Reflection on Learning week 7

WEEK 4

What statistical procedure is needed to determine an effective sample size to make a reasonable conclusion? Explain your rationale.

An appropriate sample size in a given study is considered to be one that will be involved in finding a statistically significant impact for a scientifically essential event. An appropriate sample size is one that ensures that a vital type of research question gets answered in the most appropriate way (Kraemer & Blasey, 2016). For an individual to attain this, their samples ought to be the appropriate size which should neither be too big nor too small. This is considered to be more of an art than being a science.

Sample size form of determination is defined as being the act associated with the selection of the various amounts of observations or even replicates to entail in a form of a statistical sample. The sample size is regarded as being a vital feature associated with any kind of empirical study whereby the key goal is usually to come up with inferences about a given population on the basis of the cost along with time, or the convenience associated with the collection of the data along with the need for it to provide enough statistical power (Kraemer & Blasey, 2016). In a complicated type of study, there may be a variety of distinct sample sizes for instance a stratified type of surely there would be distinct sizes for each type of stratum.

Determination of the sample size in a given quantitative type of research study is considered to be an issue. There are some elements that an individual need to put into consideration and there is no type of an easy answer. Each kind of experiment is considered to be distinct with a variety of varying degrees of certainty along with expectations. Essentially, there are three elements or even variables that one must have knowledge about in a given study each with a kind of numerical value. They are considered to be significance level associated with power and even effect size. When these identified values are identified, they are utilized with a form of table that is often found in the designs used in the determination of sample size.

To effectively determine the sample size, the first step is considered to be the selection of an effective significance level which is identified as the alpha level. An alpha level associated value of p is .05 which is commonly utilized. This means that the identified probability that the identified outcomes found are because of the chance alone is .05 or 5% and 95% of the time are considered to be a difference that is usually found between the identified control group along with the experimental group which will be statistically significant and because of the manipulation as well as the treatment (Kraemer & Blasey, 2016).

The next step is considered to be the selection of the power level. Essentially a power level associated with .8 or even 80% is usually selected. This is used to mean that the 80% of the time that the identified experiment will be considered to detect a distinction that is identified between the control along with the experimental groups if there is the existence of a difference. Finally, there is the organization of the existing data in regard to the various values for the three elements available. Thereafter all the three values that have been found should be entered into an online calculator so as to attain the most appropriate sample size (Kraemer & Blasey, 2016).

Reading through the study, you observe that the researcher used a chi-square analysis to analyze nominal and ordinal data. Is this the appropriate level of statistical analysis to answer the research question? Explain your rationale.

I disagree with the use of a chi-square type of analysis in the study for the analysis of the nominal along with the ordinal data in the study. For the two types of data, there can only be distinct kinds of analysis methods that will be used because the two types of data are different and need the use of different forms of analysis techniques. For the nominal type of data, it can be analyzed via the use of the grouping method. In this case, the various types of variables can be grouped together into some form of categories and for each of the given category, the identified frequency or even percentage can be effectively calculated.

The data can also be presented in a visual manner like in the use of a pie chart. For the ordinal type of data all the practical types, one can utilize regular parametric form of statistics which are the mean along with the model and the median. The ANOVA method can be considered to be the most effective especially for three groups or more but for two groups the Mann-Whitney U test will be the best (Berry & Mielke, 2018). 

Reading further, the researcher reports that the p-level led her to conclude that the null hypothesis was rejected. In your critique of the study, you determine that the null hypothesis is true. Do these findings impact your decision about whether to use this evidence to inform practice change? Why or why not?

It is important to note that these findings have an impact on decisions on whether to utilize the data or not. If the null hypothesis is true then it means that whatever data that was given in the study did not happen. This means that it is hard to come up with a conclusion because there is nothing to conclude because for a case of null hypothesis it usually assumes that something is equal to zero (Patton, 2014). 

 

 

References

Berry, K. J., Johnston, J. E., & Mielke, P. W. (2018). The measurement of association: A permutation statistical approach. Retrieved from https://www.worldcat.org/title/measurement-of-association-a-permutation-statistical-approach/oclc/1065538401

Kraemer, H. C., & Blasey, C. (2016). How many subjects?: Statistical power analysis in research. Retrieved from https://www.worldcat.org/title/how-many-subjects-statistical-power-analysis-in-research/oclc/1059096980 (Links to an external site.)

Patton, M. Q. (2014). Qualitative Research & Evaluation Methods. SAGE Publications US. Retrieved from https://www.worldcat.org/title/qualitative-research-evaluation-methods/oclc/1205201059