De la criticidad y los análisis de aceite

Neville’s (2003) bachelor thesis examined the efficacy of textese and the relationship between texting and spelling. Her participants were 45 British girls in secondary school. They filled in questionnaires on mobile phone ownership and texting behaviour. They also completed a spelling test in which they were asked to write down not only the Standard English spelling of words, but also how they would spell them in a text message. In addition, their speed and accuracy of reading and writing text messages in textese and Standard English was measured, as follows: by typing two messages dictated to them into a mobile phone (using the multi-press mode),12F13

one in Standard English and the other as if writing to a friend in textese, and by reading aloud two messages from a mobile phone, again, one in Standard English and the other in textese. Neville found that writing messages went significantly faster using textese, whereas reading messages went much faster when they were composed in Standard English, regardless of participants’ usual texting frequency (number of messages sent per day): textisms caused faster writing and slower reading. She also found that spelling ability correlated positively with the speed of reading and writing text messages in both Standard English and textese: good spellers were quicker at reading and writing both types of message. Moreover, there were significant positive correlations between spelling ability and textism use (number of textisms used in textese writing) as well as textism understanding (number of textisms read accurately): good spellers used more textisms in composing text messages and made

12 _{In addition to the studies already discussed by Verheijen (2013), this chapter now also} includes theses (bachelor, master, and PhD), studies on languages other than English, and studies published after Verheijen (2013) was submitted, as well as any studies that were overlooked.

13 _{Alphanumeric keypads present a choice between two text entry modes: the ‘multi-press’ or} ‘multi-tap’ mode, where a key is pressed one to four times to type the required letter, and the ‘predictive’ mode, where each key is pressed only once and predictive text input software (such as T9 or “text on nine keys”), which works with a built-in dictionary, suggests on the basis of frequency estimations what is the most likely word resulting from a particular sequence of key presses.

fewer errors in reading textisms. Neville concludes that her findings refute the idea that textese negatively affects children’s or adolescents’ linguistic ability, but these results can only be generalized to British female adolescents and their spelling skills. Plester, Wood, and Joshi (2009) studied the relationship between textisms and literacy attainment with 88 British children. Their knowledge and use of textisms was measured by eliciting spontaneous text messages, where they were presented with different scenarios and had to pretend they were in different situations. The textisms density was established by calculating the ratio of textisms to the total number of words in the messages. Standardized tests were used to assess reading ability, non- word reading ability (alphabetic or orthographic decoding),13F14 spelling ability,

vocabulary knowledge, and phonological awareness. Although no significant correlations were found between textism use and spelling or non-word reading, positive correlations were found between textism density and reading, vocabulary, and phonological awareness. Even when controlling for individual differences in age, vocabulary, phonological awareness, non-word reading ability, short-term memory, and length of time of owning a mobile phone, the extent of children’s textism use predicted their reading ability. Plester et al. thus suggest that “facility with text literacy is positively associated with [S]tandard English literacy” (158).

Kemp (2010) analysed 61 Australian university students’ proficiency with textese and links with their literacy skills. Participants filled in questionnaires on their texting behaviour and read aloud text messages in Standard English and textese from a mobile phone and wrote dictated text messages in Standard English and textese on a mobile phone, which measured their reading and writing speed and accuracy. Reading accuracy was based on number of errors; writing accuracy was based on number of textisms. They also completed standardized spelling and reading tests, and experimental tasks assessing morphological and phonological awareness. Texting frequency (number of messages typically sent and received per day) was not correlated to any of the literacy measures, but faster and more accurate reading and writing of text messages (in Standard English and textese) were neutrally or positively correlated with literacy scores. Kemp concludes that students with better literacy skills are more efficient at composing and deciphering text messages or, conversely stated, those fluent with textisms have better literacy skills. There was hardly any intrusion of textisms into the Standard English messages, which suggests that students are capable of limiting their textism use to appropriate contexts.

Bushnell, Kemp, and Martin (2011) studied the relationship between texting and spelling with 227 Australian children. Questionnaires measured their texting- related behaviours and attitudes. Their knowledge and use of textisms was assessed with a translation task in which they had to rewrite a list of conventionally-spelled words as they would in a text message. Spelling ability was measured with a standardized spelling test. On average, the children wrote about half of the words as textisms and the rest in Standard English. Children who sent text messages in real

14 _{Non-word reading tests measure participants’ accuracy of reading out non-existing words} that follow the standard language morpheme-phoneme patterns. Reading non-words requires an ability to decode unfamiliar letter strings (Crumpler & McCarty, 2004).

life produced significantly more textisms, but even those who did not produced a considerable number of them. There was a significant positive correlation between spelling skills and the proportion of textisms produced (not with any other texting variables): the greater a child’s spelling ability, the more textisms they produced. Bushnell et al. conclude that their results contradict claims of any detrimental impact of texting on spelling.

Durkin, Conti-Ramsden, and Walker (2011) explored the relationship between textism use and literacy in adolescents with and without specific language impairment (SLI). 94 participants – 47 of whom were “typically developing” (TD) and 47 had SLI – were interviewed about their texting frequency (sending and receiving messages), asked to send a spontaneous text message in reply to one sent by the experimenter, and assessed on their cognitive, language, and literacy abilities (spelling; reading efficiency and accuracy). Adolescents with SLI, who reported sending fewer text messages than their TD peers, were indeed less likely to send a reciprocal text message, and their messages were shorter and included fewer textisms than those of their TD peers. Participants with SLI who did not send a message in reply had significantly lower reading skills than those who did. Durkin et al. found significant positive correlations between literacy and textism density and the number of textism types used; the highest correlations were with spelling. Their study suggests that literacy skills are related to the choice to return a text message, the length of a message, and the use of textese. Adolescents with better literacy skills, regardless of whether they have SLI, are more likely to return a message, send significantly longer messages, and use more textisms and more different types.

In investigating associations between texting and literacy, Kemp and Bushnell (2011) also looked at the effects of texting method (predictive versus multi-press) and texting experience (texters versus non-texters). 86 Australian children read aloud text messages in Standard English and textese from a mobile phone and wrote dictated text messages in Standard English and textese on a mobile phone. Their literacy skills were measured with standardized tests of spelling, reading, and non- word reading. Children using the predictive mode turned out to be faster at writing and reading messages than those using the multi-press mode. Texting experience increased writing but not reading speed. Literacy scores did not differ significantly with texting method. Although the proportion of textisms used and literacy scores did not correlate, there were significant positive correlations between all literacy scores and textese reading speed and accuracy, as well as between spelling and reading scores and textese writing speed. Speculating on the direction of these relations, Kemp and Bushnell suggest that using textese “can reflect or even enhance children’s traditional literacy abilities” (26). Children’s Standard English was not overrun with textese: they used only few textisms in these messages.

Kreiner and Davis (2011) conducted two studies into the relationship between young adults’ texting and IMing behaviour and their spelling skills, one with 64 and the other with 40 American university students. In both studies, participants filled in questionnaires about their texting and IMing practices, took a test measuring their knowledge of textisms (they were presented with a long list of textisms and had to indicate if they had seen and/or used them), and took a standardized spelling test.

Although spelling ability did not significantly correlate with any of the measures of texting or IMing frequency (text messages or instant messages sent or received daily) in either of the studies, it positively correlated with knowledge of textisms –referred to by Kreiner and Davis as sensitivity to textisms – in both studies. In addition, spelling ability was positively correlated with reaction time to identify ‘real’ textisms in the second study (this was not measured in the first study), so better spellers tended to respond more slowly to textisms, perhaps because they are more careful with spelling. Kreiner and Davis conclude that for young adults, better knowledge of textisms is linked to better spelling skills.

Plester et al. (2011) conducted a study into Finnish children’s text messages and the relationship between their use of textisms and literacy ability. 65 Finnish children filled in questionnaires about their mobile phone use. Three methods were used for collecting text messages, yielding elicited and naturalistic data. All participants wrote text messages on paper in response to different scenarios (elicited text messages). These messages were then distributed among the participants, who wrote text messages in reply (elicited replies). 16 participants also submitted exact copies of the messages they had sent over a weekend (natural text messages). Literacy skills were assessed with standardized tests of reading (fluency, accuracy, and comprehension), non-word reading, spelling, and phonological skill; cognitive skills were assessed with tests of vocabulary, rapid serial naming, and short-term memory. For elicited text messages and elicited replies, Plester and colleagues found significant positive correlations between textism density and reading fluency, reading comprehension, phonological skill, and short-term memory, as well as with composite scores for reading [fluency, accuracy, comprehension, and non-word reading] and literacy [reading, spelling, and phonological skill], but not with spelling. For elicited texts, there was also a significant positive correlation between textism density and vocabulary. All this points towards a positive relationship between textism use and literacy. For natural text messages, there were no significant correlations with any of the literacy measures, but this may be due to the small sample size of only 16 participants.

Seeing that language prescriptivists consider textisms as particular kinds of misspellings, Powell and Dixon (2011) conducted an experiment to study the effects of exposure to textisms, misspellings, and ‘correct’ spellings on spelling performance. This operationalization of CMC use is quite different from that in other studies, but interesting nevertheless, since it yields insight into whether being exposed to (e.g. friends’) textisms and other non-standard spellings can have an effect on youths’ spelling ability, irrespective of their own use of textisms. The spelling ability of 94 British university students was assessed in pre- and post-tests before and after an exposure phase in which they were exposed to the same words as in the tests, but presented in three ways: as ‘correctly’ spelled words (e.g. tonight), as phonetically plausible misspelled words (e.g. tonite), or as textisms using a combination of letters and numbers (e.g. 2nite). A no-exposure baseline condition was included to determine the effects of textisms, misspellings, and correct spellings relative to a potential test repetition effect. Spelling scores decreased from pre-test to post-test after exposure to misspellings, but improved after exposure to correct spellings and textisms. Powell

and Dixon conclude that exposure to textisms positively affects young adults’ spelling performance.

Wood, Meachem et al. (2011) conducted a longitudinal study to determine the nature and direction of any association between textism use and literacy. Such a longitudinal method can reveal insights into the causality of the relationship. They measured the textism use of 119 British children by asking them to provide exact copies of the actual text messages they had sent within two days at the start [Time 1] and the end [Time 2] of the school year. Participants completed standardized pre- and post-tests on reading, spelling, verbal ability, phonological awareness, and phonological retrieval. Textism density correlated positively with reading and spelling, both concurrently and longitudinally. Moreover, textism use at Time 1 could predict unique variance in spelling ability at Time 2 (i.e. explain changes in spelling scores over the year) even after controlling for verbal ability, phonological awareness, and spelling ability at Time 1. Reversing the analysis revealed that reading and spelling ability at Time 1 could not predict unique variance in textism use at Time 2 (i.e. account for changes in textism use over the year). Wood and colleagues thus contest that good literacy skills make children better at using textisms or more prone to use them and suggest that, instead, there is a causal contribution of using textisms to Standard English spelling skills. However, this may be mediated by phonological retrieval skills, because when this was also controlled for, the relationship between textism use and spelling ability was no longer statistically significant.

Johnson (2012) explored the link between textisms and reading ability with 91 Australian children. Their comprehension of textisms was tested by translating common text abbreviations into Standard English. Standardized reading tests were also administered. Johnson found that children who were better at translating the textisms also had better reading fluency (speed) and sentence comprehension skills. Johnson’s findings thus suggest a positive association between children’s understanding of textisms and reading skills. Yet the external validity of this study is limited, because the translation task only involved five items, four of which were so- called initialisms, so these results cannot simply be generalized to textisms in general.

Van Dijk et al. (2016) studied the influence of textese on Dutch children’s grammar and vocabulary skills and executive functions (cognitive abilities). 55 primary school children from grades 5 and 6 participated. Van Dijk and colleagues elicited text messages by (a) having the children reply to a message sent by the experimenter, as if they had received it from a friend, and (b) in response to an everyday scenario, again as if sending a message to a friend. They also collected naturalistic messages, by having the children copy actual text messages they had sent from their own mobile phones, as well as from WhatsApp chat groups with their classmates monitored by the researchers. All messages were analysed for two characteristics of textese, textisms and omissions, and the ratio of these features to the total number of words were calculated. The children completed a questionnaire measuring texting-related behaviours, e.g. asking them about their texting frequency (number of messages sent on a weekday and a weekend day). Their language skills were measured with standardized tests, of vocabulary and grammar performance in spoken language (sentence repetition task), and so were their executive functions

(selective attention, interference inhibition, visuo-spatial working memory, and verbal working memory). Reported texting frequency was not significantly related to vocabulary or grammar, but textism and omission ratios in the elicited messages correlated positively and marginally significantly with vocabulary and grammar scores, and textisms also with selective attention scores. Still, the only significant effect found in regression analyses was that the omission ratio was a positive predictor of children’s grammar performance (even after controlling for other variables, such as age): the more words children omitted in their elicited text messages, the better they performed on the spoken grammar task. The authors hypothesize that omissions in textese train children’s grammar system, because they have to use their grammar to decide which elements can be dropped. Since only a positive effect was found, Van Dijk and colleagues conclude that children may benefit from CMC.