Preliminary Study of the Confined, Collateral, and Combined Effects of Reading and Behavioral...

Running Head: RELATIONSHIP BETWEEN READING AND BEAHVIOR PROBLEMS

Preliminary Study of the Confined, Collateral, and Combined

Effects of Reading and Behavioral Interventions: Evidence for a

Transactional Relationship

Clayton R. Cook

Christopher Daikos

John Delport

University of Washington, Seattle

Evan Dart

Tai Collins

Louisiana State University

Paul Fitts

University of Wisconsin, La Crosse

READING AND BEHAVIOR PROBLEMS 2

This article has been accepted for publication in the journal Behavioral Disorders.

Abstract

Recent correlational studies provide support for a transactional

relationship between reading and behavior problems. A

transactional relationship implies that reading problems cause

behavior problems, and vice versa. This study took this basic

finding and examined its implications for intervention.

Specifically, this study employed single-case experimental

methods to examine the collateral (e.g., reading intervention

improves behavioral performance) confined (e.g., reading

intervention improves reading performance) and combined (e.g.,

reading plus behavioral intervention) effects of reading and

behavioral interventions. Preliminary results indicated that

both reading and behavioral interventions produced confined,

collateral, and combined effects on reading and behavioral


outcomes. Although a transactional relationship was demonstrated

for the majority of participants, findings indicated that

behavior function potentially moderates the effectiveness of the

reading intervention to improve behavior, suggesting that a

transactional relationship may not be universally true. The

implications and limitations of the results, as well as

directions for future research are discussed.

KEYWORDS:

Reading problems, behavior problems, reading intervention,

behavioral interventions, comorbid reading and behavior problems


Preliminary Study of the Confined, Collateral, and Combined

Effects of Reading and Behavioral Interventions: Evidence for a

Transactional Relationship

Reading and behavior problems frequently co-occur and

combine to put students at risk for short- and long-term

detriment in social, emotional, and academic aspects of life

(U.S. Department of Justice, 2001; Walker, Ramsey, & Gresham,

2004). Epidemiological studies indicate that behavior problems

co-occur with academic deficits 50 to 80 percent of the time

(Benner, Nelson, & Epstein, 2002; Kauffman, Cullinan & Epstein,

1987). Given the high base rates of comorbidity between reading

and behavior problems, several explanations have been proffered

to describe the pathological nature of this relationship

(Hinshaw, 1992; Lane, 2004; Lynam, 1996; Trout, Nordness, Pierce,

& Epstein, 2003). The most notable of these explanations were

described by Hinshaw (1992) in a seminal article published in

Psychological Bulletin.

According to Hinshaw (1992), there are four possible

explanations for the co-occurrence of reading and behavior


problems. The first explanation contends that reading problems

set the stage for or cause behavior problems (i.e., academic

pathway). That is, difficulty learning to read causes children to

disengage from the learning process and, in so doing, develop a

range of potential behavior problems, including disruptive and

noncompliant behaviors. The second explanation involves behavior

problems serving as a causal factor in the development of reading

problems (i.e., behavior pathway). Under this pathway, children

come to school with well entrenched patterns of behavior

problems, and it is these behaviors that interfere with their

ability to benefit from reading instruction and, ultimately,

learn. The third possible explanation is that there is a

reciprocal, or transactional, relationship between reading and

behavior problems (i.e., transactional pathway). This explanation

posits that reading and behavior problems exert transactional or

bidirectional influences one another. The fourth and final

explanation argues that some third, unknown variable is

responsible for the presence of reading and behavior problems.

For example, some researchers have speculated that inattention


may be responsible for both academic and behavior problems. From

a practical standpoint, these explanations take on importance

when considering the direct implications for intervention.

Two studies provide insight about the comorbidity between

reading performance and behavior problems. The first, a study

conducted by Trzesniewski, Moffitt, Caspi, Taylor, and Maughan

(2006), used participants from the Environmental Risk (E-Risk)

Longitudinal Twin Study and found evidence in support of a

reciprocal relationship between the two. In particular, they

noted that behavior problems significantly predicted later

reading problems after controlling for a host of variables

including prior reading problems. Likewise, reading problems

predicted subsequent behavior problems after holding constant

previous behavior problems. They interpreted the findings as

implying that interventions put in place to address reading

achievement should have a comparable indirect effect on

antisocial behavior, and the converse of this should also be

true.


More recently, Morgan, Farkas, Tufis, and Sperling (2008)

examined data taken from the Early Childhood Longitudinal Study –

Kindergarten Class (ECLS-K) to investigate a possible underlying

bidirectional relationship between reading and behavior problems.

Morgan et al. controlled for a host of possible confound

variables based on family resources, demographic disparity, and

prior academic and behavior problems. Holding these variables

constant, they found reading achievement to be a strong

determinant of future behavior problems and behavior problems,

specifically off-task behavior, to be a determinant of reading

problems. Together, the results from this correlational study and

the Trzesniewki et al. (2006) provide support for the

transactional relationship between reading and behavior problems.

Other studies have demonstrated a possible reciprocal

relationship between reading and problem behaviors through the

implementation of reading and behavior interventions, indicating

that students may behave inappropriately as a way of escaping or

avoiding aversive academic tasks or activities that exceed their

academic skills (Kellam, Mayer, Rebok, & Hawkins, 1998; Reinke,


Herman, Petros, & Ialongo, 2008). For instance, Lane,

O’Shaughnessey, Lambros, Gresham, and Bebee-Frankenberger (2001)

investigated the impact of a decoding intervention on first-grade

students with reading problems, impulsivity, and conduct

problems. They found that the reading intervention produced both

improved early reading and social interaction skills. As a

follow-up, Lane et al., (2002) found comparable results in a

study of first graders with concomitant early reading and

antisocial behavior problems.

The research supporting the transactional relationship

between reading and behavior problems notwithstanding, other

research provides contradictory or mixed findings, particularly

when considering the collateral effects of reading instruction on

behavioral outcomes. For example, a recent best-evidence

synthesis by Nelson, Lane, Benner, and Kim (2011) found that

literacy instruction does not appear to have an overall

collateral effect on social adjustment. Despite the above-

mentioned research, there is limited research that has explicitly

and purposely examined the byproduct effects of reading


interventions to improve behavioral outcomes, and vice versa,

under tightly controlled experimental conditions.

Given mixed findings in the literature, it is premature to

suggest that a transactional relationship holds true for all

students with co-occurring reading and behavior problems.

Instead, of a universal transactional relationship it is possible

that certain factors moderate the relationship between reading

performance and behavior problems relationship. A moderator is a

variable that alters the relationship between two variables

(Baron & Kenny, 1986; Holmbeck, 1997). One possible moderator of

the relationship between reading instruction and behavioral

outcomes is the function of the student’s behavior problems. From

an applied behavior analytic perspective, there are two functions

of behavior: positive and negative reinforcement (Iwata, Dorsey,

Slifer, Bauman, & Richman, 1982). Positively reinforced classroom

problem behaviors are maintained because they gain access to or

come into contact with a desirable consequence, such as attention

from others, tangible item or activity, which increases the

future likelihood of the disruptive behavior (Cooper, Heron, &


Heward, 2007). Negatively reinforced classroom problem behaviors

are maintained because they allow the person to escape, avoid, or

minimize contact with an unpleasant stimulus, such as undesirable

social attention, an unfavorable directive, or aversive academic

tasks (Cooper, Heron, & Heward, 2007). Putting this into context,

it is reasonable to assume that a reading intervention may

improve both reading and behavior outcomes for students whose

function of behavior is to escape/avoid frustrating, boring or

difficult-to-complete reading tasks, but the same reading

intervention may not improve the behavior of students who engage

in behavior problems to obtain attention from others (Kern &

Clemens, 2007). In the former case, the reading intervention

serves as a functionally equivalent antecedent strategy (i.e.,

improved reading skills decreases aversiveness or difficulty of

engaging in reading tasks), while in the latter case it does

nothing to address the underlying function or purpose for the

behavior. To date, no research has investigated whether the

relationship between reading and behavior problems is moderated

by the function of behavior.


Purpose of Present Study

Assuming there is a reciprocal, transactional relationship

between reading and behavior problems, the logical prediction

that follows is that reading interventions should not only cause

improved reading performance but also improved behavioral

performance, and vice versa. Based on this logic, the primary

purpose of the present study was to examine whether reading and

behavioral interventions have collateral effects on behavior

problems and reading performance, respectively. The secondary

purpose of this study was to examine the confined and combined

effects of these interventions. Last, we were interested in

exploring whether the function of participants’ classroom

behavior problems moderated the collateral effects of the reading

intervention. For the sake of understanding the focus of this

study, it is important to understand the concepts of confined,

collateral, and combined effects of interventions and behavior

function as a moderator of intervention effectiveness.

Confined effects refer to those effects that are specific to

the purpose or designed intent of the intervention. For example,


the confined effect of a reading intervention is improved reading

performance. Collateral effects, on the other hand, refer to

those effects that are secondary byproducts of the intervention

that are not necessarily specific to the intent of the

intervention, but are nevertheless important outcomes. For

instance, improvement in a student’s reading performance as a

result a behavioral intervention is an example of a collateral

effect. The collateral effect provides evidence in support of

the causal relationship between the variable representing the

focus of the intervention and the variable representing the

secondary or byproduct effect, particularly when examined under

experimental conditions. Combined effects refer to the additive

effect that is observed when adding another intervention to one

in place. For example, a combined effect would be present if the

combination of reading and behavioral interventions produced

gains in performance greater than the gains produced by either

intervention alone. Developing an understanding of the confined,

collateral, and combined effects of reading and behavioral

interventions is important, to figure out the optimal design and


delivery of interventions for students with co-occurring reading

and behavior problems.

Based on research on functional behavior assessment, a

related research question was whether the hypothesized function

of problem behavior would moderate the effectiveness of the

reading intervention on behavioral outcomes. This research

question was generated from the distinction between negatively

and positively reinforced behaviors (Cooper, Heron, & Heward,

2007). In the case of negatively reinforced behavior problems

occurring in the classroom, academic instruction and related

activities represent unpleasant, aversive conditions and behavior

problems represent the response class being negatively reinforced

via escape/avoidance. For positively reinforced behavior

problems occurring in the classroom, peer attention represents a

common, desired stimulus that positively reinforces behavior

problems (Jones, Drew, & Weber, 2000). Throughout this study, we

refer to negatively reinforced behaviors as escape-motivated and

positively reinforced behaviors as attention-maintained, which is


consistent with the results of our functional behavior

assessments.

The distinction between escape-motivated and attention-

maintained behavior has implications for the transactional

relationship between reading and behavior problems and the

collateral effects of the reading intervention in particular.

Reading interventions should increase alternative behaviors

(i.e., reading skills) and make reading instruction and

activities less aversive or difficult thereby reducing behavior

problems. While reading interventions may be causally related to

reductions for students with escape-motivated behavior problems,

the same may not be true for children with attention-maintained

behavior problems. In the latter case, the child engages in

behavior problems to gain contact with attention, not to escape

an aversive reading instruction or activity. Thus, the reading

intervention is inconsistent with the function of behavior and

unlikely to have an impact on the child’s behavior problems.

This study included participants who were receiving special

education services under the category of specific learning


disability, but engaged in high rates of behavior problems. The

sample is suitable for publication in this journal for a number

of reasons. First, there is a continuum of students with

emotional and behavioral disorders that range from those who

receive formal identification for special education services

under the category of emotional disturbance and those whose EBD

may not be severe enough to warrant a formal diagnosis (Gagnon &

Leone, 2006). Moreover, many students with EBD are often

inappropriately placed in programs for students with learning

disabilities (Lopez, Forness, MacMillan, Bocian, & Gresham, 1998;

Svetaz, Ireland, & Blum, 2000) or simply not identified as having

an emotional disturbance because of the social maladjustment

exclusionary clause (Merrell & Walker, 2004). In these instances,

it is important to examine the specific behavioral patterns to

determine whether they are consistent. All of the student

participants included in this study exhibited externalizing

behavior patterns in the classroom that are consistent with

having or being at risk for an EBD.

Methods


Participants and Setting

This study involved six students from two special education

classrooms (6th and 7th Grade) at a middle school in a moderately

sized urban school district in Southern California. Students

were identified for participation in this study based on a

multiple gating procedure, which was developed out of the spirit

of the Systematic Screening for Behavior Disorders (Walker &

Severson, 1990). The first gate involved teachers nominating the

top six students in their class who met definitions of disruptive

classroom behavior and poor reading performance. The next gate

consisted of having teachers complete a brief behavior rating

form (Combined Frequency Index) and obtaining extant standardized

reading scores. Students who had clinically significant behavior

problems (T-score > 64) on the brief behavior rating form and

performed below the 15th percentile on the reading index from

CAT/6 California Achievement Tests 6th Edition Survey were passed

on to the final gate. The final gate consisted of a Can’t

do/Won’t do assessment to rule out students whose reading

problems fit the definition of a performance deficit (i.e., won’t


do problem) rather than an acquisition deficit (can’t do problem)

(VanDerHeyden, Witt, & Naquin, 2003). The Can’t do/Won’t do

assessment consisted of administering curriculum-based

measurement oral reading fluency probes first without an

incentive and then with an incentive. If the student’s

performance improved in the face of the incentive, then it was

determined that the student had more of a performance deficit

(won’t do problem) and was excluded from participating in this

study. This resulted in the identification of three students

from each class to participate in the study. Consent was

obtained from the parents and teachers and assent was obtained

from each student prior to the onset of the study.

The demographic information for the participants is

displayed in Table 1. The average age of the six participants was

12.3 years old. With regard to ethnicity, three of the students

were African American, two were Caucasian, and one was Latino.

In terms of gender, five of the participants were male and one

was female. All students were receiving special education

services under the category of specific learning disability.


Sage had an additional diagnosis of OHI for ADHD related

symptoms. As for the teachers, both had special education

teaching certificates. The 6th Grade teacher had been a special

education teacher in a self-contained classroom for seven years,

while the teacher in the 7th Grade class had been teaching

special education students in resource and self-contained

classrooms for 10 years.

Experimental Design

A concurrent multiple-baseline across classrooms design was

used to examine the confined, collateral and combined effects of

the reading and behavioral interventions. For each class, there

were two intervention phases, each lasting three weeks in

duration. Random assignment was used to determine the order each

class would receive the interventions (e.g., reading or

behavioral intervention first). The 6th Grade class consisting

of Frank, Ramón, and Sage was selected to receive the

interventions in the following sequence: behavioral intervention

followed by the addition of the reading intervention. The 7th

Grade class consisting of Jake, Ryan, and Marcia had an extended


baseline and received the interventions in the following

sequence: reading intervention followed by the addition of the

behavioral intervention. Although this design did not permit

replication of intervention effects across classrooms, it did

permit an analysis of within classroom replication across

participants. Experimental control was established via the

extended baseline and demonstration that reading and/or behavior

performance did not improve until the introduction of the

intervention, albeit a different intervention. The point of the

extended baseline was to demonstrate that performance remained

stable for the participants in the 7th Grade class until the

introduction of an IV, which was under the experimenter’s

control.

Measures

Reading performance. Curriculum-based measurement reading probes

were used to assess reading level and progress in response to the

interventions. Specifically, Aimsweb 6th and 7th Grade oral

reading fluency (ORF) probes were used. Aimsweb ORF probes have

been repeatedly demonstrated to be reliable and valid general


outcome measures of reading performance (Hosp, Hosp, & Howell,

2006). Standardized guidelines for administering and scoring the

probes were followed. The reading fluency score was established

by calculating the number of words read correct per minute. Three

probes were administered each assessment occasion and the median

score was used as the final ORF score for that occasion.

Problem behavior. To measure behavior problems, the Behavioral

Observation of Students in Schools (BOSS; Shapiro, 2004) direct

observation PDA was used. In particular, the behavioral response

categories of academic engaged time (AET) and disruptive behavior

(DB) were used as proxies for behavior problems. AET was defined

as instances when the student was paying attention to instruction

by looking at the teacher or speaker and working on the academic

task at hand. Examples of AET included writing, reading aloud,

raising a hand and waiting patiently, talking to the teacher or

other student about assigned material, and looking things up that

are relevant to the assignment. DB was defined as behaviors that

are not related to the task at hand and are disruptive to

learning or the classroom environment, but do not pose immediate


danger to the other peers, teachers, or property (e.g., call

outs, talking to peer when not permitted, out of seat, behavior

that draws other peers off-task, playing with object). The BOSS

utilizes a partial interval time sampling procedure to record the

percent of intervals the target student is academically engaged.

Observations were performed by three trained graduate students;

each observation lasted 30 minutes in duration; were not

performed until the observers reached 90% agreement with one

another during training sessions; and were conducted during the

language arts block, but not during the time and place in which

the reading intervention was implemented. Inter-observer

agreement was calculated on 20% of the observations during

baseline and intervention phases for each of the student

participants. The results of the IOA for each of the participants

for each phase are depicted in Table 1. Across all participants,

the mean IOA was 92% (minimum 84%; maximum 98%) and 98% (minimum

93%; maximum 100%) for AET and DB, respectively, indicating an

acceptable level of agreement (Salvia & Ysseldyke, 2004).


Functional behavior assessment. Two advanced graduate students, one

with BCBA certification and the other one working towards BCBA

certification, with extensive experience conducting FBAs and

developing behavior intervention plans performed the FBAs for

this study. A combination of indirect and direct measures was

used to conduct the FBA. The indirect measures included teacher

and student interviews derived from O’Neill et al. (1997) and the

Problem Behavior Questionnaire (Lewis, Scott, & Sugai, 1994).

Once the initial information was gleaned from the FBA interviews

about the antecedents, behaviors, and consequences, the Problem

Behavior Questionnaire was administered to cross-validate the

initial hypotheses. Based on the agreement found between these

assessments, the initial hypothesis was that four of the

participants (Frank, Ramón, Jake, and Marcia) engaged in escape-

motivated disruptive classroom behavior during academic tasks in

order to escape/avoid doing the work, while the other two (Sage

and Ryan) were engaging in attention-maintained disruptive

classroom behavior in order to gain attention from peers across

different activities.


The next step in the FBA consisted of direct observations

using an A-B-C observation form to (dis)confirm the initial

working hypotheses (O'Neill, Horner, Albin, Sprague, Storey, &

Newton, 1997). Conditional probabilities were calculated to

examine the relationship between the target behavior and

consequences under different antecedent conditions. Specifically,

the probability that the participants’ target behavior was

followed by either escape or attention was calculated during two

antecedent conditions (academic tasks vs. transition/free-time).

The results of the conditional probability analysis are depicted

in Table 2. Given that disruptive behavior is incompatible with

academic engagement, all participants were associated with

conditional probabilities that indicated the target behavior was

followed 100% of the time by escape from the academic task.

However, consistent with the initial hypotheses, differentiated

outcomes were observed when assessing the conditional

probabilities for the attention consequence during academic tasks

and the consequences of escape and attention during

transition/free-time antecedent conditions. For Sage and Ryan


(hypothesized attention-maintained), the conditional

probabilities indicated attention was associated with the target

behavior across both antecedent conditions. On the other hand,

for Frank, Ramón, Jake, and Marcia, attention was rarely

associated with the occurrence of the target behavior during both

academic tasks and transition/free-time conditions. Moreover,

the four hypothesized escape-motivated participants had near zero

levels of target behavior during transition/free time, indicating

that the target behavior was functionally related to academic

task via escape. Therefore, collectively, the results of the

multi-step FBA indicated that four of the participants’

disruptive behavior problems were escape-motivated (negative

reinforcement – Jake, Ramón, Frank, and Marcia) and the other two

participants were attention-maintained (positive reinforcement –

Sage and Ryan).

Interventions

Reading intervention. The reading intervention consisted of the

Six-Minute Solution (Adams & Brown, 2006) and small group

decoding instruction focusing on the alphabetic principle. The


Six-Minute Solution involves repeated readings of one-minute

nonfiction passages and a peer-tutoring and feedback system that

keeps students motivated and on task. Repeated reading and peer

tutoring have both been found to increase fluency (NRP, 2002).

The decoding instruction consisted of instruction targeting the

alphabetic principle, which is designed to improve knowledge

about, and interplay between phonemic awareness (i.e., sound-

spoken language correspondence) and phonics (i.e., sound-written

language correspondence). Specifically, lessons from the

Corrective Reading curriculum were used to target these areas.

Corrective Reading program has been shown to effectively improve

students’ early literacy and literacy skills (Polloway, Epstein,

Polloway, Patton, & Ball, 1986). Forty minutes of class time was

reserved each day during the study to deliver the reading

intervention protocol.

Behavioral Intervention. The behavioral intervention program

consisted of two empirically supported strategies: Check in/Check

out (CICO; Crone, Horner, & Hawken, 2004) and self monitoring

protocol. CICO is a mentor-based behavior support program that


consists of four steps: (a) morning check in with mentor, (b)

teacher evaluation and feedback, (c) check out with mentor and

performance feedback, and (d) notification to parent of student’s

performance. The four-step cycle is repeated on a daily basis

throughout the school week. Several studies have demonstrated

the efficacy of CICO to decrease the disruptive behavior of

students (Filter et al., 2007; Hawken, MacLeod, & Rawlings, 2007;

Todd, Campbell, Meyer, & Horner, 2008). The self monitoring

protocol was designed for the participants to monitor their on-

task behavior and absence of problem behavior (Crawley, Lynch, &

Vannest, 2006; Gureasko-Moore, DuPaul, & White, 2007; Peterson,

Young, Salzberg, West, & Hill, 2006; Petscher, & Bailey, 2006).

The participants were provided self monitoring charts and a

vibrating clock, which was set to vibrate on a variable interval

7-minute schedule. When the clock vibrated, the participants were

instructed to reflect on their behavior and mark down whether

they had been on-task and engaging in expected behaviors (i.e.,

on-task, hands to self, and following instructions).

Individualized goals were set for each student based on their


baseline data and participants were rewarded with an item or

activity from their reward menu each day if they met their goal.

To ensure acceptable levels of treatment integrity, graduate

research assistants assisted the teachers with implementing the

reading and behavioral interventions. Given the importance of

adequate treatment integrity to appropriately address the

research questions guiding this study, data were collected to

monitor implementation and provide performance-based feedback to

increase future integrity.

Treatment Integrity

Treatment integrity data were collected to assess the degree

to which the interventions were implemented consistently and

accurately. A combination of permanent products (e.g., self

monitoring forms and Check in/Check out attendance sheets) and

direct observation checklists were used to gather treatment

integrity data. Data were collected on three separate occasions

during each phase of intervention implementation. For the

behavioral intervention, there were six components across the

permanent product and direct observation measures that were used


to calculate percent integrity: (a) self monitoring form

completed (permanent product), (b) morning Check in/ attendance

form (permanent product), (c) afternoon Check out attendance

form, (d) student self-recorded behavior on chart (direct

observation), (e) periodic honesty check for self-monitoring

(direct observation), (f) teacher provided prompts and feedback

(direct observation). With regard to the reading intervention,

there were five components across the permanent product and

direct observation measures that were used to calculate percent

integrity: (a) attendance form for supplemental reading

instruction (permanent product), (b) progress monitoring graph

with session specific data point (permanent product), (c) teacher

or aide delivered fluency instruction (direct observation), (d)

teacher or aide delivered decoding instruction targeting phonemic

awareness or phonics (direct observation), and (e) teacher or

aide provided multiple opportunities to practice (direct

observation). The results of the treatment integrity assessment

for the behavioral intervention were as follows: Jake (m = 94%;

range = 83 - 100%), Marcia (m = 100%; range = 100%), Ryan (m =


89%; range = 83 - 100), Frank (m = 100%; range = 100%), Sage (m =

94%; range = 83 – 100%), and Ramón (m = 94%; range = 83 – 100%).

The results of the treatment integrity assessment for the reading

intervention were as follows: Jake (m = 100%; range = 100%),

Marcia (m = 100%; range = 100%), Ryan (m = 100%; range = 100%),

Frank (m = 93%; range = 80 - 100%), Sage (m = 100%; range =

100%), and Ramón (m = 100%; range = 100%). Together, these

results indicated that both interventions were implemented with

adequate levels of integrity for the participants.

Results

The data were interpreted using a combination of visual

inspection and behavior change estimates. For the ORF measure,

words read correct per minute per week (WRC per week) for each

condition was calculated as an estimate of reading growth during

baseline and intervention conditions and compared to established

growth rates (Hosp, Hosp, & Howell, 2007). For the AET and DB

measures, the mean percentages across phases were compared to

evaluate either the percent increase or decrease, depending on

the measure. The results are presented and interpreted in two


ways. First, the data are aggregated across students in each

class to examine class-level effects. Second, the individual-

level data within each classroom were visually displayed to

examine individual-level responses to the interventions, see

Figures 1, 2, and 3. The following is organized according to the

four main research inquiries guiding this study.

Research Question 1: To what extent do reading and behavioral

interventions are associated with confined effects?

As a reminder, confined effects refer to those effects that

are specific to the purpose of the intervention (i.e., reading

intervention improving reading performance and behavior

intervention improving behavior performance).

Confined effects of the reading intervention. Participants in the 7th

Grade Class were the only students to receive the reading

intervention alone. The aggregated results from the 7th Grade

Class indicated that the reading intervention was associated with

confined effects to improve reading fluency (see Figure 1).

Specifically, while the participants in the 7th Grade Class

decreased at rate of approximately 1 word per week during


baseline, they experienced an increase in 4.5 WRC per week once

the reading intervention was implemented. Comparison of these

growth rates to normative growth rates for ORF revealed that the

participants far exceeded established growth rates for students

in the same grade (Hosp, Hosp, & Howell, 2007).

Examination of the individual ORF response trajectories

indicated that all three participants in the 7th Grade Class

responded well to the reading intervention, see Figure 1. For

example, Marcia had a decreasing trendline at baseline, but

experienced an upward trend in her reading performance once the

reading intervention was implemented. Although Ryan and Jake had

slightly increasing trendlines at baseline, the slope of their

ORF data during the reading intervention phase was visibly

steeper, indicating improved reading fluency and providing

support for the confined effects of the reading intervention. The

WRC per data indicated that Marcia experienced the greatest

confined effects relative to baseline, while Ryan experienced the

weakest. However, all three participants had WRC per week

estimates during the reading intervention phase that exceeded


expected growth rates for students’ their same age (Hosp, Hosp, &

Howell, 2007).

Confined effects of the behavioral intervention. The participants in the

6th Grade Class were the only group who received the behavioral

intervention alone. The class-level results for AET and DB

indicated that the participants’ behavior improved as a result of

the behavioral intervention. Specifically, results for the AET

dependent variable indicated that while the participants were on-

task for only half of the time (49%) during baseline, their

academic engagement increased to over 70% once the behavior

intervention was introduced. This change was equal to an increase

in an hour more of academic engagement for every 6 hours of

instruction. As for the DB dependent variable, the average

percent intervals of DB decreased from 27% at baseline to 13%

during the behavior intervention only condition. These results

indicated that amount of observed DB was reduced in half with the

introduction of the behavior intervention.

Examination of the individual AET and DB data indicated that

all three participants in the 6th Grade Class responded well to


the behavioral intervention. Ramón exhibited the greatest net

gain in AET, with an increase from 37% at baseline to 79% during

behavioral intervention. Frank began baseline with the highest

AET, yet he still evidenced observable improvements in AET

following the introduction of the behavioral intervention.

Although Sage demonstrated the weakest confined effects, his AET

still improved relative to baseline, with a 15% increase in AET,

which is equivalent to roughly 10 minutes of extra time on-task

per instructional hour.

With regard to DB, the confined effects of the behavior

intervention were replicated across all three participants in the

6th Grade Class (see Figure 2). Ramón’s DB decreased the most

following the introduction of the behavior intervention

(reduction from 31% to 11%), followed by Sage (reduction from 27%

to 14%), and then Frank (reduction from 22% to 13%).

Collectively, the results across these three participants’ AET

and DB data provide support for the confined effect of the

behavioral intervention.


Research Question 2: To what extent are reading and behavioral

interventions associated with collateral effects?

Collateral effects are those effects that are secondary

outcomes, or byproducts, of an intervention. Therefore,

behavioral improvements that result from the implementation of

the reading intervention and reading gains produced as a function

of the behavioral intervention were examined separately.

Collateral effects of the reading intervention. To evaluate the

collateral effect of the reading intervention, the focus was

directed exclusively to the participants in the 7th Grade Class,

as they received the reading intervention alone while their

behavior performance was concurrently observed (see Figures 2 and

3). Results indicated that whereas the participants in the 7th

Grade Class’ average AET was 45% at baseline, once the reading

intervention was introduced their AET as a group increased to

67%. This represented a 22% increase from baseline. As for the

DB, the participants in the 7th Grade Class engaged in disruptive

behavior for an average of 32% of the intervals during baseline,

but demonstrated a reduction to 19% of the intervals once the


reading intervention was implemented. This reduction was

associated with a 13 percent decrease from baseline, which over

an instructional hour represents eight fewer minutes of DB.

With regard to individual effects, visual inspection of

Figures 2 and 3 revealed that two of the three students showed

marked improvement in AET and DB as a result of the reading

intervention. As can be seen, Marcia demonstrated the most

improvement in behavior relative to baseline, with a 48 percent

increase in AET and a 23 percent decrease in DB. Jake also

demonstrated improved behavior as a result of the reading

intervention, with an increase in AET by 19% and a decrease in DB

by 12% from baseline to the reading intervention. Ryan was the

only student who did not demonstrate a collateral effect of the

reading intervention (0% increase in AET and a 1% increase in

DB). Further interpretation of Ryan’s data is discussed below

the section below.

Collateral effect of the behavioral intervention. To assess the collateral

effects of the behavioral intervention, only data collected from

participants in the 6th Grade class were evaluated because they


were the only ones to receive the behavioral intervention alone,

see Figure 1. Beginning first with the class-level data,

collateral effects of the behavioral intervention were observed

as the participants as a whole increased from an average increase

of 0.45 WRC per week during baseline to an average increase of

4.78 WRC per week once the behavioral intervention was

implemented. This increase exceeded normative growth rates for

students their same age, and at this rate of progress the

participants would likely catch up to their peers’ performance

(Hosp, Hosp, & Howell, 2007).

Across all participants, examination of the individual-level

data provided evidence in support of the collateral effects of

the behavioral intervention. Specifically, all three of the

participants demonstrated growth in reading performance

contingent upon implementation of the behavior intervention.

Frank and Ramón demonstrated comparable collateral effects in

response to the behavioral intervention by improving at a rate of

over 5 WRC per week. Sage demonstrated the least improvement

relative to baseline. However, he still increased by an average


of 3.33 WRC per week following the implementation of the behavior

intervention, compared to only 0.67 WRC per week increase during

baseline. These results provide replicated support for the

individualized collateral effects of the behavioral intervention

to improve reading performance.

Research Question 3: To what extent are reading and behavioral

interventions associated with combined effects?

Data from both classes were used to assess the combined

effects of the reading and behavioral interventions. The presence

of combined effects was determined by visually and statistically

comparing reading and behavior performance during the combined

intervention phase to reading and behavior performance during the

reading intervention only and behavior intervention only

conditions. Both class- and individual-level data supported the

additive benefits of combining reading and behavioral

interventions. The combined effects on behavior performance are

discussed first.

While participants in the 6th Grade class averaged 76% AET

during the behavioral intervention only condition, when the


reading intervention was combined, their average increased to

85%. This increase is equivalent to six additional minutes of

academic engagement per instructional hour, which is equal to 32

extra minutes of academic engagement for a six-hour instructional

day or 160 minutes over the course of a week. Similar effects

were found for the 7th Grade Class. The average AET for the 7th

Grade Class increased from 66% during the reading intervention

only phase to 89% when the behavioral intervention was added to

the reading intervention. This 23% increase is equal to 14-

minutes of extra academic engagement per instructional hour.

Together, the class-level data indicated that the reading and

behavioral interventions demonstrated combined effects.

The class-level data revealed similar findings for the DB

outcome. While the 6th Grade group engaged in DB for 12% of the

intervals during the behavioral intervention only phase, their DB

decreased to 8% during the combined intervention phase. As for

the 7th Grade group, the average DB decreased from an average of

18% during the behavioral intervention only phase to an average

of 5% during the combined intervention phase. Across both


classrooms, the participants as a group demonstrated reductions

in DB during the combined intervention phase when compared to the

behavioral intervention only phase.

Across both classrooms, all but one of the participants

demonstrated combined effects when compared to the reading or

behavior intervention only condition, see Figure 2. Sage was the

only participant whose behavior did not improve once the reading

intervention was combined with the behavioral intervention.

Further discussion of the null finding for Sage is discussed in

the section below.

With regard to reading outcome, the addition of the reading

intervention to the behavioral intervention resulted in an

increase from 4.78 WRC per week during the behavioral

intervention only condition to 6.68 WRC per week during the

combined intervention phase for participants in the 6th Grade

class. For students in the 7th Grade class, a similar increase

from 4.74 to 6.34 WRC per week was observed when the behavioral

intervention was combined with the reading intervention. For both

classes, these increases indicated a greater likelihood of


closing the gap between their performance and their peers’ when

receiving both reading and behavioral interventions.

Results for the individual-level data revealed that all

participants demonstrated additive improvements in reading

performance when the interventions were combined together, see

Figure 1. Ryan and Frank demonstrated the greatest net gain in

WRC per week followed by Marcia and Sage, when compared to

reading or behavioral intervention only phase. Although difficult

to see in Figure 1, the slopes or trend of performance in the

combined intervention phase was steeper than the trend or slope

in the solo intervention phase, which indicated combined effect

on reading performance for all participants.

Research Question 4: To what extent does the behavior function

moderate the collateral effects of the reading intervention?

The results of the FBAs indicated that four of the

participants’ problem behavior was hypothesized to be maintained

by escape/avoidance (Ramón, Frank, Jake and Marcia) and two of

the participants’ problem behavior was hypothesized to be

maintained by social attention (Sage and Ryan). Consistent with


one of the inquiries guiding this study, visual analysis revealed

that differential findings were observed based on the

hypothesized function of the problem behavior. While the

participants with hypothesized escape-maintained problem behavior

(Jake, Ramón, Frank, and Marcia) demonstrated improvements in

problem behavior with the introduction of the reading

intervention, the participants with hypothesized attention-

maintained problem behavior (Sage and Ryan) did not show

improvements in their behavior problems when the reading

intervention was introduced either alone or in combination with

the behavioral intervention (see Figures 2 and 3). The purest

examination of the moderating role of behavior function was for

Ryan. Ryan was in the class that received the reading

intervention only. Whereas Ryan’s AET and DB demonstrated minimal

to no improvement in response to the reading intervention, Jake

and Marcia both experienced notable changes in behavior

performance.

Sage’s data also provided support for the moderating effect

of behavior function on the collateral effect of the reading


intervention, as he was in the class that received the reading

intervention in addition to the behavioral intervention. While

Sage demonstrated improvements in problem behavior with the

behavioral intervention, he did not evidence incremental

improvements in behavior when the reading intervention was added.

Unlike Sage, Frank and Ramón demonstrated additive improvements

(reductions in DB and increase in AET) once the reading

intervention was combined with the behavioral intervention. Take

together, the results for Ryan and Sage provide data supporting

behavior function as a moderator of the collateral effects of the

reading intervention.

Discussion

Recent studies indicate that a transactional relationship

between reading and behavior problems may exist (Morgan, Farkas,

Tufis, & Sperling, 2008; Trzesniewski, Moffitt, Caspi, Taylor, &

Maughan, 2006). A transactional relationship implies that reading

problems cause behavior problems and, in turn, behavior problems

cause reading problems. Direct implications for intervention stem


from this transactional hypothesis to improve the performance of

with students who co-occurring reading and behavior problems.

As expected, the reading and behavioral interventions were

associated with positive confined effects. Given prior research

supporting the efficacy of the reading (repeated readings and

decoding instruction) and behavioral (CICO/self-monitoring)

interventions used in this study, it was little surprise that

they produced confined effects. Nevertheless, the results

provide additional support for the use of these particular

reading and behavioral interventions to improve students’ reading

and behavioral performance, respectively.

Findings also supported the main hypothesis of this study.

That is, findings provided evidence supporting a transactional

relationship between reading and behavior problems in that both

collateral and combined effects were observed. The reading

intervention contributed to unique improvements in behavioral

performance, while the behavioral intervention independently

produced gains in participants’ reading performance. Moreover,

when combined together, the reading and behavioral interventions


resulted in improvements in reading and behavior above and beyond

the effects produced by either intervention implemented

independently. These results, however, were tempered by the

moderating effect of behavior function on the collateral effect

of the reading intervention.

Consistent with our prediction, the collateral effects of

the reading intervention was moderated by the hypothesized

function of the participants’ behavior problems. The effect sizes

revealed that the reading intervention had collateral effects on

behavior problems for participants whose behavior problems were

hypothesized to be escape-maintained. However, for Ryan and Sage,

whose behavior problems were hypothesized to be attention-

maintained, the reading intervention was associated with minimal

to no improvements in behavioral performance. This finding is

consistent with notion of functionally equivalent interventions

(Maag, 2005). Given that Sage and Ryan engaged in disruptive

classroom behavior to gain attention from others—not to escape

difficult, boring, or otherwise aversive academic work—the

reading intervention did not diminish the need to for Ryan and


Sage to gain attention via disruptive classroom behaviors. On the

other hand, the reading intervention likely served to diminish

the difficulty and/or aversiveness of the reading/language arts

tasks for the participants with escape-motivated disruptive

behavior, which led to a reduction in behavior problems.

These findings challenge the universality of the notion of a

transactional relationship between reading and behavior problems,

as well as provide support for a functional, rather than a

topographical, approach to understanding behavior problems. For

some students with comorbid reading and behavior problems, the

relationship between the two appears to be best explained by

Hinshaw’s (1992) fourth explanation which stipulates that some

third unknown variable is responsible for the manifestation of

reading and behavior problems. It is also possible that in some

cases reading and behavior problems may not share a common

etiology.

It is noteworthy to discuss how the behavioral intervention

impacted reading scores, considering that a ‘can't do vs. won't

do’ procedure was implemented and potential participants with


reading performance deficits were excluded from this study. A

logical question to ask is: how did behavioral intervention

positively impact reading performance? One possible explanation

is that the improved reading fluency in response to the

behavioral intervention is a byproduct of increased student

engagement. In the classroom, engagement has been argued to serve

as the behavioral conduit through which a student’s attentional

resources are devoted to learning (Connell & Wellborn, 1991),

including the academic skills they acuire (Skinner & Belmont,

1993) and their overall academic achievement (Finn & Rock, 1997).

Given the critical role student engagement plays with regard to

learning (Fredricks, Blumenfeld, & Paris, 2004; Skinner, Furrer,

Marchand, & Kindermann, 2008), students were more likely to be

receptive of the business-as-usual reading instruction while

receiving the behavioral intervention, which resulted in greater

skill acquisition. Notwithstanding this explanation, there is

also the possibility that an unknown variable could better

explain the improved reading, which is consistent with Hinshaw’s

(1992) fourth explanation.


Implications

The results revealed that the whole, or combination, of

reading and behavioral interventions is greater than the either

of its parts. The implication of this finding is that school

staff should attempt to intervene on both fronts when confronted

with students with co-occurring reading and behavior problems.

This finding takes on enhanced meaning when interpreted in light

of the response to intervention (RtI) models of service delivery.

RtI has been a major stimulus for discussion in education,

particular with regard to providing academic supports (Cook,

Sprague, Browning Wright, & Sadler, 2008; Gresham, 2005). The

data from this study indicate that RtI models designed to support

students only with regard to academic performance may be narrowly

focused. Instead, a comprehensive RtI model that integrates

academic and behavioral approaches from assessment to

intervention is likely to be the most effective service delivery

system. This assertion is supported by McIntosh, Chard, Boland,

and Horner (2006) research synthesis in which they found that

combined academic and behavior service delivery models were


associated with better school-wide academic and behavior outcomes

than multi-tiered models focused on academics or behavior alone.

Limitations and Directions for Future Research

This study, like all others, has limitations that are

important to be cognizant of in order appropriately interpret the

findings. First, given the design, we were unable to evaluate

collateral effects of the interventions across both classes to

replicate the findings. Rather, only one class received the

reading intervention or behavioral intervention only, which

limited our evaluation of the confined effects to one class per

intervention. A better design with greater external validity

would be a group-based design consisting of randomly assigning

individuals to four treatment conditions: reading intervention

only, behavioral intervention only, combined reading and

behavioral intervention, and business-as-usual control.

Second, future research delving into collateral effects of

interventions should seek to confirm the present findings as well

as address new questions. Although evidence for collateral

effects between reading and behavioral interventions is provided,


its scope is limited to the specific interventions used in the

study. More research should be conducted to explore whether

using different reading and behavioral interventions will yield

similar results. If collateral effects do not hold across many

interventions, the transactional relationship between reading and

behavior problems, becomes questionable. Further work must be

done to determine which behavioral and academic interventions

produce optimal collateral effects.

Third, descriptive methods were used to develop a hypothesis

of behavior function. It is possible that the hypothesized

behavior functions for the participants were inaccurate; thus,

undermining the results of our moderator analysis. A more

tenable approach would have been to conduct experimental

functional analyses for each of the participants to ascertain

behavior function. Given the difficulties of conducting school-

based function analyses, we used descriptive FBA methodology.

Also, credence was added to the results of our FBA in that we

observed differential results based on the hypothesized function

of each of the participants behavior problems.


Last, the sample of participants included students who were

receiving special education services under the category of

specific learning disability. The findings of this study could

potentially be different if the sample included students who had

formally been identified as having an underlying emotional

disturbance. Although this may be true, as stated earlier, many

students with specific learning disabilities have undiagnosed EBD

(Lopez, Forness, MacMillan, Bocian, & Gresham, 1998; Merrell &

Walker, 2004); therefore, the sample included in this study is

likely to be representative of the population of students with

EBD.

Conclusion

This study provides evidence in support of the transactional

relationship between reading and behavior problems. However,

results tentatively indicate that this relationship may depend on

the function of the behavior problem, thereby moderating the

effect of reading interventions on students’ behavior problems.

School practitioners can benefit from an understanding of the

causal relationship between reading and behavior problems.


Further research is needed to determine with whom and under what

circumstances reading or behavioral interventions, or the

combination of the two, can be used to effectively address the

needs of students with co-occurring reading and behavior

problems.


References

Adams, G., & Brown, S. (2006). Six-minute solution: A reading

fluency program. Frederick,

CO: Cambium Learning Sopris West.

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator

variable distinction in social

psychological research: Conceptual, strategic, and

statistical considerations. Journal of Personality and Social

Psychology, 51, 1173–1182.

Benner, G., Nelson, R., & Epstein, M. (2002). Language skills of

children with EBD: A literature

review. Journal of Emotional and Behavioral Disorders, 10, 43–59.

Cooper, J.O., Heron, T.E., & Heward, W.L. (2007). Applied behavior

analysis (2nd Ed.). Upper

Saddle River, N.J.: Pearson Prentice Hall.

Crawley, S.H., Lynch, P., & Vannest, K. (2006). The use of self-

monitoring to reduce off-task


behavior and cross-correlation examination of weekends and

absences as an antecedent to off-task behavior. Child & Family

Behavior Therapy, 28, 29-48

Crone, D. A., Horner, R. H., & Hawken, L. S. (2004). Responding to

problem behavior in

schools: The behavior education program. New York: Guilford Press.

Filter, K.J., McKenna, M.K., Benedict, E.A., Horner, R.H., Todd,

A.W., & Watson, J. (2007).

Check in/Check out: A post-hoc evaluation of an efficient,

secondary-level targeted intervention for reducing problem

behaviors in schools. Education & Treatment of Children, 30, 69-84.

Finn, J. D., & Rock, D. A. (1997). Academic success among

students at risk for school failure..

Journal of Educational Psychology, 85, 571–581.

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004).

School engagement: Potential of the

concept, state of the evidence. Review of Educational Research, 74,

59–109.


Gagnon, J. C. & Leone, P. E., (2006). Elementary day and

residential schools for Children with

emotional and behavioral disorders: characteristics of

educators and students. Education and Treatment of Children, 29, 156-

179.

Gureasko-Moore, S., DuPaul, G.J., & White, G.P. (2007). Self-

management of classroom

preparedness and homework: effects on school functioning of

adolescents with Attention Deficit Hyperactivity Disorder.

School Psychology Review, 36, 647-664.

Hawken, L.S., MacLeod, K.S., & Rawlings, L. (2007). Effects of

the Behavior Education

Program on office discipline referrals of elementary school

students. Journal of Positive Behavior Interventions, 9, 94-101.

Hawken, L. (2006). School psychologists as leaders in the

implementation of a targeted

intervention: The Behavior Education Program (BEP). School

Psychology Quarterly, 21, 91-111.


Hinshaw, S. P. (1992). Externalizing behavior problems and

academic underachievement in

childhood and adolescence: Causal relationships and

underlying mechanisms.

Psychological Bulletin, 111, 127-155.

Holmbeck, G. N. (1997). Toward terminological, conceptual, and

statisti-cal clarity in the study

of mediators and moderators: Examples from the child-

clinical and pediatric psychology literatures. Journal of

Consulting and Clinical Psychology, 65, 599–610

Hosp, M.K., Hosp. J.L., & Howell, K.W. (2007). The ABCs of CBM: A

Practical Guide to

Curriculum-Based Measurement. New York: The Guilford Press.

Jones, K.M., Drew, H.A., & Weber, N.L. (2000). Noncontingent peer

attention as treatment for

disruptive classroom behavior. Journal of Applied Behavior Analysis,

33, 343-346.

Kauffman, J., Cullinan, D., Epstein, M. (1987). Characteristics

of students placed in special


programs for the serious emotionally disturbed. Behavioral

Disorders, 12, 175-184.

Kellam, S. G., Mayer, L. S., Rebok, G. W., & Hawkins, W. E.

(1998). Effects of improving

achievement on aggressive behavior and of improving

aggressive behavior on achievement through two preventive

interventions: An investigation of causal paths. In B. P.

Dohrenwend (Ed.), Adversity, stress, and psychopathology (pp. 486-

505). London: Oxford University Press.

Kern, L., & Clemens, N.H. (2007). Antecedent strategies to

promote appropriate classroom

behavior. Psychology in the Schools, 44, 65-75.

Lane, K. L. (2004). Academic instruction and tutoring

interventions for students with emotional

and behavioral disorders. In R. B. Rutherford, M. M. Quinn,

& S. R. Mathur (Eds.),

Handbook of research in emotional and behavioral disorders (pp. 462-486).

New York:

Guliford Press.


Lane, K.L., Wehby, J.H., Menzies, H. M., Gregg, R.M., Doukas,

G.L., & Munton, S.M. (2002).

Early literacy instruction for first-grade students at-risk

for antisocial behavior. Education

and Treatment of Children, 25, 438-458.

Lewis, T. J., Scott, T., & Sugai, G. (1994). The problem behavior

questionnaire: A teacher based

instrument to develop functional hypotheses of problem

behavior in general education classrooms. Diagnostique, 19,

103–115.

Lopez, M. F., Forness, S. R., MacMillan, D. L., Bocian, K. M., &

Gresham, F. M. (1998).

Children with attention deficit hyperactivity disorder and

emotional or behavioral disorders in primary grades: In-

appropriate placement in the learning disorder category.

Education & Treatment of Children, 19, 286–299.

Lynam, D.R. (1996). Early identification of chronic offenders:

Who is the fledgling psychopath?

Psychological Bulletin, 120, 209-234.


McIntosh, K., Chard, D. J., Boland, J. B., & Horner, R. H.

(2006). Demonstration of combined

efforts in school-wide academic and behavioral systems and

incidence of reading and behavior challenges in early

elementary grades. Journal of Positive Behavior Interventions, 8, 146-

154.

Merrell, K. W., & Walker, H. M. (2004). Deconstructing a

definition: Social maladjustment

versus emotional disturbance and moving the EBD field

forward. Psychology in the Schools, 41, 899- 910.

Morgan, P.L., Farkas, G., Tufis, P.A., & Sperling, R.A., (2008).

Are reading and behavior

problems risk factors for each other? Journal of Learning

Disabilities, 41, 417-436.

O'Neill, R.E., Horner, R.H., Albin, R.W., Sprague, J.H., Storey,

K., & Newton, J.S. (1997).

Functional assessment and program development for problem behavior: A

practical handbook (2nd ed.). Pacific Grove, CA: Brooks/Cole

Publishing Co.


Polloway, E.A., Epstein, M.H., Polloway, C.H., Patton, J.R., &

Ball, D.W. (1986). Corrective

Reading Program: An analysis of effectiveness with learning

disabled and mentally retarded students. Remedial and Special

Education, 7, 41-47.

Petscher, E.S., & Bailey, J.S. (2006). Effects of training,

prompting, and self-monitoring on staff

in a classroom for students with disabilities. Journal of

Applied Behavior Analysis, 39, 215-226.

Peterson, L.D., Young, K.R., Salzberg, C.L., West, R.P., & Hill,

M. (2006). Using self-

management procedures to improve classroom social skills in

multiple general education settings. Education & Treatment of

Children, 29, 1-21.

Reinke, W. M., Herman, K. C., Petros, H., & Ialongo, N. (2008).

Empirically-derived subtypes

of child academic and behavior problems: Co-occurrence and

distal outcomes. Journal of Abnormal Child Psychology, 36, 759-777.


Rock, M.L., & Thead, B.K. (2007). The effects of fading a

strategic self-monitoring intervention

on students’ academic engagement, accuracy, and

productivity. Journal of Behavioral Education, 16, 389-412.

Shapiro, E. S. (2004). Academic skills problems workbook (rev.). New

York: The Guilford Press.

Skinner, E. A., Furrer, C., Marchand, G., & Kindermann, T.

(2008). Engagement and

disaffection in the classroom: Part of a larger motivational

dynamic? Journal of Educational Psychology, 100, 765-781.

Svetaz, M. V., Ireland, M., & Blum, R. (2000). Adolescents with

learning disabilities: Risk and

protective factors associated with emotional well-being:

Findings from the National Longitudinal Study of Adolescent

Health. Journal of Adolescent Health, 27, 340–348.

Todd, A.W., Campbell, A.L., Meyer, G.G., & Horner, R.H. (2008).

The effects of a targeted


intervention to reduce problem behaviors: Elementary school

implementation of Check in-Check out. Journal of Positive Behavior

Interventions, 10, 46-55.

Trzesniewski, K., Moffit, T.E., Caspi, A. Taylor, A., & Maughan,

B. (in press). Revisiting the

association between reading achievement and antisocial

behavior: New evidence of an environmental explanation from

a twin study. Child Development, 77, 77-88.

Trout, A. L., Nordness, P. D., Pierce, C. D., & Epstein, M. H.

(2003). Research on the academic

status of children with emotional and behavioral disorders:

A review of the literature

from 1961 to 2000. Journal of Emotional and Behavioral Disorders, 11,

198-210.

VanDerHeyden, A. M., Witt, J. C., & Naquin, G. (2003).

Development and validation of a

process for screening referrals to special education. School

Psychology Review, 32, 204–227.


Walker, H.M., Ramsey, E., & Gresham, F.M. (2004). Antisocial behavior

in school: Evidence-

based practices. Belmont, CA: Wadsworth/Thomson Learning.

Walker, H.M., & Severson, H. (1990). Systematic screening for behavioral

disorders.

U.S. Department of Justice. (2001). Crime in the United States:

2001. Washington, DC


Table 1.

Inter-observer Agreement Estimates for Each Participant by Each Phase

Participant &

Phases

AET IOA DB IOA

Jake

Baseline 92% 98%Reading only 95% 100%Combined 95% 100%

Marcia


Ryan


Frank

Baseline 92% 100%Behavioral only 96% 96%Combined 95% 100%

Sage

Baseline 85% 94%Behavioral only 88% 96%Combined 96% 94%

Ramón

Baseline 90% 86%


Behavioral only 96% 92%Combined 92% 93%

Note: all estimates were either rounded up or down based on the

decimal in the tenths.

Preliminary Study of the Confined, Collateral, and Combined Effects of Reading and Behavioral...

Documents

Transcript of Preliminary Study of the Confined, Collateral, and Combined Effects of Reading and Behavioral...