praise on task bx article

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https://doi.org/10.1177/10983007221126568

Journal of Positive Behavior
Interventions
1 –12
© Hammill Institute on Disabilities 2022
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DOI: 10.1177/10983007221126568
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Empirical Research

Teacher praise has been examined for decades by research-
ers as a form of positive reinforcement to improve student
behaviors when attention maintains behavior (Becker et al.,
1967; Brophy, 1981; Markelz, Taylor, et al., 2019;
Sutherland et al., 2000; White, 1975). It is often stated that
teacher praise is a low-intensity, effective, and efficient pro-
active classroom management strategy for academic and
social behaviors (Ennis et al., 2018; Lane et al., 2015).
Although there is debate about whether sufficient high-
quality research exists to categorically claim teacher praise
as an “evidence-based strategy” (e.g., Moore et al., 2019;
Royer et al., 2019), researchers have demonstrated the
effectiveness of teacher praise in a variety of settings with a
variety of populations (Ennis et al., 2020; Moore et al.,
2019).

There is consensus among scholars that behavior-spe-
cific praise (BSP) may be a more salient reinforcer than
general praise (GP) due to the explicit linkage between
teacher approval and a specific student behavior (Alberto
et al., 2022; Gage & MacSuga-Gage, 2017). An example of
GP is “Nice job!” An example of BSP is “I like the way you
put your things away then immediately started working on
your project, well done!” When students receive feedback
about their behavior delivered as a specific positive affirma-
tion, the student’s behavior is rewarded with attention, the
student is told which specific behavior resulted in

that attention, and their classmates have been reminded of
classroom expectations. Specific feedback is a potentially
more effective reinforcer than nonspecific feedback because
the recipient is oriented to the behavior that elicited rein-
forcement and is thus able to replicate that behavior in the
future (Cooper et al., 2020). Accordingly, BSP is recom-
mended as a Tier 1 (classroom level), Tier 2 (small group
level), or Tier 3 (individual student level) intervention to
promote desirable behaviors (Floress et al., 2020).

Praise Variety

The past empirical literature has focused on the efficacy of
BSP versus GP (Markelz, Taylor, et al., 2019). Yet, scholars
have suggested another characteristic of praise may affect
efficacy such as praise variety (Floress & Beschta, 2018;
Hager, 2012; Markelz et al., 2020). Praise variety is a topo-
graphical characteristic grounded in research related to the

1126568 PBIXXX10.1177/10983007221126568Journal of Positive Behavior InterventionsMarkelz et al.
research-article2022

1Ball State University, Muncie, IN, USA
2James Madison University, Harrisonburg, VA, USA
3University of Virginia, Charlottesville, USA

Corresponding Author:
Andrew M. Markelz, Ball State University, 749 Teachers College, 2000
W. University Ave., Muncie, IN 47306, USA.
Email: ammarkelz@bsu.edu

The Effects of Varied and Non-Varied
Praise on Student On-Task Behaviors

Andrew M. Markelz, PhD1, Benjamin S. Riden, PhD, BCBA-D2 ,
Stephanie Morano, PhD3, Alicia L. Hazelwood, MS1,
and April M. Taylor, MA1

Abstract
Research has demonstrated behavior specificity as a salient characteristic of teacher praise efficacy. Praise variety may also
be an important characteristic to reinforce desired student behavior based on research about the quality of reinforcers.
In this study, we used an alternating treatments design to examine the effects of varied and non-varied behavior-specific
praise (BSP) on two first-grade students’ on-task behaviors in general education classrooms. Visual and statistical analyses
suggest both varied and non-varied BSP increased on-task behavior, with varied BSP resulting in marginally higher levels
of on-task behavior. There was no functional relationship between varied and non-varied BSP conditions. Findings from
this study contribute to teacher praise literature as the first to empirically investigate the effects of praise variety on
student behavior. We discuss the implications of this preliminary research and encourage future inquiry into additional
characteristics of praise.

Keywords
behavior-specific praise, on-task behavior, praise variety, teacher training

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2 Journal of Positive Behavior Interventions 00(0)

quality of reinforcer (Markelz et al., 2020). The stronger or
more effective a reinforcer is, the greater likelihood of
meaningful behavior change. Floress and Beschta (2018)
suggested that teachers who praise in various ways (e.g.,
physical gestures, verbal praise, and written notes) may
become a discriminative stimulus for the availability of
praise. As students learn which teachers deliver more praise
(i.e., provide more reinforcement), they are likely to per-
form behaviors that in the past have led to teacher praise
(Greer, 2002). However, stimulus habituation is a decrease
in response to a stimulus after repeated delivery (Thompson
& Spencer, 1966). For example, at a children’s birthday
party, the first balloon that pops likely startles everyone, but
over time, as more balloons pop, the sound becomes less
startling because party-goers habituate to the sound. Similar
to a balloon popping, a student who first hears a praise
statement might take notice more readily, as the statement is
novel to the environment. However, teacher praise, like any
other stimulus (e.g., balloon popping), may become irrele-
vant if students habituate to hearing praise.

Shriver and Allen (2008) contend that individuals learn
to attend to relevant stimuli and ignore stimuli that have
little meaning or importance. Student habituation to praise
may diminish the reinforcer strength and the praise efficacy
(Markelz et al., 2020). Given the potential for praise variety
to impact the efficacy of this widely purported classroom
management strategy, an examination of praise variety lit-
erature is warranted.

Praise Variety Literature Review

Researchers have suggested that sincere praise will more
likely capture students’ attention (Bayat, 2011; Brophy,
1981; Henderlong & Lepper, 2002; McKay, 1992).
However, sincerity is a subjective and difficult to quantify
term that has been neglected in the literature. Markelz,
Riden, Floress, et al. (2022) suggest that praise variety may
increase perceived sincerity as the praise statement novelty
decreases the likelihood of habituation. Rather than habitu-
ating to hearing “Good job,” a student may believe a teach-
er’s praise statement is more genuine with statements like,
“Thank you for putting your supplies away immediately
after I asked the class.”

Few studies have empirically examined praise variety.
Floress and Beschta (2018) measured “diverse praise” in 28
kindergarten through fifth-grade classrooms and defined it
as “the use of verbal statements or gestures of approval that
are delivered in a variety of distinguishable ways in response
to desired student behavior” (p. 1191). The authors mea-
sured the rate of teacher praise delivery method toward vari-
ous student behaviors. Verbatim GP data were coded into
eight categories: (a) work/job, (b) adjective, (c) effort, (d)
compliance/appreciation, (e) gesture, (f) tangible, (g) physi-
cal, and (h) miscellaneous. Verbatim BSP data were coded

into categories based on student behavior variety (e.g., sit-
ting, working, hand-raising, following directions). For
example, if the teacher said, “Nice job sitting in your seat”
and “Good job sitting down when I asked,” within the same
observation, one diverse BSP category was counted because
both BSP statements targeted “sitting down.” On average,
teachers used 3.7 total diverse praise categories per observa-
tion (M = 18.2 min per observation) and more general
diverse praise categories (2.2) compared with specific
diverse praise categories (1.5; Floress & Beschta, 2018).

In a study examining a student-teacher’s use of effective
teaching strategies in a moderate to severe self contained
elementary classroom, Hager (2012) measured praise vari-
ety by quantifying the adjective used in the praise state-
ment. For example, if the student-teacher said, “Good job
working quietly” and “Nice work working quietly,” praise
variety was counted twice (i.e., once for “good” and once
for “nice”). During baseline, the teacher delivered an aver-
age of 7.6 varied praise statements. Following a video self-
monitoring intervention, the teacher averaged 14.8 varied
statements.

Markelz and colleagues (2020) developed and assessed
the reliability of an observation tool to measure praise spec-
ificity, contingency, and variety. Similar to Hager (2012)
who identified the statement adjective as the key compo-
nent to varied praise, Markelz et al. explained BSP variety
as “the teacher praises a student (or group of students) for a
specific behavior using a variety of descriptive language
(e.g., ‘Sam, you did a super job raising your hand.’ At the
next opportunity, the teacher praises Sam for raising his
hand ‘Sam, wonderful job raising your hand’).” Unlike
Floress and Beschta (2018) who included student behavior
as a required component of diverse praise, if a teacher said,
“Great job raising your hand” and “Great job sitting in your
seat” those statements would be counted as non-varied
since “great job” was used in both statements.

Markelz et al. (2020) determined that statement adjective
was appropriate to measure praise variety since GP does not
identify a specific behavior. Furthermore, teachers often
need to single out a specific student behavior for frequent
reinforcement. For example, a student’s behavior interven-
tion plan (BIP) may target sharing with classmates. The
teacher would want to identify instances of the student shar-
ing with classmates and deliver a dense schedule of descrip-
tive praise (“I love how you are sharing right now,” “You are
doing an excellent job sharing with your friends,” “Awesome
job, I love seeing you share with your classmates”).

Using the behavior-specific praise observation tool
(BSP-OT), Markelz, Riden, Floress, and colleagues (2022)
measured natural rates of teacher praise specificity, contin-
gency, and variety. To calculate praise variety the authors (a)
counted different adjectives used over a 15-min observation
session and then (b) divided the number of different types of
praise statements by total statements and multiplied by 100.

Markelz et al. 3

For example, if a teacher delivered 10 BSP statements and
used “great,” “wonderful,” “good,” and “love,” then the
teacher used four varied praise statements (4/10 × 100 =
40% praise variety).

Results from Markelz, Riden, Floress, et al. (2022) sug-
gested that special education teachers (n = 12) delivered
44.9% BSP praise variety and 48.6% GP praise variety per
15-min observation. General education teachers (n = 13)
delivered 44.5% BSP variety and 51.5% GP variety with no
statistical differences between special education and gen-
eral education teachers (BSP variety [U = 76.0, p = .934];
GP variety [U = 69.0, p = .643]). Preservice special educa-
tion teachers (n = 17) delivered 51.5% BSP variety and
57.7% GP variety and were excluded from statistical differ-
ence calculations due to limited observations. Across
teacher groups, low natural rates of BSP and GP were
observed per observation session (BSP M = 1.59; GP M =
7.2), which corresponded with prior research measuring
natural rates of praise delivery (Floress et al., 2018; Jenkins
et al., 2015).

Purpose of the Study

Given the identification of praise variety as a potentially
salient characteristic of praise efficacy due to potential
stimulus habituation, this study empirically examined the
effects of varied and non-varied specific praise on student
on-task behavior. Previous research has observed teacher’s
natural rates of praise variety (e.g., Markelz, Riden, Floress,
et al., 2022); however, no study has empirically examined
whether praise variety affects student behavior. The follow-
ing study adds to the literature on teacher praise by empiri-
cally manipulating praise variety to expand researchers’
understanding of praise efficacy. In other words, does var-
ied praise serve as a more salient reinforcer than non-varied
praise on student on-task behaviors? The following research
questions guided our analysis:

Research Questions

Research Question 1: Is there a functional relation
between varied BSP and increased student on-task
behavior?
Research Question 2: Is there a functional relation
between non-varied BSP and increased student on-task
behavior?
Research Question 3: Is there a difference in effective-
ness between varied BSP and non-varied BSP on student
on-task behavior?

Method

Participant and Setting Information
The study took place in a public k-5 Title 1 elementary
school in the United States with 490 students. Approximately

40% of the student population identified as a racial or ethnic
minority. Prior to study initiation, institutional review board
approval was obtained from university and school district
review boards. A recruitment email was sent to three differ-
ent principals within the school district to forward to their
general and special education teachers. The email asked
teacher volunteers to participate in a study that could help
with classroom management practices. The inclusion crite-
rion included no planned extended absences. Teacher par-
ticipants were offered a US$400 incentive to participate.
Two teachers from the same public elementary school vol-
unteered to participate in the study. Each teacher selected an
instructional period for observation to occur and identified
a target student from their classroom, who they believed
demonstrated frequent off-task behaviors, to complete a
teacher–student dyad. Voluntary consents from teachers and
students’ parents were obtained. All names have been
changed to protect participant identity.

Two certified general education teachers and one of their
students in an inclusive classroom participated in the study.
Both participating teachers were white females with
Bachelor’s in Elementary Education. Dyad A comprised a
teacher and the target student in a classroom with 20 stu-
dents total. Ms. Alisha was 41 years of age and had a total
of 8 years of teaching experience, two of those years in her
current position. Alli was a 6-year-old Black girl without a
disability. Ms. Alisha reported that Alli frequently demon-
strated off-task behaviors such as climbing on furniture,
repeatedly seeking teacher attention, and not completing
assigned activities. Alli was on a behavior intervention plan
for 3 weeks consisting of a check-in/checkout (Klingbeil
et al., 2019) when the study started. Ms. Alisha completed a
Functional Analysis Screening Tool (FAST; Iwata et al.,
2013), which indicated Alli’s off-task behavior was socially
maintained (attention/preferred item). Dyad A observations
occurred daily during the beginning of reading/writing class
where students sat at their desks, clustered in small groups
of 4 to 5 students, and participated in teacher-led activities,
such as filling in blanks to complete story sentences, spell-
ing practice, and sight word practice.

Dyad B comprised a teacher and the target student in a
classroom with 22 students total. Ms. Brenda was 21 years
of age and had 4 years of overall experience and 2 years in
her current position. Brice was a 7-year-old Black boy who
was medically diagnosed with attention deficit hyperactive
disorder (unmedicated) and was not receiving special edu-
cation services. Ms. Brenda identified Brice as a good can-
didate for the intervention due to excessive off-task
behaviors, such as not completing assignments and aggres-
sion toward other students. Brice was also receiving a
behavior intervention plan for 4 weeks with check-in/
checkout. Ms. Brenda completed a FAST, which indicated
Brice’s off-task behaviors were equally social and automat-
ically maintained (attention/preferred item and sensory
stimulation). Dyad B observations occurred daily during

4 Journal of Positive Behavior Interventions 00(0)

reading instruction where students sat at their individual
desks (Brice’s desk was isolated in the front of the class-
room) and participated in teacher-led activities, such as
choral reading, or sat on the carpet in front of a smartboard
and participated in the video-led reading activities.

Study Design

An alternating treatments design (Cooper et al., 2020) was
used to assess if a functional relation between varied/non-
varied praise and student on-task behaviors exists. This
design was used to measure whether rapid changes in praise
variety would affect student on-task behaviors. The present
study aligns with the What Works Clearinghouse version
4.1 single-case design review standards (What Works
Clearinghouse, 2020): (a) the independent variable was sys-
tematically manipulated; (b) outcome variables were mea-
sured systematically over time by more than one assessor,
and interobserver agreement (IOA) was collected for at
least 20% of all sessions across all phases; (c) the study
included at least three attempts to demonstrate an interven-
tion effect at three different points in time; (d) each phase
had a minimum of five data points, and (e) no more than
two consecutive treatments were scheduled.

Dependent Variable

The primary dependent variable was student on-task behav-
ior. Student behaviors were measured as a percentage using
whole interval recording (Ledford & Gast, 2018) with 20-s
intervals. Whole interval recording was selected because it
underestimates the duration of behavior (Ledford & Gast,
2018). On-task behavior was defined as participating in a
lesson or activity or being focused and attending to a
speaker or activity. Examples of student on-task behavior
included looking at the teacher when they were talking,
working on iPad activity (e.g., phonics practice), and rais-
ing one’s hand to participate in classroom discussion.
Students were also considered on-task if they were per-
forming typical classroom activities (e.g., taking scrap
paper to the trash can or handing out worksheets). Examples
of off-task behavior included walking around the classroom
without permission, talking with other students during
instruction, and head down on desk and not completing
assignments.

Secondary dependent variables were teacher praise char-
acteristics delivered toward the target student at any time
during observation sessions (i.e., specific/general praise,
contingent/non-contingent, and varied/non-varied praise).
Definitions of praise characteristics were used from the
BSP-OT (Markelz et al., 2020). Specific praise was defined
as a positive statement delivered by the teacher toward the
target student that identified a behavior and incorporated an
expression of approval (e.g., great job raising your hand,

Sarah). General praise was defined as a positive statement
delivered by the teacher toward the target student that was
unspecific to behavior (e.g., “Good work, Sarah”).

Specific and general praise can classify as contingent or
noncontingent. Contingency is the relationship between
two events, one being a consequence of temporal proximity
to the other event (Markelz et al., 2020). Contingent-
specific praise was defined as a positive statement provided
by the teacher, when a desired behavior occurred (contin-
gent), to inform the target student, specifically, what they
did well (e.g., a teacher tells the class to quiet down; Sarah
gets quiet, teacher responds, “Great job getting quiet,
Sarah”). Contingent general praise was a positive state-
ment provided by the teacher, when a desired behavior
occurred to inform the student generally that they did well
(e.g., Teacher tells the class to sit down; Sarah sits, teacher
responds, “Good job, Sarah”).

Specific and general praise can also classify as varied or
non-varied. Praise variety is about the quality of praise
being different or diverse (Markelz et al., 2020). Varied spe-
cific praise was defined as the teacher praising the student
for a specific behavior using a variety of descriptive lan-
guage (e.g., “Sarah, you did a super job raising your hand.”
At the next opportunity, the teacher praises Sarah for raising
her hand, “Sarah, wonderful job raising your hand”). Varied
general praise was when the teacher praises a student with
a variety of descriptive language without specifying a
behavior (e.g., “Great work, Sarah!” “Nice job, Sarah”).

Independent Variables

Given the study’s purpose to examine differences between
varied and non-varied praise on student on-task behaviors,
teacher participants needed to deliver consistent specific
and contingent characteristics of praise, prior to indepen-
dently manipulating varied praise. We trained teachers to
ensure high levels of specific and contingent praise were
being delivered (i.e., a minimum of 10 statements toward
the target student) before entering the treatment phases.
Once participants entered the treatment phase, Treatment 1
(T1) was a high rate of varied praise. Treatment 2 (T2) was
a high rate non-varied praise.

Data Collection

Dependent and independent variables were measured with
an adapted BSP-OT. Previous research suggests the BSP-OT
is a reliable tool to measure teacher praise characteristics
with an interclass correlation of .80 and a κ score of .91
(Markelz et al., 2020). The adapted BSP-OT added a stu-
dent on-task row to simultaneously collect teacher praise
characteristic data (independent variable) as well as student
on-task behavior data (dependent variable). The adapted
BSP-OT is available in the supplemental file. The first

Markelz et al. 5

author trained the primary data collector (a graduate stu-
dent). Following the 30-min training, the primary data col-
lector watched a 15-min prerecorded video of a teacher/
student dyad in an authentic classroom and used the adapted
BSP-OT to data collect teacher praise characteristics and
student on-task behavior. The primary data collector’s data
was assessed for reliability against an accurate data sheet
using kappa. A priori κ criterion of .80 was set. The primary
data collector scored kappa reliability of .95.

Procedures

Baseline. Observation sessions were set to 15-min in length
due to previous praise research on acceptable dependability
and generalizability levels of teacher praise (Floress et al.,
2021; Gage et al., 2014). A minimum of five baseline obser-
vations were conducted, but the research team also analyzed
data with a stability envelope criterion of 80% of data
within 25% of the mean to ensure baseline data stability
prior to intervention implementation (Lane & Gast, 2014).

Praise Training Condition. Once baseline conditions were met
(i.e., student on-task behaviors were stable), the first author
and primary data collector met with both teacher partici-
pants and trained them using explicit instruction (Archer &
Hughes, 2011) on the importance of specific and contingent
praise (note: praise variety was not mentioned during this
training). The first author trained teacher participants on the
definition and use of BSP; research efficacies of BSP on
student behaviors were highlighted, and the teacher partici-
pants practiced delivering BSP with their target students’
name. The trainer delivered positive and corrective verbal
feedback. Participants were then told that prior to each
observation, they would be handed an Apple Watch™
(series 4) that had a Periodic Timer application (Kelin,
2014). The timer application was programmed to deliver a
vibratory cue (i.e., tactile prompt) every 75 s. Previous
research suggests tactile prompting effectively increases
teacher praise rates (Markelz, Riden, & Hooks, 2021;
Markelz, Taylor, et al., 2019).

Participants practiced putting the Apple Watch on and
delivering BSP every tactile prompt. Participants were
instructed to deliver specific praise to their target student
following each tactile prompt, contingent on on-task behav-
iors (e.g., following directions, participating in the activity).
Participants were informed that they could deliver BSP at
any time during observations; however, the Apple Watch
prompted delivery at a minimum of 10 statements based on
previous research on BSP in childhood settings (LaBrot
et al., 2016), and previous recommendations of 6 to 10
praise statements per 15 min (Sutherland et al., 2000). The
trainer instructed teacher participants that to reach mastery
levels of praise delivery, they would need to meet the goal
of at least 10 BSP statements per observation for 3 days

before entering the treatment condition. The training lasted
approximately 30 min.

Treatment Condition. After each participant demonstrated
mastery in delivering specific and contingent praise to their
target student during observation sessions, an email was
sent describing the difference between varied and non-var-
ied praise. Teacher participants were informed that observa-
tion sessions would now alternate between T1 and T2
conditions. When the data collector handed teacher partici-
pants the Apple Watch prior to observation, they informed
the teacher which treatment was in place for that session. A
research team member randomly assigned T1 and T2 condi-
tion orders without knowledge of which condition was var-
ied or non-varied praise. To meet alternating treatments
design standards without reservations (What Works Clear-
inghouse, 2020), five sessions per treatment condition were
scheduled with no more than two consecutive treatments.

Data Analysis

We used Barton’s (2021) visual analysis tool and Lanovaz
et al.’s (2019) visual structured criterion (VSC) for alternat-
ing treatment designs to determine the presence of a func-
tional relation between experimental conditions and phases.
The visual analysis tool (Barton, 2021) is a spreadsheet that
guides users through the visual analysis process to help
determine whether a functional relation is present and how
confident the user can be in that determination. The tool
prompts users to consider the overlap between conditions,
differentiation between conditions, and the magnitude and
trend of differentiation. We computed the nonoverlap of all
pairs (NAP; Parker & Vannest, 2009) to assess overlap and
judged differentiation between conditions by evaluating
stability, trend, and effect size. We assessed stability by
determining whether 80% of data points fell within 25% of
the median of each phase (Lane & Gast, 2014) and used the
split-middle method (White & Haring, 1980) to identify
trend. To use the split-middle method, one finds the mid-
rate, mid-date, and middle point of the mid-rate and mid-
date for each phase; then draws a trend line between the
middle point of the mid-rate and mid-date (Ledford & Gast,
2018).

We also used Lanovaz et al.’s (2019) visual structured
criterion (VSC) for alternating treatments designs to sup-
plement visual analysis. In contrast to nonoverlap methods
used in Barton’s (2021) tool (i.e., NAP; Parker & Vannest,
2009), which compare overlap on a point-by-point basis
and do not account for trend, the VSC compares the relative
position of points and paths (Lanovaz et al., 2019). To use
the VSC, one counts the number of times that a data path
and/or points for one treatment condition fall above the path
and points for a second treatment condition at each session.
Then, that number is compared with a predetermined cutoff

6 Journal of Positive Behavior Interventions 00(0)

value based on the number of data points and comparisons
(Lanovaz et al., 2019).

To report a quantitative index of effectiveness (Ledford
& Gast, 2018) that captures the magnitude of behavior
change between T1 and T2 conditions, we calculated the
weighted average difference between success observations
(ADISO; Manolov & Onghena, 2018). To do so, T1 and T2
data were entered into https://manolov.shinyapps.io/
ATDesign/ where an ADISO score was calculated. The
ADISO score is expressed in the same measurement units
as the dependent variable. Thus, the ADISO score repre-
sents a weighted change in the percentage of on-task behav-
ior between T1 and T2 conditions.

Visual and Statistical Analysis Agreement

The second and third authors conducted independent visual
analyses on the graphed data collected for each participant to
determine if there were functional relations between baseline
and treatment conditions and T1 versus T2 conditions.
Barton’s (2021) visual analysis tool was used to examine level
changes, trend, variability, overlap, and immediacy of change.
The second author earned his PhD in special education and is
a Doctoral Level Board Certified Behavior Analyst. The third
author earned her Ph.D. in special education and completed
the Institute of Education Sciences/The National Center for
Special Education Research (IES/NCSER) Summer Research
Training Institute on Single-Case Intervention Research
Design and Analysis. After the independent visual analyses
were completed, agreement across 66 coding opportunities
within the visual analysis tool was 97%.

IOA

To ensure observers remained reliable using the BSP-OT,
we gathered IOA data for student on-task behavior and
praise characteristics across 27.3% (n = 6) of total observa-
tions (n = 22) for Dyad A, and 36.8% (n = 7) of total obser-
vations (n = 19) for Dyad B. Similar to observer training,
IOA scores were calculated with a priori criterion of accept-
able kappa reliability at .80. For Dyad A, baseline κ reli-
ability was .86, .89, and .93 for 33% (n = 3) of observations.
During the mastery training phase, overall κ was .91 for
33% (n = 1) of observations. T1 reliability for Dyad A was
.93 for 25% (n = 1) of observations. T2 reliability for Dyad
A was .92 for 25% (n = 1) of observations.

For Dyad B, baseline κ reliability was .95, .85, and .97
for 60% (n = 3) of observations. During the mastery train-
ing phase, kappa reliability was .92 for 25% (n = 1) of
observations. T1 reliability was .87 for 25% (n=1) and T2
reliability was .96 for 25% (n= 1) of observations.

Treatment Fidelity

During intervention conditions, data were analyzed daily to
confirm the only praise characteristic manipulated was

varied and non-varied BSP. Based on previous research on
low rate BSP (Jenkins et al., 2015; Markelz, Riden, Floress,
et al., 2022) and previous praise rate recommendations
(Floress et al., 2020), a minimum of 10 BSP statements
toward the target student per 15-min session was set so that
sufficient data could be observed. The number of GP state-
ments was recorded to document if anomalous rates of GP
occurred during any particular session. A post hoc paired
samples t test was conducted on the number of GP state-
ments between T1 and T2 for each Dyad. There was no sta-
tistical difference between the two treatments for Dyad A,
t(4) = −1.500, p = .208, and Dyad B, t(4) = −0.767, p =
.486.

Contingent BSP was measured using the BSP-OT to
confirm that consistent BSP contingency was being deliv-
ered. Due to the assumption of independence of groups not
being met, Wilcoxon signed-rank tests were used to exam-
ine post hoc statistical differences in praise characteristics.
Results indicated no statistical difference for BSP contin-
gency between T1 and T2 for both Dyads (z = −.074, p =
.941). In addition, the post hoc Wilcoxon signed-rank test
indicated no significant differences for the number of BSP
statements per T1 and T2 sessions for both Dyads (z =
−.291, p = .771).

A post hoc paired samples t test did confirm a statistical
difference of BSP variety between T1 and T2 for Dyad A,
t(4) = −5.48, p = .005, and Dyad B, t(4) = −7.64, p = .002.
The Wilcoxon signed-rank analysis confirmed this signifi-
cant difference in praise variety between T1 and T2 (z =
−2.316, p = .021). Treatment fidelity analyses suggest the
only praise characteristic manipulated between T1 and T2
was BSP variety.

Results

Visual analysis indicates a positive effect of praise (both var-
ied and non-varied) on participants’ on-task behavior, but
fails to indicate a clear difference in effectiveness between
T1 and T2 for either participant. Alli’s data are presented in
Figure 1 and Brice’s data are presented in Figure 2. Teachers’
praise data (number of BSP statements and variety percent-
age) are summarized in Tables 1 and 2.

Dyad A

During baseline, Ms. Alisha provided an average of less
than 1 BSP statement to Alli per observation (range 0–2),
and Alli was on-task an average of 49% of the time (range
27%–76%). Alli’s baseline data were variable, and a split
middle trend analysis indicated a decelerating trend. During
training, Ms. Alisha provided Alli an average of 13 BSP
statements per observation (range 12–14) and her BSP
statements averaged 41% variety (range: 36%–46%). Alli’s
percentage of time-on-task increased to an average of 70%
(range: 60%–82%).

https://manolov.shinyapps.io/ATDesign/

https://manolov.shinyapps.io/ATDesign/

Markelz et al. 7

Figure 1. Percentage of On-Task Behavior Across Conditions for Alli.

Figure 2. Percentage of On-Task Behavior Across Conditions for Brice.

During T1 sessions (i.e., varied praise), Ms. Alisha pro-
vided an average of 14 BSP statements per observation
(range: 13–15) with an average of 39% variety (range 33–
46%). During T2 sessions (i.e., non-varied praise), she pro-
vided an average of 13 BSP statements per observation
(range 10–14) with an average of 9% variety (range:
0–15%). Alli’s average percentage of time on task rose to
80% (range 64–93%) during T1 sessions and 74% (range
62–80%) during T2 sessions. Split middle trend analyses
indicated that Alli’s on-task behavior showed an accelerat-
ing trend in both T1 and T2 sessions.

We used visual analysis guided by Barton’s (2021) visual
analysis tool to determine that a functional relation was
present between both treatment conditions and on-task
behavior. For both T1 and T2, there was low overlap
between data in the baseline and praise conditions

(NAP=93% for both comparisons), and changes in level
and trend provided clear differentiation between baseline
and T1 and T2. The level of on-task behavior increased in
T1 and T2 and the trend changed from decelerating during
baseline to accelerating.

Visual analysis and the VSC (Lanovaz et al., 2019) fail
to indicate a functional relation between T1 and T2 condi-
tions. There was a high degree of overlap between T1 and
T2 data for Alli (NAP = 30%) and moderate overlap
between T2 and T1 data (NAP = 70%). Given undifferenti-
ated intervention conditions, the visual analysis tool
(Barton, 2021) indicated no functional relation between T1
and T2 for Alli. The VSC (Lanovaz et al., 2019) supported
this determination. The T1 data points and/or data path were
higher than T2 in 7 out of 8 comparisons, which falls below
the cutoff of 8 out of 8 comparisons (Lanovaz et al., 2019).

8 Journal of Positive Behavior Interventions 00(0)

Although a functional relation between treatments was
unestablished, the ADISO calculation demonstrated that T1
had a +8.5% difference in on-task behavior compared with
T2.

Dyad B

During baseline, Ms. Brenda provided an average of 1 BSP
statement for Brice per observation (range 0–2). Brice was
on-task an average of 28% of sessions (range 22–36%) and
his baseline data were stable. During training, Ms. Brenda
provided an average of 12 BSP per observation (range
8–13) and her BSP statements averaged 46% variety (range
37–58%). Brice’s percentage of time on task increased to an
average of 41% during training (range 33–53%).

During T1 sessions (i.e., varied praise), Ms. Brenda pro-
vided an average of 11 BSP statements per observation
(range 9–13) with an average of 58% variety (range 45–
66%). During T2 sessions (i.e., non-varied praise), she pro-
vided an average of 12 BSP statements per observation
(range 11–13) with an average of 9% variety (range 0–16%).
Brice’s average percentage of time on task was 66% (range
42–84%) during T1 sessions and 64% (range 44–80%) dur-
ing T2 sessions. Split middle trend analyses indicated that

Brice’s on-task behavior showed an accelerating trend in
both T1 and T2 sessions.

Barton’s (2021) visual analysis tool suggested a func-
tional relation between the intervention and on-task behav-
ior. For both T1 and T2, there was no overlap between data
in baseline and treatment conditions (NAP = 100% for both
comparisons); and differences in level and trend provide
clear differentiation between baseline and each treatment
condition.

There was substantial overlap in Brice’s data in the two
treatment conditions (NAP=46% for T1 vs. T2 and
NAP=54% for T2 vs. T1) and undifferentiated conditions.
As a result, the visual analysis tool (Barton, 2021) indicated
no functional relation between T1 and T2. We were unable
to confirm this determination using the VSC because there
were 7 comparison points between conditions and 8 are
required for VSC (Lanovaz et al., 2019). The AIDSO calcu-
lation demonstrated that T1 had a +1.6% difference in on-
task behavior compared with T2.

Discussion

Results from this study are the first in teacher praise research
to examine the efficacy of praise variety on student on-task

Table 1. Praise Statement Data for Dyad 1: Ms. Alisha and Alli.

Session Phase Treatment
BSP

frequency
Percentage
of variety

1 Baseline 0 0
2 Baseline 0 0
3 Baseline 1 0
4 Baseline 1 0
5 Baseline 0 0
6 Baseline 0 0
7 Baseline 0 0
8 Baseline 1 0
9 Baseline 0 0
10 Training 14 36
11 Training 12 41
12 Training 13 46
13 Intervention T2 14 14
14 Intervention T1 13 38
15 Intervention T1 15 33
16 Intervention T2 13 15
17 Intervention T2 14 14
18 Intervention T1 14 36
19 Intervention T2 10 0
20 Intervention T1 15 40
21 Intervention T2 12 0
22 Intervention T1 13 46

Note. BSP = behavior-specific praise; T1 = varied praise condition; T2
= non-varied praise condition.

Table 2. Praise Statement Data for Dyad 2: Ms. Brenda and
Brice.

Session Phase Treatment
BSP

frequency
Percentage
of variety

1 Baseline 0 0
2 Baseline 1 0
3 Baseline 2 0
4 Baseline 2 0
5 Baseline 1 0
6 Training 12 41
7 Training 8a 37
8 Training 12 58
9 Training 13 46
10 Intervention T1 11 63
11 Intervention T1 9a 66
12 Intervention T2 11 0
13 Intervention T2 12 16
14 Intervention T1 11 45
15 Intervention T2 13 15
16 Intervention T1 10 60
17 Intervention T2 11 0
18 Intervention T1 13 54
19 Intervention T2 12 16

Note. BSP = behavior-specific praise; T1 = varied praise condition; T2
= non-varied praise condition.
aParticipant did not meet minimum BSP criterion and was retrained prior
to subsequent session.

Markelz et al. 9

behaviors. Increases in student on-task behaviors between
baseline and both T1 and T2 conditions support consensus
in the research community that BSP may positively affect a
variety of student behaviors (Ennis et al., 2020). Although
praise research has been conducted for decades, few meth-
odologically sound studies exist to classify BSP as an evi-
dence-based practice according to the Council for
Exceptional Children (CEC) and the What Works
Clearinghouse quality indicators and standards (Moore
et al., 2019). Results from this study contribute to the grow-
ing body of rigorous research which supports BSP as a
potential evidence-based practice according to the CEC
guidelines (Royer et al., 2019).

The visual and statistical examination of varied versus
non-varied praise on student on-task behaviors indicates no
functional relation in efficacy. Varied praise did produce
slightly higher on-task behavior percentages for both stu-
dent participants; however, insufficient change to claim dif-
ferences in efficacy. Given the novelty of praise variety
research, we recommend further examination between var-
ied and non-varied praise to build a larger literature base.
Because challenges with classroom management contribute
to teacher stress and decreased self-efficacy (Bottiani et al.,
2019) and BSP is an effective, efficient, low-intensity strat-
egy (Ennis et al., 2018), further research into BSP charac-
teristics is warranted.

Future research should acknowledge a potential ceiling
effect for varied praise percentages. Participants in this
study used a total of 13 different praise statements across all
sessions (i.e., good, great, awesome, thank you, appreciate,
like, wonderful, love, nice, perfect, fantastic, excellent,
super). On average, teacher participants used only five var-
ied statements during T1 conditions. With a ceiling effect
on the number of unique statements, fluctuations in total
statements may misrepresent desired levels of percent var-
ied. For example, if a teacher uses two unique BSP state-
ments out of 4 total BSP statements; the percent varied for
that session is 50%. However, if the teacher during the next
observation uses five unique BSP statements, but 15 state-
ments in total, the percent varied for that session is 33%.
The use of more BSP statements is encouraged and aligned
with best practices; however, the teacher is “penalized”
with a lower percent variety. In other words, the more BSP
statements a teacher delivers, the more difficult it is to have
higher percentages of praise variety.

In addition to the ceiling effect on the number of unique
statements, previous research suggests cognitive load may
also play a factor in limiting the number of unique state-
ments (Markelz, Scheeler, et al., 2019). In a classroom with
competing stimuli, teachers are often focused on their peda-
gogy, curricular content, and reacting to management issues
(Maag, 2001). One explanation for consistently higher GP
rates over BSP rates is that it takes less cognitive effort to
deliver “Good job” as opposed to identifying a specific

behavior to praise (Markelz, Riden, Floress, et al., 2022).
The same may be true for delivering unique BSP state-
ments. Teachers may deliver repeated BSP statements
because it is easier than producing a unique BSP statement
every time. Based on these realities of delivering praise in a
busy classroom, it is unrealistic to expect teachers to use
proportionally higher numbers of unique statements as total
statements increase.

In a study that examined teachers’ natural rates of praise
characteristics, praise variety was around 50% on average
(Markelz, Riden, Floress, et al., 2022). Participants within
that study, however, delivered meager total BSP statements
(M = 1.6 per 15-min observation). Thus, simply delivering
two unique statements out of three total BSP statements
would result in a 66% variety. Teacher participants in the
current study averaged 39% and 58% during varied praise
treatment conditions; yet, also averaged 14 and 11 BSP
statements, respectively. The question arises, which is more
important, praise variety or the number of BSP statements?
Preliminary evidence from this study suggests the number
of total BSP statements supersedes higher percentages of
praise variety. However, we caution any definitive declara-
tions on the importance of praise variety through descrip-
tive words as a salient characteristic on student behaviors
until more research is conducted.

Future researchers of praise variety should consider that
although the BSP-OT measures praise variety by differen-
tiation in descriptive words, Floress and Beschta (2018)
identified various student behaviors as means of varying
praise statements. For explicit operationalized behaviors
(e.g., hand raising), descriptive word variation may be a
more appropriate method of varying praise. For example,
“Great job raising your hand before speaking,” and “I really
appreciate you raising your hand and not calling out.” Yet
for more ambiguous behaviors, like “on-task,” descriptive
word differentiation and various student behaviors could
contribute to praise variety. For example, a teacher could
say “I love how focused you are on your assignment right
now,” or “Great job working hard today.” Both praise state-
ments target on-task behavior and use different descriptive
words (i.e., love and great). The statements also praise two
different behaviors (i.e., focused and working hard).
Although this study lacked control for the praise of varied
student behaviors (as long as they targeted on-task), future
inquiry should examine possible interactions between these
two modes of praise variety and their possible contribution
to praise efficacy.

Implications

Current evidence that teachers may use low-rate BSP
(Floress et al., 2022) suggests teachers might not connect
best practice with actual practice. Multiple interventions
have been documented to successfully increase teachers’

10 Journal of Positive Behavior Interventions 00(0)

BSP rates with components like didactic training, self-mon-
itoring, performance feedback, and tactile prompting
(Markelz et al., 2018; Zoder-Martell et al., 2019). Preservice
teacher preparation programs or professional development
for in-service teachers must provide opportunities for teach-
ers to learn about and practice BSP to counter persistent
low-level use. Future research should continue to examine
the maintenance and generalized use of BSP following
intervention to provide guidance on best practices in sus-
tainable and efficient teacher training.

Markelz, Riden, Floress, and colleagues (2022) dis-
cussed future research to determine normative guides of
praise characteristics to provide recommendations to teach-
ers and teacher trainers. Regarding frequency of BSP deliv-
ery, ranges have been recommended to offer flexibility such
as 6 to 10 BSP statements class wide per 15-min (Sutherland
et al., 2000). Other researchers have recommended similar
ranges like 18 to 30 BSP class-wide per hour (Floress et al.,
2020). At the secondary level, researchers have recently
identified using BSP once per 2 min as superior to once per
4 min in increasing academic engaged behavior and reduc-
ing disruptive behavior (O’Handley et al., 2022). In line
with previous research, we also recommend at least 1 BSP
statement class-wide every 2 min. However, a denser sched-
ule of reinforcement may be appropriate for a particular stu-
dent given the frequency of student behavior and
reinforcement goals.

When discussing a normative guide dictating optimal
praise variety, either in professional development or in
future research, we suggest using a ratio (unique BSP state-
ments to total BSP statements) as opposed to a percentage.
Unsimplified ratios are more descriptive than percentages.
An unsimplified ratio provides the number of unique BSP
statements as well as the number of total BSP statements,
while percentages obscure actual counts and provide only a
proportion of the total. Using un-simplified ratios under-
scores the importance of the rate of praise as well as the
proportion of unique praise statements. We acknowledge,
however, that more research is needed to examine varying
ratio effects on student behaviors before definitive guide-
lines are established.

Limitations

There are several limitations to this study that require dis-
cussion. First, the BSP-OT developed and assessed for reli-
ability (Markelz et al., 2020) was amended for this study.
To measure teacher praise characteristics and their effect on
student on-task behavior, we added an additional row to the
tool that allowed data recorders to capture student on-task
data with whole interval recording. Tool amendment may
have impacted reliability; however, acceptable IOA data
suggest the tool was reliable and recorded teacher praise
characteristics as well as student on-task behavior.

Second, we collected data using a whole interval
recording approach which can underestimate the true
occurrence of behavior and thus is likely to underestimate
the level of the behavior (Fiske & Delmolino, 2012). A
behavior is marked as occurring only if it occurs for the
duration of the entire interval. For example, a student may
have been on-task for 19-s within an interval, however,
the 1-s non-response (i.e., off-task) is coded for the entire
interval as a non-occurrence. In other words, on-task
behaviors that occurred for a fraction of the interval
escape capture. Although we considered an underestima-
tion of behavior an acceptable limitation, future research
should consider other data collection methods (e.g., par-
tial interval recording).

Third, we used the VSC for alternating treatments design
(Lanovaz et al., 2019) as a supplement to visual analysis in
interpreting Alli’s data, but we were unable to use the pro-
cedure for Bryce’s data. We followed single case design
quality guidelines and used random assignment of treat-
ment condition to session and collected data across five ses-
sions for each intervention (T1 and T2). Based on random
assignment, Bryce received T1 for his first two intervention
sessions, but this session order in combination with the lim-
ited number of total sessions resulted in only seven possible
comparisons between T1 and T2. We were unable to use the
VSC as a supplement for Bryce’s data as the VSC requires
a minimum of 8 comparisons between conditions to make a
determination about functional relation. We recommend
researchers adhere to the comparison requirement or extend
the total number of sessions.

Finally, we did not collect social validity data from
teacher participants on their perceived differences between
varied and non-varied praise conditions. Measuring teach-
er’s acceptability of treatment conditions could have pro-
vided a more nuanced examination of willingness to deliver
higher percentages of praise variety. Future research should
include social validity measures when conducting praise
variety research to explore potential barriers like cognitive
overload.

Conclusion

This study’s findings add to the evidence base supporting
BSP as an effective practice to change student behavior.
Participating teachers significantly increased their rate of
BSP delivery following training, and visual analysis of
results indicated that increased praise was correlated to
increased on-task behavior for student participants. In
addition, characteristics of praise, such as variety, may
increase praise efficacy. Although a functional relation
between varied and non-varied praise was not identified,
preliminary results from this study warrant further inves-
tigation into praise variety as a salient characteristic of
BSP.

Markelz et al. 11

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.

Funding

The author(s) received no financial support for the research,
authorship, and/or publication of this article.

ORCID iD

Benjamin S. Riden https://orcid.org/0000-0002-6733-1942

Supplemental Material

Supplemental material for this article is available on the Journal of
Positive Behavior Interventions website with the online version of
this article.

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