A key statistical test in research fields including biology, economics and psychology, analysis of variance (ANOVA) is very useful for analyzing datasets. It allows comparisons to be made between three or more groups of data. Here, we summarize the key differences between these two tests, including the assumptions and hypotheses that must be made about each type of test. Show
There are two types of ANOVA that are commonly used, the one-way ANOVA and the two-way ANOVA. This article will explore this important statistical test and the difference between these two types of ANOVA. What is a one-way ANOVA?A one-way ANOVA is a type of statistical test that compares the variance in the group means within a sample whilst considering only one independent variable or factor. It is a hypothesis-based test, meaning that it aims to evaluate multiple mutually exclusive theories about our data. Before we can generate a hypothesis, we need to have a question about our data that we want an answer to. For example, adventurous researchers studying a population of walruses might ask “Do our walruses weigh more in early or late mating season?” Here, the independent variable or factor (the two terms mean the same thing) is “month of mating season”. In an ANOVA, our independent variables are organised in categorical groups. For example, if the researchers looked at walrus weight in December, January, February and March, there would be four months analyzed, and therefore four groups to the analysis. A one-way ANOVA compares three or more than three categorical groups to establish whether there is a difference between them. Within each group there should be three or more observations (here, this means walruses), and the means of the samples are compared. What are the hypotheses of a one-way ANOVA?
What are the assumptions and limitations of a one-way ANOVA?
What is a two-way ANOVA?A two-way ANOVA is, like a one-way ANOVA, a hypothesis-based test. However, in the two-way ANOVA each sample is defined in two ways, and resultingly put into two categorical groups. Thinking again of our walruses, researchers might use a two-way ANOVA if their question is: “Are walruses heavier in early or late mating season and does that depend on the sex of the walrus?” In this example, both “month in mating season” and “sex of walrus” are factors – meaning in total, there are two factors. Once again, each factor’s number of groups must be considered – for “sex” there will only two groups “male” and “female”. The two-way ANOVA therefore examines the effect of two factors (month and sex) on a dependent variable – in this case weight, and also examines whether the two factors affect each other to influence the continuous variable. What are the assumptions and limitations of a two-way ANOVA?
What are the hypotheses of a two-way ANOVA?
Interactions in two-way ANOVAThese last two hypotheses, of there being (or not being) interactions in a two-way ANOVA, refer to how the two variables in the study affect each other.This is most easily explained by going back to our walruses.If the researchers found that male walrus weight significantly decreased between December and March, but female walrus weight remained steady or slightly increased, subsequent statistical analysis may conclude that there was an interaction between the two independent variables of month and sex. These effects are not to be ignored. If we put the interactions to one side, with the results mentioned above, an incomplete analysis might conclude that walruses in general lost weight over mating season, which would ignore a reality that the decrease was driven by changes to male walrus weight. Another example could be the efficacy of a candidate drug for a disease; you can see how proper modeling of interaction effects can become critical to many biological research studies.
1. A one-way ANOVA is primarily designed to enable the equality testing between three or more means. A two-way ANOVA is designed to assess the interrelationship of two independent variables on a dependent variable. 2. A one-way ANOVA only involves one factor or independent variable, whereas there are two independent variables in a two-way ANOVA. 3. In a one-way ANOVA, the one factor or independent variable analyzed has three or more categorical groups. A two-way ANOVA instead compares multiple groups of two factors. 4. One-way ANOVA need to satisfy only two principles of design of experiments, i.e. replication and randomization. As opposed to two-way ANOVA, which meets all three principles of design of experiments which are replication, randomization and local control. One-Way ANOVATwo-Way ANOVADefinitionA test that allows one to make comparisons between the means of three or more groups of data.A test that allows one to make comparisons between the means of three or more groups of data, where two independent variables are considered. Number of Independent VariablesOne.Two. What is Being Compared?The means of three or more groups of an independent variable on a dependent variable.The effect of multiple groups of two independent variables on a dependent variable and on each other. Number of Groups of Samples Three or more.Each variable should have multiple samples.
Senior Science Writer In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores. Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.
Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half. Your dependent variable is math test scores. You measure the math skills of all participants using a standardized test and check whether they differ based on room temperature. What is an independent variable?An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study. Independent variables are also called:
These terms are especially used in statistics, where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable. Types of independent variablesThere are two main types of independent variables.
Experimental variablesIn experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ. You can apply just two levels in order to find out if an independent variable has an effect at all. You can also apply multiple levels to find out how the independent variable affects the dependent variable. Example: Independent variable levelsYou are studying the impact of a new medication on the blood pressure of patients with hypertension. Your independent variable is the treatment that you directly vary between groups.You have three independent variable levels, and each group gets a different level of treatment. You randomly assign your patients to one of the three groups:
A true experiment requires you to randomly assign different levels of an independent variable to your participants. Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation. Subject variablesSubject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables. It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Example: Quasi-experimental designYou study whether gender identity affects neural responses to infant cries.Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other. Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness. After collecting data, you check for statistically significant differences between the groups. You find some and conclude that gender identity influences brain responses to infant cries.
Professional editors proofread and edit your paper by focusing on:
See an example
What is a dependent variable?A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable. In statistics, dependent variables are also called:
The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses. Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable. Identifying independent vs. dependent variablesDistinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic paper. A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design. Here are some tips for identifying each variable type. Recognizing independent variablesUse this list of questions to check whether you’re dealing with an independent variable:
Recognizing dependent variablesCheck whether you’re dealing with a dependent variable:
Independent and dependent variables in researchIndependent and dependent variables are generally used in experimental and quasi-experimental research. Here are some examples of research questions and corresponding independent and dependent variables.
For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis. The type of test is determined by: You’ll often use t tests or ANOVAs to analyze your data and answer your research questions. Visualizing independent and dependent variablesIn quantitative research, it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x-axis (horizontal) and the dependent variable on the y-axis (vertical). The type of visualization you use depends on the variable types in your research questions:
To inspect your data, you place your independent variable of treatment level on the x-axis and the dependent variable of blood pressure on the y-axis. You plot bars for each treatment group before and after the treatment to show the difference in blood pressure. Based on your results, you note that the placebo and low-dose groups show little difference in blood pressure, while the high-dose group sees substantial improvements. Frequently asked questions about independent and dependent variablesWhat’s the definition of an independent variable?
An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study. Independent variables are also called:
What’s the definition of a dependent variable?
A dependent variable is what changes as a result of the independent variable manipulation in experiments. It’s what you’re interested in measuring, and it “depends” on your independent variable. In statistics, dependent variables are also called:
|