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One-way MANOVA using Minitab

Introduction

The one-way multivariate analysis of variance (one-way MANOVA) is used to determine whether there are any differences between two or more groups of an independent variable on more than one continuous dependent variable. It can be considered an extension of the one-way ANOVA when you have multiple dependent variables.

For example, you could use a one-way MANOVA to determine whether students' short-term and long-term recall of facts differed based on three different lengths of lecture (i.e., the two dependent variables are "short-term memory recall" and "long-term memory recall", whilst the independent variable is "lecture duration", which has four independent groups: "30 minutes", "60 minutes", "90 minutes" and "120 minutes"). Alternately, a one-way MANOVA could be used to determine whether there is a difference in salary and bonuses based on degree type (i.e., the two dependent variables are "salary" and "bonuses", whilst the independent variable is "degree type", which has five groups: "business studies", "psychology", "biological sciences", "engineering" and "law").

When there is a statistically significant difference between the groups of the independent variable, it is possible to determine which specific groups were significantly different from each other using post hoc tests. You need to conduct these post hoc tests because the one-way MANOVA is an omnibus test statistic and cannot tell you which specific groups were significantly different from each other; it only tells you that at least two groups were different.

In this guide, we show you how to carry out a one-way MANOVA using Minitab, as well as interpret and report the results from this test. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a one-way MANOVA to give you a valid result. We discuss these assumptions next.

Minitab

Assumptions

The one-way MANOVA has nine assumptions. You cannot test the first three of these assumptions with Minitab because they relate to your study design and choice of variables. However, you should check whether your study meets these three assumptions before moving on. If these assumptions are not met, there is likely to be a different statistical test that you can use instead. Assumption #4 is something that you can choose to test before your study, but is rarely checked. Assumptions #1, #2, #3 and #4 are explained below:

Assumptions #5, #6, #7, #8 and #9 relate to the nature of your data and all but #8 can be checked using Minitab. You have to check that your data meets these assumptions because if it does not, the results you get when running a one-way MANOVA might not be valid. In fact, do not be surprised if your data violates one or more of these assumptions. This is not uncommon. However, there are possible solutions to correct such violations (e.g., transforming your data) such that you can still use a one-way MANOVA. Assumptions #5, #6, #7, #8 and #9 are explained below:

In practice, checking for assumptions #5, #6, #7, #8 and #9 will probably take up most of your time when carrying out a one-way MANOVA. However, it is not a difficult task and Minitab provides many of the tools you need to do this.

In the section, Test Procedure in Minitab, we illustrate the Minitab procedure required to perform a one-way MANOVA assuming that no assumptions have been violated. First, we set out the example we use to explain the one-way MANOVA procedure in Minitab.

Minitab

Example

A researcher wants to test the effectiveness of different types of learning intervention on exam performance. Exam performance was assessed as the scores achieved in a humanities exam and a science exam. These two example scores are the two dependent variables the researcher wants to measure. The different types of learning intervention were the current learning method (called the "Regular" programme), a rote learning programme (called the "Rote" programme) and a learning programme based on reasoning skills (called the "Reasoning" programme). These three types of learning intervention are the three categories of the independent variable.

To carry out this experiment, 30 students were recruited to take part in the study. Of these 30 students, 10 students were assigned to each learning method (i.e., 10 students to the "Regular" programme, 10 students to the "Rote" programme and 10 students to the "Reasoning" programme). A one-way MANOVA was used to assess the effect of these different learning interventions on student performance in the humanities and science exams.

Minitab

Setup in Minitab

In Minitab, we set up the three variables. Under column we entered the name of the independent variable, Intervention, as follows: . Then, under column we entered the name of the first dependent variable, Humanities Score, as follows: . Then, under column we entered the name of the second dependent variable, Science Score, as follows: . The set up is as follows:

Data setup for the one-way MANOVA in Minitab

Published with written permission from Minitab Inc.

Minitab

Test Procedure in Minitab

In this section, we show you how to analyse your data using a one-way MANOVA in Minitab when the nine assumptions in the previous section, Assumptions, have not been violated. Therefore, the four steps required to run a one-way MANOVA in Minitab are shown below:

Minitab

Output of the one-way MANOVA in Minitab

The Minitab output for the one-way MANOVA is shown below:

Output for the  in Minitab

There are three rows you can interpret to get the result of the one-way MANOVA. These are the Wilks', Lawley-Hotelling and Pillai's rows. We will use the results of the Wilks' row. This reports Wilks' Lambda (Λ) and its statistical significance. Wilks' Lambda statistic is found under the Test Statistics column. You can see that Wilks' Lambda is 0.708 (to 3 d.p.). The statistical significance level (i.e., p-value) is found under the P column. You can see that it is .001 (i.e., p = .001). As such, the one-way MANOVA is statistically significant (because p < .05). In other words, there is a statistically significant difference between teaching interventions on exam performance.

Note: We present the output from the one-way MANOVA above. However, since you should have tested your data for the assumptions we explained earlier in the Assumptions section, you will also need to interpret the Minitab output that was produced when you tested for them. Also, remember that if your data failed any of these assumptions, the output that you get from the one-way MANOVA procedure (i.e., the output we discuss above) might no longer be valid and you will need to interpret the alternative Minitab output that is produced when they fail.

Minitab

Reporting the output of the one-way MANOVA

When you report the output of your one-way MANOVA, it is good practice to include:

Based on the Minitab output above, we could report the results of this study as follows:

There was a statistically significant difference in academic performance based on the teaching method applied, F (4, 112) = 5.285, p = .001; Wilk's Λ = 0.708.

In addition to reporting the results as above, a diagram can be used to visually present your results. Furthermore, you are increasingly expected to report an "effect size" in addition to your one-way MANOVA results. Effect sizes are important because whilst the one-way MANOVA tells you whether the difference between group means is "real" (i.e., different in the population), it does not tell you the "size" of the difference. Whilst Minitab will not produce these effect sizes for you using this procedure, they can be calculated from the results Minitab produces.

Portions of information contained in this publication/book are printed with permission of Minitab Inc. All such material remains the exclusive property and copyright of Minitab Inc. All rights reserved.

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