ABC Simultaneous Subtests

Gestalt Closure Triangles Matrix Analogies Spatial Memory Photo Series Sequential Subtests Hand Movements Number Recall Word Order Achievement Subtests

Faces and Places Arithmetic Riddles Reading Decoding Reading Understanding .509 .642 .599 .561 .709 .536  .522 .584 .663 .790 .774 .646  .650 .480 .702 .459 .633 .678 .354 .093 .220 .212 .481 .324 .131 .106 .387 .240 .258 .070 .277 .132 .173 .242 .685 .399 .732 .322 .412 .016 .081 .242 .221 .246 .430 .769 .657  .160 .349 .206 .257 .174 .003 .117 .198 .121 .094 .190 .136 .110 .204 .347 .194 .781 .789 WISC-R Verbal Subtests Information Similarities Arithmetic Vocabulary Comprehension Digit Span Performance Subtests Picture Completion Picture Arrangement Block Design Object Assembly Coding .758 .748 .708 .773 .711 .548 .618 .677  .707  .618 .408 .314 .297 .440 .226 .327 .161 .536  .578 .759 .700 .393 .608 .592 .269 .746  .643 .190 .381 .369 .247 .237 .015 .217 .279 .395 .258 .237 .702 .160 .225 .121 .089 .195 .358 .312 .348 .276 .119 .143 .013 .052 .146 .048 .274  Note. Coefficients  .400 are italicized. K-ABC Kaufman Assessment Battery for Children, WISC-R Wechsler

Intelligence Scale for Children. From Keith and Novak (1987).

and Places also correlated with this factor. Subtests associated most highly with this factor call for the processing of seman- tic material, that is, meanings. These subtests require an ex- aminee to think beyond the verbal presentation mode to the underlying meanings or referents. Only by accessing this un- derlying material can an individual respond correctly. The highest correlation, for example, represented Vocabulary, a subtest that repeatedly asks, “What does mean?” Riddles may be seen as a reversal of traditional vocabulary subtests, providing aspects of a word’s meaning and requir- ing examinees to provide the word (see also Kaufman & Kaufman, 1983b). This factor might be labeled Semantic Processing.

The traditional interpretation of this second factor as Ver- bal Comprehension does not account for the order of the correlations. The Vocabulary subtest appears to draw upon semantic material more heavily than Comprehension and Sim- ilarities do; whereas it does not clearly require more compre- hension or verbal activity than they do. In addition, K-ABC

Faces and Places is a largely pictorial subtest, but it too has a high correlation with the second factor. A semantic inter- pretation also potentially explains why the Wechsler nonver- bal subtests tend to correlate substantially with this factor. Finally, the fourth factor may be a Reading factor, dominated as it is by two K-ABC subtests: Reading Decoding and Read- ing Understanding.

The unrotated first factor was strong and general to all subtests. This factor closely resembled the Semantic Pro- cessing factor. The WISC-R and K-ABC subtests that cor- related highest with factor two also correlated highest with this general factor. The resemblance suggested—with con- siderable intuitive credibility—that facility with meaning is general to many types of mental tasks, including those sam- pled by both tests examined by Keith and Novak (1987).

These results did not, in themselves, suggest a factor that could readily be labeled Achievement, with the possible excep- tion of the Reading factor. Herein lay the main difficulty for the traditional interpretation of the K-ABC. The Achievement

TABLE 3.23 Pearson Correlations of K-BIT and Stanford-Binet Scores with K-TEA Scores

K-BIT Stanford-Binet

K-TEA Vocabulary Matrices

IQ

Composite V-PA Composite Mathematics Reading Spelling .52 .61 .47 .53 .45 .39 .57 .58 .47 .58 .55 .48 .66 .57 .53  Note. K-BITKaufman Brief Intelligence Test, K-TEAKaufman Test

of Educational Achievement, V-PAVocabulary-Pattern Analysis short form. From Prewett and McCaffery (1993).

subtests correlated variously with Simultaneous Processing, Semantic Processing, and Reading. Possible interpretationsare that no K-ABC subtests measure achievement; that only the K-ABC reading subtests measure achievement; and that the WISC-R Verbal IQ measures achievement rather than intel- ligence. The third interpretation seems improbable, given a long history of research addressing the Wechsler scales. As noted by Kamphaus (1993), however, both Anastasi (1988) and Kaufman (1979) have made similar arguments.

The Achievement subtests broke apart again in the five- factor solution, but not in the three-factor solution, which included no Reading factor. In the latter solution, these sub- tests all correlated with the second factor, here labeled Se- mantic Processing. As in the four- and five-factor solutions, the WISC-R Verbal subtests tended to correlate highly with this factor. Thus, despite the unity of the K-ABC Achieve- ment subtests in this solution, the question of their distinct- ness from intelligence remained.

Keith and Novak (1987), then, sought to help answer the question of whether these subtests measured intelligence or whether, perhaps, the K-ABC Verbal subtests measured achievement. Thus, the researchers factor analyzed the K-ABC with the WISC-R and the WRAT. In the four-factor solution, the three WRAT components correlated with a separate factor along with the two K-ABC reading subtests. Keith and Novak  labeled this factor Achievement. The remaining K-ABC Achievement subtests again broke apart, correlating with the Simultaneous Processing and Semantic Processing factors. The K-ABC reading subtests correlated with the Achieve- ment factor. No reading factor emerged from this analysis. The results suggest that the K-ABC reading subtests measure achievement; whereas the remaining K-ABC Achievement subtests measure intelligence, an interpretation consistent with Keith and Novak’s.

Prewett and McCaffery (1993) compared the K-BIT, the S-B 4, and the Vocabulary-Pattern Analysis (V-PA; Prewett, 1992) short form of the S-B 4. The researchers reported cor- relations between each of these tests and the K-TEA Brief  Form (K-TEA BF; Kaufman & Kaufman, 1985). Participants were 75 students enrolled in a large, urban school district in the Midwest. Of the students, 49 were male, and 26, female; 35 were White, and 40, Black. All had been referred by their teachers for a psychoeducational evaluation because of aca- demic difficulties. Their mean age was 9 years 7 months, range 6–0 to 15–9. Their mean S-B 4 Composite score was 81.6, SD 13.3.

As shown in Table 3.23, K-BIT scores correlated highest with K-TEA BF Reading, followed in turn by K-TEA BF Mathematics and Spelling. The exception was that K-BIT Matrices correlated higher with Mathematics than with Read- ing. The S-B 4 Composite and the V-PA short form behaved

as K-BIT Matrices did, correlating highest with K-TEA BF Mathematics, followed in turn by K-TEA BF Reading and Spelling.

A report by Prewett and Farhney (1994) included corre- lations of the Matrix Analogies Test–Short Form (MAT-SF; Naglieri, 1985) and the S-B 4 with the K-TEA BF. Partici- pants were 71 students in a large, urban school district; 44 were male, 27 female, 30 White, and 41 Black. The students were enrolled in Grades 1–8, with about half in Grade 3, n  20, and Grade 4, n  15. Most students qualified for the free or reduced lunch program. The participants’ teachers had referred them for psychoeducational evaluation because of academic difficulties. The average S-B 4 Composite score was 81.2, SD 12.8.

The students’ MAT-SF scores correlated with K-TEA BF Mathematics, Reading, and Spelling at  r .44, r .44, and r  .38, respectively. Their S-B 4 Composite scores corre- lated with these same measures at  r .65, r .65, and r 

.59, showing the same rank order as the MAT-SF scores but higher absolute values.

Prewett, Bardos, and Naglieri (1989) reported correlations of the MAT-SF and the DAP:QSS with K-TEA Reading and Mathematics Composites. Participants were 85 White stu- dents enrolled in a suburban, middle-class school district in central Ohio. This sample comprised a group drawn from five regular classrooms, n  46, and a group drawn from four classes for developmentally handicapped students,  n 39.

The regular group consisted of 25 males and 21 females, of whom 14 were in Grade 4 and 32 were in Grade 5. Their mean age was 10 years, 6 months, SD  0–8. Their mean MAT-SF score was 97.7, SD 11.6. The developmentally handicapped group consisted of 20 males and 19 females, of  whom 21 were in Grade 4 and 18 were in Grade 5. The mean age of this group was 10 years, 9 months, SD 0– 8. Their mean MAT-SF score was 75.8, SD  8.6. Their handicaps were considered mild.

For the regular group, MAT-SF scores produced higher correlations with K-TEA Reading Composite scores, r 

44 Relations Between Intelligence and Achievement Tests

.63, than with Mathematics Composite scores,  r  .37. All DAP:QSS scores produced low correlations with both K-TEA Composites, r  .18 to r  .20. These coefficients were corrected for restriction of range. For the developmentally handicapped group, however, MAT-SF scores produced higher correlations with K-TEA Mathematics Composite scores,  r 

.57 vs. r  .35. Moreover, DAP:QSS scores correlated sub- stantially with the Mathematics Composite scores of this group, r  .37–.50. Correlations with Reading Composite scores were again low,  r  .03–.26.

Cantwell (1966) investigated the correlations of the Stan- dard Progressive Matrices (SPM) (Raven, 1963) and the D. 48 Test (Gough & Domino, 1963) with the SAT Verbal and Mathematics subtests. As Cantwell explains, the D. 48 Test presents 44 domino problems in various progressions from simple to relatively complex. The last domino in each series is blank, and the examinee completes it. All D. 48 problems are open-ended.

The participants in Cantwell’s (1966) study were the 139 entering first-year students at a women’s liberal arts college in the Midwest. Their mean SPM score was 50.88, SD 

4.59, and their mean SAT scores were 495.13 Verbal, SD

82.55, and 486.46 Mathematics, SD  86.99. As noted by Cantwell, the small standard deviations indicated that the sample was relatively homogeneous on these variables.

Although the D. 48 Test is little known, its results were comparable to the SPM results. Scores on both tests had low correlations with SAT Verbal scores, r .37 and .36 for SPM and D. 48, respectively, but higher ones with SAT Mathe- matics scores,  r .55 and .57. This outcome is not surpris- ing, given the nonverbal character of the SPM and D. 48.

Smith, Smith, and Dobbs (1991) examined the associations of the Peabody Picture Vocabulary Test–Revised (PPVT-R) and the WISC-R with the WRAT-R, permitting a comparison of the results of both intelligence tests. Participants were 181 children from rural areas of Arkansas, referred for evaluation by their teachers because of academic difficulties. The chil- dren included 124 males and 57 females, 129 Whites and 52 Blacks. The average age was 10 years, 6 months, SD  2 years, 5 months. The average WISC-R IQs were 82.44, SD

13.68 for Full Scale; 80.14, SD   13.61 for Verbal; and 87.91, SD14.94 for Performance. The difference between Verbal and Performance IQs, with Verbal lower, suggested that some participants might have had learning disabilities.

Both the WISC-R Full Scale IQ and the PPVT-R corre- lated highest with WRAT-R Arithmetic at  r  .57 and r 

.50, respectively. WRAT-R Reading was next at  r .41 and r  .42, followed by WRAT-R Spelling at  r .33 for both Wechsler subtests. The two intelligence tests produced simi- lar results, excepting the different correlations with WRAT-R Arithmetic.

BLIND AND LOW-VISION EXAMINEES

A report by Gutterman (1983) began with a review of the literature on measurement of intelligence of blind and low- vision examinees, including relations between intelligence and achievement measures. Among the results reviewed were those of Hecht and Newland (1973; as cited in Gutterman, 1983), whose results included correlations between WISC Mental Age and scores from the Braille version of the SAT (Hayes, 1941). Participants were 69 blind students enrolled in a residential school. The researchers subdivided this sam- ple into three age groups: 9–11, 12–13, and 14–16 years. Correlations ranged widely, from .00 for SAT Spelling with the 9–11 year group to .87 for SAT Paragraph Meaning with the 12–13 year group.

Coveny (1973) reported regression results for the Perkins- Binet Tests of Intelligence for the Blind, Form U (P-B; Davis, 1980) and the WISC as predictors of SAT subtest scores. Both intelligence tests were predictive of SAT Word Rec- ognition, Arithmetic Computation and Reasoning, and Social Studies scores. Gutterman (1983) also reviewed correlations with GPA, summarized briefly here. For 115 blind children, Rich and Anderson (1965) reported correlations of .51 for WISC Verbal scores, .36 for the Children’s Tactual Progres- sive Matrices (CTPM; Rich & Anderson), and a multiple cor- relation of  r .55 for both instruments. Streitfeld and Avery (1968) administered the WAIS Verbal Scale and the Haptic Intelligence Scale for the Adult Blind (HIS; Shurrager, 1961; Shurrager & Shurrager, 1964) to 31 residential school stu- dents, 20 low vision and 11 blind. For the WAIS, the re- searchers were able to combine the two samples. Correlations with average grades were  r  .74–.81.

Gutterman’s (1983) own analysis compared the WISC-R Verbal Scale and the Perkins-Binet, as associated with WRAT scores. Participants were 52 low vision children enrolled in TABLE 3.24 Correlations of Perkins-Bineta

Scores and WISC-R Verbal Scores with WRAT Scores by Grade Level

Grade

WRAT Composite 3 5 7 9 All

Perkins-Binet  Reading Spelling Arithmetic .40 .48 .63* .33 .27 .41 .74** .67* .52 .49 .39 .28 .43** .37** .38** WISC-R Verbal Scale

Reading Spelling Arithmetic .35 .41 .57* .43 .18 .49 .87** .84** .79** .82** .80** .60** .53** .47** .60**  Note. Correlations corrected for restriction of range are shown. WISC-R 

Wechsler Intelligence Scale for Children, WRATWide Range Achieve- ment Test. From Gutterman (1983).

a

Form U.

Grades 3, 5, 7, and 9 in residential and public school pro- grams in three Ohio public school systems and an Ohio school for the blind. This sample consisted of 24 males, 28 females; 30 Whites and 22 non-Whites; 14 students in Grade 3, 14 in Grade 5, 12 in Grade 7, and 12 in Grade 9. All participants used print as their primary reading mode.

The results suggested a trend toward higher correlations at Grades 3, 7, and 9. In addition, correlations were highest for WRAT Arithmetic at Grades 3 and 5, but for WRAT Reading at Grades 7 and 9. Finally, the WISC-R Verbal Scale produced higher correlations than the Perkins-Binet in Grades 7 and 9. Sample size considerations make these observations tentative. Table 3.24 presents correlations corrected for re- striction of range.

DEAF AND LOW-HEARING EXAMINEES

In a well-designed and generally well-reported study with a review of literature, Blennerhassett, Strohmeier, and Hibbett (1994) first summarized correlations between intelligence and achievement measures for samples of deaf and low-hearing students. Most of the achievement measures sampled reading achievement; a few sampled language, writing, or spelling achievement. Results for the WISC, WISC-R, and WAIS-R Performance IQs with SAT and SAT-HI scores tended to be inconsistent, ranging from  r  .12 to r  .73. Correlations with other achievement tests, such as the WRAT-R and the Reynell Developmental Language Scales, tended to be low but had too little representation for a judgment of consistency. By contrast, K-ABC Nonverbal scores produced relatively consistent correlations with Metropolitan Achievement Test (MAT) Reading Comprehension, WRAT-R Reading and Spell- ing, and K-ABC Reading Decoding and Reading Understand- ing. These correlations ranged from .46 with MAT Reading Comprehension to .65 with K-ABC Reading Decoding (Brooks & Riggs, 1980; Kelly & Braden, 1990; Paal, Skinner, & Reddig, 1988; Phelps & Branyan, 1990; Porter & Kirby, 1986; Ulissi, Brice, & Gibbins, 1989; Watson, Goldgar, Kroese, & Lotz, 1986; Watson, Sullivan, Moeller, & Jensen, 1982; all as cited in Blennerhassett et al.). Sample sizes and other aspects of research design may be responsible for incon- sistent results, such as those found with the Wechsler scales.

Blennerhassett et al. (1994) investigated, in part, SPM and SAT-HI scores with 107 students enrolled at two residential schools for the deaf. Participants’ age range at test was 10 years, 10 months to 19 years, 3 months, M  14–7, SD 

1–11. The researchers reported sample demographics in per- centage form for comparison with a national sample of deaf  residential students. In the study sample, 64.2% of the stu- dents were male, and 35.8%, female; 57.0% were White,

25.2% Black, 15.0% Hispanic, and 2.8% Asian American. According to audiological records, hearing loss was profound for 75.2% of the students, severe for 22.9%, and moderate for 1.9% (see Blennerhassett et al. for etiology of deafness and data from a national sample).

Among the results were correlations between SPM scores and SAT Reading Comprehension, Spelling, and Language. Correlations for these tests were relatively low,  r .33, r 

.38, and r  .44, respectively. The results were consistent with, though somewhat lower than, the earlier results of  Stedman et al. (1978) with normal participants, most of  whom had Spanish surnames.

Kelley et al. (1990) explored the criterion-related validity of the WISC-R Performance IQ with scores from the Stanford Achievement Test, Special Edition for Hearing-Impaired Stu- dents (SAT-HI). Previous studies had produced low correla- tions but had employed small samples or grade-equivalent scores. Participants were 83 deaf students enrolled in a resi- dential school. Their ages ranged from 7.5 years to 16.6 years, M12.6. All participants were prelingually deaf and had bilateral, severe to profound hearing loss.

The researchers reported rank-order correlations for SAT- HI subtests Reading Comprehension, Spelling, Concept of  Number, Math Computation, and Math Applications. The correlation coefficients were relatively low ( r .32, .24, .46, .31, and .41, respectively) for scaled scores but higher (r 

.39, .33, .57, .42, and .52) for percentile ranks. All coeffi- cients except that for Spelling were statistically significant ( p.05; for Concept of Number, p .01).

The earliest study found (Brown, 1930) utilized a sample of students attending a school for deaf children. This study addressed achievement tests and other correlates of nonlan- guage mental tests. At the time, clinicians used nonlanguage tests routinely with all individuals who had “difficulty with the English language” (Brown, p. 371).

Of 390 total participants, 98 were above Grade 5. They were the population for whom correlations between mental and educational tests were obtained. The report provided little demographic information about these students. The mental tests administered included the Pintner Non-Language Men- tal Test and a point scale incorporating the Knox Cubes, three form boards, the Manikin and Feature Profile, the Mare and Foal, Healy Picture Completion I, and the Kohs Block De- signs (Arthur, 1925). Many of these activities resembled mod- ern Wechsler subtests. The education test correlates included the Stanford Achievement Arithmetic Tests and the Stanford Achievement Reading Tests.

The Pintner test correlated at r .45 with total Arithmetic but r .00 for total Reading. Similarly, the point scale cor- related at r .37 for total Arithmetic but  r  .06 for total Reading. Of note, more recent studies with deaf and low-

46 Relations Between Intelligence and Achievement Tests

hearing examinees (e.g., Blennerhassett et al., 1994; Kelley et al., 1990) have produced higher correlations with reading results, particularly when the K-ABC Nonverbal Composite is the intelligence score used.

CONCLUSIONS

The results addressed in this chapter suggest several possible generalizations. These generalizations must remain tentative, however, given the small numbers of studies addressing par- ticular populations. In addition, many of the studies found had methodological limitations, as described later in this sec- tion. For the WISC-III and its predecessors, Full Scale and Verbal IQs may be more highly associated with achievement scores than are Performance IQs (Figueroa et al., 1989; Lavin, 1996). The distinction may not apply to low IQ ranges (Smith, Smith, & Dobbs, 1991). Higher results for verbal and overall composites may also characterize the WPPSI-R and the K-BIT, when K-ABC Achievement is used as the crite- rion (Lassiter et al., 1995; but see Keith & Novak, 1987). Again for the WISC-III and predecessors, Performance IQs may have stronger associations with mathematics achieve- ment than with reading and other verbal forms of achieve- ment. Other nonverbal composite scores, such as those of the SPM and D. 48 Test, may also correlate more highly with mathematics achievement than with other achievement results (Cantwell, 1966).

In four-factor solutions of the WISC-III, the Verbal Com- prehension factor has produced higher associations than Freedom From Distractibility, Perceptual Organization, and Processing Speed (Roid et al., 1993; Teeter et al., 1993). This pattern may not hold in severe ED cases (Teeter et al., 1993). In addition, the WISC-III and its predecessors sometimes show lower associations with achievement for Hispanic ex- aminees than with White or Black examinees (Weiss et al., 1993). Ethnic and gender differences, however, are inconsis- tent with Wechsler children’s intelligence tests (e.g., Figueroa et al., 1989). For WISC-III results, correlations with reading achievement appear to be higher than with other forms of  achievement (Weiss et al., 1993). This difference may also hold true for WAIS-R results (Spruill et al., 1986; but see Cooper et al., 1988).

Not surprisingly, the K-ABC Reading subtests correlated well with SAT Reading scores, and the Achievement Global score, with SAT Vocabulary scores (Hooper, 1987). A gen- eralization from these results, however, is difficult to make with confidence. The K-ABC may have relatively high and consistent correlations when administered to deaf and hearing- impaired residential students (Blennerhassett et al., 1994). The K-ABC (Hooper, 1987), the CAS (Naglieri, 1999), and

the Wechsler scales (e.g., Roid et al., 1993; Teeter et al., 1993; Weiss et al., 1993) have produced reasonably high correla- tions with relevant achievement measures.

The studies found have a number of methodological lim- itations. A widespread limitation is that numerous studies present only Pearson correlation coefficients. The many avail- able Pearson tables do have an advantage: They are directly comparable, because they are in the same metric. Correla- tions alone are not enough, however, to indicate that a test is predictive. A correlation coefficient indicates only that two