What Do Undergraduates Learn About Human Intelligence? An Analysis of Introductory Psychology Textbooks

Abstract

SCIENTIFIC Human intelligence is an important concept in psychology because it provides insights into many areas, including neurology, sociology, and health. Additionally, IQ scores can predict life outcomes in health, education, work, and socioeconomic status. Yet, most students of psychology do not have an opportunity to take a class on intelligence. To learn what psychology students typically learn about intelligence, we analyzed 29 textbooks for introductory psychology courses. We found that over 3/4 of textbooks contained inaccurate statements. 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%). We learned that most introductory psychology students are exposed to some inaccurate information about intelligence and may have the mistaken impression that nonmainstream theories (e.g., Sternberg’s or Gardner’s theories) are as empirically supported mainstream theories (such as Spearman’s g).

Full text

What Do Undergraduates Learn About Human Intelligence? An Analysis of
Introductory Psychology Textbooks
Russell T. Warne, Mayson C. Astle, and Jessica C. Hill
Utah Valley University
ABSTRACT
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 gtheory. 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.
SCIENTIFIC ABSTRACT
Human intelligence is an important concept in psychology because it provides insights into many areas, including
neurology, sociology, and health. Additionally, IQ scores can predict life outcomes in health, education, work, and
socioeconomic status. Yet, most students of psychology do not have an opportunity to take a class on intelligence. To learn
what psychology students typically learn about intelligence, we analyzed 29 textbooks for introductory psychology courses.
We found that over 3/4 of textbooks contained inaccurate statements. 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%),
This article was published February 26, 2018.
Russell T. Warne, Mayson C. Astle, and Jessica C. Hill, Department of Behavioral Science, Utah Valley University.
This research was previously presented at the Utah Conference on Undergraduate Research on February 17, 2017, in Orem, UT; the National Conference on
Undergraduate Research on April 7, 2017, in Memphis, TN; the annual conference of the Rocky Mountain Psychological Association on April 7, 2017, in Salt
Lake City, UT; the annual conference of the International Society for Intelligence Research on July 15, 2017, in Montreal, Canada; and the annual conference of
the American Psychological Association on August 3, 2017, in Washington, DC.
The authors have made available for use by others the data that underlie the analyses presented in this paper (see Warne, 2017), thus allowing replication and potential
extensions of this work by qualified researchers. Next users are obligated to involve the data originators in their publication plans, if the originators so desire.
This project was financially supported by a Grant for Engaged Learning and an Undergraduate Research Scholarly and Creative Activities Grant from Utah
Valley University.
Copyright of this manuscript belongs to the author(s). The author(s) grant(s) the American Psychological Association the exclusive right to publish this manuscript first,
identify itself as the original publisher, and claim all commercial exploitation rights. Upon publication, the manuscript is available to the public to copy, distribute, or display
under a Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), which permits use, distribution, and
reproduction in any medium, provided that the original work is properly cited and is not used for commercial purposes. Please use APA’s Online Permissions Process
(Rightslink®) at http://www.apa.org/about/contact/copyright/seek-permission.aspx to request commercial reuse of this content.
Correspondence concerning this article should be addressed to Russell T. Warne, Department of Behavioral Science, Utah Valley University, 800 West
University Parkway MC 115, Orem, UT 84058. E-mail: rwarne@uvu.edu
Archives of Scientific Psychology 2018, 6, 32–50 © 2018 The Author(s)
DOI: http://dx.doi.org/10.1037/arc0000038 2169-3269
Archives of Scientific Psychology
www.apa.org/pubs/journals/arc

and how intelligence is measured (82.8%). We learned that most introductory psychology students are exposed to some
inaccurate information about intelligence and may have the mistaken impression that nonmainstream theories (e.g.,
Sternberg’s or Gardner’s theories) are as empirically supported mainstream theories (such as Spearman’s g).
Keywords: human intelligence, psychology textbooks, undergraduate education
Supplemental materials: http://dx.doi.org/10.1037/arc0000038.supp
Data repository: http://dx.doi.org/10.3886/ICPSR36957.v1
Psychology is one of the most popular majors in America (Halonen,
2011). The National Center for Education Statistics reports that out of
1,894,934 bachelor’s degrees awarded in the 2014 –2015 academic
year, 117,557 were in psychology (National Center for Education
Statistics, 2016, Table 322.30). The only two degree areas with more
bachelor’s degrees awarded were business and health-related profes-
sions.
Like the psychology major, introductory psychology courses have
broad appeal, which is a unique opportunity for the field to have an
educational impact across the university curriculum. These courses
provide exposure to both the natural sciences (e.g., neuroanatomy,
brain function) as well as the social sciences (e.g., personality, social),
due to psychology’s philosophical and biological origins (Barnard et
al., 1970; Halonen, 2011). Thus, the courses are popular general
education electives for nonpsychology majors, such as those studying
medicine, business, engineering, computer science, teaching, commu-
nications, religion, and the law (Barnard et al., 1970; Halonen, 2011).
Consequently, the introductory psychology course is the most fre-
quently offered psychology course at universities across the nation
(Stoloff et al., 2010), enrolling between 1.2 and 1.6 million students
annually (Steuer & Ham, 2008, p. 160). Indeed, more than 99% of
institutions of higher education in America offer introductory psy-
chology (Stoloff et al., 2010). Introductory psychology courses also
serve as “gateway courses” to later classes in the psychology major
(Hogben & Waterman, 1997), as introductory psychology is often a
prerequisite more advanced courses (Stoloff et al., 2010).
Because psychology has tremendous diversity in content, almost
every university offers an introductory psychology course in which
students are briefly introduced to many topics (American Psycholog-
ical Association [APA], 2014; Stoloff et al., 2010) via course lecture
and textbooks. To increase consistency across institutions in the
content offered, the APA formed an introductory psychology Working
Group to provide recommendations on critical content. The group
produced five pillars that should be the foundation of any introductory
course: (a) biological, (b) cognitive, (c) development, (d) social and
personality, and (e) mental and physical health (APA, 2014, p. 17).
Among the topics covered in the “social and personality pillar” are
gender, emotion, and human intelligence.
Intelligence Is Important, but Neglected
Intelligence has been subjected to more than a century of empirical
scrutiny (Detterman, 2014; Warne, 2016). One of the results today is
a hierarchical theory of intelligence called the Carroll (1993) three-
stratum theory, which states that all cognitive abilities are organized
in a hierarchy, with specific, narrow tasks (e.g., vocabulary knowl-
edge, arithmetic skills, visual memory) at the lowest level (see Figure
1). These tasks are subsumed by a smaller number of abilities that are
broader in applicability (e.g., verbal ability, mathematical reasoning,
short-term memory (STM)). At the top of the hierarchy is general
intelligence, or g,
1
which is the broadest mental ability and seems to
be used in every cognitive task. Carroll’s theory had the virtue of
informing a variety of controversies related to cognitive abilities in the
20th century. For example, Carroll recognized the importance of gas
a general ability, while also providing a space for other abilities to
make a contribution to human cognition (Warne, 2016). A related
model is the bifactor model, which posits that observed variables are
the product of gand of broad abilities (see Figure 2). The bifactor
model is mathematically related to the Carroll three-stratum theory,
with the bifactor model being a generalization of Carroll’s work and
in accordance with Carroll’s beliefs about how ginfluences perfor-
mance on specific tasks (Beaujean, 2015; Yung, Thissen, & McLeod,
1999). In recent years the bifactor model has attracted advocates (e.g.,
Canivez, 2016; Cucina & Howardson, 2017; Frisby & Beaujean,
2015), mostly because the bifactor model tends to fit the data from
cognitive test batteries better than competing models do (Cucina &
Byle, 2017).
One contrasting hierarchical model is the Cattell-Horn-Carroll
(CHC) theory (McGrew, 2009), which posits that the influence of g
onto specific tasks is fully mediated by broad, midlevel cognitive
abilities. The CHC theory denies any direct influence of gonto
specific tasks, thus favoring a structure of mental abilities shown in
Figure 1. The CHC theory forms the basis of many professionally
developed cognitive test batteries, though often these same test bat-
teries produce data that support the bifactor/Carroll three-stratum
theory more (e.g., Cucina & Byle, 2017; Cucina & Howardson, 2017),
mostly because the CHC model requires strict assumptions about the
relative contribution of variance of each task to its factor (Gignac, 2016).
Regardless of one’s preferred theoretical model, intelligence has
wide-ranging implications in real-world settings. It has strong corre-
lations with extensive variables such as income (A. R. Jensen, 1998),
job prestige (Nyborg & Jensen, 2001), life expectancy (Deary, White-
man, Starr, Whalley, & Fox, 2004), and job performance (Schmidt &
Hunter, 2004). Conversely, intelligence is negatively correlated with
criminal behavior (Beaver et al., 2013), long-term unemployment
(Herrnstein & Murray, 1996), dementia (Deary et al., 2004), death by
automobile accident (O’Toole & Stankov, 1992), and more. Exposure
to the wide spectrum of human ability is beneficial to students of all
backgrounds; Detterman (2014) stated that, “high ability students
believe that everyone is like them. They are often shocked when told
about the full range of ability and even more shocked when they
encounter it in the real world” (p. 148). For example, the critical
nature of understanding intelligence differences can be seen in recent
exchanges between the police and those with decreased intellectual
capacity, such as individuals with severe autism (e.g., Karimi, 2016).
Students of criminal justice, who are more likely to have higher
1
There is disagreement among experts about whether gis synonymous with
general intelligence. Arthur Jensen was careful to distinguish the two, whereas
Carroll (1993, pp. 591–599) thought they were synonymous. Many other
experts fall on either side of the debate, and consensus on the issue is elusive.
In this article we take the position that gis either synonymous with general
intelligence, or that the two concepts are very much alike. However, we
acknowledge that experts have good reasons to disagree with us. Readers who
desire a comprehensive argument for why gand intelligence are not the same,
see A. R. Jensen (1998, Chapter 3).
33UNDERGRADUATE LEARNING ABOUT INTELLIGENCE

intellectual ability, would be more fully prepared for their careers
through encountering people with intellectual ability, who are a dis-
proportionate share of individuals who are arrested (Beaver et al.,
2013).
In contrast to the importance of intelligence and the strength of the
evidence about the construct, psychology education has largely ne-
glected the concept. In an investigation of psychology course offer-
ings at American universities, intelligence didn’t even make the list of
psychology courses provided in at least 10% of universities (Stoloff et
al., 2010). Earlier studies of the psychology curriculum show similar
results (e.g., Perlman & McCann, 1999), and a course on intelligence
has never been part of the mainstream undergraduate psychology
curriculum (McGovern, 1992). Therefore, the little that college stu-
dents learn about intelligence occurs in classes that mostly focus on
other topics, such as cognitive or developmental psychology.
The one exception to the lack of exposure to human intelligence is
the introductory psychology course. This is a course that nearly every
undergraduate psychology student is required to take (Stoloff et al.,
2010) and that many nonpsychology students choose to take as a
general education elective (Barnard et al., 1970). To discover the
depth and breadth of undergraduate experience with intelligence in
introductory psychology courses, we conducted a study of introduc-
tory psychology textbooks.
Analysis of Psychology Textbooks
Textbook Coverage
To learn more about the undergraduate psychology curriculum
researchers often analyze textbooks. For example, in Griggs and
Marek’s (2001) study of 37 introductory psychology textbooks, they
found that even though general chapter topics were largely similar
(e.g., abnormal, cognition), the content within the chapters showed a
great degree of variation. Further, there is a wide degree of variation
among texts’ most cited authors and journal articles. Gorenflo and
McConnell (1991) found that in the list of 37,590 citations, several
prominent psychologists were missing—including Skinner, Freud,
and Piaget. However, a follow-up study by Griggs, Proctor, and Cook
(2004), found that books by these authors were cited in textbooks,
even though their journal articles were rarely cited. This demonstrates
that “there is no substantial common core either in the language used
by psychology text authors or in the psychologists cited and journal
articles referenced in these textbooks” (Griggs, Proctor, & Bujak-
Johnson, 2002, p. 452).
Textbook Accuracy
The introductory psychology textbook is difficult to produce with
uniform accuracy, as authors have only a limited area of expertise, yet
must write chapters that discuss the entire breadth of psychology. As
a result, authors can unintentionally include common misconceptions
or inaccurate findings. Ferguson, Brown, and Torres (in press) found
that when certain issues (like the idea that humans use only 10% of
their brain, the story of the murder of Kitty Genovese, and the
influence of violent media on later violent behavior) were presented,
textbook authors tended to discuss them in oversimplified terms that
ignored controversies, often avoided discussing weaknesses in the
research, or perpetuated misconceptions. Such an approach to sum-
marizing research in introductory texts is likely due to the authors’
need to aim their writing at a student audience; as a result, they present
“contested research as more consistent, generalizable to socially rel-
evant phenomena and higher quality than it was” (Ferguson et al., in
press, p. 6).
Ferguson et al.’s (in press) results, unfortunately, are not an isolated
finding. For instance, Skinner’s seminal work on operant conditioning
and his philosophy of radical behaviorism is often presented in texts
as being mutually exclusive from cognition, and textbook authors
sometimes make the claim that Skinner was not interested in inner
behavior (R. Jensen & Burgess, 1997). Rather, Skinner proposed that
the contents of “private events” (e.g., thoughts, emotions) should not
be privileged beyond overt behavior (Skinner, 1953, p. 257). Regret-
tably, among 15 textbooks, there was not a single full and accurate
account of Skinner’s views (R. Jensen & Burgess, 1997). Moreover,
some textbook authors even avoided any connection between behav-
iorism and cognition at all.
Although Ferguson et al.’s (in press) work could be discounted as
being attributable to differences in interpretation, it is regrettably
common for textbooks to include errors of fact—particularly in first
editions. Although many of first edition errors are corrected in later
editions, some errors persist across many editions, resulting in the
perpetuation of misquotations, errors of fact, omissions, and occasion-
ally factual fabrications (Habarth, Hansell, & Grove, 2011; Thomas,
2007). Although many of these errors are small, it is possible for large
errors to survive the review process. For example, descriptions of
Figure 1. A representation of a hierarchical model of intelligence. The model
depicts a hierarchy of cognitive abilities. At the bottom are specific, narrow
abilities. Highly correlated groups of these specific abilities (represented as
rectangles) coalesce into a small number of abilities that have broader impact
and are represented in the middle row of ovals. These broad abilities, in turn,
are all related via the general intelligence factor (labeled g) at the top of the
hierarchy. Although many intelligence researchers subscribe to this model, the
exact number of abilities in the middle and lower levels is a subject of much
debate (McGrew, 2009).
Figure 2. A representation of the bifactor model of intelligence. The model
depicts each specific ability (represented as rectangles) are the product of
general intelligence (labeled g) and the broad abilities (shown at the bottom of
the figure). Like the hierarchical model shown in Figure 1, the exact number
of specific and broad abilities is the subject of debate. When compared with
hierarchical models, the bifactor model tends to fit the data better (Cucina &
Byle, 2017).
34 WARNE, ASTLE, AND HILL

applied psychology areas (e.g., industrial/organizational, clinical,
counseling, school) are often subject to inaccuracy in textbook de-
scriptions (Haselhuhn & Clopton, 2008).
How authors approach writing is directly related to the quantity and
types of inaccuracies. Steuer and Ham (2008) randomly selected
portions of introductory psychology textbooks and compared the
textbook authors’ explanations and interpretations with the text’s
references. They discovered an assortment of errors, ranging from
minor citation errors, to misrepresenting the content of a source, to
plagiarism. When authors engaged in inductive referencing— by be-
coming familiar with the literature and writing about what they
learned—texts were more accurate than when authors engaged in
deductive referencing, which occurs when writers start with a precon-
ceived understanding of a topic and then search for literature support-
ing their views. Deductive referencing is often more error-prone, as
the writing process “becomes more a matter of defending [viewpoints]
than of discovering statements about scientific truth” (Steuer & Ham,
2008, p. 163).
Psychology Textbooks and Intelligence
There have been three prior studies about intelligence and psy-
chology textbooks. In one study, Griggs (2014a) analyzed textbook
coverage and course syllabi, finding that discussions on intelli-
gence were a smaller percentage of textbook space in the 21st
century than the 1980s, dropping from 6% of textbook space to 4%.
Previously intelligence was covered predominantly in its own
chapter, whereas in 21st century textbooks it was often combined
with the language and thought sections of the book. In another
study, Jackson and Griggs (2013) found that modern introductory
psychology textbooks devoted 1– 4% (median: 4%) of space to
discussing intelligence. Although this information about the per-
centage of a textbook dedicated to intelligence is worthwhile
information, it says nothing about accuracy of textbook informa-
tion on intelligence or the topics that textbook authors introduce
when discussing intelligence. To understand better what under-
graduates learn about human intelligence, more research is re-
quired.
In a more detailed study of organizational psychology textbooks
(Pesta, McDaniel, Poznanski, & DeGroot, 2015), intelligence was
discussed in an average of 3.89 paragraphs, despite the fact that
intelligence is one of the most powerful predictors of job perfor-
mance, especially in more complex jobs (Schmidt & Hunter, 1998,
2004). In comparison, these organizational behavior textbooks
discussed emotional intelligence—a construct with much less em-
pirical evidence for its existence and/or utility—in almost twice as
many paragraphs. Pesta et al. (2015) also found that discussions of
intelligence were much less accurate than discussions of emotional
intelligence.
Purpose of Current Study
Given the importance of human intelligence and the limited liter-
ature related to its inclusion in the undergraduate psychology curric-
ulum, we investigated the presentation of intelligence in the most
frequently used introductory psychology textbooks in the United
States. Following the procedures of other researchers who have in-
vestigated the accuracy of psychology textbooks (e.g., Ferguson et al.,
in press; Habarth et al., 2011; Steuer & Ham, 2008), we conducted a
study to investigate the quality of the discussion on intelligence in the
most popular psychology textbooks today. Specifically, we had two
research questions:
• Research Question 1: What are the most frequently discussed
topics related to intelligence in introductory psychology textbooks?
• Research Question 2: How accurate are introductory psychology
textbooks in their discussion of intelligence?
Because so few undergraduate students ever take a course on
intelligence and the instructor’s choice of textbook often dictates the
content of an introductory class (Miller & Gentile, 1998), we believe
that this study will provide a realistic snapshot of what undergraduates
learn about intelligence.
Method
Following Habarth et al. (2011), we decided to sample the most
popular introductory textbooks, based on sales of new textbooks. In
August 2016 we requested the 30 most popular introductory psychol-
ogy textbooks according to rankings publically available on amazon
.com. We received 29 of these books (listed in Table 1). When two
versions of a book were available, we always chose the full version
rather than the shorter, abridged version to follow the same procedure
as Habarth et al. (2011) and because these shorter versions contained
no unique information (Jackson & Griggs, 2013). A few of the
textbook authors are among the most influential living psychologists
(Diener, Oishi, & Park, 2014). The books represent eight different
publishers, including all of the major social science textbook publish-
ing companies. Cengage had the largest number of textbooks in the
sample (10), with Pearson (6), Worth (5), McGraw-Hill (3), Norton
(2), Kendall Hunt (1), Oxford University Press (1), and Wiley (1) also
having textbooks in the sample. Smaller textbook publishers are
absent from our sample, as are open source textbooks. However,
because our purposive sampling method focused on the most popular
introductory psychology textbooks and several publishers are repre-
sented, we believe that the books would provide accurate information
about what many—perhaps most—introductory psychology students
would learn about intelligence.
Gathering Data: Research Question 1
We recorded basic information about the section on intelligence in
each book. This information consisted of whether intelligence had its
own chapter in the text (or was a section of a more comprehensive
chapter, such as a chapter on cognition), the number of pages devoted
to intelligence, and the total number of pages in the textbook (not
including references, indices, or glossaries).
To investigate Research Question 1, the first author created a
coding system before the study began. Based on prior studies of
textbook content (e.g., Griggs & Marek, 2001; Griggs & Mitchell,
2002; Pesta et al., 2015; Zechmeister & Zechmeister, 2000), the first
author chose to code every textbook’s section headings, emphasized
vocabulary terms (e.g., bolded vocabulary words), and topics dis-
cussed in relationship with intelligence. Headings and emphasized
vocabulary are easy to find in textbooks and are unambiguous. To
ensure that the coding of topics was as objective as possible, the first
author decided a priori that topics would be coded at the paragraph
level because the point at which paragraphs begin or end is always
clear. If a topic was not discussed for at least one full paragraph, it was
not coded. The first author then trained the second author (an accom-
plished undergraduate student and veteran of the first author’s class on
human intelligence) in this coding scheme by coding an entire text-
book chapter together. The second author then used the system to
collect data about topics that textbook authors discussed in their
books.
From this coding process we produced a comprehensive list of
topics discussed in each textbook and also a count of the number of
35UNDERGRADUATE LEARNING ABOUT INTELLIGENCE

paragraphs that each topic was discussed in. We discovered after data
collection began that some topics could overlap when the topic of a
paragraph was a subset of a broader topic (e.g., a paragraph about twin
studies would be recorded as discussing “twin studies” and “herita-
bility research”). Early in the coding process we chose to label these
paragraphs as discussing multiple topics, and we ensured that this
decision was applied uniformly to all data.
To ensure accuracy in the coding process, the first author trained
an additional coder (another undergraduate student), who also
conducted the same coding process independently of the first and
second author on five randomly chosen textbooks. The two coders
had 100% agreement for the number of pages that a textbook
discussed intelligence, the total number of textbook pages, and
whether the textbook devoted an entire chapter to intelligence.
Additionally, the two coders were in agreement for 89.2% of
vocabulary terms, 99.2% of section headings, and 85.1% of topics
discussed. The first author examined ever discrepancy between the
two coders, and where there were discrepancies, the primary coder
(i.e., the second author) was almost always the more accurate,
detailed coder. Based on these results, the research team decided
that the primary coder was sufficiently accurate to continue using
her data without further reliability checks. Percentages of agree-
ment for all five books are available in Supplemental File 1 (p. 23).
After coding, the first two authors compiled a detailed list of every
section heading, vocabulary term, and topic in the textbooks. The first
two authors and three undergraduate research assistants then classified
each item in the comprehensive list into categories. After the catego-
ries were created, the first two authors and the three research assistants
reexamined each category and ensured that the items within each
category were truly the same. When there was doubt, the group
consulted the original passages in the textbooks and reached a con-
sensus after a discussion. In cases of ambiguity we opted to maximize
the number of categories so that the results would be more detailed. It
is important to note that the decision to maximize the number of
categories in the textbooks is an arbitrary one that could make text-
books seem to be less comprehensive than they really are. Therefore,
we also performed an alternative classification analysis in which we
attempted to minimize the number of categories. Both results are
reported for greater transparency.
Gathering Data: Research Question 2
The first author also read the section on intelligence in every
textbook and coded for (a) factual inaccuracies, (b) statements of
questionable accuracy, and (c) logical fallacies that inhibit under-
standing of intelligence research. Before the study began we chose
two standards for factual accuracy: Gottfredson’s (1997a) mainstream
statement on intelligence and Neisser et al.’s (1996) summary of
intelligence research. Gottfredson’s (1997a) article is a statement
signed by more than 50 scholars from diverse fields related to intel-
ligence research (e.g., psychometrics, behavioral genetics, cognitive
psychology, education). Neisser et al.’s (1996) article is the official
report of an APA committee to produce a summary of intelligence
research. We used these articles for two reasons. First, both represent
a summary of solid, noncontroversial findings in intelligence research.
Second, both articles are widely cited and old enough to be commonly
known.
The first author compared every statement in the textbooks with the
content of the Gottfredson (1997a) and Neisser et al. (1996) articles.
If a statement in a textbook contradicted one or both of these articles,
then he labeled it as a factual inaccuracy. In an effort to reduce
subjectivity, we decided in advance that the inaccuracy must be
Table 1
Descriptive Information for Introductory Psychology Textbooks
Citation
Full chapter on
intelligence?
# of pages on intelligence/
total # of pages # of inaccuracies
# of statements
of questionable
accuracy Fallacies
Bernstein (2016) Yes 28/690 1 2 2, 10
Bonds-Raacke (2014) No 8/289 None 1 None
Cacioppo & Freberg (2016) No 12/662 1 3 10, 11
Ciccarelli & White (2015) No 16/611 3 4 10, 11
Comer & Gould (2013) Yes 36/644 3 7 2, 3, 4
Coon & Mitterer (2016) Yes 21/593 8 6 2, 3, 4, 13
Feist & Rosenberg (2015) No 21/619 None 5 3
Feldman (2015) No 14/556 5 8 1, 12
Gerrig (2013) Yes 18/479 1 3 6, 11, 12
Gleitman, Gross, & Reisberg (2011) Yes 34/713 1 6 1, 2, 6, 10
Gray & Bjorklund (2014) No 19/693 1 3 1, 6
Griggs (2014b) No 12/438 None 1 None
Grison, Heatherton, & Gazzaniga (2017) No 17/557 1 4 6, 12
Hockenbury, Nolan, & Hockenbury (2015) No 18/659 3 6 2, 4
Kalat (2017) Yes 18/525 None 6 11
Lahey (2012) No 15/595 1 2 2, 7, 8
Lilienfeld, Lynn, Namy, & Woolf (2014) Yes 37/663 1 4 4, 13
Morris & Maisto (2016) No 19/516 1 4 None
Myers & Dewall (2015) Yes 20/691 2 8 4, 6
Nairne (2014) Yes 29/536 1 6 2
Nevid (2015) No 11/552 1 5 3
Nolen-Hoeksema, Fredrickson, Loftus, & Lutz (2014) Yes 19/639 1 1 None
Okami (2014) No 12/769 None 3 None
Pastorino & Doyle-Portillo (2016) No 13/631 1 3 3, 5, 12
Rathus (2016) No 13/376 2 2 3, 4
Schacter, Gilbert, Wegner, & Nock (2014) Yes 26/663 1 6 1, 2, 3
Wade, Tavris, & Garry (2014) No 12/615 None 4 1
Weiten (2017) No 17/565 2 6 None
Zimbardo, Johnson, & McCann (2017) No 20/581 1 10 3, 6, 12
36 WARNE, ASTLE, AND HILL

explicit and in direct contradiction to either article. Inaccurate state-
ments were noted (including a reference) and compiled so that the
number of inaccurate statements per book could be calculated.
Because Gottfredson (1997a) and Neisser et al. (1996) are not fully
comprehensive literature reviews of the entire field of intelligence
research, we believed that it would be necessary to also identify
problematic statements in a textbook which did not directly contradict
anything in the Gottfredson (1997a) and Neisser et al. (1996) articles.
We labeled these as “statements of questionable accuracy,” and our
study design designated that the first author would search for these
statements during the accuracy coding process. Just as in the coding
process for inaccurate statements, the first author compiled all of the
statements of questionable accuracy by noting them (including a
reference) in order to report a summary of the results.
After statements of questionable accuracy were compiled, we noticed
that these all fit into three categories. One type of statement of question-
able accuracy were false statements that were not addressed in the
Gottfredson (1997a) and Neisser et al. (1996) pieces. Sometimes these
statements were trivial, such as Lilienfeld, Lynn, Namy, and Woolf’s
(2014, p. 333) statement that David Wechsler was “among those classi-
fied as feeble-minded by early flawed IQ tests.” This statement (which
does not have an accompanying citation) is untrue because Wechsler was
too old to have been given an IQ test during his childhood. Therefore,
Lilienfeld et al.’s (2014) statement was labeled as being of “questionable
accuracy” because it did not contradict anything in the Gottfredson
(1997a) or Neisser et al. (1996) articles. A biographical detail about
Wechsler is minutia that most students will probably forget and few
instructors—if any—will emphasize. On the other hand, sometimes these
statements of questionable accuracy could distort students’ understanding
of intelligence. For example, two different textbooks (Nairne, 2014;
Zimbardo, Johnson, & McCann, 2017) report the results of the Minnesota
Transracial Adoption Study (Scarr & Weinberg, 1976; Weinberg, Scarr,
& Waldman, 1992) in inaccurate or overly simplified ways that make
environmental influences on IQ seem more important than many experts
would argue (e.g., A. R. Jensen, 1998; Lee, 2010; Levin, 1994; Lynn,
2015; Plomin & Petrill, 1997).
Another type of statement of questionable accuracy was statements
which recent research would call into question. For example, stereo-
type threat (Steele & Aronson, 1995) was discussed in Neisser et al.’s
(1996) article. But recently attempts at replicating stereotype threat
effects have sometimes been disappointing (e.g., Walker & Bridge-
man, 2008), and some experts have expressed doubts of the reality of
the stereotype threat phenomenon (Flore & Wicherts, 2015; Ganley et
al., 2013) and its applicability outside of the laboratory (Lee, 2010).
The last type of statement of questionable accuracy was statements
which the first author (who teaches a course on human intelligence
and has published multiple peer-reviewed articles on the topic) did not
believe would find widespread agreement among experts on intelli-
gence research. An example of this was Feist and Rosenberg’s (2015,
p. 360) claim that “fluid intelligence is not influenced by culture or the
size of your vocabulary. Instead, it simply involves how fast you learn
things.” The first author classified this as a statement of questionable
accuracy because of evidence that experience and crystallized intel-
ligence can influence fluid intelligence (e.g., Lohman, 2006) and
because some experts (e.g., Carroll, 1993) would question whether
learning speed and fluid intelligence are synonymous.
The standard for logical fallacies was taken from a list compiled by
Gottfredson (2009) of 13 logical fallacies used to dismiss research on
intelligence. These fallacies cover a wide variety of specious argu-
ments that scientists and lay people use to dismiss intelligence re-
search. Table 2 gives a brief description of the fallacies and provides
an example of each fallacy in the textbooks. Like the coding process
for textbook accuracy, the decision to use the Gottfredson (2009) list
of logical fallacies was an a priori decision. We also decided in
advance that for statements to be labeled as a fallacy, the statement
had to be explicit in applying the specious reasoning to a discussion
about intelligence. Statements that appeared to be fallacious were
transcribed and recorded.
Whether judging a statement as inaccurate, of questionable accu-
racy, or as a logical fallacy, the first author was always as conserva-
tive as possible. If the implications or subtext of a statement were
inaccurate, fallacious, or of questionable accuracy, the first author did
not code the statement as being problematic. Only statements that
explicitly met the criteria were coded as inaccurate, fallacious, or of
questionable accuracy. To verify the first author’s work, the second
author (who was very familiar with the sources the logical fallacies
and the standards of factual accuracy) examined the lists of inaccurate,
questionably accurate and fallacious statements, and removed any that
she did not fully agree had met the criteria.
Results
Descriptive Statistics
Table 1 shows that descriptive statistics of the results of our
analysis of introductory textbooks. The mean number of pages dis-
cussing intelligence was 19.5 (SD ⫽7.80), and the average length of
a textbook was 591.0 pages (SD ⫽102.71), indicating that the average
textbook author devotes 3.29% of their textbook to discussing intel-
ligence, which is in accordance with previous research indicating that
3% to 4% of introductory psychology textbook length is dedicated to
intelligence (Griggs, 2014a; Jackson & Griggs, 2013). A total of 11 of
the 29 textbooks (37.9%) dedicate an entire chapter to intelligence. In
the other textbooks, authors combined their discussion on intelligence
with sections on language, cognition, creativity, memory, and other
allied topics.
Topics Covered in Textbooks
In the analysis that maximized detail, we found 102 topics that were
discussed in at least two introductory psychology textbooks. The mean
number of these topics discussed in a textbook was 30.9 (SD ⫽9.67,
min ⫽17, max ⫽53). The 10 most common topics in textbooks were IQ
(27 books, 93.1%), Spearman’s g(27 books, 93.1%), Gardner’s theory of
multiple intelligences (27 books, 93.1%), Sternberg’s triarchic theory of
intelligence (26 books, 89.7%), the measurement of intelligence (24
books, 82.8%), psychometric validity and reliability (23 books, 79.3%),
Alfred Binet and his work (22 books, 75.9%), the Stanford-Binet intel-
ligence test (21 books, 72.4%), environmental influences on intelligence
(20 books, 69.0%), and intellectual disabilities (20 books, 69.0%). For a
full list of topics taught in at least two textbooks, see Table 3 and
Supplemental File 2.
When we minimized the number of categories, we found 40 topics
discussed in at least two textbooks (M⫽18.2, SD ⫽3.98, min ⫽13,
max ⫽29). According to this list, the 10 most common topics discussed
in textbooks were psychometrics (29 books, 100%), IQ (28 books,
96.6%), Spearman’s g(28 books, 96.6%), specific tests (e.g., Stanford-
Binet; 28 books, 96.6%), genetics and environment (28 books, 96.6%),
Gardner’s theory of multiple intelligences (27 books, 93.1%), Sternberg’s
triarchic theory of intelligence (26 books, 89.7%), the history of intelli-
gence tests (24 books, 82.8%), extremes of intelligence (23 books,
79.3%), and group differences (23 books, 79.3%). Table 3 and Supple-
mental File 2 contain the full list of topics taught in at least two textbooks.
37UNDERGRADUATE LEARNING ABOUT INTELLIGENCE

Logical Fallacies
Tables 1 and 2 show that almost every fallacy was mentioned at
least once across the books. The textbooks contained an average of
1.76 logical fallacies (SD ⫽1.21, min ⫽0, max ⫽4). The most
commonly committed fallacies were Fallacies 2 (eight books), 3 (eight
books), 4 (six books), and 6 (six books).
Fallacy 2 is committed when an author indicates that intelligence
does not exist because it is a collection of abilities that an IQ test
creator happens to choose to include on their test. For example, Coon
and Mitterer (2016) stated:
many psychologists simply accept an operational definition of intelli-
gence by spelling out the procedures they use to measure it....Thus, by
selecting items for an intelligence test, a psychologist is saying in a direct
way, “This is what Imean by intelligence.” A test that measures memory,
reasoning, and verbal fluency offers a very different definition of intel-
ligence than one that measures strength of grip, shoe size, hunting skills,
or the person’s best Candy Crush mobile game score. (p. 290)
This gives readers the idea that because “intelligence” is nothing
more than the sum of whatever tasks a psychologist arbitrarily
chooses to put on a test. However, this is not the case because a
common gfactor accounting for about half of variance on cognitive
tasks has been found across many human cultures (e.g., Carroll, 1993;
Dolan, 2000; Dolan & Hamaker, 2001; Frisby & Beaujean, 2015;
Gurven et al., 2017; Reuning, 1972) and even in nonhuman primates
(Fernandes, Woodley, & te Nijenhuis, 2014; Herndon, Moss, Rosene,
& Killiany, 1997), dogs (Arden & Adams, 2016), and rats (Anderson,
1993; Galsworthy, Paya-Cano, Monleón, & Plomin, 2002). Thus, the
existence of gis not dependent on the items on an IQ test used to
investigate it. Many different collections of cognitive tasks produce an
overall gfactor (Carroll, 1993), and these different gfactors from
different test batteries are highly correlated (usually rⱖ.95), indi-
cating their gfactors correspond to the same ability (Johnson,
Bouchard, Krueger, McGue, & Gottesman, 2004; Johnson, te Nijen-
huis, & Bouchard, 2008).
Fallacy 3 is the idea that because people learn as they age or
because IQ scores can change or fluctuate over the lifetime, it is
possible to change a person’s IQ. The most common manifestation of
Fallacy 3 in the introductory psychology textbooks was the claim that
psychologists know how to raise IQ among individuals in high quality
environments (e.g., in industrialized nations). As an example,
Schacter, Gilbert, Wegner, and Nock (2014, p. 420) listed ways in
which parents could raise their child’s IQ, including enriching preg-
nant women’s diets with polyunsaturated fatty acids and sending
children to preschool. In addition to being unsupported by any cita-
tions to the scholarly literature, Schacter et al.’s (2014) list ignores the
disappointing results from studies of efforts to raise intelligence in
most children, with the fadeout of any IQ gains occurring quickly
(Protzko, 2015; see Simons et al., 2016, for similarly disappointing
results of “brain training” studies of older adults). In evaluating
textbooks for their adherence to Fallacy 3, we acknowledged that
eliminating environmental characteristics with a known negative im-
Table 2
Gottfredson’s (2009) Logical Fallacies Used to Dismiss Intelligence Research
Fallacy # Description Citation in textbooks
1Yardstick ⴝConstruct
Item appearance and surface content reflect the actual ability an item measures. Wade et al., 2014, pp. 320–321
2Intelligence is a marble collection
Intelligence/gis a collection of specific abilities or skills that are forced to “add up”
to an IQ score. Thus, gdoesn’t really exist.
Hockenbury, Nolan, & Hockenbury, 2015, p. 308
3Nonfixedness proves malleability
Because IQ can growth or change over the lifespan, it must be possible to change
people’s IQs.
Nevid, 2015, p. 271
4Improvability ⴝEqualizability
Because skills and knowledge can be improved in groups, it must be possible to
equalize groups.
Myers & DeWall, 2015, p. 412
5Gene-environment interaction nullifies heritability
Genes and environment work together to produce phenotypes. Therefore, it is
impossible to tease apart the influence of either one.
Pastorino & Doyle-Portillo, 2016, p. 340
6Genetic similarity among humans negates differences
Humans are 99%⫹alike genetically. Therefore, that last ⬍1% must be trivial. Grison et al., 2017, p. 301
7Contending definitions of intelligence negate evidence
Because there are conflicting definitions of intelligence, no one really knows what
(if anything) IQ tests measure.
Lahey, 2012, p. 286
8Phenotype ⴝgenotype
Physical differences among humans (e.g., IQ scores) are innate, genetically
determined traits.
Lahey, 2012, p. 296
9Biological ⴝgenetic
A biological difference (e.g., brain size, reaction time) must be genetically caused. None
10 Environmental ⴝnongenetic
Environmental influences on development (e.g., parental SES) are unaffected by
individuals’ genes.
Ciccarelli & White, 2015, p. 292
11 Imperfect measurement pretext
IQ tests must perfectly measure intelligence and/or make predictions perfectly
before they can be used or trusted.
Gerrig, 2013, pp. 256–257
12 Dangerous thoughts trigger
Socially divisive or controversial ideas should be held to a higher standard of proof
before dissemination.
Feldman, 2015, p. 279
13 Happy thoughts leniency
Politically correct or socially acceptable ideas are the default belief or should be
given less scrutiny.
Lilienfeld et al., 2014, p. 353
38 WARNE, ASTLE, AND HILL

Table 3
Table of Topics Related to Intelligence in Textbooks
Maximum number of topics Minimum number of topics
Concept
Number (%) of
books Concept
Number (%) of
books
Gardner and his theory of multiple intelligences 27 (93.1%) Psychometrics 29 (100.0%)
IQ 27 (93.1%) Genetics and environment 28 (96.6%)
Spearman and g27 (93.1%) IQ 28 (96.6%)
Sternberg and his theory of triarchic intelligence 26 (89.7%) Spearman and g28 (96.6%)
Measurement of intelligence 24 (82.8%) Tests (specific) 28 (96.6%)
Validity and reliability 23 (79.3%) Gardner and his theory of multiple intelligences 27 (93.1%)
Binet and his tests 22 (75.9%) Sternberg and his triarchic theory of intelligence 26 (89.7%)
Stanford-Binet test 21 (72.4%) History of intelligence tests 24 (82.8%)
Environment 20 (69.0%) Extremes of intelligence 23 (79.3%)
Fluid and crystallized intelligence 20 (69.0%) Group differences 23 (79.3%)
Intellectual disabilities 20 (69.0%) Types of intelligence 21 (72.4%)
Wechsler and his tests 19 (65.5%) Emotional intelligence 18 (62.1%)
Emotional intelligence 18 (62.1%) Cultural bias in intelligence tests 17 (58.6%)
Extremes and giftedness 18 (62.1%) Flynn effect 17 (58.6%)
Genetics and environment 18 (62.1%) Long term outcomes 17 (58.6%)
Mental age 18 (62.1%) Theories of intelligence 15 (51.7%)
Cultural bias on intelligence tests 17 (58.6%) Self-fulfilling prophecies 14 (48.3%)
Flynn effect 17 (58.6%) Politics and controversies 13 (44.8%)
Heritability 16 (55.2%) Definitions of intelligence 12 (41.4%)
Modern intelligence tests 16 (55.2%) Biology and brain 11 (37.9%)
Theories of intelligence 15 (51.7%) Early theorists 11 (37.9%)
Genes 14 (48.3%) Cognitive psychology and intelligence 10 (34.5%)
History of intelligence tests 13 (44.8%) Evaluating intelligence tests 10 (34.5%)
Stereotype threat 13 (44.8%) Normal distribution 10 (34.5%)
Definitions of intelligence 12 (41.4%) Multifactor theories (including Thurstone’s) 8 (27.6%)
Group differences in intelligence 12 (41.4%) Hierarchical theories of intelligence (including
Carroll three-stratum model and CHC theory)
7 (24.1%)
Race differences in intelligence 12 (41.4%)
Standardization 12 (41.4%) Education system 7 (24.1%)
Twin studies 12 (41.4%) Achievement and aptitude 5 (17.2%)
Biology and the brain 11 (37.9%) Causes of g5 (17.2%)
Calculating IQ 11 (37.9%) Creativity, convergent/divergent thinking, and
creativity tests
5 (17.2%)
Evaluating intelligence tests 10 (34.5%)
Family studies of intelligence 10 (34.5%) Non-Western views/context 5 (17.2%)
Nature vs. nurture 10 (34.5%) Individual differences 4 (13.8%)
Normal distribution 10 (34.5%) Statistics 4 (13.8%)
Sex differences in intelligence 10 (34.5%) Tacit knowledge 4 (13.8%)
“Termites” 10 (34.5%) Wisdom 4 (13.8%)
Adoption studies 9 (31.0%) Adaptive testing 2 (6.9%)
Galton and his work 9 (31.0%) Animal intelligence 2 (6.9%)
Interventions 9 (31.0%) Artificial intelligence 2 (6.9%)
Long-term outcomes associated with intelligence 9 (31.0%) Human evolution 2 (6.9%)
The Bell Curve 9 (31.0%) Personality 2 (6.9%)
Test norms 8 (27.6%)
Savants 8 (27.6%)
Between- and within-group differences 7 (24.1%)
Cognitive psychology and intelligence 7 (24.1%)
Cultural differences in intelligence 7 (24.1%)
Culture reduced testing 7 (24.1%)
Education 7 (24.1%)
Factor analysis 7 (24.1%)
Aptitudes and aptitude tests 6 (20.7%)
s(specific abilities) 6 (20.7%)
Thurstone and his work 6 (20.7%)
Achievement and achievement tests 5 (17.2%)
Causes of g5 (17.2%)
Creativity and creativity tests 5 (17.2%)
General or specific intelligence? 5 (17.2%)
Learning disabilities 5 (17.2%)
Miscellaneous types of intelligence 5 (17.2%)
Non-Western views/context 5 (17.2%)
Politics and controversies 5 (17.2%)
Psychometric approach to investigating intelligence 5 (17.2%)
Psychometrics 5 (17.2%)
(table continues)
39UNDERGRADUATE LEARNING ABOUT INTELLIGENCE

pact on intelligence—such as lead poisoning, iodine deficiency, and
severe childhood neglect— can raise IQ (e.g., Huang et al., 2012).
Therefore, we only identified this fallacy if a textbook author (a)
stated that interventions to raise IQ were successful for most or all
individuals, or (b) if they did not distinguish between interventions
that are appropriate for very poor environments and interventions
designed for middle- and upper-class individuals in industrialized
nations.
Whereas Gottfredson’s (2009) Fallacy 3 is about interventions that
raise individuals’ IQ scores, Fallacy 4 concerns improving the intel-
ligence level of groups or individuals until individual differences are
eliminated. Those who subscribe to this fallacy believe that research
on the environmental impact on IQ can be used to eliminate mean
differences in IQ among groups or individuals. For example, Comer
and Gould (2013) described research that found the mean IQ score
gap between African American and White American students de-
creased from early adolescence through the end of college. Because of
these finding, Comer and Gould (2013) concluded that gaps in these
groups’ mean scores can be closed completely. However, whether
mean IQ score differences between racial groups are narrowing is a
matter of contentious debate among experts (e.g., Nisbett et al., 2012;
Murray, 2007; Rushton, 2012; Williams & Ceci, 1997). And even if
these mean score differences are narrowing, it does not indicate that
the gaps will close completely or that experts understand exactly
which societal changes are driving improvements for low scoring
individuals. Indeed, some experts have questioned whether closing
mean IQ score gaps among some demographic groups is even possible
(e.g., Woodley & Meisenberg, 2012). Although it is conceivable that
one day an environmental intervention could eliminate individual or
group differences in IQ scores, such an intervention does not exist at
this time, and there is no guarantee that it ever will (Lee, 2010). Those
who subscribe to Fallacy 4 cling to the possibility of an intervention
and ignore (or deny) its current nonexistence.
Fallacy 6 primarily focuses on the similarities of human beings,
stating that because human genes are about 99% alike, genetic dif-
ferences among people or demographic groups must be inconsequen-
tial. For example, when discussing racial differences in intelligence,
Grison, Heatherton, and Gazzaniga (2017) wrote:
The first issue to consider is whether ‘race’ is a biologically meaningful
concept....Thevast majority of genes—perhaps as many as 99.9%—are
identical among all humans....Butitisunlikely that differences in skin
color and hair type relate to the mental capacities that underlie intelli-
gence. (pp. 301, 302)
Table 3 (continued)
Maximum number of topics Minimum number of topics
Concept
Number (%) of
books Concept
Number (%) of
books
Stability of IQ 5 (17.2%)
Twin and adoption studies 5 (17.2%)
Working memory 5 (17.2%)
Eugenics 4 (13.8%)
Hierarchical theories of intelligence 4 (13.8%)
Individual differences 4 (13.8%)
Mental speed 4 (13.8%)
Reaction range 4 (13.8%)
Self-fulfilling prophecies 4 (13.8%)
Statistics 4 (13.8%)
Tacit knowledge 4 (13.8%)
Wisdom 4 (13.8%)
Army Alpha and Army Beta 3 (10.3%)
Conclusion section 3 (10.3%)
Dove Test 3 (10.3%)
Group tests of intelligence 3 (10.3%)
Intelligence and age 3 (10.3%)
Longitudinal studies 3 (10.3%)
Metacognition 3 (10.3%)
Mozart and/or Beethoven 3 (10.3%)
Reaction time 3 (10.3%)
Adaptive testing 2 (6.9%)
Animal intelligence 2 (6.9%)
Artificial intelligence 2 (6.9%)
Cattell-Horn-Carroll model of intelligence 2 (6.9%)
Chronological age 2 (6.9%)
Convergent thinking 2 (6.9%)
Criticisms of intelligence tests 2 (6.9%)
Goddard and his work 2 (6.9%)
Human evolution 2 (6.9%)
Meaning of IQ scores 2 (6.9%)
Mindset theory 2 (6.9%)
Multi-factor theories 2 (6.9%)
Personality 2 (6.9%)
Prodigies 2 (6.9%)
Spearman and Thurstone 2 (6.9%)
Terman’s theories 2 (6.9%)
Theory of mind 2 (6.9%)
Working memory and attention 2 (6.9%)
Note. For a detailed enumeration of topics in each textbook, see Supplemental File 2.
40 WARNE, ASTLE, AND HILL

Fallacy 6 is so common in the biological and social science literature
that it even has its own name: Lewontin’s fallacy, named for a biologist
who popularized it (Lewontin, 1972). Briefly, what makes this reasoning
fallacious is that this high degree of genetic similarity is what makes all
humans belong to the same species and able to interbreed. Because these
genes are identical, they cannot be responsible for any phenotypical
differences among humans. (In comparison, humans and chimpanzees
differ in about 4% of their genes; see The Chimpanzee Sequencing and
Analysis Consortium, 2005.) But this high degree of similarity does not
indicate that the relatively few genetic differences among humans are
irrelevant. Indeed, these genetic differences are responsible for at least
some of the interpersonal phenotypic variation in a wide variety of traits,
including height (Yang et al., 2015), heart disease (Pickrell et al., 2016),
aggressive behavior (Beaver, Barnes, & Boutwell, 2016), schizophrenia
(Sariaslan, Larsson, & Fazel, 2016), and intelligence (Davies et al., 2015).
Other explanations of why Lewontin’s fallacy is incorrect are available
(e.g., Edwards, 2003; Smouse, Spielman, & Park, 1982).
Extending the incorrect reasoning of Fallacy 6 to another species
shows why it is unwise to rely on genetic similarity among individuals to
dismiss phenotypic variation. All domesticated dog breeds differ by only
0.15% of their genes—a much lower level of genetic variation than
humans. By the reasoning of Fallacy 6, the breed of a dog doesn’t matter
for its owner’s use because such slight genetic differences are trivial.
Therefore, poodles can pull Iditarod sleds, and a pug is a great police dog
(see Figure 3). Indeed, many canid species (e.g., wolves, foxes, dogs,
jackals, and dingoes) are so closely related that they can interbreed
(Wayne & Ostrander, 1999). Thus, even a small percentage of genetic
diversity can contribute greatly to a population’s phenotypical variance
(The Chimpanzee Sequencing and Analysis Consortium, 2005, p. 83).
Tables 1 and 2 show that authors frequently perpetuate logical
fallacies related to intelligence in their introductory psychology text-
books. Indeed, nearly all the fallacies from Gottfredson’s (2009) list
appeared in at least one book, indicating that these specious arguments
about intelligence research are commonly disseminated. Other exam-
ples of these logical fallacies in the textbooks can be found in
Supplemental File 1 (pp. 3–21).
Inaccuracies
Only six books (20.7%) did not have any inaccuracies. These books
were Bonds-Raacke (2014); Feist and Rosenberg (2015); Griggs
(2014b); Kalat (2017); Okami (2014); and Wade, Tavris, and Garry
(2014). The remaining 23 textbooks (79.3%) contained at least one
factually inaccurate statement about human intelligence, a similar
percentage as was found in the Pesta et al. (2015) study of organiza-
tional behavior textbooks.
By far the most common type of inaccurate statement was related
to test bias (14 books, 48.2%). These inaccurate statements about test
bias often were claims that mean differences in test scores among
demographic groups (especially racial/ethnic groups) were due to test
bias. For example, Morris and Maisto (2016) stated:
Another major criticism of intelligence tests is that their content and
administration do not take into account cultural variations and, in fact,
discriminate against minorities. High scores on most IQ tests require
considerable mastery of standard English, thus biasing the tests in favor
of middle- and upper-class White people. (p. 252)
However, this directly contradicts the mainstream statement on
intelligence from Gottfredson (1997a), which stated:
Intelligence tests are not culturally biased against American Blacks or
native-born, English-speaking peoples in the U.S. Rather, IQ scores
predict equally accurately for all such Americans, regardless of race and
social class. Individuals who do not understand English well can be given
either a nonverbal test or one in their native language. (p. 14; see also
Neisser et al., 1996, p. 90)
Indeed, eliminating test bias is a commonplace procedure among
professional psychometrics that it is required before putting a cognitive,
intelligence, or educational test on the commercial market (American
Educational Research Association, American Psychological Association,
& National Council on Measurement in Education, 2014; Warne, Yoon,
& Price, 2014).
A related inaccurate statement that was found in the textbooks was
claims that intelligence simply could not be measured in any mean-
ingful way or that it was extremely challenging to measure intelli-
gence (e.g., Coon & Mitterer, 2016; Feldman, 2015; Pastorino &
Doyle-Portillo, 2016). This contradicts the mainstream view of intel-
ligence research, which stated, “Intelligence . . . can be measured, and
intelligence tests measure it well” (Gottfredson, 1997a, p. 13). Addition-
ally, one of the bedrock foundations of intelligence testing is the “indif-
ference of the indicator” (Spearman, 1927, p. 197), which states that the
surface content of intelligence test items is irrelevant. Rather, any test
item that requires cognitive effort measures—at least partially—
intelligence (Lubinski & Humphreys, 1997). This means that it is actually
easier to measure intelligence than many other psychological constructs.
Indeed, some individuals trying to measure other constructs have inad-
vertently created intelligence tests (see Gottfredson, 2004, and Reeve &
Basalik, 2014, for an example).
Another frequent topic of inaccurate statements was the claim that
intelligence is only relevant in academic settings—and not in every-
day life (e.g., Caciopo & Freberg, 2016, p. 385; Coon & Mitterer,
2016; pp. 297, 309). However, the APA statement (Neisser et al.,
1996, pp. 82– 83) and the mainstream statement on intelligence (Got-
tfredson, 1997a, p. 13) argued that intelligence tests scores do corre-
late with many nonacademic life outcomes. Indeed, there is a vast
body of research on the correlates of intelligence (Gottfredson, 1997b;
A. R. Jensen, 1998; Warne, 2016). This is why intelligence scholars
believe that “there is little that IQ doesn’t help predict” (Belasen &
Hafer, 2013, p. 615). Other topics that were the subject of inaccurate
Figure 3. The incorrect reasoning of Lewontin’s fallacy. Slight differences in
genotypes among organisms can result in major phenotype differences. Com-
pared with humans, dogs are much less genetically diverse. Ignoring these
genotype differences among organisms in the same species is nonsensical. (Art
by Bryan Johnson, caption by Russell T. Warne. Copyright: Russell T. Warne,
2017. This work is licensed under the Creative Commons CC-BY-NC-ND
License. The image—with accompanying caption—may be shared for non-
commercial purposes with attribution to the authors, but may not be altered
without permission of the copyright holder.)
41UNDERGRADUATE LEARNING ABOUT INTELLIGENCE

statements in the textbooks included the influence of environment and
genes on intelligence, culture, and racial issues related to intelligence.
Questionable Accuracy
There were several ways that textbook authors provided statements
of questionable accuracy. We will focus on the most common topics
of questionably inaccurate statements: race, stereotype threat, envi-
ronmental influences on intelligence, and Lewontin’s seed analogy.
However, readers should be aware that there were other topics that
were the subject of statements of questionable accuracy, including sex
differences in intelligence, culture and intelligence, The Bell Curve
(Herrnstein & Murray, 1996), the history of intelligence research/
testing, and childhood intervention programs. For a more detailed
summary of the statements that we found questionably accurate, see
Supplemental File 1.
Race. One of the most controversial topic in all of science is
mean race differences in intelligence (Check Hayden, 2013; Cofnas,
2016). The topic is so taboo that it is one of the few topics that some
scholars declare a priori off limits to empirical investigation (e.g.,
Kourany, 2016; Sternberg, 2005) and which is subject to regular
censorship in the scientific community (Gottfredson, 1994, 2009,
2010). Perhaps for this reason, some textbook authors (e.g., Bonds-
Raacke, 2014; Kalat, 2017; Nolen-Hoeksema, Fredrickson, Loftus, &
Lutz, 2014) avoided the topic. Because racial differences in intelli-
gence are not the most important issue related to intelligence research,
this is a valid way to handle the controversy (Hunt, 2014).
However, many authors who chose to address the controversy had
questionably accurate statements regarding race and intelligence.
Many of these revolved around the persistent finding (dating back
nearly a century) that— on average—American White examinees
score approximately 15 points higher than African Americans (e.g.,
Roth, Bevier, Bobko, Switzer, & Tyler, 2001; Yerkes, 1921, p. 707),
though some scholars contend that in recent decades the score gap has
narrowed to as little as 10 points (Hedges & Nowell, 1999; Nisbett et
al., 2012). None of the textbook authors denied the existence of these
mean score differences, though in one textbook the differences were
described as being “relatively small” (Gleitman, Gross, & Reisberg,
2011, p. 450). We classified this particular statement as being ques-
tionably accurate because a 10 –15 point mean difference would have
major consequences for individuals and society; some people may not
consider that difference “relatively small.”
But the textbook authors’ explanation of the cause(s) of mean score
differences across groups was frequently a source of questionably inac-
curate information. Although these statements took a variety of formats,
they all emphasized environmental, nongenetic causes for the score
differences across White American and African American examinees and
de-emphasized, minimized, dismissed, or ignored possible empirically
supported genetic causes. Because clear answers in this contentious
nature/nurture debate elude experts, we classified statements as being
questionably accurate if the authors’ explanation for these race differ-
ences was largely discredited or if it completely denied the role of
genetics. For example, Nevid (2015, p. 271) stated— citing evidence
from the Minnesota Transracial Adoption Study—that “being raised in an
environment that places a strong value on educational achievement...
basically canceled out the oft-cited 15-point gap in IQ between these two
groups.” However, the data clearly reported in the study shows that in
adolescence clearly showed that differences were not “basically cancelled
out.” Indeed, IQ scores of African American and multiracial adoptees
were 7–16 points lower than White adoptees’ scores and 10 –20 points
lower than the scores from White adopted parents’ biological children
(Weinberg et al., 1992, p. 123). Even the earlier report from the study—
largely seen as more favorable to an environmental argument—still
shows a 5-point mean difference between African American and White
adoptees’ IQ scores (Scarr & Weinberg, 1976, p. 732). Generally, schol-
ars in the field of intelligence see the evidence from this study—and
others (e.g., Moore, 1986)—as consistent with both environmental and
genetic hypotheses for the cause of Group IQ score differences (e.g.,
Nisbett et al., 2012).
Stereotype threat. A topic closely related to racial differences in
intelligence that appeared in 13 (44.8%) textbooks was stereotype threat.
Originally proposed by Steele and Aronson (1995), stereotype threat is
the tendency for individuals to do more poorly on an ability test when
they are reminded that their demographic group (e.g., a racial or a gender
group) performs more poorly on standardized tests than other groups. In
addition to recent difficulties in replicating these results, there is new
circumstantial evidence of publication bias in the stereotype threat liter-
ature (Flore & Wicherts, 2015; Ganley et al., 2013), which may inflate the
apparent strength of the effect.
Although it is an intriguing theory, stereotype threat is not a viable
explanation for the cause of mean racial and gender group differences
in intelligence test scores. Sackett, Hardison, and Cullen (2004)
showed that Steele and Aronson (1995) used stereotype threat to
create new mean score differences between African American and
White examinees. Therefore, Steele and Aronson’s (1995) study ex-
plained nothing about preexisting score gaps between demographic
groups (Sackett et al., 2004). However, scholarly authors, the popular
press, and psychology textbook authors interpreted stereotype threat
as explaining part or all of the score difference between groups
(Sackett et al., 2004). We found that misinterpretations have not
lessened since the Sackett et al. (2004) study. The majority (9 of 13)
textbooks in our sample that discussed stereotype threat stated that it
was a partial cause and/or a contributing factor to mean differences in
IQ scores across racial groups (Gerrig, 2013, p. 255; Gleitman et al.,
2011, pp. 455– 456; Gray & Bjorklund, 2014, p. 407; Hockenbury,
Nolan, & Hockenbury, 2015, pp. 304 –305; Kalat, 2017; pp. 306 –307;
Myers & DeWall, 2015, p. 415; Nairne, 2014, pp. 330 –331; Schacter
et al., 2014, pp. 418 – 419; Wade et al., 2014, pp. 321–322).
Environment. When examining the relative importance of envi-
ronmental and genetic factors, even the most strident hereditarians
acknowledge that individual and group differences in intelligence are
at least partially influenced by environmental variables (e.g., Gottfred-
son, 2005; Levin, 1994; Lynn, 2015; Rushton & Jensen, 2005).
In contrast, many textbook authors suggested in their books that
environmental factors were the dominant or only cause for the differ-
ences in IQ scores among individuals. For example, Schacter et al.
(2014) discussed that high socioeconomic status (SES) was the cause
of higher IQ scores, even stating, “Money can’t buy love, but it sure
appears to buy intelligence” (p. 413), and provided three paragraphs
of supporting information. Although there is no question that socio-
economic status is correlated with intelligence, evidence is clear that
individuals’ intelligence is at least a partial cause of their SES (Deary
et al., 2005; Snyderman & Rothman, 1987; Strenze, 2007) and/or that
the two variables share a common partial genetic cause (Marioni et al.,
2014; Trzaskowski et al., 2014).
Lewontin’s seed analogy. One way in which several textbook
authors minimized or denied the importance of genetic factors was by
arguing that the causes of individual IQ variability may have nothing
to do with group differences in IQ. Most of these authors illustrated
this claim with an analogy of two randomly selected groups of seeds,
one planted in favorable soil and another planted in unfavorable soil.
Because the two groups of seeds were formed randomly, any mean
differences between groups must be due to the different environments,
whereas any differences among individual plants within groups would
be attributable to genetic variation. This analogy was usually attrib-
42 WARNE, ASTLE, AND HILL

uted to Lewontin (1970),
2
who used it to argue that average IQ score
differences were unrelated to genetic differences among groups. All of
the textbook authors in this study who used the analogy (Bernstein,
2016; Gleitman et al., 2011; Gray & Bjorklund, 2014; Hockenbury et
al., 2015; Myers & DeWall, 2015; Nolen-Hoeksema et al., 2014;
Weiten, 2017) used it for the same purpose.
We did not classify the use of the seed analogy as being inaccurate
because Neisser et al. (1996, p. 95) used the same analogy to state that
within- and between-groups differences may not have the same ori-
gins. However, we classified the use of the analogy as “questionably
accurate” because we disagreed with the logic of the analogy for two
reasons. One reason is that group differences must be at least partially
caused by individual genetic differences, unless groups are either
formed randomly (as in Lewontin’s seed analogy), or are perfectly
matched genetically (as in a study of identical twins raised in different
homes). Neither of these scenarios applies to the formation of actual
human racial/ethnic groups, which have genetic differences due to
their geographically dispersed recent evolutionary histories (The 1000
Genomes Project Consortium, 2015; Tishkoff et al., 2009). Addition-
ally, the possibility of unique influences that impact intelligence
scores of one demographic group uniquely (e.g., a “legacy of slavery”
among African Americans, or an influence of societal racism that only
affects minority groups) has been empirically ruled out (Dalliard,
2014; Lubke, Dolan, Kelderman, & Mellenbergh, 2003; Rowe &
Cleveland, 1996; Rowe, Vazsonyi, & Flannery, 1994, 1995).
The second reason we classified Lewontin’s seed analogy as being
questionably accurate is that for intelligence to be heritable within groups
but not between groups, the mean environmental differences between
groups would be so large and/or so consistent that there would be little
overlap between groups’ environments (A. R. Jensen, 1998, pp. 447–
458). Indeed, assuming a within-group heritability of intelligence of 0.5
(a realistic estimate, see Deary, 2012; Gottfredson, 1997a; Neisser et al.,
1996; Plomin & Petrill, 1997), environmental differences would have to
be d⫽1.41 for two groups to have a mean IQ difference of d⫽1.0. Such
a difference would indicate that (on a normally distributed variable) only
7.9% of individuals in the lower environment group would exceed the
mean environment for the higher group. Although this size of a difference
in environments is plausible between wealthy industrialized nations and
poor undeveloped nations, it is not plausible as an estimate for the
environmental differences among groups within the United States. For
example, in the United States, 59.5% of White households have incomes
under $75,000; 22.5% of Black households have incomes of $75,000 or
more (Proctor, Semega, & Mollar, 2016, Table A-1). The discrepancies
in the adult education levels between African Americans and White
Americans are even less than the income discrepancies are (see Ryan &
Bauman, 2016, Table 1).
We acknowledge that “environment” consists of much more than just
education and income. As a result, some may argue that an aggregate
environment variable could explain the mean IQ score differences in
African Americans and White Americans. However, for this to be true,
differences in environmental variables would have to (a) be remarkably
consistent in their unfavorability toward African Americans, and (b) have
a causal impact on intelligence levels. Assuming 10 relevant environ-
mental variables average r⫽.40 in their intercorrelations, members of
the lower scoring group would have to average 0.53 standard deviations
below the higher scoring group’s mean on all variables in order to
produce an aggregate environmental difference of d⫽1.41. In a nor-
mally distributed variable, a d⫽.53 difference would indicate that 29.8%
of the lower scoring group’s members exceed the higher scoring group’s
mean. Although this is a possible mean group difference for some
variables—such as income—it is not realistic for many others, such as
education level or many variables (e.g., vaccination rates, access to early
childhood education).
3
Our point in this discussion is not to say that environment is totally
irrelevant in explaining mean group differences in IQ; such a claim is
not supported by the evidence. Rather, we wish to show that empirical
data shows that the possibility of a completely environmental expla-
nation of group differences in IQ—as Lewontin’s (1970) analogy
implies—is not plausible.
Discussion
Most Frequently Discussed Topics
The first research question in this study was, “What are the most
frequently discussed topics related to intelligence in introductory psy-
chology textbooks?” Our results indicated that many of the most fre-
quently discussed topics related to intelligence in textbooks are important
concepts to scholars in the field (e.g., IQ, the measurement of intelli-
gence, intellectual disabilities). Although there is no objective list of the
“correct” concepts that textbook authors should discuss, we believe that
most intelligence researchers would find relevance in many frequently
discussed topics listed in Table 3 (see also Supplemental File 2).
However, two concepts received much more attention from textbooks
authors than they do from the intelligence research community: Gard-
ner’s theory of multiple intelligences and Sternberg’s triarchic theory of
intelligence. Almost every textbook author included these topics in their
discussion, a finding that is in accordance with previous research showing
that Gardner’s Frames of Mind is one of the most frequently cited books
in introductory psychology textbooks (Griggs et al., 2004). Gardner’s and
Sternberg’s theories are popular with textbook authors, even though key
aspects of both theories have little empirical support. Indeed, researchers
investigating cognitive abilities using these theories often produce gin
their data anyway (e.g., Pyryt, 2000). We suggest that textbook authors
should either eliminate a discussion of these theories or present them in
a more nuanced light, exploring both the theories’ strong points and
aspects that are unsupported by empirical data. For example, it would be
fair to discuss how Gardner’s linguistic, logical-mathematical, and visual-
spatial intelligences align well with the broad midlevel factors in modern
2
As a historic sidenote, Lewontin was not the first to publish the analogy;
it originated with Thoday (1969).
3
These calculations are based on the formula provided by Schneider (2016,
p. 8). The average group difference on specific variables that contribute to a
composite is dependent on (a) the number of variables and (b) the intercorrelation
among variables. As (a) increases or (b) decreases, there is a decrease in the
average group difference needed on individual variables to produce a given group
difference on a composite variable. We chose 10 variables and an average
intercorrelation of r⫽.40 arbitrarily. But the results are not greatly different for
other plausible estimates. For example, with 20 variables and an average intercor-
relation of r⫽.40, the required average group difference for individual variables
to produce a composite mean difference of d⫽1.41 would be d⫽.37. Fifteen
variables with a mean intercorrelation of r⫽.35 would require an average group
difference of d⫽.40 to produce a composite mean difference of d⫽1.41. In
theory, one could suggest an extremely large number of environmental variables,
each with an extremely small impact on intelligence levels (e.g., with a mean
intercorrelation of r⫽.50, it would require 75 with a mean difference of d⫽.20
to produce a composite difference of d⫽1.41). But then the challenge becomes
generating a plausible list of dozens of nonredundant variables that all have a
possible causal impact on intelligence after genetic influences have been controlled
for. Given the lackluster results of randomized interventions to increase intelli-
gence (e.g., Lipsey, Farran, & Hofer, 2015; U.S. Department of Health & Human
Services, 2010) and the severe attenuation in effect sizes in correlational studies
when genetic effects are controlled for (e.g., Bouchard, Lykken, Tellegen, &
McGue, 1996), we believe that a lengthy list of variables that each have a small
detrimental impact on African Americans’ intelligence levels would be extremely
difficult to produce.
43UNDERGRADUATE LEARNING ABOUT INTELLIGENCE

intelligence tests and that there is strong evidence for these types of
abilities. However, a balanced approach to Gardner’s theory could dis-
cuss how his denial of gis at odds with over a century of psychometric
data and that Gardner never explained how to measure these intelli-
gences, nor has he embarked on any sort of systematic research program
to gather data to test his theory (Hunt, 2001; Lubinski & Benbow, 1995).
Likewise, in discussing Sternberg’s theory, most textbook authors would
be justified in praising Sternberg’s emphasis on creativity, a trait that
many psychologists and educators value (e.g., Guilford, 1950; Subotnik,
Olszewski-Kubilius, & Worrell, 2011). However, the textbooks would
improve by mentioning how Sternberg’s practical intelligence is not as
general or important as g—an empirical fact that undercuts the theory
(Gottfredson, 2003a). Discussions of the weaknesses of both theories are
widely available (e.g., Deary, Penke, & Johnson, 2010; Gottfredson,
2003a, 2003b; Lubinski & Benbow, 1995; Waterhouse, 2006), as are
rebuttals from their creators (e.g., Gardner, 1995; Sternberg, 2003; Stern-
berg & Hedlund, 2002). If textbook authors do decide to keep these
theories in their textbooks, drawing from both types of sources would
strengthen the discussion of these theories.
Spearman and gwere mentioned in most books; however, there was
often no explanation that gis the dominant theory in this field. Few
books taught about hierarchical models, and those that did usually
discussed these models in general terms. Only one textbook (Okami,
2014, pp. 455– 456) mentioned the Carroll three-stratum theory by
name, and none introduced the bifactor model or any other contem-
porary models. In contrast, nonmainstream theories (e.g., Gardner’s
and Sternberg’s) were treated as favorably— or better—than gor
modern theories of intelligence. Thus, we believe that some introduc-
tory psychology students would mistakenly think that Sternberg’s or
Gardner’s theories are as scientifically supported as Spearman’s gor
the models shown in Figures 1 and 2. This situation also created an
inherent contradiction in some of the textbooks: after writing posi-
tively about Gardner’s and/or Sternberg’s theories, some textbook
authors (e.g., Ciccarelli & White, 2015; Comer & Gould, 2013;
Nairne, 2014) then proceeded to discuss data from IQ research in
depth—much of which was based on gtheory.
Textbook Accuracy
The second research question we aimed to answer was, “How
accurate are introductory psychology textbooks in their discussion of
intelligence?” Judged solely by the number of factually inaccurate
statements, the textbooks we examined were mostly accurate. Six of
the textbooks had no factual accuracies, and no book had more than
eight inaccurate statements or eight questionably accurate statements.
Unfortunately, many of Gottfredson’s (2009) logical fallacies were
present in the textbooks. Apart from Fallacy 9, every fallacy appeared
at least once. We found this unfortunate because few instructors—and
likely no students—would likely have the background knowledge in
psychometrics, population genetics, and intelligence research to un-
derstand the problems associated with these fallacies. We urge intro-
ductory psychology textbook authors to read Gottfredson’s (2009)
chapter and then scrutinize their work to eliminate these fallacies.
Textbook Themes
Although it was not a goal in our research, certain themes emerged
during data analysis. These themes included the perspectives on
psychological testing, an environmental bias, and a minimizing of the
importance of individual differences. We will briefly discuss each of
these themes.
Testing perspectives. One unexpected finding of our study was
that most of the textbooks discussed basic psychometrics and the
principles of testing. Perhaps because scientifically based tests were
first invented to test intelligence (Fancher, 1985), many authors used
their discussion of IQ tests to teach foundational psychometric prin-
ciples, like reliability and validity. Most of these discussions were at
an appropriate level of complexity for introductory students. More-
over, these digressions into psychometric theory were usually inte-
grated seamlessly into the text.
But the psychometric discussions in some books were not ideal.
Several textbooks gave a distorted or oversimplified view of test bias
to readers. Indeed, test bias was the most common topic of the
inaccurate statements, and readers of nearly half of the textbooks in
our study would receive the impression that intelligence tests are
prima facie biased against diverse examinees. This flatly contradicts
mainstream opinion about the topic, which is that “the issue of test
bias is scientifically dead” (Hunter & Schmidt, 2000, p. 151).
Environmental bias. Although some books provided a balanced
explanation of the existence interindividual intelligence differences,
textbook authors almost always favored environmental explanations
for the differences. Prominent authors (e.g., Sternberg, Kamin, Gould,
Lewontin) who advocate a purely environmental explanation for
intelligence differences (both among individuals and among groups)
were usually cited uncritically and approvingly. But scholars who
posit a moderate or strong genetic role in intelligence differences (e.g.,
Deary, Gottfredson, A. R. Jensen, Plomin, Rushton, Lynn) were often
dismissed and rarely cited in a positive way—if they were mentioned
at all. As a result, we doubt that most students would believe that
mainstream scholars view both genetics and environment as important
determinants of individual and mean group differences in intelligence.
Although we do not know why textbook authors take this approach to
the nature-nurture debate in intelligence, our literature review raised a
few possibilities. One possibility is the deductive referencing that
Steuer and Ham (2008) identified. Another explanation could be
authors’ desire that Ferguson et al. (in press) identified to present
research on intelligent as being less ambiguous and more consistent
than it really is.
Rather than shying away from genetic influences on intelligence,
we challenge textbook authors to use intelligence research to intro-
duce the basic principles of behavioral genetics into their textbooks
(see Plomin, deFries, Knopik, & Neiderhiser, 2016, for an accessible
introduction to the topic). In recent years, the evidence that genes are
at least a partial influence of every human behavior and psychological
trait has mounted so quickly that the early 21st century may be the
dawn of a behavioral genetics revolution in psychology. Such a
revolution may be as important— or more important—for psychology
than the cognitive revolution was in the mid-20th century. Introducing
behavioral genetics concepts in introductory psychology may help
students and instructors handle these new advances in psychological
science. Because the evidence that intelligence is influenced by genes
is strong and dozens of alleles associated with IQ have already been
identified, the textbook section on intelligence research may be an
ideal place for authors to introduce behavioral genetics to psychology
undergraduates.
Minimizing the importance of individual differences in
intelligence. Probably the most surprising theme we observed in the
introduction was the tendency of some authors to minimize the
importance of individual differences in intelligence. Most frequently
this appeared in the form of a tacit acknowledgment that IQ test scores
correlate with academic success, followed by a quick denial that the
scores are important for anything else in life (e.g., Coon & Mitterer,
2016, pp. 297, 309; Lahey, 2012, p. 289; Nolen-Hoeksema et al.,
2014, p. 418; Weiten, 2017, p. 281). Although no one would say that
IQ test scores are a perfect predictor of any life outcome, we found it
44 WARNE, ASTLE, AND HILL

striking that the textbook authors who discussed the relationship
between personality traits and life outcomes did not mention similar
caveats, even though personality traits are also variables that correlate
imperfectly long-term outcomes.
The attitude that some authors had toward the importance of intel-
ligence in determining life outcomes is a noticeable contrast to the
opinions of scholars of intelligence, who often explain that “measures
of individual differences in...intelligence...arethemost accurate
predictors that we have of success in academic achievement, industrial
and professional competence and military performance” (Hunt, 2014,
p. 156; see also Belasen & Hafer, 2013, p. 615). Despite their
enthusiasm, intelligence scholars are realistic in their attitudes toward
the topic they study; frank admissions of the limits of intelligence are
common (e.g., Schmidt & Hunter, 1998). It is true that if intelligence
were relevant only in an academic setting, then it would be relatively
unimportant. Nevertheless, intelligence seems to extend to many
aspects of human life, which underscores its inclusion in introductory
psychology textbooks. Hence, we find the tendency to minimize the
importance of individual differences in intelligence puzzling.
Implications for Psychology
Some readers may question why psychologists should attend to the
content of introductory textbooks. In response, we wish to remind
readers of the importance of the introductory psychology course. With
over a million students taking introductory psychology every year, the
introductory psychology course and the assigned textbook are impor-
tant for educating nonexperts about psychological science. Further-
more, because fewer than 10% of all psychology departments offer a
course on intelligence (Stoloff et al., 2010), most psychology students
will never have the chance to correct the misconceptions that they
learn in their introductory course. Nonpsychology majors—who are a
majority of introductory psychology students (Miller & Gentile,
1998)—would be even less likely to learn correct information about
intelligence if their introductory psychology course contains inaccu-
rate information. Thus, as a field, psychologists should remain watch-
ful of the content of introductory psychology texts, as psychologists
are ethically bound to present factually correct information to their
students.
Beyond higher education implications, this study highlights the
mismatch between scholarly consensus on intelligence and the beliefs
of the general public (e.g., Cronbach, 1975; Freeman, 1923; Gottfred-
son, 1994; Snyderman & Rothman, 1987). After reading 43 inaccurate
statements, 129 questionably accurate statements about intelligence,
and 51 logical fallacies about intelligence in introductory psychology
textbooks, the reason for this mismatch became obvious to us. We
believe that members of the public likely learn some inaccurate
information about intelligence in their psychology courses. The good
news about this implication is that reducing the public’s mistaken
beliefs about intelligence will not take a massive public education
campaign or public relations blitz. Instead, improving the public’s
understanding about intelligence starts in psychology’s own backyard
with improving the content of undergraduate courses and textbooks.
Limitations
Although we feel that our findings are generally robust, some
limitations must be considered in the context of our study. First, our
sample size was limited to only 29 textbooks. We tried to ameliorate
this limitation by including the bestselling introductory psychology
textbooks. We also wish to note that this sample of textbooks is larger
than most studies of introductory psychology textbooks (e.g., Griggs
et al., 2004).
It is also possible that textbook content may not reflect what an
instructor teaches in class and additional assigned readings. However,
Griggs (2014a) stated “it is clear that introductory textbooks greatly
impact the structure of the introductory course” (p. 9). Therefore, it
can be assumed that textbooks are—at minimum—an important
source of structure and content for introductory psychology courses.
Although most instructors are free to correct or supplement their
textbook’s content on intelligence, we doubt this happens often be-
cause most instructors probably do not have extensive education in
intelligence research. Courses on intelligence are rare (Stoloff et al.,
2010), and many instructors will have never had extensive education
on the topic. For instructors trained in cognitive psychology, devel-
opmental psychology, social psychology, or other branches that are
more concerned with the generalities of human behavior, the founda-
tion that intelligence research has in individual differences may be
somewhat alien.
Another shortcoming is that we were conservative in our criticism
or the textbooks. For example, we limited our definition of a “factual
accuracy” to anything that was contradicted by the Gottfredson
(1997a) and Neisser et al. (1996) articles. This may make textbooks
appear relatively accurate; comparing textbooks to a more recent
article written by an expert (e.g., Deary, 2012) would have likely
made textbooks seem less accurate. However, we thought that the two
articles represented consensus among experts and had a foundation in
robust empirical findings. We believed these standards would reduce
the influence of our own professional opinions when judging accu-
racy.
4
Another example of our conservative standards was that topics,
inaccuracies, and logical fallacies had to be explicit. This reduced the
subjectivity of our judgments, but it also made the fallacies appear
rare. An example of this is Fallacy 13, which is that politically correct
or socially acceptable ideas are treated more leniently than controver-
sial ones. Despite the proenvironmental bias in explanations of intel-
ligence differences in many books and the frequent use of the seed
analogy, only two textbooks (Coon & Mitterer, 2016, p. 309; Lilien-
feld et al., 2014, p. 353) had explicit statements to this effect.
Some may question the standards of accuracy in this study, which
were two articles from the mid-1990s. However, we chose these
standards because we did not expect introductory textbooks to reflect
the most cutting-edge intelligence research. This is a reasonable
expectation because it takes time for findings to be replicated, theories
to develop, and for the importance of some empirical work to become
apparent. We believed that two extensively cited, mainstream articles
were sound summaries of the intelligence literature and were reason-
able standards for textbook authors to meet. The choice of older
articles also seemed fair considering research by Gorenflo and Mc-
Connell (1991), who found that it “typically takes 20 years or so
before article is perceived as being ‘classic’ by most authors of
introductory psychology texts” (p. 10).
Finally, we want readers to recognize that the data regarding
inaccurate statements, questionably accurate statements, and logical
fallacies were not checked for accuracy, as indicated by interrater
reliability analysis. Rather, the second author examined the informa-
tion compiled by the first author and removed any statements that she
did not believe met the criteria. However, this is not a completely
independent evaluation of the first author’s work because the first
author provided the second author with her education on intelligence.
We adopted this strategy for checking our data because of the limi-
tations of the skills of our research team: the first author is the only
expert on intelligence at the university, and an accomplished under-
4
After all, we were interested in the accuracy of textbooks compared with
mainstream scholarly thought—not compared with our own professional opinions.
45UNDERGRADUATE LEARNING ABOUT INTELLIGENCE

graduate was the most qualified person available to check the first
author’s work. Of course, this is not an ideal situation.
Conclusion
Despite these limitations, we believe that the findings of our study
are important for psychology educators, textbook authors, and the
field of psychology. Although introductory psychology texts incorpo-
rate many central concepts related to intelligence (e.g., intelligence
testing, Spearman’s g), they also include some nonmainstream top-
ics—specifically Gardner’s theory of multiple intelligences and Stern-
berg’s triarchic theory of intelligence. These nonmainstream topics
can be removed from textbooks, de-emphasized, or presented in a
more critical fashion to improve the accuracy of textbooks.
5
More-
over, an added emphasis on the Carroll three-stratum model and/or
bifactor models would greatly strengthen many textbooks.
For those interested in knowing more about intelligence, we suggest
starting with the Gottfredson (1997a) and Neisser et al. (1996) arti-
cles, which provide useful summaries of the scholarly literature
through the mid-1990s. The short books by Deary (2001) and Ritchie
(2015) are not only accurate, but they also have a breezy writing style
that makes them easily digestible. Those willing to tackle lengthier
tomes should consult Herrnstein and Murray’s The Bell Curve (1996)
or A. R. Jensen’s (1998) book on g. Both books stand up well to the
test of time and contain very little information that has since come into
question by mainstream scholars. Some readers will also be surprised
to find that The Bell Curve is not as controversial as its reputation
would lead one to believe (and most of the book is not about race at
all). More recent books that some may find helpful were written by
Hunt (2011) and Mackintosh (2011), both of which could also serve
as a textbook for an advanced undergraduate or basic graduate-level
course on human intelligence. Readers interested in the historical
predecessors and development of hierarchical models of intelligence
should consult Carroll (1993, Chapter 2) for a technical explanation of
various factor analysis models that found favor during the 20th century.
Beaujean (2015) provides a useful historical perspective on bifactor
models, and Canivez (2016) provides an accessible comparison of hier-
archical and bifactor models, whereas Cucina and Byle (2017) make the
same comparison across dozens of data sets. Lohman (1997) gives an
evenhanded account of the philosophical and scientific debates that
occupied historical figures in intelligence research, though modern de-
bates in intelligence research are concerned with other matters (Gottfred-
son, 2009; Gottfredson & Saklofske, 2009). An article from Nisbett et al.
(2012) provides a contrasting view to some of our opinions, especially in
relation to genetics. Regardless of the resources that readers use to learn
more about intelligence, a sincere perusal of the intelligence literature will
help any reader learn more about this important psychological construct.
5
To facilitate these changes, we intend to send all textbook authors a copy
of the article’s Supplemental File 1, which lists the problematic statements
from every textbook.
References
*References marked with an asterisk indicate textbooks included in the
analysis.
American Educational Research Association, American Psychological Asso-
ciation, & National Council on Measurement in Education. (2014). Stan-
dards for educational and psychological testing. Washington, DC: Ameri-
can Educational Research Association.
American Psychological Association [APA]. (2014). Strengthening the com-
mon core of the introductory psychology course. Washington, DC: Ameri-
can Psychological Association, Board of Educational Affairs. Retrieved
from http://www.apa.org/ed/governance/bea/intro-psych-report.pdf
Anderson, B. (1993). Evidence from the rat for a general factor that underlies
cognitive performance and that relates to brain size: Intelligence? Neuro-
science Letters, 153, 98 –102. http://dx.doi.org/10.1016/0304-3940(93)
90086-Z
Arden, R., & Adams, M. J. (2016). A general intelligence factor in dogs.
Intelligence, 55, 79 – 85. http://dx.doi.org/10.1016/j.intell.2016.01.008
Barnard, C., Carmichael, L., French, T., Hunter, W., Thurstone, L., Bronk,
D.,...Whitehorn, J. (1970). The place of psychology in an ideal university:
The report of the university commission to advise on the future of psychol-
ogy at Harvard. American Psychologist, 25, 391– 410. http://dx.doi.org/10
.1037/h0029489
Beaujean, A. A. (2015). John Carroll’s views on intelligence: Bi-factor vs.
higher-order models. Journal of Intelligence, 3, 121–136. http://dx.doi.org/
10.3390/jintelligence3040121
Beaver, K. M., Barnes, J. C., & Boutwell, B. B. (2016). Exploring the
relationship between violent behavior and participation in football during
adolescence. Youth & Society, 48, 786 – 809. http://dx.doi.org/10.1177/
0044118X13515279
Beaver, K. M., Schwartz, J. A., Nedelec, J. L., Connolly, E. J., Boutwell, B. B.,
& Barnes, J. C. (2013). Intelligence is associated with criminal justice
processing: Arrest through incarceration. Intelligence, 41, 277–288. http://
dx.doi.org/10.1016/j.intell.2013.05.001
Belasen, A., & Hafer, R. W. (2013). IQ and alcohol consumption: International
data. Intelligence, 41, 615– 621. http://dx.doi.org/10.1016/j.intell.2013.07
.019
*Bernstein, D. A. (2016). Psychology: Foundations and frontiers. Boston,
MA: Cengage Learning.
*Bonds-Raacke, J. (2014). Psychology in a complex world. Dubuque, IA:
Kendall Hunt.
Bouchard, T. J., Jr., Lykken, D. T., Tellegen, A., & McGue, M. (1996). Genes,
drives, environment, and experience: EPD theory revised. In C. P. Benbow
(Ed.), Intellectual talent: Psychometric and social issues (pp. 5– 43). Balti-
more, MD: Johns Hopkins University Press.
*Cacioppo, J. T., & Freberg, L. A. (2016). Discovering psychology: The
science of the mind (2nd ed.). Boston, MA: Cengage Learning.
Canivez, G. L. (2016). Bifactor modeling in construct validation of multifac-
tored tests: Implications for understanding multidimensional constructs and
test interpretation. In K. Schwizer & C. DiStefano (Eds.), Principles and
methods of test construction: Standards and recent advancements (pp.
247–271). Gottingen, Germany: Hogrefe.
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic
studies. New York, NY: Cambridge University Press. http://dx.doi.org/10
.1017/CBO9780511571312
*Ciccarelli, S. K., & White, J. N. (2015). Psychology (4th ed.). Upper Saddle
River, NJ: Pearson.
Cofnas, N. (2016). Science is not always “self-correcting”: Fact-value confla-
tion and the study of intelligence. Foundations of Science, 21, 477– 492.
http://dx.doi.org/10.1007/s10699-015-9421-3
*Comer, R., & Gould, E. (2013). Psychology around us. Hoboken, NJ: Wiley.
*Coon, D., & Mitterer, J. O. (2016). Introduction to psychology: Gateways to
mind and behavior (14th ed.). Boston, MA: Cengage Learning.
Cronbach, L. J. (1975). Five decades of public controversy over mental testing.
American Psychologist, 30, 1–14. http://dx.doi.org/10.1037/0003-066X.30
.1.1
Cucina, J., & Byle, K. (2017). The bifactor model fits better than the higher-
order model in more than 90% of comparisons for mental abilities test
batteries. Journal of Intelligence, 5, 27. http://dx.doi.org/10.3390/
jintelligence5030027
Cucina, J. M., & Howardson, G. N. (2017). Woodcock-Johnson-III, Kaufman
Adolescent and Adult Intelligence Test (KAIT), Kaufman Assessment Bat-
tery for Children (KABC), and Differential Ability Scales (DAS) support
Carroll but not Cattell-Horn. Psychological Assessment, 29, 1001–1015.
http://dx.doi.org/10.1037/pas0000389
Dalliard, M. (2014). The elusive x-factor: A critique of J. M. Kaplan’s model
of race and IQ. Open Differential Psychology, 27. Retrieved from https://
openpsych.net/paper/27
46 WARNE, ASTLE, AND HILL

Davies, G., Armstrong, N., Bis, J. C., Bressler, J., Chouraki, V., Giddaluru, S.,
. . . the Generation Scotland. (2015). Genetic contributions to variation in
general cognitive function: A meta-analysis of genome-wide association
studies in the CHARGE consortium (N⫽53949). Molecular Psychiatry, 20,
183–192. http://dx.doi.org/10.1038/mp.2014.188
Deary, I. J. (2001). Intelligence: A very short introduction. New York, NY:
Oxford University Press. http://dx.doi.org/10.1093/actrade/9780192893215
.001.0001
Deary, I. J. (2012). Intelligence. Annual Review of Psychology, 63, 453– 482.
http://dx.doi.org/10.1146/annurev-psych-120710-100353
Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human
intelligence differences. Nature Reviews Neuroscience, 11, 201–211.
Deary, I. J., Taylor, M. D., Hart, C. L., Wilson, V., Smith, G. D., Blane, D., &
Starr, J. M. (2005). Intergenerational social mobility and mid-life status
attainment: Influences of childhood intelligence, childhood social factors,
and education. Intelligence, 33, 455– 472. http://dx.doi.org/10.1016/j.intell
.2005.06.003
Deary, I. J., Whiteman, M. C., Starr, J. M., Whalley, L. J., & Fox, H. C. (2004).
The impact of childhood intelligence on later life: Following up the Scottish
mental surveys of 1932 and 1947. Journal of Personality and Social Psy-
chology, 86, 130 –147. http://dx.doi.org/10.1037/0022-3514.86.1.130
Detterman, D. K. (2014). You should be teaching intelligence! Intelligence, 42,
148 –151. http://dx.doi.org/10.1016/j.intell.2013.07.021
Diener, E., Oishi, S., & Park, J. (2014). An incomplete list of eminent
psychologists of the modern era. Archives of Scientific Psychology, 2,
20 –31. http://dx.doi.org/10.1037/arc0000006
Dolan, C. V. (2000). Investigating Spearman’s hypothesis by means of multi-
group confirmatory factor analysis. Multivariate Behavioral Research, 35,
21–50. http://dx.doi.org/10.1207/S15327906MBR3501_2
Dolan, C. V., & Hamaker, E. L. (2001). Investigating Black-White differences
in psychometric IQ: Multi-group confirmatory factor analyses of the
WISC-R and K-ABC and a critique of the method of correlated vectors. In
F. H. Columbus (Ed.), Advances in psychology research (Vol. 6, pp. 31–59).
Huntington, NY: Nova Science Publishers.
Edwards, A. W. F. (2003). Human genetic diversity: Lewontin’s fallacy.
BioEssays, 25, 798 – 801. http://dx.doi.org/10.1002/bies.10315
Fancher, R. E. (1985). The intelligence men: Makers of the IQ controversy.
New York, NY: Norton.
*Feist, G. J., & Rosenberg, E. L. (2015). Psychology: Perspectives and
connections (3rd ed.). New York, NY: McGraw-Hill Education.
*Feldman, R. S. (2015). Essentials of understanding psychology (11th ed.).
New York, NY: McGraw-Hill Education.
Ferguson, C. J., Brown, J. M., & Torres, A. V. (in press). Education or
indoctrination? The accuracy of introductory psychology textbooks in cov-
ering controversial topics and urban legends about psychology. Current
Psychology.
Fernandes, H. B. F., Woodley, M. A., & te Nijenhuis, J. (2014). Differences in
cognitive abilities among primates are concentrated on G: Phenotypic and
phylogenetic comparisons with two meta-analytical databases. Intelligence,
46, 311–322. http://dx.doi.org/10.1016/j.intell.2014.07.007
Flore, P. C., & Wicherts, J. M. (2015). Does stereotype threat influence
performance of girls in stereotyped domains? A meta-analysis. Journal of
School Psychology, 53, 25– 44. http://dx.doi.org/10.1016/j.jsp.2014.10.002
Freeman, F. N. (1923). A referendum of psychologists. The Century, 107,
237–245.
Frisby, C. L., & Beaujean, A. A. (2015). Testing Spearman’s hypotheses using
a bi-factor model with WAIS-IV/WMS-IV standardization data. Intelli-
gence, 51, 79 –97. http://dx.doi.org/10.1016/j.intell.2015.04.007
Galsworthy, M. J., Paya-Cano, J. L., Monleón, S., & Plomin, R. (2002).
Evidence for general cognitive ability (g) in heterogeneous stock mice and
an analysis of potential confounds. Genes, Brain, & Behavior, 1, 88 –95.
http://dx.doi.org/10.1034/j.1601-183X.2002.10204.x
Ganley, C. M., Mingle, L. A., Ryan, A. M., Ryan, K., Vasilyeva, M., & Perry,
M. (2013). An examination of stereotype threat effects on girls’ mathematics
performance. Developmental Psychology, 49, 1886 –1897. http://dx.doi.org/
10.1037/a0031412
Gardner, H. (1995). A response on four fronts. Contemporary Psychology, 40,
938 –939.
*Gerrig, R. J. (2013). Psychology and life (20th ed.). Upper Saddle River, NJ:
Pearson.
Gignac, G. E. (2016). The higher-order model imposes a proportionality
constraint: That is why the bifactor model tends to fit better. Intelligence, 55,
57– 68. http://dx.doi.org/10.1016/j.intell.2016.01.006
*Gleitman, H., Gross, J., & Reisberg, D. (2011). Psychology (8th ed.). New
York, NY: Norton.
Gorenflo, D. W., & McConnell, J. V. (1991). The most frequently cited journal
articles and authors in introductory psychology textbooks. Teaching of
Psychology, 18, 8 –12. http://dx.doi.org/10.1207/s15328023top1801_2
Gottfredson, L. S. (1994). Egalitarian fiction and collective fraud. Society, 31,
53–59. http://dx.doi.org/10.1007/BF02693231
Gottfredson, L. S. (1997a). Mainstream science on intelligence: An editorial
with 52 signatories, history, and bibliography. Intelligence, 24, 13–23.
http://dx.doi.org/10.1016/S0160-2896(97)90011-8
Gottfredson, L. S. (1997b). Why g matters: The complexity of everyday life.
Intelligence, 24, 79 –132. http://dx.doi.org/10.1016/S0160-2896(97)90014-3
Gottfredson, L. S. (2003a). Dissecting practical intelligence theory: Its claims
and evidence. Intelligence, 31, 343–397. http://dx.doi.org/10.1016/S0160-
2896(02)00085-5
Gottfredson, L. S. (2003b). On Sternberg’s “Reply to Gottfredson.” Intelli-
gence, 31, 415– 424. http://dx.doi.org/10.1016/S0160-2896(03)00024-2
Gottfredson, L. S. (2004). Intelligence: Is it the epidemiologists’ elusive
“fundamental cause” of social class inequalities in health? Journal of Per-
sonality and Social Psychology, 86, 174 –199. http://dx.doi.org/10.1037/
0022-3514.86.1.174
Gottfredson, L. S. (2005). What if the hereditarian hypothesis is true? Psy-
chology, Public Policy, and Law, 11, 311–319. http://dx.doi.org/10.1037/
1076-8971.11.2.311
Gottfredson, L. S. (2009). Logical fallacies used to dismiss the evidence on
intelligence testing. In R. P. Phelps (Ed.), Correcting fallacies about edu-
cational and psychological testing (pp. 11– 65). Washington, DC: American
Psychological Association. http://dx.doi.org/10.1037/11861-001
Gottfredson, L. S. (2010). Lessons in academic freedom as lived experience.
Personality and Individual Differences, 49, 272–280. http://dx.doi.org/10
.1016/j.paid.2010.01.001
Gottfredson, L., & Saklofske, D. H. (2009). Intelligence: Foundations and
issues in assessment. Canadian Psychology/Psychologie Canadienne, 50,
183–195. http://dx.doi.org/10.1037/a0016641
*Gray, P., & Bjorklund, D. F. (2014). Psychology (7th ed.). New York, NY:
Worth Publishers.
Griggs, R. A. (2014a). Topical coverage in introductory textbooks from the
1980s through the 2000s. Teaching of Psychology, 41, 5–10. http://dx.doi
.org/10.1177/0098628313514171
*Griggs, R. A. (2014b). Psychology: A concise introduction (4th ed.). New
York, NY: Worth Publishers.
Griggs, R. A., & Marek, P. (2001). Similarity of introductory psychology
textbooks: Reality or illusion? Teaching of Psychology, 28, 254 –256. http://
dx.doi.org/10.1207/S15328023TOP2804_03
Griggs, R. A., & Mitchell, M. C. (2002). In search of introductory psycholo-
gy’s classic core vocabulary. Teaching of Psychology, 29, 144 –147.
Griggs, R. A., Proctor, D. L., & Bujak-Johnson, A. (2002). The nonexistent
common core. American Psychologist, 57, 452– 453. http://dx.doi.org/10
.1037/0003-066X.57.6-7.452b
Griggs, R. A., Proctor, D. L., & Cook, S. M. (2004). The most frequently cited
books in introductory texts. Teaching of Psychology, 31, 113–116.
*Grison, S., Heatherton, T. F., & Gazzaniga, M. S. (2017). Psychology in your
life. New York, NY: Norton.
Guilford, J. P. (1950). Creativity. American Psychologist, 5, 444 – 454. http://
dx.doi.org/10.1037/h0063487
Gurven, M., Fuerstenberg, E., Trumble, B., Stieglitz, J., Beheim, B., Davis, H.,
& Kaplan, H. (2017). Cognitive performance across the life course of
Bolivian forager-farmers with limited schooling. Developmental Psychol-
ogy, 53, 160 –176. http://dx.doi.org/10.1037/dev0000175
Habarth, J., Hansell, J., & Grove, T. (2011). How accurately do introductory
psychology textbooks present psychoanalytic theory? Teaching of Psychol-
ogy, 38, 16 –21. http://dx.doi.org/10.1177/0098628310390912
47UNDERGRADUATE LEARNING ABOUT INTELLIGENCE

Halonen, J. (2011). Are there too many psychology majors? Retrieved from
https://pdfs.semanticscholar.org/0bac/a87447cc5df53095cecba3a92
99f7c428343.pdf
Haselhuhn, C. W., & Clopton, K. L. (2008). The representation of applied
psychology areas in introductory psychology textbooks. Teaching of Psy-
chology, 35, 205–209. http://dx.doi.org/10.1177/009862830803500310
Hayden, E. C. (2013). Ethics: Taboo genetics. Nature, 502, 26 –28. http://dx
.doi.org/10.1038/502026a
Hedges, L. V., & Nowell, A. (1999). Changes in the black-white gap in
achievement test scores. Sociology of Education, 72, 111–135. http://dx.doi
.org/10.2307/2673179
Herndon, J. G., Moss, M. B., Rosene, D. L., & Killiany, R. J. (1997). Patterns
of cognitive decline in aged rhesus monkeys. Behavioural Brain Research,
87, 25–34. http://dx.doi.org/10.1016/S0166-4328(96)02256-5
Herrnstein, R. J., & Murray, C. (1996). The bell curve: Intelligence and class
structure in American life (2nd ed.). New York, NY: Free Press.
*Hockenbury, S. E., Nolan, S. A., & Hockenbury, D. H. (2015). Psychology
(7th ed.). New York, NY: Worth Publishers.
Hogben, M., & Waterman, C. K. (1997). Are all of your students represented
in their textbooks? A content analysis of coverage of diversity issues in
introductory psychology textbooks. Teaching of psychology, 24, 95–100).
http://dx.doi.org/10.1207/s15328023top2402_3
Huang, P.-C., Su, P.-H., Chen, H.-Y., Huang, H.-B., Tsai, J.-L., Huang, H.-I.,
& Wang, S.-L. (2012). Childhood blood lead levels and intellectual devel-
opment after ban of leaded gasoline in Taiwan: A 9-year prospective study.
Environment International, 40, 88 –96. http://dx.doi.org/10.1016/j.envint
.2011.10.011
Hunt, E. (2001). Multiple views of multiple intelligence [Review of Intelli-
gence reframed: Multiple intelligence in the 21st century]. Contemporary
Psychology, 46, 5–7.
Hunt, E. (2011). Human intelligence. New York, NY: Cambridge University
Press.
Hunt, E. (2014). Teaching intelligence: Why, why it is hard and perhaps how
to do it. Intelligence, 42, 156 –165. http://dx.doi.org/10.1016/j.intell.2013.06
.018
Hunter, J. E., & Schmidt, F. L. (2000). Racial and gender bias in ability and
achievement tests: Resolving the apparent paradox. Psychology, Public
Policy, and Law, 6, 151–158. http://dx.doi.org/10.1037/1076-8971.6.1.151
Jackson, S. L., & Griggs, R. A. (2013). Psych lite: Great price, less filling.
Teaching of Psychology, 40, 217–221. http://dx.doi.org/10.1177/
0098628313487415
Jensen, A. R. (1998). The g factor: The science of mental ability. Westport,
CT: Praeger.
Jensen, R., & Burgess, H. (1997). Mythmaking: How introductory psychology
texts present B. F. Skinner’s analysis of cognition. The Psychological
Record, 47, 221–232. http://dx.doi.org/10.1007/BF03395221
Johnson, W., Bouchard, T. J., Jr., Krueger, R. F., McGue, M., & Gottesman,
I. I. (2004). Just one g: Consistent results from three test batteries. Intelli-
gence, 32, 95–107. http://dx.doi.org/10.1016/S0160-2896(03)00062-X
Johnson, W., te Nijenhuis, J., & Bouchard, T. J., Jr. (2008). Still just 1 g:
Consistent results from five test batteries. Intelligence, 36, 81–95. http://dx
.doi.org/10.1016/j.intell.2007.06.001
*Kalat, J. W. (2017). Introduction to psychology (11th ed.). Boston, MA:
Cengage Learning.
Karimi, F. (2016, July 25). Man with autism not sleeping, eating after North
Miami shooting, family says. CNN. Retrieved from http://www.cnn.com/
2016/07/24/health/north-miami-police-charles-kinsey-shooting/
Kourany, J. A. (2016). Should some knowledge be forbidden? The case of
cognitive differences research. Philosophy of Science, 83, 779 –790. http://
dx.doi.org/10.1086/687863
*Lahey, B. B. (2012). Psychology: An introduction (11th ed.). New York, NY:
McGraw-Hill.
Lee, J. J. (2010). Review of Intelligence and how to get it: Why schools and
cultures count [Book review]. Personality and Individual Differences, 48,
247–255. http://dx.doi.org/10.1016/j.paid.2009.09.015
Levin, M. (1994). Comment on the Minnesota Transracial Adoption Study.
Intelligence, 19, 13–20. http://dx.doi.org/10.1016/0160-2896(94)90049-3
Lewontin, R. C. (1970). Race and intelligence. Bulletin of the Atomic Scien-
tists, 26, 2– 8. http://dx.doi.org/10.1080/00963402.1970.11457774
Lewontin, R. C. (1972). The apportionment of human diversity. In T. Dob-
zhansky, M. K. Hecht, & W. C. Steere (Eds.), Evolutionary biology (Vol. 6,
pp. 381–398). Boston, MA: Springer US. http://dx.doi.org/10.1007/978-1-
4684-9063-3_14
*Lilienfeld, S. O., Lynn, S. J., Namy, L. L., & Woolf, N. J. (2014). Psychol-
ogy: From inquiry to understanding (3rd ed.). Upper Saddle River, NJ:
Pearson Education.
Lipsey, M. W., Farran, D. C., & Hofer, K. G. (2015). A randomized control
trial of a statewide voluntary prekindergarten program on children’s skills
and behaviors through third grade. Nashville, TN: Vanderbilt University,
Peabody Research Institute.
Lohman, D. F. (1997). The history of intelligence testing in context: The
impact of personal, religious, and scientific beliefs on the development of
theories and tests of human abilities. In R. F. Dillon (Ed.), Handbook on
testing (pp. 82–106). Westport, CT: Greenwood Press.
Lohman, D. F. (2006). Beliefs about differences between ability and accom-
plishment: From folk theories to cognitive science. Roeper Review, 29,
32– 40. http://dx.doi.org/10.1080/02783190609554382
Lubinski, D., & Benbow, C. P. (1995). An opportunity for empiricism. Con-
temporary Psychology, 40, 935–938.
Lubinski, D., & Humphreys, L. G. (1997). Incorporating general intelligence
into epidemiology and the social sciences. Intelligence, 24, 159 –201. http://
dx.doi.org/10.1016/S0160-2896(97)90016-7
Lubke, G. H., Dolan, C. V., Kelderman, H., & Mellenbergh, G. J. (2003). On
the relationship between sources of within- and between-group differences
and measurement invariance in the common factor model. Intelligence, 31,
543–566. http://dx.doi.org/10.1016/S0160-2896(03)00051-5
Lynn, R. (2015). Race differences in intelligence: An evolutionary analysis
(2nd ed.). Arlington, VA: Washington Summit Publishers.
Mackintosh, N. J. (2011). IQ and human intelligence (2nd ed.). New York,
NY: Oxford University Press.
Marioni, R. E., Davies, G., Hayward, C., Liewald, D., Kerr, S. M., Campbell,
A.,...Deary, I. J. (2014). Molecular genetic contributions to socioeconomic
status and intelligence. Intelligence, 44, 26 –32. http://dx.doi.org/10.1016/j
.intell.2014.02.006
McGovern, T. V. (1992). Evolution of undergraduate curricula in psychology,
1892–1992. In A. E. Puente, J. R. Matthews, & C. L. Brewer (Eds.),
Teaching psychology in America: A history (pp. 13–38). Washington, DC:
American Psychological Association. http://dx.doi.org/10.1037/10120-001
McGrew, K. S. (2009). CHC theory and the human cognitive abilities project:
Standing on the shoulders of the giants of psychometric intelligence re-
search. Intelligence, 37, 1–10. http://dx.doi.org/10.1016/j.intell.2008.08.004
Miller, B., & Gentile, B. F. (1998). Introductory course content and goals.
Teaching of Psychology, 25, 89 –96. http://dx.doi.org/10.1207/
s15328023top2502_2
Moore, E. G. (1986). Family socialization and the IQ test performance of
traditionally and transracially adopted Black children. Developmental Psy-
chology, 22, 317–326. http://dx.doi.org/10.1037/0012-1649.22.3.317
*Morris, C. G., & Maisto, A. A. (2016). Understanding psychology (11th ed.).
New York, NY: Pearson Education.
Murray, C. (2007). The magnitude and components of change in the black–
white IQ difference from 1920 to 1991: A birth cohort analysis of the
Woodcock–Johnson standardizations. Intelligence, 35, 305–318. http://dx
.doi.org/10.1016/j.intell.2007.02.001
*Myers, D. G., & DeWall, C. N. (2015). Psychology (11th ed.). New York,
NY: Worth Publishers.
*Nairne, J. S. (2014). Psychology (6th ed.). Belmont, CA: Cengage Learning.
National Center for Education Statistics. (2016). 2016 Digest of education
statistics. Retrieved from https://nces.ed.gov/programs/digest/2016me-
nu_tables.asp
Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci,
S.J.,...Urbina, S. (1996). Intelligence: Knowns and unknowns. American
Psychologist, 51, 77–101. http://dx.doi.org/10.1037/0003-066X.51.2.77
*Nevid, J. S. (2015). Essentials of psychology: Concepts and applications (4th
ed.). Stamford, CT: Cengage Learning.
48 WARNE, ASTLE, AND HILL

Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., &
Turkheimer, E. (2012). Intelligence: New findings and theoretical develop-
ments. American Psychologist, 67, 130 –159. http://dx.doi.org/10.1037/
a0026699
*Nolen-Hoeksema, S., Fredrickson, B. L., Loftus, G. R., & Lutz, C. (2014).
Atkinson & Hilgard’s introduction to psychology (16th ed.). Andover, UK:
Cengage Learning.
Nyborg, H., & Jensen, A. R. (2001). Occupation and income related to
psychometric g.Intelligence, 29, 45–55. http://dx.doi.org/10.1016/S0160-
2896(00)00042-8
*Okami, P. (2014). Psychology: Contemporary perspectives. New York, NY:
Oxford University Press.
O’Toole, B. I., & Stankov, L. (1992). Ultimate validity of psychological tests.
Personality and Individual Differences, 13, 699 –716. http://dx.doi.org/10
.1016/0191-8869(92)90241-G
*Pastorino, E., & Doyle-Portillo, S. D. (2016). What is psychology? Founda-
tions, applications, and integration. Boston, MA: Cengage Learning.
Perlman, B., & McCann, L. I. (1999). The most frequently listed courses in the
undergraduate psychology curriculum. Teaching of Psychology, 26, 177–
182. http://dx.doi.org/10.1207/S15328023TOP260303
Pesta, B. J., McDaniel, M. A., Poznanski, P. J., & DeGroot, T. (2015).
Discounting IQ’s relevance to organizational behavior: The “somebody
else’s problem” in management education. Open Differential Psychology,
35, 1–11. Retrieved from https://openpsych.net/files/papers/Pesta_2015a.
pdf
Pickrell, J. K., Berisa, T., Liu, J. Z., Ségurel, L., Tung, J. Y., & Hinds, D. A.
(2016). Detection and interpretation of shared genetic influences on 42
human traits. Nature Genetics, 48, 709 –717. http://dx.doi.org/10.1038/ng
.3570
Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2016). Top 10
replicated findings from behavioral genetics. Perspectives on Psychological
Science, 11, 3–23. http://dx.doi.org/10.1177/1745691615617439
Plomin, R., & Petrill, S. A. (1997). Genetics and intelligence: What’s new?
Intelligence, 24, 53–77. http://dx.doi.org/10.1016/S0160-2896(97)90013-1
Proctor, B. D., Semega, J. L., & Mollar, M. A. (2016). Income and poverty in
the United States: 2015 (U.S. Census Bureau Current Populations Reports
No. P60-256(RV)). Washington, DC: U.S. Government Printing Office.
Retrieved from https://www.census.gov/content/dam/Census/library/
publications/2016/demo/p60-256.pdf
Protzko, J. (2015). The environment in raising early intelligence: A meta-
analysis of the fadeout effect. Intelligence, 53, 202–210. http://dx.doi.org/
10.1016/j.intell.2015.10.006
Pyryt, M. C. (2000). Finding “g”: Easy viewing through higher order factor
analysis. Gifted Child Quarterly, 44, 190 –192. http://dx.doi.org/10.1177/
001698620004400305
*Rathus, S. A. (2016). PSYCH (4th ed.). Boston, MA: Cengage Learning.
Reeve, C. L., & Basalik, D. (2014). Is health literacy an example of construct
proliferation? A conceptual and empirical evaluation of its redundancy with
general cognitive ability. Intelligence, 44, 93–102. http://dx.doi.org/10
.1016/j.intell.2014.03.004
Reuning, H. (1972). Psychological studies of Kalahari Bushmen. In L. J.
Cronbach & P. J. D. Drenth (Eds.), Mental tests and cultural adaptation (pp.
171–181). The Hague, The Netherlands: Mouton.
Ritchie, S. (2015). Intelligence: All that matters. London, UK: John Murray
Learning.
Roth, P. L., Bevier, C. A., Bobko, P., Switzer, F. S., III, & Tyler, P. (2001).
Ethnic group difference in cognitive ability in employment and educational
settings: A meta-analysis. Personnel Psychology, 54, 297–330. http://dx.doi
.org/10.1111/j.1744-6570.2001.tb00094.x
Rowe, D. C., & Cleveland, H. H. (1996). Academic achievement in Blacks and
Whites: Are the developmental processes similar? Intelligence, 23, 205–228.
http://dx.doi.org/10.1016/S0160-2896(96)90004-5
Rowe, D. C., Vazsonyi, A. T., & Flannery, D. J. (1994). No more than skin
deep: Ethnic and racial similarity in developmental process. Psychological
Review, 101, 396 – 413. http://dx.doi.org/10.1037/0033-295X.101.3.396
Rowe, D. C., Vazsonyi, A. T., & Flannery, D. J. (1995). Ethnic and racial
similarity in developmental process: A study of academic achievement.
Psychological Science, 6, 33–38. http://dx.doi.org/10.1111/j.1467-9280
.1995.tb00301.x
Rushton, J. P. (2012). No narrowing in mean Black-White IQ differences—
Predicted by heritable g.American Psychologist, 67, 500 –501. http://dx.doi
.org/10.1037/a0029614
Rushton, J. P., & Jensen, A. R. (2005). Thirty years of research on race
differences in cognitive ability. Psychology, Public Policy, and Law, 11,
235–294. http://dx.doi.org/10.1037/1076-8971.11.2.235
Ryan, C. L., & Bauman, K. (2016). Education attainment in the United States:
2015. (U.S. Census Bureau Current Populations Reports No. P20-578).
Washington, DC: U.S. Government Printing Office. Retrieved from https://
www.census.gov/content/dam/Census/library/publications/2016/demo/p20-
578.pdf
Sackett, P. R., Hardison, C. M., & Cullen, M. J. (2004). On interpreting
stereotype threat as accounting for African American-White differences on
cognitive tests. American Psychologist, 59, 7–13. http://dx.doi.org/10.1037/
0003-066X.59.1.7
Sariaslan, A., Larsson, H., & Fazel, S. (2016). Genetic and environmental
determinants of violence risk in psychotic disorders: A multivariate quan-
titative genetic study of 1.8 million Swedish twins and siblings. Molecular
Psychiatry, 21, 1251–1256. http://dx.doi.org/10.1038/mp.2015.184
Scarr, S., & Weinberg, R. A. (1976). IQ test performance of Black children
adopted by White families. American Psychologist, 31, 726 –739. http://dx
.doi.org/10.1037/0003-066X.31.10.726
*Schacter, D. L., Gilbert, D. T., Wegner, D. M., & Nock, M. K. (2014).
Psychology (3rd ed.). New York, NY: Worth Publishers.
Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection
methods in personnel psychology: Practical and theoretical implications of
85 years of research findings. Psychological Bulletin, 124, 262–274. http://
dx.doi.org/10.1037/0033-2909.124.2.262
Schmidt, F. L., & Hunter, J. (2004). General mental ability in the world of
work: Occupational attainment and job performance. Journal of Personality
and Social Psychology, 86, 162–173. http://dx.doi.org/10.1037/0022-3514
.86.1.162
Schneider, W. J. (2016). Why are WJ IV cluster scores more extreme than the
average of their parts? A gentle explanation of the composite score extrem-
ity effect. Itasca, IL: Houghton Mifflin Harcourt. Retrieved from http://
www.hmhco.com/~/media/sites/home/hmh-assessments/clinical/woodcock-
johnson/pdf/wjiv/wjiv_asb_7.pdf
Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F.,
Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016). Do “brain-training”
programs work? Psychological Science in the Public Interest, 17, 103–186.
http://dx.doi.org/10.1177/1529100616661983
Skinner, B. F. (1953). Science and human behavior. New York, NY: Mac-
millan.
Smouse, P. E., Spielman, R. S., & Park, M. H. (1982). Multiple-locus alloca-
tion of individuals to groups as a function of the genetic variation within and
differences among human populations. American Naturalist, 119, 445– 463.
http://dx.doi.org/10.1086/283925
Snyderman, M., & Rothman, S. (1987). Survey of expert opinion on intelli-
gence and aptitude testing. American Psychologist, 42, 137–144. http://dx
.doi.org/10.1037/0003-066X.42.2.137
Spearman, C. (1927). The abilities of man: Their nature and measurement.
New York, NY: The Macmillan Company.
Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test
performance of African Americans. Journal of Personality and Social
Psychology, 69, 797– 811. http://dx.doi.org/10.1037/0022-3514.69.5.797
Sternberg, R. J. (2003). Our research program validating the triarchic theory of
successful intelligence: Reply to Gottfredson. Intelligence, 31, 399 – 413.
http://dx.doi.org/10.1016/S0160-2896(02)00143-5
Sternberg, R. J. (2005). There are no public-policy implications: A reply to
Rushton and Jensen (2005). Psychology, Public Policy, and Law, 11, 295–
301. http://dx.doi.org/10.1037/1076-8971.11.2.295
Sternberg, R. J., & Hedlund, J. (2002). Practical intelligence, g, and work
psychology. Human Performance, 15, 143–160. http://dx.doi.org/10.1080/
08959285.2002.9668088
49UNDERGRADUATE LEARNING ABOUT INTELLIGENCE

Steuer, F. B., & Ham, K. W., II. (2008). Psychology textbooks: Examining
their accuracy. Teaching of Psychology, 35, 160 –168. http://dx.doi.org/10
.1177/009862830803500302
Stoloff, M., McCarthy, M., Keller, L., Varfolomeeva, V., Lynch, J., Makara,
K., . . . Smiley, W. (2010). The undergraduate psychology major: An
examination of structure and sequence. Teaching of Psychology, 37, 4 –15.
http://dx.doi.org/10.1080/00986280903426274
Strenze, T. (2007). Intelligence and socioeconomic success: A meta-analytic
review of longitudinal research. Intelligence, 35, 401– 426. http://dx.doi.org/
10.1016/j.intell.2006.09.004
Subotnik, R. F., Olszewski-Kubilius, P., & Worrell, F. C. (2011). Rethinking
giftedness and gifted education: A proposed direction forward based on
psychological science. Psychological Science in the Public Interest, 12,
3–54. http://dx.doi.org/10.1177/1529100611418056
The 1000 Genomes Project Consortium. (2015). A global reference for human
genetic variation. Nature, 526, 68 –74. http://dx.doi.org/10.1038/
nature15393
The Chimpanzee Sequencing and Analysis Consortium. (2005). Initial se-
quence of the chimpanzee genome and comparison with the human genome.
Nature, 437, 69 – 87. http://dx.doi.org/10.1038/nature04072
Thoday, J. M. (1969). Limitations to genetic comparison of populations.
Journal of Biosocial Science, 1, 3–14. http://dx.doi.org/10.1017/
S002193200002318X
Thomas, R. K. (2007). Recurring errors among recent history of psychology
textbooks. The American Journal of Psychology, 120, 477– 495.
Tishkoff, S. A., Reed, F. A., Friedlaender, F. R., Ehret, C., Ranciaro, A.,
Froment, A.,...Williams, S. M. (2009). The genetic structure and history
of Africans and African Americans. Science, 324, 1035–1044. http://dx.doi
.org/10.1126/science.1172257
Trzaskowski, M., Harlaar, N., Arden, R., Krapohl, E., Rimfeld, K., McMillan,
A.,...Plomin, R. (2014). Genetic influence on family socioeconomic status
and children’s intelligence. Intelligence, 42, 83– 88. http://dx.doi.org/10
.1016/j.intell.2013.11.002
U.S. Department of Health and Human Services. (2010). Head Start impact
study. Final report. Washington, DC: U.S. Department of Health and
Human Services Administration for Children and Families Office of Plan-
ning, Research and Evaluation.
*Wade, C., Tavris, C., & Garry, M. (2014). Psychology (11th ed.). Upper
Saddle River, NJ: Pearson Education.
Walker, M. E., & Bridgeman, B. (2008). Stereotype threat spillover and SAT
scores (Research Report No. 2008 –2). New York, NY: College Board.
Warne, R. T. (2016). Five reasons to put the gback into giftedness: An
argument for applying the Cattell–Horn–Carroll theory of intelligence to
gifted education research and practice. Gifted Child Quarterly, 60, 3–15.
http://dx.doi.org/10.1177/0016986215605360
Warne, R. (2017). Introductory psychology textbooks’ coverage of intelligence
research (ICPSR36957-v1) [Data set]. Ann Arbor, MI: Inter-University
Consortium for Political and Social Research. http://dx.doi.org/10.3886/
ICPSR36957.v1
Warne, R. T., Yoon, M., & Price, C. J. (2014). Exploring the various inter-
pretations of “test bias.” Cultural Diversity and Ethnic Minority Psychology,
20, 570 –582. http://dx.doi.org/10.1037/a0036503
Waterhouse, L. (2006). Multiple intelligences, the Mozart effect, and emo-
tional intelligence: A critical review. Educational Psychologist, 41, 207–
225. http://dx.doi.org/10.1207/s15326985ep4104_1
Wayne, R. K., & Ostrander, E. A. (1999). Origin, genetic diversity, and
genome structure of the domestic dog. BioEssays, 21, 247–257. http://dx
.doi.org/10.1002/(sici)1521-1878(199903)21:3⬍247::aid-bies9⬎3.0.co;2-z
Weinberg, R. A., Scarr, S., & Waldman, I. D. (1992). The Minnesota Tran-
sracial Adoption Study: A follow-up of IQ test performance at adolescence.
Intelligence, 16, 117–135. http://dx.doi.org/10.1016/0160-2896(92)90028-P
*Weiten, W. (2017). Psychology: Themes and variations (10th ed.). Boston,
MA: Cengage Learning.
Williams, W. M., & Ceci, S. J. (1997). Are Americans becoming more or less
alike? Trends in race, class, and ability differences in intelligence. American
Psychologist, 52, 1226 –1235. http://dx.doi.org/10.1037/0003-066X.52.11
.1226
Woodley, M. A., & Meisenberg, G. (2012). Ability differentials between
nations are unlikely to disappear. American Psychologist, 67, 501–502.
http://dx.doi.org/10.1037/a0029650
Yang, J., Bakshi, A., Zhu, Z., Hemani, G., Vinkhuyzen, A. A. E., Lee,
S.H.,...theLifeLines Cohort Study. (2015). Genetic variance estimation
with imputed variants finds negligible missing heritability for human height
and body mass index. Nature Genetics, 47, 1114 –1120. http://dx.doi.org/
10.1038/ng.3390
Yerkes, R. M. (Ed.). (1921). Psychological examining in the United States
army. Washington, DC: U.S. Government Printing Office.
Yung, Y.-F., Thissen, D., & McLeod, L. D. (1999). On the relationship
between the higher-order factor model and the hierarchical factor model.
Psychometrika, 64, 113–128. http://dx.doi.org/10.1007/BF02294531
Zechmeister, J. S., & Zechmeister, E. B. (2000). Introductory textbooks and
psychology’s core concepts. Teaching of Psychology, 27, 6 –11. http://dx
.doi.org/10.1207/S15328023TOP2701_1
*Zimbardo, P., Johnson, R., & McCann, V. (2017). Psychology: Core concepts
(8th ed.). Hoboken, NJ: Pearson Education.
Received March 27, 2017
Revision received October 10, 2017
Accepted October 16, 2017 䡲
50 WARNE, ASTLE, AND HILL