Explain how the ANOVA technique avoids the problem of the inflated probability of making Type I error that would arise using the alternative method of comparing groups two at a time using the t-test f
ANOVA or analysis of variance is a statistical test used for comparison of more than two groups. The two types of AVONA in comparison is the one-way ANOVA versus the two-way ANOVA or two-factor. What is being compared in a one-way ANVOA is the means of three or more groups of an independent variable on a dependent variable (Nishisiba & Jones, 2014). In a Two-way ANVOA, what is being measured or tested is the effect of multiple groups of two independent variables on a dependent variable on each other (Nishisiba & Jones, 2014). The two-way test is mainly used to analyze the data from a study, which has the possibility to examine both the separate and combined effects of two variables on some measure of behaviour (Lewis-Beck, Bryaman & Futing, 2004). The major differences between the two are listed below in Table 1. The Factorial ANOVA or design is difficult to interpret because of all the different variables present and the different ways they can be manipulated. Furthermore, there are no limits to the number of independent variables, hence, the more variables that are included, the more difficult the interpretation of the data can be (Allen, 2017). Another reason is that as the number of conditions increases, the greater is the number of resources required to implement that design, thus increasing the possibility of having to measure the dependent variable twice (Allen, 2017). This test not only measures the effect, like the two-way ANOVA, but it also measures how the interaction effects of one factor are impacted by the categories on one or more other factors (Allen, 2017). In addition, because the different types of effects are independent, they can be interpreted independently of each other (Allen, 2017). In conclusion, the factorial designs can become difficult to interpret because of the unlimited number of independent variables, the different ways they can be manipulated, the dependent variable could possibly have to be measured twice, and there are different types of effects on the independent variables that interpreted independently from each other.
Table 1.
| Name of the test | Purpose of the analysis | # of IV | Type of IV | # of groups | # of DV | Type of DV | # of Groups of Samples |
| One-way ANOVA | Compare means of more than two independent groups. | Categorical | More than 1 | Continuous | Three or more. | ||
| Two-way ANOVA | Comparison between the means of three or more groups of data, where two IV are considered | Categorical | Multiple groups | Continuous | Each variable should have multiple samples | ||
| Factorial ANOVA | Compare means of two related groups | More than 1 | Categorical
| More than 1 | Continuous | Multiple grouping variables |
IV: independent variables
DV: dependent variables
References
Allen, M. (2017). The sage encyclopedia of communication research methods (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411
Comparing means of more than two groups: analysis of variance (anova). (2014). In Nishishiba, M., Jones, M., & Kraner, M. Research methods and statistics for public and nonprofit administrators (pp. 193-221). 55 City Road, London: SAGE Publications, Inc. doi: 10.4135/9781544307763
Lewis-Beck, M. S., Bryman, A., & Futing Liao, T. (2004). The SAGE encyclopedia of social science research methods Thousand Oaks, CA: Sage Publications, Inc. doi: 10.4135/9781412950589
