NUR 699 GC WEEK 3 DISCUSSIONS PAPER NUR 699 GC WEEK 3 DISCUSSIONS PAPER NUR 699 GC Week 3 Discussion 1 What levels of evidence are present in relation to research and practice, and why it is important regardless of the method you use? NUR 699 GC Week 3 Discussion 2 What factors must be assessed when critically appraising quantitative studies (e.g., validity, reliability, and applicability)? Which is the most important? Why? CLICK HERE TO ORDER YOUR NUR 699 GC WEEK 3 DISCUSSIONS PAPER NUR 699 GC Week 3 Assignment Evidence-Based Practice Proposal ? Section C: Literature Support Details: To begin, work through the reference list that was created in the ?Section B: Problem Description? assignment in Topic 2. Appraise each resource using the ?Rapid Critical Appraisal Checklists,? available in the textbook appendix. The specific checklist you use will be determined by the type of evidence within the resource. Develop a research table to organize and summarize the research studies. Using a summary table allows you to be more concise in your narrative description. Only research studies used to support your intervention are summarized in this table. Refer to the ?Evaluation Table Template,? available in the textbook appendix. Use the ?Evaluation Table Template? as an adaptable template. Write a narrative of 750-1,000 words (not including the title page and references) that presents the research support for the projects problem and proposed solution. Make sure to do the following: Include a description of the search method (e.g., databases, keywords, criteria for inclusion and exclusion, and number of studies that fit your criteria). Summarize all of the research studies used as evidence. The essential components of each study need to be described so that readers can evaluate its scientific merit, including study strengths and limitations. Incorporate a description of the validity of the internal and external research. It is essential to make sure that the research support for the proposed solution is sufficient, compelling, relevant, and from peer-reviewed professional journal articles. Although you will not be submitting the checklist information or the evaluation table you design in Topic 3 with the narrative, the checklist information and evaluation table should be placed in the appendices for the final paper. Prepare this assignment according to the APA guidelines found in the APA Style Guide, located in the Student Success Center. An abstract is not required. This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. You are required to submit this assignment to Turnitin. Please refer to the directions in the Student Success Center. Upon receiving feedback from the instructor, refine ?Section C: Literature Support? for your final submission. This will be a continuous process throughout the course for each section. ? ? Order Now

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What factors must be assessed when critically appraising quantitative studies?

Introduction

Quantitative research is an important part of any scientific study, but it’s not always clear how to critically appraise a quantitative study. This blog post will explore the key factors that should be assessed when critically appraising a quantitative study.

Is the study design specified?

A study design is the way in which an experiment or other research is conducted. It can be defined as “a plan for collecting data”, and it has three essential components: the method, purpose and population. The method describes how data are collected (e.g., using questionnaires), while purpose defines why we want to collect our data (e.g., to understand how people make decisions). Population refers to those who will participate in your study – this includes yourself!

In most cases, studies use observational designs where one group of participants acts as control group and another group acts as experimental group. In these types of studies:

• Observational studies compare two groups on something like health outcomes before knowing whether one treatment works better than another; this helps us understand what actually happens when people take part in certain programs or treatments;

• Experimental designs involve randomization— randomly assigning everyone into different groups so that none know ahead of time which direction their fate will go;

Does the study sample or participants represent the target population?

A study sample or participants must represent the target population. For example, a study of cancer patients must include a sample that represents all types of cancer and subgroups within each type (e.g., lung, breast).

In addition to representing the targeted group, your sample size needs to be appropriate for your research question. For example, if you want to investigate how many children aged 15 years or younger are using tobacco products in South Africa and how much time they spend doing so each day during school hours or after-school hours at home alone with their parents’ permission (i) then it would be important for this study design because there is no way for researchers who conduct surveys to determine whether their questions accurately capture all possible instances where children may use tobacco products (e.g., at school). On top of this problem might come another issue related specifically only within South Africa: many families depend upon kwasa/tobacco farming communities as sources for income; therefore some people would never allow themselves access outside these communities even if asked by researchers because they knew full well what risks existed outside these areas too much risk involved!

Were key variables measured accurately?

Assessing the validity of a quantitative study is important because it will help you to determine whether the results are reliable. It’s also helpful if you have a clear overall understanding of how your key variables were measured and what their limitations might be.

If you can’t find any information about this in the study itself, ask your researcher or supervisor for clarification on this point before continuing with your analysis.

Is there a control group?

The control group is an essential part of a quantitative study. It allows you to compare the results of your treatment group with those of a control group, which will allow you to assess whether or not there was any difference between them. For example, if your study tests whether or not adding a chemical additive improves crop yield by 25% in comparison with using no chemical additives at all (a positive result), then it would be inappropriate for your data analysis team simply to conclude that this chemical additive improved crop yields based upon their own findings—they would need one or more other studies testing this hypothesis independently in order to confirm or deny it; these additional studies could be conducted by different researchers working under different conditions (such as using different pesticides) so long as they were designed similarly enough so that they could be combined into one final report.

How were confounding variables handled?

Confounding variables are variables that could affect the results of a study. They can be controlled for by using statistical analysis, but this may not always be possible. For example, gender may be a confounding variable in an experiment on the effects of age on productivity. If you’re studying how long it takes to complete some tasks and divide them into male and female categories, you’ll need to control for this variable when analyzing your data—the more men there are in your sample population (and therefore in your experimental group), the less likely it will be that women will finish their tasks faster than men do because they’re working harder than their colleagues due to being socially stereotyped as less capable workers; conversely, if you have only one woman working with two or three men then chances are she’ll beat them handily!

Was it an appropriate statistical analysis?

Statistical analysis is the process of analyzing data, which can be done in two ways. The first is a descriptive statistical method in which variables are identified and compared between groups or conditions. In contrast, inferential methods take into account the factors influencing the results of a study and provide an understanding of what could have been observed otherwise (e.g., if there were no differences between groups). Both types of analysis must be appropriate for each stage at which they occur—for example, when using descriptive statistics on data collected from one particular population it may not be appropriate to use inferential methods because these would imply that those same results could be applied across different populations with similar characteristics but different ethnicities/educational backgrounds etc…

It is important to be clear about how the variables in a quantitative study were measured and analyzed.

Quantitative studies are based on numbers, not words. They are based on how many people did something or how many were involved in a particular activity, event or situation.

To understand how to assess the quality of a quantitative study it is important to understand what makes up a good quantitative report:

• The results should be presented clearly and concisely so that anyone reading them can understand what was measured and why it matters for their research question(s).

• The sample size should be large enough (at least 50) because small samples may result in unreliable results due to random error which can lead to false conclusions about factors affecting an outcome variable (such as high blood pressure).

Conclusion

In order to be successful in your future quantitative data analysis, it is important to understand the limitations and strengths of existing studies. The authors of this article hope that they have given you the tools needed to make good decisions about how best to analyze your own data sets; we encourage you to think critically about all of these aspects when applying quantitative methods, so as not only to create valid conclusions but also build on prior research.

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