Minimum of 350 words with at least two peer review reference in 7th edition apa style As a practice scholar, you are searching for evidence to translate into practice. In your review of evidence, you locate a quasi-experimental research study as possible evidence to support a practice change. You notice that the study aims to make a prediction that relates to correlation between study variables. The study sample size is large and normally distributed. Reflect upon this scenario to address the following.

The scenario presented involves a practice scholar seeking evidence to support a practice change. In their search, they come across a quasi-experimental research study that aims to make a prediction relating to the correlation between study variables. The study sample size is large and normally distributed. In this reflective analysis, we will evaluate the strengths and limitations of using a quasi-experimental design in this context.

Quasi-experimental research designs are commonly utilized when it is not feasible or ethical to conduct a true experimental study. These designs allow researchers to have some control over the variables being studied and enable them to make predictions about relationships between variables. However, it is important to consider the limitations of quasi-experimental designs, especially when making evidence-based practice decisions.

One strength of the quasi-experimental design in this scenario is the use of a large sample size. A large sample size increases the generalizability of the findings and enhances the statistical power to detect meaningful relationships. This is particularly vital when aiming to make predictions about the correlation between study variables. If the sample size is too small, the results may not be accurate or representative of the population, leading to unreliable predictions.

Additionally, the normally distributed sample is another strength of this study. Normal distribution implies that the data follows a bell-shaped curve, with the majority of observations falling around the mean. This is essential because the assumption of normality is required for many statistical tests and allows for valid inferences to be made. When the data is normally distributed, it indicates that the sample is likely representative of the population, further strengthening the validity of the study’s predictions.

Despite these strengths, it is crucial to recognize the limitations of quasi-experimental designs. One limitation is the lack of random assignment to groups. In a true experimental design, participants are randomly assigned to either the experimental or control group, which helps to ensure that any observed differences between the groups are caused by the intervention or independent variable. In a quasi-experimental design, however, participants are not randomly assigned, which introduces the potential for confounding variables to influence the results. Therefore, caution must be exercised when interpreting the findings, as they may not establish a causal relationship.

Another limitation of quasi-experimental designs is the potential for selection bias. In this scenario, the practice scholar has located a quasi-experimental study; however, without further information, it is unclear how the participants were selected. If the sample was not representative of the target population, the findings may not be applicable in other settings or with different populations. It is essential for researchers and practitioners to consider the external validity of the study before applying the findings to practice.

Additionally, quasi-experimental designs may be limited in the extent to which confounding variables are controlled. Unlike in true experimental designs, where random assignment helps to minimize the influence of confounding variables, quasi-experimental designs often rely on statistical techniques, such as analysis of covariance, to control for these variables. While these statistical techniques can help to account for some confounding factors, they may not completely eliminate their impact. Therefore, practitioners should be cautious when translating the findings into practice and consider potential confounding variables that may have influenced the results.

In conclusion, the quasi-experimental research study described in the scenario has both strengths and limitations. The large and normally distributed sample size enhances the generalizability and validity of the findings, particularly when making predictions about the correlation between study variables. However, the lack of random assignment, potential selection bias, and limited control over confounding variables should be taken into consideration when interpreting and applying the findings to practice. As a practice scholar, it is crucial to critically evaluate the evidence and consider these strengths and limitations before making any practice changes based on the findings of a quasi-experimental study.