To carry out adequate statistical calculations and obtain objective data on specific correlations, in academic research, special measures of central tendency are utilized – the mode, the median, and the mean. As an example of a scholarly article containing these components, the study by Butz et al. (2016) will be analyzed. In their research, the authors use t-tests as statistical tools, in particular, independent sample tests, to compare the scores for individual analysis criteria (Butz et al., 2016). This approach allows determining if the predicted correlations are credible and whether the calculations show objective relationships between the data obtained in categories.
As a key measure of central tendency, the mean is used in this article. In particular, Butz et al. (2016) analyze the rates of similarity of the data obtained from the participants involved through statistical tests and determine the average indicators. Based on this information, general conclusions are made concerning the proposed hypotheses and the correspondence of the initial assumptions to the outcomes. This measure of central tendency is the most common tool for evaluating statistical results and identifying specific correlations and coincidences. However, Butz et al. (2016) also utilize the mode as their statistical mechanism. This indicator helps reflect how high the levels of the declared variables are in the study conducted. The researchers compare two categories of participants, and based on this information, they comment on how much the outcomes of one assessment group prevail over those of the other one (Butz et al., 2016). The median is not used in this study, but the data obtained are sufficient to draw the necessary conclusions and obtain objective information regarding the results of statistical tests.
Reference
Butz, N. T., Stupnisky, R. H., Pekrun, R., Jensen, J. L., & Harsell, D. M. (2016). The impact of emotions on student achievement in synchronous hybrid business and public administration programs: A longitudinal test of control‐value theory. Decision Sciences Journal of Innovative Education, 14(4), 441-474.