Friday, September 23, 2022

What do undergraduates learn about human intelligence? An analysis of introductory psychology textbooks.

Human intelligence is an important construct in psychology, with far-reaching implications, providing insights into fields as diverse as neurology, international development, and sociology. Additionally, IQ scores can predict life outcomes in health, education, work, and socioeconomic status. Yet, students of psychology are often exposed to human intelligence only in limited ways. To ascertain what psychology students typically learn about intelligence, we analyzed the content of 29 of the most popular introductory psychology textbooks to learn (a) the most frequently taught topics related to human intelligence, (b) the accuracy of information about human intelligence, and (c) the presence of logical fallacies about intelligence research. We found that 79.3% of textbooks contained inaccurate statements and 79.3% had logical fallacies in their sections about intelligence. The five most commonly taught topics were IQ (93.1% of books), Gardner's multiple intelligences (93.1%), Spearman's g (93.1%), Sternberg's triarchic theory (89.7%), and how intelligence is measured (82.8%). Conversely, modern models of intelligence were only discussed in 24.1% of books, with only one book discussing the Carroll three-stratum model by name and no book discussing bifactor models of intelligence. We conclude that most introductory psychology students are exposed to some inaccurate information and may have the mistaken impression that nonmainstream theories (e.g., Sternberg's or Gardner's theories) are as empirically supported as g theory. This has important implications for the undergraduate curriculum and textbook authors. Readers should be aware of the limitations of the study, including the choice of standards for accuracy for the study and the inherent subjectivity required for some of the data collection process.

Kevin S. McGrew, PhD
Educational & School Psychologist
Institute for Applied Psychometrics (IAP)

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