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Further Application of Delay Discounting on Special Educator Decision-Making

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Title: Further Application of Delay Discounting on Special Educator Decision-Making
Language: English
Authors: Allison N. White-Cascarilla, Matthew T. Brodhead (ORCID 0000-0003-4567-0051), Derek D. Re, Ashley N. Walker
Source: Journal of Behavioral Education. 2025 34(1):94-108.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 15
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Special Education Teachers, Decision Making, Intervention, Student Behavior, Outcomes of Treatment
DOI: 10.1007/s10864-023-09519-3
ISSN: 1053-0819
1573-3513
Abstract: Special education teachers often make decisions about interventions to help reduce student problem behavior. There are many variables that impact how teachers make decisions regarding behavioral interventions, and the current study aimed to quantitatively evaluate how delay to treatment outcomes affect teacher decision-making. This study used the delay discounting framework to examine the effects of delays to treatment outcomes on special education teacher decision-making. Participants completed an online hypothetical delay discounting task based on the behavioral economic theory that the value of an outcome (e.g., treatment outcome) diminishes as the delay to that outcome increases over time. Our results indicate that most special education teachers discount delays in treatment effects, suggesting that special education teachers may prefer interventions that result in more immediate behavior change. Implications for future research and consultative practice are discussed.
Abstractor: As Provided
Notes: https://osf.io/3z4j6
Entry Date: 2025
Accession Number: EJ1485407
Database: ERIC
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  Value: <anid>AN0184231738;41z01mar.25;2025Apr07.04:59;v2.2.500</anid> <title id="AN0184231738-1">Further Application of Delay Discounting on Special Educator Decision-Making </title> <p>Special education teachers often make decisions about interventions to help reduce student problem behavior. There are many variables that impact how teachers make decisions regarding behavioral interventions, and the current study aimed to quantitatively evaluate how delay to treatment outcomes affect teacher decision-making. This study used the delay discounting framework to examine the effects of delays to treatment outcomes on special education teacher decision-making. Participants completed an online hypothetical delay discounting task based on the behavioral economic theory that the value of an outcome (e.g., treatment outcome) diminishes as the delay to that outcome increases over time. Our results indicate that most special education teachers discount delays in treatment effects, suggesting that special education teachers may prefer interventions that result in more immediate behavior change. Implications for future research and consultative practice are discussed.</p> <p>Keywords: Behavioral economics; Delay discounting; Decision-making; Problem behavior; Special educator</p> <p>Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</p> <hd id="AN0184231738-2">Introduction</hd> <p>Teachers are a primary provider of behavioral interventions designed to decrease problem behavior in school settings (DiGennaro Reed & Codding, [<reflink idref="bib9" id="ref1">9</reflink>]; Kasari & Smith, [<reflink idref="bib19" id="ref2">19</reflink>]). Evidence-based interventions can decrease student engagement in problem behavior (Horner et al., [<reflink idref="bib17" id="ref3">17</reflink>]); however, problem behavior is often not eliminated immediately after treatment implementation begins—there is usually some delayed period of time before problem behavior reduces to desired levels (Call et al., [<reflink idref="bib6" id="ref4">6</reflink>]). Given the finding that humans tend to devalue delayed outcomes (Critchfield & Kollins, [<reflink idref="bib8" id="ref5">8</reflink>]; Gilroy & Kaplan, [<reflink idref="bib14" id="ref6">14</reflink>]), the delay to treatment outcomes may influence teacher decision-making of behavioral interventions.</p> <p>Individuals with disabilities often engage in problem behavior in school settings (Rivera et al., [<reflink idref="bib36" id="ref7">36</reflink>]). For example, nearly one-third of children with autism exhibit problem behavior, and over 20% of those children engage in aggressive behavior (Hartley et al., [<reflink idref="bib16" id="ref8">16</reflink>]; Rivera et al., [<reflink idref="bib36" id="ref9">36</reflink>]). Problem behavior may lead to serious negative personal (e.g., self-injury), administrative (e.g., restricted placement or suspension), or social (e.g., social isolation) outcomes. Furthermore, problem behavior often impedes the delivery of educational services (Hartley et al., [<reflink idref="bib16" id="ref10">16</reflink>]; Rispoli et al., [<reflink idref="bib33" id="ref11">33</reflink>]; Rivera et al., [<reflink idref="bib36" id="ref12">36</reflink>]). Therefore, treating problem behavior for individuals with disabilities is of major concern in public schools so children can access their free and public education.</p> <p>Problem behavior of children with disabilities may, in part, be reduced via the effective delivery of behavioral interventions in school settings (Horner et al., [<reflink idref="bib17" id="ref13">17</reflink>]; Kasari & Smith, [<reflink idref="bib19" id="ref14">19</reflink>]; Rivera et al., [<reflink idref="bib36" id="ref15">36</reflink>]; Stahmer et al., [<reflink idref="bib38" id="ref16">38</reflink>]). A recent review by Rivera et al. found that antecedent-based and function-based interventions were the most common behavior interventions in school settings. These interventions significantly reduced problem behaviors. Teachers also reported they are likely to implement antecedent and function-based interventions in their classrooms due to their likelihood of successful behavior reduction.</p> <p>Teachers shoulder much responsibility for developing and implementing behavioral interventions to decrease problem behavior in school settings (DiGennaro Reed & Codding, [<reflink idref="bib9" id="ref17">9</reflink>]). In fact, teachers are required by the Individuals with Disabilities Act (IDEA) to conduct functional behavior assessments (FBA) and develop behavioral intervention plans for students with disabilities who engage in problem behavior that impedes their learning or the learning of others (Machalicek et al., [<reflink idref="bib22" id="ref18">22</reflink>]; IDEA Improvement Act, [<reflink idref="bib40" id="ref19">40</reflink>]). Put another way, IDEA mandates that teachers are responsible for choosing, developing, and then applying evidence-based interventions to treat problem behavior (West et al., [<reflink idref="bib42" id="ref20">42</reflink>]). Teachers may make treatment choices daily, as changes in student problem behavior may be dynamic and require continued modification. Teachers may also collaborate with consultants (e.g., behavior analysts) to make long-term treatment choices that address student problem behavior (Kelly & Tincani, [<reflink idref="bib20" id="ref21">20</reflink>]). Here, consultants may provide treatment recommendations, but teachers are still responsible for following and implementing those recommendations. Regardless, teachers' treatment choices are important because their decisions directly impact student outcomes (DiGennaro Reed & Codding, [<reflink idref="bib9" id="ref22">9</reflink>]). Understanding how teachers make treatment choices may lead to improvements in teacher decision-making as well as consulting practices that directly impact students who engage in problem behavior.</p> <p>Teacher decision-making may be best understood in a choice context (i.e., teachers choose to implement a treatment from various treatment options). Choice can be defined as "the distribution of operant behavior among alternative sources of reinforcement" (Pierce & Cheney, [<reflink idref="bib28" id="ref23">28</reflink>], p. 472). Put another way, a teacher selects an intervention because that choice likely results in stronger reinforcement (e.g., higher likelihood of treatment success) than other available options.</p> <p>In each choice context, a few variables likely affect how teachers make intervention decisions (Kasari & Smith, [<reflink idref="bib19" id="ref24">19</reflink>]). One variable is the length of time required to achieve reductions in problem behaviors (Call et al., [<reflink idref="bib6" id="ref25">6</reflink>]; Kasari & Smith, [<reflink idref="bib19" id="ref26">19</reflink>]). Consider an example where a student engages in problem behavior to access preferred items. For the student's teacher, the choice context may consist of two treatment options: Option 1 may be to reinforce the problem behavior by providing access to the preferred item, thereby <emph>immediately</emph> reducing the behavior; and Option 2 may be to implement a behavioral intervention designed to functionally decrease the behavior, but the teacher may observe <emph>delayed</emph> treatment effects. Option 1 may immediately reduce the student's problem behavior in the short-term, but it likely has long-term undesirable effects because reinforcing problem behavior makes it more likely to occur in the future. The rational decision may be for the teacher to select Option 2. However, with Option 2, there may be a delay until the teacher observes reductions in problem behavior (Allen & Warzak, [<reflink idref="bib1" id="ref27">1</reflink>]; Call et al., [<reflink idref="bib6" id="ref28">6</reflink>]). When immediate changes in behavior are not observed, teachers may select the less-than-optimal treatment option (i.e., Option 1) that immediately reduces problem behavior but causes problem behavior to persist over time (Allen & Warzak, [<reflink idref="bib1" id="ref29">1</reflink>]; Kasari & Smith, [<reflink idref="bib19" id="ref30">19</reflink>]). An open question then is: Do delays to treatment effects impact how teachers make decisions about treatments for problem behavior?</p> <p>The potential effects of delays to treatment outcomes on teacher intervention choices are poorly understood. One way to understand patterns of teacher decision-making is through the delay discounting framework. Delay discounting has been used to describe patterns of decision-making (Critchfield & Kollins, [<reflink idref="bib8" id="ref31">8</reflink>]; Gilroy & Kaplan, [<reflink idref="bib14" id="ref32">14</reflink>]) and refers to the finding that people often prefer smaller, immediate rewards compared to larger, delayed rewards (Madden et al., [<reflink idref="bib23" id="ref33">23</reflink>]; Odum et al., [<reflink idref="bib27" id="ref34">27</reflink>]; Weatherly et al., [<reflink idref="bib41" id="ref35">41</reflink>]). Put another way, the longer a person must wait for a reward, the less valuable that reward becomes (Bickel et al., [<reflink idref="bib3" id="ref36">3</reflink>]; Odum et al., [<reflink idref="bib27" id="ref37">27</reflink>]). For teachers implementing behavior interventions, we can assume that decreases in challenging behavior serve as the reward (Call et al., [<reflink idref="bib6" id="ref38">6</reflink>]).</p> <p>Delay discounting is highly relevant to the study of teacher decision-making. If teachers do not observe the intended problem behavior reduction outcome immediately, teachers may discount the delayed treatment (i.e., that treatment outcome is of less value) and choose treatments that result in immediate decreases in problem behavior but may be contraindicated to behavioral function or be less effective than the delayed treatment. The time required to achieve desired outcomes through behavioral interventions may also diminish teacher adherence to an intervention, thereby increasing the likelihood that teachers will choose treatments that result in an immediate decrease in behavior (i.e., reinforcing the problem behavior).</p> <p>Delay discounting is studied by presenting participants with the choice between two hypothetical rewards: a reward that is available immediately and a second reward that will be available after a delay (Bickel et al., [<reflink idref="bib3" id="ref39">3</reflink>]; Call et al., [<reflink idref="bib6" id="ref40">6</reflink>]; Rachlin et al., [<reflink idref="bib30" id="ref41">30</reflink>]). When the rewards only differ in immediacy (e.g., receiving a $100 reward today vs a $100 reward in 7 days), most participants choose the immediate reward (Call et al., [<reflink idref="bib6" id="ref42">6</reflink>]; Meyerson & Green, [<reflink idref="bib25" id="ref43">25</reflink>]). When the amount of the immediate reward decreases and the delay between reward stays constant (e.g., receiving a $50 reward today vs. a $100 reward in 7 days), most participants will eventually select the delayed reward (Call et al., [<reflink idref="bib6" id="ref44">6</reflink>]). The point where the participant selects the delayed reward over the immediate reward is called the <emph>indifference point</emph> (Bickel et al.). When multiple indifference points are obtained at various delays, an indifference curve can be graphed and analyzed using quantitative models.</p> <p>Recent studies by Call et al. ([<reflink idref="bib6" id="ref45">6</reflink>]) and Gilroy and Kaplan ([<reflink idref="bib14" id="ref46">14</reflink>]) provide an excellent framework for studying the effects of delays on teacher decision-making. Call et al. and Gilroy and Kaplan used the delay discounting framework to analyze caregivers' treatment decisions and assumed that reduction in problem behavior functions as a reward for caregivers making treatment decisions. In Call et al., participants were exposed to two delay discounting conditions. The first condition evaluated discounting of hypothetical monetary rewards, and the second condition evaluated discounting of delayed treatment outcomes. Because many studies on choice behavior have supported Mazur's hyperbolic model (Call et al., [<reflink idref="bib6" id="ref47">6</reflink>]; Madden et al., [<reflink idref="bib23" id="ref48">23</reflink>]), Call et al. fit participant data to the hyperbolic model. Overall, participants' individual responses matched the typical pattern of indifference points in both the monetary rewards and treatment outcomes conditions, and the researchers concluded that Mazur's hyperbolic equation described the data. By demonstrating that caregivers discount the value of delayed treatment outcomes, Call et al., suggested delay discounting could be an appropriate method to analyze decision-making in the treatment of problem behavior.</p> <p>Gilroy and Kaplan ([<reflink idref="bib14" id="ref49">14</reflink>]) replicated and extended the findings from Call et al. ([<reflink idref="bib6" id="ref50">6</reflink>]). Participants included 62 caregivers with concerns about their child's behavior. Like Call et al. participants in Gilroy and Kaplan were asked to make choices in a delayed monetary context and in a delayed treatment context. Consistent with the findings of Call et al., Gilroy and Kaplan found caregiver decision-making was influenced by delays, and caregivers preferred treatments that had minimal delays to achieve optimal behavior reduction.</p> <p>The purpose of the current study was to use delay discounting to quantitatively evaluate if delay to treatment outcomes impacts teacher decision-making. To do so, we aimed to replicate and extend the findings of Call et al. ([<reflink idref="bib6" id="ref51">6</reflink>]) and Gilroy and Kaplan ([<reflink idref="bib14" id="ref52">14</reflink>]). We recruited special education teachers because they are often responsible for implementing behavior treatments for students who engage in problem behavior (Rispoli et al., [<reflink idref="bib34" id="ref53">34</reflink>]; Westling, [<reflink idref="bib42" id="ref54">42</reflink>]). We analyzed delays to treatment outcomes, because previous research has hypothesized that delay to problem behavior reduction may serve as a barrier to treatment adherence (Collier-Meek et al., [<reflink idref="bib7" id="ref55">7</reflink>]); however, temporal related barriers to treatment implementation have not been quantified. Research related to teachers' implementation of behavioral interventions is important because teacher decision-making of behavioral treatments directly impacts students' future behavioral success. Additionally, when teachers do not adhere to recommended behavioral treatments, problem behavior will likely persist or increase, the student or other individuals in the environment may be at risk to harm, there may be a loss of academic time, and there may be an increase in the use pseudoscientific interventions (Rispoli et al., [<reflink idref="bib34" id="ref56">34</reflink>]; Suess et al., [<reflink idref="bib39" id="ref57">39</reflink>]). We hope that the methodology and findings of the current study inspire continued research on educator decision-making to further support teachers in implementing behavior interventions in their classrooms. Therefore, we asked the following research question: do special education teachers of students with disabilities discount delays to behavioral treatment outcomes consistent with the delay discounting model?</p> <hd id="AN0184231738-3">Method</hd> <p></p> <hd id="AN0184231738-4">Participants and Setting</hd> <p>Twenty-two special education teachers served as participants. We recruited participants through personal contacts, professional listservs (e.g., listserv), and Facebook®, and participants completed the study on their own personal computer or electronic device. The mean age of participants was 42 years old (range, 27–67). The majority of participants identified as white non-Hispanic females, and most participants held a master's degree and taught in schools for more than five years. See Supplementary Materials (Table S1) for additional reported demographic information.</p> <hd id="AN0184231738-5">Procedure</hd> <p>Participants completed the delay discounting task in the program Qualtrics (Qualtrics, Provo, UT). The delay discounting task evaluated hypothetical treatment outcomes for a hypothetical student that engages in problem behavior. The task consisted of 378 trials (described in more detail below) and took an average of 39 min (range, 16 min 11 s–2 h 5 min 13 s) to complete. Upon completing the task, participants were allowed to provide their e-mail addresses to enter a drawing to win a ten-dollar Amazon.com gift card.</p> <p>After participants consented to participate in the experiment and verified they were currently a special education teacher, participants were provided with the following statement adapted from Call et al. ([<reflink idref="bib6" id="ref58">6</reflink>]):For this experiment you will be asked to make choices about treatment options for a hypothetical student that engages in problem behavior. The treatment outcomes are hypothetical, but we ask that you make choices as they were real. Each treatment outcome represents a problem behavior no longer occurring for different amounts of time. You will see two options. One option will offer you a treatment that will stop your student's problem behavior immediately. The other option will offer you a treatment that will stop your student's problem behavior after some delay. Pick the option that you would rather have. You will continue to see two options presented to you after each choice that you make. Please continue to pick the option that you would rather have.</p> <p>The first trial then appeared on participants' screens and each subsequent trial was presented individually on separate pages (see Figure S1 in supplementary materials for trial examples). For each trial, participants were asked to choose between a delayed reward (i.e., behavior reduction after some delay) and an immediate reward (i.e., immediate behavior reduction). Trials were presented in descending choice trials and ascending choice trials. For the descending choice trials, the immediate reward began at 10 years and descended progressively across trials until the immediate reward was 0.01 years. For the ascending choice trials, the immediate reward began at 0.01 years and ascended progressively until the immediate reward was 10 years. The delayed reward always stayed constant at 10 years. The magnitude of the immediate reward varied across choice trials from 10 to 0.01 years. The delays presented varied from 1 week to 10 years. Altogether, there were 7 delays (i.e., 1 week, 2 weeks, 1 month, 6 months, 1 year, 3 years, and 10 years), and for each delay, there were 27 descending choice trials and 27 ascending choice trials for a total of 378 choice trials. See Supplemental Materials (Table S2 and S3) for immediate reward values and delays. The reward values and delays are identical to those used by Odum et al. ([<reflink idref="bib27" id="ref59">27</reflink>]) and Call et al. ([<reflink idref="bib6" id="ref60">6</reflink>]). Following completion of the task, we asked participants to answer demographic questions.</p> <hd id="AN0184231738-6">Data Analysis</hd> <p>For all analyses (unless otherwise specified), we used the Discounting Model Selector (v1.8.2; DMS) available via <ulink href="http://www.smallnstats.com/index.php?page=ModelSelector">http://www.smallnstats.com/index.php?page=ModelSelector</ulink> and described in (Gilroy et al., [<reflink idref="bib13" id="ref61">13</reflink>]). We first applied the Johnson and Bickel criteria for identifying nonsystematic delay discounting data to each participant's dataset (see Johnson & Bickel, [<reflink idref="bib18" id="ref62">18</reflink>] for criteria). Next, we used the DMS to conduct Bayesian Information Criterion to estimate which candidate discounting model best described the current datasets (Franck et al., [<reflink idref="bib11" id="ref63">11</reflink>]). Candidate models included Mazur's hyperbolic model and two hyperboloid models (Myerson & Green, [<reflink idref="bib25" id="ref64">25</reflink>]; Rachlin, [<reflink idref="bib29" id="ref65">29</reflink>]). To nontheoretically describe participants' discounting, we applied the Borges et al. (Borges et al., [<reflink idref="bib4" id="ref66">4</reflink>]) ordinal-based area-under-the-curve (AUC<subs>ord</subs>).</p> <hd id="AN0184231738-7">Results</hd> <p>Raw data with accompanying equations and calculations for Participants 1–22 are available in an open science repository (see Brodhead, [<reflink idref="bib5" id="ref67">5</reflink>]). Indifference points for each participant at each delay are presented in Table 1. Each indifference point represents the time at which participants switched from selecting the immediate reward to the delayed reward and can be interpreted as the value retained at a certain delay (Call et al., [<reflink idref="bib6" id="ref68">6</reflink>]). For example, an indifference point of 6.0 at a 1-year delay means a treatment that would result in 10 years of no problem behavior is equal to the value of a treatment that would result in 6 years of no problem behavior after 1 year of implementing a behavioral treatment. An indifference point of 6.0 at a 1-year delay means the treatment held 60% of its value after 1 year.</p> <p>Table 1 <emph>Participant indifference points, k, R</emph><sups><emph>2</emph></sups><emph>, and area under the curve values</emph></p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" rowspan="2"><p>Participant</p></th><th align="left" colspan="7"><p>Delays and indifference points</p></th><th align="left" rowspan="2"><p><italic>k</italic></p></th><th align="left" rowspan="2"><p><italic>R</italic><sup>2</sup></p></th><th align="left" rowspan="2"><p>AUC<sub>ord</sub></p></th></tr><tr><th align="left"><p>1 week</p></th><th align="left"><p>2 weeks</p></th><th align="left"><p>1 month</p></th><th align="left"><p>6 months</p></th><th align="left"><p>1 year</p></th><th align="left"><p>3 years</p></th><th align="left"><p>10 years</p></th></tr></thead><tbody><tr><td align="left"><p>1</p></td><td align="left"><p>10</p></td><td align="left"><p>9.95</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>9.95</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>2.69E−06</p></td><td char="." align="char"><p>0.91</p></td><td char="." align="char"><p>0.75</p></td></tr><tr><td align="left"><p>2</p></td><td align="left"><p>8</p></td><td align="left"><p>8</p></td><td align="left"><p>6.75</p></td><td align="left"><p>6.75</p></td><td align="left"><p>6.75</p></td><td align="left"><p>5.75</p></td><td align="left"><p>4.75</p></td><td align="left"><p>3.70E−07</p></td><td char="." align="char"><p>− 4.76</p></td><td char="." align="char"><p>0.67</p></td></tr><tr><td align="left"><p>3</p></td><td align="left"><p>10</p></td><td align="left"><p>8.45</p></td><td align="left"><p>6.75</p></td><td align="left"><p>5.25</p></td><td align="left"><p>4.75</p></td><td align="left"><p>0.8</p></td><td align="left"><p>1</p></td><td align="left"><p>1.71E−05</p></td><td char="." align="char"><p>0.77</p></td><td char="." align="char"><p>0.53</p></td></tr><tr><td align="left"><p>4<sup>*</sup></p></td><td align="left"><p>3.75</p></td><td align="left"><p>4</p></td><td align="left"><p>3.25</p></td><td align="left"><p>3.75</p></td><td align="left"><p>4</p></td><td align="left"><p>3.5</p></td><td align="left"><p>3.75</p></td><td align="left" /><td char="." align="char" /><td char="." align="char"><p>0.37</p></td></tr><tr><td align="left"><p>5</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.96</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.97</p></td><td align="left"><p>1.55</p></td><td align="left"><p>2.10E−07</p></td><td char="." align="char"><p>0.92</p></td><td char="." align="char"><p>0.93</p></td></tr><tr><td align="left"><p>6</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>9.95</p></td><td align="left"><p>8</p></td><td align="left"><p>0</p></td><td align="left"><p>4.11E−07</p></td><td char="." align="char"><p>0.95</p></td><td char="." align="char"><p>0.88</p></td></tr><tr><td align="left"><p>7</p></td><td align="left"><p>9.05</p></td><td align="left"><p>8.85</p></td><td align="left"><p>8.85</p></td><td align="left"><p>8.25</p></td><td align="left"><p>6.75</p></td><td align="left"><p>4.75</p></td><td align="left"><p>3.75</p></td><td align="left"><p>1.15E−06</p></td><td char="." align="char"><p>0.32</p></td><td char="." align="char"><p>0.73</p></td></tr><tr><td align="left"><p>8</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.6</p></td><td align="left"><p>9.2</p></td><td align="left"><p>8.95</p></td><td align="left"><p>6.75</p></td><td align="left"><p>6.25</p></td><td align="left"><p>6.86E−08</p></td><td char="." align="char"><p>0.38</p></td><td char="." align="char"><p>0.88</p></td></tr><tr><td align="left"><p>9</p></td><td align="left"><p>9.6</p></td><td align="left"><p>8.85</p></td><td align="left"><p>8.85</p></td><td align="left"><p>8.85</p></td><td align="left"><p>8.85</p></td><td align="left"><p>8.85</p></td><td align="left"><p>7.35</p></td><td align="left"><p>3.34E−08</p></td><td char="." align="char"><p>− 1.20</p></td><td char="." align="char"><p>0.88</p></td></tr><tr><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>9.95</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>9.95</p></td><td align="left"><p>0.03</p></td><td align="left"><p>2.80E−07</p></td><td char="." align="char"><p>0.88</p></td><td char="." align="char"><p>0.92</p></td></tr><tr><td align="left"><p>11</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.6</p></td><td align="left"><p>8.85</p></td><td align="left"><p>6.75</p></td><td align="left"><p>0</p></td><td align="left"><p>6.08E−07</p></td><td char="." align="char"><p>0.97</p></td><td char="." align="char"><p>0.84</p></td></tr><tr><td align="left"><p>12</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>0.425</p></td><td align="left"><p>0</p></td><td align="left"><p>2.50E−06</p></td><td char="." align="char"><p>0.92</p></td><td char="." align="char"><p>0.76</p></td></tr><tr><td align="left"><p>13</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>6.75</p></td><td align="left"><p>0</p></td><td align="left"><p>0</p></td><td align="left"><p>4.72E−06</p></td><td char="." align="char"><p>0.97</p></td><td char="." align="char"><p>0.70</p></td></tr><tr><td align="left"><p>14</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.75</p></td><td align="left"><p>9.05</p></td><td align="left"><p>8.25</p></td><td align="left"><p>5</p></td><td align="left"><p>0</p></td><td align="left"><p>1.14E−06</p></td><td char="." align="char"><p>0.98</p></td><td char="." align="char"><p>0.78</p></td></tr><tr><td align="left"><p>15</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.4</p></td><td align="left"><p>8.85</p></td><td align="left"><p>7.75</p></td><td align="left"><p>2.75</p></td><td align="left"><p>0</p></td><td align="left"><p>2.57E−06</p></td><td char="." align="char"><p>0.99</p></td><td char="." align="char"><p>0.73</p></td></tr><tr><td align="left"><p>16</p></td><td align="left"><p>9.95</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>0</p></td><td align="left"><p>2.79E−07</p></td><td char="." align="char"><p>0.87</p></td><td char="." align="char"><p>0.92</p></td></tr><tr><td align="left"><p>17</p></td><td align="left"><p>10</p></td><td align="left"><p>5</p></td><td align="left"><p>5.25</p></td><td align="left"><p>6</p></td><td align="left"><p>4.5</p></td><td align="left"><p>3.5</p></td><td align="left"><p>0.7</p></td><td align="left"><p>1.24E−05</p></td><td char="." align="char"><p>− 0.20</p></td><td char="." align="char"><p>0.49</p></td></tr><tr><td align="left"><p>18</p></td><td align="left"><p>10</p></td><td align="left"><p>10</p></td><td align="left"><p>9.2</p></td><td align="left"><p>8.35</p></td><td align="left"><p>6.75</p></td><td align="left"><p>7.85</p></td><td align="left"><p>0.005</p></td><td align="left"><p>5.29E−07</p></td><td char="." align="char"><p>0.81</p></td><td char="." align="char"><p>0.79</p></td></tr><tr><td align="left"><p>19</p></td><td align="left"><p>10</p></td><td align="left"><p>9.6</p></td><td align="left"><p>9.75</p></td><td align="left"><p>9.25</p></td><td align="left"><p>9.6</p></td><td align="left"><p>6.25</p></td><td align="left"><p>7</p></td><td align="left"><p>4.99E−08</p></td><td char="." align="char"><p>0.13</p></td><td char="." align="char"><p>0.88</p></td></tr><tr><td align="left"><p>20</p></td><td align="left"><p>9.25</p></td><td align="left"><p>9.75</p></td><td align="left"><p>9.75</p></td><td align="left"><p>8.85</p></td><td align="left"><p>8.85</p></td><td align="left"><p>6.75</p></td><td align="left"><p>0.2</p></td><td align="left"><p>6.02E−07</p></td><td char="." align="char"><p>0.95</p></td><td char="." align="char"><p>0.81</p></td></tr><tr><td align="left"><p>21</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.75</p></td><td align="left"><p>9.05</p></td><td align="left"><p>8.85</p></td><td align="left"><p>6.75</p></td><td align="left"><p>5.75</p></td><td align="left"><p>8.73E−08</p></td><td char="." align="char"><p>0.50</p></td><td char="." align="char"><p>0.87</p></td></tr><tr><td align="left"><p>22</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.95</p></td><td align="left"><p>9.4</p></td><td align="left"><p>8.85</p></td><td align="left"><p>7.25</p></td><td align="left"><p>7</p></td><td align="left"><p>4.56E−08</p></td><td char="." align="char"><p>0.29</p></td><td char="." align="char"><p>0.90</p></td></tr><tr><td align="left"><p>Aggregate</p></td><td align="left"><p>9.51</p></td><td align="left"><p>9.18</p></td><td align="left"><p>8.94</p></td><td align="left"><p>8.65</p></td><td align="left"><p>8.13</p></td><td align="left"><p>5.52</p></td><td align="left"><p>2.23</p></td><td align="left"><p>2.52E−04</p></td><td char="." align="char"><p>0.98</p></td><td char="." align="char"><p>0.77</p></td></tr></tbody></table> </ephtml> </p> <p>*Participant excluded from curve-fitting due to nonsystematic data patterns</p> <p>Of the 22 datasets, only one (participant 4) failed any of the Johnson and Bickel ([<reflink idref="bib18" id="ref69">18</reflink>]) criteria—specifically, this participant's dataset failed criterion 2. Examination of the dataset indicated haphazard responding; we excluded this dataset from further analysis, leaving 21 datasets. Note that this failure rate—4% of the sample—is well below those found in the review of the literature conducted by (Smith et al., [<reflink idref="bib37" id="ref70">37</reflink>]).</p> <p>Results of the Bayesian model selection identified the Rachlin ([<reflink idref="bib29" id="ref71">29</reflink>]) model as most probable for 14 datasets; Myerson and Green's ([<reflink idref="bib25" id="ref72">25</reflink>]) model was most probable for the remaining 7 datasets. Accordingly, we used the Rachlin ([<reflink idref="bib29" id="ref73">29</reflink>]) model to describe all 21 datasets for the remainder of this manuscript. Rachlin's model states:</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>E</mi><mfenced close=")" open="("><mi>Y</mi></mfenced><mo>=</mo><mfrac><mi>A</mi><mrow><mn>1</mn><mo>+</mo><mi>k</mi><msup><mrow><mi>D</mi></mrow><mi>s</mi></msup></mrow></mfrac></mrow></math> </ephtml> </p> <p>Graph</p> <p>where <emph>E(Y)</emph> represents the indifference point at delay <emph>D</emph>, <emph>A</emph> represents the maximum undiscounted value of the larger later outcome, <emph>k</emph> represents the delay discounting rate, and <emph>s</emph> represents the psychophysical scaling of <emph>D</emph>. We constrained <emph>s</emph> to a shared value across all datasets when fitting individual functions. However, we fitted <emph>s</emph> separately for the aggregate function across the mean indifference points to describe the group-level data. We used GraphPad Prism for all curve-fitting.</p> <p>As indicated in Table 1, the median <emph>k</emph> value across the 21 individual datasets was 5.29E−06 (IQR 1.48E−07–2.54E−06) with shared <emph>s</emph> as 1.98. For three participant datasets, <emph>R</emph><sups>2</sups> was negative, indicating poor fit and/or relatively shallow discounting—this results in the sum of squared residuals being larger than the sum of squared differences from the mean indifference point value, yielding the negative <emph>R</emph><sups>2</sups>. Of the remaining 18 datasets, median <emph>R</emph><sups>2</sups> was relatively high at 0.90 (IQR 0.47–0.96). Finally, median proportion AUC<subs>ord</subs> was 0.80 (IQR 0.72–0.88).</p> <p>Figure 1 depicts indifferent points as a function of delay to treatment gains (in days)—the open circles depict individual participant indifferent points at each delay, with larger shaded circles depicting the mean indifference point across all 22 participants. We depict only the fitted line to the aggregate data (i.e., the shaded data points). The shaded band around the aggregate function depicts the 95% confidence band of the likely location of the true curve. The aggregate function yielded <emph>k</emph> = 2.50E−04 and <emph>s</emph> = 1.16, with proportion AUC<subs>ord</subs> = 0.77.</p> <p>Graph: Fig. 1 Participant discounting as a function of delay to treatment gains</p> <hd id="AN0184231738-8">Discussion</hd> <p>This study used the delay discounting framework to understand how teachers of students with disabilities discount delays in behavioral treatment effects. The Rachlin ([<reflink idref="bib29" id="ref74">29</reflink>]) model accurately described patterns of discounting for the majority of individual participants, as well as the group. It is unsurprising that special education teachers discount delays to behavioral treatment effects. In fact, preliminary qualitative data (Collier-Meek et al., [<reflink idref="bib7" id="ref75">7</reflink>]) suggests that the length of time required to implement a behavior intervention and the rate of behavior reduction may serve as barriers to treatment adherence. However, previous research has not quantified delays to treatment outcomes as a variable that impacts teacher decision-making. The current study contributes to previous research on barriers to treatment adherence by providing preliminary evidence that delays to behavior reduction do impact teacher decision-making.</p> <p>Our results are similar to those obtained by Call et al. ([<reflink idref="bib6" id="ref76">6</reflink>]) and Gilroy and Kaplan ([<reflink idref="bib14" id="ref77">14</reflink>]), who found that delay in treatment outcomes affect parental decision-making. The alignment in findings supports delay discounting as a framework for evaluating practice-related decisions. Further, our study represents a novel extension of the delay discounting literature because it is the first to be conducted with special education teachers. We hope this successful novel application serves as a catalyst for future translational research on behavioral determinants that affect special educator decision-making (see Mace & Critchfield, [<reflink idref="bib21" id="ref78">21</reflink>], for further discussion).</p> <p>Teachers often report that managing problem behavior in the classroom is of great concern as professional development in behavior management is scarce (Rispoli et al., [<reflink idref="bib34" id="ref79">34</reflink>]). As an additional behavioral support, consultants commonly collaborate with teachers to provide recommendations that target the problem behaviors exhibited by students (Kelly & Tincani, [<reflink idref="bib20" id="ref80">20</reflink>]; O'Neil et al., [<reflink idref="bib26" id="ref81">26</reflink>]). However, teachers commonly do not adhere to consultant recommended interventions (Rispoli et al., [<reflink idref="bib34" id="ref82">34</reflink>]). There are several reasons why special education teachers may not implement a treatment as prescribed, including: (a) not having enough time to learn the treatment, (b) previous classroom responsibilities, (c) progress monitoring needs for all students, and (d) administrative limitations (Kasari et al., [<reflink idref="bib19" id="ref83">19</reflink>]). The results of the current study support the hypothesis that the delay in treatment outcomes (i.e., delay before reduction in problem behavior) may serve as an additional variable that may affect treatment implementation or adherence.</p> <p>Our results also suggest that if a treatment outcome is too delayed, special educators may select a treatment that results in immediate behavior reduction but may contraindicate behavioral function. For example, for Participant 3, the indifference point of 6.75 at a 1-month delay indicates that if treatment were to result in the desired behavior outcomes after 1-month, that treatment only holds 67.5% of its value for that participant (see Table 1). The indifference point of 6.75 at a 1-month delay suggests Participant 3 steeply discounts delayed rewards and may prefer treatments that yield immediate outcomes. The applied implications are such that Participant 3 may be more likely to select a different treatment (or cease treatment) if the recommended treatment does not result in behavior reduction within 1 month.</p> <p>Though we did not directly evaluate whether delay to treatment outcomes impacts treatment adherence (the degree to which a treatment plan is executed as prescribed; Barnett et al., [<reflink idref="bib2" id="ref84">2</reflink>]), the point at which special educators switched from selecting the delayed reward to the immediate reward may serve as a proxy measure of treatment abandonment (poor treatment adherence). Treatment adherence may be a critical variable to evaluate in a choice context because teachers' implementation of behavioral interventions is often measured by treatment adherence (DiGennaro Reed & Codding, [<reflink idref="bib9" id="ref85">9</reflink>]), and treatment adherence positively correlates with more substantial student outcomes. An avenue for future research may be to systematically manipulate various levels of adherence required for a special educator to achieve a desired treatment outcome (e.g., 50%, 75%, 90%, 95%, and 100%). For a consultant-recommended treatment, such as a behavior analyst making a recommendation for a teacher, the role of treatment adherence could be understood through the systematic manipulation of stated probabilities the teacher will adhere to treatment, relative to delay in treatment outcomes.</p> <p>Future research may also evaluate how additional behavioral supports or environmental manipulations ameliorate the effects of delay in treatment outcomes. Consider an example where a behavior analyst has recommended a teacher implement a differential reinforcement of other behavior (DRO) procedure to decrease a student's engagement in problem behavior. The DRO could yield a behavior reduction to 0% in three months. To make the 3-month delay more tolerable to the special educator, the behavior analyst could recommend the teacher implement additional evidence-based interventions (e.g., a visual schedule; see Rivera et al., [<reflink idref="bib36" id="ref86">36</reflink>]). Researchers may systematically manipulate the presence or absence of supplementary antecedent interventions that result in moderate yet more immediate treatment outcomes and evaluate the effects of those manipulations on participant preference for the delayed treatment outcome that results in desired behavior reduction. We hypothesize that incorporating an additional antecedent strategy may yield reductions in problem behavior enough to where the teacher may continue to select a recommended treatment over a long period of time, in lieu of delay. However, this hypothesis should be empirically evaluated and considered alongside other factors, such as the probability of success and relative response effort of treatment implementation.</p> <p>Related, future research may also consider evaluating the extent to which smaller and more immediate treatment results impacts teacher intervention selection (Call et al., [<reflink idref="bib6" id="ref87">6</reflink>]; Dixon et al., [<reflink idref="bib10" id="ref88">10</reflink>]). Emphasizing smaller more immediate rewards (e.g., smaller, and sooner decreases in problem behavior) as opposed to emphasizing the larger delayed reward (e.g., complete behavior reduction) may be an important consideration, because previous research (e.g., Bickel et al., [<reflink idref="bib3" id="ref89">3</reflink>]; Reed et al., [<reflink idref="bib32" id="ref90">32</reflink>]) supports the notion that people prefer smaller immediate rewards as opposed to larger delayed rewards. For example, if a student engages in an average of 20 instances of disruption during a school day (2.5 instances/hr), the long-term goal may be to decrease the number of instances of disruption to two a day (0.1 instances/hr). In this scenario, a researcher may set a few short-term behavior goals such as: reduce the target behavior to (goal 1) 2.0 instances/hr, (goal 2) 1.5 instances/hr, and (goal 3) 1.0 instances/hr. Meeting multiple short-term goals may increase the likelihood the teacher will continue to select the recommended treatment, because they are contacting rewards more quickly (i.e., they see the behavior reduce after very short delays) (Call et al., [<reflink idref="bib6" id="ref91">6</reflink>]).</p> <p>Given that teachers may discount the value of behavioral treatments that incur a delay before experiencing the desired outcomes, behavior analysts may need to employ additional strategies when collaborating with teachers. First, our results suggest transparency about the delay in treatment outcome is important. Behavior analysts may need to address the teacher's expectations or tolerance for the delay required to achieve treatment effects, especially given the fact that treatments may take extensive periods of time (e.g., six or more months) to reach behavioral goals (Call et al., [<reflink idref="bib6" id="ref92">6</reflink>]). Until a more formal survey or questionnaire can be established and validated through empirical research, behavior analysts may ask questions such as: (<reflink idref="bib1" id="ref93">1</reflink>) How long can you tolerate current rates of [problem behavior]? and (<reflink idref="bib2" id="ref94">2</reflink>) Would you be satisfied if the treatment yielded positive results in [delay]? The before mentioned questions may give the behavior analyst initial insight into the length of delay the teacher can tolerate, or how likely the teacher is to adhere to specific treatments.</p> <p>We recruited 22 participants, similar to the size of participants reported in previous delay discounting studies (Call et al., [<reflink idref="bib6" id="ref95">6</reflink>]; Madden et al., [<reflink idref="bib23" id="ref96">23</reflink>]; Meyerson & Green, [<reflink idref="bib25" id="ref97">25</reflink>]). However, the extent to which our findings generalize beyond our participants, special education teachers, is unknown. Upon visually analyzing indifference points (Table 1), participant 4 appears to be the only teacher whose selections were not sensitive to delays (i.e., their indifference points did not systematically decrease as the delay increased). The reasons why this participant did not discount delays are unknown. Results may be an artifact of the task itself or be due to unaccounted variables (e.g., perceived notions about problem behavior). Not all participants discounted delays to treatment outcomes, which points to the need for further research on how teachers make decisions regarding treatments for problem behavior.</p> <p>The time it takes to complete the task (average 39 min/participant) and the repetitive nature of the task may have influenced participant dropout (Call et al., [<reflink idref="bib6" id="ref98">6</reflink>]). Efforts to decrease the time required to evaluate delay discounting (e.g., decreasing the number of trials) may make it possible to study delay discounting with more participants (Call et al., [<reflink idref="bib6" id="ref99">6</reflink>]). One important area for future research is to assess delay discounting with shorter delays and fewer rewards. That is, delays could range from one week to 10 months (e.g., 1 week, 2 weeks, 1 month, 3 months, 10 months), and future researchers may consider reducing the number of immediate rewards (this study used 27 immediate rewards). Given the exploratory nature of this study, we used delays reported in previous research. These delays likely do not generalize to the classroom because teachers typically do not serve students for up to 10 years. Instead, delays could be amended to represent a school year (i.e., 10 months) and therefore yield more generalizable results.</p> <p>The online format of our study may have prevented participants from asking clarifying questions and may have decreased participant motivation to complete the task (e.g., no observation bias). However, the online format may be a more feasible way to access different participant populations to further explore the generality of study findings (Frye et al., [<reflink idref="bib12" id="ref100">12</reflink>]). In addition, we did not specify the target student, behavior, the effort in implementing the treatment, nor the resources required to implement the treatment (Call et al., [<reflink idref="bib6" id="ref101">6</reflink>]; Gilroy et al., [<reflink idref="bib15" id="ref102">15</reflink>]). In Call et al. ([<reflink idref="bib6" id="ref103">6</reflink>]), the researchers instructed caregivers to select hypothetical treatments based on their child's behavior. In our study, participants had their own interpretation of the severity of the behavior, the topography of the behavior, characteristics of their student, and the environment in which the treatment would be implemented. An interesting avenue for future research may be to assess how teachers discount delays in treatment effects for different topographies or severities of behavior. Similarly, future research could assess how teachers discount delays in treatment effects for students with and without disabilities.</p> <p>The delay discounting task only presented two options in each trial that may not represent real-world decisions in the classroom (Gilroy & Kaplan, [<reflink idref="bib14" id="ref104">14</reflink>]). Due to the hypothetical nature of the task and statistical analyses, only two options can be presented at once (i.e., a smaller immediate reward and a larger delayed reward). However, results from previous delay discounting studies using hypothetical outcomes do yield similar results as studies using real-world outcomes (e.g., food) (Bickel et al., [<reflink idref="bib3" id="ref105">3</reflink>]), supporting the notion that how people make decisions with hypothetical rewards mirrors how they make decisions with real-world rewards.</p> <p>Finally, our current analyses represent an analytic extension from the Call et al. ([<reflink idref="bib6" id="ref106">6</reflink>]) approach given our use of Bayesian model selection to inform the discounting model for analyses, as well as the inclusion of AUC<subs>ord</subs>. However, these analyses are not as sophisticated as the multi-level approach used by Gilroy and Kaplan ([<reflink idref="bib14" id="ref107">14</reflink>]). We note also that our study did not compare monetary versus treatment discounting, as was done in both the Call et al. and Gilroy and Kaplan studies. These previous studies found consistent discounting between monetary and treatment outcomes, so we did not seek to compare these outcomes in the present study out of respect for special educator teachers' time and the fact that our current sample size would be underpowered to adequately conduct a formal comparison. Future research would benefit from a larger sampling of special education teachers, using multi-level approaches, and comparing across different outcomes to correct these issues.</p> <p>The current study demonstrates that special education teachers' decision-making of behavioral treatments can be understood using behavioral economics. The delay discounting framework allowed us to assess one potential variable affecting treatment implementation–delay to treatment outcomes. Assessing variables that impact teacher decision-making may lead to advancements in behavioral consulting and treatment development. With the ability to assess teacher sensitivity to and preference for delays, behavior analysts can take steps to ameliorate the effects of delays on treatment outcomes, thereby improving student behavior success (Call et al., [<reflink idref="bib6" id="ref108">6</reflink>]). Of course, future research on teachers' decision-making of behavioral treatments is warranted, and the current study hopefully serves as a framework for future analysis of teacher decision-making.</p> <hd id="AN0184231738-9">Funding</hd> <p>Financial support for the first author was provided by the Bosco Family Fellowship in Autism and Michigan State University's College of Education.</p> <hd id="AN0184231738-10">Data availability</hd> <p>Raw data with accompanying equations and calculations are available at osf.io/3z4j6.</p> <hd id="AN0184231738-11">Declarations</hd> <p></p> <hd id="AN0184231738-12">Conflicts of interest</hd> <p>All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.</p> <hd id="AN0184231738-13">Ethics approval</hd> <p>All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.</p> <hd id="AN0184231738-14">Informed consent</hd> <p>Informed consent was obtained from all individual participants included in the study.</p> <hd id="AN0184231738-15">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0184231738-16"> <title> References </title> <blist> <bibl id="bib1" idref="ref27" type="bt">1</bibl> <bibtext> Allen KD, Warzak WJ. The problem of parental nonadherence in clinical behavior analysis: Effective treatment is not enough. Journal of Applied Behavior Analysis. 2000. 10.1901/jaba.2000.33-373. 11051583. 1284264</bibtext> </blist> <blist> <bibl id="bib2" idref="ref84" type="bt">2</bibl> <bibtext> Barnett D, Hawkins R, McCoy D, Wahl E, Shier A, Denune H, Kimener L. Methods used to document procedural fidelity in school-based intervention research. Journal of Behavioral Education. 2014; 23: 89-107. 10.1007/s10864-013-9188-y</bibtext> </blist> <blist> <bibl id="bib3" idref="ref36" type="bt">3</bibl> <bibtext> Bickel WK, Odum AL, Madden GJ. Impulsivity and cigarette smoking: Delay discounting in current, never, and ex-smokers. Psychopharmacology (berl). 1999; 146: 447-454. 10.1007/PL00005490. 10550495</bibtext> </blist> <blist> <bibl id="bib4" idref="ref66" type="bt">4</bibl> <bibtext> Borges AM, Kuang J, Milhorn H, Yi R. An alternative approach to calculating Area-Under-the-Curve (AUC) in delay discounting research. Journal of the Experimental Analysis of Behavior. 2016; 106; 2: 145-155. 10.1002/jeab.219. 27566660</bibtext> </blist> <blist> <bibl id="bib5" idref="ref67" type="bt">5</bibl> <bibtext> Brodhead, M. T. (2022). Special educator discounting study. Retrieved from osf.io/3z4j6</bibtext> </blist> <blist> <bibl id="bib6" idref="ref4" type="bt">6</bibl> <bibtext> Call NA, Reavis AR, McCracken CE, Gillespie SE, Scheithauer MC. The impact of delays on parents' perceptions of treatments for problem behavior. Journal of Autism and Developmental Disorders. 2015; 45: 1013-1025. 10.1007/s10803-014-2257-9. 25267069</bibtext> </blist> <blist> <bibl id="bib7" idref="ref55" type="bt">7</bibl> <bibtext> Collier-Meek MA, Sanetti LMH, Boyle AM. Barriers to implementing classroom management and support plans: An exploratory investigation. Psychology in the Schools. 2018; 56: 5-17. 10.1002/pits.22127</bibtext> </blist> <blist> <bibl id="bib8" idref="ref5" type="bt">8</bibl> <bibtext> Critchfield TS, Kollins SH. Temporal discounting: Basic research and the analysis of socially important behavior. Journal of Applied Behavior Analysis. 2001; 34: 101-122. 10.1901/jaba.2001.34-101. 11317983. 1284292</bibtext> </blist> <blist> <bibl id="bib9" idref="ref1" type="bt">9</bibl> <bibtext> DiGennaro Reed FD, Codding RS. Advancements in procedural fidelity assessment and intervention: Introduction to the special issue. Journal of Behavior Education. 2014; 23: 1-18. 10.1007/s10864-013-9191-3</bibtext> </blist> <blist> <bibtext> Dixon MR, Marley J, Jacobs EA. Delay discounting by pathological gamblers. Journal of Applied Behavior Analysis. 2003; 36: 449-458. 10.1901/jaba.2003.36-449. 14768665. 1284461</bibtext> </blist> <blist> <bibtext> Franck CT, Koffarnus MN, House LL, Bickel WK. Accurate characterization of delay discounting: A multiple model approach using approximate Bayesian model selection and a unified discounting measure. Journal of the Experimental Analysis of Behavior. 2015; 103; 1: 218-233. 10.1002/jeab.128. 25556903</bibtext> </blist> <blist> <bibtext> Frye CC, Galizio A, Friedel JE, DeHart WB, Odum AL. Measuring delay discounting in humans using an adjusting amount task. Journal of Visualized Experiments. 2016; 103: 1-8. 10.3791/53584</bibtext> </blist> <blist> <bibtext> Gilroy SP, Franck CT, Hantula DA. The discounting model selector: Statistical software for delay discounting applications. Journal of the Experimental Analysis of Behavior. 2017; 107; 3: 388-401. 10.1002/jeab.257. 28467023</bibtext> </blist> <blist> <bibtext> Gilroy SP, Kaplan BA. Modeling treatment-related decision-making using applied behavioral economics: Caregiver perspectives in temporally-extended behavioral treatments. Journal of Abnormal Child Psychology. 2020; 48: 607-618. 10.1007/s10802-020-00619-6. 31982979</bibtext> </blist> <blist> <bibtext> Gilroy SP, Kaplan BA, Leader G. A systematic review of applied behavioral economics in assessments and treatments for individuals with developmental disabilities. Journal of Autism and Developmental Disorders. 2018; 5: 247-259. 10.1007/s40489-018-0136-</bibtext> </blist> <blist> <bibtext> Hartley SL, Sikora DM, McCoy R. Prevalence and risk factors of maladaptive behaviour in young children with autistic disorder. Journal of Intellectual Disability Research. 2008; 52: 819-829. 10.1111/j.1365-2788.2008.01065.x. 18444989</bibtext> </blist> <blist> <bibtext> Horner RH, Carr EG, Strain PS, Todd AW, Reed HK. Problem behavior interventions for young children with autism: A research synthesis. Journal of Autism and Developmental Disorders. 2002; 32: 423-446. 10.1023/A:1020593922901. 12463518</bibtext> </blist> <blist> <bibtext> Johnson MW, Bickel WK. An algorithm for identifying nonsystematic delay-discounting data. Experimental and Clinical Psychopharmacology. 2008; 16; 3: 264-274. 10.1037/1064-1297.16.3.264. 18540786. 2765051</bibtext> </blist> <blist> <bibtext> Kasari C, Smith T. Interventions in schools for children with autism spectrum disorder: Methods and recommendations. Autism. 2013; 17: 254-267. 10.1177/1362361312470496. 23592848</bibtext> </blist> <blist> <bibtext> Kelly A, Tincani M. Collaborative training and practicing among applied behavior analysts who support individuals with autism spectrum disorder. Education and Training in Autism and Developmental Disabilities. 2013; 48: 120-131</bibtext> </blist> <blist> <bibtext> Mace FC, Critchfield TS. Translational research in behavior analysis: Historical traditions and imperative for the future. Journal of the Experimental Analysis of Behavior. 2010; 93: 293-312. 10.1901/jeab.2010.93-293. 21119847. 2861871</bibtext> </blist> <blist> <bibtext> Machalicek W, O'Reily MF, Beretvas N, Sigafoos J, Lancioni GE. A review of interventions to reduce challenging behavior in school settings for students with autism spectrum disorder. Research in Autism Spectrum Disorders. 2007; 1: 229-245. 10.1016/j.rasd.2006.10.005</bibtext> </blist> <blist> <bibtext> Madden GJ, Begotka AM, Raiff BR, Kastern LL. Delay discounting of real and hypothetical rewards. Experimental and Clinical Psychopharmacology. 2010; 11; 2: 139-145. 10.1037/1064-1297.11.2.139</bibtext> </blist> <blist> <bibtext> Mazur JECommons ML, Mazur JE, Nevin JA, Rachlin H. An adjusting procedure for studying delayed reinforcement. Quantitative analyses of behavior. 1987; Erlbaum: 55-73; 5</bibtext> </blist> <blist> <bibtext> Meyerson J, Green L. Discounting of delayed rewards: Models of individual choice. Journal of the Experimental Analysis of Behavior. 1995; 64: 263-276. 10.1901/jeab.1995.64-263</bibtext> </blist> <blist> <bibtext> O'Neil R, Williams R, Sprague JR, Horner RH, Albin RW. Providing support for teachers working with students with severe problem behaviors: a model for providing consulting support within school districts. Education and Treatment of Children. 1993; 16: 66-89</bibtext> </blist> <blist> <bibtext> Odum AL, Madden GJ, Bickel WK. Discounting of delayed health gains and losses by current, never- and ex-smokers of cigarettes. Nicotine & Tobacco Research. 2002; 4: 295-303. 10.1080/14622200210141257</bibtext> </blist> <blist> <bibtext> Pierce WD, Cheney CD. Behavior analysis and learning. 20135; Psychology Press</bibtext> </blist> <blist> <bibtext> Rachlin H. Notes on discounting. Journal of the Experimental Analysis of Behavior. 2006; 85; 3: 425-435. 10.1901/jeab.2006.85-05. 16776060. 1459845</bibtext> </blist> <blist> <bibtext> Rachlin H, Raineri A, Cross D. Subjective probability and delay. Journal of the Experimental Analysis of Behavior. 1991; 55: 233-244. 10.1901/jeab.1991.55233. 2037827. 1323057</bibtext> </blist> <blist> <bibtext> Reed DD, Kaplan BA, Brewer AT. A tutorial on the use of Excel 2010 and Excel for Mac 2011 for conducting delay discounting analysis. Journal of Applied Behavior Analysis. 2012; 45: 375-386. 10.1901/jaba.2012.45-375. 22844143. 3405931</bibtext> </blist> <blist> <bibtext> Reed DD, Martens BK. Temporal discounting predicts student responsiveness to exchange delays in a classroom token system. Journal of Applied Behavior Analysis. 2011; 44: 1-18. 10.1901/jaba.2011.44-1. 21541113. 3050479</bibtext> </blist> <blist> <bibtext> Rispoli M, Lang R, Neely L, Camargo S, Hutchins N, Davenport K, Goodwyn F. A comparison of within- and across-activity choices for reducing challenging behavior in children with autism spectrum disorder. Journal of Behavior Education. 2012; 22: 66-83. 10.1007/s10864-012-9164-y</bibtext> </blist> <blist> <bibtext> Rispoli M, Zaini S, Mason R, Brodhead MT, Burke MD, Gregori E. A systematic review of teacher self-monitoring on implementation of behavioral practices. Teaching and Teacher Education. 2017; 63: 58-72. 10.1016/j.tate.2016.12.007</bibtext> </blist> <blist> <bibtext> Rivera G, Gerow S, Kirkpatrick M. A review of school-based interventions to reduce challenging behavior for adolescents with ASD. Journal of Developmental and Physical Disabilities. 2019; 31: 1-21. 10.1007/s10882-018-9626-9</bibtext> </blist> <blist> <bibtext> Smith KR, Lawyer SR, Swift JK. A meta-analysis of nonsystematic responding in delay and probability reward discounting. Experimental and Clinical Psychopharmacology. 2018; 26; 1: 94-107. 10.1037/pha0000167. 29389172</bibtext> </blist> <blist> <bibtext> Stahmer AC, Rieth S, Lee E, Reisinger EM, Mandell DS, Connell JE. Training teachers to use evidence-based practices for autism: Examining procedural implementation fidelity. Psychology in the Schools. 2015; 52: 181-195. 10.1002/pits.21815. 25593374</bibtext> </blist> <blist> <bibtext> Suess AN, Romani PW, Wacker DP, Dyson SM, Kuhle JL, Lee JF, Lindgren SD, Kopelman TG, Pelzel KE, Waldron DB. Evaluating the treatment fidelity of parents who conduct in-home functional communication training with coaching via telehealth. Journal of Behavioral Education. 2014; 23: 34-59. 10.1007/s10864-013-9183-3</bibtext> </blist> <blist> <bibtext> The Individuals with Disabilities Education Improvement Act of 2004. (2004). Pub. L. No. 108–446, 101,118 Stat. 2647.</bibtext> </blist> <blist> <bibtext> Weatherly JN, Terrell HK, Derenne A. Delay discounting of different commodities. The Journal of General Psychology. 2010; 137; 3: 273-286. 10.1080/00221309.2010.484449. 20718227</bibtext> </blist> <blist> <bibtext> West EA, McCollow M, Kidwell J, Umbarger G, Cote DL. Current status of evidence-based practice for students with intellectual disability and autism spectrum disorders. Education and Training in Autism and Developmental Disabilities. 2013; 48: 443-455</bibtext> </blist> <blist> <bibtext> Westling DL. Teachers and challenging behavior. Knowledge, views, and practices. Remedial and Special Education. 2010; 31: 48-63. 10.1177/0741932508327466</bibtext> </blist> </ref> <aug> <p>By Allison N. White-Cascarilla; Matthew T. Brodhead; Derek D. Reed and Ashley N. Walker</p> <p>Reported by Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib19" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib17" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib14" firstref="ref6"></nolink> <nolink nlid="nl4" bibid="bib36" firstref="ref7"></nolink> <nolink nlid="nl5" bibid="bib16" firstref="ref8"></nolink> <nolink nlid="nl6" bibid="bib33" firstref="ref11"></nolink> <nolink nlid="nl7" bibid="bib38" firstref="ref16"></nolink> <nolink nlid="nl8" bibid="bib22" firstref="ref18"></nolink> <nolink nlid="nl9" bibid="bib40" firstref="ref19"></nolink> <nolink nlid="nl10" bibid="bib42" firstref="ref20"></nolink> <nolink nlid="nl11" bibid="bib20" firstref="ref21"></nolink> <nolink nlid="nl12" bibid="bib28" firstref="ref23"></nolink> <nolink nlid="nl13" bibid="bib23" firstref="ref33"></nolink> <nolink nlid="nl14" bibid="bib27" firstref="ref34"></nolink> <nolink nlid="nl15" bibid="bib41" firstref="ref35"></nolink> <nolink nlid="nl16" bibid="bib30" firstref="ref41"></nolink> <nolink nlid="nl17" bibid="bib25" firstref="ref43"></nolink> <nolink nlid="nl18" bibid="bib34" firstref="ref53"></nolink> <nolink nlid="nl19" bibid="bib39" firstref="ref57"></nolink> <nolink nlid="nl20" bibid="bib13" firstref="ref61"></nolink> <nolink nlid="nl21" bibid="bib18" firstref="ref62"></nolink> <nolink nlid="nl22" bibid="bib11" firstref="ref63"></nolink> <nolink nlid="nl23" bibid="bib29" firstref="ref65"></nolink> <nolink nlid="nl24" bibid="bib37" firstref="ref70"></nolink> <nolink nlid="nl25" bibid="bib21" firstref="ref78"></nolink> <nolink nlid="nl26" bibid="bib26" firstref="ref81"></nolink> <nolink nlid="nl27" bibid="bib10" firstref="ref88"></nolink> <nolink nlid="nl28" bibid="bib32" firstref="ref90"></nolink> <nolink nlid="nl29" bibid="bib12" firstref="ref100"></nolink> <nolink nlid="nl30" bibid="bib15" firstref="ref102"></nolink>
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  Data: Further Application of Delay Discounting on Special Educator Decision-Making
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  Data: <searchLink fieldCode="AR" term="%22Allison+N%2E+White-Cascarilla%22">Allison N. White-Cascarilla</searchLink><br /><searchLink fieldCode="AR" term="%22Matthew+T%2E+Brodhead%22">Matthew T. Brodhead</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-4567-0051">0000-0003-4567-0051</externalLink>)<br /><searchLink fieldCode="AR" term="%22Derek+D%2E+Re%22">Derek D. Re</searchLink><br /><searchLink fieldCode="AR" term="%22Ashley+N%2E+Walker%22">Ashley N. Walker</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Behavioral+Education%22"><i>Journal of Behavioral Education</i></searchLink>. 2025 34(1):94-108.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: <searchLink fieldCode="DE" term="%22Special+Education+Teachers%22">Special Education Teachers</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Making%22">Decision Making</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Outcomes+of+Treatment%22">Outcomes of Treatment</searchLink>
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  Data: 1053-0819<br />1573-3513
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  Data: Special education teachers often make decisions about interventions to help reduce student problem behavior. There are many variables that impact how teachers make decisions regarding behavioral interventions, and the current study aimed to quantitatively evaluate how delay to treatment outcomes affect teacher decision-making. This study used the delay discounting framework to examine the effects of delays to treatment outcomes on special education teacher decision-making. Participants completed an online hypothetical delay discounting task based on the behavioral economic theory that the value of an outcome (e.g., treatment outcome) diminishes as the delay to that outcome increases over time. Our results indicate that most special education teachers discount delays in treatment effects, suggesting that special education teachers may prefer interventions that result in more immediate behavior change. Implications for future research and consultative practice are discussed.
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