methods of data collection and analysis pdf

Methods Of Data Collection And Analysis Pdf

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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

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.

Patient Centered Medical Home

More than one method of data collection is often necessary in order to have a sample of patients that is both representative and large enough to be meaningful. There is always the concern that different interview methods will introduce a bias in responses. In the final phase of a three-year study, inner-city hypertensives were interviewed by telephone or, if that was not possible, in person. Patient-reported data were compared using discriminant function analyses to detect differences in responses by the two interview methods. Analysis showed that telephone interviews were of shorter duration than home interviews and that the combined method was less costly than an earlier home interview study of half the same cohort. No significant differences in the two interview methods were found on the basis of population distribution characteristics, completion rates, or on any of the 17 classifications analyzed. The authors' findings demonstrate that the use of more than one method of data collection with the same sample, while facilitating the augmentation of the response rate, will not necessarily bias the study results.

Be familiar with different methods for collecting and analysing qualitative data​.

Data collection

Now that you have determined what outcomes or other aspects of your program to evaluate, it is time to identify what type of data to collect and how to collect those data. Keep in mind that there is no single best evaluation design or way to collect data. The most appropriate approach is the one that will answer your evaluations questions within the limits of the resources available to you. One of the first aspects you need to consider is what type of data will best meet your needs.

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If an organization is considering whether to collect data on its own or get help from an external consultant, it will need to have enough information to make an informed decision about how to proceed. This section outlines some of the key considerations that may arise during various steps in the data collection process. There is no requirement that these steps be followed or pursued in the order that they are written. The model presented is offered as a reference tool. How data is gathered and analyzed depends on many factors, including the context, the issue that needs to be monitored, the purpose of the data collection, and the nature and size of the organization.

Data Collection Methods

Data Collection Methods

In the figure below we provide an abbreviated overview of each method. A more detailed description and explanation of each method along with its unique benefits and challenges is located below the figures. Document analysis is the most common form of data collection because it involves the gathering of existing documents and records. Surveys are probably the most recognized and popular form of data collection because they provide an easy way to collect a lot of information at once in a systematic and standardized way. If neither approach works, you can combine the approaches listed above.

Data collection is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences , humanities , [2] and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture quality evidence that allows analysis to lead to the formulation of convincing and credible answers to the questions that have been posed. Regardless of the field of study or preference for defining data quantitative or qualitative , accurate data collection is essential to maintain research integrity.

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.

Data Collection

Determine Collection Method

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. The population may be composed of a group of individuals e.

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6. What is involved in collecting data – six steps to success

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Step 4: Choose Design and Tool

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  1. Ralph S.

    examples, Census data being used to analyze the impact of education on career choice and earning. Common sources of secondary data for.

    27.11.2020 at 13:08 Reply

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