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. 2019 Sep 6;12(4):782–794. doi: 10.1007/s40617-019-00389-8

A Descriptive Analysis of Applied Behavior Analysis Research With Economically Disadvantaged Children

Brandi Fontenot 1, Margaret Uwayo 1, Sarah M Avendano 2, Denise Ross 1,✉
PMCID: PMC6834796  PMID: 31976290

Abstract

In the United States, approximately 43% of children under age 18 are considered economically disadvantaged. Research suggests that these children are at a greater risk for academic underperformance and dropping out of school than their peers who are not from economically disadvantaged backgrounds. As such, they may need effective educational interventions to improve their academic performance. The purpose of the current article is to describe the degree to which economically disadvantaged children are included in educational research in behavioral journals. Ninety-four studies were analyzed to determine the publication trends between 1968 and 2017. Studies were scored and categorized based on journal; publication year; several demographic characteristics for participants including age, income status, and disability diagnosis; and research designs, interventions, and target behaviors. Results suggest that economically disadvantaged children are increasingly included in behavior-analytic literature. However, there are opportunities for research with English language learners and children with disabilities. Implications for practice and research are discussed.

Keywords: Economically disadvantaged, Low socioeconomic status, Children, Education, Schools

In the United States, economically disadvantaged students are students whose family incomes are near the federal poverty level and, as such, qualify for federal assistance programs such as free or reduced-price lunch in school (Department of Health and Human Services, 2019). Based on this definition, approximately 50% of children in public schools attend schools where the majority of students are economically disadvantaged (U.S. Department of Education, 2018). These students are largely younger students, as children under age 12 are more likely to be from economically disadvantaged families than older children are (Jiang, Granja, & Koball, 2017). Additionally, an average of 63% of Black, Hispanic, and Native American children are economically disadvantaged when compared to an average of 30% of Asian and White children (Jiang et al., 2017). Relatedly, Black, Hispanic, and Native American students are three times more likely than White or Asian students to attend mid-poverty or high-poverty schools, which are schools where 50% or more students are economically disadvantaged (U.S. Department of Education, 2018).

Economically disadvantaged children are also more likely to underperform academically than children who are not economically disadvantaged (Wagmiller & Adelman, 2009). Between 1998 and 2017, an average of 17% of economically disadvantaged fourth-grade students read proficiently on the National Assessment of Educational Progress (NAEP) compared to 45% of fourth-grade students who were not economically disadvantaged (U.S. Department of Education, 2019). Similarly, between 1996 and 2017, an average of 29% of economically disadvantaged fourth-grade students had proficient math performance on the NAEP compared to 57% of fourth graders who were not economically disadvantaged (U.S. Department of Education, 2019). The academic progress of economically disadvantaged children is important because research suggests that only 22% of children from economically disadvantaged families graduate from high school, and this number decreases if they cannot read proficiently by third grade (Hernandez, 2012). Because dropping out of high school results in a greater risk of unemployment in adulthood (Levin & Rouse, 2012), the failure to graduate can sustain a cycle of economic disadvantage.

Given the relationship between academic performance in school and subsequent postschool outcomes, it is important to determine the degree to which applied behavior analysis has included economically disadvantaged children or organizations (e.g., schools or centers in economically disadvantaged communities) in educational research. Educational research is research that is implemented with children from early childhood through secondary school (e.g., birth to age 18) in public or private educational settings and that seeks to improve academic or social behavior. To date, analyses of school-based educational research have been conducted for studies published in the Journal of Applied Behavior Analysis (Sulzer-Azaroff & Gillatt, 1990), for children with autism (e.g., Machalicek, O’Reilly, Beretvas, Sigafoos, & Lancioni, 2007), for children with emotional-behavioral impairments (e.g., Hodge, Riccomini, Buford, & Herbst, 2006), and for young children with challenging behaviors (e.g., Conroy, Dunlap, Clarke, & Alter, 2005). However, to the authors’ knowledge, no descriptive analysis of the inclusion of economically disadvantaged children in educational research has been conducted for applied behavior analysis journals.

Because the degree to which economically disadvantaged children have been included in behavior analysis is unknown, the purpose of the current article is to describe their inclusion by (a) selecting empirical studies published in behavioral journals between 1968 and 2017 that included educational interventions, (b) describing the inclusion of economically disadvantaged children as participants, and (c) discussing potential research and practice opportunities for behavior analysts who are interested in working with economically disadvantaged children. The article begins with a descriptive assessment of applied behavior analysis research studies that were conducted with economically disadvantaged children, followed by a discussion of implications for researchers and practitioners.

Method

Three electronic databases were used to identify studies for inclusion in this analysis: PsycINFO, ERIC, and Academic OneFile. The following keywords and Boolean terms were also entered into each of three electronic databases: “low-income” OR “low-socioeconomic status” OR “poverty” OR “free and reduced lunch” OR “Title I” OR “at-risk” (not economically at risk) OR “disadvantaged” OR “deprived” OR “impoverished” OR “welfare” OR “negroes” (used to identify older papers) AND “single-subject design” AND “children” OR “adolescents” OR “teenagers” OR “students” OR “schools” OR “youth.” Additionally, with the exception of the term single-subject design, the previous keywords and Boolean terms were used to search each of the following journals individually in a university library database: the Journal of Applied Behavior Analysis (JABA), Behavior Analysis in Practice, The Analysis of Verbal Behavior (TAVB), The Behavior Analyst (TBA), the Journal of the Experimental Analysis of Behavior (JEAB), The Psychological Record, the Journal of Behavioral Education (JBE), Perspectives on Behavior Science, Behavior and Social Issues (BSS), and Behavior Interventions (BI). These journals were selected for individual analysis because they were identified as behavioral journals by the Association for Behavior Analysis International. After studies were returned, each author reviewed the abstracts or bodies of the studies to determine if the inclusion criteria were met.

Inclusion and Exclusion Criteria

Studies were included in this analysis if they (a) were published between 1968 and 2017, (b) measured or manipulated a variable related to academic or social performance, (c) included a participant who was age 18 or younger or included a participant who was in an extended special education school program (e.g., a student with a developmental disability who was in school until age 21), (d) were conducted in a school or educational center such as an after-school community setting, and (e) included a keyword that indicated that a participant or setting was economically disadvantaged (e.g., a study described its participants or a school setting as being low income or eligible for free and reduced-price lunch). Studies were excluded if they (a) included adults without children as the primary participants (e.g., teaching assistants or paraprofessionals), (b) did not specify that one or more participants or settings were economically disadvantaged, or (c) were conceptual or theoretical.

Measures and Scoring

Selected studies were analyzed and then categorized on a spreadsheet using the following categories: journal name, age of participants, number of participants, locale of the study (a U.S. Census term that specifies if a geographic area is urban, rural, or suburban), English language learner (ELL) status, disability diagnosis, terms used to indicate income status (e.g., low income, low socioeconomic status, disadvantaged), participant type (child, child and parent, child and teacher), research design, intervention, and target behavior. One person analyzed each article and recorded information for each of the measures on a drop-down box in a spreadsheet. When an author was unsure of a research design or income classification, a second author analyzed the article. Only journals that published studies with economically disadvantaged children were included in the results. Journals that did not report research with economically disadvantaged children (e.g., TBA) were not included in the results although they were analyzed (e.g., they were excluded from the mean).

Interobserver Agreement

The authors independently scored 15 of 94 (16%) total studies, according to the following categories: age of participants, number of participants, setting and locale of the study, ELL status, disability diagnosis, terms used to indicate income status, participant type, research design, target behavior, and intervention. Studies were randomly selected and assigned to each author. Interobserver agreement across all coding variables averaged 94% (range 80%–100%).

Results

The initial search returned a total of 1,930 studies. After excluding duplicates and those studies that did not meet the inclusion criteria, 94 articles met the inclusion criteria and were included in this analysis. Results are described as a percentage of the total articles reviewed but may not equal 100% because of rounding.

Publication Years

Figure 1 illustrates the number of articles published in behavioral journals from 1968 to 2017. In general, economically disadvantaged children have been increasingly included in behavior analysis research since 1968 but to varying degrees over time. For instance, 28% of articles that included economically disadvantaged children were published between 1968 and 1977. However, from 1978 to 2001, only 20% of all articles that included economically disadvantaged children were published. In fact, between 1978 and 2001, there were 14 nonconsecutive years during which economically disadvantaged children were not identified in any research articles. After 2001, economically disadvantaged children were included more often as research participants: 50% of all articles that included economically disadvantaged children were published between 2002 and 2017.

Fig. 1.

Fig. 1

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Number of educational studies with economically disadvantaged children by year

Journals

JABA has published more than half (52%) of all studies that describe economically disadvantaged children. JBE has published roughly more than one quarter (28%) of behavior analysis research that included economically disadvantaged children. Interestingly, JABA and JBE each published most of their journal articles during 10-year spans: JABA from 1968 to 1977 and JBE from 2006 to 2017. JEAB and BI published 6% of all studies. TAVB and the Journal of Early and Intensive Behavior Interventions published 2% of all studies. Twelve percent of studies were published in special education or psychology journals identified in ERIC or other databases.

Participant Types and Ages

Table 1 lists participant types and ages. Generally, economically disadvantaged children without parents or teachers were the most common participants in research articles. Specifically, elementary school children, ages 6 to 10 years, were the most frequent research participants. However, there have been periods when children from other groups were included more often than elementary school children. For example, children from birth to age 5 were the most highly researched population from 1968 to 1972 but were included less often after 1972 when compared to elementary school children. In contrast, 11- to 18-year-old children have not been included as often as younger children, representing less than 20% of all publications between 1968 and 2017. No students over age 18 were included in any research articles (e.g., older special education students).

Table 1.

Participant types, age, disability status, and English Language Learners (ELLs) statusa

Number of Studies (Percentage)
Participant Types Child 81 (86%)
Child and parent 1 (1%)
Child and teacher 12 (13%)
Child and other 0
Total 94
Age Range Birth–5 34 (36%)
6–10 45 (48%)
11–18 15 (16%)
18+ 0
Total 94
No disability 78 (83%)
Disability Status Cognitive 6 (6%)
Learning 5 (5%)
Developmental 3 (3%)
Behavior 1 (1%)
Other 1 (1%)
Total 94
English Language Learners Yes 9 (10%)
No 68 (72%)
Not specific 17 (18%)
Total 94
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aPercentages may not total to 100 because of rounding.

Disability Status and ELLs

Table 1 lists disability diagnosis and ELL status for participants. Approximately 80% of the studies did not include economically disadvantaged children with disabilities as participants. Cognitive, learning, and developmental disabilities comprised the small percentage of studies that reported disability status. One study did not specify disability status for its participants but reported that 32% of participants were generally students with disabilities (Teerlink, Caldarella, Anderson, Richardson, & Guzman, 2017). Similarly, only a small percentage of studies conducted research with economically disadvantaged ELLs. When studies included ELLs in their population, they typically did not specify if the participants were economically disadvantaged. That is, when ELLs were included in studies, researchers did not describe their income status.

Income Status, Setting, and Locale

Table 2 lists income status, setting, and locale. Between 1968 and 2001, participant income status was primarily described as low socioeconomic status or low income instead of disadvantaged or impoverished. However, after 2001, the term free and reduced-price lunch was used more often in research to describe income status. The settings that were used most frequently were K–12 schools followed by preschool settings. Fewer than 3% of all studies were conducted in clinics, day cares, or community settings. The most frequent locales for research were urban locales followed by rural locales. However, in most cases, the locale for a study was not specified.

Table 2.

Income status, setting, and localea

Number of Studies (Percentage)
Income Status Low socioeconomic status 26 (28%)
Low income 23 (24%)
Free or reduced lunch 17 (18%)
Disadvantaged 7 (7%)
Poverty 7 (7%)
Deprived 6 (6%)
Lower middle/lower class 3 (3%)
Impoverished 1 (1%)
At risk 1 (1%)
Other (e.g., Head Start) 3 (2%)
Setting

Total

Center

Preschool

K–12 School

Community center

Day care

Clinic

94

6 (6%)

24 (26%)

61 (65%)

1 (1%)

1 (1%)

1 (1%)
Total 94
Locale Urban 32 (34%)
Rural 14 (15%)
Suburban 7 (7%)
Not specified 41 (44%)
Total 94
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aPercentages may not total to 100 because of rounding.

Research Designs and Sample Sizes

Table 3 lists research designs and sample sizes. The majority of studies used reversal and withdrawal designs or multiple-baseline across-participants designs. Reversal and withdrawal designs were used most often before 1978 and less often in subsequent years. Alternating-treatments designs, multiple-probe designs, and group designs were implemented far less frequently across all publications. In terms of sample sizes, most studies had sample sizes of more than 10 children, which may have been because some researchers were providing classwide interventions. The majority of large sample sizes were studied between 2000 and 2014, whereas smaller sample sizes (e.g., one to nine participants) were studied consistently between 1968 and 2017. Interestingly, few studies specified which percentage of their sample was economically disadvantaged. For example, a study that had 10 participants may have specified that the study took place in an economically disadvantaged school but may not have specified how many of its participants were economically disadvantaged.

Table 3.

Research designs, sample sizes, target behaviors, and interventionsa

Number of Studies (Percentage)
Research Designs Reversal designs 28 (30%)
Multiple-baseline designs 20 (21%)
Withdrawal designs 11 (12%)
Alternating treatment Designs 10 (11%)
Group designs 8 (9%)
Multielement designs 5 (5%)
Other 12 (13%)
Total 94
Sample Sizes

1–5

6–10

10+

Total

33 (35%)

18 (19%)

43 (46%)

94
Target Behaviors

Reading

Disruptive behavior

Individual social skills

Language

Math

Academic other (e.g., science)

25 (27%)

20 (21%)

13 (14%)

12 (13%)

9 (10%)

9 (19%)
Nonacademic (e.g., fire drill) 4 (2%)
Classroom management 2 (2%)
Total 94
Interventions Token economy 9 (10%)
Self-management 7 (7%)
Group contingency 6 (6%)
Performance feedback 6 (6%)
Behavior skills training 5 (5%)
Computer-assisted instruction 5 (5%)
Differential reinforcement 5 (5%)
Stimulus discrimination 5 (5%)
Classwide peer tutoring 4 (4%)
Peers 4 (4%)
Teacher attention 4 (4%)
Verbal behavior 4 (4%)
Antecedent manipulation 4 (4%)
Choice 3 (3%)
Fluency 3 (3%)
Function-based replacement behaviors 3 (3%)
Teaching 3 (3%)
Social attention and reinforcement 2 (2%)
Social skills 2 (2%)
Direct instruction 1 (1%)
Premack principle 1 (1%)
Prompting 1 (1%)
Response cards 1 (1%)
Response cost 1 (1%)
No intervention 2 (2%)
Other 3 (3%)
Total 94
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aPercentages may not total to 100 because of rounding.

Target Behaviors, Interventions, and Outcomes

Table 3 lists the target behaviors and interventions. The majority of the studies examined academic reading performance more than disruptive behavior, individual social skills, language, or math. Token economy procedures were the most frequently implemented interventions, followed by self-management, group contingencies, performance feedback, and differential reinforcement interventions. Twenty-two percent of studies targeted more than one behavior, and 16% of studies implemented more than one type of intervention.

Generally, most articles reported successful outcomes for research studies that included economically disadvantaged children. For the current paper, all studies were divided into four categories to analyze their outcomes: (a) academic subjects (writing, spelling, reading, and math), (b) social behaviors (classroom management, disruptive behaviors, and social skills), (c) nonacademic subjects (physical education and safety), and (d) speech and language.

Writing and spelling

Studies that targeted writing and spelling largely reported positive treatment effects with most participants. Results of these studies demonstrated that writing accuracy and completion increased with contingent reinforcement and feedback for increased numbers of words written (Hansen & Wills, 2014; Miller & Schneider, 1970; Van Houten, Hill, & Parsons, 1975; Van Houten, Morrison, Jarvis, & McDonald, 1974). Additionally, interventions such as classwide peer tutoring, competitive team peer tutoring, self-correction, and a combination of constructed responses with traditional teacher instruction increased spelling accuracy (Greenwood, Terry, Arreaga-Mayer, & Finney, 1992; Lee-Vieira, Mayer, & Cameron, 2006; Madrid, Canas, & Ortega-Medina, 2007; McNeish, Heron, & Okyere, 1992).

Math

Studies that targeted math responses also reported positive treatment effects for most participants. Effective interventions included goal setting, self-monitoring, and self-reinforcement for completion of math problems (Stevenson & Fantuzzo, 1986); student-teacher conferences combined with goal setting and feedback about math problem completion (Rizzo & Belfiore, 2014); peer tutoring and performance feedback (Gilbertson, Witt, Singletary, & VanDerHeyden, 2007); assessment strategies to identify effective instructional strategies for individual students (Kong & Orsco, 2016; Rich & Duhon, 2014); and specific intervention strategies such as cover-copy-compare and math to mastery (Konarski, Johnson, Crowell, & Whitman, 1980; Mong & Mong, 2010).

Reading

Reading decoding, fluency, and comprehension interventions were effective for participants. Studies of early reading performance with kindergarten ELLs demonstrated that teaching letter names at a slower pace and providing see/say practice opportunities increased letter naming accuracy, fluency, and retention (Gilbertson & Bluck, 2006; Gilbertson, Maxfield, & Hughes, 2007). Training preschoolers to discriminate letters based on critical features instead of noncritical features (e.g., length of line or size of figure) also resulted in greater discrimination of similar letters (Kincaid & Weisberg, 1978; Tawney, 1972). Formal reading programs such as sound partners and corrective reading (Harris, Marchand-Martella, & Martella, 2000; Marchand-Martella et al., 2002), as well as explicit phonics and sight word instruction (Reed, 2013), increased letter sound responses for children in early elementary school and ELLs in middle school.

Effective fluency interventions for middle school and elementary school students included multiple-exemplar instruction, repeated reading, and listening passage preview (Silber & Martens, 2010); reading racetrack fluency drills (Rinaldi, Sells, & McLaughlin, 1997); longer durations of fluency practice (Ross & Begeny, 2010); and performance feedback on incorrectly and correctly read words (Eckert, Dunn, & Ardoin, 2006). Pairing reinforcers with reading increased comprehension for two of four middle school participants (Lahey, McNees, & Brown, 1973). Technology interventions increased fluency and comprehension (Council, Cartledge, Green, Barber, & Gardner, 2016; Gibson, Carledge, & Keys, 2011) but not decoding performance for elementary students (Larabee, Burns, & McComas, 2014) in three studies.

General academic behavior

Several studies targeted behaviors that were not specific to a particular subject. These studies demonstrated that academic work completion and accuracy increased when token economies, response cards, performance feedback, tangible reinforcement, and social reinforcement were used (Chadwick & Day, 1971; Gardner, Heward, & Grossi, 1994; Kelley & Stokes, 1982; McLaughlin & Malaby, 1972; Saudargas, Madsen, & Scott, 1977). One study also showed gains on IQ tests for preschoolers who were given contingent reinforcement for correct responses (Edlund, 1972), whereas another study demonstrated that preschool participants did not emit untrained equivalence relations following equivalence class training (Pilgrim, Chambers, & Galizio, 1995). Some studies showed that behavioral skills training could establish and maintain safety skills for preschoolers (Dickson & Vargo, 2017; Hanratty, Miltenberger, & Florentino, 2016) and that social skills curricula, a public address system, or group contingencies increased compliance and social skills during physical education classes (Ryan, Ormond, Imwold, & Rotunda, 2002; Sharpe, Brown, & Crider, 1995; Vidoni & Ward, 2006).

Speech-language

Studies that examined speech and/or language focused on articulation errors, grammatical speech, frequency of speech, complexity of speech, expressive vocabulary, and say-do correspondence. Results suggested that incidental teaching, modeling and reinforcement, and modified antecedents and consequences increased speech and language for general education preschoolers, elementary students, middle school students, and students in special education (Bailey, Timbers, Phillips, & Wolf, 1971; Broden, Copeland, Beasley, & Hall, 1977; Dennis, 2016; Hart & Risley, 1968, 1974, 1975, 1980; Reynolds & Risley, 1968). Two additional studies by McDowell and Caron (2010a, 2010b) used the matching law to predict how adolescent boys would allocate their choices of conversational topics with peers between settings; no interventions were used in these studies.

Social behaviors

Social behaviors included classroom management, acquisition of social skills, and reducing disruptive behaviors. Most of these studies focused on increasing appropriate social behaviors and compliance while decreasing disruptive behaviors. Results demonstrated that individual, classroom, and schoolwide social behaviors improved for economically disadvantaged students with intervention. For instance, Hanley, Fahmie, and Heal (2014) demonstrated that the Preschool Life Skills curriculum—a formal social skills training curriculum consisting of instruction, modeling, role-play, and differential reinforcement—increased appropriate requests and “self-control” behaviors for Head Start children with disruptive behaviors. These results were replicated in similar studies (Luczynski & Hanley, 2013). Other effective interventions involved increasing reinforcement for children by teaching them to self-reinforce, reinforce their peers, or reinforce their teachers to obtain more verbal approvals (Fantuzzo & Clement, 1981; Polirstok & Greer, 1977; Teerlink et al., 2017). Self-management, group contingencies (e.g., the good behavior game), and contingent reinforcement for rule following were also effective interventions for decreasing disruptive or inappropriate social behaviors while increasing appropriate behaviors (e.g., Balcazar et al., 1991; Hupp & Reitman, 1991; McGoey, Schneider, Rezzetano, Prodan, & Tankersley, 2010). Finally, providing appropriate classroom materials resulted in reducing transitions in preschool classrooms (e.g., Doke & Risley, 1991). Although most studies reported treatment effects for participants, one study that compared a response cost and levels system noted that it could not determine which treatment was most effective for decreasing disruptive behaviors (Tiano, Fortson, McNeil, & Humphreys, 2005).

Discussion

The current study was a descriptive assessment of the degree to which economically disadvantaged children were included in educational research in behavior analysis. This is an important issue to examine because national data indicate that school-age children from families with low socioeconomic status perform significantly lower on measures of academic performance than children from higher income families do (U.S. Department of Education, 2018), which places them at risk for dropping out of school and maintaining a low socioeconomic status as adults (Hernandez, 2012; Levin & Rouse, 2012). Because one half of all school-age children in the United States attend schools where the majority of children are economically disadvantaged (U.S. Department of Education, 2018), including them in behavior analysis research could contribute to more positive outcomes for this population of students.

Based on the selected journals included in this analysis, between 1968 and 2017, economically disadvantaged children were included in about 5% of behavior analysis publications to varying degrees during different periods. The majority of children were included during two periods: 1968–1977 and 2002–2017. From 1978 to 2001, the number of studies that included economically disadvantaged children decreased greatly. We suggest that the shift in publications occurred, in part, because of national educational policy and funding opportunities (Association for Professional Behavior Analysts [APBA], 2015). For example, a period of time during which economically disadvantaged children were included most frequently was after the Elementary and Secondary Act (1965) was enacted to providesupplemental funding for schools serving economically disadvantaged students. However, in 1977, after the Education for all Handicapped Children Act (1975) was enacted, children who had developmental disabilities were included more frequently in behavior analysis publications (Northup, Vollmer, & Serrett, 1993). In 2001, the No Child Left Behind Act (2002) , which mandated that schools increase the academic outcomes of economically disadvantaged students, may have influenced the increased number of studies published since 2002.

The current analysis also found that socioeconomic status was not described in most publications, which may have obscured the inclusion of economically disadvantaged children in research. This issue of excluding descriptions of socioeconomic status in research is not specific to behavior analysis. In 2007, the American Psychological Association (APA, 2007) reported similar findings in their report on socioeconomic status in psychology. To address this issue, behavior analysts may need to be intentional about describing the socioeconomic status of participants in research publications.

Because this review was a descriptive assessment of published studies in specific journals and databases, it did not include an analysis of the degree to which educational interventions produced behavior change for economically disadvantaged children (Conroy et al., 2005). However, a descriptive analysis of the results may serve as a useful preliminary assessment of the existing literature (Conroy et al., 2005). Overall, the results of the studies included in this article indicate that educational interventions in behavior analysis improved academic and social outcomes for economically disadvantaged children. Based on this descriptive analysis, behavior analysis has successfully implemented interventions to teach reading, math, and social skills to economically disadvantaged children. This analysis did not compare the effectiveness of behavior analysis to other fields or disciplines serving economically disadvantaged children. However, this may not be an important comparison because the academic performance of economically disadvantaged children is complex and influenced by a number of factors, including nutrition, health, and parenting, among others. Instead, an interdisciplinary approach to interventions in which behavior analysis contributes to a greater effort across several fields (e.g., psychology, social work, medicine) may be necessary to improve their academic outcomes. For example, seminal studies published by behavior analysts like Betty Hart and Todd Risley demonstrated the importance of early childhood interventions for economically disadvantaged children (Hart & Risley, 1995). The work that they conducted in the 1960s and 1970s, and the outcomes of national early childhood projects such as Project Follow Through in the 1970s (Becker & Gersten, 1982), has contributed to more widely used interventions for economically disadvantaged children, such as language interventions and direct instruction programs (e.g., Colker, 2014; U.S. Department of Education, 2007).

This analysis also indicates several areas where behavior analysts may provide support for economically disadvantaged students. Specifically, there are opportunities for research and intervention among adolescents and teenagers, in community centers and rural areas, and with ELLs and children with disabilities. It is noteworthy that the absence of economically disadvantaged children who were ELLs or who had disabilities in this analysis may offer an important opportunity for behavior analysts. Approximately 9.5% of public school students are ELLs (McFarland et al., 2018), which may present an opportunity to provide research-based interventions for ELLs who are economically disadvantaged (National Council on Disability, 2018). Additionally, the low number of studies that included economically disadvantaged students with disabilities in behavior analysis research may reflect the broader need for special education services in economically disadvantaged communities and schools (National Council on Disability, 2018). Research suggests that families of children with disabilities are at an increased risk for delays in diagnosis and treatment. For instance, Klin, Klaiman, and Jones (2015) noted that although 20% of all children with autism who require special education services are identified before age 3, economically disadvantaged, minority, and rural families are diagnosed approximately 1.5 years later than other children, which causes them to receive later treatment as well.

Although the limited number of articles published during the period of this review may reflect a need for researchers to report the socioeconomic status of their participants more often, the results of this review also suggest a need for more research to be conducted with economically disadvantaged children. One reason why economically disadvantaged children may not be included in research may be because of some of the societal barriers that limit their access to educational and psychological services, including parental employment hours, transportation challenges, and financial barriers (Young & Rabiner, 2015). However, another reason for the exclusion of economically disadvantaged children from research may actually be a lack of behavior analysts who are trained to work with economically disadvantaged populations. According to the APA (2007), despite the number of interventions developed by psychologists, psychology as a field has not adequately addressed the needs of individuals from poor and low-income communities. In behavior analysis, only 13% of certified behavior analysts provide services in education (excluding higher education) compared to 61% who provide services to individuals with autism (APBA, 2015). This barrier can be addressed by expanding the boundaries of competence for future and practicing behavior analysts to include economically disadvantaged populations and collaboration in school-based settings (Behavior Analyst Certification Board, 2014). This training may include, as Liu (2011) suggests in his discussion of social class in the helping professions, educating behavior analysts about the experiences and needs of economically disadvantaged individuals and the intersection between socioeconomic status and other important factors such as disability, race, gender, and aging.

In conclusion, this paper provided a descriptive analysis of the number of educational studies in behavior analysis journals that included economically disadvantaged children as participants. The results of the analysis suggest that economically disadvantaged children are increasingly included in behavior analysis journals when compared to the 1980s and 1990s. The results also suggest that there may be areas of opportunity for research and practice, including working with economically disadvantaged secondary students, students with disabilities, and ELLs and conducting research in community locations and rural areas. One limitation of this analysis is that it only described studies that reported income levels. As a result of our inclusion criteria for studies in this analysis, specifically the criterion that an article must specify a participant’s income level, there may have been more published studies that focused on the education of economically disadvantaged children but were not evaluated in this analysis. Another limitation of this analysis is that research from other disciplines that are affiliated with behavior analysis (e.g., special education, social work, and speech-language pathology) may be published in discipline-specific journals, which were also not included in this analysis. Although reviewing discipline-specific journals was beyond the scope of this article, practitioners and researchers who are interested in working with economically disadvantaged children may find it useful to review behavior-analytic research published in other fields such as special or general education.

Funding

This article was not funded.

Compliance with Ethical Standards

Conflict of Interest

Brandi Fontenot, Margaret Uwayo, Sarah Avedano, and Denise Ross declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Footnotes

Publisher’s Note

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

References

References marked with an asterisk indicate studies included in the literature review

  1. American Psychological Association. (2007). Report of the APA Task Force on Socioeconomic Status. Washington, DC: Author, Task Force on Socioeconomic Status. Retrieved from https://www.apa.org/pi/ses/resources/publications/task-force-2006.pdf.
  2. Association for Professional Behavior Analysts . 2014 U.S. professional employment survey: A preliminary report. San Diego, CA: Author; 2015. [Google Scholar]
  3. *Bailey, J. S., Timbers, G. D., Phillips, E. L., & Wolf, M. M. (1971). Modification of articulation errors of pre-delinquents by their peers. Journal of Applied Behavior Analysis, 4, 265–281. [DOI] [PMC free article] [PubMed]
  4. Balcazar Fabricio E., Majors Richard, Blanchard Katherine A., Paine Adrienne, Suarez-Balcazar Yolanda, Fawcett Stephen B., Murphy Ray, Meyer Jim. Teaching minority high school students to recruit helpers to attain personal and educational goals. Journal of Behavioral Education. 1991;1(4):445–454. doi: 10.1007/BF00946777. [DOI] [Google Scholar]
  5. Becker WC, Gersten R. A follow-up of Follow Through: The later effects of the direct instruction model on children in fifth and sixth grades. American Educational Research Journal. 1982;19:75–92. doi: 10.3102/00028312019001075. [DOI] [Google Scholar]
  6. Behavior Analyst Certification Board . Professional and ethical compliance code for behavior analysts. Littleton, CO: Author; 2014. [Google Scholar]
  7. *Bellone, K. M., Dufrene, B. A., Tingstrom, D. H., Olmi, D. J., & Barry, C. (2014). Relative efficacy of behavioral interventions in preschool children attending Head Start. Journal of Behavioral Education, 23, 378–400.
  8. *Billings, D. C., & Wasik, B. H. (1985). Self-instructional training with preschoolers: An attempt to replicate. Journal of Applied Behavior Analysis, 18, 61–67. [DOI] [PMC free article] [PubMed]
  9. *Briere, D., & Simonsen, B. (2011). Self-monitoring interventions for at-risk middle school students: The importance of considering function. Behavioral Disorders, 36, 129–140.
  10. *Broden, M., Bruce, C., Mitchell, M., Carter, V., & Hall, R. (1970). Effects of teacher attention on attending behavior of two boys at adjacent desks. Journal of Applied Behavior Analysis, 3, 199–203. [DOI] [PMC free article] [PubMed]
  11. *Broden, M., Copeland, G., Beasley, A., & Hall, R. (1977). Alternating student responses through changes in teacher verbal behavior. Journal of Applied Behavior Analysis, 10, 479–487. [DOI] [PMC free article] [PubMed]
  12. *Chadwick, B., & Day, R. (1971). Systematic reinforcement: Academic performance of underachieving students. Journal of Applied Behavior Analysis, 4, 311–319. [DOI] [PMC free article] [PubMed]
  13. *Christensen, L., Young, K. R., & Marchant, M. (2004). The effects of a peer-mediated positive behavior support program on socially appropriate classroom behavior. Education and Treatment of Children, 27, 199–234.
  14. *Cihak, D. F., Kirk, E. R., & Boon, R. T. (2009). Effects of classwide positive peer tootling to reduce the disruptive classroom behaviors of elementary students with and without disabilities. Journal of Behavioral Education, 18, 267–278.
  15. *Clark, H. B., & Sherman, J. A. (1975). Teaching generative use of sentence answers to three forms of questions. Journal of Applied Behavior Analysis, 8, 321–330. [DOI] [PMC free article] [PubMed]
  16. Colker LJ. The word gap: The early years make the difference. Washington, DC: National Association for the Education of Young Children; 2014. [Google Scholar]
  17. *Conklin, C. G., Kamps, D., & Wills, H. (2016). The effects of class-wide function-related intervention teams (CW-FIT) on students’ prosocial classroom behaviors. Journal of Behavioral Education, 26, 75–100.
  18. Conroy MA, Dunlap G, Clarke S, Alter PJ. A descriptive analysis of positive behavioral intervention research with young children with challenging behavior. Topics in Early Childhood Special Education. 2005;25:157–166. doi: 10.1177/02711214050250030301. [DOI] [Google Scholar]
  19. *Council, M. R., III, Cartledge, G., Green, D., Barber, M., & Gardner, R., III. (2016). Reducing risk through a supplementary reading intervention: A case study of first- and second-grade urban students. Behavioral Disorders, 41, 241–257.
  20. Dennis Lindsay R. The Effects of a Multi-Component Intervention on Preschool Children’s Literacy Skills. Topics in Early Childhood Special Education. 2015;36(1):15–29. doi: 10.1177/0271121415577399. [DOI] [Google Scholar]
  21. *Dickson, M., & Vargo, K. K. (2017). Training kindergarten students lockdown drill procedures using behavioral skills training. Journal of Applied Behavior Analysis, 50, 407–412. [DOI] [PubMed]
  22. *Doke, L., & Risley, T. (1972). The organization of day-care environment: Required vs. optional activities. Journal of Applied Behavior Analysis, 5, 405–420. [DOI] [PMC free article] [PubMed]
  23. *Dufrene, B. A., Doggett, R. A., Henington, C., & Watson, T. S. (2007). Functional assessment and intervention for disruptive classroom behaviors in preschool and Head Start classrooms. Journal of Behavioral Education, 16, 368–388.
  24. *Eckert, T. L., Ardoin, S., Daly, E., & Martens, B. (2002). Improving oral reading fluency: A brief experimental analysis of combining an antecedent intervention with consequences. Journal of Applied Behavior Analysis, 35, 271–281. [DOI] [PMC free article] [PubMed]
  25. *Eckert, T. L., Dunn, E. K., & Ardoin, S. P. (2006). The effects of alternate forms of performance feedback on elementary-aged students’ oral reading fluency. Journal of Behavioral Education, 15, 149–162.
  26. Edlund Calvin V. The effect on the test behavior of children, as reflected in the I.Q. scores, when reinforced after each correct response. Journal of Applied Behavior Analysis. 1972;5(3):317–319. doi: 10.1901/jaba.1972.5-317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Education for All Handicapped Children Act of 1975, P.L. 99-142, 85 U.S.C. § 773 (1975).
  28. Elementary and Secondary Education Act of 1965, P.L. 89-10, 79 U.S.C. § 27 (1965).
  29. *Fantuzzo, J. W., & Clement, P. W. (1981). Generalization of the effects of teacher- and self-administered token reinforcers to nontreated students. Journal of Applied Behavior Analysis, 14, 435–447. [DOI] [PMC free article] [PubMed]
  30. *Felixbrod, J. J., & O’Leary, K. D. (1973). Effects of reinforcement on children’s academic behavior as a function of self-determined and externally imposed contingencies. Journal of Applied Behavior Analysis, 6, 241–250. [DOI] [PMC free article] [PubMed]
  31. Gardner Ralph, Cartledge Gwendolyn, Seidl Barbara, Woolsey M. Lynn, Schley Guy S., Utley Cheryl A. Mt. Olivet After-School Program. Remedial and Special Education. 2001;22(1):22–33. doi: 10.1177/074193250102200104. [DOI] [Google Scholar]
  32. *Gardner, R., Heward, W. L., & Grossi, T. A. (1994). Effects of response cards on student participation and academic achievement: A systematic replication with inner-city students during whole-class science instruction. Journal of Applied Behavior Analysis, 27, 63–71. [DOI] [PMC free article] [PubMed]
  33. *Gibson, L. G., Cartledge, G., & Keyes, S. E. (2011). A preliminary investigation of supplemental computer-assisted reading instruction on the oral reading fluency and comprehension of first-grade African American urban students. Journal of Behavioral Education, 20, 260–282.
  34. *Gilbertson, D., & Bluck, J. (2006). Improving responsiveness to intervention for English language learners: A comparison of instructional pace on letter naming rates. Journal of Behavioral Education, 15, 131–147.
  35. *Gilbertson, D., Maxfield, J., & Hughes, J. (2007a). Evaluating responsiveness to intervention for English-language learners: A comparison of response modes on letter naming rates. Journal of Behavioral Education, 16, 259–279.
  36. *Gilbertson, D., Witt, J. C., Singletary, L. L., & VanDerHeyden, A. (2007b). Supporting teacher use of interventions: Effects of response dependent performance feedback on teacher implementation of a math intervention. Journal of Behavioral Education, 16, 311–326.
  37. Greenwood Charles R., Dinwiddie Granger, Bailey Voris, Carta Judith J., Dorsey Don, Kohler Frank W., Nelson Chris, Rotholz David, Schulte Dan. FIELD REPLICATION OF CLASSWIDE PEER TUTORING. Journal of Applied Behavior Analysis. 1987;20(2):151–160. doi: 10.1901/jaba.1987.20-151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. *Greenwood, C. R., Terry, B., Arreaga-Mayer, C., & Finney, R. (1992). The classwide peer tutoring program: Implementation factors moderating students’ achievement. Journal of Applied Behavior Analysis, 25, 101–116. [DOI] [PMC free article] [PubMed]
  39. *Greer, R. D., & Polirstok, S. R. (1982). Collateral gains and short-term maintenance in reading and on-task responses by inner-city adolescents as a function of their use of social reinforcement while tutoring. Journal of Applied Behavior Analysis, 15, 123–139. [DOI] [PMC free article] [PubMed]
  40. Hall R. V., Fox R., Willard D., Goldsmith L., Emerson M., Owen M., Davis F., Porcia E. The teacher as observer and experimenter in the modification of disputing and talking-out behaviors1. Journal of Applied Behavior Analysis. 1971;4(2):141–149. doi: 10.1901/jaba.1971.4-141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. *Hall, R. V., Lund, D., & Jackson, D. (1968). Effects of teacher attention on study behavior. Journal of Applied Behavior Analysis, 1, 1–12. [DOI] [PMC free article] [PubMed]
  42. Hanley Gregory P., Fahmie Tara A., Heal Nicole A. Evaluation of the preschool life skills program in Head Start classrooms: A systematic replication. Journal of Applied Behavior Analysis. 2014;47(2):443–448. doi: 10.1002/jaba.132. [DOI] [PubMed] [Google Scholar]
  43. *Hanratty, L. A., Miltenberger, R. G., & Florentino, S. R. (2016). Evaluating the effectiveness of a teaching package utilizing behavioral skills training and in situ training to teach gun safety skills in a preschool classroom. Journal of Behavioral Education, 25, 310–323.
  44. *Hansen, B., & Wills, H. (2014). The effects of goal setting, contingent reward, and instruction on writing skills. Journal of Applied Behavior Analysis, 47, 171–175. [DOI] [PubMed]
  45. *Harris, R. E., Marchand-Martella, N., & Martella, R. C. (2000). Effects of a peer delivered corrective reading program. Journal of Behavioral Education, 10, 21–36.
  46. *Hart, B., & Risley, T. (1968). Establishing use of descriptive adjectives in the spontaneous speech of disadvantaged preschool children. Journal of Applied Behavior Analysis, 1, 109–120. [DOI] [PMC free article] [PubMed]
  47. *Hart, B., & Risley, T. (1974). Using preschool materials to modify the language of disadvantaged children. Journal of Applied Behavior Analysis, 7, 243–256. [DOI] [PMC free article] [PubMed]
  48. *Hart, B., & Risley, T. R. (1975). Incidental teaching of language in the preschool. Journal of Applied Behavior Analysis, 8, 411–420. [DOI] [PMC free article] [PubMed]
  49. *Hart, B., & Risley, T. (1980). In vivo language intervention: Unanticipated general effects. Journal of Applied Behavior Analysis, 13, 407–432. [DOI] [PMC free article] [PubMed]
  50. Hart, B., & Risley, T. (1995). Meaningful differences in the everyday experience of young American children.
  51. *Hawkins, R. O., Musti-Rao, S., Hughes, C., Berry, L., & McGuire, S. (2009). Applying a randomized independent group contingency component to classwide peer tutoring for multiplication fact fluency. Journal of Behavioral Education, 18, 300–318.
  52. *Haydon, T., Maheady, L., & Hunter, W. (2010). Effects of numbered heads together on the daily quiz scores and on-task behavior of students with disabilities. Journal of Behavioral Education, 19, 222–238.
  53. *Haydon, T., Mancil, G., & Van Loan, C. (2009). Using opportunities to respond in a general education classroom: A case study. Education and Treatment of Children, 32, 267–278.
  54. *Herman, S., & Tramontana, J. (1971). Instructional and group versus individual reinforcement in modifying disruptive group behavior. Journal of Applied Behavior Analysis, 4, 113–119. [DOI] [PMC free article] [PubMed]
  55. Hernandez DJ. Double jeopardy: How third-grade reading skills and poverty influence high school graduation. Baltimore, MD: The Annie E. Casey Foundation; 2012. [Google Scholar]
  56. Hodge J, Riccomini PJ, Buford R, Herbst MH. A review of instructional interventions in mathematics for students with emotional and behavioral disorders. Behavioral Disorders. 2006;31:297–311. doi: 10.1177/019874290603100304. [DOI] [Google Scholar]
  57. *Hupp, S. D. A., & Reitman, D. (1999). The effects of stating contingency-specifying stimuli on compliance in children. The Analysis of Verbal Behavior, 16, 17–27. [DOI] [PMC free article] [PubMed]
  58. Individuals With Disabilities Education Act of 1990, P.L. 94-142, 104 U.S.C. § 1142 (1990).
  59. *Jacobson, J. M., Bushell, D., & Risley, T. (1969). Switching requirements in a Head Start classroom. Journal of Applied Behavior Analysis, 2, 43–47. [DOI] [PMC free article] [PubMed]
  60. Jiang Y, Granja MR, Koball H. Basic facts about low-income children: Children under 18 years, 2015. New York, NY: National Center for Children in Poverty, Columbia University Mailman School of Public Health; 2017. [Google Scholar]
  61. *Kelley, M. L., & Stokes, T. F. (1982). Contingency contracting with disadvantaged youths: Improving classroom performance. Journal of Applied Behavior Analysis, 15, 447–454. [DOI] [PMC free article] [PubMed]
  62. *Kincaid, M., & Weisberg, P. (1978). Alphabet letters as tokens: Training preschool children in letter recognition and labeling during a token exchange period. Journal of Applied Behavior Analysis, 11, 199.
  63. Klin A, Klaiman C, Jones W. Reducing age of autism diagnosis: Developmental social neuroscience meets public health challenge. Revue Neurologique. 2015;60:1–4. [PMC free article] [PubMed] [Google Scholar]
  64. *Konarski, E., Johnson, M., Crowell, C., & Whitman, T. (1980). Response deprivation and reinforcement in applied settings: A preliminary. Journal of Applied Behavior Analysis, 13, 595–609. [DOI] [PMC free article] [PubMed]
  65. *Kong, J. E., & Orosco, M. J. (2016). Word-problem-solving strategy for minority students at risk for math difficulties. Learning Disability Quarterly, 39(3), 171–181.
  66. Kratochwill, T. R., Hitchcock, J., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M., & Shadish, W. R. (2010). Single-case designs technical documentation. Retrieved from the What Works Clearinghouse website: http://ies.ed.gov/ncee/wwc/pdf/wwc_scd.pdf.
  67. *Lahey, B., McNees, M., & Brown, C. (1973). Modification of deficits in reading for comprehension. Journal of Applied Behavior Analysis, 6(3), 475–480. [DOI] [PMC free article] [PubMed]
  68. *Larabee, K. M., Burns, M. K., & McComas, J. J. (2014). Effects of an iPad-supported phonics intervention on decoding performance and time on-task. Journal of Behavioral Education, 23, 449–469.
  69. *Lee-Vieira, A., Mayer, M., & Cameron, M. (2006). Constructed-response spelling and literacy development: An application in an urban classroom. Behavioral Interventions, 21, 111–122.
  70. Levin, H. M., & Rouse, C. E. (2012). The true cost of high school dropouts. The New York Times, p. A31.
  71. Liu WM. Social class and classism in the helping professions: Research, theory, and practice. Thousand Oaks, CA: Sage; 2011. [Google Scholar]
  72. *Luczynski, K. C., & Hanley, G. P. (2013). Prevention of problem behavior by teaching functional communication and self-control skills to preschoolers. Journal of Applied Behavior Analysis, 46, 355–368. [DOI] [PubMed]
  73. *Luczynski, K. C., Hanley, G. P., & Rodriguez, N. M. (2014). An evaluation of the generalization and maintenance of functional communication and self-control skills with preschoolers. Journal of Applied Behavior Analysis, 47(2), 246–263. [DOI] [PubMed]
  74. *Machalicek, W., O’Reilly, M. F., Beretvas, N., Sigafoos, J., & Lancioni, G. E. (2007). A review of interventions to reduce challenging behavior in school settings for students with autism spectrum disorders. Research in Autism Spectrum Disorders, 1, 229–246.
  75. *Madrid, L. D., Canas, M., & Ortega-Medina, M. (2007). Effects of team competition versus team cooperation in classwide peer tutoring. Journal of Educational Research, 100, 155–160.
  76. *Marchand-Martella, N. E., Martella, R. C., Nelson, J. R., Waterbury, L., Shelley, S. A., Cleanthous, C., & Hatfield, D. (2002). Implementation of the sound partners reading program. Journal of Behavioral Education, 11, 117–130.
  77. *McDowell, J. J., & Caron, M. L. (2010). Bias and undermatching in delinquent boys’ verbal behavior as a function of their level of deviance. Journal of the Experimental Analysis of Behavior, 93, 471–483. [DOI] [PMC free article] [PubMed]
  78. McFarland J, Hussar B, Wang X, Zhang J, Wang K, Rathbun A, et al. The condition of education 2018 (NCES 2018-144) Washington, DC: National Center for Education Statistics, U.S. Department of Education; 2018. [Google Scholar]
  79. *McGoey, K. E., Schneider, D. L., Rezzetano, K. M., Prodan, T., & Tankersley, M. (2010). Classwide intervention to manage disruptive behavior in the kindergarten classroom. Journal of Applied School Psychology, 26, 247–261.
  80. *McKenna, J., Flower, A., Falcomata, T., & Adamson, R. (2017). Function-based replacement behavior interventions for students with challenging behavior. Behavioral Interventions, 32, 379–398. [DOI] [PubMed]
  81. *McLaughlin, T. F., & Malaby, J. (1972). Intrinsic reinforcers in a classroom token economy. Journal of Applied Behavior Analysis, 5, 263–270. [DOI] [PMC free article] [PubMed]
  82. *McNeish, J., Heron, T., & Okyere, E. (1992). Effects of self-correction on the spelling performance of junior high students with learning disabilities. Journal of Behavioral Education, 2, 17–27.
  83. *Miller, L., & Schneider, R. (1970). The use of a token system in project Head Start. Journal of Applied Behavior Analysis, 3(3), 191–197. [DOI] [PMC free article] [PubMed]
  84. *Mong, D. M., & Mong, K. W. (2010). Efficacy of two mathematics interventions for enhancing fluency with elementary. Journal of Behavioral Education, 19, 273–288.
  85. *Morrison, J. Q., & Jones, K. M. (2007). The effects of positive peer reporting as a class-wide positive behavior support. Journal of Behavioral Education, 16, 111–124.
  86. National Council on Disability . IDEA series: English learners and students from low-income families. Washington, DC: National Council on Disability; 2018. [Google Scholar]
  87. No Child Left Behind Act of 2001, P.L. 107-110, 20 U.S.C. § 6319 (2002).
  88. Northup J, Vollmer TR, Serrett K. Publication trends in 25 years of the Journal of Applied Behavior Analysis. Journal of Applied Behavior Analysis. 1993;26:527–537. doi: 10.1901/jaba.1993.26-527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. *Pilgrim, C., Chambers, L., & Galizio, M. (1995). Reversal of baseline relations and stimulus equivalence II: Children. Journal of the Experimental Analysis of Behavior, 63, 239–254. [DOI] [PMC free article] [PubMed]
  90. *Polirstok, S. R., & Greer, R. D. (1977). Remediation of mutually aversive interactions between a problem student and four teachers by training the student in reinforcement techniques. Journal of Applied Behavior Analysis, 10, 707–716. [DOI] [PMC free article] [PubMed]
  91. *Power, T. J., Dowrick, P. W., Ginsburg-Block, M., & Manz, P. H. (2004). Partnership-based, community-assisted early intervention for literacy: An application of the participatory intervention model. Journal of Behavioral Education, 13, 93–115.
  92. *Putnam, R., Handler, M., Ramirez-Platt, C., & Luiselli, J. (2003). Improving student bus riding behavior through a whole-school intervention. Journal of Applied Behavior Analysis, 36, 583–590. [DOI] [PMC free article] [PubMed]
  93. *Quilitch, H. R., & Risley, T. R. (1973). The effects of play materials on social play. Journal of Applied Behavior Analysis, 6, 573–578. [DOI] [PMC free article] [PubMed]
  94. *Rames-LaPointe, J., Hixson, M. D., Niec, L. N., & Rhymer, K. N. (2014). Evaluation of errorless compliance training in a general education classroom. Behavioral Interventions, 29, 253–267. doi:10.1002/bin.1391.
  95. *Rapport, M. D., Murphy, H. A., & Bailey, J. S. (1982). Ritalin vs. response cost in the control of hyperactive children: A within-subject comparison. Journal of Applied Behavior Analysis, 15, 205–216. [DOI] [PMC free article] [PubMed]
  96. Reed Deborah K. The Effects of Explicit Instruction on the Reading Performance of Adolescent English Language Learners With Intellectual Disabilities. TESOL Quarterly. 2013;47(4):743–761. doi: 10.1002/tesq.94. [DOI] [Google Scholar]
  97. *Reynolds, N. J., & Risley, T. R. (1968). The role of social and material reinforcers in increasing talking of a disadvantaged preschool child. Journal of Applied Behavior Analysis, 1, 253–262. [DOI] [PMC free article] [PubMed]
  98. *Rich & Duhon (2014). Using brief academic assessments to determine generalization strategies. Journal of Behavioral Education, 23, 401–420.
  99. *Rinaldi, L., Sells, D., & McLaughlin, T. (1997). The effects of reading racetracks on the sight word acquisition and fluency of elementary students. Journal of Behavioral Education, 7, 219–233.
  100. *Risley, T. R., & Hart, B. (1968). Developing correspondence between the non-verbal and verbal behavior of preschool children. Journal of Applied Behavior Analysis, 1, 267–281. [DOI] [PMC free article] [PubMed]
  101. *Rizzo, K., & Belfiore, P. (2014). Pairing student-teacher conferencing and self-regulation to increase mathematics performance in middle school students at risk for academic failure. Journal of Evidence-Based Practices for Schools, 14, 30.
  102. *Ross, S. G., & Begeny, J. C. (2014). An examination of treatment intensity with an oral reading fluency intervention: Do intervention duration and student-teacher instructional ratios impact intervention effectiveness? Journal of Behavioral Education, 24, 11–32.
  103. *Ryan, S., Ormond, T., Imwold, C., & Rotunda, R. (2002). The effects of a public address system on the off-task behavior of elementary physical education students. Journal of Applied Behavior Analysis, 35, 305–308. [DOI] [PMC free article] [PubMed]
  104. *Saudargas, R. W., Madsen, C. H., & Scott, J. W. (1977). Differential effects of fixed- and variable-time feedback on production rates of elementary school children. Journal of Applied Behavior Analysis, 10, 673–678. [DOI] [PMC free article] [PubMed]
  105. *Sharpe, T., Brown, M., & Crider, K. (1995). The effects of a sportsmanship curriculum intervention on generalized positive social behavior of urban elementary school students. Journal of Applied Behavior Analysis, 28, 401–416. [DOI] [PMC free article] [PubMed]
  106. *Silber, J. M., & Martens, B. K. (2010). Programming for the generalization of oral reading fluency: Repeated readings of entire text versus multiple exemplars. Journal of Behavioral Education, 19, 30–46.
  107. *Stevenson, H. C., & Fantuzzo, J. W. (1986). The generality and social validity of a competency-based self-control training intervention for underachieving students. Journal of Applied Behavior Analysis, 19, 269–276. [DOI] [PMC free article] [PubMed]
  108. Stevenson Nathan A. Effects of Planning and Goal Setting on Reducing Latency to Task Engagement for Struggling Readers in Middle School. Journal of Behavioral Education. 2015;25(2):206–222. doi: 10.1007/s10864-015-9238-8. [DOI] [Google Scholar]
  109. Sulzer-Azaroff B, Gillat A. Trends in behavior analysis in education. Journal of Applied Behavior Analysis. 1990;23:491–495. doi: 10.1901/jaba.1990.23-491. [DOI] [PMC free article] [PubMed] [Google Scholar]
  110. Tawney James W. Training letter discrimination in four-year-old children1. Journal of Applied Behavior Analysis. 1972;5(4):455–465. doi: 10.1901/jaba.1972.5-455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  111. *Teerlink, E., Caldarella, P., Anderson, D. H., Richardson, M. J., & Guzman, E. G. (2017). Addressing problem behavior at recess using peer praise notes. Journal of Positive Behavior Interventions, 19, 115–126.
  112. *Tiano, J. D., Fortson, B. L., McNeil, C. B., & Humphreys, L. A. (2005). Managing classroom behavior of Head Start children using response cost and token economy procedures. Journal of Early and Intensive Behavior Intervention, 2, 28–39.
  113. U.S. Department of Education . National assessment of Title I final report: Summary of key findings (NCEE 2007-4014) Washington, DC: Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance; 2007. [Google Scholar]
  114. U.S. Department of Education . The condition of education, 2018: Concentration of public school students eligible for free or reduced-price lunch. Washington, DC: Institute of Education Sciences, National Center for Education Statistics; 2018. [Google Scholar]
  115. U.S. Department of Education . National assessment of educational progress data explorer. Washington, DC: Institute of Education Sciences, National Center for Education Statistics; 2019. [Google Scholar]
  116. *Van Houten, R., Hill, S., & Parsons, M. (1975). An analysis of a performance feedback system: The effects of timing and feedback, public posting, and praise upon academic performance and peer interaction. Journal of Applied Behavior Analysis, 8, 449–457. [DOI] [PMC free article] [PubMed]
  117. *Van Houten, R., Morrison, E., Jarvis, R., & McDonald, M. (1974). The effects of explicit timing and feedback on compositional response rate in elementary school children. Journal of Applied Behavior Analysis, 7, 547–555. [DOI] [PMC free article] [PubMed]
  118. *Vanselow, N. R., & Hanley, G. P. (2014). An evaluation of computerized behavioral skills training to teach safety skills to young children. Journal of Applied Behavior Analysis, 47, 51–69. [DOI] [PubMed]
  119. *Vidoni, C., & Ward, P. (2006). Effects of a dependent group-oriented contingency on middle school physical education students’ fair play behaviors. Journal of Behavioral Education, 15, 80–91.
  120. Wagmiller RL, Adelman RM. Childhood and intergenerational poverty: The long-term consequences of growing up poor. New York, NY: National Center for Children in Poverty and Columbia University Mailman School of Public Health; 2009. [Google Scholar]
  121. *Wasik, B., Senn, K., Welch, R., & Cooper, B. (1969). Behavior modification with culturally deprived school children: Two case studies. Journal of Applied Behavior Analysis, 2, 181–194. [DOI] [PMC free article] [PubMed]
  122. Young AS, Rabiner D. Racial/ethnic differences in parent-reported barriers to accessing children’s health services. Psychological Services. 2015;12:267–273. doi: 10.1037/a0038701. [DOI] [PMC free article] [PubMed] [Google Scholar]

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