How-to-Write-a-Better-Thesis
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106 8 Outcomes and Results<br />
Jorge’s case was relatively straightforward. He had a lot of data, and the <strong>to</strong>ols <strong>to</strong><br />
build a variety of graphs and tables that interpreted the data in useful ways, including<br />
statistical summaries, trends, and behaviour as different variables, were tuned.<br />
Having spent months investigating the data, drawing preliminary conclusions, and<br />
then seeking confirmation (or confounds), he could then choose typical examples<br />
<strong>to</strong> illustrate his argument. He first listed the data sets and the analyses applied <strong>to</strong><br />
set context for these examples, and in some cases could then just summarize the<br />
outcome of the analysis. By having reasonably objective criteria for choosing what<br />
<strong>to</strong> present, he was able <strong>to</strong> build a persuasive qualitative description of the work he<br />
had undertaken, and how it supported his original hypothesis.<br />
Dai’s results chapter was, in the end, similar. He had <strong>to</strong> be explora<strong>to</strong>ry <strong>to</strong> find<br />
ways <strong>to</strong> consistently describe the issues encountered by the different scientists he<br />
studied, including assigning issues <strong>to</strong> scores in the range − 3 <strong>to</strong> + 3 <strong>to</strong> quantify their<br />
severity, and, separately, carefully explaining how he had decided which problems<br />
were more important or less important. Once he had identified common themes, and<br />
thus a categorization of the cases, the presentation was straightforward. That is, he<br />
could consolidate his results in<strong>to</strong> tables, and used them as the basis of a discussion<br />
showing how they confirmed his initial hypothesis.<br />
Jackie’s problem was that, fundamentally, she didn’t trust some of the data, and<br />
needed <strong>to</strong> build her arguments with unusual care. The approach we <strong>to</strong>ok was <strong>to</strong><br />
build the presentation from small units, each of which represented a single logical<br />
step and which, we felt, could be defended by a simple, unarguable case. We began,<br />
not by looking at the data, but by going back <strong>to</strong> basics and setting out criteria that a<br />
trustworthy data set should satisfy. In particular, we identified potential sources of<br />
bias or dis<strong>to</strong>rtion in the data sets, and also tabulated the kinds of fac<strong>to</strong>rs that might<br />
lead <strong>to</strong> the results observed in each case. For example, poor academic results might<br />
be a consequence of poor diet—but it might be that poor results lead <strong>to</strong> depression,<br />
which then leads <strong>to</strong> poor diet. Some studies attempted <strong>to</strong> control for such fac<strong>to</strong>rs<br />
<strong>to</strong> try and distinguish between these alternatives; other studies were less well designed.<br />
One particularly good study noted which students were siblings, so that, for<br />
example, by working from the assumption that siblings usually have similar diet<br />
then it is possible <strong>to</strong> explore the degree <strong>to</strong> which differences in performance are due<br />
<strong>to</strong> other fac<strong>to</strong>rs.<br />
This foundational analysis allowed Jackie <strong>to</strong> organize the studies by their<br />
strengths and defects, and undertake meta-analysis (that is, combined analysis of<br />
a group of studies) on the basis of which criteria each study met. She then worked<br />
through the studies re-analyzing the conclusions that had been reached, and showed<br />
that some of these conclusions could be refuted. With the field of explanations<br />
greatly reduced, she could then examine where in the data her own hypothesis was<br />
and wasn’t supported, and ultimately was able <strong>to</strong> reach nuanced conclusions. In<br />
contrast <strong>to</strong> the sometimes dogmatic assertions made in earlier work, her results<br />
were thoughtful and reasoned, and she avoided the mistake of making overly strong<br />
claims.<br />
For Don, the initial hurdle was that he had not appreciated that he was trying <strong>to</strong><br />
present what were essentially quantitative results; the approach he had taken earlier