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Evan and Marlyce have a 4-year-old son (Micah) who has cerebral palsy. Their life is very challenging because they both have to work and recently lost their home to foreclosure. Micah is being discharged to home this afternoon, and Marlyce is obviously anxious. Marlyce states, “I just feel like we are always out of touch when we go home. I have other children to care for, and we are always on the go.“ This case study is based on a longitudinal qualitative study from Canada (Woodgae, Edwards, & Ripat, 2012). 2. Answer the following questions in at least 500 words, but no more than 800 words: a. Propose a qualitative research study purpose that would help the nurse provide better care for Micah’s family. Be sure to include the purpose of the study and a rationale using your text to support your answer. b. As the researchers set up a qualitative study for families of patients such as Micah, they used purposive sampling. What does this mean, including some ideas for inclusion and exclusion factors? Use the text to support your answer c.How is data collected for this type of study? Use the text to support your answer Yes above are chapters you read for the assignment. Please go through the questions thoroughly to know what is required. Pls l need a good job.   Criteria for Qualitative assignment rubric. ( pls follow this for best grade) 1. Use of appropriate Comments 2.No errors in grammar, spelling, or sentence structure. 3. APA format is correct with the inclusion of citations and references. 4. Clear reference is made to the assignment being discussed. 5. Substantively discusses all expected content areas. 6. Includes information from professional sources and required reading to augment personal reflections/positions. 7.Clearly connects the assignment to text or reference points from previous readings, activities, and discussions. Reflects on concepts in the required readings and their impact on nursing practice. APA formatting •No title page, but citations and references are expected Read chapters 2, 5, and 7 in your LoBiondo-Wood & Haber book Chapter 2 Research questions, hypotheses, and clinical questions Judith Haber At the beginning of this chapter, you will learn about research questions and hypotheses from the perspective of a researcher, which, in the second part of this chapter, will help you generate your own clinical questions that you will use to guide the development of evidence-based practice projects. From a clinician’s perspective, you must understand the research question and hypothesis as it aligns with the rest of a study. As a practicing nurse, developing clinical questions (see Chapters 19, 20, and 21) is the first step of the evidence-based practice process for quality improvement programs like those that decrease risk for development of pressure ulcers. When nurses ask questions such as, “Why are things done this way?” “I wonder what would happen if . . . ?” “What characteristics are associated with . . . ?” or “What is the effect of ____ on patient outcomes?”, they are often well on their way to developing a research question or hypothesis. Research questions are usually generated by situations that emerge from practice, leading nurses to wonder about the effectiveness of one intervention versus another for a specific patient population. The research question or hypothesis is a key preliminary step in the research process. The research question tests a measureable relationship to be examined in a research study. The hypothesis predicts the outcome of a study. Hypotheses can be considered intelligent hunches, guesses, or predictions that provide researchers with direction for the research design and the collection, analysis, and interpretation of data. Hypotheses are a vehicle for testing the validity of the theoretical framework assumptions and provide a bridge between theory (a set of interrelated concepts, definitions, and propositions) and the real world (see Chapter 4). For a clinician making an evidence-informed decision about a patient care issue, a clinical question, such as whether chlorhexidine or povidone-iodine is more effective in preventing central line catheter infections, would guide the nurse in searching and retrieving the best available evidence. This evidence, combined with clinical expertise and patient preferences, would provide an answer on which to base the most effective decision about patient care for this population. Often the research questions or hypotheses appear at the beginning of a research article, but may be embedded in the purpose, aims, goals, or even the results section of the research report. This chapter provides you with a working knowledge of quantitative research questions and hypotheses. It also highlights the importance of clinical questions and how to develop them. Developing and refining a research question: Study perspective A researcher spends a great deal of time refining a research idea into a testable research question. Research questions or topics are not pulled from thin air. In Table 2.1, you will see that research questions can indicate that practical experience, critical appraisal of the scientific literature, or interest in an untested theory forms the basis for the development of a research idea. The research question should reflect a refinement of the researcher’s initial thinking. The evaluator of a research study should be able to identify that the researcher has: • Defined a specific question area • Reviewed the relevant literature • Examined the question’s potential significance to nursing • Pragmatically examined the feasibility of studying the research question Defining the research question Brainstorming with faculty or colleagues may provide valuable feedback that helps the researcher focus on a specific research question area. Example: ➤ Suppose a researcher told a colleague that her area of interest was health disparities about the effectiveness of peer coaching or case management in improving health outcomes with challenging patient populations such as those who are homeless. The colleague may have asked, “What is it about the topic that specifically interests you?” This conversation may have initiated a chain of thought that resulted in a decision to explore the effectiveness of a nursing case management and peer coaching intervention on hepatitis A and B (HAV and HBV) vaccine completion rates among homeless men recently released on parole (Nyamathi et al., 2015). Beginning the literature review The literature review should reveal a relevant collection of studies and systematic reviews that have been critically examined. Concluding sections in such articles (i.e., the recommendations and implications for practice) often identify remaining gaps in the literature, the need for replication, or the need for additional knowledge about a particular research focus (see Chapter 3). In the previous example, the researcher may have conducted a preliminary review of books and journals for theories and research studies on factors apparently critical to vaccine completion rates for preventable health problems like HAV and HBV, as well as risk factors contributing to the disproportionate impact of HAV and HBV on the homeless, such as risky sexual activity, drug use, substandard living conditions, and older age. These factors, called variables, should be potentially relevant, of interest, and measurable.  EVIDENCE-BASED PRACTICE TIP The answers to questions generated by qualitative data reflect evidence that may provide the first insights about a phenomenon that has not been previously studied. Other variables, called demographic variables, such as race, ethnicity, gender, age, education, and physical and mental health status, are also suggested as essential to consider. Example: ➤ Despite the availability of the HAV and HBV vaccines, there has been a low completion rate for the three-dose core of the accelerated vaccine series, particularly following release from prison. This information can then be used to further define the research question and continue the search of the literature to identify effective intervention strategies reported in other studies with similar high-risk populations (e.g., homeless) that could be applied to this population. Example: ➤ One study documented the effectiveness of a nurse case management program in improving vaccine completion rates in a group of homeless adults, but no studies were found about the effectiveness of peer coaching. At this point, the researcher could write the tentative research question: “What is the effectiveness of peer coaching and nursing case management on completion of an HAV and HBV vaccine series among homeless men on parole?” You can envision the interrelatedness of the initial definition of the question area, the literature review, and the refined research question. Examining significance When considering a research question, it is crucial that the researcher examine the question’s potential significance for nursing. This is sometimes referred to as the “so what” question, because the research question should have the potential to contribute to and extend the scientific body of nursing knowledge. Guidelines for selecting research questions should meet the following criteria: • Patients, nurses, the medical community in general, and society will potentially benefit from the knowledge derived from the study. • Results will be applicable for nursing practice, education, or administration. • Findings will provide support or lack of support for untested theoretical concepts. • Findings will extend or challenge existing knowledge by filling a gap or clarifying a conflict in the literature. • Findings will potentially provide evidence that supports developing, retaining, or revising nursing practices or policies. If the research question has not met any of these criteria, the researcher is wise to extensively revise the question or discard it. Example: ➤ In the previously cited research question, the significance of the question includes the following facts: • HAV and HBV are vaccine preventable. • Viral hepatitis disproportionately impacts the homeless. • Despite its availability, vaccine completion rates are low among high-risk and incarcerated populations. • Accelerated vaccine programs have shown success in RCT studies. • The use of nurse case management programs in accelerated vaccine programs also provides evidence of effectiveness. • Little is known about vaccine completion among ex-offender populations on parole using varying intensities of nurse case management and peer coaches. • This study sought to fill a gap in the related literature by assessing whether seronegative parolees randomized to one of three intervention conditions were more likely to complete the vaccine series as well as to identify predictors of HAV/HBV vaccine completion. The fully developed research question When a researcher finalizes a research question, the following characteristics should be evident: • It clearly identifies the variables under consideration. • It specifies the population being studied. • It implies the possibility of empirical testing. Because each element is crucial to developing a satisfactory research question, the criteria will be discussed in greater detail. These elements can often be found in the introduction of the published article; they are not always stated in an explicit manner. Variables Researchers call the properties that they study “variables.” Such properties take on different values. Thus a variable, as the name suggests, is something that varies. Properties that differ from each other, such as age, weight, height, religion, and ethnicity, are examples of variables. Researchers attempt to understand how and why differences in one variable relate to differences in another variable. Example: ➤ A researcher may be concerned about the variable of pneumonia in postoperative patients on ventilators in critical care units. It is a variable because not all critically ill postoperative patients on ventilators have pneumonia. A researcher may also be interested in what other factors can be linked to ventilator-acquired pneumonia (VAP). There is clinical evidence to suggest that elevation of the head of the bed and frequent oral hygiene are associated with decreasing risk for VAP. You can see that these factors are also variables that need to be considered in relation to the development of VAP in postoperative patients. When speaking of variables, the researcher is essentially asking, “Is X related to Y? What is the effect of X on Y? How are X1 and X2 related to Y?” The researcher is asking a question about the relationship between one or more independent variables and a dependent variable. (Note: In cases in which multiple independent or dependent variables are present, subscripts are used to indicate the number of variables under consideration.) An independent variable, usually symbolized by X, is the variable that has the presumed effect on the dependent variable. In experimental research studies, the researcher manipulates the independent variable. In nonexperimental research, the independent variable is not manipulated and is assumed to have occurred naturally before or during the study. The dependent variable, represented by Y, varies with a change in the independent variable. The dependent variable is not manipulated. It is observed and assumed to vary with changes in the independent variable. Predictions are made from the independent variable to the dependent variable. It is the dependent variable that the researcher is interested in understanding, explaining, or predicting. Example: ➤ It might be assumed that the perception of pain intensity (the dependent variable) will vary in relation to a person’s gender (the independent variable). In this case, we are trying to explain the perception of pain intensity in relation to gender (i.e., male or female). Although variability in the dependent variable is assumed to depend on changes in the independent variable, this does not imply that there is a causal relationship between X and Y, or that changes in variable X cause variable Y to change. Although one independent variable and one dependent variable are used in the examples, there is no restriction on the number of variables that can be included in a research question. Research questions that include more than one independent or dependent variable may be broken down into subquestions that are more concise. Finally, it should be noted that variables are not inherently independent or dependent. A variable that is classified as independent in one study may be considered dependent in another study. Example: ➤ A nurse may review an article about depression that identifies depression in adolescents as predictive of risk for suicide. In this case, depression is the independent variable. When another article about the effectiveness of antidepressant medication alone or in combination with cognitive behavioral therapy (CBT) in decreasing depression in adolescents is considered, change in depression is the dependent variable. Whether a variable is independent or dependent is a function of the role it plays in a particular study. Population The population is a well-defined set that has certain characteristics and is either clearly identified or implied in the research question. Example: ➤ In a retrospective cohort study studying the number of ED visits and hospitalizations in two different transition care programs, a research question may ask, “What is the differential effectiveness of nurse-led or physician-led intensive home visiting program providing transition care to patients with complex chronic conditions or receiving palliative care (Morrison, Palumbo, & Rambur, 2016)? Does a relationship exist between type of transition care model (nurse-led focused on chronic disease self-management or physician-led focused on palliative care and managing complex chronic conditions) and the number of ED visits and rehospitalizations 120 days pre- and post-transitional care interventions?” This question suggests that the population includes community-residing adults with complex chronic conditions or receiving palliative care who participated in either a nurse or physician-led transitional care program. Testability The research question must imply that it is testable, measurable by either qualitative or quantitative methods. Example: ➤ The research question “Should postoperative patients control how much pain medication they receive?” is stated incorrectly for a variety of reasons. One reason is that it is not testable; it represents a value statement rather than a research question. A scientific research question must propose a measurable relationship between an independent and a dependent variable. Many interesting and important clinical questions are not valid research questions because they are not amenable to testing. Study purpose, aims, or objectives The purpose of the study encompasses the aims or objectives the investigator hopes to achieve with the research. These three terms are synonymous. The researcher selects verbs to use in the purpose statement that suggest the planned approach to be used when studying the research question as well as the level of evidence to be obtained through the study findings. Verbs such as discover, explore, or describe suggest an investigation of an infrequently researched topic that might appropriately be guided by research questions rather than hypotheses. In contrast, verb statements indicating that the purpose is to test the effectiveness of an intervention or compare two alternative nursing strategies suggest a hypothesis-testing study for which there is an established knowledge base of the topic. Remember that when the purpose of a study is to test the effectiveness of an intervention or compare the effectiveness of two or more interventions, the level of evidence is likely to have more strength and rigor than a study whose purpose is to explore or describe phenomena. Box 2.1 provides examples of purpose, aims, and objectives. Study purpose, aims, or objectives The purpose of the study encompasses the aims or objectives the investigator hopes to achieve with the research. These three terms are synonymous. The researcher selects verbs to use in the purpose statement that suggest the planned approach to be used when studying the research question as well as the level of evidence to be obtained through the study findings. Verbs such as discover, explore, or describe suggest an investigation of an infrequently researched topic that might appropriately be guided by research questions rather than hypotheses. In contrast, verb statements indicating that the purpose is to test the effectiveness of an intervention or compare two alternative nursing strategies suggest a hypothesis-testing study for which there is an established knowledge base of the topic. Remember that when the purpose of a study is to test the effectiveness of an intervention or compare the effectiveness of two or more interventions, the level of evidence is likely to have more strength and rigor than a study whose purpose is to explore or describe phenomena. Box 2.1 provides examples of purpose, aims, and objectives. Relationship statement The first characteristic of a hypothesis is that it is a declarative statement that identifies the predicted relationship between two or more variables: the independent variable (X) and a dependent variable (Y). The direction of the predicted relationship is also specified in this statement. Phrases such as greater than, less than, positively, negatively, or difference in suggest the directionality that is proposed in the hypothesis. The following is an example of a directional hypothesis: “Nurse staff members’ perceptions of transformational leadership among their nurse leaders (independent variable) is that it is negatively associated with nurse staff burnout (dependent variable)” (Lewis & Cunningham, 2016). The dependent and independent variables are explicitly identified, and the relational aspect of the prediction in the hypothesis is contained in the phrase “negatively associated with.” The nature of the relationship, either causal or associative, is also implied by the hypothesis. A causal relationship is one in which the researcher can predict that the independent variable (X) causes a change in the dependent variable (Y). In research, it is rare that one is in a firm enough position to take a definitive stand about a cause-and-effect relationship. Example: ➤ A researcher might hypothesize selected determinants of the decision-making process, specifically expectation, socio-demographic factors, and decisional conflict would predict post decision satisfaction and regret about their choice of treatment for breast cancer in Chinese-American women (Lee & Knobf, 2015). It would be difficult for a researcher to predict a cause-and-effect relationship, however, because of the multiple intervening variables (e.g., values, culture, role, support from others, personal resources, language literacy) that might also influence the subject’s decision making about treatment for their breast cancer diagnosis. Variables are more commonly related in noncausal ways; that is, the variables are systematically related but in an associative way. This means that the variables change in relation to each other. Example: ➤ There is strong evidence that asbestos exposure is related to lung cancer. It is tempting to state that there is a causal relationship between asbestos exposure and lung cancer. Do not overlook the fact, however, that not all of those who have been exposed to asbestos will have lung cancer, and not all of those who have lung cancer have had asbestos exposure. Consequently, it would be scientifically unsound to take a position advocating the presence of a causal relationship between these two variables. Rather, one can say only that there is an associative relationship between the variables of asbestos exposure and lung cancer, a relationship in which there is a strong systematic association between the two phenomena. Testability The second characteristic of a hypothesis is its testability. This means that the variables of the study must lend themselves to observation, measurement, and analysis. The hypothesis is either supported or not supported after the data have been collected and analyzed. The predicted outcome proposed by the hypothesis will or will not be congruent with the actual outcome when the hypothesis is tested. Relationship statement The first characteristic of a hypothesis is that it is a declarative statement that identifies the predicted relationship between two or more variables: the independent variable (X) and a dependent variable (Y). The direction of the predicted relationship is also specified in this statement. Phrases such as greater than, less than, positively, negatively, or difference in suggest the directionality that is proposed in the hypothesis. The following is an example of a directional hypothesis: “Nurse staff members’ perceptions of transformational leadership among their nurse leaders (independent variable) is that it is negatively associated with nurse staff burnout (dependent variable)” (Lewis & Cunningham, 2016). The dependent and independent variables are explicitly identified, and the relational aspect of the prediction in the hypothesis is contained in the phrase “negatively associated with.” The nature of the relationship, either causal or associative, is also implied by the hypothesis. A causal relationship is one in which the researcher can predict that the independent variable (X) causes a change in the dependent variable (Y). In research, it is rare that one is in a firm enough position to take a definitive stand about a cause-and-effect relationship. Example: ➤ A researcher might hypothesize selected determinants of the decision-making process, specifically expectation, socio-demographic factors, and decisional conflict would predict postdecision satisfaction and regret about their choice of treatment for breast cancer in Chinese-American women (Lee & Knobf, 2015). It would be difficult for a researcher to predict a cause-and-effect relationship, however, because of the multiple intervening variables (e.g., values, culture, role, support from others, personal resources, language literacy) that might also influence the subject’s decision making about treatment for their breast cancer diagnosis. Variables are more commonly related in noncausal ways; that is, the variables are systematically related but in an associative way. This means that the variables change in relation to each other. Example: ➤ There is strong evidence that asbestos exposure is related to lung cancer. It is tempting to state that there is a causal relationship between asbestos exposure and lung cancer. Do not overlook the fact, however, that not all of those who have been exposed to asbestos will have lung cancer, and not all of those who have lung cancer have had asbestos exposure. Consequently, it would be scientifically unsound to take a position advocating the presence of a causal relationship between these two variables. Rather, one can say only that there is an associative relationship between the variables of asbestos exposure and lung cancer, a relationship in which there is a strong systematic association between the two phenomena. Testability The second characteristic of a hypothesis is its testability. This means that the variables of the study must lend themselves to observation, measurement, and analysis. The hypothesis is either supported or not supported after the data have been collected and analyzed. The predicted outcome proposed by the hypothesis will or will not be congruent with the actual outcome when the hypothesis is tested. Theory base The third characteristic is that the hypothesis is consistent with an existing body of theory and research findings. Whether a hypothesis is arrived at on the basis of a review of the literature or a clinical observation, it must be based on a sound scientific rationale. You should be able to identify the flow of ideas from the research idea to the literature review, to the theoretical framework, and through the research question(s) or hypotheses. Example: ➤ Nyamathi and colleagues (2015) (see Appendix A) investigated the effectiveness of a nursing case management intervention in comparison to a peer coaching intervention based on the comprehensive health-seeking and coping paradigm developed by Nyamathi in 1989, adapted from a coping model by Lazarus and Folkman (1984), and the health-seeking and coping paradigm by Schlotfeldt (1981), which is a useful theoretical framework for case management, peer coaching interventions, and vaccine completion outcomes. Statistical versus research hypotheses You may observe that a hypothesis is further categorized as either a research or a statistical hypothesis. A research hypothesis, also known as a scientific hypothesis, consists of a statement about the expected relationship of the variables. A research hypothesis indicates what the outcome of the study is expected to be. A research hypothesis is also either directional or nondirectional. If the researcher obtains statistically significant findings for a research hypothesis, the hypothesis is supported. The examples in Table 2.4 represent research hypotheses. A statistical hypothesis, also known as a null hypothesis, states that there is no relationship between the independent and dependent variables. The examples in Table 2.5 illustrate statistical hypotheses. If, in the data analysis, a statistically significant relationship emerges between the variables at a specified level of significance, the null hypothesis is rejected. Rejection of the statistical hypothesis is equivalent to acceptance of the research hypothesis. Directional versus nondirectional hypotheses Hypotheses can be formulated directionally or nondirectionally. A directional hypothesis specifies the expected direction of the relationship between the independent and dependent variables. An example of a directional hypothesis is provided in a study by Parry and colleagues (2015) that investigated a novel noninvasive device to assess sympathetic nervous system functioning in patients with heart failure. The researchers hypothesized that participants with heart failure reduced ejection fraction (HFrEF), who have internal cardiac defibrillators or CRT pacemakers, will have a decrease in pre-ejection period (reflective of increased sympathetic nervous system activity) and decrease in left ventricular ejection time (reflective of an increased heart rate) with a postural change from sitting to standing. In contrast, a nondirectional hypothesis indicates the existence of a relationship between the variables, but does not specify the anticipated direction of the relationship. Example: ➤ Rattanawiboon and colleagues (2016) evaluated the effectiveness of fluoride mouthwash delivery methods, swish, spray, or swab application, in raising salivary fluoride in comparison to conventional fluoride mouthwash, but did not predict which form of fluoride delivery would be most effective. Nurses who are learning to critically appraise research studies should be aware that both the directional and the nondirectional forms of hypothesis statements are acceptable. Relationship between the hypothesis and the research design Regardless of whether the researcher uses a statistical or a research hypothesis, there is a suggested relationship between the hypothesis, the design of the study, and the level of evidence provided by the results of the study. The type of design, experimental or nonexperimental (see Chapters 9 and 10), will influence the wording of the hypothesis. Example: ➤ When an experimental design is used, you would expect to see hypotheses that reflect relationship statements, such as the following: • X1 is more effective than X2 on Y. • The effect of X1 on Y is greater than that of X2 on Y. • The incidence of Y will not differ in subjects receiving X1 and X2 treatments. • The incidence of Y will be greater in subjects after X1 than after X2.  EVIDENCE-BASED PRACTICE TIP Think about the relationship between the wording of the hypothesis, the type of research design suggested, and the level of evidence provided by the findings of a study using each kind of hypothesis. You may want to consider which type of hypothesis potentially will yield the strongest results applicable to practice. Hypotheses reflecting experimental designs also test the effect of the experimental treatment (i.e., independent variable X) on the outcome (i.e., dependent variable Y). This suggests that the strength of the evidence provided by the results is Level II (experimental design) or Level III (quasi-experimental design). In contrast, hypotheses related to nonexperimental designs reflect associative relationship statements, such as the following: • X will be negatively related to Y. • There will be a positive relationship between X and Y. This suggests that the strength of the evidence provided by the results of a …