Assignment: Chapter 4 Applied Statistics for Healthcare Professionals

Assignment: Chapter 4 Applied Statistics for Healthcare Professionals ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Assignment: Chapter 4 Applied Statistics for Healthcare Professionals Quality Improvement Proposal Identify a quality improvement opportunity in your organization or practice. In a 1,250-1,500 word paper, describe the problem or issue and propose a quality improvement initiative based on evidence-based practice. Apply “The Road to Evidence-Based Practice” process, illustrated in Chapter 4 of your textbook, to create your proposal. Assignment: Chapter 4 Applied Statistics for Healthcare Professionals Include the following: Provide an overview of the problem and the setting in which the problem or issue occurs. Explain why a quality improvement initiative is needed in this area and the expected outcome. Discuss how the results of previous research demonstrate support for the quality improvement initiative and its projected outcomes. Include a minimum of three peer-reviewed sources published within the last 5 years, not included in the course materials or textbook, that establish evidence in support of the quality improvement proposed. Discuss steps necessary to implement the quality improvement initiative. Provide evidence and rationale to support your answer. Explain how the quality improvement initiative will be evaluated to determine whether there was improvement. Support your explanation by identifying the variables, hypothesis test, and statistical test that you would need to prove that the quality improvement initiative succeeded. While APA style is not required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA formatting guidelines, which can be found in the APA Style Guide, located in the Student Success Center. This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. Assignment: Chapter 4 Applied Statistics for Healthcare Professionals You are required to submit this assignment to LopesWrite. Refer to the LopesWrite Technical Support articles for assistance. ch4._evidence_based_practice_process.docx Chapter 4 (Reference: Grand Canyon University, 2018) https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/4 By June Helbig Essential Questions • • • • What is the difference between experimental, quasi-experimental, and nonexperimental research? How does qualitative research differ from quantitative research? What is the difference between causation and correlation? What is the difference between research and quality improvement. Introduction Health care professionals must have knowledge regarding research methods and statistical analysis. Statistical analysis is associated with evidence-based practices and is responsible for many of the new and innovative treatments and procedures performed by health care professionals every day; however, knowing about evidence-based practices and using them is not enough. One must know how the results were obtained and if the results are statistically sound (see Figure 4.1). Health care professionals are concerned with quality and providing those in their care with safe, patient-centered care. Figure 4.1 The Road to Evidence-Based Practice fullscreenClick here to enlarge Using the results of research involves incorporating those results into care provided to patients. Using new drugs, treatments, or procedures is for the sake of quality patient care. A very simple way to look at it is to consider the three Rs that make up evidence-based practice—Research, Results, and Review. The research is completed, the results are obtained, and then experts review the results of the study. If the review is found to be positive, the drug, treatment, or procedure can now considered evidence-based and can be put into practice (see Figures 4.1 and 4.2). Figure 4.2 The Three Rs of Evidence-Based Practice fullscreenClick here to enlarge • Many hospitals have professional practice committees that examine the results of research and quality improvement projects. The purpose of these committees is to find new and innovative ways to provide quality health care. Implemented changes are supported by results or evidence obtained through research. The following questions are some examples that may be asked when reviewing results connected with a research study. • Can the same results be duplicated? • What data were obtained and how were the data analyzed? • What type of research was performed? • Was it experimental, quasi-experimental, or nonexperimental research? • Was it a qualitative study or a quantitative study? • Were the research findings the result of cause and effect or were the findings the result of correlation? These and many other questions will be answered as statistics for health care professionals is learned. After reading this chapter, the health care professional will be able to understand and answer those questions. Conceptual Framework • A conceptual framework is an analytical tool used to build a research study. All research studies begin with a hypothesis. Assignment: Chapter 4 Applied Statistics for Healthcare Professionals The researcher takes that hypothesis and formulates a theory. The researcher is able to take that theory and build a conceptual framework to investigate the theory. In order to answer the hypothesis, the research process must be followed, which will lead to a result. The hope is that the result aligns with the researcher’s theory; however, the conclusion may or may not support the hypothesis. The steps taken to prove a theory are considered the building materials of a conceptual framework. The conceptual framework explains what will be investigated, how it will be investigated, and what will be needed to arrive at a conclusion. The conceptual framework defines the tools needed to answer the hypothesis and the variables the researcher or investigator will encounter along the way. Experimental Versus Nonexperimental Research When conducting experimental research, the researcher sets up the study to evaluate an experimental drug, treatment, or intervention. This type of research is a randomized control trial (RCT). Some patients receive the experimental drug, treatment, or procedure, and the other group does not. Randomization involves something similar to a coin toss (see Figure 4.3). Figure 4.3 Example: Randomization Control Study fullscreenClick here to enlarge In randomized control trials, the control group is the group in which no experimentation occurs. The control group receives customary and routine treatment. The experimental group is where the independent variable is manipulated. In randomized control research designs, one group of patients will receive the experimental drug, treatment, or procedure, and the other group of patients will receive customary treatment. Randomization is like flipping a coin (see Figure 4.3). Heads the patient is in the group that receives the experimental intervention, tails the patient receives the customary treatment. Another research scenario could be the study of a new medication. In this case, it could be the dose of the medication that is different among the groups. Assignment: Chapter 4 Applied Statistics for Healthcare Professionals This allows the researchers to evaluate the effects of dosage on the patients in different groups. Table 4.1 provides a visual example of manipulation, control, and randomization for experimental research. Table 4.1 Manipulation, Control, and Experimentation Patient Patient A (Manipulation) Drug Lisinopril Dose Intervention 20 mg Manipulation Manipulation of dosage • Patient B (Tails: Control Group) Lisinopril 20 mg ?40 mg—independent variable Control Control Group: Randomization • Coin toss Patient is randomly placed in the control group of the study, receiving customary treatment. o Nothing is changed. o Patient C (Heads: Experimental Group) Lisinopril 20 mg Experimentation Experimental Group: Randomization • Coin toss Heads: New medication; patient started on propranol 10 mg daily. o All RCTs are experimental. The research design specifies the study sample that will be selected to participate in the trial or study. Once the population is defined, the participant is randomized into either the control group or the experimental group. Different methods are used to randomize the participants. Experimental designs can establish causation. A high degree of internal validity can be obtained through similar control and experimental groups. In research, one cannot receive less than the usual customary treatment. The participant in the experimental arm can be subjected to risk by not receiving the usual and customary treatment. A good experiment minimizes the variability of the evaluation and provides unbiased evaluation of the intervention by avoiding confounding from other factors, which are known and unknown. Randomization ensures that each patient has an equal chance of receiving any of the treatments under study. (Suresh, 2011, para. 2) Assignment: Chapter 4 Applied Statistics for Healthcare Professionals There are several different methods the research investigator can use for randomization. Methods of randomization include basic randomization, which is based off of a single event, such as flipping a coin or rolling dice. Some methods are more complicated, such as opening an envelope or placing a phone call to receive a control or experimental group assignment for the patient. These different types of randomization may not always work because there may be too many independent variables. The study must conform to the rules and regulations set by the Code of Federal Regulations to remain ethical. A quasi-experimental study is able to identify why certain things happen. A quasi-experimental study does not use any form of randomization but looks for a causal relationship between receiving a treatment and not receiving a treatment. With the absence of randomization, the study can no longer be considered experimental. Quasi-experimental research designs identify treatment groups and comparison groups (see Figure 4.4). Because there is no randomization, selection may be based on similar characteristics or similar comorbidities. Extraneous variables may be responsible for jeopardizing internal validity. Extraneous variables are variables that are not foreseen. The researcher is not aware of extraneous variables when designing a research study. Figure 4.4 Quasi-Experimental Research Design fullscreenClick here to enlarge One of the most frequently used types of quasi-experimental research design is the pretestposttest design. This occurs when one group is given a pretest, which could be a medication, treatment, or procedure. This medication, treatment, or procedure is considered the independent variable. Patients are assessed pretest or prior to administration of a medication, treatment, or procedure. After the independent variable (medication, treatment, or procedure) is given or performed, a posttest is given, or the patient is reassessed. The differences between the status of the patient’s pretest (prior administration of medication, treatment, or procedure) and posttest (group after the administration of medication, treatment, or procedure) is the result of the study. There is no control group or randomization. All research participants receive the medication, treatment, or procedure.Assignment: Chapter 4 Applied Statistics for Healthcare Professionals This study design is not considered experimental because there was no randomization or control and is considered quasi-experimental research (see Figure 4.5). Figure 4.5 Quasi-Experimental Design Pretest/Posttest fullscreenClick here to enlarge Another type of quasi-experimental research design is the historical comparison design. Because quasi-experimental groups do not use randomization or control groups, the researcher may use historical group data as a control for comparison. This can be done by a retrospective chart review. For example, 15 years ago, patients were turned every 2 hours, but the mattress that existed then was just a standard mattress. Through a retrospective chart review, it was found that 10% of the patient population that was bedbound in the Intensive Care Unit (ICU) developed pressure ulcers. The mattresses in the hospital were changed this year to alternating pressure mattresses. The patients continued to be turned every 2 hours. It was found that only 5% of the bedbound population in the ICU developed pressure ulcers. This is an example of a historical comparison study. The researcher collected the same data on the same patient population, just 15 years apart (see Figure 4.6). This is also considered quasi-experimental research because no control or randomization occurred. Figure 4.6 Quasi-Experimental Historical Comparison Design fullscreenClick here to enlarge A common type of nonexperimental research is a correlational design. Correlational design looks at the association or relationship between variables. It is not like a quasi-experimental design study or randomized control trial because there is nothing new introduced in the design of the study.Assignment: Chapter 4 Applied Statistics for Healthcare Professionals There is no new medication, treatment, or procedure introduced. The correlational study looks at variables and the relationships that variables may have with each other. By seeing how variables exist naturally, one can evaluate or theorize what would happen if one of the variables were manipulated. Would there be change, and what type of change would occur? The results of a correlational study describe the relationships between the variables. The data collected can be retrospective or prospective and can be used to formulate a theory or as a foundation for a randomized control trial. There does not have to be causation with correlation as demonstrated by the example in Table 4.2 and Figure 4.7. Table 4.2 Correlation vs. Causation Table Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Eating a Healthy Diet (times per month) 2 3 4 5 6 7 8 9 10 11 12 Filling Gas Tank (times per month) 3 4 5 6 7 8 9 10 11 12 13 Figure 4.7 Correlation vs. Causation Graph fullscreenClick here to enlarge A correlational study can be simple, comparative, longitudinal, or cross-sectional. Each study’s purpose is the same—to describe the relationships between variables—but the designs differ in time periods and groups of variables. For a simple correlational design, data are collected from one group of variables over one period of time. In a comparative correlational design study, data are collected on two or more groups of variables, still over one period of time. For a longitudinal design study, data are collected for one group of variables over two or more periods of time. Lastly, cross-sectional correlational design research collects data from whatever groups the researcher has selected over just one period of time. Quantitative vs. Qualitative Research Research can be performed two ways, and both methods are defined by the type of variables that are collected as data (see Table 3.3). Quantitative research is performed by evaluating numbers and numeric variables that result in measurable data. Qualitative research is performed by evaluating nonnumeric variables. Assignment: Chapter 4 Applied Statistics for Healthcare Professionals Qualitative data are collected through descriptive characteristics that cannot be measured with numbers—observation, open-ended questions, or interview. It is through these nonnumerical variables that the research question can be answered. The type of research that is performed is determined by the researcher when developing the conceptual framework. Table 3.3 Qualitative vs. Quantitative Research Qualitative Research Quantitativ Research Numerical data ? Measurable data collected (numbers and numeric variables) Nonnumerical data ? Data are most often collected through observation, open-ended questions, or text-based interviews Quantitative research relies on measurement using the scales described previously—nominal level of measurement, ordinal level of measurement, interval level of measurement, and ratio level of measurement. The data collected are analyzed using statistical analysis to answer the research question. The type of statistical analysis used is determined when constructing the research question. Quantitative research generates numbers. The numerical information collected is reflective of the variable being analyzed. For example, gender is collected in many research studies. “Male” or “Female” is not numerical, but if 100 participants were enrolled and 40 were female and 60 were male, then the variable of gender becomes numeric. Once it is numeric, it can be manipulated and applied to all levels of measurement (see Table 4.4). Table 4.4 Variables for Each Level of Measurement Variable Nominal Level of Measurement Male M Ordinal Level of Measurement X Rank 60 1st Interval Level of Measurement 60 Ratio Level of Measurement 60/100 60 males out of 100 participants 60% Female F 40 2nd 40 40/100 40 females out of 10 participants 40% In health care, quality, patient-centered care is provided to all patients. If health care professionals want to prevent hospital-acquired pressure ulcers, the entire population cannot realistically be participants in the study, so a number is chosen that is reflective of the entire population. Many large clinical trials can enroll up to 25,000 or more participants nationally. Assignment: Chapter 4 Applied Statistics for Healthcare Professionals For health care research, that number is far too large. When performing research, health care professionals usually formulate a hypothesis in regard to a problem. The researcher may choose a percentage of the population of the hospital served, or a number is chosen that is sufficient to obtain results. Saturation, a term used with qualitative research, occurs when enough data have been collected to support results of the study. Results of a research study have generalizability, meaning the results can be applied accurately to the general population. When generalizability is present, the quantitative research study is welldesigned, and the results can be applied to the general population. • In qualitative research, data are most often collected through observation, open-ended questions, or interview. Data collected are words and not numbers. The researcher compiles lists of words, behaviors, and responses from participants as well as observational videos. The data collected is representative of commonalities observed. Participants’ rights are respected, and informed consent may be obtained if performing observational research. In order to maintain validity and reliability in qualitative research, rigor must be maintained. Rigor is consistency in data collection, as well as accuracy; as in the attention to all details. When rigor is maintained, the findings of the qualitative study are proven to be true and reliable. If a qualitative study of handwashing compliance in the intensive care unit were being performed, the researcher would be present in the intensive care unit observing staff and taking notes or video of staff washing their hands. Qualitative research can be difficult because observation or interviewing can be very time consuming. Many times, the sample size may be small because of the massive amount of data collected for the study. If the sample size is too large, there would be a lot of redundancy. Redundancy occurs when information collected is repetitive, so no new information needs to be collected. One can say the sky is blue so many times that no one needs to say it again, at which point no new data is being generated. This is called the saturation point, which occurs when no new data is being generated and the endpoint of the qualitative study is defined. When performing qualitative research, the investigator may get to the point where no new information is being obtained and decides that saturation has been met. Assignment: Chapter 4 Applied Statistics for Healthcare ProfessionalsAssignment: Chapter 4 Applied Statistics for Healthcare Professionals This may be sooner than the expected end date or later than the predetermined end date, but once no new information is being generated, the investigator can call an end to the study. Table 4.5 reflects common terms in qualitative and quantitative research. While there are some terms that are used in both methods, some are more in one. Table 4.5 Qualitative and Quantitative Research Terms Qualitative Research Quantitative Research IRB Approval ?* ? Informed Consent ?* ? Enrolls Human Subjects ? ? ? Continuous Variables Categorical Variables ? Mostly Subjective ? ? Mostly Objective Unstructured Responses ? ? Fixed Responses Inductive Reasoning ? ? ? Deductive Reasoning Randomization ? Saturation ? ? ? Statistical Data Analysis Validity ? ? Reliability ? ? Redundancy ? Generalizability ? Transferability ? Rigor ? Note. *There may be certain circumstances when informed consent and Institutional Review Board (IRB) approval are not required Qualitative studies are usually completed when the end date is reached, or the point of saturation occurs. The investigator of a qualitative research study is deeply involved in the study and many times will make decisions regarding the course of the study as the data collection evolves. Because qualitative research consists of words and not numbers, analysis takes place through the development of commonalities and themes. There are three different types of qualitative designs: phenomenology, grounded theory, and ethnography. • Phenomenology is considered empirical research because data are collected through observation and experiences. It can be through direct contact with what is being observed or through indirect contact, which is solely observation. • Grounded theory is research that takes first person observations or interviews and develops a theory or concept about the population being observed. • Ethnography is a type of qualitative research that studies cultures, everyday life, and cultural changes through observation or interview. Well-designed qualitative research has transferability as well as generalizability. Transferability is the ability to apply the results of the qualitative study to similar experiences and similar groups of peopl …=. Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10

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