Review steps covered in Understanding and Conducting Research in the Health Sciences this week, other readings, and resources, including the Methods Map Visual Search Tool from the University Library.
Complete the Research Process and Methods worksheet.
Cite 3 peer-reviewed or scholarly references.
Format your assignment according to APA guidelines. Include a title page at the beginning of your worksheet.
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Research Questions and Hypotheses Connelly, Lynne M. Medsurg NursingMedsurg Nursing; Pitman; Pitman Vol. 24, Iss. 6, (Nov/Dec 2015): 435-436.
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In this issue, Klaus and Steinwedel (2015) as well as Nash (2015) used research questions to frame the specific
purposes of their studies. In other studies, researchers may use research hypotheses. In this column, I will
discuss the nature of each and how they differ from each other. Depending on what the researcher is trying to
determine, either research questions or hypotheses may be appropriate for the study being reported.
A research question outlines the phenomena under study, who were studied, and what the researcher wanted to
know about them. In any study, this includes the specific phenomena usually expressed as variables to be studied,
the population studied, and the problem to be addressed (written in a question format) (Polit & Beck, 2014). This
should not be a broad, vague idea about the topic of interest. Novice researchers often need to narrow their focus
to what can be accomplished in a particular study. Research questions are used when no specific direction is
predicted. Some authors write a purpose statement rather than a question, but it has the same components of
phenomena, population, and specific gap in knowledge to be addressed, written as a declarative statement.
Large studies may have more than one research question that can range from simple to complex. Questions arise
when a gap exists in the knowledge about a particular area of concern (Farrugia, Petrisor, Farrokhyar, & Bhandari,
2010). Having a current understanding of the field of study thus is imperative when developing a study.
A research question might be used to direct a literature review (Nash, 2015) or a pilot study (Klaus & Steinwedel,
2015). A question is used when we do not have a particular hunch or hypothesis about the outcome of the study.
The research question for Klaus and Steinwedel was, "Is there an effect on nurses' intent to engage in personal
preparedness after a disaster preparedness intervention?" This question did not predict if the effect would be
positive (increase in intent to engage in personal preparedness) or negative (decrease in intent to engage in
personal preparedness). Researchers just wanted to know if there would be any effect.
A good research question is feasible, interesting, novel, ethical, and relevant (Farrugia et al., 2010). As another
example in a medical-surgical study on nurses' knowledge of delirium, Baker, Taggart, Nivens, and Tillam (2015)
offered four research questions:
1. What was nurses' level of knowledge of delirium?
2. What was nurses' level of knowledge of delirium risk factors?
3. Was there a correlation between nurses' years of experience, education, and practice area, and their knowledge
of delirium and its risk factors?
4. How did nurses perceive their own knowledge competency related to delirium? (p. 17)
Of note, the third question did not predict the direction of the relationship between years of experience, education,
and practice area, and nurses' knowledge of delirium and its risk factors.
A research hypothesis is a specific statement that predicts the direction and nature of the results of a study.
Hypotheses can be simple or complex, but they should be understandable to most readers. A hypothesis usually
outlines the precise relationship between two or more variables; it can represent an association between the
variables or a cause and effect relationship (Ross, 1998). With two variables, one variable is the independent
variable and the other is the dependent or outcome variable. In the case of a hypothesis, the researcher has strong
reasons to believe the results will take a certain direction based on the literature or a theory (Polit & Beck, 2014).
With or without a theory, hypotheses suggest an explanation that is logical and feasible. With a hypothesis,
readers should be able to read the article and state clearly if the hypothesis was supported or not supported by
study results. In an experiment, a comparison group is included in the hypothesis. Hypotheses should be written
clearly and be found easily in the article by readers (Haber, 2014). Data analysis should address each hypothesis
For example, in a study by Pritts and Hiller (2014) on implementation of physician-nurse patient rounding on a
medical unit, the hypotheses appeared in a separate section of the article. They were as follows:
1. Incorporating physician-nurse patient rounding will lead to increased perception of collaboration between the
2. With implementation of patient rounding, nurses will have an increase in satisfaction with their interaction with
3. Patient satisfaction with their care based on their perception of the teamwork among caregivers, including
physicians and nurses, will increase. (p. 409)
These hypotheses clearly stated the perception of collaboration, nurse satisfaction, and patient satisfaction
would increase (predicted direction) after rounding was implemented.
Research questions and hypotheses are used to guide the study and provide a framework for examining results.
They lead to the research aims, which outline specific methods and procedures the researcher will use to find the
desired answers (Pilot & Beck, 2014). Both are important to define the study's end point. The hypothesis or
question needs to be well founded and refined to produce relevant results (von Schnacky, 2014). Research
questions and hypotheses also should be developed before data collection and not modified after results to
obtain a better or more statistically significant result.
Readers of research need to understand the components of a study so they also can understand the results.
Relevant results are based on well-developed research questions (or purpose statements) and hypotheses. If
more information is needed, the references can be a starting point to help clarify the understanding of research
questions and hypotheses.
Baker, N.D., Taggart, H.M., Nivens, A., & Tillam, P. (2015). Delirium: Why are nurses confused? MEDSURG Nursing,
Farrugia, P., Petrisor, B.A., Farrokhyar, F., & Bhandari, M. (2010). Research questions, hypotheses and objectives.
Canadian Journal of Surgery, 53(4), 278-281.
Haber, J. (2014). Research questions, hypotheses, and clinical questions. In G. LoBiondo & J. Haber (Eds.), Nursing
research: Methods and critical appraisal for evidence-based practice (8th ed.) (pp. 27-55). St. Louis, MO: Mosby.
Klaus, K., & Steinwedel, C. (2015) Maggot debridement therapy: Advancing to the past in wound care. MEDSURG
Nursing, 24(6), 407-411.
Nash, T.J. (2015). Unveiling the truth about nurses' personal preparedness for disaster response: A pilot study.
MEDSURG Nursing, 24(6), 425-431.
Polit, D.F., & Beck, C.T. (2014). Essentials of nursing research: Appraising evidence for nursing practice (8th ed.).
Philadelphia, PA: Wolters Kluwer/Lippincott Williams & Wilkins.
Ross, D. (1998). Hypotheses: How the research question is asked. Orthopaedic Nursing, 17, 3.
Pritts, K.E., & Hiller, L.G. (2014). Implementation of physician and nurse patient rounding on a 42-bed medical unit.
MEDSURG Nursing, 23(6), 408-413.
Von Schnacky, C. (2014). Hypotheses and ethos of publication. European Journal of Clinical Nutrition, 68(8), 863.
Lynne M. Connelly, PhD, RN, is Associate Professor and Director of Nursing, Robert J. Dehaemers Endowed Chair,
Benedictine College Atchison, KS. She is Research Editor for MEDSURG Nursing.
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Farrugia, Patricia, BScN; Petrisor, Bradley A, MSc, MD; Farrokhyar, Forough, MPhil, PhD; Bhandari, Mohit, MD, MSc. Canadian Journal ofCanadian Journal of Surger ySurger y;; OttawaOttawa Vol. 53, Iss. 4, (Aug 2010): 278-281.
Pritts, Kristin E; Hiller, Laura G. Medsurg NursingMedsurg Nursing;; PitmanPitman Vol. 23, Iss. 6, (Nov/Dec 2014): 408-413.
Cossey, Kimberly. The Pennsylvania State University. ProQuest Dissertations Publishing, 2008. 3336013.
Guthery, Fred S; Lusk, Jeffrey J; Peterson, Markus J. Wildlife Society BulletinWildlife Society Bulletin;; BethesdaBethesda Vol. 32, Iss. 4, (Winter 2004): 1325-1332.
Glass, David J. Clinical Chemistr yClinical Chemistr y;; WashingtonWashington Vol. 56, Iss. 7, (Jul 2010): 1080-5.
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Research Questions and Mixed Methods in Health Services Research
Rationale for Integration
Integration at the Study Design Level
Integration at the Methods Level
Integration at the Interpretation and Reporting Level
“Fit” of Data Integration
Examples Illustrating Integration
Example 1. Integration in an Exploratory Sequential Mixed Methods Study— The Survival …
Example 2. Integration in a Convergent Mixed Methods Study— The Adaptive Designs …
Implications for Practice
Achieving Integration in Mixed Methods Designs-Principles and Practices.
Fetters, Michael D. Curry, Leslie A. Creswell, John W.
Health Services Research. Dec2013, Vol. 48 Issue 6pt2, p2134-2156. 23p. 1 Diagram, 3 Charts, 1 Graph.
*MEDICAL care research *MIXED methods research *EXPERIMENTAL design *SAMPLING (Process) *CASE studies *DATA transformations (Statistics) *DATA analysis *METHODOLOGY
biostatistical methods epidemiology focus groups program evaluation Qualitative research research methodology sampling survey
Mixed methods research offers powerful tools for investigating complex processes and systems in health and health care. This article describes integration principles and practices at three levels in mixed methods research and provides illustrative examples. Integration at the study design level occurs through three basic mixed method designs- exploratory sequential, explanatory sequential, and convergent-and through four advanced frameworks-multistage, intervention, case study, and participatory. Integration at the methods level occurs through four approaches. In connecting, one database links to the other through sampling. With building, one database informs the data collection approach of the other. When merging, the two databases are brought together for analysis. With embedding, data collection and analysis link at multiple points. Integration at the interpretation and reporting level occurs through narrative, data transformation, and joint display. The fit of integration describes the extent the qualitative and quantitative findings cohere. Understanding these principles and practices of integration can help health services researchers leverage the strengths of mixed methods. [ABSTRACT FROM AUTHOR]
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Achieving Integration in Mixed Methods Designs-Principles and Practices.
Mixed methods research offers powerful tools for investigating complex processes and systems in health and health care. This article describes integration principles and practices at three levels in mixed methods research and provides illustrative examples. Integration at the study design level occurs through three basic mixed method designs—exploratory sequential, explanatory sequential, and convergent—and through four advanced frameworks—multistage, intervention, case study, and participatory. Integration at the methods level occurs through four approaches. In connecting, one database links to the other through sampling. With building, one database informs the data collection approach of the other. When merging, the two databases are brought together for analysis. With embedding, data collection and analysis link at multiple points. Integration at the interpretation and reporting level occurs through narrative, data transformation, and joint display. The fit of integration describes the extent the qualitative and quantitative findings cohere. Understanding these principles and practices of integration can help health services researchers leverage the strengths of mixed methods.
Qualitative research; survey; sampling; focus groups; biostatistical methods; epidemiology; program evaluation; research methodology
This article examines key integration principles and practices in mixed methods research. It begins with the role of mixed methods in health services research and the rationale for integration. Next, a series of principles describe how integration occurs at the study design level, the method level, and the interpretation and reporting level. After considering the “fit” of integrated qualitative and quantitative data, the article ends with two examples of mixed methods investigations to illustrate integration practices.
Research Questions and Mixed Methods in Health Services Research Health services research includes investigation of complex, multilevel processes, and systems that may require both quantitative and qualitative forms of data (Creswell, Fetters, and Ivankova [ 10] ; Curry et al. [ 16] ). The nature of the research question drives the choice of methods. Health services researchers use quantitative methodologies to address research questions about causality, generalizability, or magnitude of effects. Qualitative methodologies are applied to research questions to explore why or how a phenomenon occurs, to develop a theory, or to describe the nature of an individual's experience. Mixed methods research studies draw upon the strengths of both quantitative and qualitative approaches and provides an innovative approach for addressing contemporary issues in health services. As one indication of the growing interest in mixed methods research, the Office of Behavioral and Social Sciences at the National Institutes of Health recently developed for researchers and grant reviewers the first best practices guideline on mixed methods research from the National Institutes of Health (Creswell et al. [ 14] ).
Rationale for Integration The integration of quantitative and qualitative data can dramatically enhance the value of mixed methods research (Bryman [ 4] ; Creswell and Plano Clark [ 11] ). Several advantages can accrue from integrating the two forms of data. The qualitative data can be used to assess the validity of quantitative findings. Quantitative data can also be used to help generate the qualitative sample or explain findings from the qualitative data. Qualitative inquiry can inform development or refinement of quantitative instruments or interventions, or generate hypotheses in the qualitative component for testing in the quantitative component (O'Cathain, Murphy, and Nicholl [ 40] ). Although there are many potential gains from data integration, the extent to which mixed methods studies implement integration remains limited (Bryman [ 4] ; Lewin, Glenton, and Oxman [ 30] ). Nevertheless, there are specific approaches to integrate qualitative and quantitative research procedures and data (O'Cathain, Murphy, and Nicholl [ 40] ; Creswell and Plano Clark [ 11] ). These approaches can be implemented at the design, methods, and interpretation and reporting levels of research (see Table [NaN] ).
Levels of Integration in Mixed Methods Research
Integration Level Approaches Design 3 Basic designs Exploratory sequential Explanatory sequential Convergent 4 Advanced frameworks Multistage Intervention Case study Participatory—Community‐based participatory research, and transformative Methods Connecting Building Merging Embedding Interpretation and Reporting Narrative—Weaving, contiguous
and staged Data transformation Joint display
Integration at the Study Design Level Integration at the design level—the conceptualization of a study—can be accomplished through three basic designs and four advanced mixed methods frameworks that incorporate one of the basic designs. Basic designs include ( 1) exploratory sequential; ( 2) explanatory sequential; and ( 3) convergent designs. In sequential designs, the intent is to have one phase of the mixed methods study build on the other, whereas in the convergent designs the intent is to merge the phases in order that the quantitative and qualitative results can be compared.
In an exploratory sequential design, the researcher first collects and analyzes qualitative data, and these findings inform subsequent quantitative data collection (Onwuegbuzie, Bustamante, and Nelson [ 41] ). For example, Wallace and colleagues conducted semistructured interviews with medical students, residents, and faculty about computing devices in medical education and used the qualitative data to identify key concepts subsequently measured in an online survey (Wallace, Clark, and White [ 54] ).
In an explanatory sequential design, the researcher first collects and analyzes quantitative data, then the findings inform qualitative data collection and analysis (Ivankova, Creswell, and Stick [ 23] ). For example, Carr explored the impact of pain on patient outcomes following surgery by conducting initial surveys about anxiety, depression, and pain that were followed by semistructured interviews to explore further these concepts (Carr [ 5] ).
In a convergent design (sometimes referred to as a concurrent design), the qualitative and quantitative data are collected and analyzed during a similar timeframe. During this timeframe, an interactive approach may be used where iteratively data collection and analysis drives changes in the data collection procedures. For example, initial quantitative findings may influence the focus and kinds of qualitative data that are being collected or vice versa. For example, in one study Crabtree and colleagues used qualitative findings and quantitative findings iteratively in multiple phases such that the data were interacting to inform the final results (Crabtree et al. [ 9] ). In the more common and technically simpler variation, qualitative and quantitative data collection occurs in parallel and analysis for integration begins well after the data collection process has proceeded or has been completed. Frequently, the two forms of data are analyzed separately and then merged. For example, Saint Arnault and colleagues conducted multiple surveys using standardized and culturally adapted instruments as well as ethnographic qualitative interviews to investigate how the illness experience, cultural interpretations, and social structural factors interact to influence help‐seeking among Japanese women (Saint Arnault and Fetters [ 48] ).
Advanced frameworks encompass adding to one of the three basic designs a larger framework that incorporates the basic design. The larger framework may involve ( 1) a multistage; ( 2) an intervention; ( 3) a case study; or ( 4) a participatory research framework.
In a multistage mixed methods framework, researchers use multiple stages of data collection that may include various combinations of exploratory sequential, explanatory sequential, and convergent approaches (Nastasi et al. [ 39] ). By definition, such investigations will have multiple stages, defined here as three or more stages when there is a sequential component, or two or more stages when there is a convergent component; these differences distinguishes the multistage framework from the basic mixed methods designs. This type of framework may be used in longitudinal studies focused on evaluating the design, implementation, and assessment of a program or intervention. Krumholz and colleagues have used this design in large‐scale outcomes research studies (Krumholz, Curry, and Bradley [ 27] ). For example, a study by their team examining quality of hospital care for patients after heart attacks consisted of three phases: first, a quantitative analysis of risk‐ standardized mortality rates for patients with heart attacks to identify high and low performing hospitals; second, a qualitative phase to understand the processes, structures, and organizational environments of a purposeful sample of low and high performers and to generate hypotheses about factors associated with performance; and third, primary data collection through surveys of a nationally representative sample of hospitals to test these hypotheses quantitatively (Curry et al. [ 15] ; Bradley et al. [ 3] ). Ruffin and colleagues conducted a multistage mixed methods study to develop and test in a randomized controlled trial (RCT) a website to help users choose a screening approach to colorectal cancer. In the first stage, the authors employed a convergent design using focus groups and a survey (Ruffin et al. [ 47] ). In the second stage, they developed the website based on multiple qualitative approaches (Fetters et al. [ 18] ). In the third stage, the authors tested the website in an RCT to assess its effectiveness (Ruffin, Fetters, and Jimbo [ 46] ). The multistage framework is the most general framework among advanced designs. The additional three frameworks frequently involve multiple stages or phases but differ from multistage by having a particular focus.
In an intervention mixed methods framework, the focus is on conducting a mixed methods intervention. Qualitative data are collected primarily to support the development of the intervention, to understand contextual factors during the intervention that could affect the outcome, and/or explain results after the intervention is completed (Creswell et al. [ 13] ; Lewin, Glenton, and Oxman [ 30] ). For example, Plano Clark and colleagues utilized data from a pretrial qualitative study to inform the design of a trial developed to compare a low dose and high dose behavioral intervention to improve cancer pain management—the trial also included prospective qualitative data collection during the trial (Plano Clark et al. [ 42] ). The methodological approach for integrating qualitative data into an intervention pretrial, during the trial, or post‐trial is called embedding (see below), and some authors refer to such trials as embedded designs (Creswell et al. [ 13] ; Lewin, Glenton, and Oxman [ 30] ).
In a case study framework, both qualitative and quantitative data are collected to build a comprehensive understanding of a case, the focus of the study (Yin [ 58] ; Stake [ 52] ). Case study involves intensive and detailed qualitative and quantitative data collection about the case (Luck, Jackson, and Usher [ 31] ). The types of qualitative and quantitative data collected are chosen based on the nature of the case, feasibility issues, and the research question(s). In one mixed methods case study, Luck and colleagues utilized qualitative data from participant observation, semistructured interviews, informal field interviews and journaling, and quantitative data about violent events collected through structured observations to understand why nurses under‐report violence in the workplace and describe how they handle it (Luck, Jackson, and Usher [ 32] ). Comparative case studies are an extension of this framework and can be formulated in various ways. For example, Crabtree and colleagues used a comparative case approach to examine the delivery of clinical preventive services in family medicine offices (Crabtree et al. [ 9] ).
In a participatory framework, the focus is on involving the voices of the targeted population in the research to inform the direction of the research. Often researchers specifically seek to address inequity, health disparities, or a social injustice through empowering marginalized or underrepresented populations. The distinguishing feature of a participatory framework is the strong emphasis on using mixed methods data collection through combinations of basic mixed methods designs or even another advanced design, for example, an intervention framework such as an RCT. Community‐based participatory research (CBPR) is a participatory framework that focuses on social, structural, and physical environmental inequities and engages community members, organizational representatives, and researchers in all aspects of the research process (Macaulay et al. [ 33] ; Israel et al. [ 21] , [ 22] ; Minkler and Wallerstein [ 37] ). In one CBPR project, Johnson and colleagues used a mixed methods CBPR approach to collaborate with the Somali community to explore how attitudes, perceptions, and cultural practices such as female genital cutting influence their use of reproductive health services—this informed the development of interventional programs to improve culturally competent care (Johnson, Ali, and Shipp [ 25] ). A similar variation involving an emerging participatory approach that Mertens refers to as transformative specifically focuses on promoting social justice (Mertens [ 34] , [ 35] ) and has been used with Laotian refugees (Silka [ 51] ).
Integration at the Methods Level Creswell and Plano Clark conceptualize integration to occur through linking the methods of data collection and analysis (Creswell et al. [ 14] ). Linking occurs in several ways: ( 1) connecting; ( 2) building; ( 3) merging; and ( 4) embedding (Table [NaN] ). In a single line of inquiry, integration may occur through one or more of these approaches.
Integration through Methods <
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