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How to report manova results

# How to report manova results

As can be seen in Table 1, a meaningful pattern of correlations was observed amongst most of the dependent variables, suggesting the appropriateness of a MANOVA. A one-way multivariate analysis of variance MANOVA was conducted to test the hypothesis that there would be one or more mean differences between education levels undergraduate, masters, PhD and intelligence test scores.

The multivariate effect size was estimated at. As the independent variable was associated with three levels, two eigenvalues and canonical correlations were extracted by the MANOVA.

The first eigenvalue was equal to. The canonical correlation associated with the first eigenvalue was equal to. By contrast, the second eigenvalue was equal to. To help interpret the statistically significant MANOVA effect, the standardized discriminant function coefficients were consulted. As can be seen in Table 2, the standardized discriminant function coefficents suggested that the three education levels were maximally differentiated by a canonical variate with greater weightings from the verbal2.

The estimate the group centroids i. Specifically, an alpha level of. Bonferroni adjusted. These values are suggestive of a large effect size, according to Cohen Cohen, J. A power primer. Psychological Bulletin, Enders, C. Measurement and Evaluation in Counseling and Development36 Huberty, C. Multivariate analysis of variance and covariance. Tinsley and S. Brown Eds. Handbook of applied multivariate statistics and mathematical modeling.

New York : Academic Press.The primary purpose of the two-way MANOVA is to understand if there is an interaction between the two independent variables on the two or more dependent variables.

For example, you could use a two-way MANOVA to understand whether there were differences in students' short-term and long-term recall of facts based on lecture duration and fact type i. Alternately, you could use a two-way MANOVA to understand whether there were differences in the effectiveness of male and female police officers in dealing with violent crimes and crimes of a sexual nature taking into account a citizen's gender i.

As mentioned earlier, a two-way MANOVA has generally one primary aim: to understand whether the effect of one independent variable on the dependent variables collectively is dependent on the value of the other independent variable.

This is called an "interaction effect". However, if no interaction effect is present usually assessed as whether the interaction effect is statistically significantyou would normally be interested in the "main effects" of each independent variable instead. This is somewhat akin to assessing the effect that an independent variable has on the dependent variables collectively when "ignoring" the value of the other independent variable.

On the other hand, if a statistically significant interaction is found, you need to consider an method of following up the result i. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a two-way MANOVA to give you a valid result.

We discuss these assumptions next. Do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated i. This is not uncommon when working with real-world data. However, even when your data fails certain assumptions, there is often a solution to overcome this. In practice, checking for these nine assumptions adds some more time to your analysis, requiring you to work through additional procedures in SPSS Statistics when performing your analysis, as well as thinking a little bit more about your data.

These nine assumptions are presented below:. Before doing this, you should make sure that your data meets assumptions 1, 2, 3 and 4, although you don't need SPSS Statistics to do this. Just remember that if you do not run the statistical tests on these assumptions correctly, the results you get when running a two-way MANOVA might not be valid. Consider an experiment where three teaching methods were being trialled in schools.

The three teaching methods were called "Regular", "Rote" and "Reasoning". The researchers primarily wanted to know whether the effects of the three teaching methods on students' grades in these two subjects were different based on students' gender i. The primary aim is to determine whether there is a statistically significant interaction effect.

Assuming that a statistically significant interaction effect is found, this indicates that the three teaching methods have different effects in male and female students i. Whether you find a statistically significant interaction will determine which effects in the two-way MANOVA you should interpret and what follow-up analyses you may want to run. Therefore, in this study the two continuous dependent variables were "Humanities score" and "Science score", whilst the two nominal independent variables were "intervention", which reflected the teaching methods i.However, prior to conducting the MANOVA, a series of Pearson correlations were performed between all of the dependent variables in order to test the MANOVA assumption that the dependent variables would be correlated with each other in the moderate range i.

As can be seen in Table 1, a meaningful pattern of correlations was observed amongst most of the dependent variables, suggesting the appropriateness of a MANOVA. A one-way multivariate analysis of variance MANOVA was conducted to test the hypothesis that there would be one or more mean differences between education levels undergraduate, masters, PhD and intelligence test scores.

The multivariate effect size was estimated at.

### Conduct and Interpret a One-Way MANCOVA

Prior to conducting a series of follow-up ANOVAs, the homogeneity of variance assumption was tested for all nine intelligence subscales. In all cases, the trend of the effect was linear. That is, on average, MA students were more intelligent than undergraduates and PhD students were, on average, more intelligent than MA students. Cohen, J.

A power primer. Psychological Bulletin, Cramer, E. Multivariate analysis. Review of Educational Research36 Howell, D. Statistical methods for psychology. BelmontCA : Thompson Wadsworth. Huberty, C. Multivariate analysis of variance and covariance. Tinsley and S. Brown Eds. Handbook of applied multivariate statistics and mathematical modeling.

Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. In the latter case, what are recommended formatting guidelines? The APA doesn't provide free access to their style guide. If you wish to use tables to report your contrasts, this document illustrates the general APA format for tables. Another thought: the APA does a print publication of its style book.

If you're at a university or have access to one, the library often has these types of books in their references section, although some require you to have a university ID to access those books. The choice of elements to report should depend on the publication journal article vs.

## Conduct and Interpret a One-Way MANOVA

That said, in practice you will often see that much less if reported, often only the test the authors want to interpret so you might get away with that, even in APA journals. Nicol, A. Presenting your findings: A practical guide for creating tables 6th ed. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Ask Question. Asked 8 years, 1 month ago. Active 7 years, 11 months ago.

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The p-values for the manufacturing plant are not significant at the 0. The p-values for the interaction between plant and method are statistically significant at the 0. Because the interaction is statistically significant, the effect of the method depends on the plant. Use the eigen analysis to assess how the response means differ between the levels of the different model terms. You should focus on the eigenvectors that correspond to high eigenvalues.

In these results, the first eigenvalue for method 0. Therefore, you should put higher importance on the first eigenvector. The first eigenvector for method is 0. The highest absolute value within this vector is for the usability rating.

This suggests that the means for usability have the largest difference between the factor levels for method. This information is helpful for assessing the means table.

Use the Means table to understand the statistically significant differences between the factor levels in your data. The mean of each group provides an estimate of each population mean. Look for differences between group means for terms that are statistically significant. For main effects, the table displays the groups within each factor and their means. For interaction effects, the table displays all possible combinations of the groups.

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If an interaction term is statistically significant, do not interpret the main effects without considering the interaction effects. Method and the interaction term are statistically significant at the 0. The table shows that method 1 and method 2 are associated with mean usability ratings of 4. The difference between these means is larger than the difference between the corresponding means for quality rating.

This confirms the interpretation of the eigen analysis. For example, the table for the interaction term shows that with method 1, plant C is associated with the highest usability rating and the lowest quality rating.

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However, with method 2, plant A is associated with the highest usability rating and a quality rating that is nearly equal to the highest quality rating. The univariate results can provide a more intuitive understanding of the relationships in your data.

## Interpreting multivariate analysis with more than one dependent variable

However, the univariate results can differ from the multivariate results. The main effects of both method and plant are statistically significant in the model for quality rating.In this section, we show you only the main tables required to understand your results from the one-way MANOVA and Tukey post-hoc tests.

This includes relevant boxplots, scatterplot matrix and Pearson's correlation coefficients, and output from your Mahalanobis distance test, Shapiro-Wilk test for normality, and Box's M test of equality of covariance, and if required, Levene's test of homogeneity of variance. However, in this "quick start" guide, we focus only on the four main tables you need to understand your one-way MANOVA results, assuming that your data has already met the nine assumptions required for a one-way MANOVA to give you a valid result.

The first important one is the Descriptive Statistics table shown below. This table is very useful as it provides the mean and standard deviation for the two different dependent variables, which have been split by the independent variable.

In addition, the table provides "Total" rows, which allows means and standard deviations for groups only split by the dependent variable to be known. You need to look at the second Effect, labelled " School ", and the Wilks' Lambda row highlighted in red. We can see from the table that we have a " Sig. If you had not achieved a statistically significant result, you would not perform any further follow-up tests. However, as our case shows that we did, we will continue with further tests.

To determine how the dependent variables differ for the independent variable, we need to look at the Tests of Between-Subjects Effects table highlighted in red :. It is important to note that you should make an alpha correction to account for multiple ANOVAs being run, such as a Bonferroni correction.

These differences can be easily visualised by the plots generated by this procedure, as shown below:. We do this using the Harvard and APA styles.

### Two-way MANOVA in SPSS Statistics

You can learn more about our enhanced content on our Features: Overview page. Join the 10,s of students, academics and professionals who rely on Laerd Statistics.In continuation to my previous articlethe results of multivariate analysis with more than one dependent variable has been discussed in this article.

For example in IV1 the numbers of respondents in hours category are This indicates that there are respondents who study Book 1 IV1 for hours week. The second table Table 2 is for the descriptive statistics of all the variables in the model. In this case the dependent variables are shown in row whereas the independent variables are in column.

One Way ANOVA

The interpretation of the descriptive table has already been discussed in our previous article. This tests the null hypothesis that the observed covariance matrices of dependent variables are equal across groups.

In our example the null hypothesis would be:. Covariance between score in mathematics and score in science would be same for all the students irrespective of their reading hours for each book. In other words the MANOVA can be performed only if the covariance matrices among the dependent variables are same across all the groups 5 groups in this case. So in this case the significance value is more than 0. All of them are used to test whether the vector of means of the groups are from the same sampling distribution or not.

We can choose any of them for interpretation. So we reject the null hypothesis that the IV1 are at same level for all the dependent variables. This is also significant as the p value is less than 0. So for IV2 also we reject the null hypothesis.

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In other words IV2 are at the same level for all the dependent variables. In our case we reject the null hypothesis that scores in mathematics and science is same for all the students irrespective of their reading hours for Book2. One can be interpret this as 6. It is useful in those cases if we are not able to reject the null hypothesis, or in other words if the p value is higher than 0.

For example if we are not able to reject the null hypothesis and the power observed is 0. Tip: The covariance shows the relationship between the two dependent variables. If there is no relationship between the two dependent variables then analyzing the effect of independent variable on both the dependent variable will not make sense.

Since there are more than one dependent variable, it is important to check whether the covariance or the interconnections among the dependent variable is same or not. If the covariance is different then it would not be appropriate to use the dependent variable together. It tests the null hypothesis that the error variance of the dependent variables is equal across the independent variables. This shows the results similar to normal ANOVA if separate regression tests were to run for each dependent variable instead of combining both of them.

For example in case of IV1 the first independent variable has F value of 8. So the null hypothesis can be rejected. In other words there is at least one difference in different groups of IV with respect to the first dependent variable. Similarly for IV2 the second independent variable the F value is So in this case reject the null hypothesis.

This means there is at least one difference in different groups of independent variable with respect to the first independent variables.