Factor is a term used to describe an independent variable in a study with more than one independent variable. A qualitative interaction is a particular type of quantitative interaction of two (or more) independent variables in which one independent variable reverses its effect depending on the level of the other independent variable. A statistical interaction occurs in a factorial design when two or more independent variables have an effect on the dependent variable in combination that neither independent variable has on its own. A marginal mean is the mean of a row or a column in a table that shows the cells of a study with a two-way ANOVA design. A quantitative interaction is an interaction in which the effect of one independent variable is strengthened or weakened at one or more levels of the other independent variable, but the direction of the initial effect does not change. A cell is a box that depicts one unique combination of levels of the independent variables in a factorial design. A factorial ANOVA is a statistical analysis used with one scale dependent variable and at least two nominal independent variables (also called factors); also called a multifactorial ANOVA. A two-way ANOVA is a hypothesis test that includes two nominal independent variables, regardless of their numbers of levels, and a scale dependent variable. A main effect occurs in a factorial design when one of the independent variables has an influence on the dependent variable. |