Scientific Data Collection And Analysis PdfBy Jermaine G. In and pdf 21.05.2021 at 14:29 7 min read
File Name: scientific data collection and analysis .zip
Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. The data collection component of research is common to all fields of study including physical and social sciences, humanities, business, etc.
While data analysis in qualitative research can include statistical procedures, many times analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase Savenye, Robinson, The form of the analysis is determined by the specific qualitative approach taken field study, ethnography content analysis, oral history, biography, unobtrusive research and the form of the data field notes, documents, audiotape, videotape. An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings.
Data Collection Methods
Jump to main content. Download PDF Version. This brief focuses on using mixed methods to evaluate patient-centered medical home PCMH models. The series is designed to expand the toolbox of methods used to evaluate and refine PCMH models. The PCMH is a primary care approach that aims to improve quality, cost, and patient and provider experience.
PCMH models emphasize patient-centered, comprehensive, coordinated, accessible care, and a systematic focus on quality and safety. Wisdom J and Creswell JW. February This brief and companion briefs in this series are available for download from pcmh.
The basic premise of this methodology is that such integration permits a more complete and synergistic utilization of data than do separate quantitative and qualitative data collection and analysis. The evaluation of PCMHs provide an ideal opportunity for mixed methods studies to contribute to learning about best practices in how to implement a PCMH as well as PCMH effectiveness in achieving the triple aim outcomes of cost, quality, and patient experience of care.
Mixed methods research originated in the social sciences and has recently expanded into the health and medical sciences including fields such as nursing, family medicine, social work, mental health, pharmacy, allied health, and others. In the last decade, its procedures have been developed and refined to suit a wide variety of research questions Creswell and Plano Clark, These procedures include advancing rigor, offering alternative mixed methods designs, specifying a shorthand notation system for describing the designs to increase communication across fields, visualizing procedures through diagrams, noting research questions that can particularly benefit from integration, and developing rationales for conducting various forms of mixed methods studies.
The core characteristics of a well-designed mixed methods study in PCMH research include the following:. This brief focuses on the potential uses of this methodology for PCMH research as well as on specific mixed methods designs in primary care research Creswell, Fetters, and Ivankova, that offer feasible, information-rich data that can enhance traditional quantitative research approaches.
PCMH evaluators can choose from five primary mixed methods designs depending on the research questions they want to answer and resources available for the evaluation. Validate findings using quantitative and qualitative data sources.
Evaluators can use a convergent design to compare findings from qualitative and quantitative data sources. It involves collecting both types of data at roughly the same time; assessing information using parallel constructs for both types of data; separately analyzing both types of data; and comparing results through procedures such as a side-by-side comparison in a discussion, transforming the qualitative data set into quantitative scores, or jointly displaying both forms of data.
For example, the investigator can gather qualitative data to assess the personal experiences of patients while also gathering data from survey instruments measuring the quality of care. The two types of data can provide validation for each other and also create a solid foundation for drawing conclusions about the intervention.
Use qualitative data to explore quantitative findings. This explanatory sequential design typically involves two phases: 1 an initial quantitative instrument phase, followed by 2 a qualitative data collection phase, in which the qualitative phase builds directly on the results from the quantitative phase. In this way, the quantitative results are explained in more detail through the qualitative data. For example, findings from instrument data about costs can be explored further with qualitative focus groups to better understand how the personal experiences of individuals match up to the instrument results.
This kind of study illustrates the use of mixed methods to explain qualitatively how the quantitative mechanisms might work.
Develop survey instruments. Yet another mixed methods study design could support the development of appropriate quantitative instruments that provide accurate measures within a PCMH context. This exploratory sequential design involves first collecting qualitative exploratory data, analyzing the information, and using the findings to develop a psychometric instrument well adapted to the sample under study.
This instrument is then, in turn, administered to a sample of a population. For example, a PCMH study could begin with a qualitative exploration through interviews with primary care providers to assess what constructs should be measured to best understand improved quality of care.
From this exploration, an instrument could be developed using rigorous scale development procedures DeVellis, that is then tested with a sample. In this way, researchers can use a mixed methods approach to develop and test a psychometric instrument that improves on existing measures. Use qualitative data to augment a quantitative outcomes study. An outcomes study, for example a randomized, controlled trial, with qualitative data collection and analysis added, is called an embedded design.
Within this type of an outcomes study, the researcher collects and analyzes both quantitative and qualitative data. The qualitative data can be incorporated into the study at the outset for example, to help design the intervention ; during the intervention for example, to explore how participants experience the PCMH model ; and after the intervention for example, to help explain the results.
In this way, the qualitative data augment the outcomes study, which is a popular approach within implementation and dissemination research Palinkas, Aarons, Horwitz, et al. Involve community-based stakeholders. A community-based participatory approach is an example of a multiphase design.
This advanced mixed methods approach involves community participants in many quantitative and qualitative phases of research to bring about change Mertens, The multiple phases all address a common objective of assessing and refining PCMH models. This design would involve primary care providers and staff, patients, and other providers and individuals in the community in the research process.
Key stakeholders participate as co-researchers in a project, providing input about their needs, ways to address them, and ways to implement changes.
These five research designs apply mixed methods approaches to evaluations of PCMH models. The literature details their procedures, illustrates the flow of activities through the use of shorthand notation, and reflects on strengths and limitations. Compares quantitative and qualitative data. Mixed methods are especially useful in understanding contradictions between quantitative results and qualitative findings. Fosters scholarly interaction. Such studies add breadth to multidisciplinary team research by encouraging the interaction of quantitative, qualitative, and mixed methods scholars.
Provides methodological flexibility. Mixed methods have great flexibility and are adaptable to many study designs, such as observational studies and randomized trials, to elucidate more information than can be obtained in only quantitative research. Collects rich, comprehensive data. Mixed methods also mirror the way individuals naturally collect information—by integrating quantitative and qualitative data.
For example, sports stories frequently integrate quantitative data scores or number of errors with qualitative data descriptions and images of highlights to provide a more complete story than either method would alone. Mixed methods studies are challenging to implement, especially when they are used to evaluate complex interventions such as a PCMH model. Below we discuss several challenges.
Increases the complexity of evaluations. Mixed methods studies are complex to plan and conduct. They require careful planning to describe all aspects of research, including the study sample for qualitative and quantitative portions identical, embedded, or parallel ; timing the sequence of qualitative and quantitative portions ; and the plan for integrating data.
Integrating qualitative and quantitative data during analysis is often a challenging phase for many researchers. Relies on a multidisciplinary team of researchers.
Conducting high-quality mixed methods studies requires a multidisciplinary team of researchers who, in the service of the larger study, must be open to methods that may not be their area of expertise.
Finding qualitative experts who are also comfortable discussing quantitative analyses and vice versa can be challenging in many environments. Given that each method must adhere to its own standards for rigor, ensuring appropriate quality of each component of a mixed methods study can be difficult Wisdom, Cavaleri, Onwuegbuzie, et al.
For example, quantitative analyses require much larger sample sizes to obtain statistical significance than do qualitative analyses, which require meeting goals of saturation not uncovering new information from conducting more interviews and relevance. Embedded samples, in which a qualitative subsample is embedded within a larger quantitative sample, can be useful in cases of inadequate statistical power. Requires increased resources. Finally, mixed methods studies are labor intensive and require greater resources and time than those needed to conduct a single method study.
The integration of quantitative and qualitative data in the form of a mixed methods study has great potential to strengthen the rigor and enrich the analysis and findings of any PCMH evaluation. Collecting and analyzing both quantitative closed-ended and qualitative open-ended data. Integrating the data during data collection, analysis, or discussion.
Using procedures that implement qualitative and quantitative components either concurrently or sequentially, with the same sample or with different samples. On This Page I. Mixed Methods Studies II. Advantages VI. Limitations V. Conclusion VI. References VII.
Data analysis is a process of inspecting, cleansing , transforming , and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses.
Martin Wolkewitz University of Freiburg, Germany. You can contact our Guest Editors through here. Submission is open to everyone. COVID has led to the adoption of unprecedented mitigation measures which could trigger many unintended consequences. These unintended consequences can be far-reaching and just as important as the intended o
proportion of participants who plan to major in science when they get to college and how this Data collected through quantitative methods are often believed to yield better techniques for classifying and analyzing large bodies of descriptive lab591.org s/lab591.org
Data Analysis and Interpretation
Data collection is a process of collecting information from all the relevant sources to find answers to the research problem, test the hypothesis and evaluate the outcomes. Data collection methods can be divided into two categories: secondary methods of data collection and primary methods of data collection. Secondary data is a type of data that has already been published in books, newspapers, magazines, journals, online portals etc. There is an abundance of data available in these sources about your research area in business studies, almost regardless of the nature of the research area.
Descriptions of key issues in survey research and questionnaire design are highlighted in the following sections. Modes of data collection approaches are described together with their advantages and disadvantages. Descriptions of commonly used sampling designs are provided and the primary sources of survey error are identified. Terms relating to the topics discussed here are defined in the Research Glossary.
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