Special Education

Using evidence-based practices, or those instructional techniques shown by research to improve student outcomes meaningfully, increases the performance of students with disabilities and should therefore be a priority for special educators. But how does a practice come to be considered evidence based? The unique characteristics of group experimental research (i.e., the use of a meaningful comparison group and the active manipulation of an intervention) allow research consumers to conclude whether an intervention causes desired changes in student outcomes. As such, group experimental research is one type of research that is well suited to determine evidence-based practices. Examples of group experimental research are provided from the contemporary special education literature.

Different research designs answer different questions. Educators cannot use nonexperimental quantitative research designs, such as descriptive surveys and correlational research, to determine definitively that an intervention causes improved student outcomes and is an evidence-based practice. However, such research can (a) inform educators about a number of issues related to the education of students with disabilities and (b) guide experimental research efforts that can more definitively determine whether a practice is evidence based. Additionally, in the absence of relevant, high-quality experimental research, special educators can use some types of correlational research as the best available evidence to guide their instructional and curricular decision making.

In response to Crockett's analysis of the relation of science and the instruction of students with learning disabilities, we discuss reasons why teaching in special education is infrequently affected by science and research, and we propose a model of naturalistic decision making and make recommendations for bridging the gap between research and practice based on that model. By building an extensive experiential base in using effective practices, we propose that teachers can become experts at the craft of applying research-based methods. Bringing science into the classroom by basing craft on research in this way represents one promising alternative for improving outcomes for students with and without learning disabilities.

As a result of legal protections and the effects of inclusive reforms (e.g., improved academic skills, heightened expectations), more students with disabilities are entering higher education than ever before. The priorities and understanding of university faculty members directly shape the educational experiences and success of the rapidly growing group of college students with disabilities. Previous research in this area has focused primarily on faculty members' knowledge of legal issues, general attitudes toward students with disabilities attending college, and willingness to make accommodations. This study expands the extant knowledge base by examining the priorities and understanding of 307 faculty members at an 8-campus university system regarding university students with disabilities in the following areas: Legal, Accommodations-Willingness,

Student-teaching is the foundational professional experience for most special education teachers. We investigated the influences on preservice teachers' decision-making during their student-teaching through a two-part study. In the first phase, six undergraduate student-teachers at a large Midwestern university participated in focus group. Participants indicated that they made instructional decisions in five main areas (i.e., planning, teaching style, teaching methods, behavior management, and handling of a difficult moment) that were primarily influenced by their cooperating-teacher, previous experience, and university coursework. We then generated a survey on which 51 special education studentteachers from the same university rated the degree to which these three sources influenced the five areas of instructional decision-making. Cooperating-teachers were perceived as a significantly greater influence than university coursework in handling a difficult moment, teaching methods, and planning; and were a significantly greater influence than previous experience in behavior management and planning. Previous experience was significantly more influential than university coursework in relation to teaching style and handling a difficult moment. Implications for teacher preparation and bridging the research-topractice gap are discussed.

Recognizing that evidence-based practices (EBPs) account for at least part of the effects of teachers on achievement and the critical role of teacher preparation, the CEEDAR Center professionals offer innovation configurations to promote the implementation of evidence-based instructional practices in teacher preparation activities. Innovation Configurations are designed to evaluate current teacher preparation by determining the extent to which EBPs are taught, observed, and applied within teacher preparation programs.

The aim of this article is to provide practitioners and other nonresearchers a basic understanding of research designs to aid in appropriately interpreting and applying research findings in special education. Research design provides the blueprint for conducting a research study and shapes what kind of knowledge is generated by the study. We discuss the purpose of, types of questions addressed by, and different types of studies associated with four research designs commonly used in special education-descriptive, relational, experimental, and qualitative designs, as well as briefly review an example study using each research design. The takeaway message of this article is that different research designs address different types of questions. Therefore, it is important that research consumers know what type of questions each research design addresses and interpret and apply research findings accordingly.

Evidence-based reforms that emphasize the identification and implementation of empirically validated practices are at the forefront of educational research, and recently, the Council for Exceptional Children (CEC; 2014) published a set of standards for establishing evidence-based practices in special education. Although 30 years of research supports the effectiveness of ClassWide Peer Tutoring (CWPT), an instructional strategy intended to increase engagement and improve academic outcomes for students with disabilities, a formal evidence-based review has not yet been conducted on the practice. The purpose of this study is to examine whether CWPT, as developed by the Juniper Gardens Children's Project at the University of Kansas, can be considered an evidencebased practice for academic outcomes of students with high incidence disabilities using the CEC standards. Sixteen single-case design studies met inclusion criteria; no studies addressed all the required quality indicators. Although results of multiple studies show that CWPT had positive effects, the lack of methodologically sound studies supporting the practice means that CWPT is not considered an evidence-based practice according to the CEC (2014) standards. Implications for research and practice are discussed.

This comprehensive review synthesizes findings from 29 studies in which students with disabilities utilized behavioral self-management techniques in inclusive settings. Findings suggest that consistent with 2 earlier reviews of these types of studies, most behavioral self-management techniques continue to be adaptable in inclusive settings with moderate to strong efficacy. This review identifies strengths as well common limitations of the studies, provides recommendations for future research and practice, and includes behavioral self-management resources. 1 | INTRODUCTION With few exceptions (cf. Rogers & Graham, 2008), findings from research syntheses published from the 1970s through present indicate that behavioral self-management (BSM) interventions produce moderate to strong improvements across a wide range of behaviors, individuals, and settings (Hitchcock, Dowrick, & Prater, 2003;

Special educators are urged to use research findings to inform their instruction in order to improve student outcomes. However, it can be difficult to tell whether and how research findings apply to one's own students. In this article, we discuss how special educators can consider the samples and the sampling methods in studies to examine the relevance of research findings for their students. We describe two approaches for examining the degree to which research findings apply to one's students-the classical generalization model and the case-tocase transfer model. We also note additional factors and caveats to consider when evaluating the applicability of research findings, examine the implications of samples and sampling in two research studies, and provide a takeaway message that educators should determine the relevance of research findings for their own students by examining studies' samples and the sampling methods. Ms. Jones teaches in a special education class for 4th-and 5th-graders with learning disabilities (LDs) at Mountain Ridge Elementary School. Most of her students are culturally and linguistically diverse (CLD) and she has noticed that almost all of them are struggling with reading comprehension this year. Ms. Jones wants to use research findings to guide her instructional decision making, so she searches the internet for research on an effective instructional practice that improves reading comprehension for students with LD. She is excited when she finds an experimental study showing that Practice X resulted in meaningful gains in reading comprehension for the 40 study participants with LD, so she decides to implement the practice in her class. However, after a month of using the practice, formative assessments indicate that most of her students are not responding to the new practice. Ms. Jones asks her principal, who has advanced training in educational research, how Practice X could have worked in the study she read, but not with her students. After skimming the study, her principal replies that, even though the study involves students with LD, the researchers used convenience sampling, and participants were middle school students who were not diverse; therefore, the sample is not representative of the population of students with LD, and study participants are not a close match to Ms. Jones' class-so she is not surprised that the practice was not effective for Ms. Jones' students the way it was in the study. Ms. Jones nods, but walks away confused as to why her efforts to base instruction on research were unsuccessful. Special educators are often asked to base their instructional practices on research findings. Indeed, scientific research-though far from perfect-"is the best trick we know for solving educational problems" (Crockett, 2004, Requests for reprints should be sent to Bryan G. Cook, University of Hawaii. Electronic inquiries should be sent to

p values are commonly reported in quantitative research, but are often misunderstood and misinterpreted by research consumers. Our aim in this article is to provide special educators with guidance for appropriately interpreting p values, with the broader goal of improving research consumers' understanding and interpretation of research findings. Specifically, we discuss null hypothesis significance testing, describe what p values mean and how they are reported, describe some common misconceptions of p values, and provide two examples from the research literature to illustrate how p values are used in the field. Our take-home message is that p values indicate how likely study results are to occur if the null hypothesis is true, and that p values should be cautiously interpreted. Requests for reprints should be sent to Jason C. Travers, University of Kansas. Electronic inquiries should be sent to

A major tenet of both the Individuals with Disabilities Education Act and the No Child Left Behind Act is the identification and use of evidence-based practices, or those instructional techniques shown by research as most likely to improve student outcomes meaningfully. However, much confusion exists regarding the meaning and potential applications of evidence-based practices in special education. Evidence-based practices are traditionally supported by the findings of multiple, high-quality, experimental research studies. Rather than changing the nature of teaching or limiting teachers to following prescribed methods, prioritizing evidence-based practices will allow teachers to maximize the impact of their instructional efforts.

Single-case research methods provide the basis for evaluating effective instructional approaches in special education. The purpose of this article is to provide special educators an overview of single-case research methods, with an emphasis on how these designs are used to establish whether an instructional practice relates to improved learner outcomes. Specifically, we describe (1) core principles of single-case design (SCD) research, (2) two frequently used SCDs-withdrawal and multiple-baseline designs, (3) how visual analysis of graphed data is used to examine functional relations in SCDs, (4) limitations to the generalizability of findings from individual SCD studies, and (5) two studies in the special education literature that use SCDs. Our take-home message is that SCDs can be used to determine whether an instructional intervention causes improved outcomes for students, but caution is warranted when generalizing results from individual SCD studies. Ms. Garcia teaches a resource room for students with learning disabilities at Mesa Verde middle school. She notices that one of her students, Sergio, is seldom engaged in class. Consequently, he gets little work done and is making little progress toward meeting his Individualized Education Plan (IEP) goals. Ms. Garcia searches the Internet to find research on instructional practices that improve academic engagement for students like Sergio. Most of the studies Ms. Garcia finds report statistical analyses that she does not understand. She does find a single-case design study, authored by Dr. Jerome, that makes some sense to her. In this study, a graph shows the proportion of time a middle-school student with a learning disability, Rosa, was engaged each day during math class. According to the graphs, Rosa was seldom engaged during the first five sessions of the study, under typical instruction. Rosa's engagement increased during the next five sessions when a token economy was implemented in her class. It appears that the token economy worked! But then the token economy was discontinued, and Rosa's engagement worsened. Ms. Garcia wonders why Dr. Jerome took away the token economy when it seemed to be working. Finally, Dr. Jerome implemented the token economy again, and Rosa's engagement again increased. Ms. Garcia likes having the graph of student behavior to look at and analyze. But she is not sure how to tell whether the token economy was truly effective for Rosa, and, if so, whether this study with only one participant provides meaningful evidence that token economies are generally effective for improving Requests for reprints should be sent to D.

Single-case research methods provide a basis for demonstrating that an intervention produces a reliable change in a targeted outcome for individual cases. To supplement visual analysis of data in single-case studies, researchers frequently report statistics-often referred to as effect sizes-to summarize study findings. The recent proliferation of effect sizes used in single-case research can be confusing. In this article, after reviewing single-case research, we provide an overview of common types of effect sizes used in single-case research, including overlap metrics and within-and between-participant effect sizes, and conclude with examples of these effect sizes in the single-case literature. Our take-home message is that effect sizes are useful complements to visual analysis when interpreting results of single-case design research studies.

Effect sizes are powerful tools for evaluating the practical importance of study findings that should be considered in the context of study characteristics such as participants, dependent variables, and comparison condition. In this article, we discuss how group-difference effect sizes are used to gauge the practical importance of group experimental studies. We first define different types of group-difference effect sizes and discuss how they can provide valuable information for research consumers. Second, we present guidelines for interpreting groupdifference effect sizes. Third, we discuss important contextual variables that should be taken into account when interpreting group-difference effect sizes reported in the literature. Last, we provide two examples of how group-difference effect sizes have been used in the learning disabilities research base. Ms. Perez, a resource teacher at Jefferson Elementary School, was pleased when she heard that her principal had scheduled an in-service training on effective teaching practices based on John Hattie's (2008) Visible Learning. Jefferson Elementary School was a large school with a mix of new and experienced teachers who often had differing opinions on what instructional approaches were most effective and should be used. She had heard Hattie's work discussed before, and knew that he determined the effectiveness of instructional approaches based on the results of hundreds of metaanalyses that included tens of thousands of research studies. She was hopeful that she and her colleagues could learn about the effectiveness of different instructional approaches from a definitive, research-based source. At the workshop, the presenter talked a lot about effect sizes. For example, the presenter said that ability grouping had an effect size of 0.12, which seemed to be treated as a small effect. The presenter spent more time talking about approaches with larger effect sizes, like feedback (effect size = 0.75). As the presenter went on to discuss this and other practices with similar effect sizes, Ms. Perez couldn't help but wonder, what do effect sizes of 0.12 and 0.75 mean? Does that mean she should expect her students to improve 12 percent if she used ability grouping, or that 75 percent of students would be successful if she used feedback? Without understanding what effect sizes mean, Ms. Perez wasn't sure which if any of the interventions were worth using. In this article, we provide a practical overview of effect sizes (ESs) in group-experimental research. As the term suggests, ESs represent the size of the effect or the strength Requests for reprints should be sent to B. G. Cook, University of Hawaii.