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Class, just to clarify substantive posts. The minimum word count should be no less than about 75 words so about 4 sentences at least. Substantive posts must be more than an agreement of another classmate’s or professor’s post, but you can add information that you’ve learned through your studies with scholarly sources.

Post 1:

Experimental Research is obtained using randomized control trials (RCT). “Some patients receive the experimental drug, treatment, or procedure, and the other group does not” (Helbig, 2018). For example, in an RCT that involves a drug, there is a control group that receives the standard treatment where no experimentation is applied. In contrast, the experimental group is given the drug or a dose that is being studied. After evaluating data obtained from the two groups, the effects of the drug can be determined. The randomized part involves the selection of the population. The participants are randomly assigned to the study group or the control group. “It is important to ensure that at the time of recruitment, there is no knowledge of which group the participant will be allocated to; this is known as concealment” (Hariton & Locascio, 2018). Choosing participants and assigning them to groups is often blinded. To reduce bias, the participant and care team do not know which treatment is being delivered. (Hariton & Locascio, 2018). 

A Quasi-experimental study does not use randomization and is therefore not experimental. Quasi-experimental research identifies groups, and the selection is based on certain things, such as people with similar comorbidities (Helbig, 2018). However, because there is no randomization, unforeseen variables can impact the internal validity of the research (Helbig, 2018). There is no control group, so all the participants are being assessed for the impact of the independent variable. For example, a selection of patients with breast cancer undergoing a particular treatment and providing everyone in that group a meditation session before a treatment to assess for a reduction in anxiety.

Nonexperimental research or a correlational design compares the relationship between variables (Helbig, 2018). It looks at the link between variables that exist naturally and allows for questions about what could happen if one of the variables were changed (Helbig, 2018). It may or may not show a correlation between the variables. For example, the correlation between age and cholesterol level. Another example of a correlational design would compare migraine frequency and Vitamin B12 levels. There is no control group or randomization, and no new variable is introduced.

Reference

Hariton, E., & Locascio, J. J. (2018). Randomised controlled trials – the gold standard for effectiveness research: Study design: randomised controlled trials. 
BJOG : an international journal of obstetrics and gynaecology, 
125(13), 1716. https://doi.org/10.1111/1471-0528.15199

Helbig, J. (2018). 
Statistical Analysis. Applied Statistics for Health Care. Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/4 

Post 2:

Experimental research is a kind of study that rigidly follows a scientific research design. It involves testing or attempting to prove a hypothesis by way of experimentation. It uses one or more independent variables, manipulating them and then using them on one or more dependent variables. The researchers can measure the effect of the independent variable(s) on the dependent variable(s). This kind of study is performed over some time, so that researchers can form a corroborated conclusion about the two variables. Health care facilities apply experimental research in different areas in their setting. Employee qualification: Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment. It can also be a teacher evaluation to determine qualification for position before employment (Siedlecki, S. L. 2020).

Quasi-experimental research is research that resembles experimental research but is not true experimental research. Although the independent variable is manipulated, participants are not randomly assigned to conditions or orders of conditions (Cook & Campbell, 2015). Because the independent variable is manipulated before the dependent variable is measured, quasi-experimental research eliminates the directionality problem. Based on study, after school activity can improve student learning activities and improved academic performance. You pick two comparable groups of students from separate classes, one of which uses the new program and the other does not and compare the result through testing.

Non-experimental research is the type of research that lacks an independent variable. Instead, the researcher observes the context in which the phenomenon occurs and analyzes it to obtain information. Non-experimental research happens during the study when the researcher cannot control, manipulate, or alter the subjects but relies on interpretation or observations to conclude. A researcher may wish to investigate the relationship between the class of family students come from and their grades in school (Sousa, V. D., 2007).

Experimental research is carefully planned and authenticated research. Quasi-experimental research gives the appearance of being planned and authenticated research, but falls short of the former, latter or both entities. Whereas non-experimental research is neither planned or authenticated research, developed by considered opinion and the experiences of conducting similar types of experimental research (Cook & Campbell, 2015).

References

Cook, T. D., & Campbell, D. T. (2015). Quasi-experimentation: Design & analysis issues in field settings. Boston, MA: Houghton Mifflin.

Siedlecki, S. L. (2020). Quasi-Experimental Research Designs. Clinical Nurse Specialist: The Journal for Advanced Nursing Practice, 34(5), 198–202. 

https://doi-org.lopes.idm.oclc.org/10.1097/NUR.0000000000000540

Sousa, V. D., Driessnack, M., & Mendes, I. A. C. (2007). An overview of research designs relevant to nursing: Part 1: Quantitative research designs. Revista Latino-Americana de Enfermagem, 15(3), 502–507. https://doi-org.lopes.idm.oclc.org/10.1590/S0104-11692007000300022

Post 3:

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 like a coin toss. 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. 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. This allows the researchers to evaluate the effects of dosage on the patients in different groups.

Experimental research is challenging what we thought we knew about how exercise prevents heart disease. Scientists initially believed that routine activity reduced cardiovascular risk largely by decreasing blood pressure and lowering the amount of LDL cholesterol (also known as the bad cholesterol) while raising the amount of HDL cholesterol (the good cholesterol) in the blood. This conclusion was only partly correct. By comparing the two groups it shows Exercise does in fact lower blood pressure substantially for some individuals, but for most people, this benefit of exercise is relatively small. Moreover, exercise — particularly resistance exercise, such as weight training — can raise HDL cholesterol, a change that typically takes several months to emerge, although the effect is modest — on the order of a few percentage points.

A quasi-experimental study can identify why certain things happen. A quasi-experimental study does not use any form of randomization but looks for a causal relationship between receiving treatment and not receiving 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. 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.

Nonexperimental research is a correlational design. The 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. 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.

References:

Grand Canyon University. (Ed.). (2018). Applied statistics for health care.

https://www.gcumedia.com/digital-resources/grand-canyon-university/2018/applied-statistics-for-health-care_le.php

In this issue. (2002). Color Research & Application, 27(5), 309-309.

https://doi.org/10.1002/col.10089

Post 4:

Research

Research initiatives are question-driven and concentrate on demonstrating efficacy. The basic goal of research is to produce new, generally applicable information about a certain topic for the study population, and the findings are frequently published in scholarly publications. In this situation, researchers are required to adhere to a stringent study protocol that has been authorized by the IRB, which includes getting the study participants’ agreement before beginning the project and informing the IRB of any protocol deviations as needed.

Quality Improvement

The goal of quality improvement (QI) initiatives, in contrast, is to demonstrate sustainable improvement to a particular process, system, or result inside a health-care organization utilizing, whenever feasible, the research findings generated as the basis for constructing the improvement interventions (Backhouse & Ogunlayi, 2020).

The goal of a QI project is not to produce new knowledge, as is the case with research projects, but rather to produce a number of learning lessons about what actually works and why it doesn’t.

Workplace Example Where Qualitative and Quantitative Research Is Applied

Other organizations in a comparable context and setting that are interested in duplicating the change to enhance a process or system utilizing the quick PDSA cycle technique would benefit from the empirical data produced by QI projects. Through testing cycles, we may discover what will become better and why without having to extrapolate the findings from one setting to another, as most research initiatives do. The measuring framework in QI initiatives does not focus on pre-and post-evaluation (Dixon-Woods & Martin, 2016). It involves regularly monitoring the measure of interest that you wish to enhance and developing different treatments based on lessons learned from earlier PDSA cycles. Finally, you arrive at the realization of sustainable change after a succession of interventions that were guided by research in the system that you actually want to enhance. To further improve the procedure and ultimately the result, the PDSA cycle is repeated and new changes are made. Process metrics, outcome metrics, and rebalancing metrics are critical metrics used in a quality assurance study to show how improvements are made over time. Study data are analyzed using statistical tests such as t-test, chi-square, and regression analysis, then aggregated and presented in appropriate tables and/or graphs. In contrast, IQ activity data is usually analyzed using statistical tests such as t-test, chi-square test, and regression analysis.

Qualitative research is a form of research that expands knowledge in the humanities and social sciences by examining attitudes, emotions, and other factors. For example, a study evaluating patient decisions about the use of antihypertensive drugs.

Quantitative research is a type of research that uses mathematical, logical, and statistical methods to generate reliable data and numbers. For example, age, weight, temperature, or number of diabetics.

Research results can be used to inform decision-making at many different levels, including those related to individual patient care, development of practice guidelines, health care delivery, prevention, and health promotion strategies, policy development, education initiatives, and clinical trial audits. Implementing therapeutic change often requires organizational change.

References

Backhouse, A., & Ogunlayi, F. (2020). Quality improvement into practice. BMJ, 368.

Dixon-Woods, M., & Martin, G. P. (2016). Does quality improvement improve quality? Future Hospital Journal, 3(3), 191.

Class, just to clarify substantive posts. The minimum word count should be no less than about 75 words so about 4 sentences at least. Substantive posts must be more than an agreement of another classmate’s or professor’s post, but you can add information that you’ve learned through your studies with scholarly sources.

Post 5:

The focus on patient experience and improving care is expanding. As a result, there is great demand for research and quality improvement initiatives to improve patient outcomes (Ginex, 2017). There are subtle differences, and the similarity between them overlap, which can be challenging when trying to identify the best way to investigate a clinical gap (Ginex, 2017). Both have a goal of improving patient outcomes. “Research is a systematic investigation of phenomena for the purposes of generalizing findings to a population. Researchers aim to add to the current body of knowledge about a particular subject, and results are often published in academic journals” (Merrill, 2015). Like research, quality improvement (QI) is systematic. However, the focus relates to improving outcomes, care, and processes within a particular organization (Merrill, 2015). Both research and quality improvement projects collect data. However, different procedures, such as obtaining approval or consent, are required for research. “When health care providers and nurses carry out a QI project, it may not be the implementation of something new, but an improvement upon something already in place” (Helbig, 2018). These QI projects are unique and specific to the need for improvement at that particular organization. 

When I first started working at my hospital, I noticed the lead nurse following nurses around with a clipboard. Eventually, I discovered that she was observing nurses for compliance with handwashing. I discovered that it was the responsibility of the lead nurse of the day to observe and document 10 nurses during the shift. The data was used to assess for handwashing compliance of a sample of nurses to generalize how compliant the department was as a whole. This is an example of qualitative research. Data is collected by observation and words, not numbers (Helbig, 2018). Eventually, the end date of the data collection was reached with 100% compliance. It made an impact that we hold each other accountable to being compliant in proper handwashing.

We also use quantitative data to keep track of how many days a patient has a peripherally inserted central line catheter (PICC) and compare that data to how many of those lines develop infection. This quantitative data is used to analyze and compare how long a patient has a PICC line and the risk of infection. The data has resulted in a new workflow to review line necessity with each patient and discontinue the line when not medically necessary.

Reference

Ginex, P. K. (2017). The Difference Between Quality Improvement, Evidence-Based Practice, and Research. ONS Voice, 32(8), 35.

Helbig, J. (2018). Statistical Analysis. Applied Statistics for Health Care. Retrieved from 

https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/4

Merrill, K. C. (2015). Is this quality improvement or research? American Nurse Today, 10(4), 14–1.

Post 6:

TWO GROUPS OF THE INDEPENDENT VARIABLE

Class, which one is correct? When there are two groups of the independent variable, then:

a. Only the t-test for 2 independent samples should be used.

b. Either the t-test for 2 independent samples or the Anova can be used.

c. Either the t-test for paired samples or the Anova can be used.

d. Only the Anova should be used. 

Please respond for a substantive credit.