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Classroom Observation Of Children With Adhd And Their Peers: A Classroom Observation Of Children With Adhd And Their Peers: A

Meta-analytic Review Meta-analytic Review

Michael Kofler University of Central Florida

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CLASSROOM OBSERVATION OF CHILDREN WITH ADHD AND THEIR PEERS:

A META-ANALYTIC REVIEW

by

MICHAEL J. KOFLER B.S. Tulane University, 2000

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the Department of Psychology

in the College of Arts and Sciences at the University of Central Florida

Orlando, Florida

Spring Term 2006

© 2006 Michael J. Kofler

ii

ABSTRACT

Meta-analysis of 23 between-group direct observation studies of children with ADHD

and typically developing peers indicates significant deficiencies in children with ADHD's ability

to pay attention in classroom settings. Comparison with 59 single case design studies of children

with ADHD suggests generalizability of between-group comparisons. Weighted regression

analysis determined that several methodological differences – sample characteristics, diagnostic

procedures, and observational coding schema – have significant effects on observed levels of

attentive behavior in the classroom. Best case estimation indicates that after accounting for these

factors, children with ADHD are on-task approximately 65% of the time compared to 85% for

their classroom peers. Children with ADHD were also more variable in their attentive behavior

across studies. Implications for conceptual models of ADHD are discussed.

iii

TABLE OF CONTENTS

LIST OF FIGURES ....................................................................................................................... vi

LIST OF TABLES........................................................................................................................ vii

LIST OF ACRONYMS/ABBREVIATIONS.............................................................................. viii

CHAPTER ONE: INTRODUCTION............................................................................................. 1

The Need for a Meta-Analytic Approach ................................................................................... 4

Sample Characteristics............................................................................................................ 6

Diagnostics Methods............................................................................................................... 6

Classroom variables ................................................................................................................ 7

Observation methods .............................................................................................................. 8

CHAPTER TWO: METHODOLOGY......................................................................................... 10

Literature Searches.................................................................................................................... 10

Inclusion and Exclusion Criteria............................................................................................... 11

Coding of Moderators ............................................................................................................... 12

Sample................................................................................................................................... 12

Diagnostics............................................................................................................................ 12

Classroom variables .............................................................................................................. 14

Observation variables............................................................................................................ 14

Computation of Effect Sizes ..................................................................................................... 16

Hedges' g............................................................................................................................... 16

Multiple effect sizes.............................................................................................................. 17

Nonsignificant results ........................................................................................................... 17

iv

Data Analysis ............................................................................................................................ 18

CHAPTER THREE: RESULTS................................................................................................... 21

Tier I: Moderator-independent attentional differences ............................................................. 21

Publication Bias: The File Drawer Problem ......................................................................... 21

Off-task Comparisons ........................................................................................................... 21

Effect sizes ............................................................................................................................ 22

Tier II: Moderators of obtained effect size ............................................................................... 22

Fixed Effects Model.............................................................................................................. 22

Sample................................................................................................................................... 23

Diagnostics............................................................................................................................ 24

Classroom type...................................................................................................................... 25

Observation Variables........................................................................................................... 25

Tier III: Best Case Estimation and Original Metric.................................................................. 27

Best Case Estimation ............................................................................................................ 27

Power Analysis for Future Studies ....................................................................................... 27

Original Metric...................................................................................................................... 28

Tier IV: Single Case Design Studies ........................................................................................ 28

Mean Differences.................................................................................................................. 28

Effect Size Comparisons....................................................................................................... 29

Moderator Comparison ......................................................................................................... 29

CHAPTER FOUR: CONCLUSION............................................................................................. 45

LIST OF REFERENCES.............................................................................................................. 57

v

LIST OF FIGURES

Figure 1: Original Metric Differences Between Observed Classroom Attention Of Children With Adhd And Control

Children Based On Best Case Estimation After Controlling For Methodological Differences Among Studies.

...........................................................................................................................................................................43

vi

LIST OF TABLES

Table 1: Demographic And Methodological Variables ...............................................................................................30

Table 2: Mean Off-Task Rates, Standard Difference Scores, And Effect Sizes In Children With Adhd ....................33

Table 3: Stem And Leaf Display Of 23 Hedges G Effect Sizes ..................................................................................35

Table 4: Inverse Variance Weighted Regression Results ............................................................................................36

Table 5: Predicted And Obtained Effect Sizes For Single Case Design Studies .........................................................37

Table 6: Between Group And Single Case Design Study Moderator Variable Differences........................................38

Table 7: Single Case Design Studies: Demographic And Methodological Variables .................................................39

vii

LIST OF ACRONYMS/ABBREVIATIONS

ADHD Attention-Deficit/Hyperactivity Disorder

BG Between Group

CD Conduct Disorder

CPT Continuous Performance Task

DSM-IV Diagnostic and Statistical Manual of Mental Disorders,

Fourth Edition

ES Effect Size

LD Learning Disabilities

ODD Oppositional Defiant Disorder

SCD Single Case Design

viii

CHAPTER ONE: INTRODUCTION

Attention-Deficit/Hyperactivity Disorder (ADHD) is a chronic and disabling condition

that affects an estimated three to seven percent of school-age children (APA, 2000; Francis,

1993). Inattention in the classroom is associated with several negative outcomes: These children

are academically more likely to receive lower grades and standardized test scores (Zentall,

1993), be placed in special education classrooms, have comorbid learning disabilities, and repeat

a grade level compared to their typically developing peers (Faraone et al., 1993). More than half

of all children with ADHD fail at least one grade level by adolescence (Barkley et al., 1990), and

23 percent of children with ADHD fail to finish high school (Mannuzza et al., 1993). Early

academic difficulties are predictive of later academic problems (Fergusson & Horwood, 1995;

Rapport, Scanlan, & Denney, 1999), and longitudinal outcome studies of ADHD indicate that

adults diagnosed with ADHD as children are less likely to attend college, and more likely to

have unstable marriages, lower mean SES scores, and conduct problems compared to peers of

similar intelligence (Mannuzza et al., 1993).

Environmental demands impact the expression of ADHD-related symptoms (Rapport,

Chung, Shore, Denney, & Isaacs, 2000). Children with ADHD are indistinguishable from their

peers in some environments (e.g., during recess and non-academically related work periods), but

exhibit marked behavioral differences when encountering particular structured situational

demands (Porrino et al., 1983; Whalen et al., 1978). They pay attention less often during

academic instruction (Abikoff, Gittelman-Klein, & Klein, 1977), complete academic work

inconsistently (Barkley, DuPaul, & McMurray, 1990), and shift from one activity to another at

home more frequently compared with peers (DuPaul & Stoner, 2004). Situations involving novel

1

tasks (Power, 1992; Zentall & Meyer, 1987) and/or frequent reinforcement (Douglas & Parry,

1983; Pelham, Milich, & Walker, 1986) are associated with improved behavior and performance

in children with ADHD.

Laboratory studies employing rigorous experimental paradigms such as the Continuous

Performance Task (CPT) often, yet not always, report performance differences between children

with ADHD and typically developing children (Chung, Rapport, & Denney, 2005). The highly

controlled nature of the laboratory setting allows hypotheses to be tested regarding the

underlying processes of ADHD. However, such situational demands may evoke attentional

processes that differ from those required in the natural environment (Barkley, 1991).

Ecological validity concerns necessitate the investigation of more naturalistic procedures

for characterizing behavior, such as behavioral rating scales and direct observations by

independent researchers. Ratings scales are cost- and time-efficient measures capable of

capturing overall behavioral functioning. They provide a quantitative metric of the frequency and

severity of behavior, yet remain dependent upon subjective judgments and are vulnerable to

response bias, misinterpretation of questions, and over/underestimation of behavior due to

intensity/immediacy effects (Kazdin, 1997; Rutter & Graham, 1968). In contrast, direct

observations can provide more objective information by utilizing independent, well-trained

observers and specific, operationalized behavioral definitions (Kazdin, 1997). Observational

coding schemes offer decreased threats to validity in comparison with other methods. Unlike

global rating scales of behavior, observation codes are not subject to halo effects or rater

expectation bias (Abikoff et al., 2002; Harris & Lahey, 1982; Kent, O'Leary, Diament, & Dietz,

1974). Systematic and clearly defined behavior codes also may offer improved criterion validity

(Abikoff et al., 1977). Observation coding schemes are able to differentiate between typically

2

developing children and children with ADHD, suggesting diagnostic validity (Luk, 1985;

Pelham, Fabiano, & Massetti, 2005; Platzman et al., 1992). Classroom observation methods are

more likely than laboratory methods to report significant attentional differences between children

with ADHD and their peers (Platzman et al., 1992). However, sensitivity and specificity have not

been sufficiently established to allow diagnosis based solely on an observation code, or any other

single indicator (Abikoff et al., 2002; APA, 2000; Luk, 1985). Shortcomings of direct

observation methods include increased time and labor commitments as well as a lack of

agreement on behavioral definitions and standardized observation techniques.

Attentional problems are a core feature of ADHD, defined behaviorally in the DSM-IV

by characteristics such as distractibility, organizational problems, daydreaming, and frequent

shifting from one unfinished task to another (APA, 2000). Definitions of attention in the

literature refer to preferential selection and processing of sensory information – inattention is

inferred by frequent shifts in activity, and behaviors that are not task-related (Bear, Connors, &

Paradiso, 2001; Platman et al., 1992). In direct observation studies, attention is most often

operationalized as on-task behavior (e.g., Abikoff et al., 2002; DuPaul & Rapport, 1993).

Definitions invariably involve the child physically looking at the teacher or seat work, but vary

considerably in the duration of consecutive focus needed to code the child as on- or off-task.

This operationalization is limited by the need for observable behavior, and it is noted that it is

possible to pay attention to auditory stimuli without visually focusing, just as the direction of

one's eyes may not indicate task attention (e.g., daydreaming). The former may be more relevant

in studies observing children during teacher-led lectures, whereas the latter may influence results

of studies observing children during structured seat work. In laboratory settings, on-task data has

also been used to characterize vigilance (e.g., omission errors in CPT studies; Chung et al.,

3

2005). In a natural classroom setting, however, vigilance is only one of myriad factors

influencing a child's ability to pay attention or remain on-task (DuPaul & Stoner, 2004; Whalen

et al., 1978). In addition, behavioral observations of children with ADHD performing a CPT task

are more highly correlated with direct observations of classroom on-task behavior than CPT

performance (Schatz, Ballantyne, & Trauner, 2001).

The Need for a Meta-Analytic Approach

Attentional problems in the classroom are quintessential symptoms of children with

ADHD, and are often the catalyst for clinical referrals (Pelham, Fabiano, & Massetti, 2005). A

meta-analytic review provides not only documentation of this phenomenon across studies, but

unequivocal quantification of the magnitude of these attentional difficulties compared to

typically developing children. It is an accepted tenet of attention-deficit/hyperactivity disorder

(ADHD) that affected children are off-task more frequently and/or for longer durations than their

peers. However, the magnitude of this difference varies considerably across studies. Two

previous review articles have found that the overwhelming majority of direct observation studies

report significant differences between children with ADHD and their peers (Luk, 1985; Platzman

et al., 1992). Both reviews described classroom and laboratory observation studies and found that

most studies reported significant differences between the attentive behaviors of ADHD and

normal children. Luk (1985) concluded that differences in task demands and classroom situations

influenced behavioral differences between hyperactive and comparison children, but noted that

both the specific factors and their relative effects on behavior remain unknown. Platzman and

colleagues (1992) used difference-of-proportion tests to examine whether levels of a particular

variable are more or less likely to result in significant between-group differences. They

4

concluded that significant between-group differences were more likely to occur using classroom

rather than laboratory observation, however, diagnostic source (i.e., parent, teacher, or physician

report) was unrelated to the number of studies reporting significant differences. Both reviews

noted the significant variability among behavioral coding schemes, participant age, and study

setting (Luk, 1985; Platzman et al., 1992). The reviews, however, were primarily descriptive in

nature and failed to quantify between-study differences or analyze potential moderator variable

effects on observed differences in attention between children with ADHD and typically

developing controls. Box score literature reviews (e.g., comparing the number of studies finding

versus failing to find significant group differences) do not consider study power, and results may

therefore inaccurately reflect the data (see Howard, Maxwell, & Fleming, 2000 for details and

specific examples of this phenomenon). Moderating variables warrant scrutiny because of their

potential to change the nature of dependent-independent variable relationships, with implications

for theory development, refinement, and refutation (Holmbeck, 1997).

The present meta-analysis of published and unpublished studies examines both the

magnitude and variability of observed differences in classroom attention (on-task behavior)

between children with ADHD and their peers using standardized effect size estimates. The

potential moderating relationship of sample characteristics, diagnostic methods, classroom

variables, and observational schema on the magnitude of observed attentional differences in the

classroom are discussed below and analyzed to determine whether the variability between effect

size estimates exceeds levels expected based on study-level sampling error.

5

Sample Characteristics

Despite the restricted range related to the limited number of female subjects in most

studies, gender differences in ADHD prevalence rates and theoretical differences in symptom

manifestation necessitated the inclusion of this variable in moderator analysis (Barkley, 1990).

Gender has been found to predict DSM-IV diagnostic subtype in most but not all studies of

children with ADHD, with females more likely to exhibit inattentive symptoms and males more

likely to display hyperactive behavior (Abikoff et al., 2002; Biederman & Faraone, 2004; Graetz,

Sawyer, & Baghurst, 2005; Newcorn et al., 2001; Seidman, Biederman, & Monuteaux, 2005;

Weiss, Worling, & Wasdell, 2003; Yang, Jong, & Chung, 2004). Cognitive differences have also

been found between girls and boys diagnosed with ADHD (e.g., Carlson, Lahey, & Neeper,

1986; Douglas, 1988). A recent large-scale study of nonreferred boys and girls, however, found

no gender differences on any variables of interest. These results suggest that some earlier

findings may be the result of referral bias rather than true gender differences in the expression of

ADHD symptoms (Biederman et al., 2005). Previous meta-analytic reviews of children with

physical disabilities, autism, and dyslexia have reported that matching on gender, age, and other

demographic variables significantly influences outcome measures (Lavigne & Faier-Routman,

1992; Mottron, 2004; van Ijzendoorn & Bus, 1994).

Diagnostic Methods

Comprehensive clinical interviews, including structured/semi-structured interviews and

data from multiple informants, are the gold standard for ADHD diagnosis, whereas rating scales

provide a time- and cost-efficient method of identifying children whose behaviors are similar to

those of children meeting formal diagnostic criteria (McClellan & Werry, 2000; Pelham,

Fabiano, & Massetti, 2005; Rutter & Graham, 1968). Defining samples based solely on a referral

6

appears to be the least face valid method of grouping, considering the myriad of disorders and

conditions featuring attention and behavioral problems as core or secondary features (APA,

2000). Extant research suggests that significant correlations exist between rating scale cutoff

scores and semi-structured clinical interview diagnoses, as well as between parent and teacher

rating scale scores (Biederman, Keenan, & Faraone, 1990; Hodges, 1993; McGrath, Handwerk,

Armstrong, Lucas, & Friman, 2004). However, significant unexplained variability exists between

both diagnostic tools and informants. For example, McGrath and colleagues (2004) reported

correlations of .72 and .55 between symptom endorsement on the Diagnostic Interview Schedule

for Children – Fourth Edition (DISC-IV; Schaffer, Fisher, Lucas, Dulcan, & Schwab-Stone,

2000) and Conners parent and teacher rating scales (Conners, Parker, Sitarenios, & Epstein,

1998), respectively. This finding suggests that between 48% and 70% of the variability in

symptom endorsement on the semi-structured clinical interview is not accounted for by Conners

rating scale data. The correlation between parent and teacher ratings is often reported to be

somewhat lower than these values, highlighting the importance of considering informant source

in the diagnostic process (e.g., McGrath et al., 2004).

Classroom variables

Situational variables have been found to affect hyperactive symptomatology in

classroom (Barkley, Copeland, & Sivage, 1980; Beck, Kotkin, & Swanson, 1999; Pelham,

Wheeler, & Chronis, 1998; Rapport, Murphy, & Bailey, 1982; Zentall, 1980) and other settings

(e.g., Luk, 1985; Sleator & Ullmann, 1981). In addition, the specific academic task may have

significant effects on the activity level and task attention of children with ADHD (e.g., Whalen et

al., 1978; Zentall & Meyer, 1987).

7

Observation methods

Coding. The complexity of the coding scheme represents a trade-off between the amount

of data collected and the potential for information processing limitations that may decrease data

accuracy (Markman & Notarius, 1987). Coding fewer behaviors during live sessions is

associated with improved reliability and interobserver agreement, and fewer recording errors

(Dorsey, Nelson, & Hayes, 1986; Harris & Lahey, 1982). Smith, Madsen, and Cipani (1981)

found no differences between continuous recording, interval recording, and recording by incident

on measured reliability or observed rate of behavior. The interval type used by Smith and

colleagues, however, did not correspond to any used by studies in the current analysis, whereas

the continuous and by incident types descriptively matched. Other studies suggest improved

reliability with the momentary (by incidence) compared to whole or partial intervals (Ary &

Suen, 1983; Bramlett & Barnett, 1993), and others support the use of predefined intervals

(Mehm & Knutson, 1987).

Duration. The influence of observation duration in the available literature is mixed.

Observation duration is significantly related to variables of interest in some (Leaper, Anderson,

& Sanders, 1998; Sahni, Schulze, & Stefanski, 1995) but not all (Gertz, Stilson, & Gynther,

1959; Rowley, 1978) studies in other areas. A previous meta-analytic review of expressive

behavior failed to find outcome differences based on duration of observation (Ambady &

Rosenthal, 1992), however, to our knowledge no studies of children with ADHD have directly

examined the influence of observation duration on recorded behavior. Studies examining the

time needed for reliable observational data, as measured by criterion codes and session

intercorrelations, differ in their conclusions regarding the total number of minutes and days

necessary. Some conclude generally that more observations are better, without specifying

8

minimum within- and between-day durations (Barton & Ascione, 1984; Leaper et al., 1998);

others specify a minimum of five day, 20-minute sessions (Doll & Elliott, 1994) or 30-minute

observations for three days (McKevitt & Elliott, 2005); and some fail to find an effect of

observation duration on outcomes (Rowley, 1978). Both the population of interest and the

frequency of target behavior likely influence the total observation duration needed for a reliable

and valid sampling of behavior (Haynes, 2001). The behavior of children with ADHD has been

described as consistently inconsistent, suggesting that longer observation periods may better

capture the attentional processes of these children (e.g., Rapport et al., 1982).

Observational variables, including the type and quantity of behaviors measured (Lorber,

2004; Mezulis, Abramson, Hyde, & Hankin, 2004); and subject sampling, including diagnostic

assessment method and informant, age, and gender, significantly moderate effect size estimates

in most (Bogg & Roberts, 2004; Connell & Goodman, 2002; Sergerstrom & Miller, 2004) but

not all (Lavigne & Faier-Routman, 1992) previous meta-analytic reviews in other areas.

Previous empirical studies support the influence of methodological variables, however,

the relative impact of these variables for understanding classroom attentional differences in

children with ADHD and typically developing children remains unknown (Luk, 1985).

Significant moderating variables of these children’s classroom attention will be used to predict

the overall mean effect size that would be expected if all direct observation studies employed the

most rigorous methodology (Lipsey & Wilson, 2001). These metrics facilitate conclusions

concerning overall differences in classroom attention between children with ADHD and their

peers under ideal observation conditions.

9

CHAPTER TWO: METHODOLOGY

Literature Searches

A three-tier literature search was conducted using PsycInfo, PsycArticles, ERIC,

Dissertation Abstracts International, and Social Science Citation Index. Search terms included

permutations of the ADHD diagnostic label (ADHD, ADD, attention deficit, hyperactivity,

hyperkinesis, minimal brain dysfunction/damage, MBD), class*, observ*, behav*, school, direct,

attention, and on/off-task, where asterisks serve as wildcards (e.g., observ* will return studies

with the word observation, observations, observer, etc.). Separate searches were conducted to

find rating scale validation studies (additional search terms: rat*, reliab*, valid*, scale develop*)

and single case design studies (additional search terms: functional analysis, case study, single

subject). Searches were conducted with and without an ADHD search term included. Searches

were conducted independently by two researchers (MJK and RMA), and repeated until no new

studies were located. To further expand the initial study base, table of contents searches of the

following journals likely to publish classroom observation studies of children with ADHD were

undertaken: Journal of Applied Behavior Analysis, Behavior Modification, Behavior Research

and Therapy, Behavior Therapy, Journal of School Psychology, School Psychology Review, and

Journal of Attention Disorders. After the initial searches, studies cited by articles meeting

inclusion criteria were examined (Tier II backward search), and a forward search (Tier III) was

conducted using the Social Science Citation Index to locate studies citing those meeting

inclusion criteria. These procedures generated 509 dissertations, peer-reviewed studies, and

unpublished manuscripts written since 1962.

10

Inclusion and Exclusion Criteria

Inclusion/exclusion criteria were applied after the computerized searches were

completed. They were not entered as search delimiters to avoid missing studies due to database

misclassification. Inclusion and exclusion criteria are described below, with the number of

studies omitted for each criterion in parentheses. The following served as inclusion criteria for

the review: (a) an independent direct observation of children exhibiting inattentive, hyperactive,

and/or impulsive behavior in an elementary classroom setting (204); (b) between six and 12

years of age (24); (c) on- or off-task frequency or duration data reported, or statistics reported in

between group studies from which effect size can be estimated (94); and (d) low average or

higher estimated intelligence (16). Exclusion criteria included: (a) comorbidity with other mental

health disorders beyond Learning Disabilities (LD), Oppositional Defiant Disorder (ODD), or

Conduct Disorder (CD) (11); (b) stimulant or psychotropic medication taken during observation,

or no pretreatment baseline condition (74); and (c) repeat data (e.g., study published in journal

and as book chapter; follow-up longitudinal study (4). Single case design studies were included

if any subject(s) for which individual data was reported met inclusion criteria. Studies reporting

only placebo (i.e., no medication-free baseline) conditions were excluded based on research

demonstrating significant differences in functioning between baseline and placebo conditions in

children with ADHD (e.g., Rapport, Denney, DuPaul, & Gardner, 1994). Twenty-three studies

published from 1969 to 2004 met these search criteria and included a typically developing

comparison group1. An additional 59 single case design studies published between 1962 and

2005 met the above criteria.

1 Special thanks to Carmen Himmerich for translating Lauth & Mackowiak (2004) from German.

11

Coding of Moderators

Demographic and methodological variables are shown in Table 1 and described below.

Categorical variables were coded chronologically in the order listed below, such that higher

values are associated with an addition to the variable in question (e.g., adding matched controls,

diagnostic tools, observation time).

Sample

Total N. Sample size was used when both adjusting inflated effect sizes (i.e., Hedges' g

correction) and weighting effect size contribution to analyses (i.e., inverse variance weights).

Sample size was not coded as a moderator because further analysis would violate the variable

independence assumption (Lipsey & Wilson, 2001).

Percent male. The proportion of male subjects was coded as (a) predominately or all

male, or (b) 10% or more females, based on a previous meta-analytic review of predominately

male samples (Mezulis et al., 2004).

Matching. Three matching variables were coded. Gender, age/grade, and classroom were

each divided into two groups based on whether or not the researchers matched on each variable.

Diagnostics

Method. Diagnostic method was coded as: (a) referral for behavior problems with no

further diagnostics; (b) single informant rating scale (i.e., parent or teacher); (c) multiple

informant rating scales (i.e., parent and teacher); or (d) structured or semi-structured clinical

interview. All but one study using a diagnostic structured/semi-structured clinical interview also

included rating scales from multiple informants – the exception included a parent, but not a

teacher rating scale (Roberts, 1990). All but four studies used at least one Conners (Conners et

12

al., 1998) or Achenbach (i.e., Child Behavior Checklist/Teacher Report Form; Achenbach &

Rescorla, 2001) rating scale. The four exceptions were all published before 1978.

Moniker. Diagnostic label was coded as (a) behavioral problem children; (b) hyperactive;

or (c) ADD-H/ADHD. ADD-H and ADHD groups were combined due to research suggesting

that children diagnosed as ADD-H based on the DSM-III typically meet DSM-IV ADHD

Combined Type criteria (APA, 1987/2000; August & Garfinkel, 1993; Garfinkel & Amrami,

1992). Skansgaard and Burns (1998) reported separate results for DSM-IV Combined and

Inattentive Types. The Combined Type data were used to match the subtype used in other

included studies. The sole study not to specifically exclude children only meeting inattentive

criteria was Atkins, Pelham, and Licht (1985), whose nonhyperactive subjects represent less than

20% of his total sample2. As expected, diagnostic moniker was significantly correlated with year

of publication (r = .83). Inspection of the data reveals that only two studies (Book & Skeen,

1987; Roberts, 1990) fail to adhere to the following pattern: Studies published between 1969 and

1975 referred to their experimental group as Behavior Problem Children, research published

between 1977 and 1980 referred to them as Hyperactive, journal articles from 1984 to 1993 used

the ADD-H label, and studies from 1997 to 2004 use the current ADHD moniker. A correlation

between year of publication and DSM version was not computed because only nine of the 23

studies explicitly reported the DSM version used. Publication year was not included in moderator

analysis due to redundancy with diagnostic moniker (see footnote in results section).

2 Diagnostic subtypes are used in the DSM-III and DSM-IV, but not the DSM-III-R

13

Classroom variables

Type. Studies were coded into two categories: (a) normal classroom environments with

the child's regular teacher and classmates; and (b) simulated classrooms in research settings.

Activities. The classroom activity was reported to be seat work (12), or a combination of

seat work and teacher lecture (8) in 20 of the 23 studies, with three studies not reporting. The

specific activity in which the children were engaged (e.g., structured vs. unstructured academic

tasks; math vs. language arts) was reported in only two studies, precluding inclusion as a

moderator.

Observation variables

Total duration. Observation duration was coded as (a) one to 10 minutes per observation

session; (b) 11 to 20 minutes; or (c) 21 or more minutes.

Intervals. Within-observation intervals refer to the period of time spent observing before

coding the behaviors of interest. Observation interval was coded as (a) one to 10 seconds of

observation; (b) 11 or more seconds prior to coding, or (c) by incidence. The later category

includes studies that coded each behavior as it changed rather than subdividing the total

observation period into intervals.

Days of observation. Days of observation were coded as (a) one or two days of data

recording; (b) three or four days of direct observation per subject, or (c) five or more days. For

studies reporting a range of observation days, the minimum number of days for any subject was

used.

Number of behaviors coded. The number of behaviors simultaneously coded by study

observers was coded to retain groups of approximately equal size: (a) one to six, (b) 7-12, (c) 13-

20, or (d) more than 21 behaviors.

14

Number of observers. The total number of observers collecting data for a study was not

reported in nine of the 23 studies (39.1%), precluding inclusion in weighted regression analysis

requiring listwise deletion of missing data (i.e., including this variable would have decreased by

eight the number of studies analyzed, thus severely decreasing power and generalizability). This

variable will be analyzed using the analog to ANOVA technique to determine any systematic

relationship between the number of observers and obtained effect size.

Definition of off-task. Definition of off-task behavior refers to the minimum duration a

child must be off-task to be coded as such. This category was coded as (a) partial interval, where

time equal to less than a defined observational interval (as defined above) must pass before a

child is coded on-/off-task (e.g., DuPaul & Rapport, 1993, define 15-s coding intervals, during

which a child must be off-task for two or more consecutive seconds to be coded off-task for that

interval); (b) whole interval, where the subject must be off-task the entire interval, or; (c) per

incident, for studies coding each behavior change as it occurs. If a study coded children as on-

task only when they were engaged appropriately during the entire interval, the definition of off-

task was coded as partial – logically if the child were off-task at any point during an interval, he

would not have been coded as on-task.

Observation method. Observation method was coded dichotomously. The alternating

category includes studies observing ADHD and control children on each day in a multiple

subsequent pattern. For example, the Classroom Observation Code used by Abikoff and

colleagues (1977, 1980, 1984, 1985, & 2002) requires a four-minute observation of an ADHD

child followed by four minutes observing a comparison child, repeated until 16 minutes of data is

collected for each child. The continuous category includes studies observing one child for the

15

full duration reported. The total number of days and minutes each child is observed using either

method is equal.

Computation of Effect Sizes

Hedges' g

Hedges’ g (1982) effect sizes were calculated to estimate the magnitude of differences in

observed attention between children with ADHD and control children in classroom settings.

Hedges' g provides a correction to the standardized mean difference effect size to correct for the

upward bias of studies with small sample size. Conceptually, an effect size is the amount of

difference in standard deviation units between comparison groups (Lipsey & Wilson, 2001). An

effect size of 1.0 indicates that the experimental group on average scored one standard deviation

higher than the comparison group on the outcome in question. Effect sizes (ES) are categorized

as small (ES ≤ 0.30), medium (ES ≈ 0.50), and large (ES ≥ 0.67) effects. These values are based

on an analysis of over 300 published meta-analyses, and are used in lieu of those originally

proposed by Cohen (1977). Means, standard deviations, and sample sizes were used to compute

Hedges’ g for 17 studies (77%). An additional four studies (18%) provided sample size and p- or

t-values needed for estimation, and one effect size was calculated using between-group

difference and pooled standard deviation (Solanto et al., 2001). DuPaul, Ervin, Hook, and

McGoey (1998) reported individual means for subjects, which were used for the calculation of

group means and standard deviations. The Comprehensive Meta-Analysis software package was

used to calculate effect sizes. Mean effect sizes were weighted by their inverse variance weight

(w) during subsequent analyses to correct for imprecision associated with larger standard error,

such that each study contributes in proportion to its sample size (Lipsey & Wilson, 2001).

16

Multiple effect sizes

Three studies reported data sufficient to calculate multiple effect sizes. Only one effect

size was used for each study to meet the independence assumption (Lipsey & Wilson, 2001).

Abikoff et al. (2002) reported separate descriptive statistics for males and females (N = 806 and

198, respectively), which were pooled based on recommendations by Lipsey and Wilson (2001).

Zentall (1980) provided data for both off-task frequency and duration. Duration data were used

to match the data format reported in the majority of studies. Jacob, O'Leary, and Rosenblad

(1978) provided data for both formal and informal classroom settings. The formal classroom data

was chosen because it consisted of teacher-led assignments typical of a normal classroom setting.

Nonsignificant results

Shecket and Shecket (1976) did not provide data sufficient to calculate effect size, but

reported no significant between-group differences. An effect size of 0.00 was adopted for this

study to minimize Type I error consistent with recommendations addressing the file drawer

problem (i.e., publication bias favoring large, statistically significant effects; Rosenthal, 1995).

This method is conservative in nature and may artificially decrease effect size estimates.

Excluding such studies, however, may result in artificially small p-values and larger effect size

estimates. The fail-safe N was computed to determine the potential bias of unpublished studies.

This procedure estimates the number of studies with a 0.0 effect size that would be needed to

decrease the overall mean effect size such that its confidence interval contained 0.0 (i.e., overall

no significant differences between study groups). A rank correlation test was also used to further

analyze the potential effects of publication bias (described below; Lipsey & Wilson, 2001).

17

Data Analysis

A four-tier data analytic strategy was adopted. In the first tier, Hedges’ g effect sizes

were calculated for the variable off-task in each study. Studies reporting on-task percentages

were converted to off-task using the formula 100 – on-task %. Studies reporting number of

intervals on-/off-task were converted to percentage off-task by dividing by the total number of

intervals. An initial overall mean effect size was computed for fixed effects and tested for

homogeneity using the Q statistic to determine whether the amount of variance between studies

could be attributed to random, study-level error variance (Lipsey & Wilson, 2001). A significant

Q rejects the assumption of homogeneity and requires further analysis.

Inverse variance weights (wi = 1/SE2) were calculated for each effect size in the second

tier. These values cause studies with larger sample sizes, and hence less sampling error, to be

weighted more heavily than studies with smaller sample sizes and larger standard errors. A fixed

effects weighted regression approach using SPSS for Windows 12.0 was adopted3. This approach

assumes that additional variance is systematically related to measured study variables. It

provides two measures of overall fit: QR, which reflects variance accounted for by the regression

model (p degrees of freedom, where p equals the number of predictors); and QE, variance

unaccounted for by either the model or random, study-level sampling error (k – p – 1 degrees of

freedom, where k equals the number of studies). Both statistics are distributed as chi-square.

3 Note: the QB and QW analog to ANOVA technique reported in many meta-

analytic reviews was not used for primary analyses because it inflates Type I error when used

with several moderator variables – see Lipsey & Wilson, 2001 for discussion of this problem.

18

Corrected B-weight standard error for each moderator variable was calculated and each variable

was tested against the z-distribution (Lipsey & Wilson, 2001).

In the third tier, a modified regression equation using the significant moderators from tier

two was created to estimate the mean effect size that would likely have been obtained had all

studies employed the best case combination of moderator variables found to significantly

influence study results. The regression equation is solved by selecting the value of each

moderator corresponding to empirically validated best practice (Lipsey & Wilson, 2001). To

further explicate the results, original metric scores were calculated for each group to examine the

difference in frequency of off-task behavior between children with ADHD and typically

developing controls by adding the product of the overall weighted mean effect size and the

control group standard deviation to the overall weighted mean of the control groups (Lipsey &

Wilson, 2001). This procedure is similar to computing standard difference scores for raw data –

it translates the effect size onto the control group distribution (e.g., Losier, McGrath, & Klein,

1996). Power analysis was conducted using GPower (Faul & Erdfelder, 1992) to determine the

minimum number of subjects future studies will likely need to detect between-group differences

in classroom attention given the best case effect size. Power analysis was used not as a

theoretically suspect a posteriori analysis (i.e., there is no chance of a Type II error if obtained

results are significant; Cohen, 1992), but as a first step for future research of the classroom

attention of children with ADHD.

In the final tier, the 59 single case/case study design studies were analyzed and compared

to the 23 between-group studies to determine the generalizability of the results obtained from the

above procedures. Demographic and methodological variables and ADHD off-task rates for

single case design studies are shown in Table 7. Three analyses were completed. Weighted

19

means for both groups of studies were compared using a t-test, power analysis, and area under

the curve calculations. The Jacobson and Truax (1991) model of clinical significance informed

this analysis, and computations were repeated after accounting for differences in the number of

studies in each group. The second analysis involved the calculation of effect sizes for the six

studies including a typically developing contrast group. Effect sizes could not be calculated for

the vast majority (56 of 61) of these studies because they did not include a normal control group.

In all six cases, peer group sample size was unreported and set equal to the number of children

with ADHD to provide rough estimates of effect size. Obtained effect sizes were compared to the

effect sizes predicted by the regression equation obtained in the between-group analysis. The

third analysis compared between-group and single case studies using a series of ANOVAs for

each of the significant moderators found in the between-group analyses. Bonferroni corrections

for multiple comparisons were employed.

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CHAPTER THREE: RESULTS

Tier I: Moderator-independent attentional differences

Publication Bias: The File Drawer Problem

A Fail-safe N analysis was computed to determine the likelihood that missing studies

would significantly influence the obtained mean effect sizes (Lipsey & Wilson, 2001; Rosenthal,

1991). Results indicate that an unlikely 1,829 studies would be needed to reduce the confidence

interval of the mean effect size to include zero (i.e., result in no significant differences between

off-task rates of children with ADHD and typically developing children in the classroom). A

rank correlation test (Begg & Mazumdar, 1994) for publication bias was non-significant,

Kendall's tau b = 0.155, p = 0.15. The Trim and Fill procedure suggests that zero studies are

missing from the analysis based on expected symmetry when plotting effect sizes by the inverse

of their standard errors (Duval & Tweedie, 2000). Collectively, these analyses suggest that the

effect of publication bias is minimal in the current meta-analysis.

Off-task Comparisons

Off-task rates are shown in Table 2. Children with ADHD were off-task an average of

28.15% across studies (range = 5.11% to 83.0%), compared with 14.96% (range = 0.78% to

70%) for control children for the 20 studies reporting this data. All but two studies reported

significant between-group differences (Cunningham & Siegel, 1987; Shecket & Shecket, 1976).

At the group level, children with ADHD were more variable than control children across studies.

The mean standard deviation across studies was significantly greater for ADHD groups than

control groups, t(16) = 4.76, p < .0005.

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Effect sizes

Effect sizes (ES), standard error of effect size (SEES), and standard difference scores

(SDS) are shown in Table 2. Mean weighted effect size for the 23 studies was 0.71 (95% CI =

0.62-0.79) with a range of 0.00 to 2.23. This result corresponds to a large effect based on Lipsey

and Wilson (2001) criteria. The overall test of homogeneity suggests that there is more variance

among the effect sizes than would be predicted by study-level error alone (Q = 119.81, p <

.0005). A stem and leaf representation of the distribution of effect sizes is shown in Table 3.

Tier II: Moderators of obtained effect size

Fixed Effects Model

Analysis of moderator variables was conducted on 15 studies reporting data for all

variables of interest. An ANOVA was conducted to determine whether there were systematic

differences between studies reporting all data and studies with missing data. Results indicate no

significant differences for effect size (F = 0.66, p = .43) or any moderator variables except

classroom matching (F = 7.34, p = .013) and diagnostic method (F = 9.05, p = .007). Classroom

matching, but not diagnostic method, was no longer significant after correcting for the number of

comparisons. Studies with missing diagnostic data include all four studies diagnosing based

solely on a referral for behavior problems, and two of the seven (29%) studies using single

informant rating scales. When the behavior problem code was eliminated, no significant

differences remained (F = 0.351, p = .56). Diagnostic method was therefore retained in the

model, with the code for referral deleted.

A fixed effects weighted regression model was adopted to examine the influence of

potential moderator variables on the observed variability in effect sizes. Results of the fixed

22

effects weighted regression reveal that the moderator variables described below predicted a large

amount of the variance in effect sizes (adjusted R2 = .91; QR = 91.93, p < .001; QE = 0.58, p =

ns). QR is analogous to the F-test for the χ2 distribution, wherein a significant QR indicates that

the model predicts significant variability in the effect sizes (Lipsey & Wilson, 2001). A

nonsignificant QE indicates that only subject-level sampling error remains across effect sizes

after accounting for variability explained by the model (Lipsey & Wilson, 2001). The influence

of each study variable is described below.

Sample

Total N. Sample size was reported in all 23 studies, and ranged from 16 to 1004 children,

with a median of 56 children. Sample size was used in the weighting of effect sizes and was

therefore not analyzed further.

Percent Male. Gender was reported in 20 of 23 studies, with males representing 84.6%

(range = 33% to 100%) of all subjects. The proportion of male to female subjects was not a

significant moderator in the regression analysis (z = 1.50, ns).

Matching. ADHD and control children were matched on two or more variables in 14 of

the 23 studies (61%). One study matched only on age (Werry & Quay, 1969), and seven studies

did not report matching on any variables. Fourteen (61%) studies matched based on sex, seven

(30%) based on age, 10 (44%) by grade in school, and seven (30%) by classroom. Age/grade (z

= -4.58, p < .01) and classroom matching (z = -2.45, p < .01) were both significant predictors of

effect size in the regression. Matching on these variables was associated with smaller effect

sizes. Gender matching was not used in the final regression due to lack of variability (see above).

An analog to ANOVA was conducted to determine the potential influence of gender matching on

23

obtained effect sizes (Lipsey & Wilson, 2001). Results reveal that gender matching did not

account for significant differences in effect sizes (QB = 2.90, p = .09; QW = 19.22, p = .57).

Mean Age. Mean age (8.36 years, range of means = 7.2 to 9.6 years) was reported for

both ADHD and control groups in 20 of the 23 studies (87%). Age mean was not analyzed as a

moderator due to the restricted range across studies.

Diagnostics

Method. Four general methods were used to diagnose subjects, including formal semi-

structured diagnostic interviews (26%), multiple informant rating scales (i.e., parent and teacher;

22%), single informant rating scales (teacher only, 26%; parent only, 4%) and referral from

teacher (13%) or pediatrician (4%) without subsequent reported rating scales/clinical interviews.

One study did not report diagnostic method (Werry & Quay, 1969). Of the five studies using

diagnostic clinical interviews with the parent, four (80%) also used rating scales from multiple

informants, and one added a parent rating scale. Rating scales were used in a majority of studies

(N=17; 74%) to select sample participants. Diagnostic method significantly predicted effect size

in the model (z = -2.43, p < .05), with more stringent diagnostic procedures associated with

smaller effect sizes.

Moniker. The ADHD/ADDH moniker was used in eight of 23 (35%) studies, 12 of 23

(52%) studies used the term hyperactive or hyperkinetic, and two studies described their sample

as behavioral problem children (9%). Diagnostic moniker significantly predicted effect size (z =

3.19, p < .01) after eliminating the behavior problem code due to missing data (see above), with

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studies using children with ADDH/ADHD reporting larger effect sizes than studies of

hyperactive children4.

Classroom type

Children were observed in their regular classroom environments (78% of studies) or

simulated classrooms (22%). Classroom type was linearly related to obtained effect size (z = -

1.69, p < .05), with regular classroom settings associated with larger effect sizes than simulated

classrooms.

Observation Variables

Total duration. Children's behavior was coded by trained observers for periods ranging

from 10 to 90 minutes, with a median of 16 minutes. Total duration of the observation interval

(observation length) was reported in 22 of the 23 studies. Total daily duration was not linearly

related to observed differences between time on-task for children with ADHD and their peers (z

= 1.07, ns).

Intervals. Within each session, observation intervals lasted between two and 20 seconds

before recording, with a median of 15 seconds across studies. Three studies (13%) coded by

incident and did not divide the observation session into intervals. Interval duration was linearly

related to obtained effect size (z = 2.66, p < .01), with longer intervals before coding associated

with larger effect sizes.

4 Diagnostic moniker and publication year were not entered into the model together because the high correlation between these variables violates the assumption of heteroscedasticity. Publication year was therefore omitted from the model. Rerunning the weighted regression model substituting publication year for diagnostic moniker did not change the significance of any variable or the direction of any variable's B-weight. Publication year was not a significant predictor, B-weight = 0.137, p > .05, in the alternate model.

25

Days of observation. Total sessions ranged from one to 10 days of observations, with a

median of 3 days across studies (100% reporting). Days of observation was significantly and

linearly related to observed group differences, with more days associated with larger effect sizes

(z = 4.36, p < .01).

Number of behaviors coded. Behavioral ratings by trained observers ranged from simple

recording schemes involving as few as one category (DuPaul & Rapport, 1993) to complex

observations of as many as 96 (Skansgaard & Burns, 1998) distinct behaviors across the 15

studies reporting. Many of the observers (56% of studies) were simultaneously observing 10 or

more behaviors. The mean number of behaviors observed was 14.5, with a median of 11

observations per session across the 22 studies reporting this data. The number of behaviors coded

simultaneously was significantly related to effect size, with greater numbers of behaviors

associated with larger effect size (z = 2.94, p < .01).

Number of observers. A mean of 3.71 observers were used in each of the 14 studies

reporting this information. Number of observers was not reported in nine of the 23 studies and

was therefore not included in the regression analysis, as weighted regression in SPSS requires

listwise deletion of missing data and inclusion would significantly decrease power. An analog to

ANOVA computed with available data suggests that the number of observers is not significantly

related to obtained effect size (QB = 0.96, p = .33; QW = 8.24, p = .77).

Definition of off-task. Some studies required children to remain off-task for an entire

defined interval to be coded off-task, some coded off-task only if it occurred during part of a

predefined interval, and others coded per incident without predefined intervals (nine, eight, and

two studies, respectively, with four studies not reporting). The length of time a child must be off-

26

task to be coded as such was not significantly related to obtained effect size differences between

children with and without ADHD (z = 1.33, ns)

Observation method. Eight studies used a continuous observation schema, whereas 14

alternated coding between experimental and control children within the same observation period.

One study did not report observation method. Observation method predicted the magnitude of

obtained effects, with continuous observation associated with smaller effect sizes (z = -5.94, p <

.01).

Tier III: Best Case Estimation and Original Metric

Best Case Estimation

Values corresponding to age/grade and classroom matching, regular classroom settings,

longer observation intervals, use of the ADHD moniker based on semi-structured clinical

interviews and multiple informants, fewer behaviors simultaneously coded, continuous

observation schemes, and greater total days of observation were selected based on best practice

for each significant predictor in the regression equation (Lipsey & Wilson, 2001). Solving the

regression equation using these values and corresponding B-weights (Table 4) suggests that an

effect size of 1.40 would be expected on average for studies employing this combination of

observational and diagnostic methodology, sample and classroom characteristics.

Power Analysis for Future Studies

GPower (Faul & Erdfelder, 1992) was used to determine the minimum sample size

needed by future researchers to detect significant between-group differences in observed

classroom attention of children with ADHD compared to their peers. With the best case effect

27

size of 1.4, an alpha of .05, and power of .80 as recommended by Cohen (1992), eight total

subjects (four per group) are needed to detect between-group differences.

Original Metric

Across studies, typically developing children were off-task an average of 14.96% (SD =

16.47). An effect size of 1.40, based on the above estimation of the expected average effect size

for research employing the most rigorous study design, corresponds to a 38.02% off-task average

for children with ADHD. In other words, one would expect typically developing children to be

on-task an average of 85.04% of the time, compared to 61.98% for children with ADHD. A

graphical representation of this relationship is depicted in Figure 1.

Tier IV: Single Case Design Studies

Mean Differences

A total of 1,174 and 496 children with ADHD were observed in between-group and

single case design studies (SCD), respectively. Children with ADHD were off-task a weighted

average of 28.15% (SD = 18.28) in the 23 between-group studies, compared to 49.34% (SD =

16.41) in the 59 single case design studies. This difference was significant, t(36) = -4.85, p <

.0005. The mean SCD off-task rate corresponds to 1.16 standard deviations above the between-

group mean, calculated as the difference between means divided by the between-group standard

deviation. Power analysis suggests that only 26 cases are needed to reject the null hypothesis

given a difference of this magnitude, for an alpha of .05 and power set at .80 (Cohen, 1992).

Because statistical significance is dependent on sample size, the results of the t-test may not

inform clinical significance (Jacobson & Truax, 1991). Therefore, the number of SCD cases

expected to equal or exceed 1.16 above the between-group mean was calculated to determine the

28

29

likelihood that the SCD studies come from a different population than the between-group studies.

With an average between-group sample size of 51.04, and a normal distribution assumed, one

would expect 6.28 subjects to meet or exceed 1.16 standard deviations above the mean,

calculated as 51.04 times the area under curve (AUC = .1230). Multiplying the average number

of SCD subjects by the area at or beyond the SCD mean (AUC = .5000; 8.13 x 0.5 = 4.08)

suggests that fewer subjects than expected actually score at or above 1.16 SD above the between-

group mean.

Effect Size Comparisons

Obtained and predicted effect sizes for the six SCD studies are displayed in Table 5.

Obtained effect sizes (M = 3.53, SD = 1.29) did not differ significantly from effect sizes

predicted by the regression equation, t(9) = 1.67, p = .117, ns. It is noted that the small number of

studies analyzed may have influenced the failure to find statistically significant differences.

Moderator Comparison

Seven Bonferroni-corrected ANOVAs were conducted to analyze potential differences

between between-group and SCD studies. Results are shown in Table 6. The studies differed on

three of the variables found in Tier II to significantly influence observed differences between

children with ADHD and typically developing children. In the Tier II regression equation,

observation method is associated with smaller effect sizes, whereas the number of behaviors

coded and number of observation days predicted larger magnitude differences.

Table 1: Demographic and Methodological Variables

Match Ctrl

Study Total

N Age

Mean Male/Female

(% Male) S A G C Classroom

Type

Observation Duration

(min)

Observation Interval

(sec)Werry & Quay (1969) 21 8.92 21/0 (100) Y Simulated 15 20 Forness & Esveldt (1975) 48 7.2 48/0 (100)

Y Y Y

Regular 10 6

Shecket & Shecket (1976) 36 NR

NR Regular 20 10Abikoff et al. (1977) 120 8.17 112/8 (93.3) Y Y Regular 16 15 Campbell et al. (1978) 31 7.67 26/5 (83.9) Y Y Regular 15 10 Jacob et al. (1978) 16 9.6 14/2 (87.5) Y Simulated

30 10

Klein & Young (1979) 34 NR 34/0 (100) Regular 90 10Abikoff et al. (1980) 119 8.4 114/5 (95.8) Y Y Regular 16 15 Zentall (1980) 62 7.3 62/0 (100) Y Y Y Y Regular 10 Not dividedAbikoff & Gittelman (1984) 56 8.5 54/2 (96.4) Y Y Regular 16 15 Abikoff & Gittelman (1985) 56 8.17 54/2 (96.4) Y Y Regular 16 15 Atkins et al. (1985) 47 9.14 34/13 (72.3) Y Y Y Regular NR 2 Book & Skeen (1987) 162 NR NR Y Y Regular 45 Not dividedCunningham & Siegel (1987) 60 8.71 60/0 (100) Y Y Simulated 15 5 Roberts (1990) 33 8.92 33/0 (100) Simulated 15 Not divided

DuPaul & Rapport (1993) 56 8.32 46/10 (82.1) Regular 20 15Lett & Kamphaus (1997) 55 7.94 41/14 (74.5) Regular 15 3 (27sec recording)

Nolan & Gadow (1997) 68 8.8 62/6 (91.2) Regular 30 10DuPaul et al. (1998) 26 7.7 21/5 (80.8) Y Y Y Regular 18 15 Skansgaard & Burns (1998) 24 NR 8/16 (33.3) Y Y Y

Regular 10 5

Solanto et al. (2001) 112 8.45 96/16 (85.7) Regular 16 15Abikoff et al. (2002) 1004 8.4 806/198 (80.3) Y Y Regular 16 15 Lauth & Mackowiak (2004) 110 8.47 76/34 (69.1) Y Y Y Y Regular 45 NR

Study

Obs/Day

Number of Bx Coded

Number of Observers

Definition of Off-task

Diagnostic Method

Diagnostic Moniker

Observation Method

Werry & Quay (1969) 3 17 6 Partial >5s NR Bx Prob Continuous Forness & Esveldt (1975) 6 5 6 NR TR Bx Prob Alternating Shecket & Shecket (1976) 4 19 1 NR TR

Hyperactive AlternatingAbikoff et al. (1977) 3 13 5 Whole >15s RS-M Hyperactive Alternating

Campbell et al. (1978) 2 5 NR NR PR Hyperactive NR Jacob et al. (1978) 10 6 NR Partial RS-T Hyperactive AlternatingKlein & Young (1979) 2 17 2 Whole RS-T Hyperactive AlternatingAbikoff et al. (1980) 3 12 4 Whole >15s RS-M Hyperactive Alternating Zentall (1980) 5 6 3 Partial >10s RS-T Hyperactive AlternatingAbikoff & Gittelman (1984) 3 12 7 Whole >15s RS-M ADDH Alternating Abikoff & Gittelman (1985) 3 12 7 Whole >15s RS-M ADDH Alternating Atkins et al. (1985) 7 32 2 Partial RS-T ADD AlternatingBook & Skeen (1987) 1 4 NR Incidence TR Bx Prob ContinuousCunningham & Siegel (1987) 1 16 2 Partial RS-P ADDH Continuous Roberts (1990) 1 6 NR Incidence SSI/RS-P Hyperactive ContinuousDuPaul & Rapport (1993) 1 1 NR Partial >2s SSI/RS-M ADD Continuous Lett & Kamphaus (1997) 1 13 NR Partial P&T RS ADHD ContinuousNolan & Gadow (1997) 3 5 3 Whole SSI ADHD Alternating DuPaul et al. (1998) 3 4 2 NR SSI/RS-M ADHD ContinuousSkansgaard & Burns (1998) 4 96 2 Whole RS-T ADHD Continuous Solanto et al. (2001) 1 12 NR Whole >15s SSI/RS-M ADHD Alternating Abikoff et al. (2002) 3 12 NR Whole >15s SSI/RS-M ADHD Alternating Lauth & Mackowiak (2004) 3 8 NR Partial RS-T ADHD Alternating

31

Notes. A = Age; Alternating = abab or similar method; Bx Prob = Referred for behavioral problems/hyperactivity; C = Classroom; Continuous =

Observing one child exclusively for entire observation period; Definition of Off-task = Proportion of interval needed to be coded off task; F = Females; G

= Grade; M = Multiple informants; No of Bx Coded = Number of behaviors coded; NR = Not reported; Obs/Day = Number of observation days;

Observation Duration = Minutes of observation; Observation Interval = Seconds before recording; P = Parent rating scale Only; PR = Pediatrician referral;

RS = Rating scale(s); S = Sex; SSI = Semi-structured interview; T = Teacher rating scale only; TR = Teacher Referral.

32

Table 2: Mean Off-task Rates, Standard Difference Scores, and Effect Sizes in Children with ADHD

Study

ADHD % Off-task

M (SD)

Control % Off-task

M (SD)

Std. Diff. Scores (%)

Hedges’ g Effect Sizes (Std. Error)

Werry & Quay (1969) 46.3 (12.8) 23 (15.4) 50.3 2.09 (0.53) Forness & Esveldt (1975) 47.0 (16.5)

34 (12.4) 27.7 0.88 (0.30)

Shecket & Shecket (1976) NR NR

NR 0.004

Abikoff et al. (1977) 13.1 (10.0) 2.1 (2.6) 84.2 1.50 (0.21) Campbell et al. (1978) 16.73 (15.15) 12.41 (10.88) 25.8 0.32 (0.35) Jacob et al. (1978) 15.8 (NR) 10.5 (NR) 33.3 1.41 (0.53)3

Klein & Young (1979) 39.8 (9.0) 26.6 (5.0) 33.1 1.78 (0.40) Abikoff et al. (1980) 15.1 (23.4) 4.1 (7.8) 72.8 0.62 (0.19) Zentall (1980) 15.0 (NR) 7.1 (NR) 52.2 0.45 (0.25) Abikoff & Gittelman (1984) 17.4 (12.3) 3.5 (6.6) 79.7 1.39 (0.29) Abikoff & Gittelman (1985) 15.7 (10.4)

2.5 (4.6) 84.1 1.71 (0.31)

Atkins et al. (1985) NR NR NR 0.59 (0.30)1

Book & Skeen (1987) 5.11 (4.82) 0.78 (1.47) 84.7 1.21 (0.17) Cunningham & Siegel (1987) 33.0 (NR) 26.4 (NR) 19.9 0.51 (0.26)2

Roberts (1990) 39.5 (18.8) 12.9 (20.9) 67.3 1.31 (0.39) DuPaul & Rapport (1993) 44.26 (16.56) 19.72 (11.56) 55.4 1.66 (0.31) Lett & Kamphaus (1997) 18.3 (16.5) 12.7 (12.7) 30.6 0.36 (0.29) Nolan & Gadow (1997) 30.5 (15.9) 13.3 (8.3) 56.4 1.34 (0.27) DuPaul et al. (1998) 33.0 (19.2) 9.5 (11.9) 71.2 1.31 (0.45) Skansgaard & Burns (1998) 23.8 (10.3)

4.8 (6.1) 79.8 2.23 (0.60)

Solanto et al. (2001) NR NR NR 0.58 (0.19)5

Abikoff et al. (2002) 10.6 (24.0) 3.3 (13.2) 68.8 0.38 (0.06) Lauth & Mackowiak (2004) 83.0 (12.0) 70.0 (13.0) 15.7 1.03 (0.20) Column M (SD) = 28.15 (18.28) 14.96 (16.47) 54.65 (23.71) 0.71 (0.04)6

33

34

Notes: 1Effect size calculated using N = 47, t = 2.01; 2Effect size calculated using N = 60, p = .052;

3Effect size calculated using N = 16, p = .01; 4Effect size set at zero – insufficient data to calculate effect size of

nonsignificant differences; 5Effect size calculated using differences in means (.007), common SD (.12), and N = 112;

6Standard error of effect sizes; SD = Mean of standard deviations for included studies.

Table 3: Stem and Leaf Display of 23 Hedges g Effect Sizes

Stem Leaf 2.2 2.1

2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

3 9 1, 8 6 0 1 1, 1, 4, 9 1 3 8 2 1, 8, 9 5 2, 6, 8 0

Note: A stem-and-leaf plot of classroom attentional differences. Effect

sizes are arranged by place value such that digits in the left column

represent the stem, while digits in the right columns represent the leaf.

For example, the effect sizes of .32, .36, and .38 are represented as .3 in

the left column, and 2, 6, and 8 in the right columns, respectively.

36

Table 4: Inverse variance weighted regression results

Model χ2 df χ2significance

QR 91.93 13 p < .001 QE 0.58 1 ns R2

Model = .997 Adjusted R2

Model = .912 Variable B-weight SEB Z-score Zsignificance

Age/Grade Matching -1.95042 0.426101 -4.57736 p<.01 Classroom Matching -2.02899 0.828252 -2.44973 p<.05 Percent Male 0.38487 0.255887 1.504063 ns Observation Duration (min.) 0.686629 0.640711 1.071667 ns Observation Interval (sec.) 1.212349 0.455464 2.661788 p<.01 Classroom Type -1.69 0.396484 -4.26247 p<.01 Definition Off-task 0.415595 0.311485 1.33424 ns Diagnostic Moniker 0.94618 0.296885 3.187027 p<.01 Observation Method -2.4322 0.409737 -5.93601 p<.01 Number of Behaviors Coded 0.857616 0.291726 2.939799 p<.01 Days of Observations 1.844816 0.423337 4.357798 p<.01 Diagnostic Method -0.78683 0.30749 -2.55886 p<.05 (Constant) 0.217208 1.56588 0.138713 ns Note: B-weight = unstandardized regression weight; SEB = standard error of the

regression weight.

37

Table 5: Predicted and obtained effect sizes for single case design studies

Study Predicted ES Obtained ES (SE)

Ward & Baker (1969) 3.54 3.16 (1.00) Glynn & Quinnell (1972) 2.74 5.81 (1.80) Walker & Hops (1976) 1.93 3.35 (0.76) Pelham et al. (1980) 3.28 1.82 (0.73) Kraemer (1994) D 0.80 3.52 (0.64) Fabiano & Pelham (2003) 2.20 3.52 (1.37) Mean (SD) 2.42 (1.00) 3.53 (1.29) Note: Comparison group sample size not reported in these studies. Obtained effect sizes

estimated by assuming an equal number of subjects per group. D = Dissertation; ES =

Effect size; SD = Standard deviations of means.

38

Table 6: Between group and single case design study moderator variable differences

Moderator F-value p-value

Observation Interval (seconds) 0.34 ns Diagnostic Moniker 5.18 ns Observation Method 9.39 .021 Number of Behaviors Coded 17.31 .004 Observation Days 11.98 .007 Diagnostic Method 1.84 ns Classroom Type 5.08 ns Note: p-value reported after Bonferroni correction (original p times

number of comparisons).

Table 7: Single Case Design Studies: Demographic and Methodological Variables

Study Total

N1 Age

Mean Male/Female

(% Male) Classroom

Type


(min)


(sec)

ADHD % Off-task

M (SD) Quay et al. (1967) 5 7.52 100% Regular 12.5 10 62% (NR)2

Hall et al. (1968) 6 NR 67%

Regular 30 10 60.33% (15.17) Ward & Baker (1969) 8 NR 75% Regular 15 20 74%

Buckley & Walker (1971) 44 NR 88.6% Regular 6 15 55% (NR) Glynn & Quinnell (1971) 6 NR NR Regular 35 10 53.67% (3.79)Axelrod et al. (1972) 1 NR 100% Regular 120 (2hrs)

120 (2 min)

40% (NR)2

Glynn & Thomas (1974) 9 NR 89% Regular 50 10 51.4%McCullough et al. (1974) 1 6 100% Regular 120 (2 hrs)

900 (15 min)

70.4% (8.17)2

Walker et al. (1976) 10 NR 90% Regular 12 15 64% (NR) Walker & Hops (1976) 24 NR 71% Regular 9 6 72.4% (5.85) Hay et al. (1977) 10 NR 100% Regular 10 10 46.30% (12.76)

Marholin & Steinman (1977) 8 NR 50% Regular 15 10 67%Walker (1977) D 12 NR 83.3% Regular 16 30 38.33% (10.96)Epstein & Goss (1978) 1 10 100% Regular 30 Momentary

75% (NR)

O’Leary & Pelham (1978) 7 8.67 NR Regular 15 10 53.38% (30.40)Rose (1978) 2 8 0% Regular 30 NR 25% (13.36)2 Loney et al. (1979) 24 NR 100% Regular NR NR 39% (NR) Friedling & O'Leary (1979) 4 7.58 87.5% Regular 10 15 44% (4.1%)Barkley et al. (1980) 6 8.35 100% Simulated 10 15 50.25% (9.18)2 Cameron & Robinson (1980) 3 7.8 66.7% Regular (SE) 25 10 43.31% (14.97)Pelham et al. (1980) 8 8.3 87.5% Regular 15 10 43% Rapport et al. (1980) 2 7.5 50% Regular 60 25 70.8% (12.27)2 Eastman & Rasbury (1981) 11 NR NR Regular 15 10 33.0% (11.00)Hallahan et al. (1981) 3 10.3 100% Regular (SE) 20 2 74.07% (7.13)2

Krause (1981) D 1 7 100% Regular 20 10 27%Roberts & Nelson (1981) 3 NR 100% Regular 30 10 41.53% (NA)Gettinger (1982) 8 7.9 50% Regular (SE) 30 10 82.1% (11.3)Hallahan et al. (1982) 1 8 100% Regular (SE) 20 4 54.38% (11.71)2

Lloyd et al. (1982) 3 9.33 66.7% Regular (SE) NR 2 48.93% (28.03)2

39

Study Total

N1 Age

Mean Male/Female

(% Male) Classroom

Type


(min)


(sec)

ADHD % Off-task

M (SD) Rapport et al. (1982) 2 7.5 100% Regular 20 10 58.5% (8.19)2 Rhode et al. (1983) 6 8.7 NA Regular

15 10 61.83% (10.65)Shepp & Jensen (1983) 1 7 100% Regular (SE) 60 10 38.0% (2.83)2 Rooney et al. (1984) 4 NR 50% Regular 20 2 86.95% (11.99)2

Rosen et al. (1984) 23 NR 73.9% Regular (SE) 30 10 20.26% (NR) Rapport et al. (1985) 12 NR 100% Regular 20 10 47% (NR)2 Burlton-Bennet et al. (1987) 1 6 100% Regular 30 10 74% (NR) Rapport et al. (1987) 42 8.5 88.1% Regular 20 15 46.5% (18.80)Walker (1989) D 6 NR 100% Regular 20 15 71.25% (9.01)Szymula (1990) D 8 NR 75% Regular 30 10 37.04% (NR) Bloomquist et al. (1991) 13 8.81 69.23% Regular 10 10 49.4% (10.0)

DuPaul et al. (1992) 1 7 100% Regular (SE) 15 30 58.5% (NR) Paniagua (1992) 1 10 100% Regular 10 30 88.6% (12.19)2 DuPaul & Henningson (1993) 1 7 100% Regular NR NR 61% Kraemer (1994) D 12 8.33 NR Regular 10 20 52.00% (8.88)Rapport et al. (1994) 76 8.51 86.8% Regular 20 15 44% (NR)2 Kelley & McCain (1995) 3 7.0 67% Simulated 45 15 53.67% Turner (1996) D 6 8.99 33.3% Regular 10 10 46% (NR)2 Matheson (1997) D 3 10 100% Regular 15 30 66% (range: 44-90%)

Sams (1999) D 4 NR 100% Regular 20 10 38% (NR)2 Cloward (2000) D 8 NR 63% Regular 10 6 26.16% (7.18)Swenson et al. (2000) 1 12 100% Regular (SE) 60 NA 41.33% (NR) Roberts et al. (2001) 3 NR 100% Regular 15 15 70.67% (NR) Northup & Gulley (2001) 1 NR NR Regular 5 10 10% (NR) Austin (2003) D 1 8.33 100% Regular 20 15 56% (NR) Fabiano & Pelham (2003) 1 9.92 100% Regular 50 1 33.22% (9.82) Lorah (2003) D 3 9.25 100% Regular 15 15 27% (NR)

Dobrinski (2004) D 4 8 75% Regular 20 10 56.38% (15.48) Trolinder et al. (2004) 2 2 50% Regular 20 10 47.15% (14.24)Clarfield & Stoner (2005) 1 7 100% Regular 10 15 49% (NR)

40

Study

Obs/Day

Number of Bx Coded

Number of

Observers Definition of

Off-task Diagnostic

Method Diagnostic Moniker

Observation Method

Quay et al. (1967) 10 1 1 Partial (any) TR Hyperactive AlternatingHall et al. (1968) 7 3 2 Partial >5s TR Bx Prob Continuous Ward & Baker (1969) 20 12 3 Partial (any) TR Bx Prob Continuous Buckley & Walker (1971) NR 21 NR NR T RS Bx Prob Continuous Glynn & Quinnell (1971) 10 8 4 Partial TR Bx Prob Alternating Axelrod et al. (1972) 5 1 NR Glance TR

Bx Prob ContinuousGlynn & Thomas (1974) 10 1 8 Partial TR Bx Prob Alternating McCullough et al. (1974) 5 1 NR Partial (any) TR Bx Prob ContinuousWalker et al. (1976) 10 11 11 NR T RS Bx Prob Alternating Walker & Hops (1976) 2 19 7 NR TR Bx Prob AlternatingHay et al. (1977) 7 2 3 Glance TR Bx Prob ContinuousMarholin & Steinman (1977) 7 3 2 Partial > 2s TR Bx Prob Alternating Walker (1977) D 10 1 2 NR PR Hyperactive ContinuousEpstein & Goss (1978) 3 3 2 Glance TR Bx Prob ContinuousO’Leary & Pelham (1978) 3 1 NR NR P&T RS Hyperkinetic Alternating Rose (1978) 2 3 NR Incidence >5s PR Hyperactive Incidence Loney et al. (1979) 1 2 1 NR P&T RS

Hyperkinetic NR

Friedling & O'Leary (1979) 10 2 4 Partial T RS Hyperactive AlternatingBarkley et al. (1980) 4 2 2 Partial >5s P&T RS Hyperactive ContinuousCameron & Robinson (1980) 13 1 2 Partial (any) T RS Hyperactive Alternating Pelham et al. (1980) 6 1 NR NR P&T RS Hyperactive Alternating Rapport et al. (1980) 7 1 NR Partial >2 s PR Hyperkinetic Continuous Eastman & Rasbury (1981) 5 2 4 NR T RS Impulsive

NR

Hallahan et al. (1981) 5 1 2 Glance TR Att Px AlternatingKrause (1981) D 6 8 NA NA P&T RS Hyperactive Continuous Roberts & Nelson (1981) 7 2 3 Glance T RS Bx Prob Continuous Gettinger (1982) 18 3 2 NR NR Bx Prob Alternating Hallahan et al. (1982) 8 1 2 Glance TR Att Px Continuous

Lloyd et al. (1982) 10 1 2 Glance TR Att Px AlternatingTable 7 continued

41

Study

Obs/Day

Number of Bx Coded

Number of

Observers Definition of

Off-task Diagnostic

Method Diagnostic Moniker

Observation Method

Rapport et al. (1982) 6 1 NR Partial >2s SSI/T RS ADDH Continuous Rhode et al. (1983) 5 15 NR Whole P&T RS Bx Prob Continuous Shepp & Jensen (1983) 2 1 1 NR T RS Hyperactive

Continuous

Rooney et al. (1984) 5 1 NR Glance TR

Att Px AlternatingRosen et al. (1984) 5 5 5 Partial (any) T RS Hyperactive Alternating Rapport et al. (1985) 3 1 NR Partial >2 s

P&T RS Hyperactive Continuous

Burlton-Bennet et al. (1987) 10 4 2 Partial (any) P&T RS Hyperkinetic Continuous Rapport et al. (1987) 3 1 NR Partial >2s P&T RS

ADDH Continuous

Walker (1989) D 3 1 NR Partial T RS ADHD ContinuousSzymula (1990) D 4 6 6 Partial P&T RS ADHD Continuous Bloomquist et al. (1991) 3 3 NR NR SSI/P&T RS ADHD NR DuPaul et al. (1992) 5 5 2 Partial (any) P&T RS ADHD Continuous Paniagua (1992) 10 8 NR Partial >15s T RS ADHD Continuous DuPaul & Henningson (1993) 6 2 NR Partial > 3s P&T RS ADHD Continuous Kraemer (1994) D 2 5 2 Partial P&T RS Hyperactive Alternating Rapport et al. (1994) 3 1 NR Partial >2s SSI/P&T RS ADDH Continuous Kelley & McCain (1995) 4 3 NR Partial (any) P&T RS ADHD Continuous Turner (1996) D 5 1 2 Partial >2s SSI/T RS

ADHD Continuous

Matheson (1997) D 3 2 NR Partial P&T RS ADHD ContinuousSams (1999) D 3 2 4 Partial P&T RS ADHD Continuous Cloward (2000) D 5 1 NR NR T RS ADHD Continuous Swenson et al. (2000) 1 2 2 Glance NR ADHD ContinuousRoberts et al. (2001) NR 1 NR Partial T RS Bx Prob Continuous Northup & Gulley (2001) 4 5 NR Partial NR ADHD ContinuousAustin (2003) D 15 10 NR Partial P&T RS ADHD ContinuousFabiano & Pelham (2003) 22 2 NR Whole P&T RS ADHD ContinuousLorah (2003) D 7 5 2 Partial >3s SSI/P&T RS ADHD Continuous Dobrinski (2004) D 5 1 4 NR T RS Att Px Alternating Trolinder et al. (2004) 4 1 2 NR T RS Att Px Continuous Clarfield & Stoner (2005) 2 3 NR Whole SSI/P&T RS ADHD Continuous

42

43

Notes. 1 Sample size reported as total number subjects whom met inclusion criteria for the current study and for whom data was

reported; 2 Mean and/or SD estimated from graph; A, Age; Bx Prob, Referred for behavioral problems/hyperactivity; C,

Classroom; Continuous, Observing one child exclusively for entire observation period; Definition of Off-task, Proportion of

interval needed to be coded off task; F, Females; G, Grade; Glance, Observers looked briefly at child and coded on/off-task for

that instant; M, Multiple informants; No of Bx Coded, Number of behaviors coded; NR, Not reported; Obs/Day, Number of

observation days; Observation Duration, Minutes of observation; Observation Interval, Seconds before recording; Observation

Method (Alternating, abab or similar method; P, Parent rating scale Only; PR, Pediatrician referral; RS, Rating scale(s); S, Sex;

SE, Special education classroom; SSI, Semi-structured interview; T, Teacher rating scale only; TR, Teacher Referral.

Normal Controls

62% 85%

ADHD Children

X +1 SD-1 SD

Average Percent On-task

Figure 1: Original metric differences between observed classroom attention of children with

ADHD and control children based on best case estimation after controlling for methodological

differences among studies.

44

CHAPTER FOUR: CONCLUSION

Direct observations of children with ADHD and their peers in classroom settings indicate

significant deficiencies in their ability to pay attention, and these differences are apparent

regardless of most methodological differences across studies. Children with ADHD were also

more variable in their attentive behavior, as indicated by significantly larger standard deviations

across studies for ADHD compared to control groups. The current meta-analysis quantified and

examined standardized effect size estimates of these differences, and found significant

heterogeneity across studies that could not be explained by study-level sampling error. A

weighted regression analysis of potential moderators determined that several factors, both

methodological and subject-specific, have significant effects on the observed levels of attentive

behavior in the classroom. Methodological differences, including sample characteristics,

diagnostic issues, and observational coding schema, accounted for a large percentage of between-

study variability such that no systematic variability remained across studies (adjusted R2 = .91).

An overall mean weighted effect size of 0.71 was found before considering the impact of

methodological variables. The significant heterogeneity among the 23 studies, however, suggests

that this mean may not be the best indicator of actual differences between children with ADHD

and control children. A best case estimation obtained by solving the significant moderator

regression equation using values corresponding to best practice suggests that the actual

standardized mean difference between the ability of children with and without ADHD to pay

attention in the classroom is likely closer to 1.40. Original metric analysis of this finding suggests

that across studies, typically developing children are off-task approximately 15% of observation

periods compared to 38% for children with ADHD. Stated differently, children with ADHD are

able to focus their attention in classroom settings approximately 62% of the time, compared with

45

an 85% on-task average for typically developing children. Power analysis based on Cohen’s

(1992) recommendations suggests that only four subjects per group are needed to detect an effect

of this magnitude.

Only two between-group studies failed to find significant differences in the classroom

attentive behavior of children with ADHD and typically developing children. Cunningham and

Siegel (1987), which approached significance at p =.052, utilized a design in which each ADHD

child was paired with a typically developing child, and these dyads worked together for the

duration of the study. The ADHD-Typically Developing child interaction may have contributed to

the failure to find a significant difference in attention between the groups. The finding of no

significant on-task differences in Shecket and Shecket (1976) may have been influenced by their

decision to code only one of 19 possible behaviors during each observation interval. For example,

a child coded as “Appropriate Talk with Teacher” would not have been coded as “Attending”

during that same interval, potentially decreasing substantially the base rate of the attending

category. It is unknown whether a re-analysis combining all codes consisting of on-task behavior

would have resulted in significant between-group differences. No studies reported children with

ADHD as being more attentive than control children.

Regression analysis revealed that sampling variables, diagnostic practices, and

observational methods influence obtained results and must be considered when designing or

implementing a direct observation system for children with ADHD and their peers. The impact of

these moderators is exemplified by the best case approximation: Subject matching, more rigorous

diagnostics, and longer observation of fewer behaviors were associated with an average effect

size of 1.40 (Note: the mean across studies without controlling for methodology was 0.71).

46

Single case design (SCD) studies generally corroborate the between-group findings,

although SCD studies were more likely than between-group studies to include children with

ADHD that were off-task more than half of observed intervals. This difference does not appear to

be clinically significant, however, and may be accounted for by several factors (Jacobson and

Truax, 1991). Based on area under the curve analysis, the larger off-task rates in the small-n SCD

studies can be predicted by the between-group distribution. The average between-group

distribution predicts that approximately six subjects per study will score beyond the SCD mean,

whereas on average only about 4 SCD subjects per study actually meet or exceed this value. No

significant differences were found between effect sizes predicted by the between-group regression

equation and obtained effect sizes for the six SCD studies with a typically developing comparison

group, although obtained off-task rates were generally higher than predicted. The study types

differed on three moderators found to significantly influence observed differences between the

off-task rates of children with ADHD and their peers. Two of these moderators, number of

behaviors coded and days of observations, were associated with larger effect sizes, whereas

observation method was associated with smaller effect sizes. Selection bias may have also

influenced the observed, though clinically insignificant, trend towards larger effect sizes for SCD

studies. Single case designs, like case studies, often target children with the most extreme

behavior problems for intervention. Publication bias may have also favored studies of children

whom are more frequently off-task, as small-n designs have lower power and require larger effect

sizes for statistical significance. No statistically significant difference in off-task rate, however,

was found between published (M = 53.73, SD = 16.63) and unpublished (M = 45.1, SD = 15.35)

SCD and between-group studies, t(18) = 1.71, p = .104, ns.

47

Matching subjects based on age, grade, and classroom at school were associated with

relatively lower effect size estimates. Subject matching is designed to control for myriad

extraneous variables including developmental level and the cognitive and situational demands

placed on children in academic settings (Tabachnick & Fidell, 2001). The present findings

corroborate this general trend and suggest that matching for relevant characteristics may be an

important consideration when conducting classroom observation research. The larger effect sizes

associated with natural classroom settings compared to simulation classrooms may be related to

the myriad uncontrolled situational factors present in the former (Rapport, Timko, Kofler, Sims,

& DuPaul, under review). Simulated classrooms tend to include highly trained (e.g., special

education) teachers, multiple adult staff members, smaller class sizes (i.e., low student-teacher

ratio), occur during irregular times of the year (i.e., summer), and frequently involve robust

behavior management systems and highly structured daily routines (e.g., Pelham et al., 2000;

Solanto et al., 2001).

Studies using increasingly more rigorous diagnostic criteria (for both number of

informants/settings and addition of comprehensive diagnostic interviews) were associated with

smaller overall effect sizes. This finding highlights the need for comprehensive diagnosis of

research subjects and suggests that screening measures such as rating scale cut-off scores may be

insufficient for assigning children to ADHD and control groups in research studies (Barkley et al.,

1990). No behavioral cluster is pathognomonic to ADHD without considering onset, course, and

duration of symptomatology, and systematically ruling out other possible causes or factors

associated with impaired functioning. Structured clinical interviews provide improved sensitivity

and specificity compared to rating scales, which typically assess a rater's perception of a limited,

recent period of time (e.g., last 6 months; McClellan & Werry, 2000; Rutter & Graham, 1968).

48

Systematic differences in obtained effect sizes were also dependent on the diagnostic moniker

assigned to the experimental group (i.e., hyperactive versus ADHD/ADDH). Earlier studies of

hyperactive children tended to report moderately larger effect sizes compared to newer studies

diagnosing children as ADHD/ADDH. This finding may reflect the change from a monothetic

(DSM-III-R) to a polythetic (DSM-III; DSM-IV Combined Type) categorical diagnostic scheme,

wherein children with other disorders were more likely to meet diagnostic criteria using the

former system (August & Garfinkel, 1993). Children with ADHD may be more variable in their

behavioral responses than children carrying other psychiatric diagnoses (e.g., Barkley, 1991).

Samples containing subgroups of relatively more homogeneous groups of children – for example,

children with anxiety or conduct problems – will have less variability and therefore larger effect

sizes, as variability is the denominator in effect size formulae. More recent studies using gold

standard assessment procedures and polythetic criteria may provide purer, and thus more variable,

samples of children with ADHD. Inclusion of non-ADHD children may result in decreased

group-level variability.

Observation code idiosyncrasies also influence observed differences between the ability of

children with ADHD and typically developing controls to remain attentive in the classroom.

Longer observation intervals and a greater number of observation days were associated with

larger effect sizes, whereas the duration a child must remain off-task before (s)he is coded as such

did not predict between-group differences. The high frequency of off-task behavior in children

with ADHD may be responsible for the failure of the latter to incrementally influence effect size.

These findings are consistent with some (Ary & Suen, 1983; Bramlett & Barnett, 1993), but not

all (Mehm & Knutson, 1987; Smith, Madsen, & Cipani, 1981) observational research with

children. None of the previous research, however, included samples of children with ADHD. Both

49

within-subject and between-group studies of ADHD and typically developing control children

consistently reveal that children with ADHD are more variable in their ability to remain on-task in

classroom and laboratory settings (Barkley, 1991). Longer observation intervals and a greater

number of days are more likely to provide a valid, more reliable sampling of classroom attention.

Scrutiny of within-subject experimental designs reveals that children with ADHD may exhibit 1-2

days of moderate to high on-task behavior followed by highly variable or low on-task days (e.g.,

Rapport et al., 1982). Heyman and colleagues (2001) provide a simple statistical method based on

split-half reliability for determining the total observation duration necessary based on the

frequency of behaviors of interest (see their Appendix, pp. 119-120). This method first calculates

current reliability and then provides a formula to determine the number of minutes the

observation needs to be increased/decreased to obtain a desired reliability. Adjusting the formula

will also allow the experimenter to calculate the affect of varying observation intervals on overall

reliability.

Studies alternating observations between ADHD and control children reported larger

differences in the classroom attentional processes of ADHD and control children compared to

studies observing one child per observation session. This difference may be related to the

frequency of behavior observed. The present review suggests that children with ADHD are off-

task approximately 38% of observation periods compared to 15% for control children. Available

research indicates that observed differences may be dependent on incident frequency and coding

scheme (Harris & Lahey, 1982; Haynes, 2001). Labeling a behavior as frequent or infrequent,

however, is not based on accepted, standardized criteria. The attentive behavior of children with

ADHD appears to be dissimilar from their peers in the classroom primarily during structured

academic tasks (Porrino et al., 1983). Children with ADHD show a significantly different pattern

50

of attentive behavior over time during structured academic tasks compared to their peers, with the

former showing more frequent shifts from on- to off-task and off- to on-task states (Rapport et al.,

2005). Alternating between children within an observation session, although allowing for

collection of temporally similar peer data, increases the probability of omitting these frequent

shifts during coding (Harris & Lahey, 1982).

The number of behaviors coded simultaneously was linearly and positively related to the

magnitude of attentional differences between ADHD and control children. Each behavior coded

requires the coder to recall a specific, operational definition (e.g., Abikoff, 1977). Most (Jones,

Reed, & Patterson, 1974; Mash & McElwee, 1974; Taplin & Reid, 1973), but not all (Frame,

1979) studies report an inverse relationship between the number of behaviors coded

simultaneously and recording accuracy. The failure of Frame (1979) to find this relationship may

be related to overall low agreement scores and lack of a criterion-referenced comparison (Harris

& Lahey, 1982). The use of written or computerized coding sheets may help reduce these

cognitive demands due to cuing and the ability to reference definitions (Abikoff et al., 1977).

Increasing the number of behaviors coded also requires the observer to attend to different features

of the child's behavior (e.g., watching the child's head/eyes, feet, and hands, listening to

words/vocalizations). An interaction with the defined coding interval is also likely, with a higher

number of behavioral codes requiring more observation time. For example, recording 13

behaviors during a 15-second interval may be more reliable than coding them every five seconds.

Several caveats are in order. The restricted age range of reviewed studies did not allow for

examination of differences between younger and older elementary school children. Attention,

cognition, and activity level show significant developmental trends based on extant literature

(DuPaul & Stoner, 2004) and the current finding of systematic differences in observed classroom

51

attention when matching for age. We were also unable to determine the influence of specific

academic task or differences during structured compared to unstructured classroom activities,

because most studies either observed children across several tasks or did not report this

information. Extant studies show significant variability in the on-task behavior of children with

ADHD due to differences in classroom variables (e.g., Barkley et al., 1980; Flynn & Rapoport,

1976; Jacob et al., 1978; Whalen et al., 1978; Zentall, 1980), some of which may be associated

with level of cognitive demand and/or working memory (Denney, Rapport, & Chung, 2005).

Girls with ADHD were underrepresented in available studies, which may have influenced

the failure to find a relationship between gender and observed differences in classroom attention.

Gender differences in behavioral and cognitive symptom prevalence are documented in most, but

not all studies of children referred for ADHD, with girls tending to exhibit more inattentive and

fewer hyperactive symptoms than their male counterparts (Abikoff et al., 2002; Biederman &

Faraone, 2004; Carlson et al., 1986; Graetz et al., 2005; Seidman et al., 2005; Weiss et al., 2003;

Yang et al., 2004). Interobserver agreement, a form of reliability, was not assessed because of

discrepancies in reported metrics (e.g., percent agreement, phi coefficient, r correlation, and

kappa were reported in 6, 8, 5, and 3 between-group studies, respectively, with one study not

reporting) and because all studies reported values above 0.8 for their metric.

The presence of comorbid diagnoses was not assessed in the current study. None of the

reviewed studies included comorbid diagnoses beyond LD, ODD, or CD. The presence of

comorbid disruptive behavior disorders has been found to predict increased symptoms of

aggression, interference (e.g., interrupting class, talking out of turn), and inattention in the large

MTA study (Abikoff et al., 2002). Comorbid anxiety disorders have been hypothesized to inhibit

impulsive/hyperactive behaviors in children with ADHD (Quay, 1997), however, no effects on

52

any directly observed behaviors were found in the large-scale MTA study (Abikoff et al., 2002;

Newcorn et al., 2001). Only Roberts (1990) included a psychiatric comparison group (aggressive

children), limiting our ability to conclude that inattentive symptoms are pathognomonic to

ADHD, and not merely a byproduct of general psychopathology.

We did not examine potential interaction effects among the moderator variables. For

example, coding interval may interact with the number of behaviors coded, such that longer

intervals mediate detrimental effects of attempting to simultaneously code multiple behaviors.

Weighted regression analysis in SPSS 12.0, however, requires simultaneous entry of dependent

variables, thus precluding traditional methods for testing interaction effects (Tabachnick & Fidell,

2001). It is statistically unlikely that the addition of interaction terms or other potential

moderators would provide incremental benefit, considering the large amount of variance

explained by the current model.

Results of the meta-analytic review indicate that direct observation studies of

comprehensively diagnosed ADHD and control children in natural classroom settings that match

subjects based on age and classroom, collect data over several days while limiting the number of

target behaviors, and observe subjects consecutively for longer intervals, are likely to obtain

results consistent with the best case estimation reported here. The coding schema, however,

depends on the data needed. Researchers may wish to use more codes for investigational

purposes, whereas clinicians seeking to supplement interview and rating scale data may be

interested in only a few select behaviors. In the former case, it is recommended that researchers

use videotaped observations, which allow multiple viewings and limit the number of behaviors

that must be coded at one time. It is noted, however, that the between-group studies analyzed here

53

used live observations, therefore the incremental validity of videotaped sessions was not directly

assessed.

Studies of children with ADHD reveal robust deficiencies in their ability to maintain

attention in the classroom. Classroom observations, however, record only output – the child’s

observable behavior – informing us only of the existence of an underlying problem, not its cause

or nature. Problems sustaining attention may be a core deficit of ADHD, as conceptualized in the

current DSM (APA, 2000), or they may be secondary to other, more primary deficits.

Deficits in behavioral inhibition may lead to problems with sustained attention (Barkley,

1997). An inability to suppress prepotent responses to stimuli has been argued repeatedly based

on laboratory experiments such as the Stop Signal task. However, a recent meta-analysis found

that such differences between children with ADHD and typically developing children could be

accounted for by differences in primary reaction time, with children with ADHD reacting slower

and more variably than their peers (Alderson, Rapport, Kofler, & Timko, under review).

Problems with one or more aspects of working memory may account for attentional

deficits seen in children with ADHD (see Rapport, Chung, Shore, & Isaacs, 2001 for a theoretical

overview). Preliminary evidence suggests robust deficiencies in the ability of children with

ADHD to recall short lists of phonological and short groups of visual-spatial stimuli for more than

a few seconds, compared to both typically developing peers and individuals with other psychiatric

diagnoses (Rapport, Timko, Kofler, & Alderson, 2005). Inattention and motor activity increase

linearly for all children as cognitive demands on working memory increase, although children

with ADHD exhibit a disproportional increase compared to both typically developing children

and children diagnosed with other psychiatric disorders (e.g., depression, anxiety). These rates of

change are similar across phonological and visual-spatial working memory tasks (Rapport et al.,

54

2005). Classroom studies suggest that children with ADHD are significantly more hyperactive

than their peers during in-seat classroom activities, but not during recess or other nonacademic

periods (e.g., Porrino et al., 1984). It is theoretically possible that children’s movement serves a

functional purpose such as maintaining or enhancing arousal to sustain attention and/or stimulate

working memory. Inchoate neurophysiological evidence supports this theory – areas of the

prefrontal cortex, but not the motor loop, are consistently implicated in fMRI studies of ADHD

(Castellanos, 2001; Diamond, 2000). Abnormalities in motor loop (i.e., basal ganglia and

associated areas) functioning would suggest that hyperactivity is ubiquitous in ADHD, however,

findings of primarily prefrontal abnormalities point to problems with arousal, working memory,

and the planning of behavior.

None of the reviewed studies examined the effect of time or attention by time interactions

in children with ADHD, with or without a typically developing comparison group. The failure to

examine time effects in classroom studies of attention severely limits our understanding

concerning possible underlying mechanisms and processes responsible for the attentional

difficulties associated with ADHD (e.g., whether they reflect a more general deficit in attentional

mechanisms or particular deficiencies in the ability to maintain attentional focus over time).

Specialty statistical processes, including growth mixture modeling and wavelet analyses, will

allow researchers to characterize both inter- and intra-individual changes in attention over time.

These analyses from the signal processing literature allow researchers to group participants based

on temporal patterns and analyze patterns in frequency and amplitude of behavior at the level of

the individual (Castellanos et al., 2005).

The current meta-analysis provides unequivocal evidence for the existence of classroom

attention deficits in children with ADHD. It also establishes the magnitude of these problems

55

compared to their peers, providing estimates of the classroom attentional capabilities of both

developmentally typical children and those diagnosed with ADHD. It is clear that children with

ADHD exhibit marked deficiencies in their classroom attentional ability, and the current study

provides substantial support for the inclusion of inattention as a core and pervasive symptom of

ADHD in the DSM-IV (APA, 2000) model. Currently, no single rating scale or diagnostic tool

possesses adequate positive and negative predictive power to accurately diagnose ADHD.

Inchoate research suggests that variables including seatwork completion/accuracy and verbally

intrusive behaviors may discriminate ADHD/non-ADHD children better than independent

observations of on-task behavior (see Pelham et al., 2005 for a review). It is unknown, however,

whether the use of the best case methods and standard differences in attention across all studies

presented here will allow improved diagnostic accuracy and predictive power.

56

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