Análisis e Interpretación de Resultados

Limitations of this research should be noted to inform future studies on syntactic complexity and nominal modifiers. Firstly, this study draws on a collection of a specialized corpus representing three disciplines (agronomy, applied linguistics, and industrial and

manufacturing systems engineering) and two registers (published journal research articles and master’s theses). Hence, the findings reported in this study are only meant for those disciplines and register, and may only be generalizable to their respective discourses.

Another limitation of the study could be related to the accuracy of the automated analysis of linguistic features. Several automatic grammatical analysis programs were employed in this study: the Biber tagger, the complexity program, and the Synlex syntactic complexity analyzer.

As tagging and complexity analysis are done automatically, it is important to know to what extent automatic programs do their job accurately. Conducting a reliability analysis for the tagging and the complexity program is useful to evaluate the accuracy of the automatic tags. It was explained in Chapter 3 that the accuracy of the automatic tags was checked in some studies through precision, recall, and overall reliability (e.g., Gray, 2015). In fact, despite high reliability scores for a most of the features, Gray’s (2015) reliability analysis pointed out that the automatic tagging may not produce error-free reliability statistics for all linguistic features. However, this study did not calculate the reliability information such as precision and recall. Instead, as explained in Chapter 3, the accuracy of the automatic tags was checked with a subsample of texts. A randomly selected sample of texts was hand coded, and tags assigned to the same sample by the tagger were compared. In this way, a percentage agreement was obtained for five

linguistic features. However, this may not provide robust reliability information as reported via precision, recall, and overall reliability statistics. Despite this limitation, the reliability of the tagging could be warranted considering that the Biber tagger has been widely used for research in academic writing (e.g., Biber & Gray, 2013; Biber et al., 2011; Biber et al., 2014; Staples et al., 2016). The tagger has been used similarly to those academic writing studies in this study. Thus the results of the study could be comparable to the similar studies.

As mentioned, another automatic program this study rests on is Synlex Syntactic

Complexity Analyzer. Unlike the tagging information provided by the Biber tagger, Synlex does not generate tagging data. Therefore, it was not possible to evaluate the accuracy of the tags in the Synlex analysis. However, several experiments were done to understand how Synlex

was done to explore how complex nominals are examined by Synlex. To this end, three similar sentences listed below were tested on the web-based interface of Synlex.

a. These large transfers lead to the findings. b. These energy transfers lead to the findings.

c. These energy transfers in the study lead to the findings.

These three sentences were tested for the number of complex nominals per clause and per T-unit. The results reported by Synlex showed that sentence a has one complex nominal (adjective pre- modifying a noun: large transfers). But there are no complex nominals detected by Synlex in sentence b and c. However, to the researcher’s knowledge, there are at least one complex nominal in sentence b, and two complex nominals in sentence c. Sentence b has the NP (energy transfers) where the noun energy is pre-modifying another noun. Similarly, sentence c has the NP (energy transfers), and the second complex nominal in sentence c is the PP (in the study) where the PP is post-modifying the noun (transfers). These nominal pre- and post-modifiers (nouns as nominal premodifiers and PPs as nominal postmodifiers) are viewed as complex nominals by the corpus-based grammatical complexity research and the complexity program used in a number of studies (e.g., Biber & Gray, 2010; Biber et al., 2011; Gray, 2011). In fact the measure complex nominals was added to Synlex analyzer to capture the subclausal complexity (Lu, 2011). Lu (2010, 2011) bases his complex nominals measurement on Cooper’s (1976) study and includes some linguistic features (e.g., noun clauses) that are not operationalized similarly in complexity studies done by Biber and his colleagues.

Surprisingly, while Synlex considered adjectives as nominal pre-modifiers as an example of complex nominals, it did not list nouns as nominal pre-modifiers and PPs as nominal post- modifiers as complex nominals. Such contrastive examples point out that different complexity programs used in this study may operationalize the same constructs with different linguistic

algorithms. This mismatch in the operationalization of some linguistic features may pose problems for the interpretation or comparison of the results obtained from two different analyzers. However, these two complexity programs have been widely used by the previous studies on complexity and are still being used increasingly in complexity studies. Despite their incongruent perspective to complexity analysis in some respects, both programs have powerful computational strength and produce useful complexity analysis for researchers of complexity. Considering these limitations resulting from the differing operationalization of the complexity features across different platforms, future studies could investigate the extent to which these operationalizations show variance. The future studies may also strive for closing the relative disconnect between the programs and developing a new complexity program utilizing the powerful aspects of both programs.

Additional shortcoming of the study may be found in the qualitative methods used in the third research question. For this analysis, the texts were analyzed according to the appearance of each feature in the discourse. Despite the fact that close functional analysis of the targeted

nominal modifiers was useful for observing patterns of use in context, employing another rater or analyst would result in more dependable qualitative results. As noted though, this close-up analysis revealed that some nominal modifiers play a pivotal role in science prose. For example, prepositional phrases (PPs) as nominal postmodifiers can create very long clause structures through embedded phrases. This extensive embedding manifests a highly intricate and technical language that may be hard to decipher for people outside of the respective disciplinary discourse. This meaning potential of PPs has in fact been highlighted by recent research as a prolific area for the analysis of syntactic complexity “particularly if the functional and meaning consequences of modification are brought to bear on the analyses” (Ortega, 2015, p. 92). Accordingly, further

research studies could focus on explaining the meaning-making potential of the PPs in academic discourse. Similarly, in addition to supplying quantitative findings on the normed rates of

occurrences of nominal modifiers, future research should explore their meaning-making capacities in relation to the functional demands of the registers for which texts are written.

Lastly, this study focused on nominal modifiers at the phrasal level and explored their use from the ideational metafunction of SFL, which is concerned with the meanings of the text. However, a student who has the knowledge of content in his discipline needs particular linguistic tools to express his ideas in a well-formed way (Mahboob, 2014; Martin & White, 2005).

Gaining control over expressing the content appropriately becomes more challenging when students need “to employ interpersonal or textual resources to express, clarify, or elaborate their disciplinary knowledge” (Schleppegrell, 2002, p. 128). Therefore, in addition to the ideational metafunction, students need to deploy interpersonal resources in their science writing samples. Considering the significance of interpersonal resources, future studies may focus on how interpersonal resources are used across different disciplines and registers. While doing so, they can examine clauses as unit of analysis because interpersonal resources are best reflected through clauses than phrases.