Analysis of variance (ANOVA) is a strategy aimed at finding dependencies in experimental data. Referring to the 11th chapter of the book, variance analysis is a unique tool that allows one to check some differences in the averages in any group, unlike t-tests (Foster et al., 2018). However, according to the 12th chapter, the “correlation method” plays an essential role in a deeper and more comprehensive analysis of variables, too (Foster et al., 2018). It is one of the most common statistical analysis methods, used both in the exact sciences or humanities. The study of types of variance analysis is the critical aspect based on the Internet article that will be discussed in this paper.
Referring to the external source, one can get the following interesting and helpful information. For example, variance testing requires two types of data collection: one-factor and two-factor analysis of variance. On the one hand, one-sided (one-factor) analysis compares the variance of the group’s averages in terms of a single independent variable or factor (Mackenzie, 2021). It resembles, as a rule, three or more categorical groups to establish the difference and has two possible hypotheses – zero and alternative. On the other hand, a two-way (two-factor) ANOVA sample, unlike its “analogue,” is determined in several ways and then placed in two categorical groups (Mackenzie, 2021). It examines two main factors on a dependent variable, for example, weight, and also evaluates the influence of these two factors on each other. Moreover, the main differences in these analyses are in the number of variables and categorical groups, goals, purposes, and the principles they must satisfy.
In conclusion, ANOVA is a complex but effective way to verify certain information. The primary purpose of the analysis of variance is to study the significance of the difference between the averages. This procedure is based on one-factor and two-factor “investigations,” each with its characteristics, goals, and objectives. In brief, two-factor analysis is used in the case of two metric variables, and one-factor analysis is used in the case of dependent or independent variable. However, these are only basic differences, but there are many more.
References
Foster, G. C., Lane, D., Scott, D., Hebl, M., Guerra, R., Osherson, D., & Zimmer, H. (2018). An introduction to psychological statistics. University of Missouri – St. Louis.
Mackenzie, R.J. (2021). One-way vs two-way ANOVA: Differences, assumptions and hypotheses. Technology Networks. Web.