Discussion response

Discussion Question Part 1

How might you utilize Excel descriptive statistics for data analysis research?

            Microsoft Excel is one of the most efficient software programs for data analysis during research. The researcher can get descriptive statistics from the dataset by using Excel software (Schober & Vetter, 2019). Excel can compute a collection of descriptive statistics for the dataset using a single function. For example, the given dataset will be carefully entered into the program, and the researcher will utilize the available functions to extract the critical, descriptive statistics. Descriptive statistics are used to characterize and organize the characteristics of a data collection. When doing research, the three most important types of descriptive statistics to acquire are the distribution, measures of central tendency, and data variability. A statistical distribution is included in data collection. In tables or graphs, the frequency of each possible parameter might be expressed in numbers or percentages. The mean, or average, of the dataset, is determined using measures of central tendency. Variability measures reveal the dispersion of response values. The range, standard deviation, and variance all indicate the spread.

Describe your experience with descriptive statistics. Use the session Window findings to back up your answer.

            The researcher began by entering the supplied data into an excel spreadsheet. The information was meticulously structured and documented, with the age variable at the C1 cell, the other variables following, and the last variable being Understand at the cell H1. After inputting the data into the spreadsheet, the following step was to go to the Data tab and click on Data Analysis (Divisi et al., 2017). A popup appeared after pressing the Data Analysis button, and the descriptive statistics option was chosen. The researcher then chose the input range, which included the data that the researcher wished to study. The researcher then set output choices, and the program generated the descriptive analysis. The method was straightforward and efficient since the program delivered the results with just a few instructions. The researcher also analyzed the data by applying graphs such as the histogram and a bar graph displaying the cringe variable.

The data provided included age and math anxiety levels measured on a scale of 1 to 5. The researcher calculated the descriptive statistics for the dataset. The mean value of all the entered variables was the first measure generated. For example, the afraid variable had a mean value of 3.55. The median and mode indicators of central tendency, which are essential descriptive statistics, were also generated (Schober & Vetter, 2019). Variability measures such as range, standard deviation, and variance were acquired throughout the procedure. Furthermore, the skewness and kurtosis were calculated, defining the distribution form.

Discussion Question Part 2

What are your plans for learning more about Excel, and how will the information you learned about this software benefit your future research data analysis?

            Microsoft Excel is a highly comprehensive piece of software, and to understand it better, one must progressively learn (Rayat, 2018). Platforms such as YouTube provide avenues for individuals to learn more about excel. A large selection of videos is available that offer a comprehensive overview of the many topics one may wish to learn. In addition, various other online sites provide online courses, such as Udemy and Coursera, where a person interested in excel may learn more. During the interaction with the excel program, the researcher learned various ideas, notably those related to descriptive statistics and graphical analysis. The lessons learned will be used while analyzing the data collected for the study project. For example, the researcher is in a far better position to extract descriptive statistics from the data acquired in the study on the body mass index values of the participants.

References

Divisi, D., Di Leonardo, G., Zaccagna, G., & Crisci, R. (2017). Basic statistics with Microsoft Excel: A review. Journal of Thoracic Disease, 9(6), 1734. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506133/

Rayat, C. S. (2018). Applications of Microsoft Excel in statistical methods. In Statistical Methods in Medical Research (pp. 139-146). Springer, Singapore. https://doi.org/10.1007/978-981-13-0827-7_15  

Schober, P., & Vetter, T. R. (2019). Descriptive statistics in medical research. Anesthesia and Analgesia, 129(6), 1445. https://doi.org/10.1213/ANE.0000000000004480

STUDENT #2 

How could you use Excel descriptive statistics for data analysis research? Write about your experience running descriptive statistics. 

Excel descriptive statistics provide a unique tool to summarize and analyze quantitative data.  Compared to other software, Excel is easy to use and readily available. In the Data Analysis tool, one can choose several adjacent columns for the Input Range and each column is analyzed separately. From my experience, running descriptive analysis in Excel requires one to identify the type of data and level of measurement first. For instance, the summary descriptive statistics results were only useful for age, which is a continuous variable.  The analysis returned the mean age (36.2), range (36), standard deviation (11.23), and other measures of central tendency. Inserting charts and graphs is also easy for continuous data.  For categorical data, including cringe, uneasy, afraid, worried, and understand, it is important for one to reorganize the data.  

Charts and graphs are important in summarizing and visualizing data. The variables in the data set were not suitable for bar graphs, column, line graph, and pie chart. However, I managed to create a histogram for the variable cringe

Discussion Part 2

What are your plans for learning more about Excel and how will the information you learned about this software be of benefit in your future analysis of research data?

According to Elliot et al., (2016), Excel is not only useful in analyzing research, but also in collecting and managing data for analysis. Therefore, it is important to gain in-depth understanding of Excel, including the different functions and analysis toolkit. I plan to enhance my analysis skills through practice.  While reading various tutorials and watching videos of various tests is important, I believe practicing, using different datasets is critical.  Further, realized that for one to utilize excel effectively, they must understand the different tests used to test hypothesizes.  

The exercise has introduced me to the basic of data analysis using excel data analysis toolkit. I have successfully installed the data analysis toolkit, which will help me in future analysis of research data.  According to my data analysis strategy, I will use excel to analysis data collected from the questionnaires. The questionnaire will capture both categorical and continuous data. Therefore, I will use a combination of tests, including chi-tests for categorical data and student-tests for continuous data.  I will also use descriptive statistics to evaluate data such as the number of missed appointments.