Neuropsychological assessment and the Cattell- Horn-Carroll (CHC) cognitive abilities model

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1 The University of Toledo The University of Toledo Digital Repository Theses and Dissertations 2008 Neuropsychological assessment and the Cattell- Horn-Carroll (CHC) cognitive abilities model James B. Hoelzle The University of Toledo Follow this and additional works at: Recommended Citation Hoelzle, James B., "Neuropsychological assessment and the Cattell-Horn-Carroll (CHC) cognitive abilities model" (2008). Theses and Dissertations. Paper This Dissertation is brought to you for free and open access by The University of Toledo Digital Repository. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of The University of Toledo Digital Repository. For more information, please see the repository's About page.

2 A Dissertation Entitled Neuropsychological Assessment and the Cattell-Horn-Carroll (CHC) Cognitive Abilities Model By James B. Hoelzle Submitted as partial fulfillment of the requirements for The Doctor of Philosophy in Psychology Advisor: Gregory J. Meyer, Ph.D. Committee Members: Wesley A. Bullock, Ph.D. Mary E. Haines, Ph.D. Stephen D. Christman, Ph.D. Joni L. Mihura, Ph.D. College of Graduate Studies The University of Toledo August 2008

3 An Abstract of Neuropsychological Assessment and the Cattell-Horn-Carroll (CHC) Cognitive Abilities Model James B. Hoelzle Submitted as partial fulfillment of the requirements for The Doctor of Philosophy in Psychology The University of Toledo August 2008 This study determined whether popular neuropsychological measures evaluate Cattell- Horn-Carroll (CHC) broad and narrow cognitive abilities. A thorough literature review was conducted to identify relevant datasets that would permit factor analyses of targeted instruments. Seventy-seven datasets were obtained and analyzed, or reanalyzed, to ensure methodological consistency across samples. Many factor solutions included dimensions that reflected broad CHC ability constructs, which suggests it is possible to integrate aspects of neuropsychological assessment and the CHC theory. Overall, the project is relevant to assessment practice because it connects neuropsychological tests with CHC theory, and thus facilitates accurate interpretation of performances across different measures. It ultimately brings clinical practice and cognitive theory closer together. ii

4 Acknowledgements It is challenging to appropriately acknowledge and thank everyone who has supported me as I completed this project and my formal graduate training. First and foremost, I am especially grateful for the excellent mentoring provided by my academic advisor and friend, Dr. Gregory Meyer. Dr. Meyer is responsible for introducing me to psychological assessment, fostering my curiosity in research, and helping me to think through many important professional and personal decisions. His guidance has helped me grow as a clinician, and more importantly as a person. I have no doubt many of the opportunities I have are directly related to his willingness to spend time teaching and collaborating with me. It is in an inadequate thank you, but I aspire to encourage and foster professional growth in students in the same ways that he mentored me. I am also appreciative of my dissertation committee s efforts. Drs. Wesley Bullock, Stephen Christman, Mary Haines, and Joni Mihura each provided specific feedback regarding aspects of this project, and these contributions were instrumental in making the finished project more sophisticated. I am thankful for their expertise, encouraging words, and accessibility to discuss the project and a range of other professional issues. A project of this magnitude could not have been completed without the assistance of other researchers. The following individuals were instrumental in providing relevant data or helpful comments during the completion of this project: Dan Han, Peter Hartmann, Bruce Hermann, David Kinsinger, Thomas Merten, Michael Nelson, Benjamin Pyykkonen, Cynthia Riccio, Noah Silverberg, Lisa Stanford, Mark Wilde, and iii

5 John Woodard. It was a pleasure to share ideas with these individuals, and hopefully in the future I can reciprocate the generosity they exhibited towards me. Lastly, and most personally, I am very much thankful for the unwavering support from those closest to me. Jessica, I can only imagine how frustrating it must have been at times to watch me get worked up and bogged down with different aspects of this project. Thank you for doing all you could to keep me going. I could not have made it without your support and I am forever grateful for it. You have set the bar high and it will be challenging for me to return the favor, but I have every intention of fully supporting your efforts as you complete your training. To my family, I have only recently realized how truly blessed I am to be part of such a loving family. It was remarkable and inspiring to watch over the past year as everyone did what they could to overcome an assortment of major to minor difficulties. Somehow you instilled in me the ability to think it will always work out as long as you keep at it. The completion of this project is tangible evidence of this. I am grateful for the unconditional support and encouragement you provide me each day. With all my heart, thank you. iv

6 Table of Contents Abstract Acknowledgements Table of Contents List of Tables List of Figures ii iii v ix x I. Introduction 1 Range of Abilities Evaluated by Neuropsychological Assessment 4 Intellectual Assessment 8 Psychometric Theories of Intelligence 10 Spearman s g-factor 11 Bi-factor Models of Intelligence 14 Multiple Intelligences 15 Extended Gf-Gc Models 17 Carroll s Three-Stratum Model of Intelligence 20 An Integration of Carroll s Three-Stratum Model and Extended Gf-Gc Theory 22 CHC Theory to Practice 26 Alternative Models of Intelligence 30 Defining the Gap Between Neuropsychological Assessment and the Cognitive Anilities Theory 34 Question of Interest 36 v

7 II. Methodology 37 General Methods 37 Survey of Neuropsychological Measures 37 Data Collection 45 Reanalysis of Data: Exploratory Factor Analytic Methods 46 Integration of Factors and CHC Theory 50 Hierarchical Exploratory Factor Analysis 54 Factorial Invariance 54 III. Results 58 Test Classification by CHC Construct and Reliability 58 Overview and Example of Factor Analyses 69 Attention/Concentration Measures 78 Trail Making Test 81 Paced Auditory Serial Addition Test 81 Stroop Test 82 Executive Functioning Measures 82 Wisconsin Card Sorting Test 85 Category Test 85 Controlled Oral Word Association Test 85 Language Measures 86 Boston Naming Test 89 Aphasia Screening Test 89 Multilingual Aphasia Examination 89 vi

8 Memory Measures 90 Wechsler Memory Scales 93 Rey Auditory Verbal Learning Test 93 Wide Range Assessment of Memory and Learning 94 Motor Function Measures 97 Finger Tapping Test 97 Grip Strength Test 97 Purdue Pegboard Test 98 Visual Perception Measures 98 Hooper Visual Organization Test 101 Rey-Osterrieth Complex Figure Test 101 Judgment of Line Orientation Test 101 Neuropsychological Batteries/Comprehensive Measures 102 Halstead-Reitan Neuropsychological Battery 106 NEPSY 106 Luria-Nebraska Neuropsychological Battery 107 Dementia Rating Scale 108 Neurobehavioral Cognitive Status Examination/Cognistat 109 Hierarchical Exploratory Factor Analysis 109 Factorial Invariance 110 vii

9 IV. Discussion 113 Implications for Neuropsychological Assessment 117 Implications for the CHC Model 119 Factorial Invariance 122 Methodological Limitations 123 Conclusion 125 References 126 Appendix A. Pattern Matrices 158 Appendix B. Factor Structure Congruency Coefficients 203 viii

10 List of Tables Table 1. Targeted neuropsychological tests and batteries: Rank-order popularity and percent of respondents who use each test 40 Table 2. Intrarater reliability of CHC constructs 60 Table 3. Hypothesized relationships between tests and CHC broad/ narrow ability constructs 63 Table 4. Rotated pattern matrix for Pontón et al. (2000) 75 Table 5. Targeted Attention/Concentration measures 80 Table 6. Targeted Executive Functioning measures 84 Table 7. Targeted Language measures 88 Table 8. Targeted Memory measures 92 Table 9. Targeted Motor Function measures 96 Table 10. Targeted Visual Perception measures 100 Table 11. Targeted Neuropsychological Batteries/ Comprehensive measures 104 Table 12. Targeted samples for hierarchical exploratory factor analysis 110 Table 13. Documented relationships between tests and CHC broad ability constructs 114 ix

11 List of Figures Figure 1. The integrated Cattell-Horn-Carroll (CHC) cognitive abilities Framework: Broad and narrow abilities 25 Figure 2. Pontón et al. (2000) parallel analysis 72 Figure 3. Pontón et al. (2000) results for the minimum average partial procedure 73 x

12 Chapter I Introduction Psychological assessment involves collecting a variety of test scores using different measures and methods, and interpreting the data in the context of an individual s unique history to answer specific referral questions (Meyer et al., 2001). Ideally, the outcome of an assessment is that the individual meaningfully increases their understanding of how they interact in the world. Family members, peers, and referring professionals also benefit for similar reasons: the assessment process should increase their understanding of the individual s tendencies or skills. While assessment occurs across different sub-domains of psychology and may include slightly different referral questions, the present investigation focuses on neuropsychological assessment. More specifically, it focuses on how neuropsychological instruments function in relation to theoretical models of cognitive ability. It is important to understand the goals of and procedures followed during a neuropsychological evaluation to appreciate the present project and its significance in clinical research and practice. Referral questions specific to neuropsychological 1

13 assessment include, but are not limited to, making diagnoses, evaluating the quality of improvement or decline in acute or deteriorating conditions (e.g., stroke or multiple sclerosis), planning care, determining treatment efficacy, and increasing scientific knowledge regarding brain-behavior relationships through research (Lezak, Howieson, & Loring, 2004). Proficient neuropsychologists are able to understand an individual s cognitive processes or possibly determine the location of a cerebral lesion based on data collected during an evaluation (Lezak, 2003). Lezak (2003) stated that neuropsychological assessment behavior has traditionally been viewed or organized using a three-dimensional schema. This structure includes explicitly focusing on an intellectual component and two nonintellectual components, motivation and executive functioning (i.e., the ability to initiate and carry-out goaldirected activities). Lezak et al. (2004) further clarified these domains by stating that the three-dimensional organization of relevant behaviors includes the following: (1) cognition or information-handling aspects of behavior, (2) emotions relating to attention and memory, and consistent with the earlier description, (3) executive functions. A widely accepted delineation of general cognitive functioning includes receptive functions, memory and learning, thinking, and expressive functioning subcomponents (Lezak et al., 2004). Each of these subcomponents can be additionally differentiated, although investigators have reached divergent conclusions regarding what distinctions might be most appropriate. For example, Tulving (2002) stated that there are over 100 different named types of memory, whereas Lezak and colleagues identified just seven (verbal memory, visual memory, tactile memory, incidental memory, prospective memory, remote memory, and forgetting). Largely, these subcomponents have come to 2

14 be better understood by studying what distinct anatomical structures are involved in the more specific processes (e.g., memory functions, Nolte, 2002). Being cognizant of the relationship between particular cognitive functions and anatomical structures is imperative when interpreting data and answering referral questions (e.g., implications of a cerebrovascular accident in the left frontal lobe). Neuropsychological assessment generally seeks to understand cognitive defects and deficits (Lezak, 2003). Lezak stated the true value of a neuropsychological evaluation is in determining subtle defects as opposed to those easily observed (e.g., Broca s aphasia). She defined cognitive deficits as measurable differences from an ideal or normal level functioning. It is essential that clinicians consider the most appropriate comparison group when interpreting cognitive deficits (Strauss, Sherman, & Spreen, 2006). For example, consider the performance of an individual with mental retardation on a visuospatial task. Their results could be interpreted in strikingly different manners depending on the reference group selected by the clinician. In relation to a normative sample, the performance might appear severely impaired, whereas when compared to a sample of individuals with similar general abilities, or to their own level of ability, the performance might be viewed as above-average or represent a specific cognitive strength. In short, neuropsychological assessment requires evaluating a range of cognitive skills and interpreting the collected data in reference to an appropriate comparison group to form conclusions regarding brain-behavior relationships. Studies comprised of patients with similar conditions or injuries have illustrated consistent patterns of deficits that are beneficial in understanding the implications of a person s disorder or injury, diagnosing, and understanding cerebral functioning (Banich, 3

15 2004; Lezak et al., 2003). For example, Alzheimer s disease is characterized by severe global anterograde amnesia, retrograde amnesia, visuospatial processing difficulties, and motor difficulties (Banich, 2004). Banich noted several important ways Alzheimer s disease is differentiated from more general amnesia. First, individuals with Alzheimer s disease do not typically retain procedural knowledge whereas this is typically maintained in amnestic individuals. Second, individuals extensive retrograde amnesia is inconsistent with most non-korsakoff s amnesias. Lastly, immediate auditory attention (e.g., Digit Span) problems are typically observed during the later stages of Alzheimer s disease whereas this deficit is infrequently observed in amnestic patients. If a clinician were unable to recognize this pattern of difficulties as being specific to Alzheimer s disease the individual may be misdiagnosed, which could have detrimental implications. Range of Abilities Evaluated by Neuropsychological Assessment While the current project broadly focuses on neuropsychological assessment, the more specific goal is to better understand how neuropsychological instruments evaluate cognitive abilities. Reitan and Wolfson (1993) stated that neuropsychological instrument development can be traced to two historical trends. The first is attributed to biopsychology and clinical psychology and views task performance on continuous distributions. This approach makes use of a standardized distribution of performance. The second approach views performance in a more dichotomous manner resulting in classification as either normal or abnormal, which is consistent with behavioral neurology. Initially, instruments used during assessment were selected because they successfully differentiated between groups of individuals, not because they were 4

16 congruent with empirically-derived theories of cognitive abilities (Strauss et al., 2006). For example the Binet-Simon Scale (Binet & Simon, 1905) was intended to identify children with mental retardation, whereas the Army Alpha and Beta forms (Yerkes, 1919, 1921) guided military selection. Further, tasks included within the Halstead-Reitan Battery (Reitan & Wolfson, 1993) were differentially responded to by a combined group of normal functioning individuals and patients hospitalized for difficulties unrelated to brain functioning compared to individuals with clearly documented brain damage (Reitan & Wolfson, 2004). Not surprisingly, there is variability in the range of cognitive functions evaluated by neuropsychological measures and tasks. Some instruments evaluate one function (e.g., motor functions) while others provide data related to multiple functions (e.g., learning and memory). Illustrating the range of specific abilities evaluated by neuropsychological instruments, Strauss and colleagues (2006) recent test compendium divided measures and tasks into the following nine categories: (1) general cognitive functioning/ neuropsychological batteries/ assessment of premorbid intelligence, (2) achievement, (3) executive functions, (4) attention, (5) memory, (6) language, (7) visual perception, (8) somatosensory function, olfactory function, and body orientation, and (9) motor function. Lezak et al. (2004) described the range of abilities covered by neuropsychological tests in a slightly different manner that included eight domains. They differentiated between tasks and measures evaluating: (1) perception, (2) memory, (3) verbal functions, and (4) neuropsychological batteries, which are similar to four of the categories identified by Strauss and colleagues. Diverging from Strauss et al. s distinctions, Lezak et al. combined (5) orientation and attention, and (6) executive functions and motor 5

17 performance into single areas. Further, they identified two unique areas evaluated by measures and tasks, (7) construction and (8) concept formation/reasoning. Goldstein and Beers (2004) proposed an additional manner to conceptually differentiate between measures. They identified six broad areas: (1) comprehensive neuropsychological assessment, (2) language and communication, (3) memory and learning, (4) attention, (5) abstract reasoning and problem solving, and (6) sensory-perceptual and motor functions. The obvious lack of clear congruence between classifications has clinical implications. For example, it leads to questions regarding what areas should be evaluated during assessment. Neuropsychological theories have been developed to account for the relationships between domains, which have unique corresponding biological components. For example, Luria s classical framework (1966, 1973, 1980) included three functional units. Das (2004) summarized the model by stating that the first unit is responsible for attention and arousal, the second unit processes material either simultaneously or successively, and the remaining unit is instrumental in evaluating and planning cognitive functions. In greater detail, Luria (1973) stated the first functional unit was responsible for selectively attending to relevant stimuli while inhibiting a response or directing attention to irrelevant stimuli. He identified this functioning as occurring predominantly in the brain stem and reticular activating system. The second unit is associated with the occipitalparietal lobes (simultaneous processing) and fronto-temporal lobes (successive processing) and is responsible for the manner in which we understand what we attend to. Simultaneous processing includes integrating or synthesizing stimuli into groups or 6

18 recognizing that individual components of various stimuli are interrelated 1 ; it requires a large number of neural events to occur simultaneously and cooperatively (Naglieri & Das, 2005). Successive functioning involves processing individual stimuli in a serial or successive order (Das, Naglieri, & Kirby, 1994). For example, successive coding is needed for writing. Noteworthy, many cognitive activities involve both simultaneous and successive processing. For example, understanding the syntax of speech involves appreciating the serial relation of one word to the next, which requires successive processing while the comprehension of the meaning of the sentence requires simultaneous processing. Luria (1980) clearly described the location and importance of the third functional unit as follows: The frontal lobes synthesize the information about the outside worlds and are the means whereby the behavior of the organism is regulated in conformity with the effect produced by its actions (p. 263). The Reitan-Wolfson model (Reitan & Wolfson, 2004) is an alternative framework to understand brain-based functions. It posits external information first enters the brain through one of the five senses. Next, alertness, attention, and the ability to relate prior experiences to a current situation (i.e., immediate, intermediate, and remote memory) is required to register environmental input. The model makes a distinction between processing verbal information in the left hemisphere and visual-spatial information in the right hemisphere. The highest level of central processing includes concept formation, reasoning, and logical analysis. Finally, the model posits the resulting decision from this more abstract cognitive processing is expressed as output in some manner, which has a 1 An example of a simultaneous processing task, Draw a triangle above a square that is to the left of a circle under a cross. 7

19 corresponding biological basis (e.g., speech and language functions correspond with the left cerebral hemisphere). Intellectual Assessment It is notable that there is a gap between neuropsychological measures and evolving conceptualizations of intelligence. That is, for as seemingly related as the instruments and concepts are, they have strikingly different historical backgrounds. Consistent with the goals of science, the construct of intelligence has become more complete over time. The progression is well documented (e.g. see, Brinkman, Decker, & Dean 2005; Carroll, 1993; Flanagan, Genshaft, & Harrison, 1997; Flanagan & Harrison, 2005; Flanagan, McGrew, & Ortiz, 2000; Johnson & Bouchard, 2005a; Neisser et al., 1996) and much is gained by understanding general similarities and differences across historical approaches. Taylor (1994) suggested increasingly complex cognitive models can be classified into three distinct groups of theories: psychometric, information processing, and cognitive modifiability frameworks. Psychometric or structural approaches attempt to evaluate an individual s performance along dimensions that comprise the fundamental structure of intelligence. Flanagan et al. (2000) view this approach as being the most empirically supported. Further, they stated that psychometric theories have produced numerous efficient and practical instruments. One criticism against structural or psychometric concepts of intelligence is that these concepts are data driven, which may make them overly sensitive to arbitrary methodological decisions (e.g., decisions pertaining to factor retention, method of rotation; Taylor, 1994). 8

20 Information-processing models evaluate whether an individual can efficiently process everyday tasks or problems (Taylor, 1994). This theory views human intelligence as being similar to a computer information processing system, thus more intelligent individuals have greater abilities to efficiently process information through working memory. At first glance this approach appears analogous to various neuropsychological processing models (e.g., Luria s model [Luria, 1966; 1973; 1980]; Reitan-Wolfson model [Reitan & Wolfson, 2004]). However, Taylor stated information-processing theories strive to evaluate a range of physiological characteristics such as average evoked potentials or nerve conduction velocity, which is somewhat inconsistent with neuropsychological processing models. It is challenging to incorporate this model with traditional neuropsychological assessment because physiological data are not typically collected. Thus, some view this model as having low clinical practicality or utility (Flanagan et al., 2000). On the other hand, Das et al. (1994) posit their Planning, Attention-Arousal, Simultaneous and Successive (PASS) cognitive processing model fits within the information-processing framework, even though no physiological data are directly collected. Floyd (2005) maintains there is a distinction between information-processing and psychometric theories, although he believed the integration of these models would yield clinical promise. He suggested an optimal integration might include informationprocessing theories that strive to determine what micro-level cognitive processes underlie the specific abilities identified via psychometric approaches. Although it is not clearly stated as such, this ideal integration is very much consistent with the notion of neuropsychological assessment. 9

21 The remaining approach, the cognitive modifiability or dynamic approach, considers intelligence as pertaining to how individuals adapt to, or learn to function in, their environment. Similar to information-processing theories, dynamic theories are significantly limited because they lack practical comprehensive measurement tools (Flanagan et al., 2000). At face value this approach is similar to how executive functioning is conceptualized in the neuropsychological literature. Inconsistent with the views of Flanagan et al., it could be argued that there are numerous measures and tasks that evaluate how individuals adapt and learn. An example is the Wisconsin Card Sorting Task (WCST; Heaton, Chelune, Talley, Kay, & Curtis, 1993), which requires an individual to shift problem solving strategies, form and test hypotheses, and integrate feedback into thought processes. It is noteworthy that this task does not provide a comprehensive evaluation of all cognitive abilities, which raises the question if it would be possible to conduct a thorough assessment using a strictly dynamic approach. Psychometric Theories of Intelligence While information processing theories and cognitive modifiability theories have the potential to provide rich, relevant clinical data, psychometric theories of intelligence are most applicable to clinical practice. The increased complexity of psychometric models is illustrated by considering how Spearman (1904, 1927) first presented a general or g-factor theory of intelligence and nearly 100 years later McGrew (1997, 2005) proposed an integrated Cattell-Horn-Carroll (CHC) model of cognitive abilities with three hierarchically organized levels: a general intellectual ability dimension (Stratum III level), 10 broad cognitive subdomains (Stratum II level), and 70 more specific abilities, which represent narrow cognitive subdomains (Stratum I level). This progression 10

22 coincides with a theoretical shift in attention from a singular intelligence to a more differentiated and clinically useful set of cognitive abilities, which are clearly more relevant to neuropsychological assessment. However, the distinction between intelligence and cognitive abilities is considered arbitrary by some researchers, as evidenced by some referring to the CHC framework as a model of intelligence (e.g., Flanagan et al., 2000) and others as a model of cognitive ability (e.g., Alfonso, Flanagan, & Radwan, 2005). The evolution of these psychometric models is well documented (e.g., Carroll, 1993; Flanagan et al., 1997; Flanagan et al., 2000) and will be elaborated upon. Spearman s g-factor Spearman (1904, 1927) was the first to consider an underlying dimension might comprise cognition. Spearman s g theory is typically understood as a single-factor model of intelligence, although this notion is somewhat misleading as the theory included a general factor, g, and smaller specific (s) factors, which he believed contributed to the performance of specific activities. Spearman posited g was common across various abilities and represented a fixed amount of mental energy whereas s was unique to particular activities. He believed the presence of g was an explanation for why intelligence tests were correlated with one another (Thorndike, 1997). Spearman s g- factor is an essence theory that stipulates all distinct intellectual abilities arise from one basic process (Horn & Noll, 1997). Carroll (1993) stated Spearman s work remains relevant today because it was one of the earliest attempts to develop a theory of individual differences in intelligence based on a set of correlations between tests. Alternative views of Spearman s theory consider it a two-factor theory of intelligence, with the general intellectual factor, g, representing one dimension and the set 11

23 of more specific factors, s, representing the other (e.g., Thorndike, 1997; Zachary, 1990). Spearman was steadfast in his belief that specific tests evaluated both g and s but differed in the proportion measured (Horn & Noll, 1997). The most notable applied measure that evaluated g was the original the Stanford-Binet Intelligence Scale (Terman, 1916), although later versions of this test included more complex and differentiated conceptualizations of cognitive ability (Terman & Merrill, 1937; Terman & Merrill, 1960; Terman & Merill, 1973; Thorndike, Hagen, & Sattler, 1986; Roid, 2003). Two examples of single-test measures that were reported to be pure measures of g are the Raven s (1938) matrices test and Porteus (1946) maze test, although these assumptions are not unconditionally accepted (e.g., Carroll, 1995; Horn & Noll, 1997). Spearman s g theory (1904, 1927), was not unreservedly accepted. For example, Thorndike, Lay, and Dean (1909) concluded, In general there is evidence of a complex set of bonds between the psychological equivalent of both what we call the formal side of thought and what we call its content, so that one is almost tempted to replace Spearman s statement by the equally extravagant one that there is nothing whatever common to all mental functions, or to any half of them (p.368). Thorndike (1920) later posited three types of intelligence existed, abstract intelligence, which was evaluated by intelligence tests of the time, social intelligence that included the ability to successfully work with others, and lastly mechanical intelligence, the ability to comprehend and use concrete and spatial concepts. The Wechsler scales are the most frequently used measures of intelligence and were derived from the belief that intelligence was a singular construct (Flanagan, et al., 2000; Rabin, Barr, & Burton, 2005). Although Wechsler rejected some aspects of 12

24 Spearman s g model (1904, 1927) he defined intelligence similarly as the aggregate or global capacity of the individual to act purposefully, to think rationally, and to deal effectively with his environment (Wechsler, 1939, p. 3). He viewed intelligence as being global because it characterized an individual s behavior as a whole, but maintained this capacity also included unique components that were interdependent (Zachery, 1990; Zhu, Weiss, Prifitera, & Coalson, 2004). Wechsler believed it was important to evaluate intelligence according to the two different ways it was expressed, verbally and nonverbally (i.e., in performance; Kamphaus, 1993; Reynolds & Kamphaus, 1990). He viewed a verbal-nonverbal dichotomy as representing the two ways that intelligence could be expressed, not as two independent intelligences (Flanagan et al., 2000; Tulsky, Saklofske, & Ricker, 2003; Zachary, 1990; Zhu et al., 2004). Wechsler was critical of the Binet scales because he believed they were too reliant on verbal abilities. This dissatisfaction served as an impetus to design a test that would include subtests measuring both verbal and nonverbal abilities (Thorndike, 1997). In general, Wechsler believed that the tasks used to measure intelligence were relatively unimportant, and were functionally equivalent, as long as they met psychometric criteria and were related to other variables believed to reflect intelligence (Kaufman & Lichtenberger, 2002). He focused less on absolute tasks and more on understanding an individual s relative position compared to a particular reference group. Given this focus, it is interesting that many of the tasks he included in the original measure remain in use today (Zhu et al., 2004). Although Wechsler s scales were not developed on the basis of empirically supported theories of intelligence (Flanagan, Ortiz, Alfonso, & Mascolo, 2002; Zhu et al., 13

25 2004), Zachary (1990) reported both factor analytic and criterion-related support for Wechsler s hierarchical model, which includes Full Scale IQ at the apex and verbal and performance scales as second-tier factors. The original Wechsler tests have recently undergone substantial revisions resulting in a more differentiated 4-dimensional test structure (i.e., Verbal Comprehension, Perceptual Organization, Working Memory, and Processing Speed indices) that is consistent with current cognitive theories (Zhu et al. 2004). Bi-factor Models of Intelligence Cattell (1941, 1943, 1957, 1971) proposed one of the most prominent 2-factor models of intelligence (Flanagan et al., 2000; Johnson & Bouchard, 2005a; Taylor, 1994). His original model proposed that intelligence was best conceptualized as a composite of two separate entities, fluid (Gf) and crystallized (Gc) intelligences. The former is an inherited ability developed by interaction with the environment leading to inductive and deductive reasoning. It reflects a capacity to solve problems where previously learned knowledge and skills are irrelevant (Johnson & Bouchard, 2005a). This capacity was thought to be influenced a great deal by biological and neurological factors (Flanagan et al., 2000). Gc is believed to represent the specialized or consolidated knowledge and skills relative to a specific culture. It is influenced by access to education, cultural information, and overall experiences. Initially, Gc was conceptualized as having a smaller degree of genetic influence than Gf, although this prediction has been repeatedly disconfirmed (e.g., see Horn, 1998). Several measures were developed specifically to evaluate fluid and crystallized intelligences. For example, the Comprehensive Ability Battery (CAB; Hakstain & 14

26 Cattell, 1975) operationalized the Gf-Gc model. The test consists of 20 brief primary ability tests that measure a broad range of abilities contributing to both intelligences. The Kaufman Adolescent and Adult Intelligence Test (KAIT; Kaufman & Kaufman, 1993) is a more recent measure that includes scales evaluating Gf and Gc intelligences. The likelihood that a true Gf-Gc dichotomy is adequate to describe all intellectual abilities has been widely questioned (e.g., Horn & Noll, 1997). Cattell expanded his bifactor model into a more general triadic theory in 1971 that stipulated intelligence was the interaction of capacities (i.e., individual limitations pertaining to physiology), provincial powers (i.e., localized sensory and motor abilities), and agencies (i.e., the ability to perform a specific cultural task). At face value this theory appears to have less in common with psychometrically derived intelligence theories and more in common with neuropsychological processing models. Nonetheless, it shows the evolution of his thought, his dichotomous conceptualization of intelligence transformed into something much more complex. Multiple Intelligences Bi-factor models of intelligence gradually evolved into more diverse multiple intelligences or sets of cognitive abilities. Flanagan et al. (2000) divided more complex cognitive theories into Incomplete and Complete models. Incomplete models are more differentiated models than single or bi-factor models of intelligence, although they fail to account for all cognitive abilities, as complete models do. For example, Flanagan et al. (2000) viewed Thurstone s (1938) attempt at mapping primary mental abilities as incomplete because it did not account for the total range of abilities that were later 15

27 observed, whereas Carroll s (1993) three stratum theory of cognitive abilities was viewed as constituting a complete model of cognitive abilities. Thurstone s (1938) work is notable because it was the first large study that investigated the structure of intelligence by using multiple-factor methods within a comprehensive battery of 57 tests (Carroll, 1993). Thurstone reached the conclusion that intelligence was multi-dimensional after administering a battery of tests to a large group of predominantly University of Chicago students. He initially concluded there were seven primary mental abilities, although he later included two additional abilities. The nine primary abilities identified were inductive reasoning (I), deductive reasoning (Rs), practical problem solving (R), verbal comprehension (V), associative short-term memory (Ma), spatial relations (S), perceptual speed (P), numerical facility (N), and word fluency (Fw). Initially, this work led to the development of the seven-factor Primary Abilities Battery (Thurstone & Thurstone, 1941), which was based on the belief that a pattern of scores would permit more accurate prediction of performance than an overall score. However, this hypothesis was later found to be inaccurate (Thorndike, 1997). Not surprisingly, there was much debate regarding the number of factors Thurstone (1938) retained. Spearman (1939) retained an extreme position that the data were indicative of a single factor while others were able to replicate and eventually extend the identification of factors (e.g., Carroll, 1993; Ekstrom, French, & Harman, 1979; Guilford, 1959, 1967; Hakstian & Cattell, 1974; Horn, 1972). For example, Guilford s (1959) model of intelligence (described in detail below) proposed 150 distinct factors and is a clearly more complex and differentiated model than the one Thurstone proposed. 16

28 Initially, while conclusive evidence of multiple intelligence dimensions accumulated, it was challenging to organize this information in a parsimonious way that would foster research and clinical utility. For example, Guilford (1967) used factor analytic procedures similar to Thurstone s (1938) and proposed a structural model of intelligence (SOI) that organized the concepts he believed intelligence required but not specific cognitive abilities. Guilford viewed intelligence as being comprised of five mental operations (cognition, divergent production, memory, convergent production, and evaluation) that operated across four distinct contents (figural, symbolic, semantic, and behavioral), which in combination produced six unique products (units, classes, relations, systems, implications, and transformations). There is mixed appreciation for this complex model. On one hand, there is evidence the operational and content aspects of Guilford s SOI model may have relevance in modern-day theory (Horn & Noll, 1997), whereas others have questioned the logical validity and advantage of creating a taxonomic system with universal parameters that interact in all possible ways to create a structural model of intelligence (e.g. Carroll, 1993). Extended Gf-Gc Models The Gf-Gc model evolved into a more comprehensive multi-structured theory of cognitive abilities as evidence accumulated that there were more than two components of intelligence, although curiously the Gf-Gc label remained. Flanagan et al. (2000) posited the gradual augmentation of this theory began with Horn s (1965) doctoral dissertation, which suggested the inclusion of four additional abilities, visual perception or processing (Gv), short-term memory (Short Term Acquisition and Retrieval SAR or Gsm), longterm storage and retrieval (Tertiary Storage and Retrieval TSR or Glr), and speed of 17

29 processing (Gs). In 1968 Horn added auditory processing ability (Ga) to his extended Gf- Gc theory while also refining Gv, Gs, and Glr descriptions. After a relatively quiet period of nearly 20 years, in 1991 Horn added a factor representing an individual s reaction time and decision speed (Gt), which was later conceptualized as correct decision speed (CDS; Horn & Noll, 1997). Additionally, factors reflecting quantitative ability (Gq) and broad reading/writing ability (Grw) were included based on empirical support to extended Gf- Gc models (Horn, 1991; Woodcock, 1994). Horn and Blankson (2005) described the current Gf-Gc framework to include the following eight prominent cognitive abilities: (1) Acculturation knowledge (Gc), (2) Fluid reasoning (Gf), (3) Short-term acquisition and retrieval (SAR) or short-term memory (Gsm), (4) Fluency of retrieval from long-term storage (TSR) or long-term memory (Glm), (5) Processing speed (Gs), (6) Visual processing (Gv), (7) Auditory processing (Ga), and (8) Quantitative knowledge (Gq). They noted that these broad abilities represent behavioral organizations that stem from neural structures and functions. Impressively, this framework was developed in response to five distinct kinds of evidence: (1) structural evidence, (2) developmental evidence, (3) neurocognitive evidence, (4) achievement evidence, and (5) heritability (Horn & Blankson, 2005; Horn & Noll, 1997). Horn and Blankson stated that strong structural support for the extended Gf-Gc model has been obtained across gender, levels of education, ethnicity, nationality, language, and historical period, as similar differentiated abilities are consistently identified. Empirical evidence makes clear that specific abilities change at varying rates as an individual grows older or experiences changes in neurological functioning (Horn & 18

30 Noll, 1997). Specifically, a cluster of Gf, Gsm, and Gs abilities appear vulnerable to neurological, genetic, and aging effects whereas the cluster of Gc, Glm, and Gq are not related to aging effects and represent more expertise abilities. A third cluster of abilities is comprised of Gv and Ga and have been linked to sensory modalities, which also makes them uniquely sensitive to aging processes and neurological functioning. In short, these findings suggest different abilities have unique developmental trajectories and neurological components, which supports a differentiation between them. Horn and Noll state that the best predictor of specific achievement (e.g., math) is a similar corresponding ability (e.g., Gq) rather than a more general ability (i.e., g). This finding represents achievement evidence that cognitive abilities are multidimensional and not a single entity. Heritability evidence for the extended Gf-Gc framework is not as overwhelming, as it does not conclusively support either single or multiple intelligences. This particular line of research developed in response to Cattell s (1941, 1957) belief that Gf and Gc differed in hereditability. Specifically, the traditional thought was that Gf represented primarily genetic influences that are invested in Gc. Thus, these abilities were thought to be distinct from one another. The extended Gf-Gc theory does not support one general factor (i.e., g) that subsumes all other factors. Horn and Blankson (2005) stated there is inadequate evidence to suggest a single factor satisfactorily accounts for the intercorrelations between all other abilities. Specifically, they believe it is troubling that one common factor does not reproduce across studies and that in some instances the more general factor is similar to a lower level factor. For example, Gustafsson (1984) found a higher order factor that was roughly equivalent to Gf. 19

31 The Cattell-Horn Gf-Gc cognitive abilities model served as a blueprint for the Woodcock-Johnson Psycho-Educational Battery Revised (WJ-R; Woodcock & Johnson, 1989a), thus the model has high clinical utility. McGrew (2005) stated the WJ-R was the first individually administered cognitive test evaluating the nine broad abilities specified by the theory. McGrew and Flanagan (1998) posited the abilities measured by many intelligence and neuropsychological batteries are accounted for by the nine Gf-Gc abilities despite the fact many of these measures do not explicitly state doing so. Carroll s Three-Stratum Model of Intelligence Carroll (1993, 1997) viewed the Cattell-Horn (Horn, 1985, 1988) model as being the most acceptable, comprehensive hierarchical model of cognitive abilities, although he recognized the valuable contributions of Spearman (1904), Thurstone (1938), and Vernon (1964, 1965). His major criticism of the Cattell-Horn model was that it did not accept a higher level g factor as explanation for correlations between Gf-Gc factors. Carroll was steadfast in his belief in an over-arching intelligence factor based on the results of his major survey of the literature, which included reanalyzing 460 sets of data pertaining to performance on cognitive tasks. Carroll (1997) described his ambitious goal for this project as follows: [The results were] intended to constitute a provisional statement about the enumeration, identification, and structuring of the total range of cognitive abilities known or discovered thus far. In this way it was expected to replace, expand, or supplement previous theories of the structure of cognitive abilities, such as Thurstone s (1938) theory of primary mental abilities, Guilford s (1967) structure-of-intellect theory, Horn and Cattell s (1966) Gf-Gc theory, or Wechsler s (1974) theory of verbal and performance components of intelligence. (pp. 124). 20

32 This enormous project led Carroll (1993) to propose a three-stratum theory of cognitive abilities. Carroll viewed his theory as an extension of Vernon s (1964, 1965) hierarchical model of intelligence, which specified the importance of verbal and educational experiences, and also spatial, practical, and mechanical abilities. Carroll s theory is based on the results of his factor analytic review; he subjected correlation matrices of variables from independent studies to exploratory factor analysis. Resulting factors were considered first-order factors, which were next subjected to similar factor analytic techniques to derive second-order factors. Similarly, second-order factors were next factor analyzed resulting in the conclusion that the variance contained within second-order factors was best accounted for by a single third-order factor. Carroll s model includes more than 60 primary mental abilities (Stratum I), which comprise eight broad abilities (Stratum II) that are generally consistent with those identified in the Gf-Gc theory. Carroll posited these eight broad abilities include (1) fluid intelligence, (2) crystallized intelligence, (3) general memory and learning, (4) broad visual perception, (5) broad auditory processing, (6) broad retrieval ability, (7) broad cognitive speediness, and (8) processing speed, which collectively comprised a third-order factor representing general (g) intelligence (Stratum III). In response to Horn and Noll s (1997) questioning of a general intelligence factor, Carroll (2003) conducted confirmatory analysis within the standardization sample of the WJ-R. He found that an intelligence model that included g was superior to alternative models specifying either (1) that g is equivalent to Gf or (2) that there is no g. 21

33 An Integration of Carroll s Three-Stratum Model and Extended Gf-Gc Theory McGrew (1997) first proposed an integrated Cattell-Horn-Carroll (CHC) framework to synthesize the factor analytic work conducted by Carroll (1993, 1997) and Horn and Noll (1997). He believed this was possible because of the numerous conceptual overlaps between models. For example, both frameworks included similar Gf and Gc intelligences. Additionally, they similarly proposed that short-term memory (i.e., General Memory and Learning and Short- Term Acquisition and Retrieval Gsm) abilities were distinct from storage and retrieval (i.e., Broad Retrieval Ability and Long-Term Associative Storage and Retrieval Glr) abilities. Furthermore, both models included similar sensory processing abilities (i.e., Gv and Ga), as well as two speed abilities (i.e., Broad Cognitive Speediness or Cognitive Processing Speed Gs, and Reaction Time/Decision Speed or Correct Decision Speed Gt). Given these similarities, McGrew believed the models could be integrated into a comprehensive model that would optimally (1) describe or classify individual subtests included in major intelligence batteries and (2) serve as a theoretical blueprint for future measures. Although the two models, Carroll s (1993, 1997) three-stratum theory and the Gf- Gc theory (Horn & Noll, 1997), included numerous similarities, McGrew (1997) had to resolve a number of notable discrepancies in order to integrate them. Initially, McGrew did not believe it was important to resolve the most striking difference between models, the acceptance of a higher-order g factor. Additional discrepancies that needed to be resolved included the placement of narrow abilities under broad abilities. For example, Carroll viewed reading and writing abilities as narrow abilities subsumed by Gc whereas the Gf-Gc model viewed them as a distinct broad ability (i.e., Reading/writing; Grw). A 22

34 similar discrepancy is observed with the placement of quantitative abilities. Carroll viewed quantitative reasoning as a narrow ability subsumed by Gf whereas the Gf-Gc model posited quantitative knowledge and reasoning constituted a distinct broad construct (Gq). Carroll viewed phonological awareness (e.g., phonetic coding) as being subsumed by Gc whereas the Gf-Gc model placed these abilities under a broad auditory processing construct (Ga). The remaining discrepancy noted by McGrew, Carroll placed short-term and free recall memory abilities with learning abilities under the broad General Memory and Learning factor whereas Horn (1991) differentiated between immediate apprehension and storage/retrieval abilities by having broad short-term memory ability (Gsm) distinct from long-term retrieval ability (Glr). To synthesize these models McGrew (1997) conducted confirmatory factor analysis using 37 measures from the standardization sample of the complete WJ-R battery. After testing alternative models he concluded an optimal framework (1) retained the distinction between quantitative reasoning/knowledge (Gq) and fluid intelligence (Gf), (2) included a distinct factor for reading and writing abilities (Grw), (3) placed phonological awareness abilities under auditory processing (Ga), and (4) separated short term memory (Gsm) from storage and retrieval abilities (e.g., associative memory), which fall under a broad retrieval (Glr) ability. McGrew (2005) stated that Horn and Carroll informally agreed on the CHC umbrella theory terminology in However, they continued to disagree on the existence of g. McGrew noted this disagreement initially presented a dilemma for individuals using the CHC model in research or clinical practice because it required the clinician to make a decision regarding whether or not to include g. Encouragingly, this 23