Tipos de residuos agrarios y problemática asociada

Measures of syntactic complexity focus on the use of complex and sophisticated structures of the language (Inoue, 2016). Previous studies mainly used a general variable such as length-

128 based and/or subordination-based variable as a measure of syntactic complexity. However, such methods might be insufficient to analyse syntactic complexity as a whole and could be unable to capture the multidimensional nature of the language development of L2 learners (Norris & Ortega, 2009). To overcome such weakness, Norris and Ortega (2009) suggested three measures which were considered suitable for this study because students’ level of proficiency was categorised as within or above intermediate. These three measures were namely: 1) length, which is measured by an overall length-based metric (e.g. words per chosen unit); 2) subordination, which is measured by the total number of clauses; and 3) sub-clausal, which is measured by clause length-based metric. Therefore, in the current study, the measures were operationalized as: 1) mean length of AS-unit; 2) ratio of subordination; and, 3) mean length of clauses.

4.4.2 Accuracy

Ellis and Barkhuizen (2005) suggested that both global and local measures of accuracy tend to correlate closely. These measures refer to the percentage of error-free clauses (Skehan & Foster, 1999) and the number of errors per 100 words (Mehnert, 1998). The former refers to the number of errorless clauses divided by the total number of all clauses including independent, sub-clausal units or subordinate clauses, multiplied by 100. The former measure of accuracy has been shown to be more realistic and sensitive in identifying varieties of errors between experimental conditions (Skehan & Foster, 1999). Meanwhile, the latter refers to the number of errors divided by the number of tokens, multiplied by 100. Even though this measure is not sensitive in segmenting clauses like the former, Inoue (2016) claimed that counting errors per 100 words is more reliable since it does not involve clauses and unit-based measures (AS-unit, T-unit and C-unit) which can be problematic as they can be difficult to define. As the two measures look into different aspects of accuracy, the present study employed both

129 measures to offer a more robust analysis of the participants’ speaking accuracy. However, such measures do not differentiate between the degree of severity of errors produced (Storch & Wigglesworth, 2007) which may cause difficulties in defining and determining what constitutes an error (Ellis & Barkhuizen, 2005). In order to solve this matter, Storch and Wigglesworth (2007) proposed that errors can be classified into three categories: 1) syntax (e.g. word order, word omission); 2) morphology (e.g. verb tense, subject-verb agreement, misused of determiners and preposition, errors in word form); and 3) lexis (e.g. incorrect word choice). Similar to complexity, the unlemmatised version of the speaking transcriptions within the same 200-words range was used for accuracy. However, since each transcription was truncated from the middle section, errors emerging at the point of the 200-words cut were disregarded. Therefore, in this present study, error-free clause is defined as no errors in syntax, morphology or lexis. Nevertheless, phonological features such as intonation and stress were excluded from the analysis as these were beyond the scope of the study.

4.4.3 Fluency

In this study, fluency is described as smooth, uninterrupted and hesitation-free speech within a time constraint (Tavakoli, 2011). Following the definition, fluency can be divided into three main dimensions which according to Tavakoli and Skehan (2005) are: 1) breakdown fluency, to assess pauses and silence in speech; 2) repair fluency, to assess hesitation phenomena related to dysfluency; and 3) speed fluency, to assess the articulation of language produced. However, as mentioned in the previous chapter, intonation, stress and pronunciation were not part of the study, thus 3) was disregarded.

In measuring 1) breakdown fluency, the amount of pausing is a good predictor of perceptive fluency (Guz, 2015). Therefore, filled and unfilled pauses were taken into account as previous studies revealed that frequency of these pauses significantly differentiates between fluent and

130 dysfluent speakers (Lennon, 1999). Moreover, students who are more fluent spend less time pausing (Ellis & Barkhuizen, 2005). Filled pauses were referred to as non-lexical fillers or meaningless sounds such as um, uh, er etc. (Kang, Rubin, & Pickering, 2010). On the other hand, unfilled pauses can be identified as silent pauses equal to or more than 250 milliseconds (de Jong & Bosker, 2013). The length of the silent pauses was measured by listening to each transcription and examining the waveform using Goldwave software. The number of pauses that were equal or more than 250 milliseconds were counted and added to CLAN to be analysed. Besides that, it is arguable that pauses may occur between mid-clause and end-clause in a single utterance.

Meanwhile, 2) repair fluency in this study was analysed in four ways according to Skehan and Foster (1999), namely: a) false starts (e.g. abandoned utterances before completion); b) repetitions (e.g. verbatim repeated words, phrases or clauses without modification); c) reformulations (e.g. repeated phrases and clauses with some modification); and d) replacements (e.g. lexical items that immediately replenished for another).

To sum up, a full description of the 11 measures used in the current study is presented in Table 4.2 below.

131 Table 4. 2: Measures of complexity, accuracy and fluency

Dimensions Measures Abbrev. Definition Lexical

Complexity

Lexical

diversity (D) LCD Computed by CLAN using VOCD. Lexical

diversity (G) LCG

Type ratios divided by the square root of all tokens.

Lexical Productivity

Lexical

density (D) LPD

Computed by CLAN using VOCD +sm;*,|n +sm;*,|v +sm;*,|adj

Lexical

density (G) LPG

The content word types ratio divided by the square root of all tokens.

Syntactic Complexity

Mean length

of AS-unit SMA

The total number of tokens divided by the number of AS-units.

Ratio of

subordination SRS

The total number of clauses divided by the number of AS-units.

Mean length

of clauses SMC

The total number of tokens divided by the number of clauses. Accuracy Percentage of error-free clauses APE

The total number of errorless clauses divided by the total number of all clauses, multiplied by 100.

Number of errors per 100 words

ANE The total number of errors divided by the number of tokens, multiplied by 100.

Fluency

Number of

pauses FNP

The total number of pauses (filled and unfilled) divided by the total time of speech in seconds, multiplied by 60.

Number of

repairs FNR

The total number of repairs (false starts, repetitions, reformulations and replacements) divided by the total time of speech in seconds, multiplied by 60.