Education, Faculty of
Educational and Counselling Psychology, and Special Education (ECPS), Department of
DSpace
UBCV
Guo, Yue
Master of Arts - MA
University of British Columbia
Despite the importance of multi-component writing intervention and transcription intervention to writing performance, the systematic effects of these interventions and how transcription instruction contributes to such effects are unclear. I conducted a comprehensive meta-analysis including group experimental writing intervention studies for K-12 students to determine the overall writing quantity and quality effects and the extent to which the transcription intervention contributes to such effects. A total of 71 studies with 164 effect sizes (54 effect sizes for writing quantity outcomes and 110 effect sizes for writing quality outcomes) met the inclusion criteria. The mean effect sizes (Hedges’ g) for the outcomes of writing quantity and quality were calculated separately under the Correlated and Hierarchical Effects model. Overall, the mean effect sizes were moderate to strong for both measures, with 0.57 for writing quantity and 0.71 for writing quality. These moderate to strong writing effects were consistent, regardless of students’ grade level or academic skills. For both measures, transcription intervention generated the smallest aggregated effect size, followed by multi-component writing intervention with a transcription component, and the multi-component writing intervention without a transcription component yielded the strongest aggregated effect size. These findings indicate that, even for young and/or struggling writers, multi-component writing intervention is promising, and transcription intervention only is insufficient to improve writing performance. Also, multi-component writing intervention was more effective when the instruction of transcription skills was targeted simultaneously in certain situations, such as when the intervention study explicitly taught both transcription skills and other writing skills. However, the ability to conclude the optimal combination of instruction on these writing skills for subgroups of students is limited; future studies would benefit from evaluating how the combination of writing skills can maximally contribute to improved writing performance for specific subgroups. Additionally, more intervention studies are needed, especially intervention studies related to multi-component writing intervention with a transcription component and intervention studies for students at secondary grade levels.
DOES TRANSCRIPTION INSTRUCTION MAKE WRITING INTERVENTIONS MORE EFFECTIVE? A META-ANALYSIS by YUE GUO B.A., Beijing Normal University, 2012 M.A., Beijing Normal University, 2014 M.Ed., McGill University, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Special Education) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2022 © Yue Guo, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Does Transcription Instruction Make Writing Interventions More Effective? A Meta-analysis submitted by Yue Guo in partial fulfilment of the requirements for the degree of Master of Arts in Special Education Examining Committee: Dr. Sterett H. Mercer, Professor, Department of Educational and Counselling Psychology, and Special Education, UBC Supervisor Dr. Joanna E. Cannon, Professor, Department of Educational and Counselling Psychology, and Special Education, UBC Supervisory Committee Member Dr. Edward Kroc, Assistant Professor, Department of Educational and Counselling Psychology, and Special Education, UBC Supervisory Committee Member iii Abstract Despite the importance of multi-component writing intervention and transcription intervention to writing performance, the systematic effects of these interventions and how transcription instruction contributes to such effects are unclear. I conducted a comprehensive meta-analysis including group experimental writing intervention studies for K-12 students to determine the overall writing quantity and quality effects and the extent to which the transcription intervention contributes to such effects. A total of 71 studies with 164 effect sizes (54 effect sizes for writing quantity outcomes and 110 effect sizes for writing quality outcomes) met the inclusion criteria. The mean effect sizes (Hedges’ g) for the outcomes of writing quantity and quality were calculated separately under the Correlated and Hierarchical Effects model. Overall, the mean effect sizes were moderate to strong for both measures, with 0.57 for writing quantity and 0.71 for writing quality. These moderate to strong writing effects were consistent, regardless of students’ grade level or academic skills. For both measures, transcription intervention generated the smallest aggregated effect size, followed by multi-component writing intervention with a transcription component, and the multi-component writing intervention without a transcription component yielded the strongest aggregated effect size. These findings indicate that, even for young and/or struggling writers, multi-component writing intervention is promising, and transcription intervention only is insufficient to improve writing performance. Also, multi-component writing intervention was more effective when the instruction of transcription skills was targeted simultaneously in certain situations, such as when the intervention study explicitly taught both transcription skills and other writing skills. However, the ability to conclude the optimal combination of instruction on these writing skills for subgroups of students is limited; future studies would benefit from evaluating how the combination of writing skills can iv maximally contribute to improved writing performance for specific subgroups. Additionally, more intervention studies are needed, especially intervention studies related to multi-component writing intervention with a transcription component and intervention studies for students at secondary grade levels. v Lay Summary The importance of writing to an individual’s overall success necessitates the implementation of writing intervention that can improve literacy and prevent long-term writing difficulties. The goals of this meta-analysis were to determine the systematic effects of writing intervention (i.e., transcription intervention and multi-component writing intervention with/without a transcription component) for K-12 students and the extent to which the teaching of transcription skills (i.e., spelling, handwriting and keyboarding skills) contributes to such effects. The moderate to strong writing effects were consistent, regardless of students’ grade level or academic skills. Results also showed that transcription intervention generated the smallest aggregated effect sizes, while multi-component writing intervention without a transcription component yielded the strongest aggregated effect sizes. Results support the theoretical and practical importance of multi-component writing intervention to writing performance and indicate that the implementation of multi-component writing intervention is promising, even for young and/or struggling writers. vi Preface This thesis study was originally conducted by the author Yue Guo under the supervision of Dr. Sterett Mercer. This study was designed collaboratively by the author and Dr. Sterett Mercer. Since this thesis study was a meta-analysis, no ethics application was required. Data collection (literature search, study selection, and data extraction) was completed by the author in collaboration with Dr. Bonita Squires. Data analysis was conducted by the author with input from Dr. Sterett Mercer. Dr. Sterett Mercer was also involved throughout all procedures of research methods and data analyses, advising literature search, study selection, data extraction, analysis model selection, and related R codes. Dr. Edward Kroc and Dr. Joanna Cannon contributed valuable ideas about the study design and research procedures that were described in sections of Methods and Results. Manuscript composition was completed by the author with input from Dr. Sterett Mercer, who also contributed to manuscript edits. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ ix List of Figures ................................................................................................................................ x Acknowledgements ...................................................................................................................... xi Chapter 1: Introduction ............................................................................................................... 1 1.1 Theoretical Framework .................................................................................................... 3 1.2 Importance of Transcription Skills ................................................................................... 6 1.3 Effectiveness of Transcription Interventions ................................................................... 9 1.4 Transcription Intervention for Students beyond Elementary Grades ............................. 11 1.5 Transcription Components in Multi-Component Writing Interventions ....................... 13 1.6 Present Study .................................................................................................................. 15 Chapter 2: Method ...................................................................................................................... 18 2.1 Inclusion Criteria ............................................................................................................ 18 2.2 Literature Search and Selection...................................................................................... 19 2.3 Study Coding .................................................................................................................. 22 2.3.1 Study Characteristics Coding ....................................................................................... 22 2.3.2 Study Coding Procedures ............................................................................................. 26 2.4 Data Analysis ................................................................................................................. 28 2.4.1 Effect Size Computation .............................................................................................. 28 2.4.1.1 Dependent Effect Size Estimates .......................................................................... 29 2.4.2 Meta-Analysis .............................................................................................................. 31 2.4.3 Publication Bias ........................................................................................................... 33 2.4.4 Sensitivity and Outlier Analysis .................................................................................. 34 Chapter 3: Results....................................................................................................................... 35 3.1 Study Features ................................................................................................................ 35 3.2 Meta-Analytic Results .................................................................................................... 38 3.2.1 Research Question One ................................................................................................ 38 3.2.2 Research Question Two ............................................................................................... 66 3.2.3 Research Question Three ............................................................................................. 68 viii 3.2.3.1 Does the Overall Writing Effect Vary by Student Grade Level? ......................... 69 3.2.3.2 Does the Overall Writing Effect Vary by Student Academic Difficulty? ............ 71 3.3 Publication Bias.............................................................................................................. 73 3.4 Sensitivity and Outlier Analysis ..................................................................................... 75 Chapter 4: Discussion ................................................................................................................. 84 4.1 The Overall Effects of Writing Intervention .................................................................. 84 4.2 Writing Effects by Writing Intervention Types ............................................................. 85 4.2.1 Transcription Intervention Only .................................................................................. 86 4.2.2 Multi-Component Writing Intervention without a Transcription Component ............ 89 4.2.3 Multi-Component Writing Intervention with a Transcription Component.................. 90 4.3 Moderation of Writing Effects by Student Grade Level and Academic Difficulties ..... 95 4.3.1 Moderation Effects by Student Grade Level ............................................................... 95 4.3.2 Moderation Effects by Student Academic Difficulty .................................................. 97 4.4 General Discussion ....................................................................................................... 100 4.4.1 Individual Needs and Specific Transcription Screening ............................................ 103 4.4.2 Instructional and Administrative Factors ................................................................... 107 4.5 Limitations ................................................................................................................... 110 4.6 Future Directions .......................................................................................................... 113 Chapter 5: Conclusion .............................................................................................................. 116 References .................................................................................................................................. 118 ix List of Tables Table 1 Summary of Study Descriptive Features ......................................................................... 37 Table 2 Model Comparisons between CHE Model and Reduced Model ..................................... 40 Table 3 Features of Studies, Participants, Interventions, and Effect Estimates ............................ 42 Table 4 Summary of Effect Size Estimates for Writing Quantity and Writing Quality by Moderators .................................................................................................................................... 69 Table 5 Summary of Main Effects of Interactive Items across Intervention Type, Grade Level, and Academic Difficulty ............................................................................................................... 72 Table 6 Summary of Effect Size Estimates for the Models Fit the Adjusted Datasets by Moderators .................................................................................................................................... 80 Table 7 Summary of Wald Test Results across Models ............................................................... 81 x List of Figures Figure 1 The Simple View of Writing ............................................................................................ 3 Figure 2 Flowchart of Literature Search and Selection ................................................................ 22 Figure 3 Forest Plot: The Effects of Writing Intervention on Writing Quantity .......................... 62 Figure 4 Forest Plot: The Effects of Writing Intervention on Writing Quality ............................ 63 Figure 5 Funnel Plot: Publication Bias of the Overall Writing Quantity Effects ......................... 73 Figure 6 Funnel Plot: Publication Bias of the Overall Writing Quality Effects ........................... 75 Figure 7 Residual Plot for Writing Quantity Effect Sizes CHE Model ........................................ 77 Figure 8 Plot of Cook’s Distance for Writing Quantity Effect Sizes CHE Model ....................... 77 Figure 9 Residual Plot for Writing Quality Effect Sizes CHE Model .......................................... 79 Figure 10 Plot of Cook’s Distance for Writing Quality Effect Sizes CHE Model ....................... 79 Figure 11 Plot of the Cook’s Distances for CHE Models with Moderators for Writing Quantity Outcomes ...................................................................................................................................... 82 Figure 12 Plot of the Cook’s Distances for CHE Models with Moderators for Writing Quality Outcomes ...................................................................................................................................... 83 xi Acknowledgements I would like to express my appreciation to a number of important individuals. First, I would like to thank my research supervisor, Dr. Sterett Mercer, for the knowledge and expertise he generously shared with me and the patience and genius thoughts he showed me throughout the project. He is really an excellent mentor; this research project would not have been possible without his generous support and encouragement. Second, I would like to thank Dr. Edward Kroc and Dr. Joanna Cannon for their professionalism, time, and willingness to be my committee members. Dr. Edward Kroc provided valuable suggestions on the methodologies and helped me become familiar with meta-analysis and related statistical issues. I truly appreciate Dr. Joanna Cannon’s insightful questions and ideas for this study, and her constant encouragement made me motivated. Third, I would like to thank Dr. Bonita Squires; I appreciate her diligent work in screening thousands of studies and extracting data. Finally, I would like to thank my family and friends for their constant support. To Shijie, thank you for always listening and believing in me; your love and support mean a lot to me. To my wonderful parents, thank you for your indescribable support. Thank you all of you. Chapter 1: Introduction Writing is important for literacy development and the overall success of individuals in school and the workplace (Graham & Perin, 2007; McMaster et al., 2018; National Commission on Writing, 2004). Learning to write can improve one’s cognitive abilities and contribute to the learning of other content areas (Graham & Hebert, 2010; Graham & Perin, 2007). Also, writing can provide individuals with the opportunity to express themselves and communicate with others, which supports one’s emotional health (Graham & Hebert, 2010; Smyth, 1998). In the digital era, writing is an important communication tool; writing enables individuals to communicate beyond the constraints of time and distance (Malecki, 2014). Despite the importance of writing to an individual’s overall success, many students struggle to develop writing proficiency. For example, a Canadian nationwide assessment, Student Achievement Indicators Program (SAIP; Council of Ministers of Education Canada, 2003), indicated that almost 20% of 13-year-old and 40% of 16-year-old students in Canada did not achieve age-expected levels in English writing. Also, the National Assessment of Educational Progress (NAEP) indicated that for American students in Grades 8 and 12, 76% of these students struggled to reach writing proficiency, and 20% of them were below the basic level of writing achievement (National Center for Education Statistics, 2012). These statistics highlight the importance of implementing writing interventions that can prevent long-term writing difficulties and improve literacy (Berninger, Abbott, et al., 2006; Treiman et al., 2019). Fortunately, many research studies have identified evidence-based writing interventions (Graham et al., 2013; McMaster et al., 2018). However, it is necessary to investigate which intervention components maximally contribute to improved writing skills for specific subgroups of struggling writers. 2 Transcription skills, including spelling and handwriting or keyboarding skills, are basic writing component skills that facilitate writing development (Ahmed & Wagner 2020; Graham & Perin, 2007; Kim, 2020). Transcription intervention has extensive evidence of enhancing student writing; however, few meta-analytic studies have examined how transcription intervention works beyond the elementary grade levels. Also, although writing intervention targeting multiple writing component skills (e.g., targeting both spelling and writing strategies) is recommended (Berninger & Amtmann, 2003; Kent & Wanzek, 2016), the extent to which the transcription component leads to improved writing outcomes is unclear. Accordingly, the purposes of this study are to investigate the systematic effects of writing intervention (i.e., transcription intervention, multi-component writing intervention, and writing intervention with the combination of these components) for students from kindergarten to Grade 12 (K-12). This study also aims to examine the extent to which transcription intervention contributes to these effects and whether such effects will be moderated by students’ grade levels and whether students have academic difficulties. To achieve these purposes, this study will use a meta-analytic method to determine (a) what are the overall distribution and average of effects of writing intervention for K-12 students and (b) how the transcription component contributes to the effects of multi-component writing intervention by comparing the differential effects of three types of writing intervention—transcription intervention only, multi-component writing intervention that includes a transcription component (e.g., targeting both writing strategies and spelling), and multi-component writing intervention without a transcription component (e.g., writing instruction focuses on writing strategies and text generation). This study will also examine whether two potential moderators (i.e., students’ grade levels and whether students have 3 academic difficulties) will have an impact on the overall effects of writing intervention and the differential effects across the three types of writing interventions. 1.1 Theoretical Framework Writing is a complex and difficult task for most students (Graham & Harris, 2000). Writing places a huge demand on cognitive resources, requires writing-related knowledge such as knowledge of genre, and involves motivational factors such as the writer’s attitude, self-efficacy, and confidence (Berninger, 1999; Graham & Harris, 2000; Harris & Graham, 2009). In addition, writing is shaped by environments, such as how a writer adjusts compositions depending on the target audience (Russell, 1997). Also, writing involves a complex functional system (Berninger, Abbott, et al., 2006). Even though it shares common neuropsychological processes with reading, writing also requires unique brain processes (Berninger, Abbott, et al., 2006; Limpo & Alves, 2018). This present study takes the Simple View of Writing (Berninger & Amtmann, 2003) as the theoretical framework. The Simple View of Writing specifies three critical writing component skills: text generation, transcription, and self-regulation supported by executive functions during the writing process. Based on this model, the three interrelated writing component skills work collaboratively to enable composition (see Figure 1; Berninger & Amtmann, 2003; Hebert, Kearns, et al., 2018). Figure 1 The Simple View of Writing 4 Note. Adapted from Handbook of learning disabilities (p.350), by Berninger & Amtmann, 2003, The Guilford Press. Text generation, at the top vertex of the triangle, refers to the process of creating ideas at multiple levels of language (i.e., word level, sentence level, discourse level) and text production on paper or screen by transcription (Berninger & Amtmann, 2003; Berninger et al., 2002). Transcription, at the left base vertex of the triangle, consists of handwriting or typing and spelling skills, and is critical for younger writers (Berninger & Amtmann, 2003). Transcription is the transformation of mental representations of language into the orthographic symbols of a given writing system (Berninger, 2009b; Berninger & Amtmann, 2003). The right base vertex is self-regulation, which includes many executive functioning skills necessary for successful writing such as conscious attention, goal setting, planning, reviewing, revising, self-instruction, and self-monitoring (Berninger & Amtmann, 2003; Hebert, Kearns, et al., 2018). Specifically, self-instruction refers to running conversations between writers and themselves while writing; that is, writers talk to themselves about what they should do and regulate their writing behaviors during writing process (Harris et al., 2008). Also, self-monitoring occurs when writers self-assess and then self-record their writing behaviors (e.g., Text GenerationWorking MemoryTranscription Executive Functions and Self-Regulation 5 writing strategies they use) while writing (Harris et al., 2008). Self-regulation skills coordinate multiple cognitive skills underlying writing process; that is, an individual’s writing skill, writing knowledge, writing strategy, and writing attitude all work collaboratively to support the writing process (Graham et al., 2014; Limpo & Alves, 2018). The center of the Simple View of Writing triangle is working memory—a limited cognitive resource that allows an individual to manipulate task-related information and mentally work with the information (Baddeley, 2012; Diamond, 2014). That is, writing happens within the long-term and short-term working memory environment (Santangelo & Graham, 2016; Berninger & Amtmann, 2003). During the writing process, all three writing component skills are constrained by an individual’s working memory capacity (Berninger & Winn, 2006; Herbert et al., 2018). Thus, any writing component skills requiring heavy cognitive resources will result in insufficient resources available for other writing skills and processes (Hebert, Kearns, et al., 2018). For example, beginning writers who have not developed automatic transcription skills (e.g., struggle with spelling) have to allocate more cognitive resources to foundational mechanics-level writing skills (Hebert, Kearns, et al., 2018). As a result, writers who have not obtained automaticity of transcription skills do not have adequate cognitive resources allocated to long-term working memory nor to higher-level writing component skills such as self-regulation (Hebert, Kearns, et al., 2018). In contrast, when individuals develop automaticity of the mechanics-level transcription skills, more cognitive resources are freed up to be used for higher-order writing processes and skills such as planning, monitoring and revising (Berninger et al., 2002; Childress, 2011). For these reasons, automaticity of transcription skills is a prerequisite for skilled writing and improves text generation (Berninger, 1999; Berninger et al., 2002). In addition, during the course 6 of writing development, higher-order writing component skills play an increasingly important role as composition is less constrained by mechanics-level skills (Kim & Park, 2019; Tighe & Binder, 2015). This present study takes the Simple View of Writing as the theoretical framework because it (a) includes the critical writing component skills (i.e., transcription, text generation, and self-regulation) for writing development and writing instruction (Berninger et al., 2002), (b) conceptualizes writing for both beginning and skilled writers (Berninger, 2009a, 2009b; McCutchen, 2006), (c) synthesizes diverse research studies of writing and depicts how the core component writing skills are interrelated and work collaboratively during the development of writing process (Berninger et al., 2002; McMaster et al., 2018), and (d) has been used as a theoretical framework for previous writing intervention studies and meta-analyses which include students across K-12, with and without specific disabilities, English language learners and native speakers (e.g., Berninger et al., 1994; Graham & Eslami, 2020; Hebert, Kearns, et al., 2018; McMaster et al., 2018; Puranik & AlOtaiba, 2012). 1.2 Importance of Transcription Skills Although all three writing component skills (i.e., transcription, text generation, and self-regulation) are important for an individual’s writing development (Berninger, 2009a; Berninger & Amtmann, 2003; Graham, McKeown et al., 2012; Kent & Wanzek, 2016; McCutchen, 2006), transcription skills (i.e., handwriting and spelling) are fundamental to writing development (Kent & Wanzek, 2016). Transcription skills can be operationalized by handwriting and spelling skills (Santangelo & Graham, 2016). Handwriting involves the coordination of a writer’s fine-motor skill with orthographic and letter knowledge (i.e., what is the letter’s name and how does the letter look when written; Berninger, 1999). Spelling involves the translation and mapping 7 between phonology and orthography in a given writing system; it requires knowledge of phonology, orthography, and morphology, as well as the capability of mentally holding phonological and orthographic forms in memory, and making correct phoneme-grapheme correspondences (Berninger et al., 2002; Fletcher et al., 2019; Treiman et al., 2019). Transcription skills appear to be important to target in writing interventions for K-12 students. Although digital technology (e.g., word processing software, speech synthesis) is widely used, much of the text production generated by K-12 students is handwritten (Santangelo & Graham, 2016); not to mention transcription is also involved in typing. Because transcription skills enable the translation of mental representations of language into the written symbols by handwriting or typing and spelling, improved transcription skills (i.e., handwriting and/or spelling skills) can increase a composition’s readability and support writing motivation (Berninger et al., 2002; Fletcher et al., 2019; Graham, Harris et al., 2011; Graham & Hebert, 2011; Kellogg, 2008). Specifically, poor handwriting legibility and spelling will make readers have difficulty understanding the composition and devalue the composition with a negative impression (Graham, Harris et al., 2011; Ouellette et al., 2017; Treiman, 2018). For example, Graham, Harris et al. (2011) found that for students from Grades 1 to 12, compared to a more legible version of a paper, the paper with poorer handwriting legibility was evaluated more harshly. In addition, improved handwriting and spelling skills reduce student avoidance of writing tasks, promoting a growth mindset for writing and further facilitating writing development (Berninger et al., 1991; Fletcher et al., 2019). Transcription skills significantly contribute to both writing fluency and writing quality across developmental stages (Berninger, 1999; Puranik & AlOtaiba, 2012), but these contributions differ by grade level. For example, Kent and Wanzek (2016) found that although 8 age is not a significant moderator of the relationship between transcription skills and writing performance, transcription skills decreasingly contribute to writing performance as students proceed to higher grade levels. Along with the changing magnitude, Limpo and Alves (2013) reported that for elementary school students (Grades 4-6), transcription skills directly relate to writing development and self-regulation skills, whereas, for students across Grades 7-9, transcription skills indirectly contribute to writing development through higher-order component skills such as self-regulation skills. Writing involves a complex neurofunctional system; the concurrent activation of each writing component skill enables text generation during the writing process, and each component skill contributes to writing development (Berninger et al.,1992, 2006; Berninger & Amtmann, 2003). For beginning writers, writing is mostly constrained by the capacity of transcription; however, self-regulation skills increasingly contribute to writing as students advance to higher grade levels (Berninger & Amtmann, 2003; Fletcher et al., 2019). According to the Simple View of Writing (Berninger & Amtmann, 2003), all the writing component skills are constrained by working memory capacity (Hebert, Kearns, et al., 2018). For writers struggling with transcription, spelling and/or handwriting is effortful and overly occupies cognitive resources, which results in too few resources available for other writing processes such as text generation and idea creation (Berninger, 2004; Berninger & Swanson, 1994; McCutchen, 2000; Puranik & AlOtaiba, 2012; Santangelo & Graham, 2016). In addition, if too many cognitive resources are continuously allocated to spelling and/or handwriting, the development of other writing skills such as text generation and self-regulation will be impeded as fewer cognitive resources can be used for the higher-level writing skills (Graham et al., 2002; Kellogg, 2008; Santangelo & Graham, 2016). 9 Kent and Wanzek (2016) found that handwriting fluency had a moderate relationship with writing outcomes for K-12 students, with average weighted effect sizes (i.e., Pearson’s r) of 0.49 and 0.48 for writing quality and writing production, respectively. Additionally, they found that spelling ability was weakly to moderately correlated with writing quality and writing production for K-12 students, with average weighted effect sizes (i.e., Pearson’s r) of 0.49 and 0.25, respectively (Kent & Wanzek, 2016). 1.3 Effectiveness of Transcription Interventions Four recently published meta-analyses on comprehensive writing interventions have determined the effects of early transcription interventions (Graham, McKeown et al., 2012; McMaster et al., 2018) as well as specific types of spelling and handwriting instruction (Graham & Santangelo, 2014; Santangelo & Graham, 2016). Graham, McKeown, et al. (2012) conducted a meta-analysis to identify effective writing instruction for students in the elementary grades. This meta-analysis divided all the identified studies into 13 categories and provided an analysis of the effectiveness of transcription instruction. Eight transcription studies for students from Grades 1 to 3 were included: five studies were about handwriting instruction, three studies related to teaching spelling, and one study was about keyboarding. Graham, McKeown, et al. (2012) reported a moderate effect of transcription instruction on writing quality for elementary grade students; the average weighted effect size (Hedges’ g) for writing quality was .55 and ranged from .12 to 2.40. Thus, Graham and colleagues provided empirical support for the theoretical and practical importance of the transcription component to the overall writing performance of young students. McMaster et al. (2018) conducted a meta-analysis to identify promising writing interventions for beginning struggling writers (i.e., students from kindergarten to Grade 3). They 10 examined 25 studies; specifically, based on the writing component skills involved in the writing interventions, these 25 studies were then categorized into four groups: (a) transcription interventions, (b) text generation interventions, (c) transcription plus text generation interventions, and (d) text generation plus self-regulation interventions (McMaster et al., 2018). Of the 25 studies, seven studies addressed transcription interventions designed to improve handwriting skills (4 studies) and spelling skills (3 studies) for early struggling writers; specifically, they reported mean effect sizes (Hedges’ g) of 0.46 (effect sizes range = .20 - .92) for writing quantity and 0.17 (effect sizes range = -.01 - .31) for writing quality. Thus, this study also provided empirical evidence supporting the theoretical and practical importance of the transcription component to the overall writing performance of early struggling writers. Santangelo and Graham investigated the effectiveness and efficiency of spelling and handwriting interventions for K-12 students (Graham & Santangelo, 2014; Santangelo & Graham, 2016). Santangelo and Graham’s (2016) meta-analysis concerned handwriting instruction; they examined true- and quasi-experimental studies of handwriting instruction and found that systematic handwriting instruction improved specific handwriting skills and overall writing performance. For specific handwriting skills, Santangelo and Graham (2016) examined 25 studies and reported that handwriting instruction could yield overall effect sizes (Hedges’ g) of 0.63 for handwriting fluency and 0.59 for handwriting legibility. In addition, they also examined seven studies and reported overall effect sizes (Hedges’ g) of 0.84, 1.33, and 0.48 for writing quality, writing productivity, and writing fluency, respectively. This meta-analysis provided empirical evidence supporting the theoretical and practical importance of handwriting skills to overall writing performance for students across K-12, which indirectly supported the importance of transcription skills to overall writing outcomes for students across grades. 11 Graham and Santangelo (2014) also examined the relative effectiveness of formal spelling instruction on spelling, writing, and reading performance for K-12 students. In their meta-analysis (2014), 53 studies were included to address research questions about whether formal and systematic spelling instruction was more effective and efficient than informal spelling instruction (e.g., reading instruction) and unrelated instruction (e.g., mathematics instruction). As a result, Graham and Santangelo (2014) reported that, compared to informal spelling instruction and no/unrelated academic instruction, formal spelling instruction produced greater effects on spelling outcomes with average weighted effect sizes (Hedges’ g) of 0.43 and 0.54, respectively. Moreover, greater time allocated to formal spelling instruction positively impacted spelling improvement with Hedges’ g of .70 (Graham & Santangelo, 2014). Also, formal spelling instruction had positive effects on reading performance (Hedges’ g = .44), writing performance (Hedges’ g = .19), and phonological awareness skills (Hedges’ g = .51; Graham & Santangelo, 2014). This meta-analysis supported the idea that spelling should be taught in a formal, explicit and systematic way. Also, this meta-analysis provided empirical evidence on the theoretical and practical importance of spelling skills to overall writing and reading performance for K-12 students, which indirectly supported the importance of transcription skills to overall writing outcomes for students across grades. 1.4 Transcription Intervention for Students beyond Elementary Grades Previous studies have documented the effects of explicit transcription instruction on writing performance for elementary grade students, but more evidence is required to determine if these practices are effective for students beyond the elementary grades (Graham, McKeown et al., 2012; Kent & Wanzek, 2016). For example, Graham, McKeown, et al. (2012) and McMaster 12 et al. (2018) examined the effects of early transcription intervention for (struggling) young writers; however, neither of the two meta-analyses included studies of transcription intervention for older students nor investigated whether transcription intervention is more effective for younger students or/and older students with writing difficulties. Kent and Wanzek (2016) reported that although transcription skills decreasingly influence writing performance as students proceed to higher grade levels, the relationship between transcription skills and writing performance is not significantly moderated by grade levels. Also, students may not acquire automatic transcription skills by the end of the elementary grades; for example, Graham and colleagues found that students can still develop improved handwriting skills until at least Grade 9 (Graham et al., 1998). It is reasonable to assume that transcription intervention may be effective for older students, especially older students who have writing difficulties because they may still be working to acquire these mechanics-level writing skills. Despite transcription skills continuing to have a moderate writing impact for secondary school students, writing intervention focuses less on transcription teaching for students at such grade levels compared to early school grades (Kent & Wanzek, 2016). Santangelo and Graham (Graham & Santangelo, 2014; Santangelo & Graham, 2016) focused on the writing effects of specific transcription skills interventions (i.e., spelling and handwriting instruction) for K-12 students and examined the relative effects of different approaches to teaching handwriting and spelling skills to these students; however, they did not determine the overall effects of K-12 transcription intervention in one meta-analysis nor investigate the relative effects of such intervention for subgroups of students. 13 Therefore, there is a great need to investigate whether transcription intervention for students beyond the elementary grades (i.e., for K-12 students) is warranted. In addition, it is necessary to determine if transcription intervention is more effective for younger students and older students with academic difficulties to inform teachers how best to allocate instructional time for different subgroups of students. 1.5 Transcription Components in Multi-Component Writing Interventions As Berninger et al. (1992) discussed, although a writing intervention may primarily focus on one component skill (e.g., transcription skills), it does not mean such skill should be taught in isolation. For example, Kent and Wanzek (2016) reported that exclusively teaching transcription skills may be insufficient to improve writing performance for students, regardless of whether they have academic difficulties. Although multi-component writing intervention that targets both mechanics-level (i.e., transcription skills) and high-level writing skills is recommended for maximal efficacy and efficiency and has support in the literature across grade levels for students regardless of writing level (Berninger et al., 1995; Berninger & Amtmann, 2003; Berninger et al., 2002; Graham, Bollinger et al., 2012; Graham & Harris, 2002; Limpo & Alves, 2018; Puranik & AlOtaiba, 2012), a clearer understanding of the extent to which the transcription component contributes to writing outcomes in a multi-component writing intervention is needed. According to the Simple View of Writing, Berninger and colleagues (Berninger & Amtmann, 2003; Berninger et al., 2002) proposed possible reasons supporting the effects of writing intervention which includes writing skills across all levels (i.e., writing interventions targeting both mechanics-level skills such as transcription skills and higher-level skills such text generation): (a) teaching of mechanics-level skills can be used to scaffold the learning of higher-level skills (based on the theory of zone of proximal development; Vygotsky, 1930-1934/1978), 14 (b) increasing automaticity of mechanics-level skills can support the development of higher-level skills by freeing up more cognitive resources for higher-level skills, and (c) teaching of higher-level skills can support learners’ motivation to work on the necessary but challenging mechanics-level skills. Thus, in a writing intervention targeting both mechanics-level and higher-level skills, learners can practice writing skills across all levels (from sub-word to discourse levels) in the neurofunctional system. Students can also strengthen each component skill while learning how to employ them together to get the functional writing system to develop and improve (Berninger, 1999; Berninger & Amtmann, 2003). For example, if a teacher only provides spelling instruction (i.e., a mechanics-level skill) without providing any instruction on strategies to regulate the writing process (i.e., a higher-level skill), students might get less effective writing instruction with a disconnected sense of writing activity and less practice on the coordination of writing component skills (Malecki, 2014). Specifically, students with learning difficulties or other complex needs may have less opportunity to get writing instruction focusing on higher-level writing skills because intervention may focus on decontextualized mechanics-level skills such as teaching letter-sound correspondence or spelling instruction (Berninger et al., 1995). Based on the Simple View of Writing, although multi-component writing intervention combined with transcription skills is recommended, few meta-analyses have been conducted to examine the extent to which including a transcription component contributes to writing outcomes for multi-component writing intervention. Thus, studies determining whether transcription intervention will be more effective in the context of multi-component writing intervention and whether multi-component writing intervention is more effective when transcription skills are targeted simultaneously are needed. Additionally, there is a great need to investigate whether the 15 differential effects of the three types of writing interventions will be affected by students’ grade levels and whether students have academic difficulties. In McMaster and colleagues’ meta-analysis (2018), they reviewed two multi-component writing intervention studies. In both studies, multi-component writing interventions targeting transcription (i.e., mechanics-level skill) and text generation skills (i.e., high-level skill) were implemented and compared to transcription intervention alone. Compared to transcription intervention only, both studies reported that the multi-component writing interventions produced greater effects on writing performance. Although McMaster et al. (2018) provided empirical evidence supporting the relative effect of the transcription intervention in the context of multi-component writing intervention, they only reviewed two related primary studies for young, struggling students, and they did not examine whether multi-component writing intervention is more effective when it includes a transcription component. To conclude, it is necessary to understand how the transcription component contributes to the writing effects of multi-component writing intervention for subgroups of K-12 students. It can provide empirical evidence of the theoretical importance of transcription component skills to overall writing performance and help teachers determine how best to allocate instructional time for transcription teaching for specific student subgroups (e.g., older struggling writers). 1.6 Present Study To date, meta-analysis has not been used to examine the relative importance of transcription components in multi-component writing intervention for K-12 students nor to examine the differential effects for specific student subgroups. Accordingly, this meta-analysis aims to determine how the transcription component account for writing outcomes for K-12 16 students and examine the potential influences of students’ grade levels and whether students have academic difficulties. This study will extend prior meta-analyses in two ways. First, this study will determine whether transcription intervention and multi-component writing intervention with/without a transcription component are warranted for K-12 students; that will extend previous literature by providing potential evidence of the effects of transcription intervention for students beyond elementary grades and the importance of multi-component writing intervention to the writing performance for K-12 students. Second, this study will examine the extent to which including a transcription component in multi-component writing interventions improves K-12 students’ writing outcomes. Addressing this question will extend previous literature by evaluating the differential effects of three types of writing interventions (i.e., transcription interventions only, multi-component writing interventions with a transcription component, multi-component writing interventions without a transcription component) to determine whether transcription intervention is more effective in the context of multi-component writing intervention, and whether a multi-component writing intervention is more effective when a transcription component is included. Additionally, this meta-analysis will include moderator analyses to examine whether such writing effects of the writing interventions will be affected by students’ grade levels and whether students have academic difficulties. Previous meta-analyses have traditionally evaluated these two potential moderators (e.g., Galuschka et al., 2020; Graham, McKeown et al., 2012; Kent & Wanzek, 2016; Santangelo & Graham, 2016). The moderator analysis will help teachers determine how best to allocate instructional time for different subgroups of students. Given the purposes of this study, three research questions will be addressed: (1) What is the effect of writing intervention on writing outcomes for K-12 students? 17 (2) To what extent does a transcription component contribute to the effectiveness of multi-component writing intervention for K-12 students? That is, does multi-component writing intervention with a transcription component yield greater writing improvements than transcription intervention only, and does multi-component writing intervention with a transcription component yield greater writing improvements than multi-component writing intervention without a transcription component? (3) Do grade level and students’ academic difficulties moderate the effects of writing intervention? Based on the Simple View of writing and prior studies (Berninger & Amtmann, 2003; Kim et al., 2021), I expect that writing intervention will have positive writing effects for K-12 students. In addition, I hypothesize that (a) multi-component writing intervention, regardless of whether a transcription component is included or not, will have stronger writing effects than transcription intervention, (b) multi-component writing intervention will be more effective when a transcription component is targeted simultaneously, especially for struggling writers, and (c) transcription intervention is likely to produce greater writing effects for younger students and students with specific academic difficulties compared to older students and students without academic risks. 18 Chapter 2: Method 2.1 Inclusion Criteria Consistent with the research purposes, the present meta-analysis included studies that met the following criteria: (a) studies included participants whose grade levels were in the range from kindergarten to Grade 12 (if ages were reported, the range should be from five to 18 years old); (b) studies involved explicit writing intervention (multi-component writing intervention with or without a transcription component, or transcription intervention only) with a focus on the English writing system; (c) the control condition received other interventions (interventions not related to writing skills) or a business-as-usual curriculum; (d) studies applied true- or quasi-experimental group design; (e) studies provided measures of writing quality or writing quantity for both the control and treatment conditions; and (f) studies written in English. Additionally, the inclusion criteria were updated during the screening process with more details. First, the business-as-usual control condition included instruction in the regular English Language Arts classroom and a school-established writing curriculum. Second, teachers’ professional development projects could be included, so long as (a) these studies employed group experimental design and reported students’ writing outcomes to assess the effect of the teachers’ professional development project, (b) participating teachers in the treatment condition were taught how to implement writing intervention that met the above criteria, and (c) these studies met all the other inclusion criteria (e.g., students were from Kindergarten to Grade 12). The following is the exclusion criterion: if the writing intervention for the treatment condition included other literacy and/or academic instruction (e.g., reading instruction, mathematics instruction), this study was excluded unless the other academic activities involved were aimed to support the implemented writing intervention. For example, Tracy et al. (2009) implemented a 19 Self-Regulated Strategy Development (SRSD) story writing intervention in which students learned to identify characteristics and elements of a good story by listening to teachers read. Although reading activities were involved (i.e., listening to teachers’ reading), this study was included because the reading activities aimed to develop story writing skills by teaching students to identify story elements. 2.2 Literature Search and Selection Based on the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA; Liberati et al., 2009), this systematic search used two strategies—electronic search and hand search—to identify eligible primary studies. The electronic search was conducted first, including searching the ERIC, PsycINFO, and ProQuest databases, which were followed by Google Scholar and hand search. ProQuest and Google Scholar were used to capture unpublished, grey literature for a comprehensive meta-analysis; specifically, ProQuest was used to access unpublished theses and dissertations, and Google Scholar was used for obtaining more potential qualifying studies. I conducted the electronic database search (i.e., ERIC, PsycINFO, ProQuest) in October 2021, using the combination of terms related to “transcription” which is operationalized by spelling and handwriting OR broadly “writing intervention” AND “writing outcomes” and searched the fields of Abstract, Title, and Keywords. The search terms were: (“transcription” or “spell*” or “phonics” or “orthograph*” or “morpholog*” or “phonolog*” or “word study” or “proofreading” or “handwriting” or “letter formation” or “fine motor” or “legibility” or “keyboarding” or “typing” or “writing intervention” or “writing instruction” or “writing strategy” or “writing research” or “writing treatment” or ”writing teaching”) AND ( “fluency” or “quality” or “quantity” or “writ*” or “length” or “composi*”). These terms were selected based 20 on the inclusion criteria, the Simple View of Writing, and adapted from related published meta-analyses (e.g., Galuschka et al., 2020; Graham & Santangelo, 2014; McMaster et al., 2018; Santangelo & Graham, 2016). To have a comprehensive search, this search process only had the language limit (written in English) and did not specify the publication date. The electronic database search returned 63,980 records; after 14,884 duplicates were removed, 49,096 studies were manually reviewed in the first screening by the titles and abstracts. Two screeners conducted two-round screenings to identify study eligibility. The primary screener is a graduate student in the program of Special Education, and the second screener is a postdoctoral fellow whose research focuses on the evaluation of written expression. The primary screener had taken coursework in the areas of meta-analysis and academic evaluation and intervention for students with special needs, as well as had prior research experience in the field of meta-analysis. In addition, the second screener had conducted and participated in several meta-analysis research projects with a focus on language learning and assessment. During the first screening, the two screeners independently screened 23% of all selected studies, and the initial inter-rater agreement was 98%. After all the disagreements were resolved with the inclusion criteria updated, the remaining studies were divided across two screeners. This process resulted in 1,077 studies proceeding to the second-round screening using the full text for final eligibility. During the second screening, 30% of the 1,077 studies were randomly selected and reviewed by the same two screeners independently. Due to weak reliability (Cohen’s Kappa = .58) and moderate inter-rater agreement of (i.e., 79%) for the 30% of selected articles, the two screeners independently screened all remaining studies, and then resolved any disagreements. Following these steps, 155 of the 1,077 studies were determined to meet the inclusion criteria. 21 Reasons for the exclusion of the 922 studies included: (a) 95 primary studies had participants whose age was beyond the range of this study required (e.g., postsecondary students; Woods-Groves et al., 2018); (b) 362 primary studies did not employ group experiment designs (e.g., a correlational study; Parker et al., 2018); (c) 66 primary studies had writing intervention that did not focus on the English writing system (e.g., study focused on Spanish writing; Fidalgo et al., 2008); (d) 77 primary studies did not implement explicit writing intervention (e.g., Truckenmiller, 2012); (e) 35 primary studies did not include transcription intervention nor multi-component writing intervention (e.g., the intervention was only about sentence combining; Walter et al., 2021); (f) 93 primary studies had writing intervention with other academic instruction embedded (e.g., reading instruction involved, Wissinger et al., 2021); (g) 182 primary studies only evaluated differential pedagogical methods between treatment and control conditions (e.g., compared peer-assisted learning to non peer-assisted learning; Painter, 1994); (h) 155 primary studies neither reported writing quantity (i.e., total word written) nor overall writing quality outcomes (e.g., Woodruff, 2014); and (i) five primary studies did not have available full text. Following the database search, I conducted a Google Scholar search using the following search query: “intervention” AND “writing” OR “written” OR “write”. Because Google Scholar did not recognize parentheses (Bramer et al., 2017) and can maximumly present 1,000 records for any given query (Google, n.d.), I ordered the records by relevance and reviewed the first 1,000 studies. This Google Scholar search resulted in no additional includable study I then implemented a hand search by searching the following journals from 2000 - 2022: Journal of Educational Psychology; The Journal of Educational Research; Journal of Experimental Education; Journal of Learning Disabilities; Learning Disability Quarterly; 22 Reading and Writing; Reading Research Quarterly, The Journal of Special Education. These journals were used by previous related meta-analyses (Gillespie & Graham, 2014; Graham, McKeown et al., 2012; Graham & Santangelo, 2014; Williams et al., 2018) and are representative of the given topic. This process resulted in one study included. Therefore, in total, 156 studies were eligible to be included in this meta-analysis. See Figure 2. PRISMA flowchart of literature search and selection. Figure 2 Flowchart of Literature Search and Selection 2.3 Study Coding 2.3.1 Study Characteristics Coding The following characteristics of each study were coded: (a) participants demographic information (including participants’ features), (b) the number of participants in each condition (i.e., the sample size in the treatment and control condition), (c) intervention type, (d) IdentificationScreeningEligibilityIncludedRecords after duplicates removed (n = 49,096) 14,884 duplicates removedRecords screened by title and abstract (n = 49,096) 48,019 records were excludedFull-text articles assessed for eligibility (n = 1,077)Additional eligible studies from Google Scholar (n = 0)Additional eligible studies from hand search (n = 1)Records of eligible studies (n = 155)922 records were excludedStudies identified for data extraction (n = 156)Studies included (n = 71)85 records were excludedRecords identified through ERIC, PsycINFO, and ProQuest (n = 63,980) 23 intervention features, (e) writing outcome measures, (f) study design, and (g) study quality. These characteristics were in line with the focus of this meta-analysis and adapted from previous related meta-analytic studies by Santangelo and Graham (2016), Kim et al. (2021), and Fishstrom et al. (2022). In addition, the two screeners worked as two coders to conduct the data extraction process (for the professional background of the two coders, see the section of “Literature Search and Selection”). Demographic information includes participants’ ages (i.e., the mean age of students in both the treatment and control conditions at the pretest time point), grade levels (i.e., elementary versus secondary), and participants’ features (i.e., participants with academic difficulties versus participants with diverse academic skills). Specifically, participants’ mean ages were either directly recorded from the source study or calculated based on the data the study provided. Regarding grade levels, based on the International Standard Classification of Education (ISCED; UNESCO Institute for Statistics, 2012), two education stages were considered—elementary grade level (i.e., primary education level) and secondary grade level (i.e., secondary education level including middle school and high school). Because research showed that the focal skills of writing intervention might differ by different grades (Graham & Santangelo, 2016; Kent & Wanzek, 2016), these two dimensions were used to examine whether writing effects vary as a function of grade level. Grade level was recorded directly from the primary study if the primary study reported this information (i.e., the primary study reported the intervention focused on either elementary or secondary grade level). Also, because the chronological age of 12 is the boundary between elementary and secondary education levels and the differences on grade-level categorization across local educational systems always happen at this age (UNESCO Institute for Statistics, 2012), the primary study that focused on students at this age and did not report the 24 grade level was coded based on the specific educational system of the region where this primary study was implemented. The present meta-analysis only included one primary study in this situation (i.e., Welch, 1992). Because this study happened in the U.S., we coded this primary study as elementary grade level, according to the guidance from National Center Education Statistics (Matheson et al., 1996). We then coded participants’ features as (a) students with academic difficulties and (b) students with diverse academic skills. Students with academic difficulties refer to primary studies that specifically focused on students with academic difficulties, and students with diverse academic skills refer to primary studies that did not target students with academic difficulties (e.g., studies conducted in inclusive classrooms). Because the number of studies focusing on students with specific risks (e.g., students at risk of spelling skills from Graham et al., 2002; students at risk of handwriting skills from Berninger et al., 1997; students with reading difficulties from Wolery, 1997) was small and unbalanced, these primary studies were all categorized as students with academic difficulties rather than categorizing by specific difficulty. In this meta-analysis, there was only one selected study that targeted students with emotional behaviour disorders who also had academic concerns; this primary study was categorized as one with students with academic difficulties. Intervention type refers to the types of writing intervention implemented with participants. Based on the Simple View of Writing and the targeted skills of the writing interventions in the treatment conditions, writing interventions were categorized in three groups: (a) transcription intervention only (T), (b) multi-component writing intervention with a transcription component (MT), and (c) multi-component intervention without a transcription component (M). The focal writing components of transcription intervention included only 25 spelling, handwriting, or keyboarding; that is, this type of writing intervention did not involve instruction on idea generation. Multi-component writing intervention included explicit writing intervention targeting multiple writing component skills, such as SRSD, a writing intervention targeting specific genre knowledge and the corresponding text structure, plus self-regulation skills. If a multi-component writing intervention included a transcription component, it was coded as a multi-component writing intervention with a transcription component (MT). In contrast, if a multi-component writing intervention did not target transcription-related skills, it was coded as a multi-component writing intervention without a transcription component (M). Regarding the coding of intervention type, we first wrote a summary for each study, including (a) a brief description of the interventions implemented in each condition and (b) the specific focal writing component skills of the intervention (spelling, genre knowledge, sentence-level composing, text structure, goal setting, planning, revising, etc.). Then, we categorized these writing interventions into three groups: M, T, or MT. Intervention categorization was based on the specific conditions rather than the study because some studies compared multiple treatment conditions with only one control condition. For example, Berninger et al. (2002) investigated writing effects by comparing three treatment conditions (spelling intervention, composition intervention including self-regulation strategies and text generation across language levels, and combined spelling and composition intervention) with only one control condition. Although the three comparisons were from the same study, they were coded separately and grouped into three different categories. We also recorded intervention features, including (a) length of each session in minutes and (b) duration of the intervention (i.e., the total number of sessions the intervention had); however, this information was not used for further analysis. 26 Regarding writing outcome measures coding, we recorded the following data: (a) writing quantity outcomes (i.e., text length such as the total words written reported by the primary study), (b) writing quality outcomes (i.e., overall writing quality), (c) measure name (e.g., the Wechsler Individual Achievement Test, Third Edition; Wechsler, 2009), (d) rubric types (holistic or analytic), and (d) measure type (i.e., standardized or unstandardized). For each comparison, we recorded mean scores and standard deviations of each condition (the treatment and control conditions) at both pre-and post-test for writing quantity and quality, respectively. Also, when writing quality was assessed by rubrics, both holistic and analytic scores were recorded. In addition, we coded study design as either true experimental or quasi-experimental group designs. Studies in which the assignment procedure lacked randomization were coded as quasi-experimental group designs (Shadish et al., 2002). Also, the cluster-randomized trials design by which the participants were assigned to different conditions by cluster was also coded as quasi-experimental group design (e.g., the study randomly assigned three classrooms to have SRSD writing intervention and three classrooms to the control condition; Trach et al., 2009). For study quality, we coded each study as published (i.e., studies published in peer-reviewed journal articles) or unpublished. These two dimensions were used in analyses of publication bias. 2.3.2 Study Coding Procedures Each included study was double coded by two coders. I was the primary coder and coded all the studies. Then, the second coder independently coded data that needed an inferential decision (i.e., the coding was determined by coders) and checked the coding accuracy of the data strictly recorded from the source study. Regarding the double coding, we first discussed the coding criteria and tried the first ten studies independently. After any inconsistent understandings of the coding rules were resolved, the second coder independently coded variables that needed an 27 inferential decision (i.e., grade level, participants’ features, intervention type, and study design) from a random set of 34% of studies. We then checked our initial agreement, and it achieved 96% for grade level, participants’ features, and intervention type and 92% for study design. After the coding discrepancies were resolved, the second coder then independently coded the remaining studies. Also, for information strictly recorded from the source study, the second coder checked the accuracy of all my entered data. We then identified 71 primary studies proceeding to data analysis after all disagreements were resolved. Reasons for the exclusion of the 85 studies at the coding stage were similar to those at the screening process, including (a) eight studies were duplicates; (b) eight studies did not employ a group experimental design (e.g., participants were assigned to two groups based on their different baseline handwriting abilities; Jones & Christensen, 1990); (c) 15 studies did not have explicit writing intervention (e.g., the intervention did not explicitly describe what and how writing-related skills were taught; Shumaker, 1982); (d) two studies did not have either transcription intervention or multi-component writing intervention (e.g., writing intervention only focused on different types of goals; Schunk & Swartz, 1993); (e) seven studies focused on the effects of differential pedagogical methods between treatment and control conditions (e.g., teachers played different roles in writing instruction between treatment and control conditions; Hayes, 1987); (f) three studies included reading instruction in the treatment condition; (g) 11 studies did not provide writing quantity outcomes nor writing quality outcomes; (h) 26 studies did not report enough statistical information to calculate the standardized mean effect sizes (e.g., Berninger et al., 1995); and (i) five studies did not have available full text. 28 2.4 Data Analysis To examine the effects of writing intervention on writing quality and writing quantity, effect sizes for these two measures for each comparison (i.e., treatment condition versus control condition) and the summary effect sizes across studies were calculated. I applied the Correlated and Hierarchical Effects working model (CHE; Pustejovsky & Tipton, 2021) with the Robust Variance Estimation meta-regression (RVE; Hedges et al., 2010) to investigate the overall and differential writing effects across the three types of writing intervention. Additionally, moderator analyses were conducted to test how grade level and whether students had academic difficulties contributed to the writing intervention effects. 2.4.1 Effect Size Computation I first computed within-study effect size estimates. Hedges’ g (i.e., standardized mean difference; Borenstein et al., 2009) of writing quantity and writing quality for each comparison was calculated. Hedges’ g, calculated by dividing the mean difference between the treatment and control conditions by the pooled standard deviation, is a widely used effect size estimate that can correct small sample bias and can be comparable and synthesized across studies (Borenstein et al., 2009). In addition, effects on writing quantity and writing quality were assessed and meta-analyzed separately; that is, I calculated within-study effect size estimates for writing quantity and writing quality separately and conducted separate meta-analyses for the two writing constructs. Regarding the mean difference between the treatment and control conditions, I only compared their post-test mean scores (i.e., mean difference = mean post-test scores on the treatment condition – mean post-test scores on the control condition); that is, I did not adjust the pretest mean difference. Although the What Works Clearinghouse Study Review Guide (Institute 29 of Education Sciences, 2018) suggested that baseline differences can impact study quality, especially for a quasi-experimental design study, the Cochrane Handbook (Higgins & Green, 2011) argued that a synthetic study could include only the post-test mean scores only, change scores from baseline only, or the combination of the two types of mean difference. Also, the prior related meta-analyses did not conduct pretest adjustments consistently—some adjusted pretest scores (e.g., Graham & Santangelo., 2014; Kim et al., 2022) but several did not (e.g., McMaster et al., 2018). After reviewing data characteristics of this present meta-analysis, I only calculated the mean difference of post-test scores for the following reasons: (a) around half of quantity measures and almost one-third of quality measures did not have pretest scores, (b) participant numbers were inconsistent between pre-and post-test such that the identification of which set of participants contributed to the change scores from baseline was difficult, (c) a number of quasi-experimental primary studies reported no significant difference on the academic skills between two conditions at baseline, though some of these studies did not report baseline mean scores. Also, for studies that reported both analytic scores and holistic scores from rubrics, I only used the mean difference of the holistic score to compute Hedges’ g. In addition, for studies that implemented an analytic rubric without a holistic score (e.g., Collopy, 2008), I calculated effect size estimates for each analytic score separately; then, a composite effect size estimate was generated by averaging the analytic effect size estimates. This method was also used by related previous meta-analyses (e.g., Graham and Santangelo, 2014; Kim et al., 2022). 2.4.1.1 Dependent Effect Size Estimates Several types of dependent within-study effect size estimates were present in the data. Three types of dependency were found: (a) a study reported multiple measures for quantity or 30 quality writing outcomes, (b) a study employed multiple comparisons (i.e., multiple treatment conditions compared with the same control condition), and (c) a study reported writing outcomes by independent cohorts (e.g., school districts, grade level). For studies with multiple measures, if the study reported writing outcomes at multiple time points (e.g., Deshler et al., 2003 reported both post-test writing quality outcomes and the maintenance score), I only used the immediate post-test scores to calculate the corresponding effect size estimates. Second, if the study provided multiple writing measures for a given writing outcome (e.g., writing quality) on the same participants, the effect size estimate of each measure could be calculated and included in further meta-analyses. To determine which effect size estimates were calculated and included, I checked the measure’s reliability and alignment to the intervention from the source study. Specifically: (a) if the source study included both standardized and unstandardized writing quality measures, I only used the standardized writing quality measure; (b) if the source study had multiple writing quality measures at relatively similar reliability levels (all implemented measures were standardized or unstandardized) and did not provide a clear description of the alignment of these measures (e.g., whether the writing quality measures tested the skills taught by the intervention, or whether the writing quality measures required other language skills), I used all the measures that were implemented; (c) if the source study had multiple writing quality measures at relatively similar reliability levels (all implemented measures were standardized or unstandardized) but clearly described which measures were more aligned with the writing skills taught by the intervention (e.g., a measure required a near transfer writing skills versus the one asked for far transfer), I only used the measures more aligned to the intervention; and (d) if the source study included more than one 31 writing quality measure but did not clearly mention their reliability and the alignment to the intervention, I used all of these measures. Regarding studies with multiple comparisons (i.e., a study with more than one treatment condition versus only one control group), the effect size estimates for each comparison were calculated and included in further meta-analyses. Also, for studies that reported writing outcomes by independent groups (e.g., Green et al., 2021 provided writing quantity and writing quality for two school districts separately), the effect size estimates for each group were calculated separately, and all were included in the meta-analyses. To conclude, the dependent within-study effect size estimates were caused by studies with multiple measures, multiple comparisons, or independent cohorts. To maximize statistical power and not inflate Type I error, I then conducted meta-analyses with all the within-study effect size estimates involved for writing quantity and writing quality separately. 2.4.2 Meta-Analysis Due to the within-study effect size estimates’ dependence, I ran meta-analyses for writing quantity and quality separately using the Correlated and Hierarchical Effects working model (CHE; Pustejovsky & Tipton, 2022). CHE is an expanded Robust Variance Estimation meta-regression (RVE; Hedges et al., 2010), and it combines the features of two original RVE models—the Hierarchical Effects model (HE) and the Correlated Effects model (CE; Hedges et al., 2010; Pustejovsky & Tipton, 2022). Specifically, CHE allows within-study effect size estimates to be correlated and assumes heterogeneity exists in the true effect size parameters at both the within-study and between-study levels (Pustejovsky & Tipton, 2022), while (a) the HE model assumes that within-study effect size estimates are independent, but dependence in the true effect sizes exists and (b) the CE model assumes that the within-study effect size estimates 32 are correlated, but there is no heterogeneity in the true effect size to be estimated within each study (i.e., fixed but dependent within-study effect structure; Pustejovsky & Tipton, 2022). In addition, both CHE and CE models specify a constant correlation between any pair of effect size estimates at both within- and between-study levels (Pustejovsky & Tipton, 2022). Regarding the data structure of this study, dependent within-study estimates were inevitable because source studies contributed more than one effect size estimate to the meta-analyses. Second, it was reasonable to expect heterogeneity in the true effect size parameters at both within-study and between-study levels because of the complex dependence structure (i.e., studies with multiple comparisons, measures, or cohorts) and other distinct features of the source studies (i.e., studies with different types of writing intervention for students at different grade levels with or without academic difficulties). Thus, to account for the random effects at both the within- and between-study levels and the dependent effect size estimates, the CHE model was applied to this study. In addition, compared to the method-of-moments estimators from the HE and the CE model, this present meta-analysis used restricted maximum likelihood (REML) to estimate variance, which then was used to generate fully efficient weights (Pustejovsky & Tipton, 2022). Based on the evidence from Pustejovsky and Tipton (2022), the REML estimator with the fully efficient weight matrices will return more accurate meta-regression coefficients (i.e., summary effect size estimates), especially with heterogeneity at both the within- and between-study levels. Also, I used Wald tests to conduct hypothesis tests of each meta-regression model because the Wald tests allow multiple contrasts across regression coefficients, including pairwise comparisons across all meta-regression coefficients (e.g., different levels of moderators) and joint tests that regression coefficients differ from zero (Pustejovsky & Tipton, 2022; Pustejovsky, 33 2022). The clubSandwich and metafor packages in R programming software were used in this study (Pustejovsky, 2022; Viechtbauer, 2010). After the categorical indicators were dummy coded (i.e., grade levels, students’ features, study design, and intervention type), to examine the overall effect sizes of the writing intervention and the specific and differential effects across the three types of writing intervention (i.e., RQ 1 and RQ 2), I ran separate CHE meta-regressions for writing quality and writing quantity. In addition to assessing whether writing intervention had statistically significant effects on the writing outcomes, I also used Wald tests to check if writing effects differed across the three types of writing intervention. Also, to examine how grade level and students’ features affected writing outcomes (i.e., RQ 3), I added these two moderators (i.e., students’ grade levels and whether they had academic difficulties) to the CHE working models ran for RQ 1, then followed by the related Wald tests. Detailed results will be reported in the “Results” chapter. 2.4.3 Publication Bias Because the available studies may not comprehensively represent all the existing evidence for a meta-analysis, reporting bias (e.g., publication bias) is inevitable. For example, because studies with stronger effects and/or statistically significant results are more likely to be published, publication bias is a common concern of meta-analytic research studies, even when unpublished studies are included (Borenstein et al., 2009; Ferguson & Brannick, 2012). To reduce the likelihood of publication bias, I conducted a comprehensive literature search (e.g., no time limits, search beyond peer-reviewed academic journals), and this present meta-analysis included grey literature (e.g., unpublished theses and dissertations). Then, I used a funnel plot (i.e., Egger’s regression; Egger et al., 1997; Viechtbauer, 2010) to detect and visualize potential publication bias in the meta-analytic results (i.e., the overall 34 writing effects). Specifically, Egger’s regression with standard error of the observed effect sizes for the y-axis was used because this strategy can accommodate dependent data structure with RVE while diagnosing publication bias (Rodgers & Pustejovsky, 2021; Sterne & Egger, 2001). Because grey literature was included in this study, I also ran meta-regression models with publication status (published versus unpublished) as a moderator to assess whether writing effects depended on studies’ publication status. 2.4.4 Sensitivity and Outlier Analysis Given the impact of outliers and influential cases on the robustness of meta-analytical results, I conducted residual analyses (i.e., externally standardized residuals) and examined Cook’s distances for potential outliers and influential cases, respectively (Viechtbauer, 2010; Viechtbauer & Cheung, 2010). Specifically, outliers are effect size estimates with extreme values compared to other cases; they may contort the average effect sizes or may only have little impact on the average effect sizes if they are excluded (Viechtbauer & Cheung, 2010). However, influential cases will result in considerable changes in the average effect sizes if excluded from the model (Viechtbauer & Cheung, 2010). In this study, I examined outliers for the models of the overall writing quantity and quality separately; then, I identified if influential cases existed in these models. 35 Chapter 3: Results Study results are reported in two steps. First, I describe the overall descriptive features of all the identified studies. Then, I report meta-analytic results according to each research question. Table 1 summarizes the number of effect sizes and studies. 3.1 Study Features Overall, this meta-analysis consists of 71 studies with a total of 164 effect size estimates. Specifically, 65 studies that reported writing quality yielded 110 effect size estimates; individual studies contributed one to eight effect size estimates, with 1.7 effect size estimates on average. Also, 33 studies that reported writing quantity generated 54 effect size estimates; individual studies contributed one to four effect size estimates, with 1.7 effect size estimates on average. Because the numbers of studies reporting writing quantity and writing quality are unbalanced, all statistical analyses for the two writing constructs were separately conducted. Of the 54 effect size estimates (33 studies) for writing quantity outcomes, nine studies employed transcription intervention with 12 effect size estimates, 20 studies implemented multicomponent writing intervention without a transcription component with 36 effect size estimates, and five studies applied multicomponent writing intervention with a transcription component with six effect size estimates. Of the 110 effect size estimates (65 studies) for writing quality outcomes, nine studies focused on transcription intervention with 14 effect size estimates, 45 studies taught multicomponent writing intervention without a transcription component with 77 effect size estimates, and 14 studies implemented multicomponent writing intervention combined with a transcription component with 19 effect size estimates. In sum, multicomponent writing intervention contributed the most effect sizes for both writing quantity and writing 36 quality. Also, for all the three writing intervention types, the number of effect size estimates for writing quality was greater than for writing quantity. Regarding grade level, a majority of the studies happened in elementary school (23 studies reported writing quantity, and 45 studies reported writing quality) and yielded 38 and 79 effect size estimates for writing quantity and writing quality, respectively. For secondary school, 16 effect size estimates were for writing quantity, while 31 were for writing quality. Regarding whether students had academic difficulties, compared to students with academic difficulties, more studies were for students with diverse academic skills and happened in inclusive learning environments (i.e., included both learners with special needs and typically developing learners), generating 28 effect size estimates (from 18 studies) and 67 effect size estimates (from 40 studies) for writing quantity and writing quality, respectively. Also, of the 69 effect size estimates from students with academic difficulties, 26 of the effect size estimates from 17 studies were for writing quantity, while 43 of the effect size estimates from 26 studies were for writing quality. Regarding publication status, this meta-analysis includes 21 published studies with 30 effect size estimates for writing quantity and 41 studies with 65 effect size estimates for writing quality. In addition, 12 unpublished studies with 24 effect size estimates for writing quantity and 24 unpublished studies with 45 effect size estimates for writing quality are also identified. Regarding study design, this present meta-analysis identifies 13 studies that employed randomized controlled trials (i.e., true experimental design) with 11 effect size estimates (from seven studies) for writing quantity and 18 effect size estimates (from 12 studies) for writing quality. Also, this meta-analysis includes 26 and 53 studies using quasi-experimental designs with effect size estimates of 43 and 92 for writing quantity and quality, respectively. 37 Additionally, I cross-tabulated (a) intervention types and grade level and (b) intervention types and whether students had academic difficulties. Table 1 summarizes the cross-tabulation results with the number of effect size estimates for both measures. Table 1 Summary of Study Descriptive Features Features Writing Quantity Outcomes Writing Quality Outcomes Number of Effect Sizes Number of Studies Number of Effect Sizes Number of Studies All identified studies 54 33 110 65 Intervention type Transcription (T) 12 9 14 9 Multi-component without a transcription component (M) 36 20 77 45 Multi-component with a transcription component (MT) 6 5 19 14 Grade level Elementary 38 23 79 45 Secondary 16 10 31 20 Risk coding Student with diverse academic skills 28 18 67 40 Students with academic difficulty 26 17 43 26 Study design Randomized controlled trial design (RCT) 11 7 18 12 Quasi-experimental design (QED) 43 26 92 53 Publication status Published 30 21 65 41 Unpublished 24 12 45 24 Intervention type * grade level T*elementary 11 8 13 8 T*secondary 1 1 1 1 M*elementary 23 12 50 27 M*secondary 13 8 27 18 MT*elementary 4 3 16 12 MT*secondary 2 2 3 2 Intervention type * risk type 38 Features Writing Quantity Outcomes Writing Quality Outcomes Number of Effect Sizes Number of Studies Number of Effect Sizes Number of Studies T*students with diverse academic skills 1 1 0 0 T*students with academic difficulty 11 8 14 9 M*students with diverse academic skills 24 15 58 33 M*students with academic difficulty 12 7 19 13 MT*students with diverse academic skills 3 3 9 8 MT*students with academic difficulty 3 2 10 6 Note. M = Multi-Component Writing Intervention without a Transcription Component; MT = Multi-Component Writing Intervention with a Transcription Component; T = Transcription Intervention; RCT = Randomized Controlled Trial (True Experimental Design); QED = Quasi-Experimental Design. 3.2 Meta-Analytic Results Meta-analytic results are reported by each research question. Table 3 provides detailed information about the characteristics of studies, participants, interventions, and the effect size estimates. Figures 3 and 4 display the distribution of the estimated effect sizes with the corresponding weights of all the identified studies for writing quantity and writing quality, respectively. 3.2.1 Research Question One To answer research question one (i.e., What is the effect of writing intervention on writing outcomes for K-12 students?), I calculated 54 estimated effect sizes for writing quantity and 110 effect size estimates for writing quality and ran CHE meta-analyses for the two 39 measures separately. Overall, statistically significant and moderate to strong effects of writing intervention on both measures were detected. For writing quantity outcomes, on average, students receiving writing intervention had more than half of a standard deviation improvement compared to students in the control groups, with a moderate effect size (Hedges’ g = 0.57). The 95% confidence interval (CI; 95% CI [0.32, 0.82], p < .001) indicates that this average effect size differs statistically from zero. Figure 3 displays that across the 33 studies (K = 54), effect size estimates (Hedges’ g) ranged from -1.11 to 7.77. A high degree of heterogeneity was detected (I2 = 90.78%), with the estimates of between-study variance τ2 = .23 and the within-study variance ω2 = .24. The study-level I2 statistic indicates that 45.14% of the total variance can be attributed to between-study heterogeneity. The within-study level I2 suggests that the true within-study variance accounts for 45.64% of the total variance. Also, the 95% prediction interval (PI) reveals that 95% of the true effect sizes in new studies will fall between -0.79 to 1.93 (95%PI [-0.79, 1.93]), indicating that writing intervention may not always yield positive effects on writing outcomes across contexts. These statistics suggest substantial heterogeneity exists across effect sizes within and between studies. Therefore, moderator analysis is encouraged to explain this heterogeneity. For writing quality outcomes, across 65 studies (K = 110), writing intervention yielded positive effects on writing quality with a robust summary effect size estimate (Hedges’ g) of 0.71; on average, students receiving writing intervention scored 0.71 standard deviation higher than students in the control group. The 95% CI (95%CI [0.49, 0.93], p < .001) indicates that the average writing quality effect statistically differs from zero. The effect size estimates (Hedges’ g) ranged from -0.76 to 5.57; see Figure 4 for their distribution and the corresponding weights. 40 Substantial heterogeneity was also found (I2 = 95.89%); specifically, the study-level heterogeneity accounted for 75.03% (I2 = 75.03%, τ2 = .58) of the total variance, and the within-study level heterogeneity explained 20.86% (I2 = 20.86%, ω2 =0.16) of the total variance. The bigger between-study variance suggests substantial heterogeneity across studies. Thus, a study-level moderator analysis is necessary to examine if systematic discrepancies depend on moderators. In addition, compared to writing quantity outcomes, more various approaches to assessing writing quality outcomes might partially account for the different magnitude and distribution of variance between writing quantity and quality effects. Also, the 95% PI [-0.99, 2.41] reveals that writing intervention may negatively affect writing quality in some contexts, suggesting that writing intervention should be implemented with caution. In addition, to check the performance of the multi-level models, I compared the CHE working model with the corresponding standard random-effects meta-analytic model by constraining the within-study correlation of estimated and true effect sizes to be zero (i.e., assuming all the effect size estimates were independent). For writing quantity outcomes, the CHE working model did not statistically significantly perform better than the standard random-effects model. In contrast, the CHE working model presented a better fit for the data for writing quality outcomes than the standard random-effects model (See Table 2). Although the CHE working model performed inconsistently for writing quantity and quality outcomes, in the present study, I still use the CHE working model for all meta-analyses for both two measures because it better represents the data structure in which within-study effect sizes are not independent. Table 2 Model Comparisons between CHE Model and Reduced Model 41 Note. Full model = CHE Working Model; Reduced model = Standard Random-Effects Model with Between-Study Heterogeneity Constrained to Zero; df = Degree of freedom; AIC = Akaike; BIC = Bayesian Information Criterion; LRT = Likelihood Ratio Test. Models Writing Quantity Writing Quality df AIC BIC LRT p df AIC BIC LRT p Full model 3 141.291 147.202 3 270.682 278.756 Reduced model 2 141.551 145.491 2.260 = .133 2 298.756 304.139 30.074 < .0001 42 Table 3 Features of Studies, Participants, Interventions, and Effect Estimates Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Allen & Lembke, 2022 Published RCT Elementary Academic difficulty 26 T: Morphological awareness intervention C: reading or math instruction T Writing quality 0.50 Asaro-Saddler et al., 2018 Published QED Secondary Academic difficulty 30 T: Summary Writing Strategy C: Business as usual M Writing quality 3.88 Asaro-Saddler et al., 2018 Published QED Secondary Academic difficulty 30 T: Summary Writing Strategy C: Business as usual M Writing quantity 7.77 Barnes, 2015 Unpublished QED Elementary n 42 T: Writers’ Club C: No extra writing M Writing quality 0.58 Bates, 1988 Unpublished QED Elementary n 86 T: Computer-assisted writing project C: Business as usual M Writing quality 0.38 Bates, 1988 Unpublished QED Elementary n 86 T: Computer-assisted writing project C: Business as usual M Writing quantity 0.46 Berninger, Rutberg, et al., 2006 Published QED Elementary Academic difficulty 94 T: Writing club C: Assessment only (no intervention) MT Writing quality 0.32 Berninger et al., 1997 Published QED Elementary Academic difficulty 48 T: Motor imitation handwriting instruction C: Phonological awareness control condition T Writing quality 0.21 Berninger et al., 1997 Published QED Elementary Academic difficulty 48 T: Visual cue handwriting instruction C: Phonological awareness control condition T Writing quality 0.22 Berninger et al., 1997 Published QED Elementary Academic difficulty 48 T: Memory retrieval handwriting instruction T Writing quality 0.38 43 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size C: Phonological awareness control condition Berninger et al., 1997 Published QED Elementary Academic difficulty 48 T: Visual cue & memory retrieval handwriting instruction vs. C: Phonological awareness control condition T Writing quality 0.93 Berninger et al., 1997 Published QED Elementary Academic difficulty 48 T: Copy handwriting instruction C: Phonological awareness control condition T Writing quality 0.23 Berninger et al., 2002 Published QED Elementary Academic difficulty 24 T: Spelling training C: Keyboarding and composing practice without any instruction T Writing quality (Informative essay) 0.22 Berninger et al., 2002 Published QED Elementary Academic difficulty 24 T: Composition training C: Keyboarding and composing practice without any instruction M Writing quality (Informative essay) 0.06 Berninger et al., 2002 Published QED Elementary Academic difficulty 24 T: Combined spelling with composing training C: Keyboarding and composing practice without any instruction MT Writing quality (Informative essay) 0.19 Berninger et al., 2002 Published QED Elementary Academic difficulty 24 T: Spelling training C: Keyboarding and composing practice without any instruction T Writing quality (Persuasive essay) 0.29 Berninger et al., 2002 Published QED Elementary Academic difficulty 24 T: Composition training C: Keyboarding and composing practice without any instruction M Writing quality (Persuasive essay) 1.58 44 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Berninger et al., 2002 Published QED Elementary Academic difficulty 24 T: Combined spelling with composing training C: Keyboarding and composing practice without any instruction MT Writing quality (Persuasive essay) 2.06 Boykin et al., 2019 Published RCT Elementary n 37 T: Computer-based graphic organizer (CBGO) with embedded self-regulated learning (SRL) strategies C: Traditional writing instruction M Writing quality 1.30 Boykin et al., 2019 Published RCT Elementary n 37 T: computer-based graphic organizer (CBGO) with embedded self-regulated learning (SRL) strategies C: Traditional writing instruction M Writing quantity 0.32 Bui et al., 2006 Published QED Elementary Academic difficulty 14 T: Demand Writing Instruction Model (DWIM) comprehensive strategic writing program C: Traditional writing instruction M Writing quality 0.07 Bui et al., 2006 Published QED Elementary n 99 T: Demand Writing Instruction Model (DWIM) comprehensive strategic writing program C: Traditional writing instruction M Writing quality 0.37 45 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Bui et al., 2006 Published QED Elementary Academic difficulty 14 T: Demand Writing Instruction Model (DWIM) comprehensive strategic writing program C: Traditional writing instruction M Writing quantity 0.94 Bui et al., 2006 Published QED Elementary n 99 T: Demand Writing Instruction Model (DWIM) comprehensive strategic writing program C: Traditional writing instruction M Writing quantity 0.29 Carlisle, 2016 Unpublished RCT Elementary Academic difficulty 27 T: Morphological awareness intervention C: Reading or math instruction T Writing quality 0.60 Carlisle, 2016 Unpublished RCT Elementary Academic difficulty 27 T: Morphological awareness intervention C: reading or math instruction T Writing quantity 0.18 Case-Smith et al., 2014 Published QED Elementary n 132 T: Write Start C: business as usual MT Writing quality -0.06 Christensen, 2005 Published QED Secondary Academic difficulty 50 T: Handwriting program C: Daily completion of a written journal T Writing quality 1.23 Christensen, 2005 Published QED Secondary Academic difficulty 50 T: Handwriting program C: Daily completion of a written journal T Writing quantity 2.27 Coe et al., 2011 Published QED Elementary n 4161 T: Students whose teachers had 6+1 Trait Writing model C: Students M Writing quality 0.48 46 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size whose teacher did not have it Collopy, 2008 Published QED Elementary n 100 T: Students whose teachers had Six Traits C: Students whose teachers did not have it MT Writing quality 0.31 Crowhurst, 1987 Published QED Elementary n 50 T: Persuasive schema & writing practice C: Novels reading and book report writing (no intervention) M Writing quality 1.08 Cuenca-Sanchez et al., 2012 Published RCT Secondary Academic difficulty 21 T: SRSD with a self-determination training C: Business as usual M Writing quality 3.33 Cuenca-Sanchez et al., 2012 Published RCT Secondary Academic difficulty 21 T: SRSD with a self-determination training C: Business as usual M Writing quantity 2.07 De La Paz & Graham, 2002 Published QED Secondary n 58 T: SRSD C: Traditional writing curriculum M Writing quality 1.07 De La Paz & Graham, 2002 Published QED Secondary n 58 T: SRSD C: Traditional writing curriculum M Writing quantity 0.72 Deshler et al., 2003 Published QED Elementary n 69 T: Demand Writing Instructional Model (DWIM) C: Business as usual M Writing quality -0.02 Deshler et al., 2003 Published QED Elementary n 90 T: Demand Writing Instructional Model (DWIM) C: Business as usual M Writing quality 0.44 Dolin, 2016 Unpublished QED Elementary n 313 T: Handwriting Without Tears handwriting curriculum C: Business as usual T Writing quantity 0.13 47 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Elshirbini Abd-ElFatah Elashri, 2013 Published QED Secondary n 74 T: Genre-based approach C: Traditional writing instruction M Writing quality 1.69 Ewoldt, 2018 Unpublished QED Secondary Academic difficulty 31 T: Reverse engineering instruction with non-coded graphic organizer C: Business as usual M Writing quality 0.76 Ewoldt, 2018 Unpublished QED Secondary Academic difficulty 21 T: Reverse engineering instruction with letter-coded graphic organizer C: Business as usual M Writing quality 0.04 Ewoldt, 2018 Unpublished QED Secondary Academic difficulty 31 T: Reverse engineering instruction with non-coded graphic organizer C: Business as usual M Writing quantity 1.10 Ewoldt, 2018 Unpublished QED Secondary Academic difficulty 21 T: Reverse engineering instruction with letter-coded graphic organizer C: Business as usual M Writing quantity -0.06 Finlayson & McCrudden, 2021 Published QED Elementary n 71 T: SRSD C: Business as usual M Writing quality 1.20 Finlayson & McCrudden, 2021 Published QED Elementary n 71 T: SRSD C: Business as usual M Writing quantity 1.11 Galbraith, 2014 Unpublished QED Secondary n 160 T: Adapted SRSD curriculum C: Traditional writing curriculum M Writing quality 0.28 Taylor, 2009 Unpublished QED Elementary n 140 T: Focused Practice Instructional Approach (FPIA) C: Business as usual M Writing quality 0.85 48 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Graham et al., 2018 Published QED Elementary Academic difficulty 30 T: Handwriting and spelling instruction C: Phonological awareness instruction T Writing quality -0.13 Graham et al., 2018 Published QED Elementary Academic difficulty 30 T: Handwriting and spelling instruction C: Phonological awareness instruction T Writing quantity 0.14 Graham et al., 2002 Published RCT Elementary Academic difficulty 54 T: Spelling instruction C: Math instruction T Writing quality 0.06 Graham et al., 2002 Published RCT Elementary Academic difficulty 54 T: Spelling instruction C: Math instruction T Writing quantity -0.32 Graham et al., 2000 Published RCT Elementary Academic difficulty 36 T: Handwriting instruction C: Phonological awareness instruction T Writing quality -0.01 Green et al., 2021 Published QED Elementary (School A) n 51 T: Write Away C: Big Writing programme (business as usual) M Writing quality 2.18 Green et al., 2021 Published QED Elementary (School B) n 39 T: Write Away C: Big Writing programme (business as usual) M Writing quality 2.11 Green et al., 2021 Published QED Elementary (School A) n 51 T: Write Away C: Big Writing programme (business as usual) M Writing quantity 0.60 Green et al., 2021 Published QED Elementary (School B) n 39 T: Write Away C: Big Writing programme (business as usual) M Writing quantity -1.11 Green, 2017 Unpublished QED Secondary n 32 T: Write Away process with social studies C: Big Writing programme (business as usual) M Writing quality 0.70 Green, 2017 Unpublished QED Secondary n 35 T: Write Away process with social studies and English C: Big Writing programme (business as usual) M Writing quality 0.90 Green, 2017 Unpublished QED Secondary n 36 T: Write Away process with English M Writing quality -0.34 49 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size C: Big Writing programme (business as usual) Green, 2017 Unpublished QED Secondary n 32 T: Write Away process with social studies C: Big Writing programme (business as usual) M Writing quantity -0.40 Green, 2017 Unpublished QED Secondary n 35 T: Write Away process with social studies and English C: Big Writing programme (business as usual) M Writing quantity 0.33 Green, 2017 Unpublished QED Secondary n 36 T: Write Away process with English C: Big Writing programme (business as usual) M Writing quantity -0.69 Hammann & Stevens, 2003 Published QED Secondary n 15 T: Summarization combined with text structure instruction C: no intervention M Writing quality (Deserts text) 0.47 Hammann & Stevens, 2003 Published QED Secondary n 20 T: Summarization combined with text structure instruction C: no intervention M Writing quality (Ruins text) -0.30 Hebert et al., 2021 Published RCT Elementary n 41 T: Structure Writing intervention C: Business as usual M Writing quality (Simple description) 0.69 Hebert et al., 2021 Published RCT Elementary n 41 T: Structure Writing intervention C: Business as usual M Writing quality (Compare & Contrast) 0.79 Hebert et al., 2021 Published RCT Elementary n 41 T: Structure Writing intervention C: Business as usual M Writing quality (Sequence) 1.08 Hebert et al., 2021 Published RCT Elementary n 41 T: Structure Writing intervention C: Business as usual M Writing quality (Problem solution) 0.75 50 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Hebert et al., 2021 Published RCT Elementary n 41 T: Structure Writing intervention C: Business as usual M Writing quality (Cause & Effect) 0.97 Hebert, Bohaty, et al., 2018 Published RCT Elementary Academic difficulty 61 T: Structures Writing intervention C: Mathematics writing M Writing quality -0.10 Hooper et al., 2013 Published QED Elementary Academic difficulty 58 T: Process Assessment of the Learner (PAL) C: Business as usual MT Writing quality -0.76 Hughes, 2018 Unpublished QED Elementary n 67 T: Mobile-based graphic organizer (MBGO) with embedded self-regulated learning strategies & adult feedback C: Business as usual M Writing quality 3.96 Hughes, 2018 Unpublished QED Elementary n 81 T: mobile-based graphic organizer (MBGO) with embedded self-regulated learning strategies without adult feedback C: Business as usual M Writing quality 1.49 Hughes, 2018 Unpublished QED Elementary n 67 T: mobile-based graphic organizer (MBGO) with embedded self-regulated learning strategies & adult feedback C: Business as usual M Writing quantity 0.16 Hughes, 2018 Unpublished QED Elementary n 81 T: mobile-based graphic organizer (MBGO) with embedded self-regulated learning strategies without adult feedback C: Business as usual M Writing quantity -0.19 51 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Jones, 2015 Published RCT Elementary n 74 T: Interactive writing C: Business as usual MT Writing quality 2.11 Jung et al., 2017 Published QED Elementary Academic difficulty (students with disabilities) 27 T: Handwriting combined with spelling instruction C: Business as usual T Writing quantity 1.24 Jung et al., 2017 Published QED Elementary Academic difficulty (students at risk) 19 T: handwriting combined with spelling instruction C: Business as usual T Writing quantity 0.22 Kelley, 1984 Unpublished QED Elementary n 104 T: Sentence Paragraph Structure Approach writing intervention C: Reading instruction M Writing quality 1.68 Kelley, 1984 Unpublished QED Elementary n 101 T: Six-step Writing Approach C: Reading instruction M Writing quality 2.21 Kennedy, 2008 Unpublished QED Secondary n 116 T: Format Oriented Language Approach (FOLA) C: No intervention M Writing quality 0.46 Lanza, 2009 Unpublished QED Elementary n 82 T: Scaffolded model of writing instruction C: No intervention M Writing quality 0.10 Leary, 1990 Unpublished RCT Elementary (Grade 3) Academic difficulty 24 T: Temporal Sequencing Strategy C: a business as usual M Writing quantity (Narrative essay) 0.95 Leary, 1990 Unpublished RCT Elementary (Grade 5) Academic difficulty 24 T: Temporal Sequencing Strategy C: a business as usual M Writing quantity (Narrative essay) 0.97 Leary, 1990 Unpublished RCT Elementary (Grade 3) Academic difficulty 24 T: Temporal Sequencing Strategy C: a business as usual M Writing quantity (Expository essay) 0.08 52 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Leary, 1990 Unpublished RCT Elementary (Grade 5) Academic difficulty 24 T: Temporal Sequencing Strategy C: a business as usual M Writing quantity (Expository essay) -0.25 Lesaux et al., 2014 Published QED Secondary N 746 T: Academic Language Instruction for All Students (ALIAS) C: Business as usual M Writing quality 0.12 MacArthur et al., 1995 Published QED Elementary Academic difficulty 168 T: Computers and Writing Instruction Project (CWIP) C: No interventiona MT Writing quality (Narrative essay) 0.34 MacArthur et al., 1995 Published QED Elementary Academic difficulty 164 T: Computers and Writing Instruction Project (CWIP) C: No interventionb MT Writing quality (Informative essay) 0.10 MacArthur et al., 1995 Published QED Elementary Academic difficulty 168 T: Computers and Writing Instruction Project (CWIP) C: No interventionc MT Writing quantity (Narrative essay) 0.30 MacArthur et al., 1995 Published QED Elementary Academic difficulty 164 T: Computers and Writing Instruction Project (CWIP) C: No interventiond MT Writing quantity (Informative essay) 0.04 McCutchen et al., 2022 Published QED Elementary n 513 T: Morphology-infused vocabulary intervention C: Business as usual MT Writing quality 0.37 McCutchen et al., 2022 Published QED Elementary n 513 T: Morphology-infused vocabulary intervention vs C: Business as usual MT Writing quantity 0.08 McKeown et al., 2018 Published QED Elementary n 685 T: Students whose teachers had SRSD practice-based professional development C: students whose teachers did not have it M Writing quality 0.34 53 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size McKeown et al., 2018 Published QED Elementary n 685 T: Students whose teachers had SRSD practice-based professional development C: students whose teachers did not have it M Writing quantity -0.19 McMaster et al., 2020 Published RCT Elementary n 51 T: Students whose teacher had data-based instruction for early writing outcomes (DBI-TLC) C: Business as usual MT Writing quality 0.56 McMaster et al., 2020 Published RCT Elementary n 51 T: Students whose teacher had data-based instruction for early writing outcomes (DBI-TLC) C: Business as usual MT Writing quantity 0.57 Moye, 1993 Unpublished QED Elementary n 121 T: Cognitive Process of Instruction (CPOI) C: Modified process writing instruction (regular classroom) M Writing quality -0.44 Palermo & Thomson, 2018 Published QED Secondary n 542 T: NC Write (Automated Writing Evaluation systems; AWE) & traditional writing instruction C: Traditional writing instruction M Writing quality 1.22 Palermo & Thomson, 2018 Published QED Secondary n 542 T: NC Write (AWE) & traditional writing instruction C: Traditional writing instruction M Writing quantity 1.15 Palermo & Thomson, 2018 Published QED Secondary n 557 T: SRSD & NC Write (AWE) M Writing quality 1.44 54 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size C: Traditional writing instruction Palermo & Thomson, 2018 Published QED Secondary n 557 T: SRSD & NC Write (AWE) C: Traditional writing instruction M Writing quantity 1.32 Popham, 2019 Unpublished QED Elementary n (all participants) 186 T: Students whose teachers had online SRSD Writing to Learn training C: Business as usual M Writing quantity 0.44 Popham, 2019 Unpublished QED Elementary n (participants without academic difficulty) 162 T: Students whose teachers had online SRSD Writing to Learn training C: Business as usual M Writing quantity 0.47 Popham, 2019 Unpublished QED Elementary Academic difficulty 13 T: Students whose teachers had online SRSD Writing to Learn training C: Business as usual M Writing quantity 0.29 Potkewitz, 1984 Unpublished QED Elementary Academic difficulty 61 T: Weehawken program C: Remedial reading program M Writing quality (Expository essay) 2.31 Potkewitz, 1984 Unpublished QED Elementary Academic difficulty 61 T: Weehawken program C: Remedial reading program M Writing quality (Descriptive essay) 2.99 Puma et al., 2007 Published QED Elementary n 2405 T: Writing Wings C: Business as usual MT Writing quality 0.02 Ray, 2017 Unpublished QED Secondary Academic difficulty 8 T: SRSD C: American College Testing (ACT) math preparation M Writing quality (ACT prompts) 1.34 Ray, 2017 Unpublished QED Secondary Academic difficulty 8 T: SRSD C: ACT math preparation M Writing quality 5.57 55 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size (WIAT-IIIe Essay) Sadauskas, 2014 Unpublished RCT Secondary n 31 T: Sparkfolio with self-authored social media C: Business as usual M Writing quality 0.33 Sadauskas, 2014 Unpublished RCT Secondary n 31 T: Sparkfolio without self-authored social media C: Business as usual M Writing quality 0.28 Schrodt, 2015 Unpublished RCT Elementary n 27 T: Writer's workshop with mindset instruction C: Business as usual M Writing quality 1.71 Scott, 2009 Unpublished QED Secondary Academic difficulty 55 T: SRSD C: No intervention M Writing quality 0.37 Silver, 2013 Unpublished QED Secondary n 61 T: Writing workshop & teacher-supported goal setting & self-edit C: Business as usual M Writing quality 1.40 Silver, 2013 Unpublished QED Secondary n 58 T: Writing workshop & self-edit by a generic checklist C: Business as usual M Writing quality 0.60 Silver, 2013 Unpublished QED Secondary n 61 T: Writing workshop & teacher-supported goal setting & self-edit C: Business as usual M Writing quantity 1.61 Silver, 2013 Unpublished QED Secondary n 58 T: Writing workshop & self-edit by a generic checklist C: Business as usual M Writing quantity 1.07 Smith, 2009 Unpublished QED Elementary n 102 T: Vocabulary instruction and Step Up to Writing (SUTW) C: Business as usual M Writing quality (Narrative essay) -0.23 Smith, 2009 Unpublished QED Elementary n 102 T: Vocabulary instruction & Step Up to Writing (SUTW) C: Business as usual M Writing quality (Expository essay) -0.04 56 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Smith, 2009 Unpublished QED Elementary n (English Language Learners) 102 T: Vocabulary instruction & Step Up to Writing (SUTW) C: Business as usual M Writing quality (Narrative essay) -0.25 Smith, 2009 Unpublished QED Elementary n (English Language Learners) 102 T: Vocabulary instruction & Step Up to Writing (SUTW) C: Business as usual M Writing quality (Expository essay) -0.15 Smith, 2009 Unpublished QED Elementary n 102 T: Writing summary instruction & SUTW C: Business as usual M Writing quality (Narrative essay) -0.18 Smith, 2009 Unpublished QED Elementary n 102 T: Writing summary instruction & SUTW C: Business as usual M Writing quality (Expository essay) -0.17 Smith, 2009 Unpublished QED Elementary n (English Language Learners) 102 T: Writing summary instruction & SUTW C: Business as usual M Writing quality (Narrative essay) -0.04 Smith, 2009 Unpublished QED Elementary n (English Language Learners) 102 T: Writing summary instruction & SUTW C: Business as usual M Writing quality (Expository essay) -0.07 Sousa, 2015 Unpublished QED Elementary (Grade 3) n 233 T: Students whose teachers Dynamic Writing Keys C: Students whose teachers didn't have this training M Writing quality 0.23 Sousa, 2015 Unpublished QED Elementary (Grade 4) n 264 T: Students whose teachers Dynamic Writing Keys C: Students whose teachers didn't have this training M Writing quality 0.46 57 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Sousa, 2015 Unpublished QED Elementary (Grade 5) n 214 T: Students whose teachers Dynamic Writing Keys C: Students whose teachers didn't have this training M Writing quality 0.04 Steelman, 1990 Unpublished QED Secondary n 42 T: Type (keyboarding) & newspaper writing program C: Business as usual MT Writing quality 0.79 Steelman, 1990 Unpublished QED Secondary n 45 T: Newspaper writing program on paper and pencil C: Business as usual M Writing quality 0.59 Steelman, 1990 Unpublished QED Secondary n 43 T: Type (keyboarding) & newspaper writing program C: Business as usual MT Writing quantity 0.81 Steelman, 1990 Unpublished QED Secondary n 46 T: Newspaper writing program on paper and pencil C: Business as usual M Writing quantity 0.48 Taylor, 1985 Published QED Elementary n 46 T: Writing instruction in social studies C: No intervention M Writing quality -0.55 Theoharopoulos, 1994 Unpublished QED Secondary Academic difficulty 75 T: STARRE writing model C: District literature-based curriculum M Writing quality 0.90 Therrien et al., 2009 Published QED Secondary Academic difficulty 40 T: Essay Test-Taking Strategy C: Business as usual M Writing quality (Measure was close to the intervention) 0.67 Therrien et al., 2009 Published QED Secondary Academic difficulty 40 T: Essay Test-Taking Strategy C: Business as usual M Writing quality (Measure was a near 0.49 58 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size transfer of the intervention) Tracy et al., 2009 Published QED Elementary n 126 T: SRSDf C: Business as usual M Writing quality (Picture story narrative essay) 0.39 Tracy et al., 2009 Published QED Elementary n 126 T: SRSDg C: Business as usual M Writing quality (Personal narrative essay) 0.57 Tracy et al., 2009 Published QED Elementary n 126 T: SRSDh C: Business as usual M Writing quantity (Picture story narrative essay) 0.58 Tracy et al., 2009 Published QED Elementary n 126 T: SRSDi C: Business as usual M Writing quantity (Personal narrative essay) 0.64 Troia & Graham, 2002 Published RCT Elementary Academic difficulty 20 T: Writing strategies instruction C: Business as usual M Writing quality (Narrative essay) 0.14 Troia & Graham, 2002 Published RCT Elementary Academic difficulty 20 T: Writing strategies instruction C: Business as usual M Writing quality (Opinion essay) -0.23 Troia & Graham, 2002 Published RCT Elementary Academic difficulty 20 T: Writing strategies instruction C: Business as usual M Writing quantity (Narrative essay) 0.03 59 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Troia & Graham, 2002 Published RCT Elementary Academic difficulty 20 T: Writing strategies instruction C: Business as usual M Writing quantity (Opinion essay) 0.50 Tsiriotakis, 2013 Unpublished QED Elementary n 177 T: Structured writing instruction with self-regulation strategies C: Business as usual M Writing quality (Narrative essay) 2.88 Tsiriotakis, 2013 Unpublished QED Elementary n 177 T: Structured writing instruction with self-regulation strategies C: Business as usual M Writing quality (Argumentative essay) 2.76 Tsiriotakis, 2013 Unpublished QED Elementary n 177 T: Structured writing instruction with self-regulation strategies C: Business as usual M Writing quantity (Narrative essay) 0.77 Tsiriotakis, 2013 Unpublished QED Elementary n 177 T: Structured writing instruction with self-regulation strategies C: Business as usual M Writing quantity (Argumentative essay) 1.75 Walter et al., 2021 Published QED Elementary Academic difficulty 41 T: Morphological spelling instruction C: Business as usual T Writing quality -0.06 Walter et al., 2021 Published QED Elementary Academic difficulty 41 T: Morphological spelling instruction C: Business as usual T Writing quantity 0.01 Wanzek et al., 2017 Published QED Elementary Academic difficulty 38 T: Spelling intervention C: No intervention T Writing quantity 0.30 Wanzek et al., 2017 Published QED Elementary Academic difficulty 40 T: Handwriting intervention C: No intervention T Writing quantity 0.50 Wanzek et al., 2017 Published QED Elementary Academic difficulty 40 T: Spelling and handwriting intervention C: No intervention T Writing quantity 0.35 Welch, 1992 Published QED Elementary Academic difficulty 18 T: PLEASE metacognitive strategy C: Business as usual M Writing quality 2.07 60 Studies Publication Status Design Grade level Risk type N Intervention Description Intervention Type Writing Outcomes Effect Size Wetzel, 1985 Unpublished QED Elementary n 183 T: Computer writing process program C: Business as usual MT Writing quality (Measure a) -0.31 Wetzel, 1985 Unpublished QED Elementary n 180 T: Computer writing process program C: Business as usual MT Writing quality (Measure b) -0.28 Wolbers, 2008 Published QED Secondary Academic difficulty 33 T: Strategic and Interactive Writing Instruction (SIWI) C: Business as usual MT Writing quality (Informative essay) 1.16 Wolbers, 2008 Published QED Secondary Academic difficulty 33 T: SIWI C: Business as usual MT Writing quality (Narrative essay) 1.03 Wolbers, 2008 Published QED Secondary Academic difficulty 33 T: SIWI C: Business as usual MT Writing quantity 0.85 Wolbers et al., 2018 Published QED Elementary Academic difficulty 63 T: Strategic and Interactive Writing Instruction (SIWI) C: Business as usual MT Writing Quality (Personal narrative essay) 0.10 Wolbers et al., 2018 Published QED Elementary Academic difficulty 63 T: SIWI C: Business as usual MT Writing quality (Persuasive essay) -0.40 Wolery, 1997 Unpublished QED Elementary Academic difficulty 28 T: Writing-based spelling/phonological instruction C: Reading-only instruction T Writing quality 0.14 Wolery, 1997 Unpublished QED Elementary Academic difficulty 28 T: Writing-based spelling/phonological instruction C: Reading-only instruction T Writing quantity 0.11 Note. The studies are alphabetically ordered by the first author’s last name; RCT = Randomized Controlled Trial (true experimental design); QED = Quasi-Experimental Design; n = Students with Diverse Academic Skills; N = Number of Participants; M = Multi-Component Writing Intervention without a Transcription Component; MT = Multi-Component Writing Intervention with a Transcription Component; T = Transcription Intervention; SRSD = Self-Regulated Strategy Development abcdThis study did not clearly describe what intervention the control group received; we coded it as no intervention. eWIAT-III = Wechsler Individual Achievement Test – Third Edition. fghiThis study used SRSD representing both Self-Regulated Strategy Development and Self-required strategy development, but these represented the same intervention in this primary study. 62 Figure 3 Forest Plot: The Effects of Writing Intervention on Writing Quantity Weights 63 Figure 4 Forest Plot: The Effects of Writing Intervention on Writing Quality Weights 64 65 66 3.2.2 Research Question Two To answer research question two (i.e., To what extent does the transcription component contribute to the effectiveness of multi-component writing intervention for K-12 students?), I calculated average effect size estimates by type of writing intervention (i.e., transcription intervention, multi-component writing intervention with/without transcription components) and reported the differential effect sizes across the three interventions under the CHE working model (See Table 4). Overall, all three types of writing intervention yielded positive effects for both writing quantity and quality outcomes. While the average effect size estimates ranged broadly across the three writing interventions for writing quality outcomes (Hedges’ g from 0.17 to 0.85), the effect sizes were relatively insensitive to the writing intervention types for writing quantity outcomes (Hedges’ g from 0.40 to 0.66). Also, based on the Wald tests, I found that the writing quality effects varied as a function of the writing intervention type while writing quantity effects did not differ by the type of writing intervention. However, for both measures, multi-component writing intervention, regardless of whether a transcription component was included, yielded stronger effects than transcription intervention, though the differences were not statistically significant. Specifically, for writing quantity outcomes, moderate average effect size estimates were found from transcription intervention (Hedges’ g = 0.40, 95% CI [-0.14, 0.93], p = .124, K = 12) and multi-component writing intervention with a transcription component (Hedges’ g = 0.50, 95% CI [-0.01, 1.01], p = .053, K = 6). However, only multi-component writing intervention without a transcription component generated statistically significant writing quantity effects (Hedges’ g = 0.66, 95% CI [0.30, 1.03], p = .001, K = 36). Given the small number of effect size estimates yielded from transcription interventions and multi-component writing interventions 67 targeting transcription, it seems reasonable that statistically significant effects were not detected from these two types due to limited statistical power. As reported above, multi-component writing intervention without a transcription component yielded the largest average effect size; in contrast, the smallest average effect size estimate was from transcription intervention. It might be because, compared to the other two writing interventions, the writing quantity measure, which requires compositional-level writing skills, was a relatively far transfer measure for some transcription interventions (e.g., transcription intervention only focused on the instruction of spelling skills). Although the average effect size estimates differed across the three writing interventions, the hypothesis that the effects across the three types of writing intervention were equal could not be ruled out by the robust Wald test [F (2, 9.37) = 0.44, p = .657]. The unbalanced number of effect size estimates across the three groups might partially explain this result. Complementarily, the total true variance (τ2 = .51) from this model was very similar (even larger) to the estimate from the previous model that did not include writing intervention type as a moderator (τ2 = .47), suggesting that writing intervention type might not help to explain the true variance between studies. In contrast to writing quantity outcomes, based on the Wald test [F (2, 6.65) = 8.75, p = .014], writing quality effects varied by writing intervention type. Specifically, the robust pairwise Wald test [F (1, 5.74) = 20.1, p = .005] shows a statistically significant difference in the average effect size estimates between multi-component writing intervention without a transcription component and transcription intervention, suggesting that the effects on writing quality differed for these two types of writing intervention. Specifically, students receiving multi-component writing intervention without a transcription component scored 0.85 standard 68 deviation higher than students in the control conditions and yielded the strongest average estimated effect (Hedges’ g = 0.85, K = 77, 95% CI [0.58, 1.12], p < .001), while students having transcription intervention had the weakest average effect size estimate (Hedges’ g = 0.17, K = 14, 95% CI [-0.14, 0.49], p = .224). Across the three types of writing intervention, regardless of whether transcription components were included, both types of multi-component writing intervention yielded statistically significant writing quality effects. Specifically, the multi-component writing intervention with a transcription component generated a moderate effect (Hedges’ g = 0.56, K = 19, 95% CI [0.10, 1.01], p = .021) and the multi-component writing intervention without a transcription component yielded a strong average effect. Similar to the Wald test, the true variance of this model with writing type as the moderator (τ2 = .70) slightly decreased when compared to the model ignoring writing intervention type (τ2 = .74), indicating that the type of writing intervention can account for some heterogeneity of the writing quality effects. However, this conclusion might be tentative due to the unbalanced number of effect size estimates across the three groups of writing intervention and statistical power concerns (the small number of effect size estimates generated from the transcription intervention and the transcription component involved in multi-component writing intervention). 3.2.3 Research Question Three To answer research question three (i.e., Do grade level and students’ academic difficulties moderate the effects of writing intervention?), I fit CHE working models, with the moderators of students’ grade level and whether students had academic difficulties, for the outcomes of writing quantity and writing quality, separately. Table 4 summarizes the effect size estimates, along with the corresponding statistics by moderators. 69 Table 4 Summary of Effect Size Estimates for Writing Quantity and Writing Quality by Moderators Moderators Writing Quantity Writing Quality K Hedges’ g 95% CI p K Hedges’ g 95% CI p Intervention type Transcription (T) 12 0.40 [-0.14, 0.93] .124 14 0.17 [-0.14, 0.49] .224 Multi-component without a transcription component (M) 36 0.66 [0.30, 1.03] .001 77 0.85 [0.58, 1.12] < .001 Multi-component with a transcription component (MT) 6 0.50 [-0.01, 1.01] .053 19 0.56 [0.10, 1.01] .021 Grade level Elementary 38 0.33 [0.15, 0.51] .001 79 0.60 [0.33, 0.87] < .001 Secondary 16 1.15 [0.41, 1.88] .007 31 0.98 [0.58, 1.37] < .001 Risk type Students with diverse academic skills 28 0.50 [0.22, 0.77] .001 67 0.73 [0.48, 0.99] < .001 Students with academic difficulty 26 0.68 [0.23, 1.13] .006 43 0.67 [0.28, 1.06] .002 Note. K was the number of effect size estimates. 3.2.3.1 Does the Overall Writing Effect Vary by Student Grade Level? Collectively, the pattern of the average estimated effect sizes for writing quantity and quality outcomes across the two grade levels was consistent. For both outcomes, writing intervention yielded statistically significant and positive effects for students across two grade levels (i.e., elementary grade level and secondary grade level). Although statistically different effects across two grade levels were only detected for writing quantity outcomes, the average estimated effect sizes for both writing quantity and quality outcomes appeared to be larger for students at secondary grade levels. Specifically, for writing quantity outcomes, the robust Wald test supported the heterogeneity test and indicated statistically significant differences in writing quantity effects across two grade levels [F (1, 14.5) = 6.18, p = .026]. Moreover, I found that the total true variance (τ2 = .37) of this model with grade level as the moderator was smaller than the one (τ2 70 = .47) for the CHE model when ignoring students’ grade level, suggesting that the grade level can account for some heterogeneity. The average effect size estimate for students at elementary school was weak to moderate (Hedges’ g = 0.33, K = 38, 95% CI [0.15, 0.51], p = .001), while writing intervention generated strong effects for students at the secondary level (Hedges’ g = 1.15, K = 16, 95% CI [0.41, 1.88], p = .007). Compared to the average effect size and the corresponding statistics for students at elementary grade levels, the smaller number of effect sizes for students at secondary grade levels limited the statistical power, which might partially account for the larger p-value of the stronger average effect size for students at secondary grade levels. Moreover, the interpretation of the true magnitude of the average writing quantity effect for students at secondary grade levels needs to consider its wide confidence interval (95% CI [0.41, 1.88]). Additionally, the conclusion and interpretation of the statistically significant different writing quantity effects across students at elementary and secondary grade levels should also be limited because the confidence intervals of the corresponding estimated effects for students at these two grade levels nearly overlapped. Similar to writing quantity outcomes, statistically significant and positive writing quality effects were found for both grade levels. Specifically, the average effect size estimate was moderate for students at elementary grade levels (Hedges’ g = 0.60, K = 79, 95% CI [0.33, 0.87], p < .001), while it was strong for students at secondary grade levels (Hedges’ g = 0.98, K = 31, 95% CI [0.58, 1.37], p < .001). Although the average estimated effects differed for the two grade levels, the Wald test was not statistically significant [F (1, 34.4) = 2.68, p = .111], indicating the hypothesis that the average writing quality effects were equal across grade levels could not be ruled out. Moreover, the total true variance of this CHE model (τ2 = .736) tended to be trivially smaller than that of the model without considering grade level as the moderator (τ2 = .741). Thus, 71 both the Wald test and heterogeneity test indicate that writing quality effects did not vary as a function of student grade level. 3.2.3.2 Does the Overall Writing Effect Vary by Student Academic Difficulty? Overall, neither writing quantity nor writing quality effects differ as a function of whether students had academic difficulties. Also, for both writing outcomes, statistically significant effect sizes for both students with academic difficulties and students with diverse academic skills were found. Specifically, for writing quantity outcomes, given the results of the Wald test [F (1, 20.6) = 0.616, p = .442], although I could not rule out the hypothesis that writing quantity effects were equal across two academic difficulty categories, students with academic difficulties obtained larger average effect (Hedges’ g = 0.68, K = 26, 95% CI [0.23, 1.13], p = .006) than students with diverse academic skills (Hedges’ g = 0.50, K = 28, 95% CI [0.22, 0.77], p = .001). Moreover, the true total variance (τ2 = .50) for this model with whether students had academic difficulties as the moderator was even larger than that (τ2 = .47) for the model without any moderator, suggesting that students baseline academic skills may not explain the heterogeneity. For writing quality outcomes, similar moderate to strong, statistically significant average effects for students with academic difficulties (Hedges’ g = 0.67, K = 43, 95% CI [0.28, 1.06], p = .002) and students with diverse academic skills (Hedges’ g = 0.73, K = 67, 95% CI [0.48, 0.99], p < .001) were detected. Moreover, the robust Wald test [F (1, 35.9) = 0.08, p = .778] supports that writing quality effects did not vary by whether students had academic difficulties or not. Similar to the writing quantity outcomes, the total true variance (τ2 = .75) of this model with the moderator of whether students had academic difficulties was even larger than that (τ2 = .74) for the model ignoring any moderators, supporting the result of the robust Wald test and indicating that student academic difficulty cannot explain the heterogeneity. 72 In addition, I examined the interactive effects across the three moderators (i.e., the type of writing intervention, student grade level, and whether students had academic difficulties) under the CHE working model on writing quantity and quality separately. However, due to the inadequate number of primary studies and effect sizes of some levels of the three moderators (e.g., only one transcription intervention study for students at secondary grade levels and no transcription intervention study reporting writing quality effects for students with diverse academic skills), I only computed the descriptive main effects of each interactive item of these three moderators and did not conduct any inferential analyses. Table 5 summarizes the descriptive interactive effects across writing intervention types, grade levels, and whether students had academic difficulties. Table 5 Summary of Main Effects of Interactive Items across Intervention Type, Grade Level, and Academic Difficulty Moderators Writing Quantity Writing Quality K Hedges’ g K Hedges’ g Intervention type * Grade level T * Elementary 11 0.19 13 0.06 M * Elementary 23 0.43 50 0.81 MT * Elementary 4 0.25 16 0.44 T * Secondary 1 2.27 1 1.23 M * Secondary 13 1.05 27 0.95 MT * Secondary 2 0.98 3 1.07 Intervention type * Academic difficulty T * Students with academic difficulty 11 0.43 14 0.21 M * Students with academic difficulty 12 1.06 19 0.79 MT * Students with academic difficulty 3 0.44 10 0.62 T * Students with diverse academic skills 1 0.13 M*Students with diverse academic skills 24 0.55 58 0.79 MT*Students with diverse academic skills 3 0.54 9 0.52 Note. M = Multi-Component Writing Intervention without a Transcription Component; MT = Multi-Component Writing Intervention with a Transcription Component; T = Transcription Intervention; K = the Number of Effect Size Estimates. 73 3.3 Publication Bias Given publication bias is inevitable, I evaluated potential publication bias for the overall writing effects. Overall, both writing quantity and writing quality effects did not differ as a function of studies’ publication status. Figure 5 and Figure 6 present the funnel plots for the two writing outcomes, respectively. For writing quantity outcomes, I did not find strong evidence of publication bias. Specifically, the funnel plot (see Figure 5) shows that studies’ effect sizes were distributed roughly symmetrically around the average effect size, and most studies’ effect sizes were similar to the average effect size. However, this funnel plot (see Figure 5) also presents that some studies at the top were outside the funnel lines, and one study (i.e., Asaro-Saddler et al., 2018) with a higher standard error (less precise effect size estimate) was far away from the average effect size. Then, I calculated the average effect size estimates by studies’ publication status under the CHE working model to examine whether writing effects vary by studies’ publication status. Although the average estimated effect for published studies (Hedges’ g = 0.65, K = 30, 95% CI [0.27, 1.03], p = .002) was larger than that for unpublished studies (Hedges’ g = 0.46, K = 24, 95% CI [0.12, 0.79], p = .012), the robust Wald test [F (1, 23.6) = 0.675, p = .420] indicates that they were not statistically different. Thus, the overall writing quantity effects did not differ as a function of studies’ publication status. Figure 5 Funnel Plot: Publication Bias of the Overall Writing Quantity Effects 74 For writing quality outcomes, the funnel plot (see Figure 6) did not indicate potential publication bias, though this figure shows slight asymmetry. Based on this funnel plot (see Figure 6), although studies at the top were roughly symmetric around the average effect size, these studies’ effect sizes were slightly skewed with a downtrend slope of higher standard error along with a larger estimated effect size. Also, there were some studies at the top outside the funnel lines, and one unpublished study (i.e., Ray, 2018) was far away from the average estimated effect with a large standard error. Then, I calculated the differential effects between published and unpublished studies by the CHE working model. Although the average estimated effects tended to be larger for the unpublished studies (Hedges’ g = 0.83, K = 45, 95% CI [0.41, 1.25], p < .001) than for published studies (Hedges’ g = 0.64, K = 65, 95% CI [0.38, 0.90], p < .001), the robust Wald test shows the differences were not statistically significant [F (1, 47.7) 75 = 0.629, p = .432]; indicating that writing quality effects did not vary by studies’ publication status. Figure 6 Funnel Plot: Publication Bias of the Overall Writing Quality Effects 3.4 Sensitivity and Outlier Analysis Because the publication bias visual analysis indicates potential outliers, I conducted residual analyses and calculated Cook’s distances to examine potential outliers and influential cases for CHE working models for the overall writing quantity and quality outcomes separately (Viechtbauer, 2010; Viechtbauer & Cheung, 2010). Then I fit the adjusted CHE working models with the potential influential case removed to examine whether the overall writing quantity and quality effects were impacted considerably by the removed cases. Collectively, these analyses did not indicate influential cases for the original models (i.e., the CHE working models with all 76 identified cases and were used for all the above analyses) either for writing quantity outcomes or writing quality outcomes. For writing quantity outcomes, consistent with the publication bias analysis, both residual analysis and Cook’s distances indicate that the primary study Asaro-Saddler et al. (2018), which had the largest residual value with the least precision (residual value = 7.27, SE = 1.21) across all the cases, was a potential outlier and potential influential case (Cook’s distance = 0.32). Although this value of Cook’s distance is smaller than three times the corresponding average effect size (Hedges’ g = .57), I still ran an adjusted CHE working model with this potential influential case excluded to examine whether the overall writing quantity effects were impacted by this case substantially. Based on this adjusted model, a moderate and statistically significant average effect size estimate was detected (Hedges’ g = 0.50, K = 53, 95% CI [0.29, 0.71], p < .001). Although the average estimated effect size of this adjusted model with the potential influential case removed was slightly smaller than that of the original model with this potential influential case (Hedges’ g = 0.57, K = 54, 95% CI [0.32, 0.82], p < .001), the two models led to the same conclusion that students receiving writing intervention performed about half of a standard deviation improvement than students in the control group, and the average writing quantity effects were statistically significant. In addition, I found that the total variance of the model without the potential influential case (τ2 = .32) was smaller than the original model (τ2 = .47). Therefore, the original CHE model, including the case of Asaro-Saddler et al. (2018), fitted the data structure and was robust. Figure 7 plots the distribution of residuals across writing quantity effect sizes, and Figure 8 presents the Cook’s distances. 77 Figure 7 Residual Plot for Writing Quantity Effect Sizes CHE Model Figure 8 Plot of Cook’s Distance for Writing Quantity Effect Sizes CHE Model For writing quality outcomes, inconsistent with the publication analysis, the primary study Ray (2018) was not an influential case (Cook’s distance = 0.02), though it had the largest -3-2-10123456781 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53ResidualsObserved CasesResiduals and Standard Errors for the Model of Writing Quantity OutcomesResidualStandard Error 78 estimated effect size (Hedges’ g = 5.57) and largest residual value (residual value = 4.87). Due to the small sample size of Ray (2018), its effect size only weighted 0.27% to the overall writing quality effects; this small percentage of weights may partially explain that Ray (2018) was not an influential case. Moreover, according to the analyses of residuals and Cook’s distances for the model of writing quality outcomes (see Figures 9 and 10), I found that the primary study Asaro-Saddler et al. (2018) had the third largest residual value (residual value = 3.20) but the largest Cook’s distance (Cook’s distance = 0.11) across all the cases. Although the Cook’s distance of Asaro-Saddler et al. (2018) was not more than three times the average writing quality effect size, I still conducted an adjusted CHE working model that did not include this case to examine whether this case considerably impacted the overall writing quality effects. Similar to the writing quantity outcomes, the adjusted CHE working model with Asaro-Saddler et al. (2018) removed yielded a consistent conclusion with the original model with this case included. Based on the adjusted model, a moderate and statistically significant average effect size estimate (Hedges’ g = 0.67, K = 109, 95% CI [0.46, 0.88], p < .001) was detected. Compared to the original model (Hedges’ g = 0.71, K = 110, 95%CI [0.49, 0.93], p < .001), although the average effect size from the adjusted model appeared to be smaller, they were both statistically significant. Additionally, the total variance of the adjusted model (τ2 = .67) was smaller than that of the original model (τ2 = .73). Therefore, Asaro-Saddler et al. (2018) was not an influential case for the model of the overall writing quality outcomes and the conclusions based on the original meta-analytic models were robust. 79 Figure 9 Residual Plot for Writing Quality Effect Sizes CHE Model Figure 10 Plot of Cook’s Distance for Writing Quality Effect Sizes CHE Model -2-101234561 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 100 103 106 109ResidualsObserved CasesResiduals and Standard Errors for the Model of Writing Quality OutcomesResidualStandard Error 80 In addition to the overall writing quantity and quality effects, I also examined whether the potential influential case (i.e., Asaro-Saddler et al., 2018) had considerable impacts on the moderation effects by fitting adjusted CHE models (i.e., the model without this potential influential case) with the moderators of the writing intervention type, grade level, and whether students had academic difficulties for the outcomes of writing quantity and quality, separately. For each moderator, neither the average effect sizes (both writing quantity and writing quality effects) nor their corresponding results of Wald tests differed considerably between the original and adjusted models, indicating the conclusions of the moderator analyses based on the original models were robust. Table 6 summarizes the effect size estimates of the adjusted models by moderators, and table 7 compares the results of Wald tests between the original models and the models that fit the adjusted datasets by moderators. Table 6 Summary of Effect Size Estimates for the Models Fit the Adjusted Datasets by Moderators Moderators Writing Quantity Writing Quality K Hedges’ g 95% CI p K Hedges’ g 95% CI p Intervention type Transcription (T) 12 0.39 [-0.12, 0.90] .117 14 0.17 [-0.16, 0.50] .251 Multi-component without a transcription component (M) 35 0.56 [0.27, 0.84] < .001 76 0.81 [0.55, 1.07] < .001 Multi-component with a transcription component (MT) 6 0.45 [-0.03, 0.93] .058 19 0.53 [0.09, 0.98] .023 Grade level Elementary 38 0.33 [0.14, 0.52] .002 79 0.59 [0.33, 0.87] < .001 Secondary 15 0.94 [0.37, 1.52] .006 30 0.86 [0.54, 1.18] < .001 Risk type Students with diverse academic skills 28 0.49 [0.21, 0.78] .002 67 0.73 [0.47, 0.99] < .001 Students with academic difficulty 25 0.51 [0.20, 0.82] .003 42 0.57 [0.23, 0.92] .002 Note. K was the number of effect size estimates. 81 Table 7 Summary of Wald Test Results across Models Models Writing Quantity Writing Quality df F p df F p Intervention Types Original Model 2 0.44 .657 2 8.75 .014 Adjusted Model 2 0.23 .802 2 7.64 .017 Grade Level Original Model 1 6.18 .026 1 2.68 .111 Adjusted Model 1 5.47 .036 1 1.68 .204 Academic Difficulty Original Model 1 0.62 .442 1 0.08 .778 Adjusted Model 1 0.01 .931 1 0.58 .450 Note. Adjusted Model = The Model without the Potential Influential Case (i.e., Asaro-Saddler et al., 2018); Original Model = The Model with All the Identified Cases; df = Degree of Freedom. To check if other potential influential cases exist in the models with moderators, I examined Cook’s distances for the CHE models by moderators (i.e., intervention type, grade level, and academic difficulty) for writing quantity and quality separately. Overall, no Cook’s distance was more than three times its corresponding average effect size. Also, Asaro-Saddler et al. (2018) had the largest Cook’s distance across all the cases, except for two models (models with the intervention type as the moderators for both writing quantity and quality outcomes). Specifically, for the model with the moderator of writing intervention type for writing quantity outcomes, a case from the primary study (i.e., Christensen, 2005) yielded the largest Cook’s distance (Cook’s distance = 0.68). Also, for the model with this moderator for writing quality outcomes, the largest Cook’s distance (Cook’s distance = 1.16) was from a case contributed by Berninger et al.’s (2002) study. Given that they were both smaller than three times the corresponding average effect sizes, they were not identified as potential influential cases. These analyses indicate the conclusions of moderator analyses based on the original CHE models were 82 robust. Figures 11 and 12 plot Cook’s distances for CHE models with moderators for writing quantity and writing quality outcomes. Figure 11 Plot of the Cook’s Distances for CHE Models with Moderators for Writing Quantity Outcomes 83 Figure 12 Plot of the Cook’s Distances for CHE Models with Moderators for Writing Quality Outcomes 84 Chapter 4: Discussion The primary goal of this meta-analysis was to determine the systematic effects of writing intervention (i.e., multi-component writing intervention and transcription intervention) for K-12 students and the extent to which transcription intervention contributes to these effects, as well as to estimate how the overall writing effects and the differential effects are moderated by grade levels and whether students have academic difficulties. As discussed above, the concurrent activation and coordination of multiple writing component skills are required to enable writing (Berninger & Amtmann, 2003). Transcription skills are fundamental to writing development, and multi-component writing intervention simultaneously targeting multiple skills highlighted as important in writing theories is in line with the multi-dimensional construct of writing. Therefore, evaluating the effectiveness of these two types of writing intervention and their combination (i.e., how well multi-component writing intervention combined with basic-level writing skills work) is both theoretically and practically important. Overall, I found that writing intervention generated moderate to strong effects on both writing quantity and quality outcomes (Hedges’ g from 0.33 to 1.15) across subgroups of students and various writing intervention approaches. These findings are promising, given the importance of writing to an individual’s academic development and overall success (Graham & Perin, 2007; Kim et al., 2021; McMaster et al., 2018). 4.1 The Overall Effects of Writing Intervention Collectively, I found statistically significant and moderate to strong effects on writing quantity (Hedges’ g = 0.57) and writing quality outcomes (Hedges’ g = 0.71) across 54 and 110 effect size estimates, respectively. These results are consistent with previous meta-analyses (Graham, McKeown et al., 2012; Graham & Perin, 2007; Kim et al., 2021), suggesting that 85 writing intervention is effective for K-12 students. However, the ranges of effect sizes for both writing quantity and quality outcomes were broad, with Hedges’ g from -1.11 to 7.77 and from -0.76 to 5.57, respectively. Since this meta-analysis synthesized heterogeneous primary studies, multiple factors can contribute to the broad range of effect sizes, such as intervention contents, instructional and administrative conditions, and students’ features. Additionally, the negative writing effects that were included in the 95% prediction intervals of the true effects for both writing quantity and quality outcomes reveal that writing intervention may impede writing performance in some contexts. These results encourage moderator analyses to investigate which writing intervention components can maximally boost writing skills for which subgroups of students by which approaches. 4.2 Writing Effects by Writing Intervention Types To determine how the transcription component contributes to the effects of multi-component writing intervention, I calculated both writing quantity and quality effects by types of writing intervention and examined whether multi-component writing intervention with a transcription component yielded greater writing effects than transcription intervention only (i.e., MT versus T) and/or than writing intervention without a transcription component (i.e., MT versus M). Overall, consistent with my hypothesis, multi-component writing intervention, either with or without the instruction of transcription skills, tended to generate stronger descriptive effects for both writing quantity and quality outcomes than transcription intervention only (i.e., MT > T and M > T). The stronger effects of multi-component writing intervention were consistent, regardless of the students’ grade levels or whether they had academic difficulties. This finding provides empirical evidence that specific writing skills should not be taught in isolation (Berninger et al., 1992). 86 In addition, compared to transcription intervention only, the stronger effects of multi-component writing intervention with a transcription component (i.e., MT > T) indicate that transcription intervention is more effective in the context of multi-component writing intervention, addressing prior research’s question on the necessity of writing intervention targeting both mechanics-and high-level skills (e.g., Berninger & Amtmann, 2003; Kim et al., 2021; McMaster et al., 2018). This finding is consistent with my hypothesis and supports the theoretical model (i.e., the Simple View of Writing; Berninger & Amtmann, 2003) that the complex neurofunctional system of writing requires the contribution of both mechanics- and high-level writing component skills to enable composition. Contrary to what I hypothesized, the aggregated writing effects of multi-component writing intervention appeared to be stronger when it did not include the instruction of mechanics-level skills (i.e., MT < M). Although the differences were not statistically significant, it still indicates that the average estimated writing effects of multi-component writing intervention tend to be smaller when transcription skills are targeted simultaneously. However, in certain situations, the multi-component writing intervention appeared to have stronger writing effects when targeting both mechanics-and high-level skills (i.e., MT > M). 4.2.1 Transcription Intervention Only Regarding transcription intervention only, the weak to moderate effects on both writing quantity (Hedges’ g = 0.40) and quality (Hedges’ g = 0.17) were consistent with previous meta-analyses (Graham & Santangelo, 2014; McMaster et al.,2018). Although these positive effect sizes were not statistically significant, they still indicate that teaching mechanics-level writing skills can improve not only specific transcription skills but also overall writing performance. However, the small number of both writing quantity and quality effect sizes (12 effect sizes for 87 writing quantity outcomes and 14 effect sizes for writing quality outcomes) and the considerably varied effect sizes across all the included transcription intervention studies might partially account for the non statistically significant results. Moreover, the broad ranges of Hedges’ g for transcription intervention (from -0.31 to 2.27 for writing quantity and from -0.13 to 1.23 for writing quality) indicate that writing effects were inconsistent across contexts. I did not find a consistent pattern across the four identified negative writing effects (three for writing quality and one for writing quantity). Among these negative effects, only Graham et al. (2002) reported that they had the control group yield three outliers that students created extremely long stories. Regarding students’ features, all four negative effect sizes were for elementary grade level students (including early and upper elementary grade levels) with academic difficulties (including specific handwriting and spelling difficulties and slower learners); however, we also found that primary studies implementing transcription intervention for students with academic difficulties at elementary grade level had strong writing effects. Therefore, future studies aimed at evaluating in what instructional conditions transcription intervention can maximally improve writing performance for subgroups of students are needed. In addition, the overall weak to moderate effects of transcription intervention indicate that the implementation of the transcription intervention only might not be the perfect approach to improving writing performance in natural settings. In addition, for transcription intervention only, the strongest writing quantity and quality effects were from the transcription intervention targeting handwriting skills, indicating that compared to spelling intervention, handwriting intervention may appear to have stronger transfer effects to overall writing performance. Specifically, Christensen (2005) implemented a specific handwriting intervention and had the strongest writing quantity (Hedges’ g = 2.27) and writing 88 quality effects (Hedges’ g = 1.23) for secondary grade level students with handwriting difficulties (i.e., students with low levels of orthographic-motor integration). Similarly, for students at the elementary grade level, Berninger et al. (1997) with a handwriting intervention had the strongest writing quality effects (Hedges’ g = 0.93), and Jung et al. (2017) with spelling and handwriting combined intervention had the strongest writing quantity effects (Hedges’ g = 1.24). This finding is consistent with previous studies (Graham & Santangelo, 2014; Santangelo & Graham, 2016) and provides evidence that for students at elementary grade levels, overall writing performance is best predicted by students’ writing speed and legibility (Berninger, 2000; Berninger et al., 1997). Additional evidence was from Wanzek et al.’s (2017) study, which examined the writing quantity effects of spelling intervention, handwriting intervention, and the combination of spelling and handwriting intervention for first graders with written expression difficulties. In this study, although they did not directly compare writing effects across the three different specific transcription interventions (they compared each transcription intervention to a control condition that did not have any intervention), I found that handwriting intervention generated the strongest writing quantity effects (Hedges’ g = 0.50), followed by the combination of handwriting and spelling intervention (Hedges’ g = 0.35), and the spelling intervention had the lowest effects (Hedges’ g = 0.30) by comparing their descriptive writing outcomes. Although I am unable to conclude that handwriting intervention is more effective than spelling intervention for compositional level effects, these findings do indicate that the writing effects may differ across types of specific transcription intervention in certain conditions. Future studies would benefit from examining which specific transcription skills or the combination of the instruction of 89 transcription skills can maximally contribute to improved writing performance. Additionally, studies aimed at evaluating whether these effects vary by subgroups of students are also needed. 4.2.2 Multi-Component Writing Intervention without a Transcription Component Regarding multi-component writing intervention without a transcription component, statistically significant effects for measures of writing quantity (Hedges’ g = 0.66, p = .001) and writing quality (Hedges’ g = 0.85, p < .001) were detected. The complex nature of the writing process in which the coordination of multiple writing skills is required to enable composition may account for these moderate to strong and statistically significant results (Berninger & Amtmann, 2003). In addition, these statistically significant effect sizes were not only detected on the overall writing quantity and quality effects but also for subgroups of students, except for writing quantity outcomes for students with academic difficulties. However, the average writing quantity effect for multi-component writing intervention without a transcription component was still strong for students with academic difficulties and almost reached conventional statistical significance (Hedges’ g = 1.06, p = .063). The large variation with the wide confidence interval (95% CI [-0.08, 2.21]) and the relatively small number of related effect sizes (K = 12) might account for the non-statistically significant result. These findings indicate that multi-component writing intervention without a transcription component is promising and encouraged, even for young writers or students with academic difficulties. Collectively, these generally consistent statistically significant results are encouraging that effective tools to teach multiple writing skills simultaneously in natural contexts do exist, which can empower practitioners given their limited instructional time. Moreover, across all the identified studies implementing multi-component writing intervention without a transcription component, we found that many related effect sizes were 90 generated from the SRSD intervention or multi-component writing intervention with self-regulated skills explicitly added, with 20 and 21 effect sizes for writing quantity and quality, respectively. This finding is consistent with previous meta-analyses (Kim et al., 2021). SRSD is a well-researched multi-component writing intervention framework that includes explicit writing strategy instruction (i.e., genre-specific and general writing strategies) while scaffolding students to self-regulate the writing process including the teaching of several self-regulation related skills necessary for successful writing (Harris et al., 2008). In addition to the research evidence, resources to support SRSD training appear to be free or low-cost (e.g., free online sample lessons; Harris et al., 2008; IRIS Center, n.d.). Thus, SRSD might be a good approach and choice of multi-component writing intervention for practitioners to be implemented in natural contexts. 4.2.3 Multi-Component Writing Intervention with a Transcription Component As discussed above, contrary to my hypothesis, the aggregated writing effects appear to be smaller for multi-component writing intervention targeting both mechanics- and high-level writing component skills than for multi-component writing intervention without a transcription component. Given that writing skills across levels may reciprocally support each other (Berninger & Amtmann, 2003; Berninger et al., 2002), I expected that multi-component writing intervention would yield stronger writing effects when a transcription component was added. However, the smaller effect size for multi-component writing intervention with a transcription component from the present study does not invalidate the importance and necessity of the implementation of writing intervention with a combination of mechanics-and high-level writing skills. First, the magnitude of the average writing effect sizes of multi-component writing intervention targeting a transcription component was moderate (Hedges’ g = 0.50 for writing quantity outcomes and Hedges’ g = 0.56 for writing quality outcomes); moreover, the average 91 writing quantity effects for this type of writing intervention appeared to be very close to the effects for multi-component writing intervention without a transcription component. Second, multi-component writing intervention with a transcription component generated a statistically significant writing quality effect (p = .021). Although the average writing quantity effect of this type of writing intervention was not statistically significant (p = .053), it almost reached the conventional criterion with a small number of related effect sizes (K = 6). These findings indicate that the writing intervention including both mechanics- and high-level skills are generally effective. Third, I identified two primary studies (i.e., Berninger et al., 2002; Steelman, 1990) that directly examined the writing effects of multi-component writing intervention with a transcription component and multi-component writing intervention without a transcription component by comparing them to the same control group. In these two studies, the writing effects of multi-component writing intervention had greater effects when the instruction of transcription skills was targeted. Specifically, Steelman (1990) implemented a newspaper writing program for students with diverse academic skills at secondary grade levels. This study compared the writing effects of the newspaper writing program combined with intensive keyboarding instruction (i.e., MT) versus the effects of the newspaper writing program with pencil and paper (i.e., M) versus the effects of the business-as-usual curriculum. In this study, the newspaper program generated the strongest writing quantity and quality effects when keyboarding instruction was explicitly added (i.e., MT > M). Similarly, Berninger et al. (2002) implemented spelling intervention, composition intervention (including text generation and self-regulation for informative and persuasive genres), and the combined spelling and composition intervention for Grade 3 students with 92 compositional fluency difficulties and compared these three interventions to the same control group in which students just practiced keyboarding and wrote different topics without any instruction. In the persuasive writing samples, the writing quality effect from the composition intervention combined with spelling instruction was the strongest (Hedges’ g = 2.06), followed by the composition intervention (Hedges’ g = 1.58), then the spelling intervention (Hedges’ g = 0.29). These findings provide evidence that multi-component writing intervention with a transcription component can be more effective when the other factors (e.g., study design, participants’ features, intervention dosage) are controlled. In addition, both this present study and prior meta-analyses have provided evidence that teaching specific transcription skills can improve both the specific targeted transcription skills and overall writing performance (Graham & Santangelo, 2014; McMaster et al.,2018; Santangelo & Graham, 2016). Thus, it is necessary to examine in what conditions for whom that transcription component can maximally contribute to the effectiveness of the multi-component writing intervention. In this meta-analysis, given that all the writing quantity effects from the multi-component writing intervention including a transcription component had the corresponding writing quality effects in the same direction, the following discussion of this type of writing intervention focuses on the writing quality effects. Regarding multi-component writing intervention with a transcription component, writing effects tended to be larger for primary studies that clearly described how specific transcription and other writing skills were explicitly taught and/or with balanced time on each (i.e., studies with Hedges’ g higher than 0.56; ranging from 0.56 to 2.11), whereas primary studies that did not clearly allocate instructional time to both or that did not provide explicit instruction on both had smaller effects. For example, Berninger et al. (2002) 93 explicitly taught spelling (by teaching alphabetic principle) for 10 minutes and composition (including text generation and self-regulation) for 10 minutes for Grade 3 students with low compositional fluency. In this primary study, they taught spelling skills based on alphabetic principle and used specific writing process strategies (e.g., graphics organizers) to teach compositional skills. McMaster et al.’s (2020) study examined the writing effects of a teacher’s professional development program, including the implementation of the writing intervention and decision-making tools. Regarding the implemented writing intervention, McMaster et al. (2020) provided the minilesson plan consisting of six sessions for transcription instruction (including the teaching of spelling and handwriting skills) and seven sessions for text generation (including sentence-and passage level construction); additionally, they added the instruction of self-regulation to the session of discourse-level text generation. Additional evidence was from the following primary studies that clearly outlined the focal writing skills with explicit instruction on each but might not have balanced instructional time on each writing skill. Steelman (1990) taught a computer-based newspaper writing program for students at secondary grade levels, including the teaching of keyboarding skills and text generation, such as organization and writing strategies. Although this study seemed not to balance the instructional time between the teaching of mechanics-and high-level skills, this study described their transcription instruction (i.e., keyboarding instruction) as intensive and students were expected to achieve a typing proficiency before they went to the composition instruction. Jones (2015) implemented an interactive writing program, including teaching transcription and composition skills to kindergarteners. This study clearly listed which transcription and text generation skills were taught (e.g., letter-sound correspondence, the writing process and procedures for different genres and audiences). Moreover, Wolbers (2008) that implemented 94 Strategic and Interactive Writing Instruction for students who were d/Deaf or hard of hearing at secondary grade levels was the only exception in that it yielded strong effects but did not clearly list all the targeted writing skills. In this study, the intervention included the teaching of both mechanics- and high-level skills, but they only clearly described which high-level writing component skills were taught. Although this study did not report which specific mechanics-level skills were taught, they did emphasize they had explicit instruction on these skills, such as spelling. However, as mentioned above, primary studies that had multi-component writing intervention with a transcription component but without the authentic combination of explicit instruction on each writing skill appeared to have smaller writing effects. Specifically, some of them mainly focused on the teaching of transcription skills with rare attention to the teaching of compositional skills (e.g., Case-Smith et al., 2014), while some of them allocated most instructional time to compositional-level instruction with implicit and little instruction on transcription skills (e.g., MacArthur et al., 1995). All these studies had relatively weak writing effects. Although this present study is unable to conclude that the approach to combining the instruction of writing component skills is the only factor contributing to the weak effects of these studies, these findings still indicate the importance of the authentic combination of explicit instruction on each writing skill to writing effects. Future studies aimed at evaluating the extent to which instructional time allocation contributes to writing effects and whether such effects would vary by subgroups of students are encouraged. 95 4.3 Moderation of Writing Effects by Student Grade Level and Academic Difficulties 4.3.1 Moderation Effects by Student Grade Level Compared to students at elementary grade levels, both writing quantity and quality effects appeared to be stronger for students at secondary grade levels, though only the differential writing quantity effects achieved conventional statistical significance. In addition, based on the descriptive results of interactive effects between grade level and writing intervention type, for all three types of writing interventions, students at secondary grade levels had stronger writing quantity and quality effects than students at elementary grade levels. Specifically, for multi-component writing intervention without a transcription component, the stronger effects of students at secondary grade levels are consistent with my hypothesis and may be explained by the theoretical model (i.e., the Simple View of Writing; Berninger & Amtmann, 2003) that students at secondary grade levels who possibly have developed automaticity of transcription skills have more cognitive resources freed up to be used for higher-order writing skills. Thus, compared to students at the elementary grade levels, multi-component writing intervention without an emphasis on transcription skills may better meet secondary school students’ needs. In addition, for multi-component writing intervention with a transcription component, the stronger aggregated writing effects for students at secondary grade levels are reasonable, given the primary studies identified in this meta-analysis. I identified three cases from two studies (i.e., Steelman, 1990; Wolbers, 2008) implementing writing interventions combining both mechanics-and high-level skills for students at secondary grade levels, and they all generated strong effects due to their authentic combination of explicit instruction on transcription-and compositional-level skills. Additionally, although students in these two studies were at secondary grade levels, 96 they all needed transcription instruction support. Steelman (1990) taught keyboarding instruction to students who were unfamiliar with computer keyboarding skills, and Wolbers (2008) designed and taught metalinguistic instruction to students who were d/Deaf or hard of hearing. However, the identified primary studies for students at elementary grade levels having this type of writing intervention were heterogeneous, with a wide range of writing quality effects of Hedges’ g from -0.76 to 2.11 and consistently weak writing quantity effects of Hedges’ g from 0.01 to 0.09. Various reasons can account for the inconsistent effects for students at elementary grade levels, such as research design (e.g., a primary study had statistically significant differences on pre-test scores favouring the control condition when compared to the treatment condition; Hooper et al., 2013) and intervention contents (e.g., the intervention did not authentically combine the instruction of different writing component skills; McCutchen et al., 2022). For transcription intervention, comparing the writing effects for students across grade levels is meaningless because only one primary study was identified for students at the secondary grade level. For students at secondary grade levels, Christensen (2005) implemented a specifically designed handwriting intervention for students with handwriting difficulties and generated Hedges’ g of 1.23 and 2.27 for writing quality and quantity, respectively. This finding is consistent with my hypothesis that transcription intervention is effective for older writers with transcription difficulties and indicates that a given intervention can be strongly effective if it meets students’ specific needs. However, the current study is unable to conclude further related implications or evaluate the differential effects across grade levels because only one related effect size with the focus on students at secondary school levels was identified. 97 For students at elementary grade levels, contrary to my hypothesis, transcription intervention had small writing quantity and quality effects, with Hedges’ g of 0.19 and 0.06, respectively. Although these aggregated writing effects were weak, transcription intervention tended to have larger writing effects for young writers with weak academic skills than for students without academic difficulties. Specifically, transcription intervention yielded moderate to strong writing quantity and/or quality effects for students with specific handwriting or spelling difficulties, at least with written expression difficulties. For example, Berninger et al. (1997) implemented handwriting intervention for first graders at risk of handwriting skills and compared differential writing quality effects across different methods to teach handwriting skills. In their study, one of the approaches to teaching handwriting generated strong writing quality effects (Hedges’ g = 0.93). Additionally, Jung et al. (2017) implemented the transcription intervention combining spelling and handwriting instruction for students with written expression difficulties and had strong writing quantity effects (Hedges’ g = 1.24). In this study, the transcription intervention was still, to some degree, specific to their participants because Jung et al. (2017) reported that their screening procedures focused on the spelling subtest of Woodcock-Johnson III Tests of Achievement (WJ-III; Woodcock et al., 2001). However, transcription intervention only had small writing effects for young writers without any risk of academic difficulties. Dolin (2016) taught handwriting intervention for kindergarteners and first graders and yielded weak writing quantity effects (Hedges’ g = 0.13). 4.3.2 Moderation Effects by Student Academic Difficulty Regarding the moderator of whether students had academic difficulties, no statistically significant differences in writing effects (both quantity and quality writing effects) across students with academic difficulties and students with diverse academic skills were detected, 98 though there were distinct patterns evident in the descriptive effects. Specifically, for writing quantity outcomes, students with academic difficulties obtained stronger effects than students with diverse academic skills; in contrast, for writing quality outcomes, the effects were smaller for students with academic difficulties. Given the technical features of writing quantity and quality measures, these findings are reasonable and indicate the importance of having adequate sensitivity to the main areas of growth expected during the implementation of a particular intervention. In this meta-analysis, writing quality was operationalized as the holistic scores reported by the primary studies (including studies that directly used holistic scores and studies that calculated the holistic scores by sub-analytic scores), and writing quantity was operationalized as the total words written provided by the primary studies. For writing quality, although holistic scores are commonly used and can be robust (Kim et al., 2014; Malecki, 2014; McMaster & Espin, 2007), these scores do not reflect the performance of students on specific writing skills. Because holistic scores consider all aspects of a writing sample (e.g., transcription skills, organization, ideas) and do not inform students and practitioners which specific writing skills have been improved and which specific skills need more instruction, these scores might not be sensitive enough to detect students’ growth on specific writing component skills, especially for students with special needs. However, the writing quantity measure (i.e., counts of total words written in this meta-analysis) is a reliable and robust approach to assessing mechanics-level writing skills but is insensitive to other complex dimensions of writing skills such as text organization and idea development (Malecki, 2014). Especially for older students whose writing becomes more complex, total words is not a reliable writing performance indicator and does not strongly correlate with writing quality (Malecki, 2014; McMaster & Campbell, 2008). 99 Given the technical features of these two measures, compared to students with diverse academic skills, the holistic scores may less sensitively detect the improved writing skills of students with academic difficulties. For students with academic difficulties, some of their writing component skills may be significantly enhanced by the current writing intervention, but some of their writing component skills may not be improved considerably unless more intensive instruction is added. However, for students with diverse academic skills, their improved overall writing performance may not be constrained by any specific writing component skills. Thus, the smaller average effects on the holistic scores for students with academic difficulties are reasonable because the holistic scores cannot identify the strengths and weaknesses in specific writing component skills. I also found two primary studies that can provide evidence on this point (i.e., Christensen, 2005; Graham et al., 2018). These two studies implemented transcription intervention for students with specific transcription difficulties, and both studies had stronger writing quantity effects than writing quality effects. Additionally, compared to students with academic difficulties who had relatively consistent writing quantity (Hedges’ g = 0.68) and quality effects (Hedges’ g = 0.67), students with diverse academic skills had much stronger effects for writing quality (Hedges’ g = 0.73) than writing quantity (Hedges’ g = 0.50). This finding is expected given that the writing quantity measure is more sensitive to assess mechanics-level writing skills but does not adequately assess high-level writing skills (Malecki, 2014). For students without academic difficulties, because they need relatively little support on transcription-level skills (either they had developed the automaticity of these skills or they obtained these skills quickly), their improvement might be more evident on higher-level writing skills, which are not reflected in total words count. 100 These findings indicate that growth sensitivity of a particular measure may vary across subgroups of students, which is consistent with previous studies (e.g., McMaster & Espin, 2007). However, in this meta-analysis, we are unable to conclude a pattern of which measure is the most appropriate for which subgroups of students because the identified studies include other heterogeneous factors, such as different intervention contents and research designs. Future studies aimed at examining the extent to which the growth sensitivity of a given assessment varies by subgroups of students are encouraged. 4.4 General Discussion To my knowledge, the present meta-analysis is the first study determining the extent to which the instruction of specific and/or combined writing component skills in line with the theoretical model (i.e., M, T, and MT interventions) contributes to the writing effects for K-12 students. Overall, regardless of students’ grade levels or academic skills, I found that transcription intervention consistently had weaker writing effects, whereas multi-component writing intervention, either with or without a transcription component, had stronger writing effects. Although most differential writing effects across the three writing intervention types did not reach the conventional statistical significance, these findings still indicate that (a) transcription intervention only may be insufficient to produce improved writing skills, (b) multi-component writing intervention without a transcription component would be encouraged even for young and/or struggling writers, and (c) multi-component writing intervention targeting both mechanics- and high-level writing skills can be promising, at least under certain conditions. First, even for beginning writers, compared to multi-component writing intervention, the writing effects appeared to be smaller for transcription intervention only. In this meta-analysis, I identified three primary studies including kindergartners; also, these three primary studies had 101 these three types of writing intervention, respectively. Among these three studies, transcription intervention generated the weakest writing effect. Specifically, Dolin’s (2016) study taught handwriting intervention for kindergarteners and first graders and yielded weak writing quantity effects with Hedges’ g of 0.13. Schrodt (2016) implemented a multi-component writing intervention including instruction on growth mindset, writing strategies (e.g., self-talk), and grammar for kindergarteners and had strong writing quality effects (Hedges’ g = 1.71). Jones (2015) taught an interactive writing intervention, including the instruction of transcription skills and sentence-and discourse-level skills, and found strong writing quality effects (Hedges’ g = 2.11). Although these three primary studies had different writing effects (one writing quantity and two writing quality effects), this finding is promising that writing intervention combining both mechanics- and high-level writing skills generated the strongest writing effects (i.e., Jones, 2015), followed by multi-component writing intervention without a transcription intervention (i.e., Schrodt, 2016), and transcription intervention only had the weakest writing effects (i.e., Dolin, 2016). It indicates that even for beginning writers, the implementation of transcription intervention only is insufficient to improve writing performance, and the implementation of multi-component writing intervention (either with or without a transcription component) is encouraged. Moreover, Dolin (2016) was the only identified transcription intervention study that was for students without academic risks. Its weak writing quantity effects provide additional evidence writing intervention only focusing on mechanics-level skills is insufficient, even for very beginning writers without academic risks. Moreover, consistent with my hypothesis, for beginning writers who still need support on transcription skills, multi-component writing intervention is more effective when a transcription 102 component is involved (i.e., Jones, 2015). However, because Jones (2015) had the longest total instruction time among all three studies, the extent to which the instructional dosage contributed to the strongest effects of the multi-component writing intervention with a transcription component is unclear. Future studies that would allow such evaluations are needed (e.g., the extent to which the writing effects of a given writing intervention differ by instructional dosage). Also, these beginning writers did not have academic difficulties; it is unknown whether the differential writing effects across the three types of writing intervention would vary by beginning writers with or without specific transcription difficulties or other academic difficulties. Future studies that would permit such analyses are encouraged. Second, as previously discussed, compared to transcription intervention, multi-component writing intervention, even without transcription instruction, tended to have stronger writing quantity and quality effects for students at elementary grade levels and students with academic difficulties who may still need to allocate heavy cognitive resources to foundational mechanics-level skills. Similarly, I also found that multi-component writing intervention targeting both mechanics- and high-level writing skills tended to have consistently strong writing effects across subgroups of students, ranging from kindergarteners to secondary grade level students, from students without risk of academic performance to students with diverse academic concerns. These findings suggest that this type of writing intervention can meet diverse learners’ needs and improve their writing performance, regardless of their grade levels or whether they have academic difficulties. For example, Jones’ (2015) study implementing an interactive writing program (including the instruction of transcription and composition skills) was for kindergarteners and had strong writing quality effects (Hedges’ g = 2.11). Similarly, Wolbers’ (2008) study also 103 generated strong writing quantity and quality effects (Hedges’ g = 0.85 for writing quantity, Hedges’ g = 1.03 and 1.16 for writing quality of narrative and informative essays, respectively), but for students at secondary grade levels who were d/Deaf or hard of hearing. Berninger et al. (2002) implemented a writing intervention that combined both spelling and composition instruction for Grade 3 students with writing difficulties and yielded strong writing quality effects on persuasive writing samples (Hedges’ g = 2.06). In this study, students had difficulties with compositional fluency rather than specific transcription difficulties. It indicates that not only the students struggling with mechanics-level skills but also students who have difficulties with compositional-level skills can benefit from this type of writing intervention. Although these results are promising that multi-component writing intervention with a transcription component can help diverse learners, this present study is unable to conclude a pattern of which components or the combination of the components can maximally contribute to enhanced writing performance for subgroups of students. 4.4.1 Individual Needs and Specific Transcription Screening Because students’ needs are complex and their improvement is specific to what they have learned, writing intervention should be most effective if it meets individuals’ needs. Consistent with my hypothesis, I found that more than one primary study with strong writing effects mentioned they focused on students’ needs and designed and modified the writing intervention based on students’ problems. For example, Wolbers (2008) and Steelman (1990) implemented multi-component writing intervention with a transcription component for secondary grade level students who needed support on transcription skills. Wolbers’ (2008) study emphasized that the instruction of metalinguistic skills was specifically designed for the participating students who were d/Deaf or hard of hearing. Steelman (1990) also taught keyboarding instruction to students 104 who were unfamiliar with computer keyboarding skills. In addition, in this present study, the strongest writing quantity and quality effects for transcription intervention were from the primary study (i.e., Christensen, 2005) focusing on students at secondary grade levels. In Christensen’s (2005) study, a handwriting intervention was specifically implemented for students with handwriting difficulties (i.e., students with low levels of orthographic-motor integration). Also, in this intervention, students did not advance to the next level until they could master the handwriting skills taught currently. These methods could help this intervention better meet participants’ needs and be appropriate to the level of their academic skills. Additional evidence was found in Berninger et al. (2002). In this study, writing quality effects were measured by both informative and persuasive writing samples. As discussed above, in persuasive writing samples, the composition intervention combined with spelling instruction yielded the greatest writing quality effects. However, in the informative writing samples, the writing quality effects from each type of writing intervention (i.e., spelling intervention, composition intervention, and combined composition and spelling intervention) were not only smaller than the corresponding effects in persuasive writing samples but also had distinct patterns across the three types of writing intervention. Specifically, the spelling intervention generated the largest effects (Hedges’ g = 0.22), followed by the composition intervention combined with the spelling instruction (Hedges’ g = 0.19), and the composition intervention had the weakest effect (Hedges’ g = 0.06). Berninger et al. (2002) explained that the stronger writing effects in the persuasive writing samples were because the persuasive writing intervention better met the participants’ needs. Berninger and colleagues thought that the informative writing intervention was just at students’ current developmental stage of writing because these students had already received 105 considerable informative writing training in their regular classrooms. However, the persuasive writing intervention that taught new writing-related knowledge and skills to these students and was designed based on their zone of proximal development was just beyond and not very far from students’ developmental stage of writing. Thus, it is reasonable that the spelling intervention had the greatest writing quality effects in the informative intervention because the instruction of compositional components had been taught before the intervention. These findings indicate that a given intervention which is specifically designed and implemented based on the needs and features of the participants can be strongly effective. Regarding students with specific transcription difficulties, strong writing effects from the teaching of transcription skills were expected. However, inconsistent with my hypothesis, I found that even for students with specific transcription difficulties, most identified transcription interventions generated weak writing effects. In this meta-analysis, I identified five primary studies (Berninger et al., 1997; Christensen, 2005; Graham et al., 2000; Graham et al., 2002; Graham et al., 2018) with 10 effect sizes for students with specific spelling and/or handwriting difficulties. All these five primary studies implemented transcription intervention. Except for strong writing effects from Christensen (2005) and Berninger et al. (1997), the other writing effects (i.e., Hedges’ g) ranged from -0.32 to 0.38. In addition, in this meta-analysis, because all the effect sizes for these students were generated from transcription intervention only, I am unable to examine whether instruction of other writing component skills combined with transcription skills can have strong writing effects for students struggling with specific transcription skills. Given that students with specific transcription difficulties may need extra related support, multi-component writing intervention targeting both mechanics- and high-level writing skills would be expected to generate robust 106 writing effects for students with specific transcription difficulties. Moreover, compared to multi-component writing intervention without a transcription component, the combination of instruction on both mechanics- and high-level writing skills might also generate stronger writing effects for students who need extra support on transcription skills. This hypothesis might explain the findings in some studies that under certain conditions, multi-component writing intervention is more effective when a transcription component is added. Although only transcription intervention was implemented for students with specific transcription difficulties, I did find some indirect evidence from identified primary studies—MacArthur et al. (1995) and McCutchen et al. (2022)—supporting that multi-component writing intervention with a transcription component might be effective for students with specific transcription difficulties. MacArthur et al. (1995) implemented a computer-based writing intervention, including the instruction of keyboarding skills, text generation and writing strategies for students with learning disabilities at the elementary grade level and yielded weak writing effects (Hedges’ g = 0.12 in the informative writing sample and Hedges’ g = 0.34 in the narrative writing sample). Although this study included instruction on keyboarding, they paid little attention to teaching this skill which to some degree made this intervention become a multi-component writing intervention without a transcription component. The insufficient instruction on transcription-level skills might account for the weak writing effects because students with learning disabilities might struggle with specific transcription skills. In contrast, McCutchen et al. (2022) implemented morphological intervention combined with the instruction of sentence- and discourse-level writing for students at elementary grade levels without risk of academic difficulties and generated a weak writing effect (Hedges’ g = 0.37). In this study, although their intervention included both mechanics- and high-level writing 107 skills, they mainly focused on transcription instruction. This instructional design made this multi-component writing intervention with a transcription component resemble a transcription intervention. The little instructional time for high-level writing skills might lead to this effect because writing intervention focusing on sentence-and discourse-level skills may better meet the needs of these students who did not have transcription-related difficulties. As previously mentioned, this present meta-analysis did not find or include studies that directly examined whether the writing effects of multi-component writing intervention with a transcription component differed for students with or without specific transcription difficulties. Future intervention studies that would permit such evaluation are recommended. Also, future intervention studies aimed at developing and evaluating the optimal combination of explicit instruction on writing component skills for specific subgroups of students are encouraged. More broadly, future studies would benefit from examining the extent to which the screening of transcription difficulties contributes to matching students to specific intervention conditions (e.g., the combination of the instruction of writing component skills and approaches to assessing students’ responsiveness to the intervention). 4.4.2 Instructional and Administrative Factors In addition to intervention contents (e.g., instruction on writing component skills) and students’ features (e.g., students’ grade levels and academic skills), instructional and administrative factors such as intervention dosage, approaches to delivering, implementation fidelity, the associated assessments, even study design impact the effects for a particular writing intervention. For example, in this meta-analysis, the lowest writing quantity and quality effects were partially explained by poor implementation fidelity (i.e., Green et al., 2021) and study design (i.e., Hooper, 2013). 108 For writing quantity outcomes, Green et al. (2021) taught a writing strategy instruction with self-regulation and peer revision for Grade 5 students in two schools (i.e., school A and school B) by different teachers. In this primary study, school B had the lowest negative writing effect (Hedges’ g = -1.11) among all the included effect sizes, while school A yielded a moderate writing quantity (Hedges’ g = 0.60). This study reported that school A had a high degree of implementation fidelity; however, school B only achieved very limited implementation fidelity at the beginning of this intervention (i.e., in week two), and its implementation fidelity increased as this intervention was delivered and reached a reasonable degree. Moreover, this study also reported that students in both intervention and control conditions in school A were highly engaged, but in school B, students in the control condition were considerably more engaged than those in the intervention condition. These findings support the importance of implementation fidelity to the effects of a given intervention and encourage future intervention studies to report, analyze, and increase their implementation fidelity. For writing quality outcomes, the smallest effect size (Hedges’ g = -0.76) was found in Hooper et al. (2013). They implemented the Process Assessment of the Learner lesson plans (PAL), including the teaching of composition strategies, handwriting, and spelling skills for elementary school students at risk of academic difficulties. This study randomly assigned students to intervention and control conditions at the school level (i.e., quasi-experimental design) but reported statistically significant differences on the pre-intervention scores between the two conditions favouring the control group. Moreover, although the intervention group had a faster rate of writing skills growth than the control group, the control group students kept obtaining greater writing outcomes than the intervention group, though their differences were not statistically significant after the intervention. 109 Additional evidence supporting the importance of instructional conditions to the writing effects was from Christensen’s (2005) study, which had the strongest writing quantity and quality effects for transcription intervention only. Among all the identified transcription intervention studies, Christensen (2005) implemented the handwriting intervention at the highest frequency that students received the intervention every day. Moreover, for all the identified transcription studies that implemented daily transcription intervention (i.e., Christensen, 2005; Dolin, 2016; Wolery, 1997), Christensen (2005) had the longest teaching time for each session (i.e., 20 minutes per session) with the longest duration (i.e., the total number of sessions; eight weeks). Although these examples can support the importance of instructional conditions of a particular writing intervention to writing effects, in the present study, my ability to conclude a systematic pattern about the extent to which the intervention delivery (i.e., instructional and administrative conditions) can maximally contribute to improved writing performance was limited due to heterogeneous identified studies. As previously reported, in addition to the identified diverse subgroups of students and different intervention contents, the present meta-analysis also included primary studies that had different instructional and administrative conditions, employed different research designs and used various writing assessments. Also, I did not identify any study that directly examined whether writing effects differ by different intervention dosages with other factors controlled (e.g., the same combination of instructional writing skills, the same writing measures). Therefore, component analysis with a broader scope, such as instructional conditions and study design, is needed. Future primary studies examining the impact of instructional conditions on writing effects for subgroups of students and determining whether such effects will differ as a function of various combinations of 110 instructional skills are needed (e.g., how much spelling instruction at which frequency in a multi-component writing intervention will be the most effective for young writers with specific spelling difficulties). Besides related empirical research studies, I also suggest that the implementation of these evidence-based writing interventions should be incorporated into a system that coordinates assessment data (i.e., data about early screening and progress monitoring) and validated interventions, such as the data-based individualization (DBI) framework in the context of Response to Intervention (RTI). Through early screening of student baseline skills and ongoing progress monitoring of student response, practitioners are empowered to implement these interventions timelier, more appropriately, and flexibly, which helps the implementation of these interventions better meet individuals’ needs. For example, future studies aimed at examining whether writing effects vary by different approaches to screening students’ baseline skills are encouraged. Also, future studies about increasing the feasibility of evidence-based writing intervention in natural settings (e.g., how to help practitioners coordinate the multi-component writing intervention combined with spelling instruction in the DBI system and how can improve teachers’ acceptability of the implementation of these interventions in school settings) are suggested. 4.5 Limitations The results of this present study and the interpretation and implication of these findings should be considered with the following limitations. First, although this study tried to include studies as comprehensively as possible, I only included writing intervention studies focusing on the English writing system. Given the common and universal neurofunctional process during writing and the unique features of specific orthography (e.g., shallow versus deep orthographies 111 and logographic versus alphabetic writing systems), future synthetic studies would benefit from focusing on or including primary studies for writing systems other than English. It is also necessary to investigate whether the writing effects differ across writing systems. Second, based on the universal educational stages (UNESCO Institute for Statistics, 2012) and the related information from primary studies, this present study divided grade levels into elementary and secondary grade levels. Future meta-analyses with other grade-level categorizations that conduct analyses of differential writing effects by grade level are encouraged. For example, based on the requirement of writing tasks at school and the developmental trajectories of writing, Berninger and colleagues (Berninger et al., 1995; Berninger & Chanquoy, 2012) believed that kindergarten to Grade 1 (early childhood writers who could use writing to express themselves) and Grade 4 (middle childhood writers who could integrate reading and writing and revise their written samples) were two important developmental points. Future meta-analyses examining whether writing effects would differ by such grade-level categorizations (i.e., K-4 versus 5-12) are needed. Third, in this meta-analysis, this present study coded different specific types of academic difficulties (e.g., reading difficulties, specific spelling difficulties, and written expression difficulties) to a single category; future studies aimed at evaluating the extent to which screening of specific academic difficulties or different levels of a particular academic difficulty (e.g., the same academic concern but at mild vs. severe degrees) contributes to the writing effects are needed. In addition to students’ academic skills, future studies aimed at developing and evaluating writing interventions with a focus on other groups of students, such as English language learners, are encouraged. As discussed above, such studies can provide evidence of the extent to which the specific intervention components or the combination of such intervention 112 components contribute to the improved writing effects for specific subgroups of students and help practitioners better match students to specific intervention conditions. Similarly, this present study coded both handwriting and spelling instruction to a single group (i.e., transcription intervention), but future studies are encouraged to investigate the extent to which the specific transcription skills (i.e., spelling, handwriting, and keyboarding skills) contribute to the effectiveness of multi-component writing intervention Fourth, this meta-analysis only included primary studies employing true- or quasi- experimental group designs. Because many academic intervention studies use single-case designs, especially for students with specific special needs or newly developed interventions, future meta-analyses would benefit from including studies employing single-case research designs to have a more comprehensive understanding of the area of writing intervention. Fifth, this study coded the cluster-randomized trials design as quasi-experimental group design; however, coding the cluster-randomized trials design as true experimental group design is also encouraged because the cluster-randomized trials design is conceptualized closer to the true experiments theoretically. Future meta-analyses would benefit from this coding approach because it can balance the number of effect sizes between categories of true experimental and quasi-experimental group designs. Sixth, because this study only included multi-component writing intervention with/without a transcription component and transcription intervention only, single-component writing intervention studies, so long as the single targeted skill was not transcription, were intentionally excluded. Similarly, this present study required compositional-level measures (writing quantity and/or writing quality assessments) reported by primary studies because overall writing performance was the target; thus, studies that did not report writing quantity or writing 113 quality outcomes were excluded. Especially for transcription intervention, most of the primary studies were excluded due to no compositional-level measures being included. This process may account for the relatively small number of identified transcription intervention studies. However, these excluded studies may also have important findings about how transcription intervention contributes to improved specific transcription skills. Finally, due to the limited number of effect sizes of some levels of the moderators (e.g., only one study for students at secondary grade levels having transcription intervention and no studies for students with diverse academic skills having transcription intervention), I was unable to conduct interactive analyses across moderators of intervention types, grade levels, and whether students have academic difficulties. 4.6 Future Directions Thus, several directions for future studies in the area of writing interventions are highlighted. First, more writing intervention studies are needed; for example, relatively few studies have evaluated multi-component writing intervention with a transcription component (only 14 primary studies with six writing quantity and 19 writing quality effect sizes). Future studies focusing on developing and evaluating writing interventions targeting both mechanics- and high-level writing skills are especially needed. Also, compared to students at elementary grade levels, writing intervention studies conducted with students at secondary grade levels are relatively few, especially studies of transcription intervention and multi-component writing intervention with a transcription component for students at secondary grade levels. Future related studies with secondary grade level students aimed at identifying the maximal effects of writing interventions are encouraged. Moreover, for students with specific transcription difficulties, I only found studies implementing transcription intervention for these students. Future studies aimed at evaluating 114 how multi-component writing intervention contributes to writing performance for these students are needed. For example, studies should determine whether the writing effects of multi-component writing intervention with a transcription component vary for students with or without specific transcription difficulties. In addition, in this meta-analysis, studies focusing on students with academic difficulties included diverse academic issues (e.g., learning disabilities, written expression difficulties); thus, future studies examining the extent to which the screening for specific transcription difficulties contribute to matching students to specific intervention conditions are encouraged. Second, as discussed above, future studies with component analysis that would permit evaluating which intervention components can maximally contribute to improved writing performance for specific subgroups of students are encouraged. Although in the current study, the aggregated writing effects of multi-component writing intervention were stronger when it did not include a transcription component, I also found in certain situations, multi-component writing intervention was more effective when it combined both mechanics- and high-level skills. Future studies would be helpful to investigate which combination of instruction on writing component skills (e.g., which specific transcription skills should be combined with which high-level writing skills) can be effective for all writers and which combination can be more effective for certain subgroups of students, as well as examine whether such effects vary by different delivery approaches to determine a better way to implement these interventions in natural settings. More specifically, for writing intervention targeting multiple writing component skills, studies that determine how the dosage and frequency of each instructional writing skill contribute to improved writing effects and evaluate the interactive effects between instructional conditions and students’ features are needed. 115 Third, because research studies about the development and evaluation of a writing intervention might be conducted by the same research teams or authors, more replicated research studies about a particular writing intervention from other independent research teams in different contexts (e.g., in other educational systems or writing systems) are encouraged. For example, SRSD is well-researched; however, most related studies have happened in the U.S., and many of them have been conducted by the developers and the same authors (e.g., Dr. Steve Graham, Dr. Karen Harris, and their former students). Finally, because students’ needs are complex and dynamic, the extensive positive evidence for interventions does not guarantee the effects of these interventions for specific groups of students or individuals. Therefore, incorporating these evidence-based writing interventions with pre-test screening and ongoing progress monitoring is highly encouraged. Future studies aimed at how incorporating evidence-based writing interventions in a data-based decision system such as DBI in natural settings are encouraged. For example, future studies would benefit from evaluating which would be the most appropriate screening assessment to match different subgroups of students to specific intervention conditions and which and at what frequency would be the most efficient approach to monitoring students’ ongoing responsiveness to a particular intervention. These studies would help practitioners select and modify the components of the evidence-based writing interventions to meet students’ needs better, which would increase the acceptability, feasibility, and social importance of these evidence-based writing interventions. 116 Chapter 5: Conclusion This comprehensive meta-analysis examined the effects of writing intervention on overall writing performance for K-12 students and investigated how the teaching of mechanics-level skills contributes to these effects. Results indicate that writing interventions are consistently effective across student subgroups (i.e., elementary grade level versus secondary grade level, students with academic difficulty versus students with diverse academic skills) and writing outcomes (i.e., writing quantity and writing quality). In addition, regardless of student subgroup, the writing effects of transcription intervention were consistently smaller when compared to multi-component writing intervention, either with or without a transcription component (i.e., M/MT > T). This finding indicates that transcription intervention only is insufficient to improve writing performance, even for young writers and/or struggling writers. Moreover, multi-component writing intervention without a transcription component had stronger aggregated writing effects than multi-component writing intervention combining both mechanics- and high-level writing skills (i.e., M > MT). However, multi-component writing intervention with a transcription component not only generated moderate writing effects but also had stronger writing effects than multi-component writing intervention without a transcription component in certain situations (i.e., MT > M). Due to little research on multi-component writing intervention with a transcription component and a small number of primary studies for students at secondary grade levels, I was unable to conclude interactive effects across moderators. Future studies related to multi-component writing intervention with a transcription component are encouraged, as well as such studies conducted with specific subgroups of students, such as students with specific transcription difficulties or students at secondary grade 117 levels. Moreover, future research aimed at developing and evaluating which writing component skills and how the combination of these skills can maximally contribute to improved writing performance is needed. Finally, to improve the acceptability and flexibility (i.e., better meet individual’s needs) of these evidence-based interventions in natural settings, future studies about how to incorporate these interventions into the data-based decision system (e.g., DBI) are encouraged. 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Thesis/Dissertation
eng
Special Education
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Mercer, Sterett H.
Does transcription instruction make writing interventions more effective? : a meta-analysis
Text