ORIGINAL PAPER

The Effects of Describing Antecedent Stimuliand Performance Criteria in Task Analysis Instructionfor Graphing

Bryan C. Tyner1,2 • Daniel M. Fienup1,2

Published online: 10 December 2015

� Springer Science+Business Media New York 2015

Abstract Task analyses are ubiquitous to applied behavior analysis interventions,

yet little is known about the factors that make them effective. Numerous task

analyses have been published in behavior analytic journals for constructing single-

subject design graphs; however, learner outcomes using these task analyses may fall

short of what could be considered socially significant by educators and the behavior

analytic community. To investigate ways to enhance task analysis instruction,

graphing performance was compared between groups receiving either a task anal-

ysis that simply described the necessary responses or the same task analysis sup-

plemented with descriptions of relevant antecedent stimuli and performance criteria,

or the consequences of correctly performing each step. Participants using the sup-

plemented task analysis demonstrated more accurate graphing behavior compared

with those using the task analysis without these descriptions. Implications of

enhancing task analysis effectiveness by linking instructions to the three-term

contingency are discussed.

Keywords Task analysis � Graphing instruction � Computer-based instruction �College students

Introduction

Task analysis (TA) is widely used in applied behavior analysis (ABA) for teaching

the completion of behavior chains. Task analysis refers to the process of ‘‘breaking

down a complex skill into smaller, teachable units, the product of which is a series

& Bryan C. Tyner

bctyner@gmail.com

1 Department of Psychology, Queens College, CUNY, Flushing, New York, NY 11367, USA

2 Department of Psychology, The Graduate Center, CUNY, New York, NY, USA

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DOI 10.1007/s10864-015-9242-z

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of sequentially ordered steps’’ (Cooper et al. 2007, p. 437), as well as the permanent

product of that process used for instruction purposes. Task analysis is an evidence-

based practice (Wong et al. 2010) and has been used for teaching a wide range of

skills including vocational skills to learners with intellectual disabilities (Cuvo et al.

1978), reading interventions to middle school teachers (Browder et al. 2007), and

graphing methods to college students and behavior analysts (Lo and Konrad 2007;

Dixon et al. 2009). Although many studies demonstrate the effectiveness of existing

TAs and of interventions using TAs in their procedures, very little research has

evaluated how to develop effective TAs for such uses. In 1967, Annett and Duncan

wrote that identifying ‘‘what to describe and on what level of detail’’ (p. 1) are two

central challenges when developing TA for use in industry. To the best of our

knowledge, there exists little empirical support for solutions to this problem.

A primary resource for learning about TA development is Cooper et al.’s (2007)

Applied Behavior Analysis textbook, which recommends three methods for

constructing and validating TA (also reported in Snell and Brown 2006): (1)

observe competent individuals engage in the behavior (2) consult expert performers

of the task, and (3) perform the behavior chain yourself. These strategies provide

guidance to practitioners developing TA, but do not answer questions about the

level of detail and range of stimuli that should be described in TA instruction. Crist

et al. (1984) parametrically evaluated (i.e., manipulated along a continuum) the

number of responses described per step in three TAs of vocational tasks for

individuals with intellectual disabilities. Participants received a 28-step TA with

either one response at a time (28 total steps), two responses at a time (14 total steps),

or four responses at a time (7 total steps). Researchers found that participants made

more errors when more responses were combined (i.e., the 7-step condition). More

recently, Graff and Karsten (2012) found that minimizing the jargon used in TA

instruction and supplementing text with pictures and examples produced greater

learner accuracy compared with TA instruction without these revisions. These

studies indicate that manipulating some parameters of TA instruction may affect

learner performance; however, which components of TA instruction are necessary

and sufficient for optimal learner performance is unknown. Data demonstrating

relations between TA components and learner performance may inform the

development of instruction materials used in research and applied settings.

A common use of TA is for teaching ABA students and practitioners to create

single-subject design graphs. Graphing data facilitate the identification of behavior–

environment relations, and ability to construct single-subject graphs is a require-

ment of the Behavior Analytic Certification Board task analysis (BACB 2012);

however, graphing software is complex, and learning to use it may be difficult for

some individuals. For these reasons, a number of TAs for creating graphs in

Microsoft Excel have been published in ABA journals (e.g., Carr 2008; Carr and

Burkholder 1998; Dixon et al. 2009; Lo and Konrad 2007; Lo and Starling 2009;

Pritchard 2008; Reed 2009; Touchette et al. 1985). Each of these TAs is a sequenced

list of responses in the behavior chain and often contains pictures of relevant

stimuli. Despite their similarities, each published TA varies in the number of steps,

number of pictures, sequence of responses, and level of detail. For example, Lo and

Konrad’s (2007) TA consisted of 110 steps including 66 screenshots of the software,

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while Pritchard’s consists of 33 steps and no pictures. The majority of empirical

research on graphing TA demonstrates that presenting a particular TA positively

affected participants’ graphing performance, establishing the general efficacy of TA

graphing instruction. However, no research has evaluated the effects of manipu-

lating qualities of TA instruction. Without data to guide instruction design, many

parameters of TA instruction, such as which qualities of the task and the level of

detail with which to describe them, are based on subjective opinion and trial and

error.

While TA instruction typically includes descriptions of discrete responses,

adding descriptions of relevant antecedent stimuli may enhance learner performance

(Cooper et al. 2007); however, the effectiveness of these descriptions has not been

researched. Some research demonstrates that describing performance criteria, or the

consequences of correct responses, improves responding in academic tasks

(Johnston and O’Neill 1973) and may also improve TA instruction. For example,

graphing TA may be enhanced by describing the changes to the graph the learner

should observe when correctly completing instructions to manipulate menu options

and buttons. Describing relevant antecedent stimuli, the required responses, and the

consequences of correctly emitting those responses may enhance instructional

effectiveness and improve learner outcomes by conceptually linking TA instruction

to the three-term contingency.

In spreadsheet and graphing software environments, relevant antecedents may

include graph elements not formatted according to publication standards, such as

colored data points, and descriptions of the physical characteristics of the software

components that one must manipulate in order to reformat them. Performance

criteria include descriptions of what a graph should look like once a step or series of

steps is completed. For example, after completing several steps to modify the

features of the data path, the user should see a black data path with black data

points. As a first step toward understanding components of TA that may enhance

instructional outcomes, this study compared the effects of TA with and without

descriptions of relevant antecedents and performance criteria on the accuracy and

speed of constructing a reversal design graph. It is hypothesized that graphing

accuracy will be higher when using TA instruction supplemented with these details.

Method

Participants and Setting

Sixteen undergraduate students enrolled in an undergraduate course on the

introduction to psychology participated and earned research credit toward the

course’s research requirement. The researcher recruited the participants using an

online research recruitment system. GraphPadTM StatMateTM statistical software—

designed for calculating power analyses—used the mean, standard deviation, and

sample sizes of participant accuracy data reported in a comparison of video- and

text-based TA instruction for creating a multiple baseline graph (Tyner and Fienup

2015). This analysis estimated that six participants were necessary per group for a

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minimum of 80 % power with a = .05. In total, eight participants were randomly

assigned to each instructional group.

Instruction took place in a research laboratory containing three computer work

stations, each equipped with a Windows�-based computer including a 48.26-cm

monitor, keyboard, and mouse, with Microsoft Excel 2007 installed.

Materials

Materials included a sign-in sheet, two versions of a task analysis for constructing a

graph, a tutorial program to present the TAs, and a social validity and demographics

questionnaire (see Table 1) used to assess differences in relevant skills and

experience prior to instruction.

Table 1 Participant demographics and social validity questionnaire

Item

#

Question Control Supplemented

M SD M SD p

1 Number of relevant courses 2.0 2.1 4.3 4.1 [.05

2 Overall computer skills 3.0 1.1 3.0 0.6 [.05

3 Frequency of using excel 1.4 0.8 1.5 0.8 [.05

4 Number of graphs made on computer 14.3 18.9 10.0 12.2 [.05

5 Graphing is an important skill for students to learn 4.1 0.7 4.0 1.1 [.05

6 Students should be able to graph independently and without

assistance

3.7 0.5 3.5 1.0 [.05

7 The tutorial was a good method for teaching graphing 3.9 1.2 3.6 1.0 [.05

8 The tutorial I just used helped improve my graphing skills 3.4 1.3 3.8 0.8 [.05

9 I can now create each of the graphs I learned without

assistance

3.1 0.9 3.6 0.8 [.05

10 I am better at graphing in Excel than I was before using the

tutorial

3.4 1.1 3.3 1.2 [.05

11 Would prefer using a tutorial like this over attending a

classroom lecture

3.6 1.5 3.8 1.0 [.05

Yes No Yes No p

12 Would recommend the tutorial to others

who wanted to learn how to graph

5.0 2.0 5.0 1.0 [.05

Questions 1 and 4 were fill-in-the-blank. Questions 2 through 11 were Likert scale format. Question 2

ranged from beginner (1) to proficient (5). Question 3 ranged from rarely (1) to frequently (5). Questions

4 through 11 ranged from strongly disagree (1) to strongly agree (5). Question 12 was answered yes or

no. Seven control participants and six experimental participants completed the questionnaire

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Task Analyses

Researchers developed two TAs for constructing the same reversal design graph,

one for control and one to test the effects of supplemental descriptions of relevant

antecedents and performance criteria. The control TA was based on the TA used by

Tyner and Fienup (2015) for constructing multiple baseline graphs. Development of

the TA-incorporated graphing methods is described in published graphing

instruction research (e.g., Dixon et al. 2009; Lo and Konrad 2007) and publication

guidelines of the American Psychological Association (APA 2010; Nicol and

Pexman 2010). The final version of the control TA described the response sequence

for making a reversal design graph, including how to: (a) organize the data table and

insert the graph, (b) format the data series and chart area, (c) change the value of the

axes, (d) align data points with tic marks, (e) insert the chart, axis, and condition

labels, (f) insert phase-change lines, (g) lift the y-axis off the x-axis, and (h) copy

and paste the graph and all components as an image for submission for publication

purposes.

The supplemented TA included all of the text contained in the control TA as well

as descriptions of relevant antecedent stimuli and performance criteria for each

response. Relevant antecedents were defined as physical descriptions of the

topographical characteristics of the stimuli within the software user interface that

the user was instructed to manipulate above and beyond descriptions of actions to

make. Examples of relevant antecedents include the color, shape, and location of

buttons, icons, and/or menu labels, or of more salient stimuli near them that might

be used to approximate their locations. Performance criteria were defined as

descriptions of the graph after correctly completing the present step. For example,

after inserting phase-change lines, the line should be straight without any blurry

segments which indicate it is angled, and the space between the line and data points

on either side should be equal. Descriptions of performance criteria were linked to

the graph–component checklist used for performance assessment. Figure 1 presents

a side-by-side comparison of the instructions provided for inserting phase-change

lines in each tutorial.

Tutorials

The learning environment in this study was similar to that described by Tyner and

Fienup (2015). The TA instruction was displayed in a PowerPoint� slide show (see

Fig. 1) positioned on the left third of the screen with Excel on the right two-thirds of

the screen. Each slide contained buttons along the bottom of the window to navigate

backward or forward one slide and to a table of contents. The control tutorial

contained 32 slides containing two to fifteen sentences each. The supplemented

tutorial was identical to the control tutorial except it contained one additional

instructional slide and between zero and five more sentences per slide describing

either relevant antecedent stimuli or performance criteria. The presentation and

sequence of steps was otherwise identical.

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Dependent Variables

The dependent variables were graphing accuracy and duration. Graphing accuracy

was defined as the percentage of graph elements formatted correctly according to

APA guidelines for the publication of figures (APA 2010; Nicol and Pexman 2010)

and those found in ABA publications on graphing research (e.g., Dixon et al. 2009;

Lo and Konrad 2007; Tyner and Fienup 2015) and text books (e.g., Cooper et al.

2007). Researchers evaluated final graphs using a 28-question checklist of graph

components (available upon request). Checklist items were scored as correct or

incorrect, and the number of correct items was counted and divided by 28 to

calculate percentage correct for each participant. Graphing duration was defined as

the number of minutes that passed from the time the participant clicked the ‘‘Begin’’

button to the time the participant notified the researcher of his or her completion of

the graph as recorded by the researcher.

Procedure

Researchers assigned each participant to receive either the control or supplemented

TA using block random assignment. Group assignment was made in pairs of two

participants using a random number generator to ensure equal group sizes.

Before a participant arrived to the experiment, the researcher arranged the

assigned tutorial alongside a blank Microsoft Excel spreadsheet on the computer

screen. When a participant arrived, the researcher logged the date of participation on

Fig. 1 Screen shots of the control tutorial (left) and the supplemented (right). Both images present thesame step for formatting phase-change lines. The last two paragraphs in the supplemented tutorial showexamples of descriptions of performance criteria that were omitted from the control tutorial

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the next available line in the sign-in sheet, which provided the participant ID

number and the condition assignment for each participant. Next, the researchers sat

the participant at a computer and explained the consent form, and each participant

provided voluntary informed consent. Then the researcher encouraged the

participant to ‘‘try your best’’ and the participant began the TA instruction. The

researchers provided no feedback or additional instructions for graphing. When a

participant indicated that he or she was done, the researcher provided a copy of the

demographics and social validity questionnaires to fill out and return before leaving.

Interobserver Agreement

An undergraduate research assistant independently coded 100 % percent of all

participant graphs for the purpose of calculating interobserver agreement (IOA). If

the researcher and assistant scored an item on the checklist the same (correct or

incorrect), the item was rated as an agreement. If the two observers scored the item

differently, the item was rated as a disagreement. IOA was calculated for each graph

by dividing the number of checklist items scored in agreement by the total number

of checklist items and multiplying by 100. Mean IOA was 95.4 % (SD = 4.9 %;

Range = 85.7–100 %) for all graphs. Chronbach’s alpha (a) was calculated as an

additional assessment of IOA using both observers’ calculated percentage of correct

for all 16 participants. Chronbach’s alpha represents the extent to which participant

scores vary due to variance in their performance versus variance in rater assessment

(Osbourne 2008), and indicated a high degree of agreement in this study, a = .993.

Results

Table 1 summarizes participants’ self-report of the number of relevant college

courses taken (question 1) and computer, graphing, and Excel experience (questions

2 through 4). Responses were compared between groups using an independent t test,

and no significant differences were found, p[ .05. Group similarity in these

measures suggests that observed performance differences between groups are

attributable to the manipulation of the independent variable. Assumptions for

statistical tests of significance—such as the presence of outliers and the normality of

distribution for t test data—were evaluated using visual analysis (Laerd Statistics

2015).

Graphing Performance

Separate one-tailed Mann–Whitney U tests were conducted to evaluate differences

between groups in percentage of graph elements formatted correctly (accuracy) and

minutes to construct each graph (duration). This test was used because of the small

sample size and evidence of skewed distributions in accuracy scores and graphing

duration (see Fig. 2).

The top panel of Fig. 2 presents accuracy by group and shows that six of eight

participants (75 %) who received the supplemented TA scored higher than the most

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accurate participant who received the control TA. On average, participants who

received the TA supplemented with descriptions of relevant antecedents and

performance criteria formatted a significantly higher percentage of graph elements

47.8

72.3

0

10

20

30

40

50

60

70

80

90

100Pe

rcen

tage

Cor

rect

Accuracy

47.152.8

0

10

20

30

40

50

60

70

80

90

100

Min

utes

Group

Time

Control Supplemental

Fig. 2 Data points represent individual participant data. The top panel shows the percentage of checklistitems scored as correct for each participant, and the bottom panel shows minutes to complete. Darker datapoints indicate overlapping data points. The black bar indicates the group mean

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correctly (M = 72.32, SD = 25.59) compared with those who received the control

TA without these details (M = 47.77, SD = 21.51), U = 11, p = .032. The effect

size of this difference was calculated using Hedges’ g, which indicated a large

effect, g = 0.98 (Lakens 2013).

The bottom panel of Fig. 2 displays graphing duration by group. Overall, there

was considerable variability within groups and considerable overlap between

groups. On average, participants who received supplemental descriptions of relevant

antecedents and performance criteria completed the graph in 52.80 min

(SD = 9.80), and those using the control TA completed the graph in 47.10 min

(SD = 15.80); however, the observed difference was not statistically significant,

U = 23, p[ .05.

In order to account for some of the high variability in these measures, graphing

accuracy and duration were correlated using the Pearson product-moment corre-

lation. Figure 3 presents a scatter plot of the percentage of graph elements formatted

correctly and minutes to complete the graph for each group. There was a strong

positive correlation between minutes spent on the task and percentage correct for

participants using the supplemented TA (r = .757, n = 8), which was statistically

significant, p = .03. There was also a strong positive correlation for participants

using the control TA (r = .684, n = 8); however, it was not statistically significant,

Fig. 3 Data points represent individual participant data. The y-axis presents the percentage of checklistitems scored as correct for each participant, and the x-axis presents minutes to complete. Squares and thesolid trendline represent data for participants who received the control TA; circles and the dashedtrendline represent data for participants who received the supplemented TA. Darker data points indicateoverlapping data points

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p[ .05. Based on these product-moment correlations, the coefficients of determi-

nation were r2 = .57 for participants using the supplemented and r2 = .47 for those

using the control TA. These values represent the proportion of variance in graphing

accuracy for each group (47 and 57 %, respectively) that can be attributed to the

amount of time participants spent on the graphing task.

Social Validity

Table 1 shows participants’ agreement with statements regarding the researchers’

goals (questions 5 and 6), method (question 7), outcomes (questions 7 through 10)

of the tutorial, and preference for the tutorial over classroom lecture (question 11).

Agreement was reported on a five-point Likert scale, and responses to these

questions were compared with separate Mann–Whitney U tests. Question 12

assessed whether they would recommend the tutorial to others, and was compared

between groups using a Chi-square (v2) statistic. One participant from the control

condition and two participants from the supplemented condition opted not to

complete the social validity questionnaire. Overall, participants in both groups

tended to agree that learning to graph was important, and task analysis instruction

was appropriate, that the tutorial improved their graphing skills, and that they would

recommend the tutorial to others; however, there were no differences in social

validity responses between groups, p[ .05.

Discussion

This study demonstrated that a TA supplemented with descriptions of relevant

antecedent stimuli and performance criteria for correct responses produced

significantly more accurate graphing behavior compared with the control TA, with

a large effect size. Although the supplemented TA included considerably more text

for participants to read, the instruction did not take significantly longer to complete.

This may represent a trade-off in response allocation in that participants completing

control instruction read relatively brief instructions and spent more time locating the

respective stimuli on the screen to click, while participants completing the

supplemented TA spent additional time reading instructions and less time locating

the stimuli to click. Differences in accuracy may suggest that the supplemented TA

had greater instructional control or that descriptions of performance criteria

prompted self-assessment and correction of errors.

The relationship between time and accuracy visualized in Fig. 3 may account for

some of the variability in both of these variables. There is a moderate correlation

between minutes to complete and graphing accuracy for both groups; however, the

scatter plot and the group difference in r2 suggest that the effects of increases in the

amount of time allocated to the task produced larger gains in accuracy for

participants who used the supplemented TA compared to those who used the control

TA. This finding makes intuitive sense: Variance in scores is attributable to both

(a) differences in instructional effectiveness between the TAs used and (b) the time

allocated to the task; however, the effects of increasing the amount of time spent on

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task is only true to the extent that instruction is effective. Therefore, increasing the

amount of time spent learning a task is more cost-effective when using more

effective instructions.

This study extends the findings of previous TA research (e.g., Crist et al. 1984;

Graff and Karsten 2012) by identifying descriptions of relevant antecedent stimuli

and performance criteria as details that improve learner performance. Although the

benefit of detail in task analysis instruction has been acknowledged (e.g., Cooper

et al. 2007; Crist et al. 1984), there is a paucity of research on the types of detail and

their contribution to learner performance. For example, research has demonstrated

that presenting TA steps individually produced better performance than presenting

them grouped (Crist et al. 1984), but it did not evaluate the effects of different types

of details. Cooper et al. (2007) emphasized the importance of describing the

antecedent stimuli to which a learner must respond; however, no research has

examined the effects of doing so. This study supports the assertion of Cooper et al.

by demonstrating that task analysis instruction produces greater graphing accuracy

when it includes descriptions of relevant antecedent stimuli and performance

criteria. This study also extends research conducted by Graff and Karsten (2012) by

identifying two specific details that may enhance written instruction. One limitation

of this study, however, is that relevant antecedents and performance criteria were

presented simultaneously; therefore, it is impossible to determine the extent to

which describing either or both of these details contributed to the observed

performance differences. Future research should evaluate the relative role of

descriptions of relevant antecedent stimuli and performance criteria in task analysis

instruction. Furthermore, this study evaluated whether describing these details

influenced TA effectiveness, but did not seek to identify the optimal amount of

detail as suggested by Annett and Duncan (1967). Future research may involve

parametric analysis of the amount of detail with which to describe a task, as too

little detail may leave instruction unclear, and too much detail may be unnecessary

for accurate performance or even overwhelm the learner.

This study also extends research on graphing instruction (e.g., Lo and Konrad

2007; Dixon et al. 2009). Graphing is a complex and difficult task, and the design of

effective graphing instruction is likewise difficult. Effective graphing instruction

may reduce the response effort of constructing a graph and increase the probability

that clinicians graph client behavior. When graphing client behavior, differentiation

and trends in behavior may be more easily detected, which may facilitate the

identification of functional relations (Fahmie and Hyanley 2008) and improve

treatment evaluation and decisions. Therefore, research on graphing instruction is

socially valid for behavior analysts.

Task analysis instruction is also commonly delivered to typically functioning

adults to guide research and intervention procedures. A recent survey on the types of

training received by ABA practitioners (DiGennaro Reed and Henley 2015) found

that written instructions were the second most common form of pre-service training

provided upon initial hire, received by 67.94 % of respondents (n = 142). For

example, TA instruction is frequently a procedural component in research and

interventions for staff and parent training (e.g., McKeel et al. 2015), behavioral

skills training (Duncan et al. 2013), video modeling (Lambert et al. 2014), and

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treatment integrity (Cook et al. 2015). Therefore, written instructions are widely

used, and a more thorough understanding and more empirical resources identifying

best practices in TA construction may support treatment delivery and enhance client

outcomes. In addition, the results of the present study may also help inform the

development of other text-based instruction, such as text books or self-instruction

manuals that describe how to conduct behavioral assessments (e.g., Ramon et al.

2015). However, because this study was conducted with a college-student

population engaging in a very specific skill, the generality of these results to other

populations—such as to children with developmental disabilities—and tasks other

than graphing may be premature. Future research should verify the generality of

these data to other populations and target behaviors, as well as to TAs delivered in

formats other than on computers.

One limitation of the present study is that no pretest was administered to assess

participants’ preexisting graphing skills. A posttest-only between-groups design was

used for two reasons. First, during previous research (Tyner and Fienup 2015),

requiring participants to complete more than one graph was too time intensive, and

requiring participants to use complex graphing software without instruction may be

aversive. In addition, graphing without instruction during baseline assessments

provides participants the opportunity to explore the software and to learn either correct

or incorrect methods for completing the task, which would have threatened internal

validity. No significant differenceswere found between groups regarding participants’

self-report of relevant computer skills, education, and experience or their values

regarding graphing ability, which may indicate that observed differences in graphing

performance can be attributed to the effects of describing relevant antecedent stimuli

and performance criteria for the graphing task. However, it should also be noted that

this study used a small sample size (n = 8 per group). The power analysis was

conducted using preexisting participant data for graphing accuracy, but not for social

validity or demographic questions. Therefore, this study may have failed to identify

true differences in relevant graphing skills and experience due to lack of power. For

example, the number of relevant courses was higher for the group that received the

supplemented TA. Conversely, participants in the group that received the control TA

reported having made more graphs. Future research should either test for preexisting

graphing skills or control for them, such as by using a matched samples design. It is

also worth noting that the small group size may have limited the accuracy with which

participants’ agreement with the social validity statements differed between groups. It

is possible that a larger sample size would identify such differences. Future research

may also consider evaluating participant preference between TA types, or evaluate

performance using within-subject research methods.

The present results are of immediate use to ABA instructors and staff trainers who

have encountered challenges in teaching others to graph. The present study is also a

preliminary step toward answering the question of ‘‘what to describe and onwhat level

of detail’’ (Annett and Duncan 1967, p. 1) in TA instruction. These results suggest that

TA instruction may be enhanced by informing instruction through an analysis of the

three-term contingency. Instruction may be enhanced by embedding descriptions of

the necessary individual responses within descriptions of relevant antecedent stimuli

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and the consequences of correct responses. The generality of the present study should

be evaluated by replicating this study using other populations and skill sets.

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