Behavior jargon and teacher acceptability of positive behavior interventions

Abstract

The current study examined elementary school teachers’ acceptability of a positive behavioral intervention based on the use of jargon and non jargon language during behavioral consultation. One-hundred and one kindergarten through grade six teachers responded to the Usage Rating Profile-Intervention Revised (URP-IR) after reviewing a vignette describing a positive behavioral intervention utilizing jargon or non-jargon language. No statistically significant difference existed between acceptability and usage of a positive behavioral intervention when described in either jargon or non-jargon terms.

Keywords

behavioral consultation intervention jargon school age

The type of language used when describing behavioral interventions to teachers should be of particular interest to consultants in the schools, such as school psychologists and educational psychologists. Specifically, should consultants use jargon (technical terms such as reinforcing, operantly condition, intervention, compliant) or nonjargon (conversational terms such as rewarding, teach, plan, habit) language when describing behavioral interventions to teachers during consultation? Concern for the type of language used during consultation was first noted in 1981, when Kazdin and Cole examined whether behavioral modification procedures and the use of jargon in describing behavioral methods affected evaluation of presented treatments by undergraduate students enrolled in elementary and secondary classes required of education majors. This research revealed that phrasing treatments using jargon resulted in more positive evaluations than humanistic and neutral procedures, suggesting a preference for treatments described in jargon terminology. Witt, Moe, et al. (1984) also examined type of language used in describing classroom interventions. The authors noted that acceptability ratings for three types of descriptions (pragmatic, humanistic, behavior) did not significantly differ. This finding was supported by Rhoades and Kratochwill (1992), whose research revealed that consultant language (jargon versus nonjargon) did not cause acceptability ratings to differ significantly. However, more recent research (Hyatt & Tingstrom, 1993) noted that jargon descriptions were associated with higher intervention acceptability ratings under certain conditions, such as when punishment-based and reinforcement-based interventions were compared (punishment-based was rated higher) and presentation format was examined (interventions written in jargon terms were rated higher than the same interventions presented via videotape).

Because it has been shown that acceptability of an intervention is important for ensuring high levels of integrity (Reimers, Wacker, & Koeppl, 1987) and acceptability of an intervention may be influenced by the language used in the description (Kazdin, 1981; Rhoades & Kratochwill, 1992; Witt, Moe, et al. 1984), the type of language used when describing a behavioral intervention is a topic that requires further research and significant attention by consultants. If consultants are cognizant of the type of language (jargon versus nonjargon) that teachers prefer when behavioral interventions are described, teachers’ acceptance and adherence to the intervention may be inadvertently promoted; thus improving intervention outcome and ultimately student success.

Despite the importance of recognizing the role that language plays in teachers’ adherence to and acceptance of behavioral interventions, the vast majority of research conducted in this area occurred during the conclusion of the 20th century. As such, the current study sought to update the existing literature on this topic by addressing the following research question.

Does the type of language used (jargon or nonjargon) by consultants when discussing and describing positive behavioral interventions significantly affect total acceptability and usage ratings on the URP–IR? Based on prior research (Rhoades & Kratochwill, 1992), it was hypothesized that the type of language (jargon versus nonjargon) used by consultants to describe positive behavioral interventions would not significantly affect total acceptability and usage ratings on the URP–IR.

Acceptability and behavioral interventions

The acceptability of behavioral interventions in the school setting has been linked to information shared about the intervention beforehand and the nature of the behaviors addressed. Boone Von Brock and Elliott (1987) and Kazdin (1981) found that providing teachers with information on the effectiveness of the intervention influenced acceptability ratings. In addition, Witt, Martens, and Elliott (1984) discovered that interventions that required more time and involvement to implement were less acceptable to teachers. It has also been concluded that positive or reinforcing interventions received more acceptable ratings than negative or non-reinforcing interventions (Elliott, Witt, Galvin, & Peterson, 1984; Kutsick, Gutkin, & Witt, 1991), and those interventions producing negative side effects were also rated as undesirable (Kazdin, 1981). Furthermore, interventions, in general, that address severe behavior problems have been rated as more acceptable (Martens, Witt, Elliott, & Darveaux, 1985; Witt, Moe, Gutkin, & Andrews, 1984).

Similarly, previous research has suggested that the language used to describe an intervention influenced the manner in which an intervention was perceived by others (Woolfolk & Woolfolk, 1979; Woolfolk, Woolfolk & Wilson, 1977). Interventions described in behavioral terms have been rated more negatively than interventions described in humanistic or neutral terms and the acceptability of interventions using behavioral techniques were rated more negatively regardless of whether or not they were presented using jargon (Kazdin & Cole, 1981). Interventions described in a pragmatic manner rather than using behavioral or humanistic jargon were rated by teachers to be most acceptable (Witt, Moe, et al., 1984).

When Hyatt et al. (1991) examined the effects of technical language on intervention acceptance, the authors hypothesized that after reading a case describing problematic behavior written either using jargon or non-jargon terminology, a preference for nontechnical language among experienced teachers would be found, but undergraduate students would rate interventions described in jargon terms as more acceptable. Results indicated that teachers rated the jargon description of the intervention as more acceptable than the non-jargon explanation, whereas there was no difference in students’ acceptability ratings. These findings contradict earlier studies (Witt, Moe, et al., 1984; Woolfolk et al., 1977; Woolfolk & Woolfolk, 1979) in which jargon descriptions were rated less favorably. However, in one of Kazdin and Cole’s (1981) experiments, interventions described in jargon terms were rated as more acceptable than those described in ordinary terms. Such conflicting findings support the need for ongoing research to determine the type of language preferred by teachers when describing a behavioral intervention.

In an attempt to clarify conflicting results, Rhoades and Kratochwill (1992) examined teacher acceptability of behavior interventions presented in jargon terminology. Sixty regular education teachers were assigned to one of four conditions: Technical language with teacher involvement, technical language without teacher involvement, nontechnical language with teacher involvement, or nontechnical language without teacher involvement. Participants viewed a video of a teacher engaging in consultation with a psychologist and then completed the Intervention Rating Profile–15 (IRP–15) to assess acceptability. Rhoades and Kratochwill found that the highest acceptability ratings were given to the vignette in which teacher involvement was low and jargon was used by the psychologist. Rated as least acceptable was the vignette with low teacher involvement and nontechnical language used by the psychologist. These findings by Rhoades and Kratochwill further confirm the variability in outcomes when attempting to determine the type of language preferred by teachers when describing behavioral interventions.

A different approach to assessing jargon usage during consultation was taken by Knotek (2003). Knotek set out to qualitatively examine how jargon and slang affected the problem identification stage of student study teams. Results revealed that when jargon and slang were used during student study team meetings, participants did not reflect upon their language. In addition, the use of jargon and slang were associated with a lack of clarification among the student study team participants. Knotek further indicated that when professional jargon was utilized, the student study team’s conceptualization of the problem was unclear and disjointed, often with assumptions being made about the problem.

Given the results of the existing research, the use of jargon during consultation is conflicting. Research has shown that improving a consultee’s understanding of an intervention can improve acceptability of that intervention, resulting in higher compliance (Reimers et al., 1987). Eckert, Russo, and Hier (2008) present best practices as avoiding jargon and technical language; however, due to the conflicting results of previous research and lack of current research in this area, additional investigation is needed to determine whether consultants should use jargon or nontechnical language when describing behavioral interventions to teachers.

Methodology

Participants

Upon receiving approval from an Institutional Review Board (IRB), participants were obtained through an internet search such as ‘school districts in (state)’. The individual school district websites were then explored to identify if the teacher email addresses were accessible to the community. Accessible teacher email lists were obtained from school districts throughout the United States, Hawaii, and Alaska. From these lists, 5,000 K-6 teachers in at least ten states were sent emails to seek their participation. One hundred and one elementary school (K-6) teachers ultimately participated. Participants who were employed in a school setting but were not K-6 teachers were excluded. Participants were largely white (97%) female (85.1%) general education (71.3%) teachers with six or more years of experience (48.6%). Approximately half the participants (48.5%) were from rural school districts and virtually all school districts were in Pennsylvania (98%). Participants (36.6%) were typically between the ages of 31 and 40 and the majority (59.4%) had never taken a class in behavior analysis. See Tables 1 and 2.
Table 1.
Demographic characteristics of participants

Variable N %

Terminology

Jargon 43 42.6

Non-jargon 58 57.4

Gender

Male 15 14.9

Female 86 85.1

Race/ethnicity

White 98 97.0

Puerto Rican 1 1.0

Spanish origin 1 1.0

Hispanic 1 1.0

State

Pennsylvania 99 98.0

Alaska 2 2.0

Table 2.
Teaching characteristics of participants

Variable N %

Educational attainment

Bachelor’s 18 17.8

Master’s 78 77.2

Educational specialist 2 2.0

Doctorate 3 3.0

Grade taught

Kindergarten 19 18.8

First 11 10.9

Second 8 7.9

Third 12 11.9

Fourth 13 12.9

Fifth 28 27.7

Sixth 10 9.9

Type of class taught

General education 72 71.3

Special education 10 9.9

Specialized education 19 18.8

Behavior analysis course

Yes 41 40.6

No 60 59.4

Measures and materials

Usage Rating Profile for Intervention (URP–IR).

The URP-IR is an extension of the IRP-20 and the URP-I (Chafouleas, Riley Tillman, Briesch, & Chanese, 2008; Wittt & Martens, 1983). The URP-IR is a 60-item five factor self-report measure designed to assess influences on the usability of academic interventions (Briesch et al., 2013). The 60 URP-IR items are spread across the following six domains: Acceptability, understanding, family- school collaboration, feasibility, system climate, and system support. These domains are based on research related to the development of other measures (URP-I; Chafouleas et al., 2009; IRP-20; Martens et al., 1985). Factor analysis of the URP-IR yielded acceptable levels of internal consistency for five of the six subscales (α > 0.70). The systems support factor had lower reliability (α = 0.67), but the authors attributed this to the small number of items that loaded onto this factor. In addition to the acceptable internal consistency, the factors were found to be weakly correlated with one another. By having factors that are weakly correlated, the validity of the scale is increased because such findings indicate each factor is distinct. Overall, Briesch et al. (2013) concluded that the acceptability and feasibility factors were improved from previous versions.

The present study examined teacher acceptability and usage of a positive behavioral intervention described in jargon terms and in non-jargon terms when using the Usage Rating Profile – Intervention Revised (URP–IR). There is limited published research on the URP–IR, and the URP–IR has yet to be used in research that specifically examines teacher acceptability and usage of behavioral interventions. Because the strongest factor of the URP–IR is acceptability, which also contains the most items, using this factor to determine acceptability of behavioral interventions would seem appropriate. Therefore, this study utilized the URP–IR to determine teacher preference for interventions described in jargon and non-jargon terms. Examination of these factors will provide consultants with information on how to approach behavioral consultation with teachers, particularly what type of language (jargon versus non-jargon) to use when describing behavioral interventions, and help to clarify previous conflicting findings.

Vignettes

Two vignettes of a positive behavioral intervention, one described in non-jargon terms (Appendix A) and one described in jargon terms (Appendix B), were used for the study. The vignettes were modified from Hall and Didier’s (1987) vignettes to make them appropriate for the current investigation. More specifically, Hall and Didier’s vignettes served as a model for the format, verbiage, and structure of explanation of the intervention; however, presenting behaviors, student names and interventions differed completely from Hall and Didier’s original vignettes. In addition, a brief explanation of the student and a presenting problematic behavior was provided, which was also modeled after Hall and Didier’s original research. Hall and Didier provided permission for modification of their original vignettes as well as the brief description of the student and corresponding problematic behavior prior to the initiation of the study.

The URP–IR was used to measure intervention acceptability and usage. The URP–IR assesses the factors that influence the likelihood of intervention usage. The final version of the URP–IR consists of 29 items rated on a six-point Likert scale ranging from 1, strongly disagree, to 6, strongly agree, and six factors: Acceptability, understanding, family- school collaboration, feasibility, system climate, and system support. The highest possible score on the URP–IR is 164, and the lowest possible score is 39, with higher scores indicating increased acceptability and potential usage (Briesch et al., 2013).

Research design

The current study was quantitative in nature. Demographic information was collected, and the URP–IR was used to obtain acceptability and usage ratings of a positive behavioral intervention described in one of two ways: Jargon terms or nonjargon terms. Acceptability and usage of a positive behavioral intervention, as measured by the URP–IR was the dependent variable. The independent variable consisted of the language used (jargon or nonjargon).

Method and procedure

To ensure anonymity, participants responded to an online survey designed using SurveyMonkey®. The capacity to track Internet addresses was disabled to eliminate the digital tracking of the individuals accessing the survey. Furthermore, no identifying information was collected as part of the survey.

In an attempt to approximate a minimal degree of random assignment, participants whose last name started with A through M were instructed to click on the first SurveyMonkey® link provided in the email; and participants with last names that began with N through Z were instructed to click on the second SurveyMonkey® link to complete the study. If teachers taught more than one grade level, they were advised to select the grade level in which they spent the majority of their time teaching. Participants were made aware of the anonymity precautions and were then asked to respond to demographic questions (gender, age, years of experience, etc.), before being directed to the study content.

Participants were instructed to read the vignettes and answer the corresponding questions on the URP–IR as they related to the vignette, which required approximately 15 to 20 minutes. Participation was concluded upon completion of the URP–IR. The URP–IR was scored later by the primary author and the data were entered by the primary author into the IBM SPSS Statistics® program. The participants did not have access to the scores corresponding to their responses.

Results

A one-way analysis of variance (ANOVA) was conducted to determine whether the type of language (jargon versus non-jargon) used by consultants affected total acceptability and usage ratings on the URP–IR. The results revealed that there was not a significant difference between the type of language used (jargon versus non-jargon) and total acceptability and usage ratings on the URP–IR, F(1, 99) = 0.05, p = 0.823 as represented in Table 3. Therefore, the use of jargon or non-jargon terminology did not affect acceptance by elementary school (K-6) teachers of a positive behavioral intervention, as described by a consultant. The results of this analysis are presented in Table 3. The means and standard deviations by types of terminology are presented in Table 4. In addition, for the acceptability and usage ratings on the URP–IR, the variances were equal for the positive behavioral intervention described in jargon and non-jargon language, F(1, 99) = 0.123, p = 0.727. The mean acceptability and usage rating for each group was also almost exactly the same (jargon, M = 136.12; non-jargon, M = 136.79) as represented in Table 4.
Table 3.
Analysis of variance for jargon versus non-jargon vignette

Source SS df MS F P

Between groups 11.31 1 11.31 0.050 0.823

Within groups 22,209.94 99 224.34

Total 22,221.25 100

Table 4.
Means and Standard Deviations for total score on the URP–IR by vignette

95% Confidence Interval for Mean

Vignette N M SD SE Lower Upper

Jargon 43 136.12 14.75 2.25 131.58 140.66

Non-jargon 58 136.79 15.14 1.99 132.81 140.78

Total 101 136.51 14.91 1.48 133.56 139.45

Discussion

Previous research on the topic of type of language to use in behavioral consultation when describing a positive behavioral intervention has yielded conflicting results (Hyatt et al., 1991; Hyatt & Tingstroms, 1993; Kazdin & Cole, 1981; Knotek, 2003; Witt, Moe, et al., 1984; Woolfolk & Woolfolk, 1979; Woolfolk et al., 1977). The findings of the current research display no significant difference between elementary school (K-6) teachers’ ratings on overall acceptability and usage of a positive behavioral intervention described in jargon or non-jargon terminology.

More specifically, the current results indicated that elementary school (K-6) teachers did not have different levels of acceptability and usage for a positive behavioral intervention described in jargon or non-jargon terms by a consultant when rated on the URP–IR. These results are consistent with previous research (Kazdin & Cole, 1981; Knotek, 2003; Rhoades & Kratochwill, 1992; Witt, Moe, et al., 1984; Woolfolk, et al., 1977; Woolfolk & Woolfolk, 1979).

The findings of the current study occur with several noted limitations in internal and external validity. First, the dependent variable of overall acceptability and usage could have been influenced by the measurement tool (URP–IR) utilized in this study. Although the URP–IR has been shown, through factor analysis, to have weakly correlated factors and acceptable levels of internal consistency on five of the six subscales (Briesch et al., 2013), the present study is the first to utilize the URP–IR to measure overall teacher acceptability and usage of a positive behavioral intervention when described in jargon versus non-jargon terminology. Thus, there is no foundational research for comparison purposes and no way of knowing whether the URP–IR was a valid and reliable tool for measuring overall acceptability and usage of a positive behavioral intervention.

The present research was completed as an online survey through SurveyMonkey®. Therefore, there is no way of knowing whether participants thoroughly read the vignettes and URP–IR questions. If participants rushed through the vignettes and questions, accuracy in responding would be comprised. Furthermore, characteristics of the teachers who chose to participate may have impacted the results of this study. The teachers who participated in the present study may be more willing to engage in behavioral consultation, which ultimately could influence their preference, or lack thereof, for a positive behavioral intervention described in jargon versus nonjargon terminology. Without interpersonal interaction with the participants in the study, there is no way of determining whether specific characteristics may have affected ratings on the URP–IR.

The participants in this study also were not randomized when assigned to the jargon or non-jargon terminology intervention description groups. Participants were assigned to either group based on the first letter of their last name. This approach to assigning participants to groups resulted in an uneven number of participants in each group. Although the participants did not know which vignette they would be presented with upon beginning the survey, it is possible that participants could have viewed both vignettes and deduced the purpose of the study. Furthermore, it is possible that participants did not follow the instructions and completed the wrong or both vignettes. Therefore, complete randomization of groups would have ensured lack of bias in group assignment as well as assure compliance to directions.

Although the sample size of the current study (101 participants) was within the range of previous research conducted on this topic (Hyatt & Tingstrom, 1993; Hyatt et al, 1991; Kazdin & Cole, 1981; Rhoades & Kratochwill, 1992; Witt, Moe, et al., 1984; Woolfolk & Woolfolk, 1979; Woolfolk, et al., 1977), it was quite small when considering the number of teachers (approximately 3.1 million) in the United States (National Center for Education Statistics, 2011). This is considered a poor response rate and, therefore, the sample may not be truly representative of the teacher population as a whole, and generalizability may be limited.

The second threat to external validity is the lack of demographic diversity of the sample used for this study. Respondents in the current study were almost all from Pennsylvania (98.0%), despite recruitment emails being sent to teachers with accessible addresses throughout the country. It is quite possible that these emails were routed to SPAM folders and/or teachers chose to respond because they were familiar with the email stem in the sender’s address (@university.edu). In addition, most of the participants in this study were White (97.0%), female (85.1%) general education teachers (71.3%), thus limiting the ability to generalize the current findings to other demographic categories.

Lastly, in addition to possibly affecting the interval validity of the study, the analog nature of this research may have affected external validity. Using a survey to collect the data was the most feasible approach, but also probably the least realistic. Without the interpersonal interaction that occurs during actual behavioral consultation practice in schools, it is difficult to control for other variables that may interfere with and affect measured outcomes, such as consultant likeability.

The current study is the only examination of the topic in over 20 years; thus, it is important for researchers to continue exploring teacher preference for positive behavioral interventions described in jargon or non-jargon terminology, as such language may inadvertently promote teachers’ adherence and acceptance of the intervention. Future research should emphasize randomization of participants into groups to ensure equal group size for more reliable statistical analysis. Furthermore, recruitment of a larger and geographically diverse sample may provide insight into whether preferences differ by the area of the United States in which the teacher is practicing. For example, individual scores on each of the factors of the URP–IR in addition to the total score could be investigated to determine whether preferences differ on each factor. Demographic information (e.g., age, gender, education, grade taught, years taught, state teaching in, type of district teaching in, type of classroom teaching [general education versus special education], whether a behavior analysis class was taken, and race/ethnicity) could be analyzed to determine whether specific preferences for jargon versus non-jargon terminology emerge.

Additional and ongoing research that examines teacher preferences in behavioral consultation is needed for consultants to successfully continue to navigate the consultative relationship. The opportunities for expansion of the current research are considerable and necessary in order for consultants to fully understand the role jargon or non-jargon terminology plays in behavioral consultation practices.

Variable	N	%
Terminology
Jargon	43	42.6
Non-jargon	58	57.4
Gender
Male	15	14.9
Female	86	85.1
Race/ethnicity
White	98	97.0
Puerto Rican	1	1.0
Spanish origin	1	1.0
Hispanic	1	1.0
State
Pennsylvania	99	98.0
Alaska	2	2.0

Variable	N	%
Educational attainment
Bachelor’s	18	17.8
Master’s	78	77.2
Educational specialist	2	2.0
Doctorate	3	3.0
Grade taught
Kindergarten	19	18.8
First	11	10.9
Second	8	7.9
Third	12	11.9
Fourth	13	12.9
Fifth	28	27.7
Sixth	10	9.9
Type of class taught
General education	72	71.3
Special education	10	9.9
Specialized education	19	18.8
Behavior analysis course
Yes	41	40.6
No	60	59.4

Source	SS	df	MS	F	P
Between groups	11.31	1	11.31	0.050	0.823
Within groups	22,209.94	99	224.34
Total	22,221.25	100

					95% Confidence Interval for Mean
Jargon	43	136.12	14.75	2.25	131.58	140.66
Non-jargon	58	136.79	15.14	1.99	132.81	140.78
Total	101	136.51	14.91	1.48	133.56	139.45

Footnotes

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.

Author biographies

Katie Shemanski is a School Psychologist and Board Certified Behavior Analyst (BCBA) for the Western Wayne School District in Northeast Pennsylvania and an adjunct faulty at Philadelphia College of Osteopathic Medicine. She received her PsyD in school psychology from Philadelphia College of Osteopathic Medicine and a graduate certificate in applied behavior analysis from the Pennsylvania State University. Her research interests include applied behavior analysis, behavioral consultation, and crisis intervention and prevention.

Francis J. DeMatteo is a Nationally Certified School Psychologist (NCSP) and earned his graduate degrees in educational psychology and school psychology at Indiana University of Pennsylvania. Dr DeMatteo is currently an Associate Professor and Director of the School Psychology Program in the Department of Psychology at Humboldt State University in northern California. His research interests include: Family-school relations, providing feedback results to parents, and post-school transition.

Allison Fruehan is graduate student in the School Psychology Program at Marywood University and she is completing her 1200 hour field based internship at Anchorage School District in Alaska. Allison’s research interests include: Culturally responsive suicide prevention and intervention, high school students with Autism Spectrum Disorder, and family-school relations.

Appendix A: Nonjargon Vignette and Intervention

Appendix B: Jargon vignette and intervention

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					95% Confidence Interval for Mean
Vignette	N	M	SD	SE	Lower	Upper
Jargon	43	136.12	14.75	2.25	131.58	140.66
Non-jargon	58	136.79	15.14	1.99	132.81	140.78
Total	101	136.51	14.91	1.48	133.56	139.45