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An investigation of the academic processing speed of students with emotional and behavioral disorders served in public school settings.

Abstract

Little is known about the academic processing speed (i.e., rapid automatic naming and academic fluency) of children and adolescents with emotional and behavioral disorders (EBD) served in public school settings. A cross-sectional design was used to investigate the (a) percentage of K-12 students with EBD served in public school settings with academic processing speed deficits; (b) mean level and stability of academic processing speed exhibited by K-12 students with EBD served in public school settings; (c) differences in the academic skills, IQ, social adjustment, and language skills of students with and without processing speed deficits; and (d) the relative contribution of academic processing speed, academic skills, and language to the prediction of the social adjustment problems (i.e., total, externalizing, internalizing, and attention). Results indicated that: (a) a majority of the sample (57%) of students with EBD exhibited academic processing speed deficits; (b) the overall academic fluency standard score was more than three-fourths of a standard deviation below the mean for the norm group; (c) statistically significant differences were found between students with and without processing speed deficits across IQ language, academic achievement, and social adjustment measures; and (d) with one exception (i.e., internalizing problems), academic fluency predicted all social adjustment domains and predicted total and attention problems above and beyond language or academic skills. Limitations, implications, and areas of future research are discussed.

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Research into characteristics of children and adolescents with emotional and behavioral disorders (EBD) that impact school performance continues to inform the field. For example, in recent years we have gained insights into the nature of the language and achievement deficits that tend to co-exist with the emotional and behavioral difficulties of the nearly 500,000 students with EBD served in U.S. public schools (U.S. Department of Education, 2001). However, there remain areas related to school performance for students with EBD that merit attention. One of these areas is cognitive processing speed, which is the time required to process academic information. Adequate processing speed enables learners to perform basic tasks such as word reading or math computation without conscious effort, thereby allowing the learner to focus more attention on the more complex tasks of comprehending text or solving math problems. Understanding the nature of the processing speed of students with EBD may have direct implications on the type and intensity of academic intervention recommended by school personnel. We begin with a discussion of the language skills and academic functioning of students with EBD.

Across areas of language and academic functioning, students with EBD lag behind their peers without verified handicapping conditions. In the area of language functioning, for example, Nelson, Benner, and Cheney (2005) reported that 68% of a cross-sectional population of public school students with EBD experienced clinical language deficits. Specifically, the percentage of students who scored below the mean of the norm group on standardized measures of total language, receptive language, and expressive language, were 85%, 77%, and 89%, respectively. Moreover, Nelson et al. (2005) found that students with EBD who exhibited externalizing problem behaviors (i.e., aggression, delinquent, attention problems) were more likely to experience language deficits than students who evidenced internalizing problem behaviors. These findings from cross-sectional research corroborate earlier results from a systematic review of the literature (i.e., Benner, Nelson, & Epstein, 2002) indicating that as many as 9 out of 10 children with EBD served in public school settings may have language deficits. Taken together, research suggests that not only do EBD and language deficits co-occur at a relatively high rate, but that the externalizing problem behaviors of students with EBD appear to be related to language functioning.

In the area of academic functioning, a convergence of recent evidence also points to global delays in skill development for students with EBD that begin early and worsen over time. In their study of 155 K-12 students with EBD, Nelson, Benner, Lane, and Smith (2004) reported large academic achievement deficits across the areas of reading, writing, and math relative to the norm group of a well-established standardized measure of global achievement. In fact, an effect size discrepancy of 0.94 was reported across the major indices, indicating that about 83% of students scored below the norm group across all content areas. The reporting of large academic deficits dovetails with findings of a comprehensive review of academic status studies for the population of children and adolescents with EBD (Trout, Nordness, Pierce, & Epstein, 2003). Trout and colleagues indicated that 91% (i.e., 31 of 35) of the academic status reports they reviewed over a 40-year time frame (i.e., 1961 to 2000) reported academically deficient findings (i.e., below grade level or years behind peers). Moreover, the magnitude of academic delays for students with EBD was most often between one and two years below grade level, with academic delays reported early on and continuing throughout schooling. Trout and colleagues' findings are bolstered by the work of Anderson, Kutash, and Duchnowski (2001), whose research compared the academic achievement of students with EBD and learning disabilities over time in reading and math. Anderson et al. found the reading achievement scores of students with EBD failed to show adequate improvement over the five-year period, whereas the achievement of students with learning disabilities improved significantly.

Regarding the relationship of academics and EBD, Nelson et al. (2004) also conducted multiple regression analyses to examine the relative contribution of externalizing and internalizing problem behaviors on the reading, written language, and mathematics achievement in a sample of 155 students with EBD. The researchers found that 27%, 38%, and 37% of the variance in the reading, written language and mathematics achievement of students with EBD was accounted for using the Teacher's Rating Form (TRF; Achenbach, 1991a) Internalizing and Externalizing Broad-band scores. Overall, students with EBD who exhibited externalizing problem behaviors (i.e., aggression, delinquent, attention problems) were more likely to experience academic achievement deficits (i.e., reading, written language, mathematics) than students who evidenced internalizing ones (i.e., withdrawn, somatic complaints, anxious/depressed, social problems, thought problems). These results were consistent with those of earlier investigations (e.g., Abikoff et al., 2002; Lane, O'Shaughnessy, Lambros, & Gresham, 2001; Mattison, Spitznagel, & Felix, 1998) indicating that conduct (e.g., aggression, delinquency) and attention problems were related to academic achievement.

Processing Speed

Processing speed is operationalized in multiple ways in fields such as cognitive psychology (e.g., Fry & Hale, 1996), language development (e.g., Catts, Fey, Zhang, & Tomblin, 1999), reading disabilities (e.g., Compton, 2003), and genetics (e.g., Davis et al., 2001). In fact, there are many related terms (e.g., naming speed, rapid naming, lexical retrieval, temporal processing, information processing, response time). Adequate processing speed enables learners to perform basic tasks without conscious effort. In other words, it enables learners to perform simple tasks with automaticity so that attention can be focused on more complex tasks. For example, in reading, fluent word recognition enables students to focus on deeper comprehension of text. In math, facility with basic facts enables students to focus on more complex algorithms and problem solving. Moreover, processing speed underlies many cognitive skills including reading word recognition, reading comprehension, verbal ability, and verbal reasoning (e.g., Fry & Hale, 1996).

In the field of reading disabilities, much attention has focused on processing speed. Although researchers agree that insufficient ability in processing phonological information is the core deficit in many children with reading disabilities, researchers are continuing to explore the role of processing speed as a separate contributor to reading problems (Catts, Gillispie, Leonard, Kail, & Miller, 2002; Schatsch-neider, Carlson, Francis, Foorman, & Fletcher, 2002; Wolf, Bowers, & Biddle, 2000). The processing speed task most commonly examined is rapid automatic naming, which requires students to rapidly name visual symbols such as colors, objects, numbers or letters. Some researchers argue that the relationship between naming speed and early reading is a result of the phonological nature of the naming speed task (Schatschneider et al., 2002; Torgesen, Wagner, Rashotte, Burgess, & Hecht, 1997). Others support the hypothesis that naming speed also taps into non-phonological processes that are related to early reading development (Catts et al., 2002; Wolf et al., 2000). This second hypothesis, the double-deficit hypothesis, asserts that there are three types of students with reading problems, those with (a) phonological deficits, (b) naming speed deficits, or (c) a combination of the two. Students with deficits in both of these areas typically experience the greatest difficulty learning to read (Wolf & Bowers, 1999).

Although researchers from several fields have studied the role of processing speed, current research on its prevalence and impact on students with EBD is relatively limited. Researchers have indicated that language and cognitive processing speed may contribute to the externalizing behavior problems of students with EBD (Hooper, Roberts, Zeisel, & Poe, 2003; Hooper & Tramontana, 1997; Rogers-Adkinson, 2003). For example, Mattison, Hooper, and Carlson (2006) recently reported that 62.9% of a sample of students meeting EBD criteria who attended a separate elementary school for students with disabilities evidenced processing speed deficits. In their study, Mattison and colleagues administered a neuropsychological screening to 35 students with serious behavioral and emotional problems. Test results indicated that nearly 2 in 3 students with EBD performed at or below the 2nd percentile on a subtest measuring processing speed [i.e., Speeded Naming subtest of the NEPSY (Korkman, Kirk, & Kemp, 1998)].

Our aim was to add to the limited literature by focusing on academic processing speed. Academic processing speed is operationalized in this study to include efficient visual processing, working memory, long term memory and executive functioning that is required to produce correct responses to rudimentary reading, mathematical and written language stimuli (see Berninger & Richards, 2002; Woodcock, McGrew, & Mather, 2001). We used two measures of the academic processing speed construct: 1) rapid automatic naming to measure processing speed and 2) academic fluency as a direct measure of reading, math, and writing fluency. In this study we explored the prevalence of academic processing speed deficits as well as the possibility that academic fluency may be an underlying variable that contributes to the social adjustment problems experienced by students with EBD. The purposes of this study were to investigate the: (a) percentage of K-12 students with EBD served in public school settings with academic processing speed deficits; (b) mean level and stability of academic fluency exhibited by K-12 students with EBD served in public school settings; (c) differences in the academic skills, IQ, social adjustment, and language skills of students with and without academic processing speed deficits; and (d) relative contribution of academic fluency, academic skills, and language to the prediction of the social adjustment problems (i.e., total externalizing, internalizing, and attention) of this population.

Method

Participants

One hundred sixty three (133 boys and 30 girls) students (K-12) receiving special education services for EBD in a medium sized urban school district in the Midwest served as participants in the present study. These students were part of an initial pool of 260 students (20 each from kindergarten through 12th grade) who were randomly selected from all of the students receiving special education services for EBD. Project staff contacted the parents/guardians of the initial pool of students to explain the purposes of the study and, if applicable, obtain informed consent and child assent to participate in the project. Approximately 64% of the parents/guardians allowed their children to participate in the present study. This resulted in an initial pool of 166 students. Three of these students were not included in the analyses because complete dependent measure data were unavailable. One hundred percent of these children assented to participate in the study.

The mean age, age of onset (age when formally diagnosed as EBD), hours of special education services per day, and mean Full Scale IQ score for four grade level groups (i.e., K-3rd, 4th-6th, 7th-9th, 10th-12th) are presented in Table 1. The specific number, approximate percentage, and gender breakdown of the 163 participants at each grade level follows: (a) kindergarten, n = 11 (7% of total sample), 10 boys and 1 girl; (b) 1st grade, n = 17 (10%), 15 boys and 2 girls; (c) 2nd grade, n = 14 (9%), 12 boys and 2 girls; (d) 3rd grade, n = 15 (9%), 11 boys and 4 girls; (e) 4th grade, n = 14 (9%), 10 boys and 4 girls; (f) 5th grade, n = 13 (8%), 9 boys and 4 girls; (g) 6th grade, n = 12 (7%), all boys; (h) 7th grade, n = 12 (7%), 10 boys and 2 girls; (i) 8th grade, n = 13 (8%), 11 boys and 2 girls; (j) 9th grade, n = 14 (9%), 11 boys and 3 girls; (k) 10th grade, n = 13 (8%), 10 boys and 3 girls; (1) 11th grade, n = 10 (6%), 9 boys and 1 girl; and (m) 12th grade, n = 5 (3%), 3 boys and 2 girls. One hundred thirty-seven (84%) of the participants were Caucasian, 20 (12%) were African American, 3 (2%) were Latino, and 3 (2%) were Native American. Gender and ethnicity were not considered in subsequent analyses because of limited numbers. These variables are not discussed further.
Table 1

Characteristics of Participants by Grade Level Group

 Grade Level Group

 K-3 4-6 7-9 10-12 Sample

Characteristic (n = 57) (n = 39) (n = 39) (n = 28) (N = 163)

Gender (1), (2)

Female 16% 20% 18% 21% 18%
 (9) (8) (7) (6) (30)

Male 84% 80% 82% 79% 82%
 (48) (31) (32) (22) (133)

Age 7.71 10.97 14.15 17.10 11.64
 (1.18) (0.86) (.99) (1.22) (3.65)

Age of Onset 5.79 7.78 9.62 11.16 8.10
 (1.60) (1.61) (2.47) (3.33) (2.97)

Hours of 1.17 0.99 1.67 1.72 1.34
Special
Education Per Day
 (1.91) (1.04) (1.44) (1.40) (1.29)

Full Scale IQ 92.96 100.77 95.58 99.35 96.71
 (15.10) (13.66) (15.54) (18.71) (15.71)

Note. (1) Values represent percentages. (2) Numbers in parentheses
represent the numbers of females and males, respectively, in each grade
level group. All other values in parentheses represent standard
deviations.


Research Design

A cross-sectional research design (Martella, Nelson, & March-and-Martella, 1999) was used to collect information on the 163 randomly selected participants within a contemporaneous 4-month time span. All of the data were collected February through May of the 2001-02 academic year.

Dependent Measures

Five categories of dependent measures were collected: (a) academic processing speed, (b) social adjustment, (c) academic achievement, (d) language, and (e) student record search to collect information on ethnicity, hours of special education per day, and IQ. Each student's special education teacher completed the social adjustment measure. Six trained data collectors administered the academic processing speed, academic achievement, and language measures. The data collectors also conducted the student record search. A description of the dependent measures follows.

Academic Processing speed. Two academic processing speed measures were used. These measures were carefully chosen to account for age-related changes in processing speed from early childhood through adolescence (Hale, 1990; Kail, 1986). First, the Clinical Evaluation of Language Fundamentals-Third Edition (CELF-III; Semel, Wiig, & Secord, 1995) Rapid Automatic Naming subtest was used to measure the rapid automatic naming (RAN) of participants. Students were presented with 36 randomly distributed colors, geometric shapes and color shape combinations. The students were asked to name the presented 36 colors, geometric shapes and color shape combinations in serial format (left to right, top to bottom) as quickly and accurately as possible. The number of errors and completion time were used to determine whether the student either passed or did not pass compared to the number of errors and completion time expected for the corresponding chronological age. The CELF-III RAN criterion for academic processing speed deficit was a non passing performance. The psychometric properties of the CELF-III RAN indicate strong content validity and adequate construct validity (Impara & Plake, 1998).

Second, the Woodcock Johnson Tests of Achievement-Third Edition (WJ-III; Woodcock et al., 2001) Academic Fluency cluster was used to assess academic fluency in the areas of math, reading, and writing. The Academic Fluency skills cluster is comprised of three WJ-III subtests: Math Fluency, Reading Fluency, and Writing Fluency. For the Math Fluency subtest, students write the answers to basic addition, subtraction and multiplication facts within a three-min time limit. For the Reading Fluency subtest, students read simple sentences and circle yes or no to indicate whether the statement is true or false. This subtest also has a three-min time limit. For the Writing Fluency subtest, students write sentences about stimulus pictures using the 3 words provided with each picture. This subtest has a seven-min time limit. The WJ-III criterion for academic processing speed deficit was performance below one deviation (<85) from the standardized mean on the WJ-III Academic Fluency cluster. The median cluster test retest reliability of the WJ-III Academic Fluency cluster is .93. The median cluster reliabilities of the WJ-III Math Fluency, Reading Fluency, and Writing Fluency subtests are .90, .90, and .88, respectively. The overall median Academic Fluency cluster reliability is .93. The authors of the WJ-III indicate that the WJ-III Academic Fluency cluster is a valid measure of broad processing speed ability, particularly after age 9 (Woodcock et al., 2001).

Social adjustment. The TRF (Achenbach, 1991a) was used to measure the social adjustment of participants. The TRF consists of 113 problem items such as difficulty following directions, disturbing other pupils, and disrupting class discipline. The teacher rates the child on each item indicating the severity of the problem on a scale of 0 (no problem) to 2 (severe problem). The TRF scoring profile provides a total scale score (Total Problems), two broad band scale scores (Internalizing and Externalizing), and eight narrow band subscale scores (Withdrawn, Somatic Complaints, Anxious/Depressed, Social Problems, Thought Problems, Attention Problems, Delinquent Behavior, and Aggressive Behavior). The broad band Internalizing scale score is based on the sum of the Withdrawn, Somatic Complaints, and Anxious/Depressed scale scores. The broad band Externalizing scale score is based on the Delinquent Behavior and Aggressive Behavior scale scores. The narrow band Social Problems. Thought Problems, and Attention Problems scale scores are not included on either the broad band Internalizing or Externalizing scale scores. The recommended borderline and clinical cut off scores for Total Problems, Externalizing, and Internalizing scales are 60 to 63 and 64 or greater, respectively. The recommended borderline and clinical cut off scores for the With drawn, Somatic Complaints, Anxious/Depressed, Social Problems, Thought Problems, Attention Problems, Delinquent Behavior, and Aggressive Behavior scales are 67 to 69 and 70 or greater, respectively. The TRF test-retest and internal consistency values for the broad and syndrome scales are reported in the test manual as ranging from. 62 to .96 and .72 to .95, respectively (Achenbach, 1991b).

Academic achievement. The WJ-III is a comprehensive achievement test measuring both broad based and specific reading, math, writing, and language skills. The Academic Skills and Academic Applications clusters of the WJ-III (Woodcock et al., 2001) were used to measure the academic achievement of participants. The WJ-III subtests for the Academic Skills cluster are Letter-Word Identification, Calculation, and Spelling, and Academic Applications cluster are Passage Comprehension, Applied Problems, and Writing Samples. The test-retest reliabilities for the Letter-Word Identification, Calculation, and Spelling subtests are .94, .86, and .90, respectively. The test-retest reliabilities for the Passage Comprehension, Applied Problems, and Writing Samples subtests are .88, .93, and .87, respectively. The WJ-III Academic Skills and Academic Applications show strong median cluster reliabilities at .95 and .96, respectively (Woodcock et al., 2001).

Language. The core subtests of the CELF-III (Semel et al., 1995) were used to measure the language skills of participants. The CELF-III core subtests include sentence structure, word structure, concepts and directions, formulated sentences, word classes, recalling sentences, sentence assembly, and semantic relationships. The CELF-III scoring profile provides a total test score (Total Language), two primary scale scores (Receptive and Expressive), and six subtest scores (three each comprise the Receptive and Expressive primary scale scores). The six subtests used to compute the Total, Receptive, and Expressive scores differ with age. The three Receptive (Sentence Structure, Concepts and Directions, and Word Classes) and Expressive (Word Structure, Formulated Sentences, and Recalling Sentences) subtests for students 6 to 8 years differ from the Receptive (Concepts and Directions, Word Classes, and Semantic Relationships) and Expressive (Formulated Sentences, Recalling Sentences, and Sentence Assembly) subtests for students 9 years and older. Regardless of age, the Receptive and Expressive test scores are based on the sum of the three respective sub-test scores. The Total Language score is based on the sum of the six Receptive and Expressive subtest scores. The test-retest reliabilities of the Receptive and Expressive scales are .86 and .88, respectively (Semel et al., 1995).

Student records. The school records of each participant were searched to collect information on their ethnicity, hours of special education services per day, and IQ. Information on ethnicity and hours of special education services per day were available for all 163 participants. In regards to IQ, the Full Scale, Verbal, and Performance scores of the participants were recorded. Completed IQ scores were available for 152 of the participants. Thus, 11 participants with missing IQ data were excluded from statistical analyses involving IQ data (i.e., pairwise exclusion).

Procedures

Training. Data collectors were trained to administer the CELF-III and WJ-III. Eight-hour training sessions on the administration and scoring procedures for these measures, building rapport, and managing behavior during testing situations occurred weekly for one month. The total number of training hours was 32. Training sessions were conducted using the training procedures outlined by the authors of the CELF-III. To demonstrate mastery of test administration, data collectors were observed delivering each subtest to a child under simulated conditions until mastery of subtest administration was reached. Fidelity was assessed using a modified version of the observation checklist created by authors of the CELF-III and WJ-III. When the data collector administered the whole test with 95% fidelity under simulated test conditions, the data collector was approved to test in the schools.

Fidelity. Fidelity checks were conducted prior to test administration and on every third test administration. Fidelity was calculated by dividing total number of occurrences (e.g., following testing script) and non-occurrences (e.g., not following testing script) by the total number of occurrences for each of the items on the observation checklists for the WJ-III and CELF-III. Item by item fidelity for administration of the CELF-III ranged from 97 to 100%. Item by item fidelity for administration of the WJ-III ranged from 95 to 100%. Overall fidelity was 99% and 97% for administration of the CELF-III and WJ-III, respectively.

Testing. The CELF-III and WJ-III were administered in a quiet area at the participant's school. To improve participant attention to each CELF-III and WJ-III task, testing was divided into 15 to 20 min segments over 2 days.

Scoring and Data Entry. Scoring agreement checks on all WJ-III, CELF-III, and TRF protocols were conducted at two phases of data collection. First, each protocol was checked for scoring accuracy by two of the authors after initial scoring by research assistants. More specifically, each protocol was checked to determine that items were completed, raw scores were computed accurately for each subtest, and standard scores were derived accurately. Agreement was calculated by dividing the number of agreements by agreements plus disagreements and multiplying by 100. An agreement was recorded when the agreement check calculations aligned with calculations made after initial scoring. Agreement in scoring WJ-III, CELF-IIII, and TRF protocols was 98% (range = 96% to 100%), 99% (range = 96% to 100%), and 99% (range = 98% to 100%), respectively. Second, all of the scores were checked for accuracy by researchers following initial data entry. Agreement in entering all data was 99%. Initial errors made in scoring or data entry were corrected.

Results

Prevalence of Academic Processing Speed Deficits

A total of 93 (57%) out of 163 students evidenced an academic processing speed deficit. The percentage of students who did not pass the CELF-III Rapid Automatic Naming subtest was 35% (n = 57). The percentage of students scoring in the low average range or below one deviation (<85) from the standardized mean on the WJ-III Academic Fluency cluster was 45% (n = 74). Twenty-three percent of students (n = 38) met CELF-III and WJ-III criteria for an academic processing speed deficit.

Mean Level and Stability of Academic Processing Speed Deficits

The mean WJ-III Academic Fluency cluster and subtest standard scores by grade level group (K-3, 4-6, 7-9, and 10-12) and overall are presented in Table 2. Overall WJ-III Academic Fluency and associated subtest standard scores (i.e., Reading, Writing, and Math Fluency) fell in the average range. Thus, the overall academic fluency scores of participants did not fall in the academic processing speed deficit range (<85). However, overall WJ-III Academic Fluency cluster and associated subtest scores declined over the grade level groups. Overall WJ-III Math Fluency standard scores dropped from average to low average after grade 3 and gradually declined through 10-12th grades. Effect size discrepancies were computed by subtracting the mean of the sample from the mean of the norm group then dividing by the standard deviation of the norm group (i.e., Xnorm group - Xsample / SDnorm group). The effect size discrepancy for the Academic Fluency cluster for the overall sample was .8. This indicates that approximately 79% of students scored below the mean of the norm group on the Academic Fluency cluster. The effect size discrepancies for the Reading, Math, and Writing Fluency subtests were .6, .9, and .5, respectively. A similar pattern was generally found across the grade level groups.
Table 2

Mean Academic Fluency Scores of Participants by Grade Level
Group

 Grade Level Group

 K-3 4-6 7-9 10-12 Total Effect
 Size

Scale/Subtest (n = 57) (n = 39) (n = 39) (n = 28) (N = 163)

 M(SD) M(SD) M(SD) M(SD) M(SD)

WJ-III

Academic 89.3 88.3 88.2 86.2 88.2 .8
Fluency (14.7) (14.5) (12.1) (19.9) (15.1)

 Reading 95.1 91.4 90.9 90.2 92.0 .6
 Fluency (12.3) (13.6) (12.3) (18.5) (14.0)

 Math Fluency 93.8 84.0 81.6 80.0 85.9 .9
 (13.2) (17.2) (12.8) (17.7) (16.0)

 Writing 98.7 88.8 89.9 89.6 91.5 .5
 Fluency (14.9) (16.0) (13.9) (19.1) (16.2)

Note. WJ-III is the Woodcock Johnson Tests of Achievement-Third Edition
(Woodcock, McGrew, & Mather, 2001).


Comparisons Between Students With and Without Academic Processing Speed Deficits

Repeated Measures Analyses of Covariance (ANCOVA) were conducted using one between group factor (academic processing speed group-deficit and no deficit) on four within group factors: IQ (Full Scale, Verbal, and Performance standard scores), Language (CELF-III Total, Expressive, and Receptive standard scores), Academic Achievement (WJ-III Academic Skills and Academic Applications standard scores), and Social Adjustment (TRF Total, Externalizing, Internalizing, and Attention Problems t-scores). Age was used as a covariate for each ANCOVA. Statistically significant differences were found between students with and without academic processing speed deficits on the IQ [F(1, 163) = 21.5, p <.001], Language [F(l, 163) = 53.3, p<.001], Academic Achievement [F(l, 163) = 91.7, p<.001 ], and Social Adjustment [F(1, 163) = 6.6, p <.05] within group factors.

Standard score means, standard deviations and analyses of co-variance (ANCOVA) for students with and without academic processing speed deficits are presented in Table 3. With two exceptions (TRF Externalizing and Internalizing Problems), statistically significant differences were found between students with and without academic processing speed deficits on all IQ (Full Scale, Verbal, and Performance), Language (Total, Expressive, and Receptive), Social Adjustment (Total and Attention Problems), and Academic Achievement (Academic Skills and Academic Applications) scales. Age was used as a covariate in all cases.
Table 3

Standardized Means, Analyses of Covariance (ANCOVA)
F-statistics by Group

 Group

Factor/Scale Processing No Processing F (df = 1)
 Deficit Deficit

 (N = 93) (N = 70)

 M (SD) M (SD)

IQ (1)

 Full Scale 91.9 (14.9) 103.3 (14.6) 21.5 ***
 Verbal 89.4 (16.5) 102.2 (13.5) 24.8 ***
 Performance 94.3 (16.7) 105.4 (16.1) 16.5 ***

Language

 Total 76.8 (14.5) 93.5 (13.1) 53.3 ***
 Expressive 74.5 (14.4) 90.6 (11.6) 50.3 ***
 Receptive 81.8 (17.0) 97.9 (16.3) 36.0 ***

Social Adjustment

 Total Problems 67.4 (8.1) 63.9 (7.9) 6.6 *
 Externalizing Problems 66.9 (8.4) 63.8 (10.3) 3.3
 Internalizing Problems 60.4 (9.5) 58.4 (8.4) 12.7 **
 Attention Problems 64.1 (8.6) 59.6 (5.8) 12.7 **

Academic Achievement

 Academic Skills 87.0 (13.7) 104.8 (12.2) 48.6 ***
 Academic Applications 86.7 (11.7) 100.1 (11.4) 034.2 ***

Note. *** [rho]<.001, ** [rho]<.01, * [rho]
<.05. Age was used as the covariate for each ANCOVA. The
values in parentheses represent standard deviations.

(1.) Participants with missing IQ data were excluded from statistical
analysis when the analysis involved IQ data (i.e., pairwise exclusion).
There were 87 and 65 students with and without processing speed
deficits, respectively, when differences on IQ measures were compared.


Relative Contribution of Academic Processing Speed on Social Adjustment

Multiple regression analyses were used to assess the contribution of academic processing speed, academic skills, and language skills to the prediction of total problems, externalizing, internalizing, and attention problems. We controlled for any variation due to age of onset before entering three sets of constructs into the regression formula (i.e., academic processing speed, academic skills, and language skills). Regression diagnostics were conducted prior to conducting these analyses to screen data for deviant cases that may be extreme outliers and/or have undue influence on the results (Pedhazur, 1999). Influential cases have a significant effect on values of regression statistics either uniquely or in combination with other observations. In order to detect influential cases, the following regression diagnostics were examined: (1) leverage (detects cases that affect the regression line); (2) Cook's D (detects cases that are influential due to their values on Y, X, or both); and (3) Standardized DFBETA (detects cases that affect the regression coefficient). The results of the regression diagnostics indicated that there were no deviant cases or outliers that would unduly influence the results of the regression analyses. Additionally, collinearity diagnostics indicated that the predictive variables were not a linear combination of one another. The obtained condition index in all cases was [less than 10. A condition index of 30 to 100 indicates moderate to strong collinearity (Fox, 1991).

The criterion variables for the regression analyses were the TRF Total Problems, Externalizing Problems, and Internalizing Problems scores. The same three predictor constructs were entered into each of the regression analyses. These predictor constructs included (a) Academic Fluency (WJ-III Reading, Math, and Writing Fluency); b) Total Language (CELF-III Expressive and Receptive Language scales); and c) Academic Skills (WJ-III Letter-World Identification, Calculation, and Spelling). Each of these constructs was entered first (after age of onset), middle, and last position in the regression analysis. This enabled us to establish the initial contribution of the externalizing, internalizing, and academic fluency constructs when the other predictors were not present (i.e., first position) and the final contribution of each construct after the other ones were entered into the equation (i.e., final position). Entry in the final position allowed us to examine which of the externalizing and internalizing constructs contributed to the prediction of academic skills and applications above and beyond the contribution of the other construct. These analyses also provided information on the combined contribution of the externalizing, internalizing, and academic fluency constructs to the prediction of academic skills and applications.

In all cases, the probability of F to enter was <.05. and to remove [greater than].10. When all variables were entered into the regression formula, 16%, 11%, 10% and 21% of the variance in the total problems, externalizing, internalizing, and attention problems was accounted for, respectively. The academic fluency construct contributed to the overall fit-of-the-model when entered in the first (following age of onset) and the last position in the regression analyses for total problems and attention problems. In the case of the regression analyses for total and attention problems, the t-test for the Beta weight for academic fluency was statistically significant ([rho]<.05) when this construct was in either the initial or final position. This construct contributed to the overall fit-of-the-model when entered in the first (following age of onset) in the regression analyses for externalizing problems. Thus, the WJ-III Reading, Math, and Writing Fluency scores contributed to the prediction of total problems, externalizing, and attention problems. Thus, students with ED who exhibit academic fluency deficits (reading, math, writing) were more likely to experience total, externalizing, and attention problems than students who evidenced academic skill or language deficits.

Discussion

The literature on school performance-based characteristics of students with EBD served in public school settings is replete with data regarding the behavioral, academic, and language skills of this population. This body of research helps understand the population of students with EBD and improve intervention efforts. Findings from the present study extend our knowledge of school performance-based characteristics to include the academic processing speed of students with EBD. In concert with what is known about the academic and language achievement of students with EBD, the academic processing speed of this population appears to both lag behind those without disabilities and contributes to academic and behavioral functioning. Several findings warrant discussion.

First, delays existed for students with EBD when compared to normative samples and remained stable over time. Specifically, 57 percent of a cross-sectional sample of students with EBD served in public schools evidenced an academic processing speed deficit when comparisons of scores on standardized (WJ-III) or criterion-referenced (CELF-III RAN subtest) measures of academic processing speed were made. Moreover, the effect size discrepancy for the academic processing speed measure was .8. This indicates that approximately 79% of students scored below the mean of the norm group on the WJ-III Academic Fluency cluster. Prevalence findings are bolstered by those of Mattison and colleagues (2006), who found that nearly 63% of a sample of elementary students meeting state EBD eligibility requirements evidenced processing speed delays. Not only was a larger proportion of the sample in that study found to exhibit processing speed delays as measured on a neuropsychological task, but also the delays were more severe (i.e., more than two standard deviations below the mean). Coinciding with academic achievement findings, the gap in the academic fluency of students with EBD and their normative counterparts appears to widen over time (Nelson et al., 2004).

Second, statistically significant differences across a number of descriptive variables were reported between students with EBD who exhibited academic processing speed deficits and those who did not. In fact, statistically significant differences were indicated for 10 of 12 broad variables related to academic achievement, language, social adjustment, and IQ. The only scales in which statistically significant differences were not found were TRF Internalizing and Externalizing Problems. These results are consistent with research findings in several fields, including genetics (e.g., Davis et al., 2001), language (e.g., Catts et. al., 2001), reading disabilities (e.g., Compton, 2003), and the neuro-sciences (Wolf et al., 2000). Research across multiple disciplines seems to indicate that processing speed is critical to proficient reading and many cognitive skills including verbal ability and reasoning.
Table 4

Multiple Regression Analyses for Academic Fluency, Academic
Skills, and Total Language

 Initial Entry Entry in last Position

 Simple R2 F

Construct df R F p- Change Change p

 Total Problems

Age of Onset 1 .10 1.18 .280
Academic Fluency 4 .35 4.25 .003 .09 3.94 .010
Academic Skills 7 .10 2.10 .085 .03 1.53 .211
Total Language 9 .18 1.44 .234 .01 .33 .719

 Externalizing Problems

Age of Onset 1 .17 3.86 .052
Academic Fluency 4 .29 2.87 .026 .03 1.26 .290
Academic Skills 7 .26 2.16 .077 .02 .861 .463
Total Language .9 .18 1.44 .234 .01 .33 .719

 Internalizing Problems

Age of Onset 1 .07 .65 .420
Academic Fluency 4 .27 2.39 .054 .06 2.64 .053
Academic Skills 7 .18 .97 .424 .02 1.05 .375
Total Language 9 .12 .63 .595 .00 .23 .796

 Attention Problems

Age of Onset 1 .06 .43 .511
Academic Fluency 4 .42 6.74 .000 .08 4.13 .008
Academic Skills 7 .35 4.42 .002 .03 1.69 .172
Total Language 9 .23 2.36 .074 .00 .32 .724


Finally, students with EBD who exhibited academic fluency deficits (reading, math, writing) were more likely to experience total, externalizing, and attention problems than students who evidenced academic skill or language deficits. With one exception (i.e., internalizing problems), academic processing speed predicted all social adjustment domains. More notably, this construct predicted total and attention problems above and beyond language or academic skills. In other words, even when language and academic skills were accounted for, academic fluency contributed significantly to differences in total and attention problems. The amount of variance in the social adjustment of students with EBD accounted for by academic fluency above and beyond language and academic skills was 9%. Thus, the predictive strength of academic fluency to the social adjustment of students with EBD appears to be robust and moderate in magnitude. Academic fluency appears to impact the overall social adjustment of students with EBD to a greater degree than language or academic skills. This finding extends the work of previous researchers who found that language and cognitive processing speed contribute to the social adjustment problems of students with EBD, particularly those who display externalizing behaviors (Hinshaw, 1992; Hooper et al., 2003; Hooper & Tramontana, 1997; Mattison et al., 2006; Rogers-Adkinson, 2003).

Limitations

Before discussing the implications of these findings, several limitations should be noted. First, the sample of children was drawn from one school district in one geographic location and may not be representative of the general population of public school students with EBD. It is possible that the findings may not generalize to other students in other geographical regions and schools. Future research should replicate these finding across varied contexts. Second, related to the first limitation, 36% of parents/guardians failed to consent to their child's participation in the study. Although we were unable to detect any differences in the characteristics between parents/guardians who provided consent and those who did not, it is unclear whether the sample was representative of the entire population of students with EBD served by the school district. Third, the language skills and social adjustment skills of students with EBD were studied with only one dependent measure, respectively. Although the CELF-III and TRF are technically adequate and widely used measures of language and social adjustment, respectively, the relative degree of relationship between language and social adjustment skills may vary based on the particular combination of measures used. Replications are necessary using different measures of language and social adjustment. In the area of social adjustment, for example, research that incorporates both deficit-oriented and strength-oriented instruments would extend the literature. Fourth, the range of variables that were available to enter into each of our regression models was relatively restricted. A more complete set of demographic, developmental, contextual, and biological set of variables may have revealed more about the factors that influence the social adjustment and academic processing speed of students with EBD served in public school settings. Nevertheless, future research is needed to identify the full range of variables that affect the social adjustment of students with EBD served in public school settings. Finally, the correlational nature of our research does not allow us to make causal comparisons. Research using true experimental designs is needed to clarify the nature of the relationship between the social adjustment and academic processing speed of students with EBD.

Implications

There are three implications emanating from the present study. First, academic processing speed of students with EBD should be a focus of ongoing classroom assessment and service evaluation. Recently, researchers in the field of EBD have made convincing arguments for preventative and remedial efforts that begin with and are continuously informed by a comprehensive assessment of performance-based characteristics. For example, Lane, Beebe-Frankenberger, Lambros, and Pierson (2001) promoted an early assessment approach that incorporated measures of reading and behavior into the identification of children at risk for EBD. The findings of Nelson and his colleagues (2003, 2004) make a strong case for the addition of language measures to a comprehensive repertoire. Our findings lend support to the notion that measures of academic processing speed should augment assessment and evaluation practices.

Assessment should not stop once an initial comprehensive assessment is completed, either. Based on over 30 years of scientific research, Curriculum Based Measurement (CBM) was designed to assess and build academic processing speed or automaticity with academic tasks (e.g., Deno, Fuchs, Marston, & Shinn, 2001). Passage of recent legislation (e.g., Individuals with Disabilities Education Improvement Act of 2004; Individuals with Disabilities Education Act of 1997) high lights the need to assess educational need, write measurable goals, monitor progress, report progress to parents, and make revisions in the IEP to address any unexpected lack of progress. CBM can be used to monitor overall student progress as well as document progress in improving academic processing speed. CBM not only provides teachers and parents technically adequate assessment data, it also has produced significant results on the performance and motivation of students with EBD. Researchers have found that CBM produces moderate to large effect sizes (ES >.5) on the academic processing speed of students with high incidence disabilities, including those with EBD (Shinn, 2002).

Second, academic processing speed may influence how well students with or at risk of EBD respond to intervention. A meta-analytic review of the literature revealed that rapid automatic naming was the most influential learner characteristic to predict the effectiveness of reading interventions (Nelson, Benner, & Gonzalez, 2003). Researchers have found large effect sizes when the academic processing speed measures of always-responsive students versus never-re-sponsive kindergarten and first grade children are compared (ES = 1.57) (Al Otaiba, 2001). Researchers have reported that approximately 50% of students with EBD and other high-incidence disabilities do not respond to academic interventions (Fuchs et al., 2001). As noted earlier, Anderson, Kutash, and Duchnowski (2001) found the reading achievement scores of students with EBD tended to plateau or decline over five years, whereas the achievement of students with learning disabilities improved significantly. Thus, although there is ample evidence that students with EBD tend to be nonresponsive to generally effective instruction, building the academic processing speed of this population may improve responsiveness.

Improving the academic processing speed and treatment responsiveness of students with EBD will require the implementation of research-based instructional practices that have been demonstrated to improve academic achievement (Epstein, Nelson, Trout, & Mooney, 2005; U.S. Department of Education, 2001). The type of instruction greatly impacts the responsiveness of students with EBD to instruction. Two reviews of the literature on reading interventions for children with EBD concluded that instruction delivered in a one-on-one format either by trained volunteers, peers, or teachers was an effective method for increasing motivation to read and improving reading skills (Coleman & Vaughn, 2000; Elbaum, Vaughn, Hughes, & Moody, 2000). Another series of reviews highlighted teacher-, peer-, and child-mediated interventions that have demonstrated positive results. While indicating numerous technical and other inadequacies in the intervention literature for students with EBD, Epstein and colleagues (Epstein et al., 2005; Mooney, Ryan, Uhing, Reid, & Epstein, 2005; Pierce, Reid, & Epstein, 2004; Ryan, Reid, & Epstein, 2004) nonetheless provided researchers interested in ameliorating school performance deficits with a cadre of potentially effective tools.

Finally, this study underscores the need for research into the underlying cognitive processes that contribute to language, academic, and social adjustment difficulties of public school students with EBD. Future research could build on two promising lines of research. First, several researchers have highlighted the role of neuropsychological functioning as a mediating factor in the relationship between EBD and language deficits (Hinshaw, 1992; Hooper et al., 2003; Hooper & Tramontana, 1997; Rogers-Adkinson, 2003). Researchers have found that externalizing EBD is related to deficits in three specific domains of functioning: 1) Language based verbal skills; 2) executive cognitive functioning (i.e., self-control functioning); and 3) processing speed (i.e., language and cognitive) (Giancola, 2000; Hooper et al., 2003; Lynam & Henry, 2001; Mattison et al., 2006). Second, researchers have demonstrated that evidence-based interventions increase neurological activity in areas of the brain that regulate information processing. Shaywitz and colleagues (2004) examined the effects of evidence based phonological processing intervention on the reading fluency and brain organization of 77 right-handed 6 to 9 year-old children with reading disabilities. These researchers found that children in the experimental condition (n = 37) had made significant gains in reading fluency and demonstrated increased activity in the left hemisphere regions of the brain, particularly the occipitotemporal systems that are critical to processing speed. These researchers also found that the occipitotemporal improvements in the brain were maintained one year following the study and that as the systems on the left side of the brain developed, the systems on the right no longer needed to compensate for the lack of development on the left side. These researchers concluded that the use of evidence-based phonological processing interventions facilitate the development of the fast-paced neural systems that underlie skilled reading and language development. Future investigations into the relationships between EBD, academic, and language deficits should build upon extant research on the underlying cognitive processes involved in language and social development.

Note

(1) The Wechsler Intelligence Scale for Children, 3rd Edition (WISC-III: Wechsler, 1991) was used with 131 participants, Stanford-Binet Intelligence Scale (Thorndike, Hagen, Sattler, & Delaney, 1986) with 17 participants, Kaufman Assessment Battery for Children (K-ABC: Kaufman & Kaufman, 1983) with 4 participants. Thus, IQ scores were not available for 11 of the 163 participants. 2 The formula used to compute the effect sizes was: (Xnorm group--Xsample/SDnorm group).

Acknowledgment

Preparation of this manuscript was supported in part by grants from the U.S. Department of Education, Office of Special Education Programs (No. H324X010010, H324D010013, and H325D990035). Opinions expressed do not necessarily reflect the position of the U.S. Department of Education, and no endorsement should be inferred.

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Gregory J. Benner

University of Washington

Jill H. Allor

Southern Methodist University

Paul Mooney

Louisiana State University

Correspondence to Gregory J. Benner, Ph. D., University of Washington, Tacoma Education Program, Box 358435, 1900 Commerce St., Tacoma WA 98402; e-mail: gbenner@u.washington.edu
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