The Effect of Choice-Making as an
Intervention for Problem Behavior:
A Meta-Analysis
Karrie A. Shogren
Michael N. Faggella-Luby
Sung Jik Bae
Michael L. Wehmeyer
University of Kansas
Abstract: Supporting people with disabilities in expressing preferences and making choices is
a core value in positive behavior support. Indeed, in recent years, the field has increasingly
focused its attention on the importance of making choices and the potential benefits of choicemaking opportunities in enhancing the quality of life of people with disabilities. In addition,
an emerging database is suggesting that providing opportunities to make choices can serve as
an intervention for decreasing problem behavior. The authors of this article examine the efficacy of the use of choice-making as an intervention for reducing problem behavior through a
meta-analysis of single-subject research studies using choice-making as an intervention. A
search of the PsycINFO and ERIC databases yielded 13 studies that met the meta-analysis criteria, with interventions affecting 30 participants. The impact of choice interventions was evaluated using the percentage nonoverlapping data and percentage zero data metrics. Overall,
providing choice opportunities resulted in clinically significant reductions in the number of
occurrences of problem behavior. The authors discuss the benefits of utilizing choice as an
intervention and provide future directions for research in this area.
The negative impact of problem behavior on the quality of
life of people with disabilities and their families has been
adequately documented through research. In recent years,
researchers have devoted increased attention to the impact
of environmental variables on problem behavior (Carr et
al., 2002; Carr et al., 1999; Dunlap, Foster-Johnson, Clarke,
Kern, & Childs, 1995; Dunlap & Kern, 1993; Dunlap, KernDunlap, Clarke, & Robbins, 1991; Koegel, Koegel, & Dunlap, 1996). Specifically, researchers in the area of positive
behavior support have introduced methods that focus on
(a) redesigning environments to make problem behaviors
“irrelevant, inefficient, and ineffective” (Horner, 2000,
p. 182) and (b) providing education in positive behaviors
that “increase the likelihood of success and personal satisfaction in normative academic, work, social, recreational,
community, and family settings” (Carr et al., 2002, p. 4).
Positive behavior support is undergirded by a focus on
person-centered values and the principles of normalization (Carr et al., 2002; Koegel et al., 1996). Moreover, the
movement toward a focus on positive behavior support
places considerable emphasis on promoting and enhanc-
228
ing self-determination (Wehmeyer, Baker, Blumberg, &
Harrison, 2004).
The importance of self-determination received more
attention in the 1990s, when research on promoting and
enhancing the self-determination of youth and adults with
disabilities emerged, largely as a result of a U.S. Department of Education initiative (Ward & Kohler, 1996). One
factor that spurred this educational emphasis on selfdetermination was an emerging recognition of the lack of
choice-making opportunities available to people with disabilities (Brotherson, Cook, Cunconan-Lahr, & Wehmeyer,
1995; Carr et al., 2002; Dunlap, Kern-Dunlap, Clarke, &
Robbins, 1994; Stancliffe & Wehmeyer, 1995; Wehmeyer,
2002; Wehmeyer & Bolding, 1999, 2001). Promoting
choice-making skills and opportunities has remained a
focus of interventions for promoting self-determination
(Algozzine, Browder, Karvonen, Test, & Wood, 2001). The
result has been both identification of and development of
methods to teach people with disabilities the skills they
need to make effective choices (Browder, Cooper, & Lim,
1998; Gothelf, Crimmins, Mercer, & Finocchiaro, 1994;
Journal of Positive Behavior Interventions
Volume 6, Number 4, Fall 2004, pages 228–237
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Martin & Marshall, 1997; Reid, Parsons, & Green, 1991;
Wehmeyer, Agran, & Hughes, 1998) and identification of
methods to increase choice-making opportunities in the
lives of these individuals (Lancioni, Bellini, & Oliva, 1993;
Lancioni, Oliva, Meazzini, & Marconi, 1993; Parsons,
Harper, Jensen, & Reid, 1997; Parsons & Reid, 1990; Reid
& Parsons, 1991). In turn, enhanced opportunities for
choice-making have been associated with positive outcomes in a variety of settings, including increased adaptive
behavior (Heller, Miller, Hsieh, & Sterns, 2000), work performance, task engagement, assignment accuracy, social/
communicative behavior, and eye contact (Kern et al.,
1998; Lancioni, O’Reilly, & Emerson, 1996) for people with
a wide range of disabilities, including intellectual and developmental disabilities, emotional disorders, pervasive
developmental disorders, and severe and multiple disabilities (Heller, Miller, & Factor, 1999; Kern et al., 1998; Lancioni et al., 1996).
A few researchers are now examining the provision of
choice-making opportunities as an intervention for reducing problem behavior. This seems promising for several
reasons. First, providing choice-making opportunities is a
first step in directing more attention toward the importance of self-determination to positive behavior support
and in the prevention of problem behavior. Furthermore,
given that some researchers argue that problem behavior
can be an expression of preference (Dattilo & Mirenda,
1987; Dunlap et al., 1995; Wehmeyer et al., 1998) or a
means of exerting control over one’s life (Wehmeyer, 1999),
providing choice-making opportunities may enable people
with disabilities to communicate their wishes and desires
in more appropriate ways.
The potentially reinforcing effects of expressing a preference, above and beyond the reinforcing value of the option selected, have also been discussed (DeLeon et al.,
2001; Fisher & Mazur, 1997; Fisher et al., 1999; Fisher,
Thompson, Piazza, Crosland, & Gotjen, 1997; Romaniuk
& Miltenberger, 2001; Thompson, Fisher, & Contrucci,
1998). Perhaps this is related to feelings of increased control over one’s environment (Dattilo & Rusch, 1985; Romaniuk & Miltenberger, 2001). In a recent review of the
literature on research into preference and choice opportunities, Romaniuk and Miltenberger concluded that existing research on choice interventions has generally shown
that such interventions have a positive impact on the lives
of individuals with disabilities, although the authors suggested that there is a long way to go in fully understanding
the mechanisms for this effect.
In the present study, we synthesized current knowledge regarding the impact of choice-making interventions
on problem behavior by conducting a meta-analysis of existing studies. A meta-analysis can provide insight into the
overall effect of an intervention, and by using a common
metric for evaluation, one may systematically investigate
overall effectiveness (Scotti, Evans, Meyer, & Walker, 1991;
Scruggs & Mastropieri, 1998; Scruggs, Mastropieri, & Casto,
1987). Furthermore, by systematically examining the differences in percentage nonoverlapping data (PND) and
percentage zero data (PZD) metric values across participant and intervention characteristics, we may gain further
information concerning variables that influence the efficacy of choice interventions. Because the majority of studies of choice interventions with people with disabilities
used single-subject research designs, the PND (Scruggs &
Mastropieri, 1998; Scruggs et al., 1987) and PZD (Scotti
et al., 1991) metrics were calculated to examine treatment
efficacy.
Method
PROCEDURE
We conducted a search of the PsycINFO and ERIC databases to identify all studies published prior to March 2003
that reported the effect of choice interventions on problem
behaviors exhibited by people with disabilities. A variety of
search terms and search term combinations were used, including choice + disability, problem behavior + choice, disability + choice + behavior, choice + mental retardation,
choice + autism, and choice + emotional disturbance. We
found additional articles in the reference sections of the articles identified through the PsycINFO and ERIC searches
and from review articles of the effects of choice interventions (Kern et al., 1998; Lancioni et al., 1996; Romaniuk &
Miltenberger, 2001; Stalker & Harris, 1998). Articles were
included for analysis if they met the following criteria:
(a) the recipient of the intervention had an identified
disability, (b) a choice intervention was implemented to
reduce problem behavior, (c) occurrences of problem behavior were measured as a dependent variable, and
(d) the effect of the intervention on problem behavior(s)
was reported graphically, with a clearly identifiable baseline (no choice) phase and intervention (choice) phases.
Our search yielded 13 articles from six journals, with a
total of 30 participants. Eight participants were girls or
women (mean age = 11.1 years) and 22 were boys or men
(mean age = 10.1 years). The majority of the interventions
(85%) involved school-age children, ranging from 5 to 21
years of age. However, one study targeted a 50-year-old
man living in a group home (Bambara, Koger, Katzer, &
Davenport, 1995), and another study targeted a 4-year-old
boy in a general education preschool (Frea, Arnold, & Vittimberga, 2001). The majority of these studies used the
term problem behavior globally to encompass a number of
different behaviors exhibited by study participants, including acting aggressively, engaging in noncompliance, leaving the area, exhibiting off-task behavior, and destroying
property.
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Journal of Positive Behavior Interventions
Each study was coded to determine (a) the type of
choice intervention used, (b) the type of activity in which
the choice procedure was embedded, (c) whether training
was provided to participants on how to make choices prior
to intervention, and (d) the type of experimental design
used to examine the effect of the choice intervention. In
coding study characteristics, we identified two types of
choice interventions:
1. interventions that allowed participants to choose
the order in which they completed assigned
tasks, subsequently called task order (62% of
studies) and
2. interventions in which participants chose
between two activities, subsequently called
either/or (38% of studies).
We identified three types of activities: academic, daily living, and vocational. However, because several studies used
some combination of activities, type of activity was coded
as follows: academic (46%), daily living (31%), vocational
(0%), or combination (23%). Preintervention training in
how to make choices was coded as present (15%) or absent
(85%), subsequently called choice training. Finally, the type
of experimental design was coded as reversal (ABAB,
69%), multiple baseline (23%), or other (8%).
We also collected information on each participant
(n = 30), including (a) gender, (b) age, (c) diagnosis,
(d) setting, (e) type of behavior, and (f) whether a functional assessment or analysis of the participant’s problem
behavior had been conducted and used to design the intervention. Girls and women constituted 27% of the sample, and boys and men constituted 73%. Each participant’s
age was recorded in years and ranged from 4 to 50 (M =
10.4). Based on participants’ primary diagnoses, we identified five disability categories. The exception was one child
whose primary diagnosis was listed as “Down syndrome”
with a secondary diagnosis of mental retardation. This
child was included in the mental retardation category. The
five categories were emotional disturbance (17% of participants), autism (23%), developmental disabilities (13%),
attention-deficit/hyperactivity disorder (13%), and mental
retardation (33%). The setting in which the intervention
was conducted for each participant was identified by its
level of inclusiveness and categorized into three groups: inclusive (i.e., a general education classroom or community
setting; 20%), segregated but community-based (i.e., a selfcontained class or resource room in a public school or
group home; 40%), and segregated (i.e., an institution or
inpatient facility; 40%). Although the majority of studies
used problem behavior or a similar term to describe a wide
array of behaviors, we were able to classify problem behavior for the majority of participants into two categories:
problem behavior that involved aggression to self or others
(50%) and problem behavior that did not include aggression (33%), subsequently called aggressive and nonaggressive (see Note).
We examined each study meeting the criteria described previously for treatment efficacy by using the PND
and PZD metrics as indices of behavior change. PND is a
measure of the proportion of nonoverlapping data between the baseline and treatment phases. It is calculated by
dividing the number of treatment data points that fall
below the lowest baseline data point by the total number of
data points in the treatment phase, multiplied by 100
(Scruggs et al., 1987). PND scores can range from 0% to
100%, with higher scores indicating more effective treatments. Scotti et al. (1991) suggested specific criteria to
evaluate the practical implication of PND values, with
PND scores ranging from 50% to 80% identifying the
treatment as questionable, scores greater than 80% and less
than 99% as fair, and scores greater than 99% as highly effective. When baseline data reach a floor level of performance (i.e., zero), the result will be a PND of zero, which
in some circumstances may not be an appropriate representation of treatment effects. For this reason, Scruggs
et al. developed a rule to discriminate between appropriate
and inappropriate PND calculations. The rule states that
when not more than three baseline data points, nor less
than 331⁄3% of total baseline data points (to account for
differing numbers of data points) reach a floor level, PND
cannot be calculated (see Scruggs et al., 1987, for further
information and examples).
PZD is a measure of the degree to which an intervention reduced and maintained a behavior at zero levels,
which is arguably the goal of most interventions aimed at
reducing problem behavior. It is calculated by identifying
the first data point in the treatment phase that reached
zero and calculating the percentage of data points from
that point onward that remained at the zero level (Scotti
et al., 1991). PZD scores also range from 0% to 100%, with
higher scores indicating more effective treatments. Scotti
et al. suggested specific criteria to evaluate the practical
implication of PZD values, with PZD scores under 18%
identifying the treatment as ineffective, scores from 18%
to 54% identifying it as questionable, scores from 55% to
80% identifying it as fairly effective, and any score over
80% indicating that the treatment is highly effective.
ANALYSES
We calculated PND and PZD scores for each unique treatment phase that was identified in the studies and its preceding baseline. For studies that utilized a BABA design
with one or more participants, we analyzed only treatment
phases with a baseline preceding them. One study used a
multielement design followed by a choice intervention
(Peterson, Caniglia, & Royster, 2001), and for this study the
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free-play condition, which the authors described as a control condition, was used as the baseline comparison to
calculate PND and PZD statistics. We also calculated descriptive statistics for PND and PZD scores across each
unique intervention and participant variable.
Based on recommendations by Scruggs et al. (1987)
and Scotti et al. (1991), we used nonparametric procedures
to analyze for the effects of the various intervention and
participant characteristics on PND and PZD scores. To examine the effect of the type of choice-making intervention,
activity, and choice pretraining on treatment efficacy, we
conducted separate Kolmogorov-Smirnov two-sample
tests, with PND and PZD scores as dependent variables.
Because participants were associated with varying numbers of PND and PZD scores due to the number of unique
treatment phases in single-subject research designs, we calculated average PND and PZD scores for each participant
and conducted analyses using these mean scores to examine the effect of gender, age, disability, and setting on
treatment efficacy. For purposes of analysis, we used a frequency distribution to identify two age groups. The first
group consisted of children ages 4 to 7 years and represented 47% of the sample. The second group was composed of participants ages 8 through 50 years and
represented 53% of the sample. To examine the effect of
gender, age, and type of problem behavior on treatment
efficacy, we conducted separate Kolmogorov-Smirnov
two-sample tests with each participant’s mean PND and
PZD score as the dependent variable. We examined the effects of disability and setting with separate Kruskal-Wallis
one-way ANOVAs, with each participant’s average PND
and PZD scores again used as the dependent measure.
The first and second authors assessed the reliability of
PND and PZD scores by independently calculating these
scores for each unique treatment phase and its preceding
baseline; agreement was scored when the first and second
authors obtained identical PND and PZD scores for each
unique phase. Reliability was calculated by dividing the
number of agreements by the number of agreements + disagreements, multiplied by 100. Reliability was 97% for
both PND and PZD scores.
Results
PND and PZD analyses resulted in 56 unique PND scores
and 59 unique PZD scores. The difference in these numbers of unique scores resulted from the inapplicability of
the PND procedure for three graphs, based on the criteria
for calculating the PND statistic discussed previously. PND
and PZD scores showed significant variability across treatment phases. The overall mean PND score was 65.7%
(SD = 41.0), with a range of 0% to 100%. The overall mean
PZD score was 42.3% (SD = 42.4), with a range of 0% to
100%. Mean PND and PZD scores for each of the intervention characteristics are presented in Table 1, and mean
PND and PZD scores by participant characteristic are presented in Table 2.
The results of the Kolmogorov-Smirnov two-sample
tests are presented in Table 3. The only significant effect
was that of gender on PZD scores (Kolmogorov-Smirnov
Z score = 1.49, p = .02). The mean PZD score for boys and
men was 53.7%, whereas the mean score for girls and
women was 15.6%, as shown in Table 2, meaning that the
former had a higher level of elimination of behavior than
Table 1. Means and Standard Deviations of PND and PZD Scores for Intervention Characteristics
Variable
PND (M)
(SD)
PZD (M)
(SD)
Choice training
Yes
No
61.3
66.6
(44.3)
(40.7)
50.6
40.6
(48.8)
(41.3)
Choice design
Task order
Either/or
63.4
72.0
(42.5)
(37.0)
36.4
59.7
(41.7)
(40.9)
Activity
Academic
Daily living
Combination
64.1
78.7
56.5
(44.9)
(31.7)
(41.7)
29.7
63.5
43.6
(37.1)
(39.8)
(46.9)
Research design
Reversal (ABAB)
Multiple baseline
Other
69.4
49.5
0.0
(40.2)
(40.0)
(N/A)
39.6
57.2
22.2
(41.1)
(49.4)
(N/A)
Note. PND = percentage nonoverlapping data; PZD = percentage zero data.
232
Table 2. Means and Standard Deviations of PND and PZD Scores for Participant Characteristics
Variable
PND (M)
(SD)
PZD (M)
(SD)
Age (yrs.)
4–7
8–50
78.0
53.6
(34.3)
(40.3)
47.4
40.2
(41.2)
(37.3)
Gender
Female
Male
68.7
61.4
(41.6)
(39.1)
15.6
53.7
(29.7)
(36.9)
Setting
Inclusive
Segregated in community
Segregated
45.3
63.8
72.5
(42.9)
(38.3)
(38.2)
35.4
39.6
51.6
(47.2)
(43.0)
(30.8)
Disability
Emotional disturbance
Autism
Developmental disabilities
Attention-deficit/hyperactivity disorder
Mental retardation
91.7
66.7
63.4
65.4
55.1
(11.8)
(47.1)
(30.5)
(45.2)
(41.1)
67.2
55.1
32.9
32.3
32.5
(41.0)
(35.4)
(27.3)
(40.3)
(42.2)
Behavior
Aggressive
Nonaggressive
Missing data
78.4
54.3
46.5
(30.0)
(43.6)
(44.6)
60.0
40.1
1.1
(34.9)
(38.0)
(2.5)
Functional assessment/analysis results
Yes
No
65.4
62.3
(43.9)
(37.1)
33.2
49.6
(41.2)
(36.9)
Note. PND = percentage nonoverlapping data; PZD = percentage zero data.
Table 3. Kolmogorov-Smirnov Results for Intervention and
Participant Characteristics
Variable
Kolmogorov-Smirnov Z
Study characteristics
Choice training
PND
PZD
Choice type
PND
PZD
Participant characteristics
Age
PND
PZD
Gender
PND
PZD
Behavior
PND
PZD
Functional assessment/analysis
PND
PZD
Note. PND = percentage nonoverlapping data; PZD = percentage zero data.
Sig.
.41
.74
.99
.65
.53
1.08
.94
.19
.99
.51
.29
.96
.53
1.49
.94
.02
.82
.98
.52
.29
.54
.88
.94
.42
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did the latter. The results of the Kruskal-Wallis one-way
ANOVA are presented in Table 4. No significant effects
were found.
Discussion
These findings provide preliminary support for the benefit
of providing choice-making opportunities as an intervention for problem behavior. Choice-making interventions
resulted in treatment levels below the lowest baseline data
point 65.7% of the time, and 42.3% of treatment points
after the first zero point remained at the zero level. Although the criteria for evaluating the practical implication
of the PND and PZD values proposed by Scotti et al.
(1991) suggest that these scores fall in the questionable
range with regard to efficacy, we believe that (a) the criteria for evaluating the potential benefit of treatments
should not be applied universally and (b) other factors
must also be taken into account. As was discussed previously, there are several reasons that providing opportunities for people with disabilities to make choices is an
important component of providing support to people with
disabilities. First, there is a growing acknowledgement that
people with disabilities have the same rights as all persons
to exert control in their lives and to express preferences
and have those preferences determine decisions about
their lives. Second, promoting choice-making opportunities (and teaching choice-making skills, when necessary)
is a key component of promoting and enhancing selfdetermination. Enhanced self-determination has, in turn,
been linked to more positive adult outcomes for students
with disabilities (Wehmeyer & Palmer, 2003; Wehmeyer &
Schwartz, 1997) and more positive perceptions of quality
of life for adults with disabilities (Wehmeyer & Schwartz,
1998). Therefore, the finding that providing opportunities
for people with disabilities who engage in problem behaviors to make choices has the impact of reducing problem
behavior is, in some ways, an additional benefit of doing
what needs to be done for other reasons.
Furthermore, the finding that two thirds of treatment
points improved compared to baseline and that more than
40% of treatment points stayed at zero once that level was
reached simply by providing choice opportunities suggests
not that this is a moderately beneficial treatment, but
rather that if all interventions included a choice-making
component, the impact of those treatments might be considerably enhanced (although empirical research is necessary to determine if this hypothesis is true). More attention
has been devoted in recent years to the concept of clinical
significance, which Kadzin (1999) defined as
the practical or applied value or importance of the effect of
the intervention—that is, whether the intervention makes
a real (e.g., genuine, palpable, practical, noticeable) difference in everyday life to the clients or to others with whom
the client interacts. (p. 332)
Table 4. Kruskal-Wallis One-Way ANOVA Results for
Intervention and Participant Characteristics
Variable
Study characteristics
Activitya
PND
PZD
Research designa
PND
PZD
Participant characteristics
Settinga
PND
PZD
Disabilityb
PND
PZD
χ2
p value
2.29
5.08
.32
.08
3.39
0.82
.18
.66
2.54
.85
.28
.65
1.36
5.09
.85
.28
Note. PND = percentage nonoverlapping data; PZD = percentage zero data.
adf = 2 for each analysis. bdf = 4 for each analysis.
Kadzin related clinical significance to improved quality of
life. He suggested that regardless of its statistical significance, an intervention that enhances a person’s quality of
life could be of great significance to the person or those
around him or her. We suggest that incorporating choicemaking opportunities into behavioral treatment programs
fits into this category.
EFFECT OF INTERVENTION AND
PARTICIPANT CHARACTERISTICS
In the analysis of the differential effects of study and participant characteristics, the only variable that demonstrated a statistically significant effect was gender, with
boys and men showing higher levels of elimination of
problem behavior but no differences in reduction of problem behavior. This finding may be an artifact of the traditionally differential, and oftentimes lacking, attention
given to the problem behavior of girls and women. Men
and boys traditionally get more attention for less frequent
or intense problem behavior (Rousso & Wehmeyer, 2001),
and perhaps girls and women labeled as having problem
behavior in need of intervention were more likely to have
pervasive and serious behaviors that were less amenable to
elimination. More research needs to be done to explore
this finding.
Although none of the other variables achieved statistical significance, by looking at the trends in PND and PZD
scores across these variables, areas for future investigation
can be identified. We suggest that for some variables, nonsignificant values may have resulted from our limited sample size. Despite this limitation, however, we believe our
methodology was the most appropriate for assessing the
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Journal of Positive Behavior Interventions
data. We also suggest that despite the nonsignificant values, by critically inspecting the trends in PND and PZD
scores across a number of variables, we can provide some
preliminary direction for future research.
In terms of participant characteristics, this data set
supports previous research suggesting that interventions
to reduce problem behavior are most effective when implemented at an early age (Dunlap & Fox, 1996; Dunlap,
Johnson, & Robbins, 1990; Fox, Dunlap, & Cushing, 2002).
The mean PND score for children ages 4 years to 7 years in
the data set was 78%, whereas the mean PND score for
children and adults ages 8 years to 50 years was 54%. Future research will need to explore this further and determine which factors are related to the mechanisms for the
effect of early choice interventions (e.g., prevention of
learned helplessness, increased feelings of control).
Participants who displayed aggressive behavior showed
a trend toward greater reduction and elimination of problem behavior than did participants who displayed nonaggressive behavior. These results need to be viewed with a
great deal of caution, however, as five participants were
excluded from the analyses because their specific target
behaviors were not described. These trends suggest that
choice interventions had less impact on nonaggressive
(mainly off-task) behaviors, although an average 54% reduction and 40% elimination of these behaviors occurred.
In addition, although trends suggested differential effects
of choice interventions across these two categories of behavior, there was still significant variation in the efficacy of
the choice interventions within these two categories, as in
the majority of participant and study variables we analyzed. It will be important in future research to examine
the influence of the combination of within-subject factors
on the efficacy of choice interventions.
The impact of using the results of a functional assessment or analysis to design the choice intervention did not
result in noticeable differences in the reduction of participants’ problem behaviors. In studies that did use functional assessment or analysis, we did find some indication
that problem behaviors maintained by specific contingencies, such as escape from demands, may be more affected
by opportunities for choice than problem behaviors maintained by other contingencies (Dunlap et al., 1995; FosterJohnson, Ferro, & Dunlap, 1994). For participants whose
functional assessment or analysis results suggested escape
as the reinforcement contingency (n = 8), the mean reduction in problem behavior (77%, range = 0%–100%) was
notably higher than for other contingencies (n = 3, M =
33%) and higher than in participants who did not have
functional assessment or analysis results (n = 19, M =
53%). Future research should continue to investigate the
degree to which functional assessment or analysis results
affect the efficacy of choice interventions.
It is important to look at the long-term effects of
choice-making interventions across different environ-
ments, given the degree to which environments vary in
their provision of choice opportunities (Stancliffe, 2001;
Stancliffe & Abery, 1997; Stancliffe, Abery, & Smith, 2000;
Stancliffe & Wehmeyer, 1995) and the ability of choice interventions to support comprehensive lifestyle change. As
Horner and colleagues (1990) noted,
The positive/nonaversive approach focuses on the lifestyle
of the individual, in addition to the frequency, duration,
and intensity of the challenging behaviors (Horner, Dunlap, & Koegel, 1988). Behavioral support should result in
durable, generalized changes in the way an individual behaves, and these changes should affect the individual’s access to community settings, to social contact, and to a
greater array of preferred events. (p. 127)
The type of choice design—task order or either/or—appeared to have little impact on the degree to which behavior was reduced, suggesting that different forms of choice
interventions may have similar levels of efficacy in regard
to reducing problem behavior. More specific analyses of
the impact of the meaningfulness of the choice to the
participant, such as an assessment of social validity, may
provide further information about the impact of choice
interventions.
Finally, the type of research design used was associated with some variability across the interventions. When
multiple-baseline designs were used, the PZD scores
tended to be higher, but when reversal designs were used,
the PND scores tended to be higher. The effect of research
design on the obtained results needs to be considered and
systematically evaluated for the production of differing effects that are simply an artifact of the design.
LIMITATIONS OF THE STUDY
Aside from the previous discussion of the small sample
size and limitations introduced in data analysis, there were
other limitations to the present study that should be considered in evaluating its implications. First, we made no attempt to evaluate the quality of the articles included in the
analyses, beyond including only articles published in peerreviewed journals. This means that any methodological issues or flaws in the studies had no impact on our analyses.
Second, there were a number of variables we were unable
to incorporate into our analyses. One of these is the body
of research that examines the differential effect of preference and choice. There is considerable debate as to whether
making a choice provides an additional benefit over simply
accessing a preferred item, with some researchers having
suggested that choice is a reinforcing event in and of itself
(Fisher & Mazur, 1997; Fisher et al., 1999; Fisher et al.,
1997) and others having indicated that choice-making
provides an added benefit only in some circumstances and
with certain populations (Cole, Davenport, Bambara, &
Ager, 1997; Parsons & Reid, 1990). However, more atten-
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tion is being directed to the assertion that choice-making
only provides a benefit when and if it provides “real” and
meaningful options for the person. Cullen (1999) argued
that the main question in determining the effect of choice
interventions should be the significance of the choice opportunity for the individual. Cullen suggested that people
with disabilities make few real or meaningful choices in
their lives, and if choice interventions are simply providing
alternatives, or choices between options of which none are
very highly preferred, the choice-making opportunities
may have no real meaning for the individual and may not
affect his or her problem behavior. This issue is important,
yet it is rarely addressed in empirical examinations of the
efficacy of choice interventions. None of the studies included in our analysis provided an assessment of the social
validity of the choice opportunity for the participants or
individuals around them. To develop a more in-depth understanding of the effect of choice, lifestyle variables
should routinely be assessed.
Michael N. Faggella-Luby, MEd, is a doctoral student in
special education at the University of Kansas, Center for Research on Learning. His research interests include issues of
self-determination related to meeting the needs of diverse
learners, especially individuals with learning and reading
disabilities. Sung Jik Bae, MA, is a doctoral student in special education at the University of Kansas. His current research focuses on the development of transition and
self-determination assessments for individuals with disabilities. Michael L. Wehmeyer, PhD, is an associate professor in
the Department of Special Education, director of the Kansas
University Center on Developmental Disabilities, and associate director of the Beach Center on Disability, all at the
University of Kansas. His research focuses on promoting selfdetermination for individuals with cognitive and developmental disabilities and technology use by people with intellectual disabilities. Address: Karrie A. Shogren, University of
Kansas, Beach Center on Disability, 1200 Sunnyside Ave.,
Rm. 3136, Lawrence, KS 66045.
CONCLUSIONS
NOTE
Given the early stages of research into the effect of choice
interventions on problem behavior, we believe this study,
despite its limitations, provides guidance and support for
future research in this area. Our findings support those of
Romaniuk and Milternberger (2001), who suggested that
choice interventions have clear benefits for individuals
with disabilities, particularly when considered in terms of
clinical significance, quality of life, and complexity of
problem behaviors. As many of these reviews suggested,
however, more research needs to address the mechanisms
for choice-making’s effectiveness; the reasons individuals
respond differently to choice opportunities; and the manner in which choice fits into broader concepts, such as selfdetermination, community participation, and positive
behavior support.
Making choices is a part of everyday life for the majority of people in the United States. It allows most individuals to experience control over their lives and, along
with other skills, enables them to act as causal agents
in their lives. The latter is the central function of selfdetermination, a contributing factor in increased quality of
life (Schalock, 2000; Wehmeyer & Schalock, 2001;
Wehmeyer & Schwartz, 1998). If, as a field, we are interested in improving the quality of life of people with disabilities, increasing choice-making opportunities and
reducing problem behaviors are critical.
Specific behaviors were not attributed to each participant in
Romaniuk et al. (2002); instead, these authors provided a
global description of all targeted behaviors across participants, making it impossible to determine if the individual
participants did or did not display aggression (n = 5, 17% of
paticipants). For detailed information regarding the distribution of study and participant characteristics across each of
the reviewed studies, please contact the authors.
ABOUT THE AUTHORS
Karrie A. Shogren, MA, is a doctoral student in special education at the University of Kansas. Her current research interests include methods of promoting and enhancing the
self-determination of youth and adults with disabilities.
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Action Editor: Glen Dunlap