Diagrama de trabajo

Criticism of readability studies has been documented for as long as such research has been carried out (e.g. Hargis 2000). Davison and Kantor (1982) criticise readability measures and investigate their accuracy in their own study. They find that editing a text to aim for a certain readability grade, e.g. reducing sentence length, has consequences for the way in which information is given and interpreted. They argue that topic, focus, inference, and point of view are also of importance.

Duffy (1985) criticises established indices, his main criticism being that the variables on which the indices are built are not the most accurate means of measurement, for example, sentence or word length. Schriver (1989) highlights the need to take other factors into account e.g. reading skill, subject knowledge, motivation, context and purpose of reading. The passages used in the comparison with the text to be tested also present a problem. In many cases these passages were very short and therefore do not provide a comprehensive baseline measure (see Klare 1984). Additionally, the criterion passage used in indices has varied, thus presenting possible issues in consistency.

Homan et al. (1994) find differences in their subjects’ responses to items estimated to be equal to their readability level and those estimated to be above this level (ibid., p. 356). They also stress that text creation be employed in conjunction with readability indicators by making use of an average readability level for the text as a whole, thus allowing for individual items to be above or below the intended level (ibid., p. 349).

Redish (2000) highlights the weakness of readability indices and promotes the case of usability studies. She draws attention to the fact that most indices were designed for American grade-school students and are therefore not appropriate for use with adult readers, and that most of the indices and associated research is out of date (ibid., p. 133). She also states that indices usually count certain text features, such as sentence length, because they are easy to count (ibid.). She draws attention to features that are not counted, for example: suitable content for the audience, text organisation (headings, index, tables of contents, layout, and familiarity with vocabulary).

Schriver (2000) reports on criticism of readability indices from the 1970s and comments on their validity today. She criticises indices that take syllables per word and words per sentence into account, thereby penalising texts that do not make extensive use of full stops – thus simply adding full stops where other punctuation is more appropriate is said to increase readability. She also describes cases where writers “write to the formulas” (ibid., p. 140) and impose limits on word length.3 _{This usage had already been mentioned by Klare (1984),}

who pointed out that the indices should not be used as a guide for text production in the first place.

Giles and Still (2005) highlight the problems that the more commonly used readability indices pose to technical writing. They propose the Golub Syntactic Density Formula, which examines sentence syntax at a deeper level than the methods of syllables per word and words per sentence, i.e. at the clausal and phrasal level. However, such a method shares some of the aforementioned shortcomings of readability indices in that it relies wholly on linguistic information.

In defence of the indices, Klare (2000) provides further information and advice on the correct use of readability indices. In addition, he points out that although some were intended for student readers, they may be applied to adults also. He (ibid.) acknowledges the drawbacks of several indices and the criticism they received and moves onward to focus on producing readable documentation for the domain of computing, where controlled language is mentioned as a being of value.

Collins-Thompson and Callan (2005) provide an example of readability measures being adapted to suit more modern needs. They attempt to develop a way for information retrieval systems to match texts to the reading ability of student users. They find that traditional readability measures, such as Flesch- Kincaid4 _{are not applicable to Web pages, and adopt a statistical model to classify}

Web pages according to reading difficulty. They find that an approach based on

3 _{There is an interesting parallel here with controlled language, which will be discussed later.} 4 _{The Flesch-Kincaid formula converts the Flesh score into grades based on the American}

vocabulary analysis is accurate in this case, yet such use of vocabulary presents issues of domain-specificity.

Connatser (1999) argues that the use of traditional readability indices such as Flesch is redundant in the domain of technical support documentation as “most audiences of technical documents read to do” (ibid., p. 284). He concludes that the implementation of usability testing is therefore more appropriate in this context; other researchers such as Hargis (2000) come to a similar conclusion.

Overall, it appears that evidence exists for the accurate use of readability indices to measure linguistic phenomena. However, the interaction with the reader presents a confounding variable, yet the text cannot be read without a reader in the first place. Other research points to the inadequacies of readability indices for some purposes, and the employment of additional methods such as usability testing to supplement, or even replace, readability tests, has been widely suggested. Therefore, it is not advisable to rely solely on readability indices for concrete results, unless correlations have been found with other measures beforehand. If such correlations are found then readability indices can be reused in similar conditions without additional methods. On the other hand, as readability indices are extremely resource-cheap, it would not be advisable to ignore them completely, once the user is sure of what they do and do not measure.

In conclusion, for the purposes of this research, and in line with most uses of the term ‘readability’, it is understood here as something that can be largely controlled a priori, by means, for example, of a controlled language or style guide. Readability can also be measured a posteriori using an index. While such an approach to readability may appear to be reductionist, it has the merit of being highly operationalisable, once suitable features of texts have been isolated that can reasonably be assumed (on the basis of prior research) to correlate well with the ease with which humans (with certain attributes) can understand a text. Given this understanding of readability we can say that readability indices measure rather than predict readability, which is an attribute of texts only (Klare 1974-5). To the extent that they can predict anything, it can be stated that they are a factor in predicting comprehensibility (see below), which depends crucially on attributes of the reader as well as the text; but a high (i.e. good) readability

index by itself is neither necessary nor sufficient to ensure that a given human will easily understand a text. Such a position allows the metrics which are designed to solely measure textual elements to do so. The aforementioned additional human factors are not easily quantifiable and will be addressed elsewhere, in particular in section 2.5 on eye tracking.