EFFICACY OF AND PREFERENCE FOR REINFORCEMENT ANDRESPONSE COS

EFFICACY OF AND PREFERENCE FOR REINFORCEMENT
AND
RESPONSE COST IN TOKEN ECONOMIES
ERICA S. JOWETT HIRST
SOUTHERN ILLINOIS UNIVERSITY
CLAUDIA L. DOZIER
UNIVERSITY OF KANSAS
AND
STEVEN W. PAYNE
STATE UNIVERSITY OF NEW YORK
Researchers have shown that both differential reinforcement and
response cost within token
economies are similarly effective for changing the behavior of
individuals in a group context
(e.g., Donaldson, DeLeon, Fisher, & Kahng, 2014; Iwata &
Bailey, 1974). In addition, these
researchers have empirically evaluated preference for these
procedures. However, few previous
studies have evaluated the individual effects of these procedures
both in group contexts and in
the absence of peers. Therefore, we replicated and extended
previous research by determining
the individual effects and preferences of differential
reinforcement and response cost under both
group and individualized conditions. Results demonstrated that
the procedures were equally
effective for increasing on-task behavior during group and

individual instruction for most chil-
dren, and preference varied across participants. In addition,
results were consistent across partici-
pants who experienced the procedures in group and
individualized settings.
Key words: differential reinforcement, independent group
contingency, preference, response
cost, token economy
The token economy is a common behavioral
intervention that has been demonstrated to be
effective for increasing appropriate behavior
and decreasing inappropriate behavior for many
populations across different settings (Doll,
McLaughlin, & Barretto, 2013; Hackenberg,
2009; Kazdin, 1977). Token economies involve
delivery, removal, or both delivery and removal
of conditioned reinforcers (e.g., tokens and
points) that can be exchanged for back-up rein-
forcers (e.g., prizes, treats, and leisure activ-
ities). When tokens are delivered contingent on
appropriate behavior or for the absence of inap-
propriate behavior, these procedures are termed
differential reinforcement of alternative behavior
(DRA) or differential reinforcement of other
behavior (DRO), respectively. When tokens are
removed contingent on inappropriate behavior
or for the absence of appropriate behavior, this
procedure is termed response cost (RC).
An advantage of token economies is that
they can be implemented with a group of indi-
viduals as a general behavior-management strat-
egy during small-group instruction or as a

classwide intervention. Classwide behavior-
management strategies such as token economies
should be considered to address minor disrup-
tive behavior, to increase motivation for learn-
ing, or as a complement to an individualized
intervention. However, general behavior-
management strategies may not be effective in
isolation for some individuals who engage in
severe problem behavior or have more intense
Correspondence concerning this article should be
addressed to Claudia L. Dozier, Department of Applied
Behavioral Science, University of Kansas, Lawrence, Kan-
sas 66045 (e-mail: [email protected]).
doi: 10.1002/jaba.294
JOURNAL OF APPLIED BEHAVIOR ANALYSIS 2016, 49,
329–345 NUMBER 2 (SUMMER)
329
deficits in learning. These individuals may
require more individualized, function-based
assessment, intervention, and additional sup-
port. Regardless, token economies are common
in classrooms and numerous other environ-
ments because they are likely to create motiva-
tion for changes in behavior for most
individuals in the group, creating a more man-
ageable and effective learning environment.
After numerous studies were conducted to
demonstrate the effectiveness of reinforcement
and RC procedures in token economies,

researchers began to compare the effectiveness
of these two procedures (e.g., Brent & Routh,
1978; Broughton & Lahey, 1978; Iwata & Bai-
ley, 1974; Panek, 1970). Overall, most studies
that have compared differential reinforcement
(DR) to RC have demonstrated equal effective-
ness of the two procedures (e.g., Capriotti,
Brandt, Ricketts, Espil, & Woods, 2012;
Donaldson, DeLeon, Fisher, & Kahng, 2014;
Iwata & Bailey, 1974; McGoey & DuPaul,
2000). However, these results are limited in
two important ways. First, most studies
involved the use of group contingencies (i.e.,
the implementation of the procedures in the
context of a group in which others are present),
which may have influenced responding. For
example, comments made or behaviors mod-
eled by others in the group may have influ-
enced target responding. Second, most studies
reported only group averages with respect to
target behavior, which does not allow analysis
of individual differences. For example, Iwata
and Bailey (1974) compared DRO and RC for
decreasing rule violations and increasing on-
task behavior of 15 children in a classroom.
During DRO, tokens were delivered at the end
of a 3- to 5-min interval if no rule violations
occurred during that interval. During RC,
tokens were removed at the end of an interval
if any rule violations occurred during that inter-
val. The children could earn or lose up to
10 tokens throughout a 30-min math period,
and the tokens could be exchanged for snacks
and free time. Results showed that the proce-
dures were similarly effective for reducing rule

violations and off-task behavior. However, the
authors reported group averages, which may
not be representative of individual responding.
Furthermore, because the study was conducted
as a group intervention, the influence of peer
behavior on target responding is unknown.
More recently, Donaldson et al. (2014) com-
pared DRO and RC for decreasing the disrup-
tive behavior of 12 first-grade students.
Although the procedures were implemented in
a group context, the authors reported both
group-average outcomes and individual out-
comes. Group-average data showed low to zero
levels of problem behavior; however, an analysis
of individual data showed that responding dur-
ing DRO was somewhat variable for four of
the 12 participants. Although this study, along
with Iwata and Bailey (1974) and most others,
provides preliminary evidence regarding the
effectiveness of reinforcement and RC when
used in a token economy, because the proce-
dures were implemented in a group context,
the influence of peers on target responding is
unknown. For example, individuals may show
an increase or decrease in target behavior
because their peers are (a) engaging in a target
behavior, (b) prompting them to engage in a
target behavior, (c) providing reinforcers (e.g.,
attention) for them to engage in appropriate
target behavior, (d) implementing punishers
(e.g., reprimands) for not engaging in a target
behavior (Salend & Kovalich, 1981), or
(e) extinguishing previously reinforced target
behavior (e.g., no longer delivering attention).
Therefore, to further isolate the effects of rein-

forcement and RC contingencies in token
economies, conducting the comparison while
students work independently or are otherwise
not in the presence of others might be
important (Capriotti et al., 2012; Sindelar,
Honsaker, & Jenkins, 1982). Furthermore,
comparing responding of a single individual
when in the presence and absence of peers to
ERICA S. JOWETT HIRST et al.330
determine whether changes in responding are
associated with the presence or absence of peers
would be useful.
In addition to comparing the effectiveness of
DR and RC procedures in individual and
group contexts, considering preference is also
important; however, only two studies that have
compared DR and RC in token economies
have empirically evaluated preference
(Donaldson et al., 2014; Iwata & Bailey,
1974). Iwata and Bailey (1974) compared the
effects of DRO and RC for reducing disruptive
classroom behavior displayed by 15 elementary
school special-education students. To deter-
mine preference across the procedures, the
experimenters conducted a choice assessment
during which each child was given the opportu-
nity to select which token procedure would be
implemented for a particular session. After all
children made a selection, the chosen token
procedure was implemented for each child. The
results showed that four students chose DRO

most often, five students chose RC more often,
and six students switched their selection across
opportunities. Donaldson et al. (2014) used a
similar procedure and found that six of the
12 children preferred RC, four children pre-
ferred DRO, and two children had approxi-
mately equal preference.
These studies provide evidence that prefer-
ence varies among individuals; however, the
results are limited, at least in Donaldson
et al. (2014), because children made selections
vocally and in the presence of their peers
(Iwata & Bailey, 1974, did not provide infor-
mation regarding how or where children made
a selection). Therefore, some children’s selec-
tions may have been influenced by the presence
or behavior (e.g., choices or comments) of their
peers (Donaldson et al., 2014). To isolate indi-
vidual preference, it is important to conduct a
preference assessment when the child is not in
the presence of his or her peers (e.g., Layer,
Hanley, Heal, & Tiger, 2008). For example,
Layer et al. (2008) presented choices on an
upright board in front of each child with the
choices facing the child (not visible to other
children) and then had the child use a motor
response (i.e., pointing), rather than a vocal
response (i.e., stating which procedure he or
she liked best), to make his or her selection.
This procedure controlled for both visual and
auditory observation of other children’s choice.
Overall, given the demonstrated effectiveness
of DR and RC but unknown influence of peers

and lack of empirical data for preference in the
absence of peers, further research is warranted.
The current study involved several evaluations
that replicate and extend previous research. The
purpose of the first evaluation was to replicate
research directly comparing the effectiveness of
DR and RC procedures in a group setting. The
second purpose was to provide a direct compar-
ison of the effectiveness of DR and RC proce-
dures for the on-task behavior of individual
children engaged in a solitary work task. The
third purpose was to evaluate individual prefer-
ence of all children in the absence of peers.
Finally, responding of individuals who partici-
pated in both the small-group activity and the
solitary work task was compared to determine
if the presence of peers influenced responding.
STUDY 1: DR VERSUS RC (GROUP)
Method
Participants and setting. Three groups of
three typically developing preschool-aged (3 to
5 years old) children who attended a university-
based preschool program participated. All chil-
dren could follow multistep instructions (e.g.,
walk to your cubby, hang up your jacket, and
come sit on the floor) and communicated using
vocal speech. We conducted sessions 3 to 5 days
per week, once or twice per day, in a quiet area
of the classroom separate from all other chil-
dren. During each session, only one group of
participants was present. Participants sat next
to one another on the floor on designated mats
across from the experimenter, and one to two

331REINFORCEMENT AND RESPONSE COST
data collectors and relevant session materials
were present.
Materials. During all sessions, small-group
activity materials were present. Materials
included plastic letters and numbers for expres-
sive labeling and individual bingo boards with
various items (i.e., plastic buttons and jewels)
for matching. During some sessions, tokens
(i.e., pennies) were present that could be earned
or lost. Tokens were attached to and removed
from laminated strips of paper (approximately
10.2 cm by 30.5 cm) with 10 square pieces of
Velcro. Participants earned access to a toy room
with tangible items (e.g., stickers, plastic rings,
spin tops, sticky hands), edible items (e.g.,
gummies, Smarties, Skittles, and M&Ms), and
leisure activities (e.g., video games and DVDs)
via token exchange following some sessions
(DR and RC). Different-colored materials (pos-
ters and token boards) were present during
each of the different conditions to aid in dis-
crimination between conditions.
Response measurement and interobserver agree-
ment. Trained graduate and undergraduate stu-
dents collected data using paper and a pencil.
The dependent variable was percentage of
intervals with on-task behavior. We defined on-
task behavior as sitting on a mat (i.e., bottom
on the mat), keeping hands to oneself (i.e.,

keeping hands in lap unless instructed to
manipulate activity materials), and sitting
quietly (i.e., talking only when the experi-
menter asked or called on the participant to
respond). We partitioned sessions into 5-s
intervals and scored on-task behavior for each
child using a momentary-time-sample proce-
dure. That is, at the end of every 5-s interval
(signaled by an auditory cue), the data collector
scored whether each child was on task at that
moment. After each session, we collected data
for on-task behavior of an individual child by
dividing the number of intervals on task by the
total number of intervals in the session and
converting the result to a percentage. In addi-
tion, for two groups, experimenters collected
data on the number of tokens that remained on
each participant’s board at the end of a DR ses-
sion or the number of empty spaces on each
participant’s board at the end of each RC ses-
sion. We later subtracted the number of empty
spaces counted after RC sessions from 10 to
compare number of net tokens in each session.
Two independent observers collected data
for at least 30% of sessions and then calculated
interobserver agreement for on-task behavior by
dividing the number of 5-s intervals during
which both observers agreed by the total num-
ber of intervals and converting the result to a
percentage. We defined an agreement for on-
task behavior as both observers scoring or not
scoring the occurrence of the behavior in a
given interval. We calculated interobserver
agreement for token count using the total

method. That is, we divided the smaller num-
ber of tokens that remained on a board (at the
end of each DR session) or were missing from
the board (at the end of each RC session) by
the larger number and converted the result to a
percentage. Interobserver agreement averaged
93% (range, 73% to 100%) for on-task behav-
ior and 99% (range, 88% to 100%) for token
count.
Procedure. All sessions lasted 5 min. During
all sessions, the participants sat next to one
another and in front of the experimenter in a
small area away from the other children in the
classroom. In addition, the experimenter placed
bingo boards with pieces and token boards
(in some sessions) in front of each participant
and a colored poster board on the wall in front
of the children. Before the start of the first ses-
sion of each condition, the experimenter
described the rules and the session contingen-
cies and required each participant to practice
engaging in related behaviors (e.g., sitting
quietly, talking out of turn, keeping hands in
lap, and touching materials) to experience the
consequences associated with each behavior.
During the 5-min sessions, the experimenter
provided continuous individual and group
instructions to name letters and numbers (e.g.,
the experimenter held up a plastic letter and
said, “Caroline, what letter is this?” and “Can

everybody tell me what letter this is?”) and
place a marker on a specific bingo board letter
or number (e.g., “Ok everyone, put a gem on
the letter d”). The experimenter delivered sev-
eral instructions during a session in a way that
was similar to instructions delivered during a
classroom activity; however, the rate at which
instructions were provided varied depending on
responding. During all sessions, if a child
(or children) responded correctly, the experi-
menter delivered praise, and if any child did
not respond correctly, the experimenter
prompted the correct response and then moved
on to another instruction.
First, the experimenter conducted baseline
sessions to determine the level of on-task
behavior in the absence of programmed conse-
quences. Next, the experimenter practiced
token trading with the participants. That is, the
experimenter gave each child tokens and the
opportunity to trade the tokens for various
items (e.g., prizes and snacks). Next, we com-
pared DR and RC to determine their effects on
on-task behavior. During DR and RC sessions,
the experimenter observed each participant in
the group at the same moment every 30 s on
average (ranging from 15 to 45 s) according to
a schedule based on a pseudorandom number
generator in Excel. We created three versions of
the schedule and rotated across sessions to
reduce the likelihood that the participants
would learn a schedule. During each scheduled
observation and depending on the condition,
the experimenter quietly delivered a token to
every child who was on task at that moment

(DR) or removed a token from any child who
was off task at that moment (RC). The experi-
menter did not say anything when delivering or
removing a token. We used the same schedules
across both conditions; therefore, the possible
number of net tokens across conditions was
equal (i.e., 10 tokens). In addition, the last
opportunity to earn or lose a token was at the
last second of each session; therefore, no partic-
ipant could earn or lose all tokens before the
end of the session.
After each DR and RC session, an experi-
menter took the participant to a room that
contained many different toys, leisure activities,
edible items, and trinkets that were not found
in the preschool classroom and gave the partici-
pant the opportunity to trade tokens for edible
items or trinkets or engagement with a toy or
leisure activity. A participant could trade one
token for 1 min to play with a toy or leisure
activity, one token for one edible item to con-
sume, or three tokens for one trinket to take
home. Each participant could spend the num-
ber of tokens he or she had for any combina-
tion of the above. All participants traded all
tokens at the end of a session. We used a mul-
tielement design in which we rapidly alternated
baseline, RC, and DR conditions to compare
the effects of the different procedures on on-
task behavior.
Baseline. Before the start of all baseline ses-
sions, the experimenter described the rules and
contingencies for the session and posted a white

board on the wall in front of the participants.
The experimenter stated the rules as follows:
“Today it’s white, and there are no tokens.
When we start, you need to sit on your mat,
keep your hands to yourself, and raise your
hand to talk.” During the session, the experi-
menter did not provide any programmed con-
sequences for any behavior, with the exception
of responses to correct and incorrect responding
(as mentioned above).
Differential reinforcement. Before the start of
all DR sessions, the experimenter described the
rules and contingencies for the session, posted a
green poster board on the wall in front of the
participants, and placed a green board with no
tokens on the floor in front of each participant.
The experimenter stated the rules as follows:
“Today you get the green board, and it doesn’t
have any tokens. If you stay on your mat, keep
your hands to yourself, and raise your hand to
talk, you will get a token. If you get off your
mat, touch your friends, or talk during some-
one else’s turn, you will not get any tokens.
When small group is done, you can trade
your tokens for prizes and candy. If you don’t
have any tokens, you don’t get anythi ng.”
Each participant had his or her own token
board. Throughout the session, the experi-
menter watched a timer, and during a sched-
uled observation, placed a token on the token

board of any participant who was on task.
The experimenter did not deliver any pro-
grammed consequences for participants who
were not on task.
Response cost. Before the start of all RC ses-
contingencies for the session, posted a red
poster board on the wall in front of the partici-
pants, and placed a red board with 10 tokens
in front of each participant. The experimenter
stated the rules as follows: “Today you get the
red board, and it has 10 tokens. If you stay on
your mat, keep your hands to yourself, and
raise your hand to talk, you will keep your
tokens. If you get off your mat, touch your
friends, or talk during someone else’s turn, you
will lose tokens. When small group is done,
you can trade your tokens for prizes and candy.
If you don’t have any tokens, you don’t get
anything.” During the session, the experi-
menter followed the variable momentary obser-
vation schedule as in the DR condition;
however, when a scheduled observation
occurred, the experimenter did not deliver con-
sequences for any participant who was on task
and removed a token from any participant’s
token board who was not on task.
Choice. When we observed stable levels of
responding in the DR and RC phases for
each participant, we conducted a preference
assessment to determine the procedure that
each participant preferred. We conducted this
evaluation with Groups 2 and 3 only because
one participant in Group 1 left the preschool

before evaluation of preference. We used a pro-
cedure similar to that used by Layer
et al. (2008) to evaluate preference. Before each
session, the experimenter placed the stimuli
(i.e., different-colored token boards and materi-
als) associated with each type of condition (i.e.,
baseline, RC, and DR) on the floor where the
experimenter conducted sessions. We presented
the DR token board without tokens present
and the RC token board with all tokens on the
board. Near each of the token boards was a
small strip of paper that matched the color of
the stimuli (e.g., a green strip of paper was
placed in front of the the DR token board).
The experimenter called each participant to the
small-group area one at a time and reminded
him or her of the contingencies associated with
each set of materials. Next, the experimenter
asked the participant to pick which session he
or she liked best by placing the colored strip of
paper associated with the selected condition
into a canvas bag. When the participant made
a selection, he or she was asked to go play in
another area of the classroom until this proce-
dure was repeated with each participant. This
method reduced the likelihood that a partici-
pant’s choice would be influenced by other
children’s prompts or comments or by obser-
ving the choices of other members in the
group. Although it is possible that children
could have discussed their choices with a peer
before his or her selection, informal observa-
tions suggest that this did not occur. However,
we did observe participants occasionally discuss
their choices after all participants had made a

selection. After all participants independently
made a selection, the experimenter called them
to the small-group area, drew a color from the
bag, then explained the contingencies in place
for the chosen session. After the experimenter
had explained the contingencies for the chosen
procedure, the experimenter implemented the
type of session chosen as described above. We
determined individual preference by counting
the number of selections of each procedure; the
procedure that an individual selected most
often was identified as the preferred procedure.
During the choice phase, we calculated inter-
observer agreement for selection of a procedure
using a total agreement method. That is, we
scored an agreement if both observers agreed
which procedure the participant selected and a
disagreement if the two observers disagreed.
Thus, interobserver agreement for selection
of a procedure for a particular session was
either 100% (the two observers agreed) or 0%
(the two observers disagreed). Interobserver
agreement for selection was 100% for all
participants.
Results
Figure 1 displays graphs of the percentage of
intervals of on-task behavior for all participants
in Groups 1, 2, and 3 and individual cumula-

tive selections and experimenter-selected proce-
dures during the choice phase for Groups
2 and 3. During the initial baseline, most parti-
cipants engaged in moderate to low levels of
on-task behavior, although participants in
Group 1 engaged in somewhat higher levels of
on-task behavior. When we compared DR and
RC, we observed similarly high levels of on-task
behavior for six of the nine participants (93%
during DR and 95% during RC) and higher
levels of on-task behavior during RC for three
participants (Adam, Molly, and Carl). When
we evaluated preference, one participant
switched his selections but selected DR more
than RC (Paul), two participants switched their
selections but selected RC more than DR (Judy
and Molly), and three participants selected RC
exclusively (Carl, Jack, and Lance).
Table 1 provides a summary of results with
respect to percentage of selections during the
choice phase and average net tokens yielded
during the DR and RC comparison phase. We
did not evaluate preference or calculate net
tokens for Group 1; therefore, Table 1 includes
data only for participants in Groups 2 and
3. Preference results show that one participant
chose DR more than RC (Paul), and the other
five participants chose RC more than
DR. Also, three of six participants had an aver-
age difference of at least 0.5 tokens between
the two procedures, and all three participants
(Molly, Carl, and Lance) preferred response
cost, which was the procedure for which more
net tokens were yielded.

STUDY 2: DRA VERSUS RC
(INDIVIDUAL)
Method
The purposes of Study 2 were twofold. The
first purpose was to replicate Study 1 by com-
paring the effectiveness of and preference for
DR and RC in the context of an independent
work task. The second purpose was to compare
responding of participants in Studies 1 and
2 to evaluate the influence of the presence of
peers.
Participants and setting. Thirteen typically
developing preschool-aged (3 to 5 years old)
children (three of whom participated in Study
1) and one child with cerebral palsy (Brianna),
who were enrolled in a university-based pre-
school program, participated. All children could
follow multistep instructions and communi-
cated using vocal speech. We conducted ses-
sions 3 to 5 days per week, once or twice per
day, in session rooms that contained tables,
chairs, and relevant session materials. The
experimenter, one participant, and one or two
data collectors were present for each session.
Materials. During all sessions, we placed
worksheets with printed letters and shapes and
markers on a child-sized table, and two chairs
were available for the child and experimenter.
In addition, we placed toys from the preschool
classroom (e.g., puzzles, dolls, toy cars, coloring
book, and crayons) on the floor on the opposite

side of the session room. Tokens were identical
to those used in Study 1. We also used
different-colored token boards and poster
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ment. Trained graduate and undergraduate stu-
dents collected data using handheld computers.
The dependent variable during all sessions was
percentage of intervals of on-task behavior. We
defined on-task behavior as the first instance of
walking to the work table, the first instance of
removing the lid of the marker, moving the
marker approximately within the boundaries of

the printed lines of a worksheet, and turning
over pages to access a new worksheet. We did
not score on-task behavior if the participant
was scribbling or drawing pictures on the work-
sheet or making patterns (e.g., dashed lines or
dots) within the printed boundaries of the let-
ters or shapes. We partitioned sessions into 5-s
intervals and scored on-task behavior using
partial-interval recording. That is, we scored
on-task behavior if it occurred during any por-
tion of the 5-s interval. Next, we converted
data to a percentage by dividing the number of
intervals during which the child was on task by
the total number of intervals in the session. We
also collected data on the frequency of token
delivery (i.e., when the experimenter placed a
token on the token board) and token removal
(i.e., when the experimenter removed a token
from the token board).
We calculated interobserver agreement for
on-task behavior as in Study 1 and calculated
interobserver agreement coefficients for token
delivery or removal by dividing the session time
into 5-s intervals and comparing observer data
on an interval-by-interval basis. If exact agree-
ment occurred (i.e., both observers scored or
did not score a token delivery or removal
within a 5-s interval), we gave a score of 1 for
that interval. For any disagreements, we divided
the smaller score in each interval by the larger.
We then summed interval scores, divided them
by the total number of observation intervals,
and converted the result to a percentage. Inter-
observer agreement for on-task behavior was
93% (range, 73% to 100%) and for token

delivery or removal it was 96% (range, 78%
to 100%).
Design. We used a multielement design for
10 participants to compare the effects of the
different procedures on on-task behavior, and
we conducted sessions in a quasirandom order.
In addition, for two of these participants, we
used a reversal design following the multiele-
ment design to rule out discrimination failure
or carryover effects during the multielement
comparison. However, because we conducted
the reversal designs after the participants had a
history of both procedures, we used a reversal
design with four participants to determine
levels of responding during DRA before and
after a history of RC.
Procedure. All sessions lasted 5 min. Before
the first session of each condition, the experi-
menter described the session contingencies and
required the participant to practice engaging in
related behaviors (i.e., tracing or playing with
toys) to experience the consequences associated
with each behavior, as in Study 1. For example,
the experimenter required the participant to
practice tracing by providing a vocal and model
prompt (i.e., “Try tracing like this,” while
demonstrating tracing), and used physical guid-
ance as necessary. After the participant prac-
ticed tracing, the experimenter provided the
Table 1
Percentage of Selections and Average Net Tokens Yielded
for Participants in Study 1 (Group Analysis)

% selections Average net tokens
Participant Group DR RC DR RC
Paul 2 67 33 9.8 9.9
Molly 2 22 78 8.5 9.4
Judy 2 11 89 9.4 9.0
Carl 3 0 100 7.3 9.1
Jack 3 0 100 9.1 9.1
Lance 3 0 100 8.9 9.6
relevant consequences and repeated the contin-
gency for that particular phase (e.g., “Look,
you got a token because you were tracing.”).
Before the start of each subsequent session dur-
ing a particular phase, the experimenter
described the session contingencies (see condi-
tion descriptions below).
First, we conducted baseline sessions to
determine the level of on-task behavior in the
absence of programmed consequences. Next,
the experimenter practiced token trading
with the participant, as in Study 1. During
DRA and RC sessions, the experimenter deliv-
ered or removed tokens according to the
same variable momentary schedule used in
Study 1; however, the experimenter conducted
observations on a fixed 30-s schedule for
four participants (Brianna, Mark, Zoey, and
Sam), who participated later in the study, to

simplify data collection. In addition, after each
DRA and RC session, participants traded
tokens for prizes, candy, and access to leisure
items.
Baseline. Before the start of all baseline ses-
contingencies for the session and placed a white
board with no tokens near the participant. The
experimenter stated the rules as follows: “Today
you get the white board, and there are no
tokens. When we start, you can either work
on tracing or play with toys. If you are
working (i.e., tracing), nothing will happen, if
you are not working, nothing will happen.”
During the session, the experimenter did not
provide programmed consequences for any
behavior.
Differential reinforcement of alternative behav-
ior. Before the start of all DRA sessions, the
experimenter described the rules and contin-
gencies for the session and placed a green board
with no tokens near the participant. The exper-
imenter stated the rules as follows: “Today you
get the green board, and it doesn’t have any
tokens on it. When we start, you can either
work on tracing or play with toys. If you are
working, you will get a token; if you are not
working, you will not get a token. At the end,
you can trade your tokens for prizes and
snacks. If you don’t have any tokens, you don’t
get anything.” Throughout the session, the
experimenter watched a timer. If the partici-
pant was on task at the time of a scheduled

observation, the experimenter placed a token
on the token board. If the participant was not
on task at the time of the scheduled observa-
tion, the experimenter did not provide any pro-
grammed consequences.
Response cost. Before the start of all RC ses-
contingencies for the session and placed a red
board with 10 tokens near the participant. The
experimenter stated the rules as follows: “Today
you get the red board, and it has 10 tokens on
it. When we start, you can either work on trac-
ing or play with toys. If you are working, you
will keep your tokens; if you are not working,
you will lose tokens. At the end, you can trade
your tokens for prizes and snacks. If you don’t
have any tokens, you don’t get anything.”
Throughout the session, the experimenter
watched a timer. If the participant was on task
at the time of a scheduled observation, the
experimenter did not provide any programmed
consequences. If the participant was not on
task at the time of a scheduled observation, the
experimenter removed a token from the token
board.
Choice. When we observed stable levels of
responding in the DRA and RC evaluations,
we conducted a preference assessment to deter-
mine the procedure that each participant pre-
ferred. Before each session, the experimenter
placed the stimuli (i.e., poster and token
boards) associated with each type of condition
(i.e., baseline, RC, and DRA) near the partici-
pant and reminded him or her of the contin-

gencies associated with each set of materials.
For example, the experimenter reminded the
participant that the white board means that
there are no tokens; the green board means that
he or she can earn tokens if he or she is tracing;
and the red board means that he or she could
keep his or her tokens if he or she is tracing.
The experimenter switched the placement of
the different sets of stimuli and materials each
session. After the experimenter reminded the
participant of the contingencies associated with
each set of materials, the experimenter asked
the participant to pick (by pointing to or
touching a set of materials) which session he or
she wanted to do. When the participant made
the selection, the experimenter explained the
contingencies in place for the session (e.g.,
“You picked green, you will get a token when I
see that you are working on tracing.”). After
the participant chose a procedure, the experi-
menter implemented the chosen type of session
as described above. The experimenter con-
ducted sessions until we observed a stable pat-
tern of selections. During the choice phase, we
calculated interobserver agreement as in Study
1; it was 100% for all participants.
Results
Figure 2 shows the results for 10 of the
14 participants. During the initial baseline, all

participants engaged in moderate to low levels
of on-task behavior, and these levels remained
low throughout the evaluation (with the excep-
tion of Adam, Frank, and Martin, who engaged
in variable levels of on-task behavior during
baseline). When we compared DRA and RC
using a multielement design, we observed
(a) similar levels of on-task behavior for eight
of the 10 participants (average of 88% during
DRA and 85% during RC), (b) higher levels of
on-task behavior during DRA for one partici-
pant (Emily; 94% during DRA and 82% dur-
ing RC), and (c) higher levels of on-task
behavior during RC for one participant (Adam;
47% during DRA and 65% during RC). When
we compared DRA and RC using a reversal
design for two participants (Anna and Caro-
line), we observed similar and high levels of on-
task behavior as during the multielement evalu-
ation. When we evaluated preference, two par-
ticipants selected DRA exclusively (Paul and
Frank), three participants switched their selec-
tions but selected DRA more than RC (Martin,
Emily, and Adrianna), three participants
switched their selections but selected RC more
than DRA (Elisa, Adam, and Anna), and two
participants selected RC exclusively (Collin and
Caroline).
Figure 3 shows the results for Brianna,
Mark, Zoey, and Sam. During baseline ses-
sions, all participants engaged in low to zero
levels of on-task behavior. When we compared
DRA and RC using a reversal design only, we
observed similar and high levels of on-task

behavior for three of the four participants
(Brianna, Mark, and Zoey); however, we
observed higher levels of on-task behavior dur-
ing RC for one participant (Sam; 62% during
DRA and 90% during RC). These data suggest
that a history of response cost is not likely to
influence responding during DRA.
Table 2 provides a summary of results from
Study 2 with respect to the percentage of selec-
tions in the choice phase and the net tokens
yielded for participants during the DRA and
RC comparison phases. We evaluated prefer-
ence for 10 of the 14 participants and calcu-
lated net tokens for all participants. Preference
results show that five participants chose DR
more than RC and five chose RC more than
DR. Although these results are similar to those
of previous studies (e.g., Donaldson et al.,
2014; Iwata & Bailey, 1974), these results were
somewhat different than those of Study 1. That
is, the majority of participants preferred RC in
Study 1, but only half of the participants pre-
ferred RC in Study 2. Also, five of the 10 parti-
cipants in Study 2 for which we also assessed
preference had an average difference of at least
0.5 tokens between the two procedures, and
four of these five participants (Frank, Paul,
Adam, and Anna) preferred the procedure that
yielded more net tokens.
GENERAL DISCUSSION

Overall, DR and RC were effective proce-
dures for increasing the on-task behavior of the
majority of children who participated in a
group activity (Study 1), and these findings
replicated those of previous research (e.g.,
Donaldson et al., 2014; Iwata & Bailey, 1974).
However, similar to Donaldson et al. (2014)
and Tanol, Johnson, McComas, and Cote
(2010), the procedures were differentially effec-
tive for some individuals in the group, which
suggests that analyzing individual data is
5 10 15 20 25 30 35 40 45 50
0
20
40
60
80
100
BL
Paul
DRA vs RC Choice
5 10 15 20 25 30 35 40
0

20
40
60
80
100
BL
Elisa
DRA vs RC Choice
5 10 15 20 25 30
0
20
40
60
80
100
BL
Frank
ChoiceDRA vs RC
10 20 30 40 50 60 70

0
20
40
60
80
100
BL
Adam
DRA vs RC Choice
5 10 15 20 25
0
20
40
60
80
100
BL
Martin
DRA vs RC Choice

5 10 15 20 25 30 35 40
0
20
40
60
80
100
BL
Collin
DRA vs RC Choice
5 10 15 20 25 30 35 40 45
0
20
40
60
80
100
BL
Emily

DRA vs RC Choice
10 20 30 40 50 60 70 80
0
20
40
60
80
100
BL
Anna
DRA vs RC Choice RC RC D D
5 10 15 20 25 30
0
20
40
60
80
100

BL
Adrianna
DRA vs RC Choice
10 20 30 40 50 60 70 80 90 100
0
20
40
60
80
100
Caroline
BL
DRA vs RC Choice D D DRA RC RC
%
I
n
te
rv
al
s
(O

n
t
as
k
)
Sessions
Figure 2. Percentage of on-task behavior for Paul, Frank,
Martin, Emily, Adrianna, Elisa, Adam, Collin, Anna, and
Caroline during RC, DRA (also denoted as D during the short
reversal phases for Anna and Caroline), and baseline in
the comparative analysis and choice phases. The symbol used
for each data point during the choice phase represents the
condition selected by the participant for that session.
important because these differences may not
have been observed if we reported only group
averages. The importance of analyzing individ-
ual data is further supported by the results of
Study 2, which showed differential effects for
three participants (Adam, Emily, and Sam),
whereas the overall results suggest that the two
procedures are equally effective.
Several variables might have influenced
results of the current study, including the type
of contingency used (individual vs. group
oriented) and the experimental design. Results

showed that the comparative effectiveness of
the procedures was the same for all three
participants who participated in Studies 1 and
2 (Adam, Anna, and Paul). That is, RC was
more effective than DR for Adam during the
group activity and solitary work task, and the
procedures were equally effective for Anna and
Paul under both conditions. These results sug-
gest that the presence of peers did not influ-
ence the comparative effectiveness of DR and
RC. However, an analysis of the results for
Adam and Anna shows that these participants
engaged in 10% to 20% higher levels of on-
task behavior during the group evaluation than
in the individual evaluation. These results ten-
tatively suggest that the presence of peers may
enhance the effectiveness of the procedures for
some children. Because both procedures
resulted in equally higher levels of responding
in the presence of peers, it could be that obser-
ving a peer receiving a token increases the
value of the token or functions as a discrimina-
tive stimulus for on-task behavior (during DR
conditions). In addition, the aversiveness of
token loss might also be enhanced when
tokens are removed in the presence of peers
(during RC).
Although the relative efficacy of DR and RC
was not influenced by the use of group-
oriented contingencies, the overall effectiveness
of the procedures was greater during the group
activity. These higher levels of on-task behavior
during the group activity may have been due
to the differential effort or task difficulty across

tasks in the group activity and individual activ-
ity (i.e., it may have been more effortful to
trace letters than to keep one’s hands in one’s
lap and sit on the mat). In addition, higher
levels of on-task behavior in the group activity
may have been due to the absence of a salient
alternative task, as was provided in the individ-
ual activity (i.e., toys were available). However,
there were many alternative tasks available dur-
ing the group activity, such as playing with or
manipulating the bingo boards and pieces and
leaving the mat to join other activities in the
classroom.
2 4 6 8 10 12
0
20
40
60
80
100
%
I
nt
er
va
ls

(
O
n
T
as
k)
Brianna
DRA RC
BL
DRA
2 4 6 8 10 12
0
20
40
60
80
100
%
I
nt
er

va
ls
(
O
n
T
as
k)
Mark
DRA RC
BL
DRA
2 4 6 8 10 12
0
20
40
60
80
100
%
I

nt
er
va
ls
(
O
n
T
as
k)
Zoey
DRA
BL
RC DRA
5 10 15 20 25 30
0
20
40
60
80
100

%
I
nt
er
va
ls
(
O
n
T
as
k)
BL
Sam
DRA RC DRA
Sessions
RC
Figure 3. Percentage of on-task behavior for Brianna,
Mark, Zoey, and Sam during RC, DRA, and baseline.
We used a multielement design in Study

1 and for 10 participants in Study 2. Thus,
similar effects observed across DR and RC may
have been due to multiple-treatment interfer-
ence because of the rapid alternation of condi-
tions that were similar in numerous respects.
Although we attempted to control for multiple-
treatment interference by including session
rules and discriminative stimuli, we also
attempted to address this concern by evaluating
the effects when a different design was used.
For two participants in Study 2 (Anna and Car-
oline), in which we used both a multielement
design and a reversal design to compare the
effects of DR and RC, we found similar results
regardless of which design was used. In addi-
tion, for four participants in Study 2 (Brianna,
Mark, Zoey, and Sam), in which we used only
a reversal design to compare DR and RC, we
showed similar levels of on-task behavior across
the two procedures as well as similar levels of
on-task behavior regardless of whether DR was
conducted before or after RC. These data sug-
gest that the use of a multielement design was
unlikely to influence the results.
With respect to preference, five of the 15 par-
ticipants in the choice evaluation preferred DR,
and the other 10 participants preferred RC. As
suggested in previous research (e.g., Donaldson
et al., 2014), several variables may have influ-
enced preference for the different procedures.
Participants may select the reinforcement pro-
cedure to avoid the loss condition, as observed
by Pietras, Brandt, and Searcy (2010), who
found that when they equated net tokens, par-

ticipants avoided the procedure that involved
token loss. In addition, participants may prefer
reinforcement, specifically when reinforcer
delivery is spaced evenly throughout the ses-
sion, because token delivery signals time pro-
gression through the session. That is, token
delivery provides feedback regarding the dura-
tion of the session, which may be valuable,
especially with young children.
With respect to preference for RC, the
potential aversion associated with RC may have
been eliminated because participants did not
contact loss often; as Donaldson et al. (2014)
noted, one participant mentioned preference
for RC due to losing few tokens. However,
additional variables also warrant consideration.
First, some participants may have preferred RC
because selection of the RC procedure results
in the delivery of all tokens; therefore, access to
all tokens may function as a reinforcer for selec-
tion of that procedure. In addition, selection of
RC over DR may be because, from the child’s
perspective, starting with tokens is viewed as
not having to work for the tokens. That is, the
procedure appears to be less effortful. To rule
out influence of the presence of tokens, future
researchers might evaluate preference under
conditions in which the tokens are present for
DR and RC (i.e., a cup of tokens next to the
DRA token board and tokens attached to the
RC board) or the tokens are not present (i.e.,
placing colored strips of paper representing
each procedure or asking the participant which
procedure he or she would like to do).
Other variables that might influence prefer-

ence in the current study are the consequences
that followed selection of a particular condition
Table 2
Percentage of Selections and Average Net Tokens Yielded
for Participants in Study 2 (Individual Analysis)
% selections Average net tokens
Participant DR RC DR RC
Frank 100 0 8.9 8.3
Paul 100 0 9.6 9.1
Martin 82 18 9.3 9.3
Adrianna 75 25 9.6 9.7
Emily 67 33 8.5 8.2
Adam 28 72 4.1 5.3
Elisa 21 79 8.4 8.2
Anna 18 82 7.6 8.8
Collin 0 100 9.8 9.1
Caroline 0 100 5.7 5.4
Brianna 8.7 8.7
Mark 9.4 9.6
Zoey 9.4 8.7
Sam 6.1 8.8
(DRA vs. RC) and the net tokens earned
within a particular condition. Participants in
the group evaluation may have chosen a differ-
ent procedure the next time they were offered a

choice if the experimenter did not implement
the procedure they had chosen in a given ses-
sion. However, an evaluation of data for parti-
cipants in Study 2 showed that participants
switched their selection during subsequent
choice opportunities when the session that the
experimenter implemented after a selection did
not match the initial selection on 38% (Paul),
38% (Molly), and 50% (Judy) of selections.
These results suggest that switches in selections
were not influenced by whether the session that
was implemented matched the procedure they
had selected, and these findings are consistent
with those of Layer et al. (2008).
Previous researchers have evaluated the
potential influence of net tokens across DR and
RC conditions. Iwata and Bailey (1974) calcu-
lated the average number of net tokens for the
class, and Donaldson et al. (2014) calculated
individual net token averages; both studies
found that net tokens were similar across proce-
dures. Although the number of net tokens was
similar, because some participants preferred one
procedure over another, it could be that even
slight differences may influence preference. In
the current study, we were able to evaluate
preference for 15 participants (twice with Paul)
and found that seven of the 14 children who
participated once (and Paul on one occasion in
Study 2) yielded an average difference of at
least 0.5 tokens between the two procedures.
Of these eight participants, seven preferred the
procedure for which more net tokens were
yielded during the comparison phase. However,
in previous research and in the current study,

experimenters did not manipulate the number
of net tokens. Therefore, the influence of net
tokens on preference is unknown, and research
on this variable is warranted.
Another point of discussion relates to
best practice guidelines. The general
recommendation is to use reinforcement-based
procedures when possible (Bailey & Burch,
2005). Therefore, because RC is a negative
punishment procedure (Kazdin, 1977), RC
often is not recommended before implementa-
tion of positive reinforcement procedures.
However, given that (a) RC is just as effective
as reinforcement, (b) RC has limited side
effects (Kazdin, 1972), (c) more participants
preferred RC in the current study, and
(d) previous researchers have also found prefer-
ence for punishment procedures (e.g., Hanley,
Piazza, Fisher, & Maglieri, 2005), reconsidera-
tion of best practice appears to be warranted.
Perhaps the use of effective and preferred pro-
cedures should be considered best practice
(e.g., Hanley, 2010).
There are several areas for future research.
First, we were able to compare responding of
only three individuals who participated in both
the group activity and solitary work task; there-
fore, our conclusions about the effects of peer
presence are limited, and future researchers
should consider conducting this evaluation with
a larger number of participants. Second,
because we conducted both preference evalua-
tions in Studies 1 and 2 in the absence of peers,

we were unable to compare choice in the pres-
ence versus absence of peers.
Third, we did not collect data on side effects
of the procedures, which may be important,
specifically with the possibility of negative side
effects (e.g., emotional responding or increases
in problem behavior) when RC procedures are
used. However, little to no negative side effects
have been reported during the use of RC proce-
dures (Conyers et al., 2004; Kazdin, 1972) nor
were negative side effects observed in the cur-
rent study.
Fourth, future researchers should include a
measure of accuracy. In the current study, we
selected on-task behavior because it was age
appropriate, but we did not measure the accu-
racy of responding. Iwata and Bailey (1974)
showed decreases in rule violations without
increasing correct responding. Because on-task
behavior is a prerequisite for accurate respond-
ing in many situations, correct responding
should increase as children are attending; there-
fore, future researchers should measure changes
in accuracy when reinforcement and punish-
ment contingencies are in effect for on-task
behavior.
Fifth, we arranged individual contingencies,
rather than interdependent group-oriented con-

tingencies or dependent group-oriented contin-
gencies. Individual and interdependent group-
oriented contingencies require that the teacher
monitor the behavior of each child and then
deliver consequences based on the behavior of
each child individually or for the behavior of
the group, respectively; on the other hand, a
dependent group-oriented contingency requires
that a teacher monitor the behavior of only one
child in the group. Herman and Tramontana
(1971) found no difference in the effectiveness
of individual and group contingencies and sug-
gested that group contingencies may be easier
for teachers. Therefore, future researchers
should compare DR and RC using dependent
and interdependent group-oriented contingen-
cies (see Litow & Pumroy, 1975, for a brief
review of group contingencies).
Finally, because we associated specific colors
with the different procedures, children’s choices
for procedures may have been based on prefer-
ence for color rather than procedure. However,
anecdotal reports do not suggest that partici-
pants had strong preferences for colors (i.e., it
was not common for participants to report
color preference during the choice evaluation).
Future researchers might control for the influ-
ence of color preferences by using low or mod-
erately preferred colors for the stimuli used for
the DR and RC procedures (e.g., Luczynski &
Hanley, 2009) or changing the colors associ-
ated with the procedures throughout the study.
In summary, there are several important
implications of the current study. First, the

results suggest that both DR and RC are
similarly effective; therefore, teachers might use
the procedure that more children prefer or that
is easier to implement in a classroom setting.
Second, the presence of peers does not appear
to influence the relative efficacy of the proce-
dures; therefore, future researchers might con-
tinue to conduct comparisons of DR and RC
in group settings for more efficient data collec-
tion. Finally, considerations for best practice
should take into account preference, given the
large number of participants who preferred RC.
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jsp.2010.06.001
Received December 2, 2014
Final acceptance October 8, 2015
Action Editor, Jeanne Donaldson
EFFICACY OF AND PREFERENCE FOR REINFORCEMENT
AND RESPONSE COST IN TOKEN ECONOMIESSTUDY 1:
DR VERSUS RC (GROUP)MethodParticipants and
settingMaterialsResponse measurement and interobserver
agreementProcedureBaselineDifferential reinforcementResponse
costChoiceResultsSTUDY 2: DRA VERSUS RC
(INDIVIDUAL)MethodParticipants and
settingMaterialsResponse measurement and interobserver
agreementDesignProcedureBaselineDifferential reinforcement
of alternative behaviorResponse costChoiceResultsGENERAL
DISCUSSIONREFERENCES
131
The Token Economy: A Recent Review and Evaluation

Christopher Doll
1
; T. F. McLaughlin
2
; Anjali Barretto
3
1
Gonzaga University, East 502 Boone Avenue, Spokane, WA
99258-0025, USA
[email protected]
2
99258-0025, USA
[email protected]
3
99258-0025, USA
[email protected]
Abstract – This article presents a recent and inclusive review of
the use of token
economies in various environments (schools, home, etc.).
Digital and manual

searches were carried using the following databases: Google
Scholar, Psych Info
(EBSCO), and The Web of Knowledge. The search terms
included: token economy,
token systems, token reinforcement, behavior modification,
classroom management,
operant conditioning, animal behavior, token literature reviews,
and token
economy concerns. The criteria for inclusion were studies that
implemented token
economies in settings where academics were assessed. Token
economies have been
extensively implemented and evaluated in the past. Few articles
in the peer-
reviewed literature were found being published recently. While
token economy
reviews have occurred historically (Kazdin, 1972, 1977, 1982),
there has been no
recent overview of the research. During the previous several
years, token
economies in relation to certain disorders have been analyzed
and reviewed;
however, a recent review of token economies as a field of study
has not been

carried out. The purpose of this literature review was to produce
a recent review
and evaluation on the research of token economies across
settings.
Key Words – Digital Search; Future Research; Literature
Review; Research;
Token Programs
1 Introduction
This article presents a recent and inclusive review of the use of
token economies in various settings.
Digital and manual searches were carried using the following
databases: Google Scholar, Psych Info
(EBSCO), and The Web of Knowledge. The search terms
included: token economy, token systems,
token reinforcement, behavior modification, classroom
management, operant conditioning, animal
behavior, token literature reviews, and token economy concerns.
The criteria for inclusion were studies
that implemented token economies in settings where academics
were assessed.

International Journal of Basic and Applied Science,
Vol. 02, No. 01, July 2013, pp. 131-149
Doll, et. al.
132 Insan Akademika Publications
2 History of Token Systems
Token systems, in one form or another, have been used for
centuries and have evolved notably to
systems used today. Clay coins, which people could earn and
exchange for goods and services, in the
early agricultural societies were part of the transition from
simple barter systems to more complex
economies (Schmandt-Besserat, 1992). Before that, however,
incentives- based structures were
created and sustained in a variety of cultures and as part of
many institutions within those cultures.
Governments used the influencing abilities of rewards to shape

behaviors in battle and throughout
society. Rewards have ranged from tangible prizes to socially
significant titles (Doolittle, 1865;
Duran, 1964; Grant, 1967). During the first century, Grant
(1967) explained that accomplishments of
gladiators were rewarded with property, prizes, and crowns.
Carcopino (1940) described charioteers
in Rome during that same time being rewarded with their
freedom after repeated victories. In ancient
China, soldiers received colored peacock feathers for bravery in
battle (Doolittle, 1865). Several
military institutions in ancient civilizations utilized these
systems of merit and rewards to incentivize
behavior. From the Aztecs in the 15
th
century (Duran, 1964), as well as the militaries of modern
times,
the use of titles of distinction and medals to reward actions
were common methods to promote certain
types of behavior, or responses. Modern research peaked in the
1970‟s where there was substantial
study surrounding psychiatry, clinical psychology, education,
and mental health fields (Kazdin, 1977).
Token economy systems have also been employed to modify
animal behavior (Addessi, Mancini,

Crescimbene, & Visalberghi, 2011; Malagodi, 1967; Sousa,
Matsuzawa, 2001). Malagodi‟s (1967)
study involving rats established a mechanism of exchange
between marbles, which the rats earned
through a dispenser, and an edible primary reinforcer. In that
study, token reinforcement under fixed
and variable interval schedules were shown to be as effective as
the edible primary reinforcer to
increase lever pressing. In another study, Wolf (1936)
compared the effectiveness of exchangeable
tokens, nonexchangeable tokens, and food to find that
exchangeable tokens and food were comparable
in reinforcing ability. These studies clearly show that tokens,
when paired with a primary reinforcer
are effective at modifying certain behaviors in animal subjects.
Cowles (1937) found similar results
with exchangeable tokens when he taught chimpanzees new
learning tasks. In Sousa and Matsuzawa‟s
(2001) study, not only did chimpanzees perform similarl y with
tokens as they did with direct food
rewards, but the researchers found that chimpanzees were able
to collect and save several tokens
before exchanging them.

The military as well as mental health and educational facilities
have increased their use of incentives
to shape behavior. Tangible items given as rewards evolved to
tokens which could be exchanged for
certain privileges and rewards. This evolution of the token
economy was a catalyst for increasingly
novel and diverse utilization of token-reinforcement systems.
One example of how token systems
have been applied in an institutional setting was Alexander
Maconochie‟s “Mark System”
implemented with a prison population during the 1840‟s
(Kazdin, 1977). This token-based system
improved the conditions under which many prisoners lived;
furthermore, it attempted to create an
incentive-driven system to reward positive behavior rather than
give aversive consequences to
prisoners. Within this “Mark System,” sentences were
converted to “marks” and the prisoners sought
to reduce these “marks,” or tokens, through good behavior
within the prison system. Upon reaching a
certain level of tokens, the prisoner could then be released. The
prisoners exchanged their tokens for
necessary items such as food, shelter, and clothes (Kazdin,
1977). A variation of the token economy

under Maconochie was the inclusion of a response cost
component where negative or institutionally-
labeled aberrant behaviors resulted in the withdrawal of
“marks.” Unique approaches such as the
Mark System have helped evolve the reward and cost structures
resulting in “serious achievements in
reform, rehabilitation, and token economies” (Kazdin, 1977).
Doll, et. al. International Journal of Basic and Applied Science,
Vol. 02, No. 01, July 2013, pp. 131-149
www.insikapub.com 133
3 Early History of Token Systems in the Schools
3.1 Token, tracking, exchange
Educational systems have employed token economies as a
means to manage students for several
decades (Kazdin, 1982). The need to educate large numbers of
children and the demand for

meaningful education helped to evolve the application of these
token-based systems. As noted
previously, titles of distinction as well as tangible property have
all been used to incentivize
individuals and their behavior. In schools, a variety of
incentives have acted and continue to serve as
the rewards earned for certain defined target behaviors
(Boniecki & Moore, 2003; Lolich,
McLaughlin, & Weber, 2012; McLaughlin & Malaby, 1975). As
early as the 7
th
century, a monk in
Southern Europe gave out biscuits of leftover dough, also
known as “petriolas” or “little rewards,” to
give to children who learned their prayers (Kazdin, 1977).
Later on in the 1100‟s, Birnbaum (1962)
noted that using rewards such as nuts, figs, and honey were
commonly implemented by educators as
incentives for learning. In the 16
th
century, Skinner (1966) described instances where fruit and
cake
was advocated by Erasmus in order to help children learn Greek
and Latin.

Within the past several centuries, the modern forms of the token
economy have been increasingly used
in the education of society. Two of those systems came to the
United States during the 1800‟s. Joseph
Lancaster‟s “Monitorial System” originated in England in the
early part of the century and came to
New York in 1805. This system, when implemented in New
York schools, contained a more explicit
use of tokens and of response cost. More-able peers were
“Monitors” for less-able peers and each
skill-group was awarded different sets of privileges and prizes,
based on level. The Monitorial System
allowed for the creation of helper teachers which allowed for
the teaching of large numbers of
students. The solution to this problem of larger classes helped
to spread this program across the
nation. A second system, Excelsior, established itself during
the latter part of the 1800‟s when the
United States was experiencing significant growth in the use of
token economies (Kazdin, 1977). This
system consisted of giving out “Excellent(s)” and “Perfect(s)”
designations to students for pro-social
and pro-academic behaviors. These “Excellents” and “Perfects”
were exchanged for “Merits,” which

in turn were saved and exchanged for a special certificate from
the teacher attesting to great
performance. In both of these systems, prizes and rewards
acted to make the token more powerful in
affecting behavior. Furthermore, in both of these token-
reinforcement systems, back-up reinforcers
and prizes were integral in their setups and sustainment.
3.2 Definition of a Token System
Token economies have been extensively researched throughout
the last several decades and applied in
a variety of settings. Teachers and caretakers have used these
systems in general education, special
education, and community-based settings. Because of the
variety of token-based systems and the ease
at which teachers can implement them, token economies are
widely used across the nation.
The behavioral principles employed in token systems are based
primarily upon the concept of operant
conditioning (Kazdin, 1977; McLaughlin & Williams, 1988).
Within a token economy, tokens are
most often a neutral stimulus in the form of “points” or tangible

items that are awarded to economy
participants for target behaviors. In a token-reinforcement
system, the neutral token is repeatedly
presented alongside or immediately before the reinforcing
stimulus. That stimulus may be a variation
of edibles, privileges, or other incentives. By performing this
process of repeating presentations of
neutral tokens before the reinforcing stimulus, the neutral token
becomes the reinforcing entity. As the
participants in the token experience the pairing of token and a
previously reinforcing items, the token
Vol. 02, No. 01, July 2013, pp. 131-149
Doll, et. al.
itself may acquire reinforcing properties as a result. The token
economy gains its utility and power to
modify behavior when the neutral tokens become secondary
reinforcers. The effectiveness of this

process has been noted by Miller and Drennen (1970). They
demonstrated that when praise is a
neutral stimulus, it could become a conditioned reinforcer
through pairing it with another reinforcing
event.
3.2.1 Target behaviors of token economies
A token economy is often implemented because there are target
behaviors that teachers would like to
increase or reduce. These behaviors must be identified by those
who work in such classrooms.
Changes in these target behaviors often improve the classroom-
learning environment or the needs for
that specific institution. Token economies can be used to
minimize disruptions in a classroom as well
as increase student academic responding. This can depend on
the classroom and the priorities of the
teacher. However, most teachers employ a token system to
manage both academic and social
behaviors (McLaughlin & Williams, 1988).
In a token economy it is important to clearly outline the target
behaviors for the students as well as the

teacher (Kazdin, 1977). When a teacher is first implementing a
token-reinforcement system it has
been recommended that desired behaviors are orally
communicated, written down, or otherwise
clearly explained or modeled to the participants (Alberto &
Troutman, 2012; McLaughlin & Williams,
1988). This communication with the participants is crucial and
directly related to the effectiveness and
efficiency of the system (Alberto & Troutman, 2012; Cooper,
Heron, & Heward, 2007).
3.2.2 Tokens
In order to establish and sustain a token economy system there
needs to be tokens. These tokens then
serve as a way to provide consequences. Tokens can be tangible
gaming-style chips, tickets, coins,
fake money, marbles, stickers, or stamps (McLaughlin &
Williams, 1988). They can also come in the
form of more abstract items in the form of points or checkmarks
given by the teacher or the economy‟s
“manager.” The choice of tokens can depend on the setting,
population, manager‟s or teacher‟s

preference, cost, among other considerations. Population and
setting considerations are related to
what type of tokens are going to be applicable for certain
participants. A younger group, or students
with developmental or cognitive delays, may well benefit from
more tangible items like coins or cards,
than more abstract items in the form of points or checkmarks
(McLaughlin & Williams, 1988;
Stainback, Payne, Stainback, & Payne, 1973). Tangible tokens
provide a concrete representation of
the number of tokens earned which can then be exchanged for
rewards (B. Williams, R. Williams, &
McLaughlin, 1989). When choosing tokens, the teacher‟s
preference, especially in relation to cost,
must be considered. Also, the choice of the token should
include the difficulty or impossibility of the
token itself being duplicated and flooding the classroom with
tokens not under the control of the
teacher. These factors must impact the types of tokens, which
are used within the system, the
frequency at which they are delivered, and ultimately the back-
up rewards that are available to give
value to the tokens.

3.2.3 Back-up rewards
Back-up rewards are the items that the students or persons have
indicated they are willing to work.
Their desirability has been used to assign the number of tokens
that are needed to purchase or take part
Vol. 02, No. 01, July 2013, pp. 131-149
in this reward (Kazdin, 1977). Without these back-up rewards,
the tokens have no exchangeable
value. Also, tokens without value can negatively alter an
individual‟s motivation (Wolf, 1936). The
more back-up rewards in the token system, the more substantial
the reinforcing strength becomes
through pairing of tokens and rewards (B. Williams, R.
Williams, & McLaughlin, 1989). Back-up
rewards have also been used in the home settings where they
have included: ski trips, video games,
movies, or lunch at a chosen restaurant (Rustab & McLaughlin,

1988). Even with this variety of back-
up rewards, the monetary reward has been used very effectively
(Jordan, McLaughlin, & Hunsaker,
1980). This is likely due to money‟s exchangeable abilities and
its ability to act as one of the ultimate
Generalized Conditioned Reinforcers.
3.2.4 The exchange
An important part of the token economy is the exchange of
tokens for certain back-up rewards chosen
by the economy‟s manager or students and in part by the needs
and preferences of the participants.
The value of the token is a function of the reinforcers which are
able to back-up their value (Kazdin,
1977). At the end of the period where tokens have been given,
the teacher will decide to begin the
exchange process.
When a conditioned reinforcer like a token is exchanged for a
variety of privileges and rewards, the
token is referred to as a generalized conditioned reinforcer
(Kazdin, 1977). Generalized tangible

conditioned reinforcers, which can be exchanged for a variety
of items, are used very frequently in
behavior modification programs (Kazdin, 1977). Tokens or
generalized conditioned reinforcers also
come in the form of money used in society. The more items or
rewards you can exchange for the
token, the more powerful the token becomes. Money and other
generalized conditioned reinforcers are
more valuable than any single reinforce because they can
purchase a variety of back-up reinforcers
(Kazdin, 1977). The power of generalized conditioned
reinforcers was assessed when Sran and
Borrero (2010) compared behaviors reinforced by tokens which
could be exchanged for a single
highly preferred item with tokens which could be exchanged for
a variety of preferred items. They
found, while degrees of preference varied, all participants were
shown to deliver higher rates of
responding during sessions where tokens could be exchanged
for a variety of preferred items.
During the early implementation of the token economy,
especially for lower-functioning persons, it is
important to have frequent exchange periods where participants
can be quickly reinforced and target

behaviors can increase (O‟Leary & Drabman, 1971). Infrequent
exchange periods at the beginning of
a token economy‟s implementation may prevent this type of
system from working effectively. It is
important to determine and adapt the exchange period based on
classroom needs (Kazdin, 1977;
McLaughlin & Williams, 1988). For some participants,
especially those with Attention Deficit
Hyperactivity Disorder (ADHD), the immediacy in which a
back-up reinforcer is received will be the
most influential dimension a token economy, making the time
between token and exchange crucially
important (Neef, Bicard, & Endo, 2001; Reed & Martens, 2011).
One of the important considerations
when carrying-out a token economy is its impact on the
classroom environment or setting. The
exchange period should be quick to complete and not
significantly impact the ability of the teacher to
manage the classroom or particular setting. Based on these
considerations, it is important to schedule
exchange periods at the end of the class period, during a
naturally occurring transition, or possibly at
the end of the day or week.

There are many different ways in which a token exchange can
take place. Many types of exchange
systems have been implemented (Kazdin, 1977; McLaughlin,
1975). Tokens may be exchanged as
soon as they are earned (Bushell, 1978), at the end of a certain
time period (McLaughlin & Malaby,
Vol. 02, No. 01, July 2013, pp. 131-149
Doll, et. al.
1972), or after a variable time period (McLaughlin & Williams,
1988). At the end of the token-reward
period, there may be a catalog of items and privileges, a “store”
where the participant is able to
exchange tokens or a predetermined back-up reinforcer.
Additionally, free-time itself may function as
its own generalized conditioned reinforce as it gives the
participants access to a variety of back-up
rewards.

When the system is in place, teachers may choose an exchange
time based on classroom schedule or
student needs. Token economy exchange periods could take
place at the end of a 50-minute class
throughout the day, daily, weekly, or biweekly. The
effectiveness of the token economy may decrease
as more if more time passes between presentation of the token
and exchange for the backup reinforcers
(Kazdin, 1977; Neef et al., 2001; Reed & Martens, 2011).
Variability of the exchange times as
opposed to fixed time periods where tokens are traded for back-
up rewards have been shown to
increase response rates as well as maintenance of the behavior
(McLaughlin & Malaby, 1976).
According to McLaughlin and Malaby (1976), executing
variable exchange times within a token
economy is effective and an important consideration for any
teacher or economy manager to consider.
3.3 Variations of Token Economies
3.3.1 Response cost

During a response cost system, tokens are taken away as
students engage in certain pre-defined
behaviors. When tokens are taken from the student that is the
cost of the behavior. In this variation of
the token economy, each unwanted behavior will have a cost
which results in the confiscation of a
determined amount of tokens. Response cost is very commonly
used to suppress behavior (Kazdin,
1977). The most commonly used form of response cost is the
withdrawal of tokens or fines. Token
economies are unique because tokens can be presented or
removed (Kazdin, 1977; McLaughlin &
Malaby, 1977a). Hall et al. (1972) employed response cost to
reduce whining in a young child. The
researchers used slips of paper given to the boy with his name
printed on them. The slips were taken
away for negative behaviors. Even when these slips had no
apparent value, this response cost system
drastically reduced negative behaviors. Iwata and Bailey (1974)
compared token reinforcement and
response cost in a special education classroom. Both were
equally effective at improving behaviors.
However, the teacher was more negative with the students when
response cost was used in the

classroom. In McLaughlin and Malaby (1977a), token
reinforcement and response cost system was
found to be more effective at increasing target behavior than
token reinforcement alone. Achievement
Place, (Kirigan, Braukman, Atwater, & Wolf, 1982), where at-
risk youth are often sent to learn
important social and academic skills, so they can be placed back
into mainstream society, effectively
implements a token reinforcement system with response cost to
reduce severe behaviors while
increasing pro-social and academic behaviors (Ayllon & Azrin,
1968; Bailey, Wolf, & Phillips, 1970;
McLaughlin & Malaby, 1977a). In general, token economies
with and without a response cost
component have been effective in different settings. It is
important to note; however, that a program
solely reliant on response cost and punishment-oriented
management are less likely to result in
creating pro-social behaviors in the participants (Iwata &
Bailey, 1974; Kazdin, 1977). This is
interesting considering that, in some studies, there seems to be a
preference by the teachers of response
cost when compared to a token reinforcement only system
(McGoey & DuPaul, 2000). In McGoey

and DuPaul (2000), a preschool class compared stickers
rewarded to students and stickers being
removed for off-task behavior. They found them to be equally
effective. This finding replicates Iwata
and Bailey. However, it is important to consider that
reinforcement for specific target behaviors is
more likely to develop pro-social responses as alternatives for
the behaviors to being suppressed
(Kazdin, 1977).
Vol. 02, No. 01, July 2013, pp. 131-149
3.3.2 Lottery systems
Instead of a token economy where behaviors earn tokens to be
exchanged at later period, lottery-based
systems add an additional component to the exchange period.
In this type of economy, target

behaviors are rewarded with a token, or ticket and at the end of
the reward period there is a lottery to
determine which individuals earn a backup reward. This can
minimize the amount of backup rewards
delivered in the token economy by choosing only a select
number of tokens, or tickets, to exchange. A
weakness of this type of system would be some ages and
populations may be difficult to affect without
a direct correspondence of tokens and backup rewards
(McLaughlin & Williams, 1988).
3.3.3 Individual vs whole class
It will be up to the teacher or manager of the economy to
determine whether tokens will be awarded to
entire groups or to individuals within the group. The advantage
of developing a group-oriented token
economy is the ease of which teachers may implement and track
tokens and rewards (Kazdin, 1977).
These class-wide systems have also been well documented and
seem to be useful in reducing
unwanted behavior (Bushell, Wrobel, & Michaelis, 1968;
Packard, 1970). Consequences in these
class-wide economies can be group or individually

administered, depending on the system chosen.
Packard (1970) evaluated a token economy under a group
contingency in four elementary school
classes where off-task behavior was a concern. In Packard‟s
study, certain class periods were chosen
for each grade and a class goal was assigned to raise on-task
behavior. When the class met the criteria
for on-task behavior, they were given points which could then
be exchanged for group or individually
assigned rewards (Packard, 1970). The results in that study
showed baseline levels of below 10% on-
task behavior rise to between 70-100% on-task behaviors during
class periods once the group-
contingent token economy was implemented (Packard, 1970).
3.3.4 Level systems
Level systems are a variation of token economy. In these
systems, different levels correspond to
different degrees of participant behavior. For example,
increasing preferred target behaviors may
result in higher levels which then translate to higher rates of
reinforcement and privilege while

unwanted behaviors may result in a decreased rate of
reinforcement or loss of privileges. In one level
system, each participant was assigned a shape or character and
every 2-4 hours, would be moved up or
down the six-level system (Filcheck, McNeil, & Greco, 2004).
Each system can be monitored
differently; however, the movement from one level to another
based on participant behavior which
results in varying levels of reinforcement. Filcheck et al.
(2004) compared a system where efficiency
was a priority and all rewards were able to be dispensed within
three minutes. The researchers found
this efficient exchange to be beneficial during class times. The
ability to efficiently dispense rewards
and levels make these systems easily customized based on the
needs of the setting.
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Doll, et. al.

3.4 Efficacy of Token Systems
3.4.1 General Outcomes
Research with individuals in classroom settings using token
economies has been firmly established the
efficacy of token reinforcement in altering a wide range of
responses (Kazdin, 1977). There is a
significant need for effective behavior management systems.
Lavigne (1998) notes that children
behavior problems are increasing, with estimates ranging from 2
to 17% of the population. This rate
of children with behavior problems is highlighting the demand
for behavior management systems
which are data-based and effective. Token Economy systems
are able to have a profound impact on
schools, classrooms, and community-based settings. One
variation of the token economy, a response
cost system, is known to have produced higher levels of on-task
behavior than when compared to
medication (Rapport, Murphy, & Bailey, 1982). The structure

and implementation of the token
economy is important as noted by Kazdin (1977) where he
describes the effectiveness of
reinforcement depends on: the delay between performance of
response and delivery of reinforcement,
the magnitude and quality of the reinforcer, and the schedule of
reinforcement. Many factors are
important in the consideration of a token economy. Whether or
not reinforcement takes place on a
continuous or intermittent basis can impact the likelihood of
maintenance (Kazdin, 1977).
3.4.2 Preschool
Token economies in the preschool setting have been utilized
with a variety of modifications to this
behavior-management system (Filchek et al., 2004; McGoey &
DuPaul, 2000). As the need for
behavioral interventions increase, it is important for preschool
teachers to be aware of these token-
oriented procedures, and using these systems classroom-wide
may be a great pro-active benefit
(Filcheck et. al., 2004).

Filcheck et al. (2004) compared the effectiveness of a class -
wide token economy level system with
parent-training techniques in managing aberrant behaviors.
These authors note that class-wide
application of the token economy has not been previously
analyzed. However, group and individual
application of token systems have effectively reduced disruptive
behavior in other settings (Bushell,
Wrobel, & Michaelis, 1968; Packard, 1970). The classroom in
Filcheck et al. was described as “out of
control” and was chosen for behavioral intervention. The token
economy used was a level system
where the top three levels included sunny faces which get
increasingly happy, the center level is the
starting point and is blank and white, while the bottom three
levels include cloudy faces that get
increasingly greyer and sad (Filcheck et al., 2004). In this
system, promotion to different levels
within the preschool class allowed participants to complete
certain activities while other children, who
were not promoted, were continuing with the pre-determined
class schedule. Furthermore, at the end
of certain activities, all participants with “positive” behavior
levels receive additional rewards like

stickers or activities with the teacher. In this system, the level
system was found to decrease rates of
inappropriate behaviors; additionally, when the parent training
was implemented further decreases
occurred (Filcheck et al., 2004). It is important to consider that
in this study the training time
necessary for each of the two behavior management tools. In
this study, the Level System took 4
hours and 30 minutes to train staff on including all consultation
and feedback time; however, the
parent training took 11 hours and 30 minutes (Filcheck et. al.
2004). In term so effectiveness and time
efficiency, the level system seemed to have the greatest rate of
positive return.
Additional studies have shown rapid behavioral improvement
when a token economy is implemented.
A study involving a sticker chart in McGoey and DuPaul (2000)
was managed by teachers placing
Vol. 02, No. 01, July 2013, pp. 131-149

stickers on a classroom board when they “caught” students
being on-task. When a student earned a
certain number of small stickers, they were rewarded with a big
sticker (McGoey & DuPaul, 2000).
For the response cost portion of this study, stickers were
removed contingent on being off-task and
when the session ended, the big sticker was kept or removed
from the chart. These token economy
and response cost systems resulted in large decreases of
aberrant behavior (McGoey & DuPaul, 2000).
Implementing token economies in a preschool setting, Sran and
Borrero (2010) compared two
variations of this behavior management system. In this study,
tokens that were exchanged for a variety
of preferred items were shown to be more effective than tokens
that could only be exchanged for one
highly preferred item. These results are consistent with
previous research which shows generalized
conditioned reinforcers are more reinforcing than a single
reinforce (Kazdin, 1977).
3.4.3 Elementary school

Elementary school classrooms, based on research study volume,
seem to be one of the most common
settings in which token economy systems are used (Coupland &
McLaughlin, 1981; Ruesch &
McLaughlin, 1981; Thompson, McLaughlin, & Derby, 2011).
Many studies exist which show the
effectiveness of this type of behavior management tool. One of
these studies, employed a free time
reward when five tokens had been earned (Ruesch, McLaughlin,
1981). The rationale that free time
would consist of a variety of reinforcers made it unlikely that
satiation would occur (Kazdin, 1977). In
Ruesch and McLaughlin, (1981) a clear increase in student
assignment completion took place. When
token economies were used to decrease inappropriate behavior
by rewarding being on task, there is
proven effectiveness with this behavior management system
(Coupland & McLaughlin, 1981). Under
a token economy with sixth grade participants, points were
given and subtracted for appropriate and
inappropriate behavior respectively (McLaughlin & Malaby,
1976).

McLaughlin and Malaby (1977a) compared token reinforcement
with and without response cost in a
special education elementary classroom. In McLaughlin and
Malaby‟s (1977a) study, ten participants
were asked to write letters for a several minute session where
they earned no token reinforcement
during baseline, token reinforcement during the next phase, and
token reinforcement plus response
cost during the final phase. The overall results were such that,
in this elementary classroom, token
reinforcement plus response cost resulted in higher rates of
target behavior (McLaughlin & Malaby,
1977a). In another study, McLaughlin and Malaby (1976)
analyzed assignment completion under
different schedules of token exchange. During that study
involving a fifth and sixth grade class, points
were earned or taken away depending on whether children
displayed appropriate or inappropriate
behavior. The results showed that participants had higher rates
of appropriate behavior, as measured
through assignment completion, when there were a variable
number of days between token award and
exchange (McLaughlin & Malaby, 1976). According to the
authors, McLaughlin and Malaby (1976)

note that such a system where variable exchange days were
implemented should be considered for any
teacher or economy manager interested in impacting the rates of
assignment completion.
3.4.4 Middle school
Middle school classrooms have seen many instances of positive
behavioral outcomes as part of a token
economy (Flaman & McLaughlin, 1986; Maglio & McLaughlin,
1981; Swain & McLaughlin, 1998;
Truchlicka, McLaughlin, & Swain, 1998). Maglio and
McLaughlin (1981) note the importance of a
teacher‟s ability to manage the token system in their study
where a student‟s partial self-management,
with teacher supervision, of points along with back-up
reinforcers resulted in a significant decrease of
inappropriate behaviors. Besides social behavior, academic
improvement has also been seen during
Vol. 02, No. 01, July 2013, pp. 131-149

Doll, et. al.
token reinforcement (Flaman & McLaughlin, 1986). Flaman
and McLaughlin‟s study took place in a
junior high school drop-out prevention program where the
subject rarely completed an assignment
unless given one-on-one assistance. In that study, correct
answers on a worksheet resulted in 1-2
points per problem that could be exchanged for free-time on a
classroom microcomputer. This study
increased the rate of correct answers from 34% to 69% correct
during the first phase, and to 79%
during the second phase of token reinforcement (Flaman &
McLaughlin, 1986). A second system
where assignment accuracy was a concern included bonus points
(Swain & McLaughlin, 1998). In
that study, four middle school special education students w hich
were previously being managed by a
token reinforcement system were offered fifty extra bonus
tokens or points for assignment scores
greater than 80% (Swain & McLaughlin, 1986). This bonus
contingency resulted in an increase of

math accuracy. When response cost is implemented in a high
school setting, positive results are
possible (Truchlicka, McLaughlin, & Swain, 1998). Truchlicka
et al. (1998) implemented a response
cost to an already functioning token reinforcement system. In
this system, an accuracy goal of 85%
was required to earn token reinforcers; however, if that
accuracy level was not reached, tokens were
removed or privileges were denied. This study concluded that
the response cost phase resulted in a
higher rate of accuracy for each subject. The implementation of
a point gain or point lose system had
a greater impact than a token reinforcing system.
3.4.5 High school
Implementation of token economies in the high school setting
occurs at a much lower rate than when
compared to elementary school or middle school settings. This
may be attributed to the fact that
teachers are more apprehensive towards this type of system;
alternatively, the lower rate of occurrence
could be due to a perceived lack of effectiveness.

In a study by Crawford and McLaughlin (1982), token
reinforcement was evaluated as a means to
increase on-task behavior. This study was conducted in a high
school within a self-contained special
education classroom with a 15-year-old student. The student
was given tokens and worked for a
chosen back-up reinforce which cost 30-40 cents worth of
tokens. In this study there was a clear
increase in on-task behavior during the token-reinforcement
phases. According to the study, on-task
behavior from the student more than doubled when tokens were
first introduced (Crawford &
McLaughlin, 1982).
3.4.6 College or University
Token systems in college settings have also been assessed for
effectiveness. Participation in class
within all settings is a priority and a goal for many teachers and
professors, and two studies
specifically, aimed to analyze the impact of tokens on
classroom participation in college settings.
Jalongo (1998) determined that only approximately 10% of

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