# Dissertation Results

For instance, you would use MANOVA when testing whether male versus female participants (independent variable) show a different determination to read a romantic novel (dependent variable) and a determination to read a crime novel (dependent variable).

When reporting the results, you first need to notice whether the so-called Box’s test and Levene’s test are significant. These tests assess two assumptions: that there is an equality of covariance matrices (Box’s test) and that there is an equality of variances for each dependent variable (Levene’s test).

Both tests need to be non-significant in order to assess whether your assumptions are met. If the tests are significant, you need to dig deeper and understand what this means. Once again, you may find it helpful to read the chapter by Andy Field on MANOVA, which can be accessed here.

Following this, you need to report your descriptive statistics, as outlined previously. Here, you are reporting the means and standard deviations for each dependent variable, separately for each group of participants. Then you need to look at the results of “multivariate analyses”.

You will notice that you are presented with four statistic values and associated *F* and significance values. These are labelled as Pillai’s Trace, Wilks’ Lambda, Hotelling’s Trace, and Roy’s Largest Root. These statistics test whether your independent variable has an effect on the dependent variables. The most common practice is to report only the Pillai’s Trace. You report the results in the same manner as reporting ANOVA, by noting the *F* value, degrees of freedom (for hypothesis and error), and significance value.

However, you also need to report the statistic value of one of the four statistics mentioned above. You can label the Pillai’s Trace statistic with V, the Wilks’ Lambda statistic with A, the Hotelling’s Trace statistic with T, and Roy’s Largest Root statistic with Θ (but you need report only one of them).

Finally, you need to look at the results of the Tests of Between-Subjects Effects (which you will see in your output). These tests tell you how your independent variable affected each dependent variable separately. You report these results in exactly the same way as in ANOVA.

Here’s how you can report all results from MANOVA:

*Males were less determined to read the romantic novel* (M = 4.11, SD = .58) *when compared to females* (M = 7.11, SD = .43). *Males were also more determined to read the crime novel* (M = 8.12. SD = .55) *than females* (M = 5.22, SD = .49). *Using Pillai’s Trace, there was a significant effect of gender on the determination to read the romantic and crime novel* (V = 0.32, *F* (4,54) = 2.56, *p* = .004). *Separate univariate ANOVAs on the outcome variables revealed that gender had a significant effect on both the determination to read the romantic novel (F*(2,27) = 9.73, *p* = .003) *and the determination to read the crime novel (F*(2,27) = 5.23, *p* = .038).

**Qualitative data largely encompass longer and more detailed responses.**

If you have conducted things like interviews or observations, you are likely to have transcripts that encompass pages and pages of work.

Putting this all together cohesively within one chapter can be particularly challenging. This is true for two reasons. First, it is always difficult to determine what you are going to cut and/or include. Secondly, unlike quantitative data, it can often be difficult to represent qualitative data through figures and tables, so condensing the information into a visual representation is simply not possible. As a writer, it is important to address both these challenges.

When considering how to present your qualitative data, it may be helpful to begin with the initial outline you have created (and the one described above). Within each of your subsections, you are going to have themes or headings that represent impactful talking points that you want to focus on.

Once you have these headings, it might be helpful to go back to your data and highlight specific lines that can/might be used as examples in your writing. If you have used multiple different instruments to collect data (e.g. interviews and observations), you are going to want to ensure that you are using both examples within each section (if possible). This is so that you can demonstrate to more well-rounded perspective of the points you are trying to make. Once you have identified some key examples for each section, you might still have to do some further cutting/editing.

Once you have your examples firmly selected for each subsection, you want to ensure that you are including enough information. This way, the reader will understand the context and circumstances around what you are trying to ‘prove’. You must set up the examples you have chosen in a clear and coherent way.

Students often make the mistake of including quotations without any other information. It is important that you embed your quotes/examples within your own thoughts. Usually this means writing about the example both before and after. So you might say something like, “One of the main topics that my participants highlighted was the need for more teachers in elementary schools. This was a focal point for 7 of my 12 participants, and examples of their responses included: [insert example] by participant 3 and [insert example] by participant 9. The reoccurring focus by participants on the need for more teachers demonstrates [insert critical thought here]. By embedding your examples in the context, you are essentially highlighting to the reader what you want them to remember.

Aside from determining what to include, the presentation of such data is also essential. Participants, when speaking in an interview might not do so in a linear way. Instead they might jump from one thought to another and might go off topic here and there.

It is your job to present the reader with information on your theme/heading without including all the extra information. So the quotes need to be paired down to incorporate enough information for the reader to be able to understand, while removing the excess.

Finding this balance can be challenging. You have likely worked with the data for a long time and so it might make sense to you. Try to see your writing through the eyes of someone else, which should help you write more clearly.