Data collection methodology refers to the process of collecting information from different sources to analyze it and obtain an answer to the research question. Among the standard methods for data collection, whether quantitative or qualitative, are (Surveys - Interviews - Observation - Experiments - Case Studies - Secondary data analysis), (Epstein, 2020).
The Data Collection Methodology:
In the paper presented in the previous discussion, the author shows that the data was collected using Internet surveys, where the questions were about Apple's commercial reputation and customer loyalty to its brand. As mentioned earlier, this methodology falls under quantitative data and descriptive statistics.
The Distribution of the Data Set:
According to Luo, Kao & Pang (2003), Data distribution refers to how the data is spread across its range of values, providing its central tendency, diversity, and skewness. Several data distribution methods exist: (Normal, Skewed, Bimodal, and Uniform distribution).
In the presented research paper, descriptive statistics such as percentages and standard deviations can be used since the data consists of categorical responses. Responses such as (Agree - Strongly agree - Disagree) are not continuous numeric values. Therefore, when using descriptive statistics, frequencies can be interpreted as a measure of central tendency and change rather than specifying the type of distribution (Pinson & Brosdahl, 2014).
The Choice of the Data Set:
I see that this paper successfully chose descriptive statistics. This branch of statistics can analyze and summarize numerical data and use the measures of mean, median, mode, standard deviation, range, and percentage to describe the characteristics of data groups adequately and clearly. Descriptive statistics can also be used to summarize categorical data, such as frequencies and different categories of data (Proches, 2011).
The Benefits of this type of distribution over another kind:
According to Proches (2016), Descriptive statistics describe and summarize data; inferential statistics generally assume and generalize data from small samples. Descriptive statistics are used to understand data, while inferential statistics infer data, test hypotheses, and predict results. There is no preference between descriptive and inferential statistics, as both have complementary uses in research procedures.
Conclusion
Datasets are distributed by shape, center, and spread. This is done through several methods that fall under descriptive and inferential statistics and can be represented by graphs. The shape of the distribution can be symmetric or skewed, while the center is represented by the mean, median, and mode. The spread of a distribution can be measured by its range, standard deviation, and interquartile range (Manikandan, 2011).
References
Epstein, Michael (2020). Statistics Unit 3: Descriptive Measures: Averages / Variability / Distributions. Retrieved from: https://www.amplaboratory.org/classes/statistics/statistics-unit-3-introduction-to-statistics-and-data-collection/ (pass: teaching stats) (sections 1 and 2 only)
Luo, A., Kao, D., & Pang, A. (2003, May). Visualizing spatial distribution data sets. In VisSym (Vol. 3, pp. 29-38).
Manikandan, S. (2011). Measures of central tendency: The mean. Journal of Pharmacology and Pharmacotherapeutics, 2(2), 140.
Pinson, C., & Brosdahl, D. J. (2014). The Church of Mac: Exploratory examination on the loyalty of Apple customers. Journal of Management and Marketing Research, 14, 1.
Proches, S. (2016). Descriptive statistics in research and teaching: Are we losing the middle ground? Quality and Quantity, 50(5), 2165-2174. Retrieved from ProQuest One Academic.