About: Data collection is a research topic. Over the lifetime, 14533 publications have been published within this topic receiving 337552 citations. The topic is also known as: data gathering.
Papers published on a yearly basis
TL;DR: In this review the usual methods applied in systematic reviews and meta-analyses are outlined, and the most common procedures for combining studies with binary outcomes are described, illustrating how they can be done using Stata commands.
Abstract: In this review the usual methods applied in systematic reviews and meta-analyses are outlined. The ideal hypothesis for a systematic review should be generated by information not used later in meta-analyses. The selection of studies involves searching in web repertories, and more than one should be consulted. A manual search in the references of articles, editorials, reviews, etc. is mandatory. The selection of studies should be made by two investigators on an independent basis. Data collection on quality of the selected reports is needed, applying validated scales and including specific questions on the main biases which could have a negative impact upon the research question. Such collection also should be carried out by two researchers on an independent basis. The most common procedures for combining studies with binary outcomes are described (inverse of variance, Mantel-Haenszel, and Peto), illustrating how they can be done using Stata commands. Assessment of heterogeneity and publication bias is also illustrated with the same program.
•01 Jan 1980
TL;DR: In this article, the context of educational research, planning educational research and the styles of education research are discussed, along with strategies and instruments for data collection and research for data analysis.
Abstract: Part One: The Context Of Educational Research Part Two: Planning Educational Research Part Three: Styles Of Educational Research Part Four: Strategies And Instruments For Data Collection And Researching Part Five: Data Analysis
TL;DR: The authors operationalize saturation and make evidence-based recommendations regarding nonprobabilistic sample sizes for interviews and found that saturation occurred within the first twelve interviews, although basic elements for metathemes were present as early as six interviews.
Abstract: Guidelines for determining nonprobabilistic sample sizes are virtually nonexistent. Purposive samples are the most commonly used form of nonprobabilistic sampling, and their size typically relies on the concept of “saturation,” or the point at which no new information or themes are observed in the data. Although the idea of saturation is helpful at the conceptual level, it provides little practical guidance for estimating sample sizes, prior to data collection, necessary for conducting quality research. Using data from a study involving sixty in-depth interviews with women in two West African countries, the authors systematically document the degree of data saturation and variability over the course of thematic analysis. They operationalize saturation and make evidence-based recommendations regarding nonprobabilistic sample sizes for interviews. Based on the data set, they found that saturation occurred within the first twelve interviews, although basic elements for metathemes were present as early as six...
TL;DR: Qualitative research produces large amounts of textual data in the form of transcripts and observational fieldnotes, and the systematic and rigorous preparation and analysis of these data is time consuming and labour intensive.
Abstract: This is the second in a series of three articles Contrary to popular perception, qualitative research can produce vast amounts of data. These may include verbatim notes or transcribed recordings of interviews or focus groups, jotted notes and more detailed “fieldnotes” of observational research, a diary or chronological account, and the researcher's reflective notes made during the research. These data are not necessarily small scale: transcribing a typical single interview takes several hours and can generate 20–40 pages of single spaced text. Transcripts and notes are the raw data of the research. They provide a descriptive record of the research, but they cannot provide explanations. The researcher has to make sense of the data by sifting and interpreting them. #### Summary points Qualitative research produces large amounts of textual data in the form of transcripts and observational fieldnotes The systematic and rigorous preparation and analysis of these data is time consuming and labour intensive Data analysis often takes place alongside data collection to allow questions to be refined and new avenues of inquiry to develop Textual data are typically explored inductively using content analysis to generate categories and explanations; software packages can help with analysis but should not be viewed as short cuts to rigorous and systematic analysis High quality analysis of qualitative data depends on the skill, vision, and integrity of the researcher; it should not be left to the novice In much qualitative research the analytical process begins during data collection as the data already gathered are analysed and shape the ongoing data collection. This sequential analysis1 or interim analysis2 has the advantage of allowing the researcher to go back and refine questions, develop hypotheses, and pursue emerging avenues of inquiry in further depth. Crucially, it also enables the researcher to look for deviant or negative cases; that is, …
01 Aug 1984
TL;DR: This chapter discusses ethical issues in Survey Research, as well as methods of data collection and analysis, and types of error in Surveys.
Abstract: Preface 1. Introduction Reasons for Surveys The Components of Surveys Purposes and Goals of This Text 2. Sampling The Sample Frame Selecting a One-Stage Sample Multistage Sampling Making Estimates From Samples and Sampling Errors How Big Should a Sample Be? Sampling Error as a Component of Total Survey Error Exercise 3. Nonresponse: Implementing a Sample Design Calculating Response Rates Bias Associated With Nonresponse Reducing Nonresponse in Telephone or Personal Interview Surveys Reducing Nonresponse to Mail Surveys Reducing Nonresponse to Internet Surveys Multimode Data Collection Correcting for Nonresponse Nonprobability (or Modified Probability) Samples Nonresponse as a Source of Error Exercise 4. Methods of Data Collection Major Issues in Choosing a Strategy Summary Comparison of Methods Conclusion Exercise 5. Designing Questions to Be Good Measures Increasing the Reliability of Answers Avoiding Multiple Questions Types of Measures/Types of Questions Increasing the Validity of Factual Reporting Increasing the Validity of Answers Describing Subjective States Question Design and Error Exercises 6. Evaluating Survey Questions and Instruments Defining Objectives Preliminary Question Design Steps Presurvey Evaluation Design, Format, and Layout of Survey Instruments Field Pretests Survey Instrument Length Conclusion Exercise 7. Survey Interviewing Overview of Interviewer Job Interviewer Recruitment and Selection Training Interviewers Supervision Survey Questions Interviewing Procedures Validation of Interviews The Role of Interviewing in Survey Error Exercise 8. Preparing Survey Data for Analysis Formatting a Data File Constructing a Code Approaches to Coding and Data Entry Data Cleaning Coding and Data Reduction as Sources of Errors in Surveys 9. Ethical Issues in Survey Research Informing Respondents Protecting Respondents Benefits to Respondents Ethical Responsibilities to Interviewers Conclusion 10. Providing Information About Survey Methods Exercise 11. Survey Error in Perspective The Concept of Total Survey Design Error in Perspective Conclusion References Index About the Author
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