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. 2021 Jan 6;14(1):131–140. doi: 10.1007/s40617-020-00508-w

Behavior Analysis Goes to School: Teacher Acceptability of Behavior-Analytic Language in Behavioral Consultation

Meara X H McMahon 1,✉, Zachary R Feldberg 1, Scott P Ardoin 1
PMCID: PMC7900384  PMID: 33732583

Abstract

Effective communication is a vital component of behavioral consultation. Behavioral consultants (e.g., behavior analysts, school psychologists) are responsible for drafting behavior intervention plans, delivering accessible training, and providing concise and consumable feedback to teachers. Their reliance on technological descriptions to communicate behavioral principles and procedures may yield poor social validity and hinder the consultant–teacher relationship. In this study, we recruited 164 teachers through Amazon Mechanical Turk and administered a survey to (a) evaluate the social acceptability of technical and nontechnical language used in behavioral consultation across a variety of student populations and (b) gain information about teachers’ experiences with behavioral consultation. Implications are discussed for training and the provision of behavioral consultation services.

Keywords: Behavioral consultation, Social validity, Social acceptability, Jargon, Amazon Mechanical Turk

Behavior analysts have long relied on a precise vernacular to describe basic principles and accompanying technological descriptions of behavior (Baer et al., 1968; Cooper et al., 2007). Much of the field’s language stems from the experimental analysis of behavior and the work of B. F. Skinner, who sought to identify and explain key mechanisms in operant conditioning (Skinner, 1938, 1953). Teaching principles and associated terminology is a core requirement for graduate training programs in order to produce students eligible to sit for the Board Certified Behavior Analyst (BCBA) exam (Association for Behavior Analysis International, 2020a, 2020b; Pastrana et al., 2016). Technical language appears imperative for both the science and practice of applied behavior analysis (ABA), as using terminology improves communication and allows for the effective dissemination of research among applied researchers and practitioners. Behavior analysts are then able to replicate procedures with documented integrity and reproduce similar results, a cornerstone of the science of human behavior and the field of ABA (Baer et al., 1987).

Since its inception, the general public has criticized behavior-analytic terminology for lacking interpretation, failing to align with socially acceptable mentalistic terms, and often containing misleading relations with nontechnical language (Critchfield et al., 2017; Leigland, 2002). Early researchers, including Ogden Lindsley and Montrose Wolf, identified a need to incorporate socially acceptable language and evaluate the social validity of behavioral goals, treatment procedures, and effects of those procedures among stakeholders (Lindsley, 1991; Wolf, 1978). Their work first suggested that using behavioral jargon may negatively influence the social acceptability of treatment procedures.

In Becirevic et al. (2016), researchers assessed the general public’s impressions of technical behavior-analytic intervention terminology and nontechnical substitute terms. In a crowdsourced survey, participants were asked to rate the social acceptability of six behavioral interventions when applied to commonly served treatment populations. Findings revealed that the general public rated nontechnical language as more socially acceptable than technical language for all terms except for “reinforcement.” These findings are not entirely surprising, as technical language can negatively influence comprehension, preference, emotional ratings, and acceptability ratings across consumer populations, especially for those with little to no training with using ABA principles (Banks et al., 2018; Critchfield et al., 2017). Perhaps most concerning is the possibility that a consultant’s use of technical language might negatively impact the integrity with which a teacher implements an intervention, resulting in the diminished likelihood of the intervention’s success (Jarmolowicz et al., 2008; St. Peter Pipkin et al., 2010). McGimsey et al. (1995) discovered that training graduate students in consultation skills improved both parent integrity with time-out procedures and child behavioral outcomes. A consultant’s ability to translate technical language into laypersons’ terms may be necessary to effectively train teachers and achieve successful student outcomes (Allen & Warzak, 2000; Helton & Alber-Morgan, 2018).

Behavioral consultation (BC) is an indirect service-delivery model that assembles the collaborative effort of stakeholders concerned with student needs (e.g., teachers, school psychologists, behavior interventionists, and administrators) to aid teachers in helping students to meet academic and behavioral goals (Bergan, 1977; Noell & Witt, 1999). In a BC model, the classroom teacher implements an intervention developed from the collaborative effort of the teacher and consultant. Teachers play an active role across all four stages of the BC model: (a) problem identification, (b) problem analysis, (c) treatment implementation, and (d) treatment evaluation (Bergan, 1977). In the problem identification stage, the consultant interviews the teacher, asking them to identify and operationalize the target behavior of concern, and attempts to better understand behavior–environment relations by examining antecedents and consequences. The problem analysis stage seeks to identify barriers to effective intervention (e.g., a lack of acceptability, resources, or accountability) and involves training the teacher on baseline data collection and collaboration to develop an agreed-upon plan that incorporates the use of evidence-based interventions. During the treatment implementation stage, the consultant trains the teacher on a prescribed intervention and provides ongoing coaching and performance feedback for the purpose of improving procedural integrity (Noell et al., 2005). In the final stage, treatment evaluation, the consultant and teacher examine the effectiveness and social validity of the intervention.

Good communication is integral to successfully implementing the BC model and developing a high-quality relationship between the teacher and consultant. A strong working alliance may positively influence social validity and procedural integrity of intervention procedures and mitigate negative outcomes from burnout (Wehby et al., 2012). As such, language is one factor to consider when examining methods to increase social validity and improve teachers’ procedural integrity of implementing behavioral strategies. Researchers have revealed a significant interaction effect between the impact of language and teacher involvement on social acceptability (Rhoades & Kratochwill, 1992). Teachers only appointed high social acceptability ratings with technical language when teacher involvement was low, which poses a problem for consultants. Effective BC requires both teacher collaboration and intervention implementation, meaning that teacher involvement must be high, as they are the individuals responsible for providing direct services to students. In a study by Witt et al. (1984), researchers assessed the acceptability of case descriptions containing scenarios and proposed interventions, where they manipulated the language type (i.e., behavioral, pragmatic, and humanistic) and the severity of behavior problem (i.e., mild or severe). Findings revealed that teachers rated pragmatic descriptions (i.e., logical consequences) as significantly more acceptable than behavioral or humanistic descriptions (i.e., expression of feelings). Additionally, teachers rated all interventions as more acceptable when they were given a case scenario with severe problem behavior. These findings suggest that language can differentially impact teachers’ acceptability and that behavioral consultants may experience an easier time “selling” interventions to address more severe problem behavior.

An increasing number of school districts are recruiting BCBAs to fill the role of behavioral consultants. Specifically, in a 9-year span from 2010 to 2018, the demand for applicants with a BCBA certification grew by 1,942% (Behavior Analyst Certification Board, 2019). In 2014, over a quarter of these jobs were in educational services, with hiring needs for BCBAs ranking second to school psychologists (Burning Glass Technologies, 2015). Behavior-analytic training programs offer more courses and in-depth training in behavioral assessment and intervention compared to school psychology programs, most of which offer one or two courses on behavioral strategies and consultation (Fischer et al., 2019). Furthermore, there is a need for improved training and specialization in behavior analysis and autism that is not currently being met by school psychology programs (Starling et al., 2019). Although behavior analysts may be better equipped to address these needs, very few training programs offer any coursework on or practicum experience in BC, warranting additional training of these skills (Shepley et al., 2017).

As more schools seek the expertise and services of BCBAs to serve as behavioral consultants, it is important to continuously evaluate teachers’ impressions of consultants’ performance (Shepley & Grisham-Brown, 2019). In tandem with the growing use of evidence-based practice and principles of ABA in education (e.g., positive behavioral interventions, functional behavior assessment; Samudre et al., 2020), teachers may be more familiar with behavioral jargon than indicated in research conducted many years ago (e.g., Eckert & Hintze, 2000; Gresham & Lopez, 1996). The purpose of this study was to assess how the language used to describe interventions might impact teachers’ social acceptability ratings. Findings could guide BCBAs and behavioral consultants in providing clearer and more consumable consultation to teachers and other school personnel.

Method

Participants and Materials

One hundred sixty-four teachers located in the United States completed a survey about behavior-analytic intervention language and their experiences with BC and ABA. Participants were recruited using the online crowdsourced labor market Amazon Mechanical Turk (MTurk). Recruiting through MTurk yields a population of valid representation that is similar to the national demographic and is often more representative than those obtained through other methods of participant recruitment (e.g., college students, online forums; Sheehan & Pittman, 2016). Researchers posted a request that participants complete a survey (referred to by MTurk as a human intelligence task [HIT]) distributed using Qualtrics online survey software (https://www.qualtrics.com). Researchers set eligibility criteria for the screening survey to ensure participants had a history of completing MTurk work of acceptable quality. Participants had to (a) be located in the United States, (b) have a HIT approval rate of greater than or equal to 95%, and (c) have at least 100 HITs approved (Amazon Mechanical Turk, 2017); 4,513 individuals participated in the screening survey, with 4,316 individuals (95.6%) not identifying as teachers. The full survey was made available to participants who met the following inclusionary criteria: First, they must have completed a bachelor’s degree or higher, and second, they must currently be employed as an early childhood, elementary, or secondary school teacher. Of the 197 teachers who met inclusionary criteria, 33 teachers (16.8%) did not respond to the full survey, and 1 teacher (<1%) disagreed to voluntarily participate after informed consent. Table 1 provides demographic information for all participants.

Table 1.

Demographic Characteristics of Teachers

Factor n %
Gender
  Female 125 76
  Male 39 24
Age
  <30 years 43 26
  30–49 years 104 63
  50–54 years 6 4
  ≥55 years 11 7
Race/ethnicity
  American Indian/Alaska Native 3 2
  Asian 3 2
  Black/African American 5 3
  Hispanic/Latino 9 6
  Native Hawaiian/Other Pacific Islander 0
  White 141 85
  Other 3 2
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Design and Procedures

The present study used a multistage survey design (Springer et al., 2016) to target teachers through MTurk. Researchers administered two surveys across a 3-week period—a screening survey and the full survey, both of which are available from the first author. The purpose of the screening survey was to discreetly identify teachers and minimize the likelihood that participants would lie to gain access to a larger payoff. Researchers administered the full survey to teachers previously identified in the screening survey.

The screening survey requested that participants provide broad information related to their level of education and current occupation. Screening out participants who reported earning less than a bachelor’s degree or employment in a field other than educational services involved directing them to the end of the survey using Qualtrics display logics. If participants reported working in educational services, Qualtrics display logics also allowed researchers to administer follow-up questions related to working with children and teaching. At the end of the survey, Qualtrics assigned each participant a random five-digit identification number to submit to MTurk that corresponded with their MTurk worker ID. This enabled researchers to pay participants $0.05 for completing the survey (i.e., by accepting HITs) and make the full survey available to only the participants who met qualifications. In the rare event that Qualtrics distributed the same random identification number to more than one participant, responses were cross-verified using the survey submission date and time. This occurred for 198 participants (i.e., 99 participants had overlapping random identification numbers) in the screening survey; however, no participants in the full survey had overlapping random identification numbers.

The full survey was adapted from one developed by Becirevic et al. (2016) for administration to the general public via MTurk. The survey requested that teachers rate the social acceptability of six technical behavior-analytic terms and six nontechnical substitutes across eight student populations. See the Appendix for the prompt provided before each term. Technical and nontechnical terms were presented to teachers in a randomized order. Technical behavior-analytic (and nontechnical substitute) terms included (a) “escape extinction” (“follow-through training”), (b) “negative reinforcement” (“relieving consequences”), (c) “negative punishment” (“penalty”), (d) “chaining” (“teaching a sequence of responses”), (e) “shaping” (“flexible learning”), and (f) “reinforcement” (“rewarding”). A semantic differential scale with labels that ranged from completely unacceptable to completely acceptable was displayed for each of the eight student populations with a sliding bar in the center of the scale under the label neutral. Teachers were instructed to rate the acceptability of each term across each student population, which included (a) infants/toddlers, (b) preschool/prekindergarten, (c) elementary kindergarten to Grade 2, (d) elementary Grades 3–5, (e) middle Grades 6–8, (f) high school Grades 9–12, (g) special education, and (h) emotional/behavioral disorders.

Teachers also answered basic demographic questions and follow-up questions related to teaching and exposure to the field of ABA. These items included gender, age, race/ethnicity, student populations served through teaching, student populations credentialed to teach, and teaching experience in years. Additionally, teachers were asked whether they had worked with a behavioral consultant before and whether they were familiar with the field of ABA. If teachers reported having worked with a behavioral consultant before, a follow-up question was displayed asking if the consultant was a BCBA. If teachers indicated familiarity with the field of ABA, additional follow-up questions were displayed regarding the avenue in which they knew about the field (e.g., are credentialed or have a family member who is). Table 2 presents a summary of teaching experiences, including education and practice. Teachers received a $2.50 bonus for submitting the survey via MTurk.

Table 2.

Summary of Teaching Experiences

Factor n %
Highest degree earned
  Bachelor’s degree 88 54
  Master’s degree 70 42
  Doctorate/professional degree 6 4
Teaching experience
  <4 years 26 16
  4–9 years 68 42
  10–15 years 28 17
  ≥15 years 42 26
Behavioral consultation experience
  BCBA 36 22
  Behavioral consultant 55 34
  No experience 73 45
Served through teaching
  Infants/toddlers 25 15
  Preschool/prekindergarten 52 32
  Elementary kindergarten to Grade 2 80 49
  Elementary Grades 3–5 87 53
  Middle Grades 6–8 82 50
  High school Grades 9–12 67 41
  Special education 61 37
  Emotional/behavioral disorders 47 29
  Other (e.g., ELLs, college) 5 3
Credentialed to teach
  Infants/toddlers 24 15
  Preschool/prekindergarten 54 33
  Elementary kindergarten to Grade 2 94 57
  Elementary Grades 3–5 98 60
  Middle Grades 6–8 97 59
  High school Grades 9–12 72 44
  Special education 34 21
  Emotional/behavioral disorders 19 12
  Other (e.g., ELL, gifted, AE, college) 7 4
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Note. BCBA = Board Certified Behavior Analyst; ELLs = English language learners; AE = alternative education.

Data Analysis

Researchers conducted a two-way, repeated-measure analysis of variance (ANOVA) to examine the effect of terms and student population, and any interaction effect, on the social acceptability rating. An examination of the studentized residuals found no score greater than ±3, indicating the data were free of outliers. However, a Shapiro–Wilk’s test of the studentized residuals indicated that the data were not normally distributed (p < .05). An examination of the skewness found that there was little consistency of skewness across the combined levels of the terms and student populations, meaning a traditional transformation of data was not possible. Further, Mauchly’s test of sphericity indicated that the assumption of sphericity was violated for the two-way interaction, χ2(3002) = 12,207.276, p = .000. We therefore applied the Greenhouse–Geisser correction with an ϵ = .284, indicating only a moderate correction was appropriate.

Next, the social acceptability ratings of technical and nontechnical terms for each student population were compared using a Wilcoxon matched-pairs signed-rank test. The Wilcoxon matched-pairs signed-rank test is a nonparametric equivalent to the paired t-test (Wilcoxon, 1945). It is an appropriate method for this analysis because it allows for the comparison of two distributions (e.g., ratings of “escape extinction” and ratings of “follow-through training”) across a sample when the pairs are dependent. Dependence existed in this situation because the same respondent rated both terms for each matched pair. This test calculates the rank from 1 to N of each pair using the absolute difference of the pair (e.g., the rating for “escape extinction” minus the rating for “follow-through training”). Then, after excluding any pairs with zero difference, the test calculates the sign of the difference using the sign function. The signed difference and rank are multiplied and then summed to create the sum of the signed ranks. This value, called W, follows a specific, unique distribution with no simple expression where H0 is rejected if |W| > Wcritical. Statistically significant comparisons had a p-value that was less than .05, indicating a significant difference between the technical and nontechnical ratings.

Cross-Study Comparison

Mean acceptability ratings were compared to the general population data reported in Becirevic et al. (2016) for cases in which the target populations overlapped. This was the case for high school Grades 9–12 (high school students in Becirevic et al.), preschool/prekindergarten (preschool children), infants/toddlers (infants/toddlers). Additional independent comparisons included special education and emotional/behavioral disorders (children with special needs) and elementary kindergarten to Grade 2 and elementary Grades 3–5 (elementary-aged students). Two trained coders independently extracted data from Becirevic et al. using WebPlotDigitizer (Rohatgi, 2019), a computer software tool that has demonstrated high intercoder reliability (i.e., 94% of studies in exact agreement) and validity (i.e., r = .989, p < .001) in extracting data plotted on axes from preexisting XY-plots charts (Drevon et al., 2017). We calculated exact agreement intercoder reliability for a total of 45 data points (100%) by dividing the number of agreements to the whole number by the number of agreements plus disagreements, then multiplying by 100, to yield a percentage. Coders reached one disagreement (i.e., one data point differed by one), and exact agreement intercoder reliability equaled 96%.

Results

The two-way, repeated-measures ANOVA revealed a significant interaction and main effects of the term and student population on social acceptability ratings. Applying the Greenhouse–Geisser correction resulted in a significant interaction between the term and the student population, F(21.89, 3568.20) = 18.12, p = .000; the term, F(6.22, 1013.42) = 148.93, p = .000; and the population, F(2.42, 394.24) = 45.94, p = .000. When controlling for the effect of terms, social acceptability ratings were lowest for infants/toddlers (M = 54.3, 95% CI [52.1, 56.4]) and preschool/prekindergarten (M = 57.9, 95% CI [56.0, 59.8] ) and highest for middle Grades 6–8 (M = 65.1, 95% CI [63.2, 67.0]) and high school Grades 9–12 (M = 65.4, 95% CI [63.3, 67.5]), as seen in Figure 1. A comparison of the terms controlling for the effect of the population found a statistically significant difference between the social acceptability ratings of the nontechnical and technical terms for all term pairs, except for “reinforcement” and “rewarding” (Figure 2).

Fig. 1.

Fig. 1

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Rating by Population Controlling for Term. Note. Means and 95% CIs for social acceptability ratings. SPED = special education; EBD = emotional/behavioral disorders.

Fig. 2.

Fig. 2

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Rating by Term Controlling for Population. Note. Means and 95% CIs for social acceptability ratings. NS = not statistically significant.

Table 3 presents the descriptive statistics of the social acceptability ratings for each term across student populations and the results of the Wilcoxon matched-pairs signed-rank test. Teachers assigned the highest social acceptability rating to “reinforcement” (M = 86.1, SD = 18.4) and the lowest rating to “negative punishment” (M = 32.7, SD = 32.5). They rated nontechnical terms as more acceptable than technical terms for 43 of the 48 comparisons across student populations (90%). Differences between the technical and nontechnical terms were found to be statistically significant (p ≥ .05) for 85% of terms (41 out of 48 comparisons) across student populations. When compared to social acceptability ratings from the general population (i.e., data extracted from Becirevic et al., 2016), teachers rated both technical and nontechnical terms as more socially acceptable with the exception of “negative punishment” (see Table 4).

Table 3.

Descriptive Statistics and Results of the Wilcoxon Matched-Pairs Signed-Rank Test for Technical/Nontechnical Terms Across Student Population

Paired terms Student Population Term total
Infants/ toddlers Preschool/ pre-K Elementary K–2 Elementary 3–5 Middle 6–8 High school 9–12 SPED EBD
Escape extinction M (SD) 36.6 (30.1) 39.3 (29.8) 43.1 (31.4) 45.6 (31.0) 47.7 (30.7) 46.1 (32.3) 46.1 (32.3) 46.1 (32.6) 44.2 (31.4)
Mdn 37.0 41.0 50.0 51.0 51.0 51.0 51.0 50.0 50.0
Follow-through training M (SD) 69.8 (30.7) 76.1 (27.0) 80.8 (22.5) 84.2 (18.0) 86.1 (16.6) 85.6 (19.2) 83.4 (18.9) 84.1 (17.8) 81.3 (22.4)
Mdn 78.5 85.5 87.0 90.0 92.0 94.0 87.5 87.5 88.0
W 1415.0* 1044.5* 775.5* 624.5* 578.0* 662.5* 829.0* 853.0*
Negative reinforcement M (SD) 28.7 (31.4) 31.4 (31.4) 34.3 (31.4) 38.6 (32.5) 42.9 (33.3) 45.8 (34.2) 31.5 (31.3) 31.7 (31.5) 35.6 (32.6)
Mdn 16.5 22.0 30.5 37.5 43.0 49.0 23.0 26.0 30.0
Relieving consequences M (SD) 52.3 (28.3) 55.3 (26.8) 56.7 (26.3) 57.4 (26.8) 57.6 (27.8) 56.8 (29.0) 60.5 (27.0) 60.1 (27.1) 57.1 (27.4)
Mdn 51.5 55.5 57.0 58.0 58.0 56.5 61.5 63.0 58.0
W 2580.0* 2426.0* 2653.5* 3160.5* 3622.0* 4219.0* 1683.5* 1644.0*
Negative punishment M (SD) 22.6 (30.1) 25.5 (30.1) 29.8 (30.7) 35.7 (31.9) 43.2 (32.6) 47.0 (34.6) 27.8 (30.5) 29.8 (32.2) 32.7 (32.5)
Mdn 7.0 13.5 21.5 29.5 44.5 48.0 17.5 17.0 23.0
Penalty M (SD) 27.8 (30.2) 34.0 (31.0) 41.7 (30.8) 49.5 (30.0) 58.9 (28.1) 63.9 (30.0) 42.8 (29.9) 41.1 (30.6) 45.0 (32.1)
Mdn 16.0 25.5 42.0 51.5 65.0 72.0 43.0 39.0 50.0
W 2688.0* 2820.5* 2647.5* 2312.5* 2250.5* 1997.0* 2340.0* 3046.5*
Chaining M (SD) 34.5 (33.9) 38.6 (35.7) 39.9 (35.3) 41.3 (35.6) 42.1 (36.9) 41.6 (36.5) 44.7 (38.7) 43.7 (37.6) 40.8 (36.3)
Mdn 29.0 37.5 41.5 46.5 47.5 47.0 48.0 47.5 42.0
Teaching a sequence of responses M (SD) 65.5 (28.6) 71.5 (24.5) 77.0 (21.0) 79.5 (20.2) 79.5 (22.0) 78.1 (24.8) 79.9 (22.1) 81.4 (20.6) 76.5 (23.6)
Mdn 67.5 74.5 81.0 83.0 83.0 85.0 85.5 86.0 82.0
W 1831.5* 1707.0* 1062.5* 818.0* 558.5* 588.5* 1088.5* 808.0*
Shaping M (SD) 71.4 (26.1) 73.4 (24.4) 75.1 (22.8) 74.0 (22.9) 71.2 (24.4) 68.8 (26.8) 75.9 (23.1) 73.9 (23.9) 72.9 (24.4)
Mdn 75.5 78.5 80.0 78.0 73.5 72.5 80.5 79.0 78.0
Flexible learning M (SD) 75.0 (28.3) 78.9 (25.9) 80.3 (24.1) 81.6 (21.6) 82.6 (20.9) 83.4 (19.9) 85.5 (21.5) 85.2 (21.5) 81.6 (23.3)
Mdn 84.0 89.5 89.5 89.0 90.0 90.5 95.0 93.0 90.0
W 3830.5 3707.0* 3743.0* 3345.0* 2869.5* 2366.0* 2429.0* 2200.5*
Reinforcement M (SD) 83.3 (21.5) 85.3 (19.7) 86.5 (17.9) 87.3 (16.3) 87.2 (17.2) 86.0 (18.9) 86.8 (18.5) 86.8 (16.8) 86.1 (18.4)
Mdn 94.0 95.5 95.5 95.0 95.0 96.5 98.0 95.0 96.0
Rewarding M (SD) 83.9 (20.6) 86.1 (17.9) 86.5 (17.7) 86.0 (17.6) 82.5 (19.6) 78.7 (22.5) 86.9 (17.7) 84.8 (19.9) 84.4 (19.4)
Mdn 92.0 93.0 92.0 91.0 89.0 85.5 95.0 94.0 92.0
W 2914.5 3186.5 3278.5 3625.5 4309.5* 4662.5* 3197.0 3474.5
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Note. W is the test statistic of the Wilcoxon matched-pairs signed-rank test. A p value of less than .05 demonstrated a statistically significant comparison between technical and nontechnical terms for each student population. SPED = special education; EBD = emotional/behavioral disorders.

*p < .01.

Table 4.

Cross-Study Comparison of Mean Social Acceptability Ratings for Each Term

Teachers General populationa
Technical Terms
  Escape extinction 44.2 33.5
  Chaining 40.8 22.8
  Negative reinforcement 35.6 33.9
  Negative punishment 32.7 34.5
  Shaping 72.9 —
  Reinforcement 86.1 68.6
Nontechnical terms
  Follow-through training 81.3 67.8
  Teaching a sequence of responses 76.5 69.6
  Relieving consequences 57.1 55.4
  Penalty 45.0 43.7
  Flexible learning 81.6 —
  Rewarding 84.4 —
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Note. Each term was rated from 0 to 100.

aData from Becirevic et al. (2016).

Discussion

Terminology continues to be a factor that can negatively influence the social acceptability of behavior-analytic interventions among teachers. For 90% of technical/nontechnical term comparisons in the present study, teachers indicated that the nontechnical term was more socially acceptable than the technical term. “Reinforcement” was the only technical term that teachers afforded significantly higher acceptability ratings to and only when applied to older students (i.e., middle and high school). Although teacher data demonstrated higher ratings as compared to previous acceptability research conducted with a general population from Becirevic et al. (2016), for the majority of terms (i.e., eight of nine, 89%), findings were similar across populations sampled in that technical language was rated as less acceptable overall.

Findings in the current study provide support for BC as an area of growth for behavior analyst preparation programs. Few institutions of higher education with an Association for Behavior Analysis International verified course sequence include coursework with an emphasis on BC (Shepley et al., 2017). This is somewhat surprising provided the demand for BCBAs to meet the behavioral needs of schools. Furthermore, consultation skills that include using replacement terms are not emphasized in coursework despite decades of social validity research (Critchfield et al., 2017; McGimsey et al., 1995). Marketing interventions to a wider audience may involve changing the language used in research and practice; it can be difficult to step outside the confines of jargon that is deeply embedded within behavior-analytic training, but it is not impossible. For example, “nonremoval of the spoon” and “physical guidance” are nontechnical terms widely used in the feeding literature as replacement terms for “escape extinction” (Kerwin et al., 1995). In the current study, the median value of social acceptability ratings for “escape extinction” and “chaining” demonstrated the largest discrepancies between technical and nontechnical terms across student populations (i.e., 38- and 40-point differences). These findings suggest similar actions, such as the adoption of replacement terms by BCBAs serving as behavioral consultants, are warrented.

Controlling for the effects of the terms, our analysis indicated that teachers rated intervention terms as less socially acceptable for younger students (i.e., infant/toddler, preschool/prekindergarten, elementary kindergarten to Grade 2) when compared to older students (i.e., middle and high school). This could be due to the prevailing myth that young children do not require intervention because they will “grow out of it,” despite the plethora of research that supports intervening early as a preventative strategy for children at risk of developing challenging behavior and other disorders (e.g., Bufferd et al., 2012; Hanley et al., 2007; Kellam et al., 2011). It is also possible the word “intervention” alone raised concern in the present study. Teachers afforded only moderate acceptability ratings for elementary Grades 3–5, but also for special education and emotional/behavioral development students who regularly receive intervention services. These findings suggest that behavioral consultants may want to avoid labeling interventions altogether, especially for younger students. BCBAs serving as behavioral consultants are tasked with being ethical practitioners and advocates of science who must also navigate professional relationships and collaborate with teachers and other members of the student support team during intervention planning to ensure students’ academic and behavioral well-being (Brodhead, 2015; Knotek, 2003). Thus, the findings in this study warrant consideration for avoiding intervention labels and providing guidance to teachers and school personnel in understanding how and why behavior analysts apply the principles of ABA to the development of individual student and class-wide interventions (Helton & Alber-Morgan, 2018).

In addition to dispelling myths, behavior analysts may be tasked with reframing competing misunderstandings through the BC process (Allen & Warzak, 2000; Coles et al., 2015). Consultants may guide teachers to reexamine their existing beliefs about any proposed interventions that are met with apprehension or resistance. The problem analysis stage in the BC model includes a scheduled meeting with the student support team in which the behavioral consultant works with the team to develop an intervention plan (Bergan, 1977). Intervention planning may be viewed as a proactive strategy for addressing barriers to treatment, as opposed to reactive strategies, such as performance feedback (Noell et al., 2005). An anecdote from Coles et al. (2015) involved a teacher who was told to provide reminders and consequences contingent on student rule violations, but the teacher only provided the reminders, despite performance feedback, because she believed reminders without consequences was her way of showing her students she cared for and supported them. The consultant reframed this belief by explaining how providing consequences that decrease misbehavior could be another form of caring for her students. However, addressing this concern during intervention planning may have mitigated the teacher’s resistance in the first place.

In the present study, we attempted to extend the findings of Becirevic et al. (2016) in relation to teachers’ reported experiences with BC. Given the format of the presented question, a limitation of this study was a lack of clarity regarding participating teachers’ experience with the terms evaluated. That is, we did not measure learning history, which could influence acceptability ratings and interpretations of novel terminology. Technical and nontechnical comparisons were potentially influenced by teachers’ perceived knowledge rather than the terminology itself. Future research may assess participants’ background knowledge within the survey in order to account for this confound. Perhaps the most apparent conclusion from the present study is the disconnect between technical and nontechnical terms. Exposing preservice teachers and teachers to didactic teaching would provide both foundational knowledge and practical applications of behavior-analytic principles to the school setting. The face value of behavior-analytic language may be unacceptable, but didactic teaching paired with modeling, rehearsal, and feedback, sometimes referred to as behavioral skills training, can produce positive behavioral outcomes and change teachers’ existing attitudes toward behavior-analytic jargon (Watson & Kramer, 1995).

Additionally, although researchers presented terms in a randomized order to mitigate order effects, a participant’s rating on one term potentially influenced their ratings of later terms. The inclusion of distractor terms may have decreased the saliency of the experimental manipulation in the survey (Gibson et al., 2011). Finally, the acceptability ratings of teachers in our sample may not reflect the views of all teachers in the United States. However, it should be noted that convenience samples traditionally used in academia, such as those collected from a single school, district, or region, could produce a demographically biased sample (Paolacci & Chandler, 2014). Although we did not collect participant information related to geographical location, this survey was available to teachers across the United States and yielded a demographic sample that closely approximated gender, race/ethnicity, and educational-level data from the Digest of Education Statistics for the 2015–2016 school year (National Center for Education Statistics, 2017). Future research may benefit from collecting additional information related to geographical location, institute type (e.g., public, private, charter school), and community (e.g., urban, suburban, rural) to determine if these variables influence ratings. Additionally, participants in the present study reflected a larger pool of early career teachers (i.e., younger in age and teaching experience), and the ratings of seasoned teachers may be underrepresented in this sample.

Technical and nontechnical terms may be used in both vocal and written language to communicate individualized behavioral interventions to teachers, parents, and school personnel. Acceptability is but one component of social validity, and there is little to no evidence to suggest that increased acceptability will ensure procedural integrity (Noell et al., 2005). There is, however, evidence that the use of nontechnical language in intervention plans may result in an increased understanding and higher procedural integrity than the use of technical language (Banks et al., 2018; Jarmolowicz et al., 2008). Finally, with nontechnical language, BCBAs risk speaking loosely. One example from Traub et al. (2017) is the use of “planned ignoring” to describe extinction for attention-maintained problem behavior. In avoiding the word “extinction,” ignoring problem behavior, in a literal sense, may not be the best alternative, ethically or logistically. Rather, “minimizing attention” may be more concise and convey the message in a better manner.

Author Note

We thank Yvette Bean for her assistance with coding.

Appendix: Social Acceptability Survey Prompt

graphic file with name 40617_2020_508_Figa_HTML.jpg

Funding

We thank the Owen Scott family for funding this research project.

Data Availability

Data and materials can be made available from the first author.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethics approval

All procedures performed in this study involving human participants were approved by the University of Georgia Institutional Review Board (Protocol ID STUDY00006519).

Informed consent

Researchers obtained informed consent from all individual participants included in the study.

Consent for publication

Researchers obtained consent for the publication of individual data from all participants included in the study.

Code availability

Not applicable.

Footnotes

Research Highlights

• Behavior analysts would benefit from using nontechnical language over technical language, especially when working with teachers who serve younger students.

• Serious consideration should be given to substituting technical terms with nontechnical terms for “escape extinction” and “chaining” in both consultation research and practice.

• Behavior analysts and behavior analysis graduate programs would benefit from additional training in behavioral consultation.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Data Availability Statement

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