The Various Forms of ANOVA
An analysis of variance (ANOVA) is an appropriate statistical analysis when assessing for differences between groups on a continuous measurement (Tabachnick & Fidell, 2013). Depending on the research goal, one can use different types of ANOVAs, such as one-way, two-way, or repeated measures.
Between-Subjects ANOVA: One of the most common forms of an ANOVA is a between-subjects ANOVA. Researchers apply this analysis to examine differences between independent groups on a continuous variable. Within this “branch” of ANOVA, there are one-way ANOVAs and factorial ANOVAs.
A one-way ANOVA assesses differences in one continuous variable across one grouping variable. For example, a one-way ANOVA would be appropriate if the goal of research is to assess for differences in job satisfaction levels between ethnicities. In this example, there is only one dependent variable (job satisfaction) and ONE independent variable (ethnicity).
A factorial ANOVA examines the effects of multiple independent variables. A factorial ANOVA examines job satisfaction differences by ethnicity and education level. In this example, there is only one dependent variable (job satisfaction) and TWO independent variables (ethnicity and education level). Researchers apply a factorial ANOVA when there are two or more independent variables.
Within-Subjects ANOVA: A within-subjects ANOVA is appropriate when examining for differences in a continuous level variable over time. A within-subjects ANOVA is also called a repeated measures ANOVA. Researchers frequently use this test with a pretest and posttest design, but it does not limit to two time periods. Researchers use the repeated measures ANOVA to examine differences over two or more time periods. For example, this analysis would be appropriate if the researcher seeks to explore for differences in job satisfaction levels, measured at three points in time (pretest, posttest, 2-month follow up).
Mixed-Model ANOVA: A mixed model ANOVA, sometimes called a within-between ANOVA, is appropriate when examining for differences in a continuous level variable by group and time. Researchers frequently apply this type of ANOVA in quasi-experimental or true experimental designs. This analysis would be applicable if the purpose of the research is to examine for potential differences in a continuous level variable between a treatment and control group, and over time (pretest and posttest).
ANCOVA: An analysis of covariance (ANCOVA) is appropriate when examining for differences in a continuous dependent variable between groups, while controlling for the effect of additional variables. The “C” in ANCOVA denotes that a covariate is being inputted into the model, and this covariate examination can be applied to a between-subjects design, a within-subjects design, or a mixed-model design. Researchers frequently use ANCOVAs in experimental studies to account for the effects of an antecedent (control) variable.
MANOVA: Finally, a multivariate analysis of variance (MANOVA) is an extension on the ANOVA, and is appropriate when examining for differences in multiple continuous level variables between groups. For example, a MANOVA would be applicable if assessing for differences between ethnicities in job satisfaction AND intrinsic motivation levels of participants. In this example, job satisfaction and intrinsic motivation are the continuous level dependent variables. The MANOVA can be conducted with multiple independent variables, and can also include covariates (i.e., MANCOVA).
References
Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics, 6th ed. Boston: Allyn and Bacon.
Take the Course:
We work with graduate students every day and know what it takes to get your research approved.
- Address committee feedback
- Roadmap to completion
- Understand your needs and timeframe