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Doing Academic Research is a concise, accessible, and tightly organized overview of the research process in the humanities, social sciences, and business. Conducting effective scholarly research can seem like a frustrating, confusing, and unpleasant experience. Early researchers often have inconsistent knowledge and experience, and can become overwhelmed - reducing their ability to produce high quality work. Rather than a book about research, this is a practical guide to doing research. It guides budding researchers along the process of developing an effective workflow, where to go for help, and how to actually complete the project. The book addresses diversity in abilities, interest, discipline, and ways of knowing by focusing not just on the process of conducting any one method in detail, but also on the ways in which someone might choose a research method and conduct it successfully. Finally, it emphasizes accessibility and approachability through real-world examples, key insights, tips, and tricks from active researchers. This book is a highly useful addition to both content area courses and research methods courses, as well as a practical guide for graduate students and independent scholars interested in publishing their research.

Context: Communities, funding agencies, and institutions are increasingly involving community stakeholders as partners in research. Community stakeholders can provide firsthand knowledge and insight, thereby increasing research relevance and feasibility. Despite the greater emphasis and use of community-academic partnerships (CAP) across multiple disciplines, definitions of partnerships and methodologies vary greatly, and no systematic reviews consolidating this literature have been published. The purpose of this article, then, is to facilitate the continued growth of this field by examining the characteristics of CAPs and the current state of the science, identifying the facilitating and hindering influences on the collaborative process, and developing a common term and conceptual definition for use across disciplines. Methods: Our systematic search of 6 major literature databases generated 1,332 unique articles, 50 of which met our criteria for inclusion and provided data on 54 unique CAPs. We then analyzed studies to describe CAP characteristics and to identify the terms and methods used, as well as the common influences on the CAP process and distal outcomes. Findings: CAP research spans disciplines, involves a variety of community stakeholders, and focuses on a large range of study topics. CAP research articles, however, rarely report characteristics such as membership numbers or duration. Most studies involved case studies using qualitative methods to collect data on the collaborative process. Although various terms were used to describe collaborative partnerships, few studies provided conceptual definitions. Twenty-three facilitating and hindering factors influencing the CAP collaboration process emerged from the literature. Outcomes from the CAPs most often included developing or refining tangible products. Conclusions: Based on our systematic review, we recommend using a single term, community-academic partnership, as well as a conceptual definition to unite multiple research disciplines. In addition, CAP characteristics and methods should be reported more systematically to advance the field (eg, to develop CAP evaluation tools). We have identified the most common influences that facilitate and hinder CAPs, which in turn should guide their development and sustainment.

There is a growing awareness of the importance of academic vocabulary, and more generally, of academic language proficiency, for students' success in school. There is also a growing body of research on the nature of the demands that academic language places on readers and writers, and on interventions to help students meet these demands. In this review, we discuss the role of academic vocabulary within academic language, examine recent research on instruction in academic vocabulary, considering both general academic words and discipline-specific words, and offer our perspective on the current state of this research and recommendations on how to continue inquiry and to improve practice in this area. We use the metaphor of 'words as tools' to reflect our understanding that instruction in academic vocabulary must approach words as means for communicating and thinking about disciplinary content, and must therefore provide students with opportunities to use the instructed words for these purposes as they are learning them.

Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly publications as the basis of subsequent analyses remains a common bottleneck in the current practice. What can we do to reduce the risk of missing something potentially significant? How can we compare different search strategies in terms of the relevance and specificity of topical areas covered? In this study, we introduce a flexible and generic methodology based on a significant extension of the general conceptual framework of citation indexing for delineating the literature of a research field. The method, through cascading citation expansion, provides a practical connection between studies of science from local and global perspectives. We demonstrate an application of the methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios and corresponding retrieval strategies, namely a query-based lexical search (one dataset), forward expansions starting from a groundbreaking article of LBD (two datasets), and backward expansions starting from a recently published review article by a prominent expert in LBD (two datasets). We particularly discuss the relevance of areas captured by expansion processes with reference to the query-based scientometric visualization. The method used in this study for comparing bibliometric datasets is applicable to comparative studies of search strategies.

Purpose – The purpose of this paper is to present a method for a structured literature review (SLR). An SLR is a method for examining a corpus of scholarly literature, to develop insights, critical reflections, future research paths and research questions. SLRs are common in scientific disciplines dominated by quantitative approaches, but they can be adapted in accounting studies since quantitative and qualitative approaches are commonly accepted. Design/methodology/approach – A literature review, as a piece of academic writing, must have a logical, planned structure. The authors also argue it requires tests based on qualitative and quantitative methods. Therefore, the authors describe ten steps for developing an SLR. Findings – The SLR method is a way that scholars can stand “on the shoulders of giants” and provide insightful and impactful research that is different to the traditional authorship approaches to literature reviews. Research limitations/implications – Traditional literature reviews can have varied results because of a lack of rigour. SLRs use a process that, through a set of rules, potentially offers less bias and more transparency of the execution and measures and techniques of validation and reliability. Practical implications – SLRs provide an approach that can help academics to discover under-investigated topics and methods, nurturing, therefore, the development of new knowledge areas and research approaches. Originality/value – The paper presents accounting researchers with an opportunity to develop insightful and publishable studies, and also serves as a basis for developing future research agendas in the accounting field. The authors advocate the SLR method especially to higher degree research students and emerging scholars as a way of potentially developing robust and defensible research agendas and questions.

Recognition of the factors affecting the medical students’ academic success is one of the most important challenges and concerns in medical schools. Hence, this study aimed to investigate the mediating effects of metacognitive learning strategies and learning-related emotions in the relationship between academic self-efficacy with academic performance in medical students. Methods The present study was carried out on 279 students of medicine studying at Shiraz University of Medical Sciences. The students filled out three questionnaires: academic emotions (AEQ), metacognitive learning strategies, and academic self-efficacy questionnaires. The data were analyzed using SPSS and Smart PLS3. Results The results of structural equation modeling revealed that the students’ self-efficacy has an impact on their learning-related emotions and metacognitive learning strategies, and these, in turn, affect the students’ academic performance. Moreover, learning-related emotions influence the metacognitive learning strategies, which in turn mediate the effect of emotions on academic performance. Discussion The results of this study revealed that metacognitive strategies and learning-related emotions could play a mediating role in the relationship between students’ self-efficacy and academic performance.

Policymakers, funders, and researchers today view research-practice partnerships (RPPs) as a promising approach for expanding the role of research in improving educational practice. Although studies in other fields provide evidence of the potential for RPPs, studies in education are few. This article provides a review of available evidence of the outcomes and dynamics of RPPs in education and related fields. It then outlines a research agenda for the study of RPPs that can guide funders' investments and help developing partnerships succeed.

The appropriate application of research methods and statistical analyses used in the studies directly affects the quality of scientific studies. Due to the possibility of employing an incorrect statistical technique, it is crucial to choose a statistical method based on the study’s data and research objectives. This study aimed to evaluate whether statistical techniques applied in the theses and dissertations were appropriate for planning surveys or experiments and analyzing data, and to identify common mistakes that master’s and doctoral students made when using statistical techniques for the intended goals. The study reviewed 139 master’s theses and doctoral dissertations submitted to seven agricultural and environmental sciences disciplines at a leading Open, Distance and e-learning university in Africa between 2015 and 2020. These dissertations and theses used mixed and quantitative research methods. The analysis of variance test was the most often used statistical test, according to the results, followed by the student t -test and the Chi-square test. At least one blatant methodological error was found in 41.0% of theses and dissertations, either in the data collection process or in the data analysis. Examples of these errors include the use of a simple random sampling technique despite the heterogeneous population units, the conversion of count responses to binary responses and percentages for fitting logistic and general binomial regression models, and the incorrect modeling of correlated data using generalized linear models. The results of this study will create greater awareness of the common errors that postgraduate students make when using statistical methods to design experiments or sample surveys and analyse data. In addition, the findings inform the university management to plan for specific training in statistical methods appropriate for a range of academic fields.

The academic ecology of China’s elite universities has been deeply shaped by internationalization through the efforts of returnee faculty members. This study explores how to internationalize the Chinese academic ecology and create world-class universities by examining the experiences and perceptions of returnee faculty members in China’s elite universities. Using a qualitative method, 18 returnees from three top universities were interviewed to present their in-depth insights into the dynamics of the academic ecosystem in China. The study findings highlight that bringing international academic peer review and tenure track mechanisms into China’s current higher education system is necessary to meet the international academic standard pursued by world-class universities. Encouraging publications in English works to benefit the internationalization of China’s academic ecology. Returnee faculty members underwent a re-adaptation socialization process in Chinese local academic discourse and used self-mockery to describe themselves as “abandoned orphans” to express their marginalized status in their local academic networks and communities. The returnees found that their academic identities needed to match the academic context of Chinese universities, especially in social science. The discussions also highlighted the current tensions in the drive to internationalize China’s elite universities and the need to strengthen indigenous elements, international faculty practices, and local requirements, in addition to the career interests of returnees and local peers. These tensions contribute to the international awareness, engagement, and advances in the focus on Chinese characteristics to serve local and national agendas.

With data becoming a centerpiece of modern scientific discovery, data sharing by scientists is now a crucial element of scientific progress. This article aims to provide an in-depth examination of the practices and perceptions of data management, including data storage, data sharing, and data use and reuse by scientists around the world. The Usability and Assessment Working Group of DataONE, an NSF-funded environmental cyberinfrastructure project, distributed a survey to a multinational and multidisciplinary sample of scientific researchers in a two-waves approach in 2017-2018. We focused our analysis on examining the differences across age groups, sub-disciplines of science, and sectors of employment. Most respondents displayed what we describe as high and mediocre risk data practices by storing their data on their personal computer, departmental servers or USB drives. Respondents appeared to be satisfied with short-term storage solutions; however, only half of them are satisfied with available mechanisms for storing data beyond the life of the process. Data sharing and data reuse were viewed positively: over 85% of respondents admitted they would be willing to share their data with others and said they would use data collected by others if it could be easily accessed. A vast majority of respondents felt that the lack of access to data generated by other researchers or institutions was a major impediment to progress in science at large, yet only about a half thought that it restricted their own ability to answer scientific questions. Although attitudes towards data sharing and data use and reuse are mostly positive, practice does not always support data storage, sharing, and future reuse. Assistance through data managers or data librarians, readily available data repositories for both long-term and short-term storage, and educational programs for both awareness and to help engender good data practices are clearly needed.