The Selection of Data Collection Methods

Many early researchers and students sometimes face the challenges with the type of data they need and the methods that can help them collect data for their academic research. Some of them often struggle with selecting appropriate data types and methods, leading to delays in research progress. Scientific data collection is one of the most important stages of academic research. Poor data leads to poor and unreliable estimated results that cannot be used for shaping public policy, and helping governments and other stakeholders make informed decisions. Thus, this review aims to equip beginners with tools to design robust studies and complete their research efficiently. This study reviewed the relevant literature on the types of data and the data collection methods in social sciences. The narrative literature review focused on the studies that are relevant to the topic and are published in English. The study used published articles from databases such as ResearchGate, Scopus, Google Scholar, and Web of Science. The findings depicted that data can be categorized based on three main dimensions. These include the Nature of data (Qualitative, quantitative data or Mixed methods) and (Categorical or Continuous), Data source (Primary or Secondary data), Data collection (Cross-sectional, Longitudinal or Time series data). The literature search also showed that the widely used methods of data collection in social sciences include Interviews, Focus Group Discussions, Key Informant Interviews, Observations and Secondary data sources such as Archives, Records and Published documents. In general, researchers should choose the right type of data and data collection method that can answer their research objectives. This will ensure that they collect valid and reliable data that can guide policymakers and the government.

Who is sensitising whom? A participatory interview guide development as an awareness tool within a health care research project

Orthodontic education, besides some other factors, influences the confidence level of recently graduated dentists to diagnose subjects seeking orthodontic treatment and provide treatment plans. This study aims to explore the factors that influence the confidence level of undergraduate dental students to diagnose subjects seeking orthodontic treatment and provide treatment plans after graduation. A qualitative study was undertaken using focus groups and interviews. Purposive sampling was used, focus groups and face to face semi-structured interviews were adopted as the method of data collection. Three focus groups were evaluated including fifth-year undergraduate, postgraduate orthodontic students, and recently graduated general dentists. In addition, eleven interviews were performed with the fifth-year undergraduate students. Data collection ceased upon reaching data saturation, with thematic analysis employed for qualitative data analysis. Six themes and two sub-themes were extracted from the data. More focus on the theory was highly noticed, and most of the participants exhibited a low level of self confidence in extrapolating orthodontic diagnosis and establishing treatment plans after graduation. In addition, they believed that practical teaching sessions mostly deal with wire bending and demanded to expand the clinical orthodontic knowledge to be able to indicate the right diagnosis and initiate proper treatment plans. Clinical and practical training alongside theoretical foundation represent key factors in orthodontic education required to obtain confidence in orthodontic practice among dental undergraduate students and alumni. The findings help in enhancing undergraduate programs via providing a clinically oriented orthodontic education from student perceptions.

A potentially powerful method of social-scientific data collection and investigation has been created by an unexpected institution: the law. Article 15 of the EU’s 2018 General Data Protection Regulation (GDPR) mandates that individuals have electronic access to a copy of their personal data, and all major digital platforms now comply with this law by providing users with “data download packages” (DDPs). Through voluntary donation of DDPs, all data collected by public and private entities during the course of citizens’ digital life can be obtained and analyzed to answer social-scientific questions – with consent. Thus, consented DDPs open the way for vast new research opportunities. However, while this entirely new method of data collection will undoubtedly gain popularity in the coming years, it also comes with its own questions of representativeness and measurement quality, which are often evaluated systematically by means of an error framework. Therefore, in this paper we provide a blueprint for digital trace data collection using DDPs, and devise a “total error framework” for such projects. Our error framework for digital trace data collection through data donation is intended to facilitate high quality social-scientific investigations using DDPs while critically reflecting its unique methodological challenges and sources of error. In addition, we provide a quality control checklist to guide researchers in leveraging the vast opportunities afforded by this new mode of investigation.

Hak T. Data collection in qualitative research. Huisarts Wet 2004;47(11):502-8. This article discusses the main methods of data collection in qualitative research. Ways of achieving appropriate levels of validity and reliabilityand of avoiding selection bias are discussed. Because qualitative research is characterised by an iteration between data collection and analysis, it is quite common that multiple types of data collection are used in a single research project, each of which plays a role in a specific phase of the research project. Unfortunately, the most common research design published in medical journals is the ‘cross-sectional qualitative survey’, in which conclusions are based on data from one-off unstructured or semi-structured interviews with a limited number of individuals (usually patients), selected in a more or less unsystematic manner (‘convenience sample’). This design is rather problematic. Data collection through focus groups is also a popular method used in medical and health services research. It is often invalid in terms of the research objective and question. The best qualitative research consists of consecutive parts. Each of these is steered by a research objective and a question that is dependent on the results of the preceding parts. In each part, a method of data collection is chosen that is appropriate for that part's particular research objective and question.

This study discusses the Optimization of the Use of Islamic Religious Education Laboratories in the Practice of Student Worship at State Junior High Schools in Pangkajene District, Pangkep Regency. The main problem is how the Islamic Religious Education Laboratory can be optimized as a facility and infrastructure that supports the development and progress of Islamic Religious Education. The author uses a research approach with qualitative methods in data collection, as well as testing and analyzing data qualitatively. The research paradigm is constructivism; data sources are Principals, PAI Teachers, Students (primary data) and documents related to the management of the PAI Laboratory (secondary data); The research instrument is the researcher himself and is assisted through data collection tools, namely observation guidesheets, interview guidelines and document study guidelines; Data collection techniques are participation observation, in-depth interviews, and documentation; and testing the validity of the research results applying source triangulation and triangulation techniques. The research was carried out in four State Junior High Schools in Pangkajene District, Pangkep Regency. The results of the study showed that 1) The condition of PAI laboratories in the schools studied varied in terms of availability and quality of facilities. All schools have PAI laboratories, but there are gaps in infrastructure and equipment due to differences in budget allocation. 2) Laboratory use of PAI showed variation in intensity and effectiveness. Some schools have integrated the use of laboratories into the curriculum, while others still face challenges in optimization. 3) The practice of students' worship varies in the level of consistency and understanding. The majority of students have awareness of the importance of worship, but the level of practice varies. 4) Factors that affect the use of PAI laboratories include the availability of infrastructure, teacher competence, budget allocation, school management, student motivation, and education policies. This study recommends increasing budgets, developing teacher competencies, integrating curriculum, collaboration between schools, utilizing technology, and periodic evaluations to optimize the use of PAI laboratories and improve students' practice of worship.

Qualitative research explores complex phenomena encountered by clinicians, health care providers, policy makers and consumers. Although partial checklists are available, no consolidated reporting framework exists for any type of qualitative design. To develop a checklist for explicit and comprehensive reporting of qualitative studies (in depth interviews and focus groups). We performed a comprehensive search in Cochrane and Campbell Protocols, Medline, CINAHL, systematic reviews of qualitative studies, author or reviewer guidelines of major medical journals and reference lists of relevant publications for existing checklists used to assess qualitative studies. Seventy-six items from 22 checklists were compiled into a comprehensive list. All items were grouped into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. Duplicate items and those that were ambiguous, too broadly defined and impractical to assess were removed. Items most frequently included in the checklists related to sampling method, setting for data collection, method of data collection, respondent validation of findings, method of recording data, description of the derivation of themes and inclusion of supporting quotations. We grouped all items into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. The criteria included in COREQ, a 32-item checklist, can help researchers to report important aspects of the research team, study methods, context of the study, findings, analysis and interpretations.

BackgroundHealthcare systems aim to enhance the health status and well-being of the individuals and populations they serve. To achieve this, measuring and evaluating the quality and safety of services provided and the outcomes achieved is essential. Like other countries, Romania faces challenges regarding the quality of care provided in its public hospitals. To address this, the Romanian Ministry of Health initiated reforms in 2022, including implementing a pay-for-performance model based on quality indicators. This paper presents a descriptive analysis of processes, methods, results and lessons learned from developing and piloting a set of Quality of Care indicators for Romanian public hospitals.MethodsWorld Health Organization’s Athens Office on Quality of Care and Patient Safety assisted Romania in developing and piloting a set of quality-of-care indicators for public hospitals. The development phase included defining indicator domains, identifying potential indicators across these domains, and defining the final indicator set. The piloting phase involved selecting and recruiting piloting hospitals, developing data collection and validation methods and tools, training hospital staff, and collecting and analysing indicator data. Piloting ended with an evaluation workshop. Mixed, quantitative and qualitative methods were used, including literature reviews, stakeholder consultation workshops, survey instruments developed for this study, modified Delphi panels and consensus-building meetings. National stakeholders were actively involved throughout the process.ResultsFour priority domains were defined for quality-of-care indicators for Romanian public hospitals: patient safety, patient experience, healthcare workforce training and safety, and clinical effectiveness. 25 core indicators were selected across these domains. During the pilot, hospitals achieved an average completion rate of 90% for data submission, with all domains rated equally relevant during post-pilot evaluations. Lessons included the need for supportive legislation, improved internal auditing practices and enhanced staff training, refinement of indicator data collection methods and alignment of indicators with hospital-specific contexts.ConclusionsThis work presents a significant stride in improving Romanian public hospitals’ quality of care and patient safety. It underscores the importance of high-level commitment, stakeholder engagement, and robust data practices in driving successful quality improvement efforts. Emphasising the role of data-driven and patient-centric approaches in achieving optimal healthcare outcomes, lessons learned offer insights for the continuation of quality improvement work in Romania but also for healthcare systems elsewhere.

The demand for the evaluation of the quality of patient and client care has increased in public health nursing as in all disciplines of nursing. In Hong Kong this demand led to the development of a collaborative study with the Department of Health to evaluate the quality of public health nursing in the maternal and child health centres. A multiple case study design was developed to undertake the research using both qualitative and quantitative methods of data collection. The findings from the study demonstrated not only implications for clinical practice which have been reported elsewhere, but also methodological issues for the evaluation of care. These findings indicate three major issues for consideration. The first issue is that of the cultural context of care which includes perceptions of care such as demands on the service and expectations of care. The second issue is the use of health data, including both the methods of recordings as well as the sources of data. The final issue is that of the method of data collection, in particular the implications of the use of language in data collection tools. The findings suggest that both the use of professional language and the need for translation have implications for data collection methods. Indeed in the author's view the development of methodologies for the evaluation of care must address culturally specific issues, particularly where English is not the first language of subjects in the study. In addition the use the use of language in the method of data collection highlights more general issues raised by the use of translation in the collection and analysis of qualitative data.

Researchers in general and those conducting English Language Teaching (ELT) studies in particular have been using alternative data collection methods and strategies due to recent technological advancements, natural events, as well as personal researchers’ predicaments. In this article, we present our research insights into remote qualitative data collection from the perspective of doctoral students. Using a joint-autoethnography research design, we share our reflections on collecting data from two different studies conducted in English as a Foreign Language (EFL) contexts. We support these reflections with similar literature about remote data collection. Our focus is on the use of remote observations and remote surveys in qualitative studies. In our settings, while collecting data through remote observation, the major challenges we faced were related to technological management for the use of in-class video-recordings. Consequently, the change to audio-recordings demonstrated to be appropriate in such contexts. Furthermore, the use of remote qualitative surveys for data collection taught us that despite a high rate of initial acceptance for participation in other studies, the response rate in our study was quite low. As a result of this challenge, additional data collection methods were used in the relevant study. These experiences reveal that there will be a need to improve our procedures for a successful collection of remote qualitative data, not only in our future studies, but also for those researchers intending to use these data collection methods in similar situations.

The main objective of this study is to compare factors that effecting entrepreneur intention of higher Education Institute (HEI) students between B40 family with M40 and T20 family in Melaka. Factors such as initiative, seek on opportunities, act on opportunities, information seeking, concern for high quality of work, commitment to work contract, efficiency orientation, systematic planning, problem solving, self-confidence and assertiveness are tested the correlation with entrepreneur intention. A total of 475 respondents were collected to give their response, where 316 respondents came from B40 family and 159 respondents from M40 and T20 family around HEI in Melaka. Data collection method using structured questionnaire and deliver to respondents by google form apps. The comparative study includes the level of entrepreneur intention for both groups. Hypotheses testing of factors toward entrepreneur intention for both group also tested. At the end, researcher seek comparative analysis the dominant factor that effecting entrepreneur intention among students for both groups. The research finding show that the level of entrepreneur intention among students from M40 and T20 family is higher than students from B40 family. Finding also state that all factors have positive significant relationship with entrepreneur intention for both group but in different situation. Two factors are most dominant factor toward entrepreneur intention for student from M40 and T20. While for students from B40, there are four factors most dominant toward entrepreneur intention. As conclusion, the university can focus to certain factors for both groups of student to improve entrepreneur intention among them.

Summary A variety of programs have been written for the computer scoring of multiple choice questions, but the design of the program alone is not a complete solution to the problems of scoring. The program is only a part of a much wider system which must cover methods of accurate data collection, through to suitable dissemination of results. Fortunately, the parts of such systems may be manipulated independently, and we have utilized our own methods of data collection, which have been established for some time, together with our own scoring procedures, to introduce a method of result display which maximizes the return of information to students. Because this is only a special method of displaying the candidates’ results, all other facilities of our major scoring program can be used concurrently. We believe that assessment is central to the learning and teaching processes, and we have therefore formulated a set of procedures which we call SLAM (Self Learning Assessment Module). SLAM is more flexible than a...

Background: Community organizations are essential in addressing health and social needs for populations often underserved by traditional healthcare systems. Recognizing the importance of program evaluation, these organizations increasingly seek to improve their services, demonstrate impact, and secure funding. However, they frequently face barriers such as limited resources, time constraints, and lack of in-house expertise. Evaluation training programs have been developed to bridge this gap to support community organizations in building evaluation capacity. Despite the availability of such programs, few undergo rigorous evaluation themselves, resulting in limited evidence of their effectiveness in fostering sustainable evaluation capacity within community settings. Addressing this need, this study presents key practices and findings from LaboEval, a five-year initiative launched in 2019 to strengthen evaluation capacity in community organizations across Quebec, Canada. Approach: LaboEval was developed through an iterative, participatory curriculum design process led by a steering committee of community representatives, students, and evaluation experts. The program offered seven online modules, each accompanied by follow-up coaching sessions grounded in a practical participatory evaluation framework. Modules covered essential topics such as stakeholder engagement, theory of change, data collection methods, knowledge transfer, and strategies for embedding evaluation within organizational practices. Implemented across 16 diverse organizations within the health and social services sectors, LaboEval emphasized flexibility to accommodate the schedules and needs of participants. Mayne's six-step contribution analysis was employed in the fifth year to assess LaboEval's impact on both individual and organizational evaluation capacities. A mixed-methods approach was applied, combining secondary data analysis, semi-structured interviews, document reviews, surveys, and literature reviews to ensure thorough data collection and analysis. Results: Findings indicate that LaboEval significantly enhanced individual and organizational evaluation skills and practices among participating organizations. Data reported increased confidence in conducting evaluations and improved integration of evaluation into their organizations’ processes. These improvements include more consistent use of logic models, incorporating evaluation activities into routine operations, and adopting appropriate data collection and analysis methods. The program's participatory design, which incorporated direct input from community stakeholders, contributed to its relevance, practicality, and success. Additionally, the modular structure and flexible implementation enhanced participant engagement and retention by addressing organizational constraints. However, challenges remained, particularly regarding the dissemination of evaluation findings by the organizations, which limited the broader impact of their evaluations on policy discussions. Implications: LaboEval offers important insights for both researchers and practitioners engaged in capacity-building initiatives. Its participatory curriculum design highlights the importance of involving organizations in the training program development process, enabling the creation of contextually relevant training that aligns with their specific needs and objectives. Contribution analysis provided a structured evaluation approach, yielding concrete, evidence-based insights into the effectiveness of training strategies and the pathways through which they build evaluation capacity. These insights illuminated specific outcomes, such as increased confidence and skills in evaluation practices, and highlighted how these pathways can refine future capacity-building initiatives and enhance their practical impact. The next phase of LaboEval will focus on expanding the program to additional organizations and enhancing mechanisms to support sustained evaluation practices.

This study is an integrative review of research trends, data collection methods, operational definitions of key terms, and rationales in Korean research on textbook use by mathematics teachers over the past 13 years. Based on the Temporal phases of curriculum use framework (Stein, Remillard, & Smith, 2007, p. 322), we examined the phases of curriculum use in each study and identified trends in the data collection and analysis methods of each paper. We identified the understanding of the terms teacher’s perception and textbook use in the studies and analyzed how the theoretical framework of each study justified the research questions and methods. The results showed: First, a significant number of studies focused on the transformation stage from written to intended, including teachers’ beliefs, orientations, and knowledge about curriculum use. Second, surveys were the most common method of data collection, followed by interviews, classroom practice/observation and text analysis in similar proportions. Surveys and interviews often did not provide a specific analytical framework for analyzing the results but teaching practice/observation and text analysis were mostly based on previous research and derived results through an analytical framework. Third, to examine teacher’s perceptions, researchers mainly examined the appropriateness/effectiveness of mathematics textbooks or specific content areas. And for textbook use, they examined the teaching methods of mathematics textbooks or specific content areas and cases of task modification. Fourth, few studies fully justified their research questions with a theoretical framework, and only about half of the studies justified their research methods with a theoretical framework. Implications for research and practice are discussed.

The main objective of this research is to identify the factors that influence the attitude of customers towards baby care products (BCP), especially nursing bottle in retailer perspective. The method of data collection used in this study is survey method. The retailers are surveyed and primary data is collected from them. Convenience sampling method is used for data collection. The population was unknown and the sample size is taken 201. Structured questionnaire has been designed and used to collect the needed primary data. Data collected through questionnaire are analyzed using weighted average and simple linear regression. From the analysis it is found that retailers tend to stock BCPs from manufacturers or stockists who offer timely delivery of products, resolve complaints in timely fashion and offer the best credit policy. This study shows the willingness level of the retailers to deal with branded BCP apart from currently they are dealing. For customers, product attributes and personal factor influence the attitude of customer towards nursing bottle in retailer perception. Rural and urban customers have the same preference for baby care products.