for conceptualization and problem-solving. Gardner differs from the other pluralists, howev- er, in his attempt to base MI theory upon neurological, evolutionary, and cross-cultural evidence (Gardner, 1993, p. xii). In the first edition of his MI theory, thirty years ago, Gardner (1983) adopted a very individualistic point of view in exploring various intelli- gences. In a newer edition of MI theory, however, Gardner (1993) places more emphasis on the cultural and contextual factors involved in the development of the seven intelli- gences. Gardner retained the original seven intelligences, but acknowledged the possibil- ity of adding new intelligences to the list. For example, he has worked on an eighth intel- ligence - the intelligence of the naturalist - to be included in his list of multiple intelli- gences (Gardner, 1995, p. 206).Robert Sternberg identifies Gardner's MI theory as a systems approach, similar to his own triarchic theory. Although he appreciates Gardner's assessments at a theoretical level, he believes them to be a psychometric nightmare. The biggest challenge for advo- cates of Gardner's approach, then, is to demonstrate the psychometric soundness of their instrument. Sternberg is calling for hard data that would show that the theory works operationally in a way that will satisfy scientists as well as teachers. Sternberg's own theory promises the broader measurement implied by the triarchic theory (Sternberg, 1985). His theory provides "process scores for componential processing, coping with novelty, automatization, and practical-contextual intelligence, and content scores for the verbal, quantitative, and figural content domains" (Sternberg, 1991, p. 266).Sternberg's observations on Gardner's theory should be kept in mind in attempts to create tests based on his theory. However, in the educational setting his theory can be used as a framework in planning a program that would meet the needs of different learn- ers (Tirri, 1997). Gardner has shown a special interest in how schools encourage the different intelligences in students (Gardner, 1991). …

[1]  D. Kaplan Structural Equation Modeling: Foundations and Extensions , 2000 .

[2]  D. Gursky The Unschooled Mind. , 1991 .

[3]  Jacob Cohen Statistical Power Analysis , 1992 .

[4]  L. L. Thurstone,et al.  The nature of intelligence , 1926 .

[5]  Rolph E. Anderson,et al.  Nederlandse samenvatting en bewerking van 'Multivariate data analysis, 4th Edition, 1995' , 1998 .

[6]  K. Tirri How Finland Meets the Needs of Gifted and Talented Pupils , 1997 .

[7]  J. S. Long,et al.  Testing Structural Equation Models , 1993 .

[8]  H. Gardner Reflections on Multiple Intelligences: Myths and Messages. , 1995 .

[9]  D. R. Johnson,et al.  Ordinal measures in multiple indicator models: A simulation study of categorization error. , 1983 .

[10]  J. Guilford,et al.  The nature of human intelligence. , 1968 .

[11]  P. Nokelainen Modeling of Professional Growth and Learning: Bayesian approach , 2008 .

[12]  L. Cronbach Coefficient alpha and the internal structure of tests , 1951 .

[13]  P. Bentler,et al.  Evaluating model fit. , 1995 .

[14]  T. E. Dinero Scale development. , 1996, Journal of health & social policy.

[15]  R. Sternberg Beyond IQ: A Triarchic Theory of Human Intelligence , 1984 .

[16]  R. Hoyle Structural equation modeling: concepts, issues, and applications , 1997 .

[17]  Donald Hedeker,et al.  Longitudinal Data Analysis , 2006 .

[18]  M. Browne,et al.  Alternative Ways of Assessing Model Fit , 1992 .

[19]  P. Nokelainen,et al.  Identification of multiple intelligences with the Multiple Intelligence Profiling Questionnaire III , 2008 .

[20]  Peter Congdon Bayesian statistical modelling , 2002 .

[21]  Leo J. Th. van der Kamp,et al.  Longitudinal Data Analysis: Designs, Models and Methods , 1999 .

[22]  R. Sternberg Death, taxes, and bad intelligence tests , 1991 .

[23]  Seana Moran Assessing and developing multiple intelligences purposefully , 2011 .

[24]  Lee J. Cronbach,et al.  Alpha Coefficients for Stratified-Parallel Tests , 1965 .

[25]  A. Goldberger,et al.  Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable , 1975 .

[26]  C Loehlin John,et al.  Latent variable models: an introduction to factor, path, and structural analysis , 1986 .

[27]  H. Jones,et al.  Frames of Mind , 1969, Mental Health.

[28]  R. Shavelson,et al.  Self-Concept: Validation of Construct Interpretations , 1976 .

[29]  Howard B. Lee,et al.  Foundations of Behavioral Research , 1973 .

[30]  M. MacDougall Moving beyond the nuts and bolts of score reliability in medical education: Some valuable lessons from measurement theory , 2011 .

[31]  P. Nokelainen,et al.  Conceptual Definition and Empirical Validation of the Spiritual Sensitivity Scale , 2006 .

[32]  Petri Nokelainen,et al.  Measuring Multiple Intelligences and Moral Sensitivities in Education , 2011 .

[33]  D. Goleman,et al.  Social Intelligence , 2006 .

[34]  D. Goleman The Value of Emotional Intelligence , 2006 .

[35]  C. Stein,et al.  Structural equation modeling. , 2012, Methods in molecular biology.

[36]  K. Murphy,et al.  Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests, Second Ediction , 1998 .

[37]  L. Tucker,et al.  A reliability coefficient for maximum likelihood factor analysis , 1973 .

[38]  Henry Tirri,et al.  B-Course: A Web-Based Tool for Bayesian and Causal Data Analysis , 2002, Int. J. Artif. Intell. Tools.

[39]  Marja-Liisa Malmivuori The dynamics of affect, cognition, and social environment in the regulation of personal learning processes : The case of mathematics , 2001 .

[40]  K. Vehkalahti Reliability of measurement scales , 2000 .

[41]  Petri Nokelainen,et al.  Conceptual Modeling of Self-rated Intelligence-profile , 2002 .

[42]  Doug Lennick,et al.  Moral Intelligence: Enhancing Business Performance and Leadership Success , 2005 .

[43]  Robert F. DeVellis,et al.  Scale Development: Theory and Applications. , 1992 .

[44]  K. Tirri MODELING A SELF-RATED INTELLIGENCE-PROFILE FOR THE VIRTUAL UNIVERSITY , 2003 .

[45]  A. Bandura Reflections on self-efficacy , 1978 .

[46]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[47]  Jay Magidson,et al.  Advances in factor analysis and structural equation models , 1980 .

[48]  P. Nokelainen,et al.  What contributes to vocational excellence? Characteristics and experiences of competitors and experts in WorldSkills London , 2012 .