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Mixed-Methods Approaches in Special Education Research

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Title: Mixed-Methods Approaches in Special Education Research
Language: English
Authors: Love, Hailey R., Cook, Bryan G., Cook, Lysandra
Source: Learning Disabilities Research & Practice. Nov 2022 37(4):314-323.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 10
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Descriptors: Mixed Methods Research, Research Methodology, Special Education, Educational Research, Research Design, Students with Disabilities, Learning Disabilities
DOI: 10.1111/ldrp.12295
ISSN: 0938-8982
1540-5826
Abstract: Mixed-methods research can uniquely inform special education practice by combining qualitative and quantitative research approaches. However, its distinct features can also make mixed-methods research difficult to understand and apply. In this article, we provide an introduction to mixed-methods research purposes, designs, and quality considerations to help practitioners critically consume and apply this type of research when working with students with learning disabilities and their families. We describe three sample research studies to illustrate mixed-methods designs and contributions. Our take-home message is that mixed-methods research (a) requires unique research practices to meaningfully combine qualitative and quantitative research approaches in a single study and (b) can be particularly useful for informing special education practice in real-world contexts.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1360499
Database: ERIC
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  Value: <anid>AN0161103546;7mj01nov.22;2023Jan04.05:46;v2.2.500</anid> <title id="AN0161103546-1">Mixed‐Methods Approaches in Special Education Research </title> <sbt id="AN0161103546-2">MIXED‐METHODS APPROACHES IN SPECIAL EDUCATION RESEARCH</sbt> <p>Mixed‐methods research can uniquely inform special education practice by combining qualitative and quantitative research approaches. However, its distinct features can also make mixed‐methods research difficult to understand and apply. In this article, we provide an introduction to mixed‐methods research purposes, designs, and quality considerations to help practitioners critically consume and apply this type of research when working with students with learning disabilities and their families. We describe three sample research studies to illustrate mixed‐methods designs and contributions. Our take‐home message is that mixed‐methods research (a) requires unique research practices to meaningfully combine qualitative and quantitative research approaches in a single study and (b) can be particularly useful for informing special education practice in real‐world contexts.</p> <p> <emph>Ms. Cynthia is the special education teacher in a cotaught inclusive first‐grade classroom. Taylor, a multilingual learner identified with a learning disability (LD), is not making expected progress in her writing skills. Ms. Cynthia is reading research about evidence‐based writing interventions but is particularly interested in culturally responsive writing instruction shown to be effective with students who are multilingual. She is also hoping to find studies conducted in cotaught classrooms</emph>.</p> <p> <emph>She identifies several studies showing that a particular writing program may be effective. She is drawn to one study the authors described as using mixed‐methods research that reports how the writing program was developed by conducting a qualitative thematic analysis of multilingual students' writing samples. Then, the researchers used a single‐case design to show the intervention improved the writing performance of five multilingual students, two of whom were identified as having LD. Additionally, the researchers interviewed both the general education and special education teachers in participating classrooms to understand the practicality of the program in addition to its effectiveness. Although Ms. Cynthia was not familiar with all the procedures used in this mixed‐methods study, she liked that the study showed the program was (a) developed based on the writing development of multilingual children, (b) shown to be effective for students like Taylor, and (c) seen as practical by coteachers</emph>.</p> <p>Mixed‐methods research purposefully integrates, or combines, quantitative and qualitative research approaches[<reflink idref="bib1" id="ref1">1</reflink>] (Creswell & Plano Clark, [<reflink idref="bib5" id="ref2">5</reflink>]). Mixed‐methods research may involve collecting multiple types of data, analyzing data in multiple ways, and/or answering multiple related research questions that require different methods (Fetters & Molina‐Azorín, [<reflink idref="bib7" id="ref3">7</reflink>]). Notably, the multiple methods, or approaches, used in mixed‐methods research should all contribute to the overarching purpose of a study and should be systematically and meaningfully integrated. The combination of the methods should create new meaning beyond what any of the methods could generate alone.</p> <p>Mixed‐methods research is particularly useful for informing special education practice and policy in real‐world contexts, such as classrooms that serve students with LD (Klingner & Boardman, [<reflink idref="bib15" id="ref4">15</reflink>]). For instance, understanding nuances of special education practice through the integration of multiple methods is helpful when identifying effective and inclusive practices for students with disabilities from culturally and linguistically diverse backgrounds (Kozleski, [<reflink idref="bib16" id="ref5">16</reflink>]); planning to apply practices across different types of settings (Love & Corr, [<reflink idref="bib20" id="ref6">20</reflink>]); and adapting evidence‐based practices to be responsive to particular populations, contexts, or circumstances (Klingner & Boardman, [<reflink idref="bib15" id="ref7">15</reflink>]). Mixed‐methods research can also help identify when and why practices may or may not be effective due to contextual features, implementation demands, or other circumstances (Howie et al., [<reflink idref="bib13" id="ref8">13</reflink>]). Finally, because mixed‐methods research can highlight the influence of different processes on outcomes, it is particularly useful for examining the effectiveness and critical features of instructional supports (Corr et al., [<reflink idref="bib4" id="ref9">4</reflink>]).</p> <p>Although mixed‐methods research has many uses and benefits, it can sometimes be difficult to make sense of how it informs practice because it combines procedures, approaches, and assumptions associated with both quantitative and qualitative methods. The purpose of this article is to provide an introduction to the purposes, designs, and unique considerations of mixed‐methods research to help practitioners critically consume and apply it. In the following sections, we first discuss purposes for mixed‐methods research, highlighting practical challenges this type of research design can address. Then, we describe core mixed‐methods research designs and provide examples from research studies including students with LD. Finally, we consider factors for assessing the rigor of mixed‐methods research. While mixed‐methods research can be used for a variety of purposes, in this article, we focus on mixed‐methods research that examines instructional practices or interventions. Our take‐home message is that mixed‐methods research (a) requires unique research practices to meaningfully combine qualitative and quantitative research approaches in a single study and (b) is useful for informing special education practice within real‐world contexts.</p> <hd id="AN0161103546-3">PURPOSES FOR MIXING RESEARCH METHODS IN SPECIAL EDUCATION</hd> <p>Although mixed‐methods research can be conducted for many purposes, we highlight three ways in which it can be used to inform special education practice, particularly how practitioners can identify, implement, and adapt practices for students with LD. A common thread throughout these purposes is that mixed‐methods research combines the strengths of quantitative and qualitative research to help practitioners learn and be responsive to student, family, and teacher perspectives and better understand complex teaching and learning processes.</p> <hd id="AN0161103546-4">Strengthening Evidence</hd> <p>Combining quantitative (e.g., numbers that can be statistically analyzed) and qualitative (e.g., recorded narratives or text that can be analyzed by identifying and applying categorical codes and/or themes) research approaches is beneficial because doing so draws on multiple ways of knowing and can therefore lead to deeper and broader understandings of educational practices, contexts, and outcomes (Corr et al., [<reflink idref="bib4" id="ref10">4</reflink>]). That is, mixed‐method studies can examine features of a context, practice, or process to (a) strengthen study findings when results from the different methods coincide or reinforce one another and (b) highlight meaningful complexities of practice when they do not. For example, a mixed‐method study that uses quantitative methods to examine whether a writing program improves outcomes for students with LD as well as qualitative interviews to explore teachers' and students' perspectives on the intervention's acceptability (i.e., how well it meets participant needs) deepens the evidence provided regarding the intervention's effects beyond what either approach could have provided separately. In such a study, student perspectives could help practitioners understand whether and how the writing program alleviated students' fatigue, hopelessness, and anxiety—experiences that are more common for students with LD than students without LD and that may mediate academic outcomes (e.g., Goegan & Daniels, [<reflink idref="bib10" id="ref11">10</reflink>]). Understanding both the effectiveness of a practice and what teachers and students thought about the practice is helpful for practitioners as they determine whether to use the intervention.</p> <hd id="AN0161103546-5">Identifying and Responding to Difference</hd> <p>Mixed‐methods research embraces differences in outcomes, contexts, and processes. Accordingly, it can help identify unintended consequences (both beneficial or challenging) of practices and interventions as well as contradictory or divergent findings (Corr et al., [<reflink idref="bib4" id="ref12">4</reflink>]; Fetters & Molina‐Azorín, [<reflink idref="bib8" id="ref13">8</reflink>]). Because qualitative data are typically broader and more open‐ended than quantitative data, they can shed light on changes that occur outside of the planned intervention as well as trade‐offs, compromises, or innovations that participants made (Fetters & Molina‐Azorín, [<reflink idref="bib8" id="ref14">8</reflink>]). For instance, a mixed‐methods study of a writing intervention could examine whether it improved the writing skills of students with LD through pre‐ and postintervention assessments (a quantitative method) as well as interview teachers (a qualitative method) to understand when, why, and how they adapted the practice to make it work in their classrooms. Such study goes beyond a typical quantitative evaluation of intervention effectiveness by providing detailed teacher‐derived knowledge that can be useful for other practitioners, like Ms. Cynthia. Practitioners could learn potential ways to adapt an intervention from teachers who implemented it in order to make the practices more effective for their students and context.</p> <p>Additionally, it is important to recognize that the practices and supports that benefit some students may not benefit others. This is one reason why practitioners should closely examine the sample and sampling methods in research studies to determine how similar the research participants are to the students with whom they would like to use the practices (Cook & Cook, [<reflink idref="bib3" id="ref15">3</reflink>]). Students of color and students who are multilingual learners tend to be underrepresented in special education research used to establish evidence‐based practices, which is largely single‐method quantitative research (e.g., Sinclair et al., [<reflink idref="bib26" id="ref16">26</reflink>]; West et al., [<reflink idref="bib29" id="ref17">29</reflink>]). This lack of representation can contribute to challenges as teachers try to work with students and families of color. For example, Keel et al. ([<reflink idref="bib14" id="ref18">14</reflink>]) found that teachers did not understand students' and their families' cultural background and reasonings during postschool transition planning with Latino students with LD. This negatively impacted teachers' ability to individualize planning.</p> <p>Mixed‐methods research offers unique opportunities to engage stakeholders throughout the research process when designing and assessing practices (Fetters & Molina‐Azorín, [<reflink idref="bib8" id="ref19">8</reflink>]; Molina‐Azorín & Fetters, [<reflink idref="bib23" id="ref20">23</reflink>]), which is particularly important when examining inequities and exploring ways to support multiply marginalized groups (e.g., students of color with disabilities; Waitoller & Annamma, [<reflink idref="bib28" id="ref21">28</reflink>]). Accordingly, mixed‐methods research can uniquely be used to iteratively develop, evaluate, and refine culturally relevant and strengths‐based practices based on multiple types of data and analyses regarding the unique perceptions and outcomes of marginalized students (Kozleski, [<reflink idref="bib16" id="ref22">16</reflink>]). Such information is useful for teachers to identify instructional practices that are culturally, linguistically, and socioeconomically responsive for multiply‐marginalized students with disabilities (Klingner & Boardman, [<reflink idref="bib15" id="ref23">15</reflink>]; Kozleski, [<reflink idref="bib16" id="ref24">16</reflink>]).</p> <hd id="AN0161103546-6">Understanding Processes Influencing Practice Implementation and Effectiveness</hd> <p>Combining quantitative and qualitative methods can also illustrate why and how a practice produces (or does not produce) the desired outcome. This information is useful for special education practitioners because it can help them determine whether and how they can achieve similar outcomes in their class and with their students. While quantitative analysis is well suited for questions related to the effects of practices on student outcomes, qualitative research is particularly useful for understanding dynamic processes and contextual features that impact practice implementation and effectiveness, including school characteristics and practitioner perspectives (Kozleski, [<reflink idref="bib16" id="ref25">16</reflink>]). For example, teachers of students with LD may hold an array of beliefs about how learning difficulties impact students' ability to meet learning goals, and these beliefs can influence whether and how teachers use certain practices to support students' learning (Lauterbach et al., [<reflink idref="bib17" id="ref26">17</reflink>]). Thus, information about teacher beliefs, likely measured through qualitative data collection (e.g., interviews) would be beneficial when assessing intervention implementation and outcomes for students with LD, likely measured through quantitative data collection (e.g., frequency counts of practice implementation, student assessments). Administrators could use such information to determine how to support teachers' knowledge about and ability to effectively educate students with LD.</p> <p>Mixed‐methods research can explore not only the effectiveness of a practice but also processes and issues underlying its effects, such as feasibility or practicality (i.e., how easy a practice is to implement or access), supports and barriers to implementation, contextual features that impact implementation, and the perspectives of stakeholders (Klingner & Boardman, [<reflink idref="bib15" id="ref27">15</reflink>]). In this way, mixed‐methods research can expand the focus and potential impact of a single research study. For example, if a practitioner is determining what writing supports to use, like Ms. Cynthia, it is beneficial to understand why and how a practice works. A mixed‐methods study can combine an examination of the effects of a writing intervention for multilingual students with LD with a qualitative exploration of how those effects were attained. The researchers may interview students to identify when and how they used the strategies taught in the intervention, whether the strategies improved their motivation to write, and the extent to which the strategies reflected and built on their cultural and linguistic identities. Thus, mixed‐methods research can simultaneously give insight into whether and how teaching practices contribute to academic outcomes as well as provide practitioners with evidence about a wider array of effects. In sum, mixed‐methods research is a powerful tool that can (a) inform practitioners about the effectiveness of practices; (b) provide information about diverse perspectives, contexts, and experiences; and (c) contribute to a nuanced understanding of how practices work.</p> <hd id="AN0161103546-7">BASIC MIXED‐METHODS RESEARCH DESIGNS</hd> <p>There are many ways to design mixed‐methods studies (e.g., Plano Clark & Ivankova, [<reflink idref="bib25" id="ref28">25</reflink>]). Among these, Creswell and Plano Clark ([<reflink idref="bib5" id="ref29">5</reflink>]) identified the following three basic designs that are used frequently: explanatory sequential, exploratory sequential, and convergent. These designs differ in the timing and relationship of quantitative and qualitative research approaches in the study. (See Table 1 for descriptions of the designs.) Familiarity with these core mixed‐methods designs can help practitioners understand the purpose and main components of mixed‐method studies, and identify how different sources of data are used.</p> <p>1 TABLE Mixed‐Methods Research Core Designs</p> <p> <ephtml> <table><thead><tr><th align="left">Term</th><th align="left">Description</th><th align="left">Common Applications</th></tr></thead><tbody><tr><td>Explanatory Sequential</td><td>One type of data collection and analysis (typically quantitative) is followed by a second type of data collection and analysis (typically qualitative). The qualitative strand typically is used to explain or expand on the quantitative results.</td><td><list list-type="Bullet"><list-item><p>Examine the effectiveness of an intervention and understand the context, processes, and/or conditions under which effects occurred.</p></list-item><list-item><p>Collect robust information about the practicality of intervention procedures and the importance of intervention goals and outcomes after an intervention or practice was implemented.</p></list-item><list-item><p>Examine the effectiveness of a practice and explore why it was or was not effective.</p></list-item></list></td></tr><tr><td>Exploratory Sequential</td><td>One type of data collection and analysis (typically qualitative) is followed by a second type of data collection and analysis (typically quantitative). The qualitative strand typically is used to broadly explore a topic, which is then more narrowly examined using quantitative approaches.</td><td><list list-type="Bullet"><list-item><p>Assess the generalizability of patterns identified through qualitative research approaches.</p></list-item><list-item><p>Assess a specific hypothesis that was developed based on qualitative findings.</p></list-item><list-item><p>Develop and evaluate a new or adapted practice, intervention, or measure based on the perspectives expressed by a targeted group.</p></list-item></list></td></tr><tr><td>Convergent</td><td>Simultaneous quantitative and qualitative data collection and analysis; strands are then combined or compared for joint interpretation. Multiple types of data from each participant, case, or research session may be collected and analyzed or a single source of data may be analyzed using both quantitative and qualitative analyses.</td><td><list list-type="Bullet"><list-item><p>Explore a broad research topic (e.g., inclusive education) by collecting and combining different types of data from different participants (e.g., surveys with parents, focus groups with students and teachers).</p></list-item><list-item><p>Simultaneously collect multiple types of data (e.g., peer rankings and interviews about peer interactions) from a single group of participants, such as students with LD.</p></list-item><list-item><p>Administer a single measure that can be analyzed both quantitatively and qualitatively, such as a survey with closed‐ended questions (responses are analyzed statistically) and open‐ended questions (responses are analyzed by looking for themes).</p></list-item></list></td></tr></tbody></table> </ephtml> </p> <p>Regardless of the type of design used, readers should identify certain key features present in all mixed‐methods research in order to understand and interpret study findings: (a) the research purpose(s) or research questions; (b) theories or epistemologies (i.e., ways of viewing knowledge) that guide the study; (c) when and how data were collected; (d) how data were analyzed; (e) and when, how, and why research methods were integrated. The final feature, integration of quantitative and qualitative research approaches to achieve the study's purposes, is a key and unique element of all mixed‐methods designs (Fàbregues & Molina‐Azorín, [<reflink idref="bib6" id="ref30">6</reflink>]). Integration may be apparent in the overall research design and purpose (e.g., asking research questions that require a mixed‐method research design to answer) and/or occur through sampling, data collection, data analysis, or data interpretation processes.</p> <p>In the following sections, we describe the three basic designs, provide example studies, and highlight the key features of the studies and some of the ways in which they met research quality criteria. We focus on the ways researchers integrated methods to achieve rigorous mixed‐methods research that can inform practice.</p> <hd id="AN0161103546-8">Explanatory Sequential Design</hd> <p>In an explanatory sequential research design, quantitative data collection and analysis is conducted first, followed by qualitative data collection and analysis. The qualitative strand is used to explain or expand on the quantitative results (Creswell & Plano Clark, [<reflink idref="bib5" id="ref31">5</reflink>]). For instance, researchers using an explanatory sequential design may first quantitatively analyze district discipline data (e.g., office referrals, suspensions, and expulsions) and then conduct qualitative classroom observations to understand the context and processes through which discipline occurs and escalates. Another common application of the explanatory sequential design are intervention studies where quantitative analyses of student or teacher outcomes are followed by collecting in‐depth qualitative interviews or focus groups that explore participant perspectives on the practicality of the intervention procedures and whether the goals and outcomes were important. Thus, an explanatory sequential research design can provide information about the impact of practices for a population, explore the circumstances or conditions under which effects occurred, and identify participant perspectives on previously implemented practices.</p> <p>Marino et al. ([<reflink idref="bib22" id="ref32">22</reflink>]) used an explanatory sequential design to examine the impact of a universal design for learning (UDL)‐enhanced science curriculum on the learning and engagement of students with LD. In the study, fourth‐ and fifth‐grade science teachers alternately delivered two UDL‐enhanced science curriculum units and two traditional non‐UDL units. The UDL units incorporated interactive video games designed to deliver life science content using UDL principles (e.g., direct feedback and guidance through steps, virtual dictionary that explained key terms, opportunities for students to design or adjust their own path through the game). Additionally, the UDL units incorporated alternative text (e.g., supplementary text that explained science vocabulary and concepts for struggling readers).</p> <p>Students with and without LD completed paper‐and‐pencil tests assessing their knowledge before and after each unit (a quantitative assessment). Additionally, a subset of students participated in focus groups after all four units (qualitative data collection). The researchers statistically analyzed the test performance of students with and without LD. The focus group transcripts were coded (qualitatively analyzed) and themes were identified that revealed students' engagement during the two types of units and what supported their learning and engagement. After initial themes were identified, the researchers asked participating teachers and students to help clarify the researchers' interpretation, a process called member checking that is important for qualitative research trustworthiness (Leko et al., [<reflink idref="bib19" id="ref33">19</reflink>]).</p> <p>Interesting, the researchers found little effect of the UDL‐enhanced features on students' test performance. That is, there was no significant difference in students' improvements from pretest to posttest between the traditional curriculum units and the UDL‐enhanced units for students in general or for students with LD specifically. However, students reported higher engagement during the UDL units than the non‐UDL units. Students with LD specifically said that the alternative text was more accessible than their textbook. Students also reported that they were actively engaged during the video game portions of the UDL‐enhanced units. They discussed supporting and learning from each other and being excited to see how science concepts worked. For instance, one student said, "It was cool to see how genetics actually happened in a plant shop" (Marino et al., [<reflink idref="bib22" id="ref34">22</reflink>], p. 94).</p> <p>For their final interpretations, the researchers juxtaposed these seemingly conflicting findings. By integrating the quantitative and qualitative findings, the authors pointed to several potential explanations and practical implications. Notably, students' comments about their test performance helped explain why UDL units did not result in higher test scores despite students' increased engagement. The researchers found that students with LD contrasted their engagement during the units with their ability to answer the test questions. While the video games allowed them to show their knowledge in multiple interactive ways, the traditional assessment did not. Additionally, the researchers noted that the traditional units included instructional features that allowed teachers to teach directly to the test whereas the UDL units focused more on interactive problem‐solving. Among other conclusions, the researchers pointed to the need for assessments to draw on UDL guidelines to ensure students are able to adequately show their knowledge. The mixed‐methods approach allowed researchers to examine the complex relationship between engagement and learning that resulted in insights that would not have been captured by either a quantitative or qualitative approach alone, leading to practical implications for teachers.</p> <hd id="AN0161103546-9">Exploratory Sequential Design</hd> <p>Exploratory sequential research designs are also sequential in their timing (one methodology followed by another methodology); however, for this type of design, researchers collect and analyze qualitative data first, followed by collecting and analyzing quantitative data. The data collected through qualitative research approaches are often used to broadly explore a topic, which is then more narrowly examined using quantitative approaches (Creswell & Plano Clark, [<reflink idref="bib5" id="ref35">5</reflink>]). The qualitative data can help researchers identify relevant variables for subsequent quantitative data collection and analysis or support the development of measures or interventions that are then quantitatively assessed. For instance, a researcher may conduct qualitative focus groups to learn about the experiences of students of color with LD and use that information to adapt  an evidence‐based practice to be more culturally responsive. They could then test the effectiveness of the adapted practice using quantitative data collection and analysis. As another example, researchers may use initial focus groups with teachers to understand professional development support needs and then develop a survey with closed‐ended questions and statistically analyze them to examine the pervasiveness of specific needs identified in the focus groups. In short, exploratory sequential designs can provide useful findings for practitioners who are interested in understanding newly developed measures or interventions.</p> <p>In their innovative study using concept mapping, Nalavany et al. ([<reflink idref="bib24" id="ref36">24</reflink>]) used an exploratory sequential design to explore the psychosocial experiences of individuals with dyslexia. In Phase I of the study, researchers conducted interviews and focus groups that built on photos participants had taken to represent their experiences with dyslexia. Additionally, researchers recorded participant descriptions on the photos and kept them as artifacts. The authors argued that using multiple means to solicit participant perspectives capitalized on the creative and visual strengths of many people with dyslexia. The researchers qualitatively analyzed Phase I interviews and photos to identify 75 statements that reflected the most common participant observations about their life with dyslexia. Interview and focus group participants were invited to reflect on, modify, and contribute to final decisions about what statements would be used in Phase II, a process of member checking.</p> <p>In Phase II, another set of participants, which included interested participants from Phase I, sorted the statements into groups that "make sense and fit together" (Nalavany et al., [<reflink idref="bib24" id="ref37">24</reflink>], p. 69). Participants then completed a questionnaire on which they rated each statement on a scale from 1 (<emph>strongly disagree</emph>) to 7 (<emph>strongly agree</emph>) based on how well each item described their personal experience with dyslexia. The researchers completed a statistical cluster analysis of the sorting task to generate a concept map that reflected the ways in which participants most commonly grouped statements. Participants were also invited to comment on the final cluster map, another opportunity for member checking. Finally, researchers statistically analyzed the statement ratings to identify the mean rating for each statement and for each cluster of statements.</p> <p>The concept map revealed nine clusters that represented common experiences for people with dyslexia. The highest rated cluster was "Why can't they see it?" An example statement within that cluster was "Other people don't see the amount of hard work that someone with dyslexia puts forth to accomplish the same task." Qualitative analyses helped researchers conceptualize the experience of disability in a complex and participant‐driven way whereas quantitative analyses helped them clearly identify the most common perspectives as well as statistically confirm whether different perspectives and experiences were related to each other (based on the statement groupings).</p> <p>In this study, integration took place at multiple points. First, in line with the explanatory sequential design, qualitative data collection and analysis informed quantitative data collection and analysis—researchers used the interviews, focus groups, and photos to identify the statements that were sorted and rated. Additionally, methods were integrated when researchers used member checking, which is traditionally a quality feature of qualitative research, during the final stage of quantitative cluster analysis, to help name and contextualize the clusters determined by quantitative analysis. The mixed‐methods approach allowed researchers to qualitatively examine and represent the experiences of people with dyslexia in a way that capitalized on their strengths and highlighted what people with dyslexia felt were the most pressing issues or experiences. Subsequently, the researchers quantitatively identified the most commonly held perceptions. Thus, integrating the methods both expanded and added nuance to the knowledge that came from the study.</p> <hd id="AN0161103546-10">Convergent Design</hd> <p>Studies with a convergent research design use simultaneous quantitative and qualitative data collection and analysis. Data from both research approaches are then combined or compared for joint interpretation (Creswell & Plano Clark, [<reflink idref="bib5" id="ref38">5</reflink>]). Researchers may simultaneously collect multiple types of data from each participant or case. For example, a researcher may administer a survey with both closed‐ and open‐ended questions that are analyzed using rigorous quantitative and qualitative methods, respectively. A convergent design may also involve researchers collecting multiple types of data in a single session or site visit. For instance, researchers may ask students with LD to draw a response to a reading passage and annotate the passage as they read in addition to answering multiple‐choice reading comprehension questions. In addition to scoring students' performance on the comprehension assessment, the researchers could use a content analysis approach to analyze the drawings and annotations to more thoroughly understand how the students engaged with and made sense of the passage, including ways they drew on their background knowledge.</p> <p>A convergent design may also be used to analyze a single source of data (e.g., observation videos, interview transcripts) using both quantitative and qualitative analyses. For instance, researchers could explore the dynamics of individualized education program (IEP) meetings by videotaping the meetings. The recordings could be quantitatively analyzed by statistically comparing the amount of time different participants (e.g., parents, teachers, related service providers, administrators, students) spoke. Additionally, the recordings could be qualitatively analyzed by examining speech content using thematic or discourse analyses to identify patterns in verbal and nonverbal communication. Together, the quantitative and qualitative analyses provide a robust investigation of the conversations and how decision‐making occurred. Convergent designs are often conducted to understand a broad research topic using multiple data sources that are triangulated (i.e., compared for similarities in findings). Therefore, such studies can be useful when practitioners are looking for deep and multifaceted information about a practice, a group of learners, or a topic of interest.</p> <p>Mariage et al. ([<reflink idref="bib21" id="ref39">21</reflink>]) conducted a convergent design to evaluate Tier‐2 reading comprehension instruction for five students who were identified as struggling readers. The five students included two students with disabilities, including one student with LD. The instruction program included introducing students to close reading, instructing them to set goals for reading, and teaching them two close reading strategies. Additionally, students were taught discussion skills, including how to contribute ideas, actively listen, and build on others' ideas. Before and after the intervention, students were given two passages to read and asked to identify the main idea for each paragraph in the passage. The students annotated the passages in any way that would help them retain the information. Additionally, the students were observed during a class discussion, completed a quiz assessing their comprehension of the passage, and were given reading assessments to assess their oral reading fluency and comprehension.</p> <p>Quantitative data included the number of correct quiz items students answered and the number and type (i.e., question, clarification, comment, connection) of text annotations for each passage. Discussion transcripts were qualitatively coded to identify patterns and themes within the discussions. Additionally, certain features of the discussions were quantitatively summarized (e.g., counts of total speaking turns, total number of initiation‐response sequences, number of times each type of discussion strategy was used). Thus, the discussion transcripts (collected during both preintervention and postintervention) were a single source of data that researchers analyzed both quantitatively and qualitatively. After the intervention, students also completed a quantitative survey with questions about the effectiveness of each strategy, whether the strategies helped the group as a collective, their enjoyment of the strategies, and whether they would like to use the strategies in the future. They rated each item on a scale from 1 (<emph>strongly disagree</emph>) to 4 (<emph>strongly agree</emph>). The students' teacher was also interviewed and asked to comment on the intervention as a whole and each strategy according to ease of implementation, effectiveness, teaching enjoyment, and likelihood of future use.</p> <p>Results indicated that students' reading fluency, comprehension assessment scores, and multiple‐choice quiz scores increased after the intervention. All five students also annotated the text more after the intervention and used a wider range of annotation strategies. Further, students' discussions changed after the intervention. While preintervention talk mostly consisted of what the authors coded as procedural talk (e.g., who was going to speak next, which passage the student was talking about), after the intervention, students initiated questions, drew upon their background knowledge to make sense of the content, and asked for clarifications. Additionally, postintervention discussion included more initiation‐response sequences, sequences were longer, and more students were involved in each sequence. Both students and teachers reported general satisfaction with the strategies and interest in using the strategies in the future. The teacher also noted what supports made the intervention easier to teach, including the reference posters provided and having the lessons in advance.</p> <p>Analyzing a single source of data (e.g., discussion observations) both quantitatively and qualitatively is a common method of integration within mixed‐methods research. The authors also combined quantitative and qualitative findings to strengthen their final conclusions that answered their research questions. Together, the quantitative and qualitative findings indicated that (a) the intervention package improved students' reading comprehension based on both their abilities to answer questions about a passage and to discuss a passage, (b) students changed how they thought about text and used different strategies and scaffolds after receiving the intervention, and (c) the students and teacher were generally satisfied with the intervention.</p> <hd id="AN0161103546-11">CONSIDERATIONS FOR EVALUATING MIXED‐METHODS RESEARCH RIGOR</hd> <p>Although there are many considerations and approaches for assessing the rigor or quality of mixed‐methods research, key indicators of quality to bear in mind when reading studies include (a) adherence to single‐method quality indicators for qualitative and quantitative research and (b) appropriate integration of methods to address the research questions and purpose.</p> <hd id="AN0161103546-12">Adhering to Quantitative and Qualitative Research Quality Indicators</hd> <p>Quantitative and qualitative data in mixed‐methods studies should generally be collected and analyzed according to expectations for rigor within that method (Heyvaert et al., [<reflink idref="bib11" id="ref40">11</reflink>]). That is, qualitative data collection and analysis in mixed‐methods studies should adhere to recommendations for credible and trustworthy qualitative research and the specific qualitative methods being used (e.g., acknowledgment of researcher positionality, implementing member checks, using rich descriptions to report study findings, interviews and focus groups are audio‐recorded and transcribed; Leko et al., [<reflink idref="bib19" id="ref41">19</reflink>]). For instance, all three sample studies described here analyzed qualitative data based on verbatim transcripts developed from audio‐recorded conversations, and Nalavany et al. ([<reflink idref="bib24" id="ref42">24</reflink>]) employed member checking to decide on the final statement clusters. Meanwhile, quantitative data collection and analysis should adhere to standards for rigor in the specific quantitative design used (e.g., clearly describing intervention and comparison conditions, participants are comparable across conditions in group experimental research; see Gersten et al., [<reflink idref="bib9" id="ref43">9</reflink>]; Horner et al., [<reflink idref="bib12" id="ref44">12</reflink>]; Thompson et al., [<reflink idref="bib27" id="ref45">27</reflink>]). Additionally, quantitative and qualitative research components in mixed‐methods studies should address the types of research questions and purposes that they are designed to answer (for review of single‐method research designs and purposes, see Cook & Cook, [<reflink idref="bib2" id="ref46">2</reflink>]). For example, when examining the implementation and effects of an instructional practice, qualitative methods should be used to explore stakeholders' perceptions and experiences rather than establish causality. In the previously described studies, both Marino et al. ([<reflink idref="bib22" id="ref47">22</reflink>]) and Nalavany et al. ([<reflink idref="bib24" id="ref48">24</reflink>]) used focus groups and/or interviews to learn participants' perspectives but did not use these data to examine whether the practices being examined resulted in improved student outcomes.</p> <p>Quantitative and qualitative methods are sometimes used in ways that diverge from single‐method expectations. This is most common when researchers transform data (i.e., quantitizing qualitative data or qualitizing quantitative data) and, therefore, change the nature of the data and how they are used. Quantitizing qualitative, or narrative, data is much more common than qualitizing quantitative, or numeric, data. For example, researchers may interview participants and analyze the transcripts by counting the number of times that a certain perspective was mentioned and then statistically compare that number across participant groups (quantitizing). Bazeley ([<reflink idref="bib1" id="ref49">1</reflink>]) noted three expectations for qualitative data that are converted into quantitative variables: (a) sufficient number of participants or cases to allow statistically meaningful analyses; (b) an explicit reason why quantitizing the data is more appropriate than keeping the data in the original narrative form (e.g., the exact number of times a code occurs across interviews with different participant groups is relevant to the research question and purpose); and (c) any codes that are quantitized should be dichotomous (e.g., present‐absent, positive‐negative). Although analyzing qualitative data in this way diverges from qualitative research analysis standards, it can provide meaningful insight for some research purposes in mixed‐methods studies.</p> <p>If practitioners observe that a method is not used according to its single‐method standards and it is not because the data were transformed, they should check how the researchers addressed divergence from single‐method expectations. In such cases, researchers may use a less rigorously implemented method to complement other study findings to achieve a specific research purpose or align with their stated theoretical or epistemological lens. For example, researchers could combine findings from qualitative interviews with findings from a quantitative survey with a small sample size if their research purpose is primarily to gain an in‐depth understanding of participant perspectives rather than to generalize findings. In that case, the survey serves to complement or supplement the interviews, which are being prioritized, and is not serving its traditional purpose (generalization). When methods diverge from single‐method standards, researchers should explain the reason for the divergence based on their research purpose and theoretical or epistemological lens, place findings in the context of related research (e.g., note correspondence with findings from related studies), and discuss limitations to the application of study findings. For example, Nalavany et al. ([<reflink idref="bib24" id="ref50">24</reflink>]) did not predetermine the number of statement clusters, as is common in the type of statistical analysis they used, because using member checks with participants to determine the clusters was more relevant to their research purpose and epistemology, and supported trustworthiness of the qualitative data analysis. That decision aligned with the authors' stated intention to prioritize participant perspectives and strengths. The authors also contextualized their findings based on other research about adults with dyslexia and noted that they were not making claims about the generalization of these findings or any causal relationships.</p> <hd id="AN0161103546-13">Meaningful Integration</hd> <p>Mixed‐methods studies should purposefully integrate quantitative and qualitative research approaches, and researchers should describe how integration addresses the overarching research purpose or questions (Fàbregues & Molina‐Azorín, [<reflink idref="bib6" id="ref51">6</reflink>]). For instance, if a study's primary purpose is to develop and assess a new measure or practice, quantitative and qualitative approaches should be combined, or integrated, to support that development, as seen in the opening vignette. Likewise, data integration in a research study that seeks to combine different types of data on a phenomenon (e.g., teacher attitudes toward inclusive education) should include some process of direct comparison between the quantitative and qualitative findings (e.g., a table that compares results of a quantitative survey with qualitative themes from focus group interviews). Data integration should also match the research design (Fàbregues & Molina‐Azorín, [<reflink idref="bib6" id="ref52">6</reflink>]). For example, the process of integration within an explanatory sequential research study should allow the researchers to draw conclusions that use the qualitative findings to explain or directly build on the quantitative findings. Meaningful integration would not be shown by a study that simply collected, analyzed, and reported the quantitative and qualitative findings separately.</p> <hd id="AN0161103546-14">CONCLUSION</hd> <p>Mixed‐methods research can effectively inform practice because it examines practices, contexts, and outcomes by combining quantitative and qualitative research approaches (Creswell & Plano Clark, [<reflink idref="bib5" id="ref53">5</reflink>]; Klingner & Boardman, [<reflink idref="bib15" id="ref54">15</reflink>]). In particular, mixed‐methods studies can (a) strengthen evidence that helps identify effective practices for students with LD with a variety of backgrounds and support needs; (b) support understanding of and responses to diverse populations, contexts, or circumstances, including informing the adaptation of evidence‐based practices; and (c) support understanding of the complex processes, critical features, contextual characteristics, and other considerations that may impact whether and how a practice is effective. Mixed‐methods research capitalizes on the strengths and purposes of quantitative and qualitative approaches and combines them to gain unique insights. When reading mixed‐methods research, practitioners should check for key study features (e.g., research purpose and questions; theories or epistemologies used; when and how the data were collected, analyzed, and integrated), qualities associated with the specific mixed‐methods design, appropriate use of quantitative and qualitative methods, and how researchers meaningfully integrated the methods to address the research question(s). In sum, mixed‐methods research requires some unique research practices, but it can meaningfully inform special education practice in real‐world contexts through the unique knowledge gained from integrating quantitative and qualitative research. Figure 1 provides additional resources for readers interested in learning more about mixed‐methods research.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/7MJ/01nov22/ldrp12295-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ldrp12295-fig-0001.jpg" title="1 Resources for additional information about mixed‐methods research." /> </p> <p></p> <ref id="AN0161103546-16"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> While research is often divided into quantitative and qualitative categories, these are broad classifications that each represents a variety of ways to collect, analyze, and represent data. For the purposes of this paper, we focus on combining multiple types of data (e.g., numeric and narrative), methods (e.g., standardized assessment, interviews, observations, document review), and analyses (e.g., statistical and thematic).</bibtext> </blist> </ref> <ref id="AN0161103546-17"> <title> References </title> <blist> <bibtext> Bazeley, P. (2006). The contribution of computer software to integrating qualitative and quantitative data and analyses. Research in the Schools, 13, 64 – 74.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref46" type="bt">2</bibl> <bibtext> Cook, B. G., & Cook, L. (2016). Research designs and special education research: Different designs address different questions. Learning Disabilities Research & Practice, 31 (4), 190 – 198. https://doi.org/10.1111/ldrp.12110</bibtext> </blist> <blist> <bibl id="bib3" idref="ref15" type="bt">3</bibl> <bibtext> Cook, B. G., & Cook, L. (2017). Do research findings apply to my students? Examining study samples and sampling. Learning Disabilities Research & Practice, 32 (2), 78 – 84. https://doi.org/10.1111/ldrp.12132</bibtext> </blist> <blist> <bibl id="bib4" idref="ref9" type="bt">4</bibl> <bibtext> Corr, C., Snodgrass, M. R., Greene, J. C., Meadan, H., & Santos, R. M. (2020). Mixed methods in early childhood special education research: Purposes, challenges, and guidance. Journal of Early Intervention, 42 (1), 20 – 30. https://doi.org/10.1177/1053815119873096</bibtext> </blist> <blist> <bibl id="bib5" idref="ref2" type="bt">5</bibl> <bibtext> Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research. Sage.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref30" type="bt">6</bibl> <bibtext> Fàbregues, S., & Molina‐Azorín, J. F. (2017). Addressing quality in mixed methods research: A review and recommendations for a future agenda. Quality & Quantity, 51 (6), 2847 – 2863. https://doi.org/10.1007/s11135‐016‐0449‐4</bibtext> </blist> <blist> <bibl id="bib7" idref="ref3" type="bt">7</bibl> <bibtext> Fetters, M. D., & Molina‐Azorín, J. F. (2017). The Journal of Mixed Methods Research starts a new decade: The mixed methods research integration trilogy and its dimensions. Journal of Mixed Methods Research, 11 (3), 291 – 307. https://doi.org/10.1177/2F1558689817714066</bibtext> </blist> <blist> <bibl id="bib8" idref="ref13" type="bt">8</bibl> <bibtext> Fetters, M. D., & Molina‐Azorín, J. F. (2020). Utilizing a mixed methods approach for conducting interventional evaluations. Journal of Mixed Methods Research, 14 (2), 131 – 144. https://doi.org/10.1177/1558689820912856</bibtext> </blist> <blist> <bibl id="bib9" idref="ref43" type="bt">9</bibl> <bibtext> Gersten, R., Fuchs, L. S., Compton, D., Coyne, M., Greenwood, C., & Innocenti, M. S. (2005). Quality indicators for group experimental and quasi‐experimental research in special education. Exceptional Children, 71 (2), 149 – 164. https://doi.org/10.1177/001440290507100202</bibtext> </blist> <blist> <bibtext> Goegan, L. D., & Daniels, L. M. (2022). Online learning for students with learning disabilities and their typical peers: The association between basic psychological needs and outcomes. Learning Disabilities Research & Practice, 37, 140 – 150. https://doi.org/10.1111/ldrp.12277</bibtext> </blist> <blist> <bibtext> Heyvaert, M., Hannes, K., Maes, B., & Onghena, P. (2013). Critical appraisal of mixed methods studies. Journal of Mixed Methods Research, 7 (4), 302 – 327. https://doi.org/10.1177/1558689813479449</bibtext> </blist> <blist> <bibtext> Horner, R. H., Carr, E. G., Halle, J., McGee, G., Odom, S., & Wolery, M. (2005). The use of single‐subject research to identify evidence‐based practice in special education. Exceptional Children, 71 (2), 165 – 179. https://doi.org/10.1177/001440290507100203</bibtext> </blist> <blist> <bibtext> Howie, E. K., Campbell, A. C., Abbott, R. A., & Straker, L. M. (2017). Understanding why an active video game intervention did not improve motor skill and physical activity in children with developmental coordination disorder: A quantity or quality issue? Research in Developmental Disabilities, 60, 1 – 12. https://doi.org/10.1016/j.ridd.2016.10.013</bibtext> </blist> <blist> <bibtext> Keel, J. M., Cushing, L. S., & Awsumb, J. M. (2018). Post‐school visions and expectations of Latino students with learning disabilities, their parents, and teachers. Career Development and Transition for Exceptional Individuals, 41 (2), 88 – 98. https://doi.org/10.1177/2165143417708997</bibtext> </blist> <blist> <bibtext> Klingner, J. K., & Boardman, A. G. (2011). Addressing the "research gap" in special education through mixed methods. Learning Disability Quarterly, 34 (3), 208 – 218. https://doi.org/10.1177/0731948711417559</bibtext> </blist> <blist> <bibtext> Kozleski, E. B. (2017). The uses of qualitative research: Powerful methods to inform evidence‐based practice in education. Research and Practice for Persons with Severe Disabilities, 42 (1), 19 – 32. https://doi.org/10.1177/1540796916683710</bibtext> </blist> <blist> <bibtext> Lauterbach, A. A., Brownell, M. T., & Bettini, E. A. (2020). Expert secondary content‐area teachers' pedagogical schemas for teaching literacy to students with learning disabilities. Learning Disability Quarterly, 43 (4), 227 – 240. https://doi.org/10.1177/0731948719864417</bibtext> </blist> <blist> <bibtext> Leko, M. M. (2014). The value of qualitative methods in social validity research. Remedial and Special Education, 35 (5), 275 – 286. https://doi.org/10.1177/0741932514524002</bibtext> </blist> <blist> <bibtext> Leko, M. M., Cook, B. G., & Cook, L. (2021). Qualitative methods in special education research. Learning Disabilities Research & Practice, 36 (4), 278 – 286. https://doi.org/10.1111/ldrp.12268</bibtext> </blist> <blist> <bibtext> Love, H. R., & Corr, C. (2022). Integrating without quantitizing: Two examples of deductive analysis strategies within qualitatively driven mixed methods research. Journal of Mixed Methods Research, 16 (1), 64 – 87. https://doi.org/10.1177/1558689821989833</bibtext> </blist> <blist> <bibtext> Mariage, T. V., Englert, C. S., & Mariage, M. F. (2020). Comprehension instruction for Tier 2 early learners: A scaffolded apprenticeship for close reading of informational text. Learning Disability Quarterly, 43 (1), 29 – 42. https://doi.org/10.1177/0731948719861106</bibtext> </blist> <blist> <bibtext> Marino, M. T., Gotch, C. M., Israel, M., Vasquez III, E., Basham, J. D., & Becht, K. (2014). UDL in the middle school science classroom: Can video games and alternative text heighten engagement and learning for students with learning disabilities? Learning Disability Quarterly, 37 (2), 87 – 99. https://doi.org/10.1177/0731948713503963</bibtext> </blist> <blist> <bibtext> Molina‐Azorín, J. F., & Fetters, M. D. (2019). Building a better world through mixed methods research. Journal of Mixed Methods Research, 13 (3), 275 – 281. https://doi.org/10.1177/1558689819855864</bibtext> </blist> <blist> <bibtext> Nalavany, B. A., Carawan, L. W., & Rennick, R. A. (2011). Psychosocial experiences associated with confirmed and self‐identified dyslexia: A participant‐driven concept map of adult perspectives. Journal of Learning Disabilities, 44 (1), 63 – 79. https://doi.org/10.1177/0022219410374237</bibtext> </blist> <blist> <bibtext> Plano Clark, V. L., & Ivankova, N. V. (2017). Mixed methods research: A guide to the field. Sage.</bibtext> </blist> <blist> <bibtext> Sinclair, J., Hansen, S. G., Machalicek, W., Knowles, C., Hirano, K. A., Dolata, J. K., Blakely, A. W., Seeley, J., & Murray, C. (2018). A 16‐year review of participant diversity in intervention research across a selection of 12 special education journals. Exceptional Children, 84 (3), 312 – 329. https://doi.org/10.1177/0014402918756989</bibtext> </blist> <blist> <bibtext> Thompson, B., Diamond, K. E., McWilliam, R., Snyder, P., & Snyder, S. W. (2005). Evaluating the quality of evidence from correlational research for evidence‐based practice. Exceptional Children, 71 (2), 181 – 194. https://doi.org/10.1177/001440290507100204</bibtext> </blist> <blist> <bibtext> Waitoller, F. R., & Annamma, S. A. (2017). Taking a spatial turn in inclusive education: Seeking justice at the intersections of multiple markers of difference. In M. Tejero Hughes & E. Talbott (Eds.), The handbook of research on diversity in special education (pp. 23 – 44). John Wiley.</bibtext> </blist> <blist> <bibtext> West, E. A., Travers, J. C., Kemper, T. D., Liberty, L. M., Cote, D. L., McCollow, M. M., & Stansberry Brusnahan, L. L. (2016). Racial and ethnic diversity of participants in research supporting evidence‐based practices for learners with autism spectrum disorder. The Journal of Special Education, 50 (3), 151 – 163. https://doi.org/10.1177/0022466916632495</bibtext> </blist> </ref> <aug> <p>By Hailey R. Love; Bryan G. Cook and Lysandra Cook</p> <p>Reported by Author; Author; Author</p> <p></p> <p>Hailey R. Love is an Assistant Professor in the Department of Rehabilitation Psychology and Special Education at the University of Wisconsin‐Madison and received her PhD from the University of Kansas. Her research interests center on (a) early childhood inclusive and equitable education for multiply‐marginalized young children with disabilities, (b) family‐professional partnerships with families of color, and (c) applications of mixed methods research in special education.</p> <p>Bryan G. Cook is a Professor in the Special Education Program at the University of Virginia School of Education and Human Development and received his PhD in Special Education at the University of California at Santa Barbara. His primary lines of inquiry include open science, conducting meta‐research on the special education research base, and evidence‐based practice.</p> <p>Lysandra Cook is an Associate Professor in the Special Education Program at the University of Virginia School of Education and Human Development, and received her PhD from Kent State University. Her scholarly interests revolve around translating research to practice in special education, including (a) identifying and implementing evidence‐based practices, (b) supporting pre‐ and in‐service teachers to be critical consumers of research, and (c) researching high‐quality teacher preparation.</p> </aug> <nolink nlid="nl1" bibid="bib15" firstref="ref4"></nolink> <nolink nlid="nl2" bibid="bib16" firstref="ref5"></nolink> <nolink nlid="nl3" bibid="bib20" firstref="ref6"></nolink> <nolink nlid="nl4" bibid="bib13" firstref="ref8"></nolink> <nolink nlid="nl5" bibid="bib10" firstref="ref11"></nolink> <nolink nlid="nl6" bibid="bib26" firstref="ref16"></nolink> <nolink nlid="nl7" bibid="bib29" firstref="ref17"></nolink> <nolink nlid="nl8" bibid="bib14" firstref="ref18"></nolink> <nolink nlid="nl9" bibid="bib23" firstref="ref20"></nolink> <nolink nlid="nl10" bibid="bib28" firstref="ref21"></nolink> <nolink nlid="nl11" bibid="bib17" firstref="ref26"></nolink> <nolink nlid="nl12" bibid="bib25" firstref="ref28"></nolink> <nolink nlid="nl13" bibid="bib22" firstref="ref32"></nolink> <nolink nlid="nl14" bibid="bib19" firstref="ref33"></nolink> <nolink nlid="nl15" bibid="bib24" firstref="ref36"></nolink> <nolink nlid="nl16" bibid="bib21" firstref="ref39"></nolink> <nolink nlid="nl17" bibid="bib11" firstref="ref40"></nolink> <nolink nlid="nl18" bibid="bib12" firstref="ref44"></nolink> <nolink nlid="nl19" bibid="bib27" firstref="ref45"></nolink>
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  Data: Mixed-Methods Approaches in Special Education Research
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  Data: Mixed-methods research can uniquely inform special education practice by combining qualitative and quantitative research approaches. However, its distinct features can also make mixed-methods research difficult to understand and apply. In this article, we provide an introduction to mixed-methods research purposes, designs, and quality considerations to help practitioners critically consume and apply this type of research when working with students with learning disabilities and their families. We describe three sample research studies to illustrate mixed-methods designs and contributions. Our take-home message is that mixed-methods research (a) requires unique research practices to meaningfully combine qualitative and quantitative research approaches in a single study and (b) can be particularly useful for informing special education practice in real-world contexts.
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      – SubjectFull: Research Methodology
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      – SubjectFull: Special Education
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      – SubjectFull: Students with Disabilities
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      – TitleFull: Mixed-Methods Approaches in Special Education Research
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            – D: 01
              M: 11
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 0938-8982
            – Type: issn-electronic
              Value: 1540-5826
          Numbering:
            – Type: volume
              Value: 37
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Learning Disabilities Research & Practice
              Type: main
ResultId 1