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3 Methods

3.3 Collecting and analysing the text corpus

3.3.3 Textual analysis

The textual analysis involved four steps: adapting the taxonomy, scanning the corpus (first electronically and then manually), calculating frequencies of each sub-category, and then identifying trends of how markers were used in the essays by analysing the concordance lines. In order to adapt the taxonomy, 50 essays were closely read in order to identify the categories and types that would likely be prominent in the full corpus. For the close reading, at least one essay belonging to each genre written at each school was randomly selected. Thereafter, further essays were added to this sample where required. For example, in order

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to confirm that the commentary genre encompassed an overtly personal style of writing, further commentaries were closely read. The trial-and-error process of adapting the taxonomy involved several iterations of compiling search terms and scanning the corpus. In order to cast a wide net, search terms from previous studies were included, despite not being found during the close reading, as well as conjugations of each search term. The list of search terms was adjusted for the purposes of each article, based on the results that were retrieved. In total, over 1,000 types of metadiscourse were used to conduct the analyses in all four articles.

Only those terms found to function as metadiscourse were reported. For example, although 668 search terms were initially compiled for the analysis of epistemic stance and engagement (see article 3), only 543 of these were found to fulfil stance functions in the data material. It should be noted that, while some previous studies have used search terms to identify larger metadiscoursal units (e.g. Ädel, 2006), the search terms themselves (the punctuation marks, words and phrases) are considered to be the metadiscoursal units in this project.

The corpus was electronically scanned using the concordancing function in the corpus analysis tool, #Lancsbox (Brezina et al., 2015). Although other tools were considered (e.g. Wordsmith Tools, Scott, 2020;

Antconc, Anthony, 2019), #Lancsbox was chosen. #Lancsbox is freeware that allows users to upload corpora that can quickly be retrieved. To scan the corpus, search terms were sorted by sub-category and used to scan the essays. The resulting concordance lines were copied into Microsoft Excel and the hits were manually analysed. If hits were part of a quote, or did not function as metadiscourse, they were discounted from the analysis. To illustrate, extract 3 contains a number of markers that were discounted due to their being part of a quote. Extract 4 contains an instance of “could”, which can function as a plausibility hedge, but functioned as a modal verb of ability.

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3) Dorian says to Lord Henry […] “You are certainly my best friend.

No one has ever understood me as you have” (Literary essay, Sweden)

4) One thing I liked in his work was how he could ask questions and immediately offer an answer for them. (Commentary, UK) None of the markers were counted as belonging to more than one sub-category. For example, in extract 5, “I” was counted as a self-mention and “believe” was counted as a first-person hedge:

5) I believe this is what drives people to be sexist (Opinion piece, UK)

The polysemic types “just” and “only” were found to function as amplifiers (6), universals (7), and downtoners (8). These two types were analysed separately to ensure that they were categorised correctly.

6) The statistics found on www.opensecrets.org/industries illustrates just how big the sums of money are. (Political essay, Norway)

7) It is clear that technology in modern society, isn't going anywhere. In fact, it will only keep on advancing. (Opinion piece, UK)

8) This is nothing more than just a common phrase used to tease people that are acting selfish. (Political essay, Sweden)

Once the manual analysis was complete, the frequencies of each sub-category in each text were calculated. Initially, I counted metadiscourse features per 100 words (see articles 1 and 4), but later counted markers per 1,000 words (see articles 2 and 3). This change was made because low frequencies were easier to interpret when calculated per 1,000 words than when calculated per 100 words. For each individual essay, the frequencies of each sub-category were entered into SPSS (IBM corp.

2017), which was used to calculate the means and standard deviations of each sub-category.

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Using the frequencies and the concordance lines, trends in how upper secondary pupils use metadiscourse markers were identified. In some cases, these trends suggest how certain sub-categories may vary across educational contexts and genres. However, a number of confounding variables (e.g. writing conditions, L1 backgrounds) restricted the use of inferential statistics, so the findings are mainly used to offer preliminary insights and suggest avenues for future research.

Statistical tests were used for investigating the pupils’ use of attitude markers across educational contexts and genres in article 4. Descriptive tests were conducted to check whether the data met assumptions of normality. Several categories were identified that did not meet these assumptions. This was due to high levels of skewness and/or kurtosis.

The data still did not meet assumptions of normality when it was transformed (using both the log10 and reciprocal transformations; Field, 2018). Thus, non-parametric tests (the Mann-Whitney U test and the Kruskal Wallis test) were used to compare metadiscourse features across the educational contexts and genres. Although some sub-categories met assumptions of normality, non-parametric tests were used for all sub-categories in order to ensure that the analysis was manageable. In dealing with data that do not meet assumptions of normality, non-parametric tests rank the scores of a target variable and compare whether the given groups within these rankings are significantly different. In order to report results from these tests, the median and median absolute deviation were used. The alpha value, which is the probability of drawing false conclusions, was set at p < .05. P-values2 of p < .01 were considered to be highly significant. When running the Kruskal Wallis test, which compares scores across three or more groups, there is a risk of Type I error. This is when the null hypothesis is incorrectly rejected, often as a consequence of comparing scores across several small groups. In order

2 P-values represent the probability that significant results were randomly produced. A p-value of p < .05 indicates that there is a 5% chance that the null hypothesis has been falsely rejected.

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to avoid Type I error, the Bonferroni correction was applied (Field, 2018), which involves adjusting the p-value according to the number of hypotheses being tested.