Assessing L2 Reading Texts at the Intermediate Level: an Approximate Replication of Crossley, Louwerse, Mccarthy & Mcnamara

Lang. Teach. (2008), 41:3, 409–429 c Cambridge University Press doi:10.1017/S0261444808005077

Replication Studies

Assessing L2 reading texts at the intermediate level: An approximate replication of Crossley, Louwerse, McCarthy & McNamara (2007)

Scott A. Crossley Mississippi State University, USA [email protected]

Danielle S. McNamara University of Memphis, USA [email protected]

This paper follows up on the work of Crossley, Louwerse, McCarthy & McNamara (2007), who conducted an exploratory study of the linguistic differences of simplified and authentic texts found in beginner level English as a Second Language (ESL) textbooks using the computational tool Coh-Metrix. The purpose of this study is to provide a more comprehensive study of second language (L2) reading texts than that provided by Crossley et al. (2007) by investigating the differences between the linguistic structures of a larger and more selective corpus of intermediate reading texts. This study is important because advocates of both approaches to ESL text construction cite linguistic features, syntax, and discourse structures as essential elements of text readability, but only the Crossley et al. (2007) study has measured the differences between these text types and their implications for L2 learners. This research replicates the methods of the earlier study. The findings of this study provide a more thorough understanding of the linguistic features that construct simplified and authentic texts. This work will enable material developers, publishers, and reading researchers to more accurately judge the values of simplified and authentic L2 texts as well as improve measures for matching readers to text.

1. Introduction

An important textual distinction in the field of second language (L2) reading is made between authentic and simplified texts (Crossley,Louwerse, McCarthy & McNamara 2007). While the linguistic differences between these reading texts might seem subtle, the theoretical impact that they have made is quite sizeable. At present, there is no concurrence within the field as to the true value of these texts and whether language learners are better served by one type over the other (Day & Bamford 1998). Additionally, while authentic text is theoretically more appealing (Swaffar 1985; Bacon & Finnemann 1990; Tomlinson, Dat, Masuhara & Rubdy 2001), the majority of L2 reading texts at the beginner and intermediate levels use simplified input and many specialists highlight the practical value of simplified text (Shook 1997; Young 410 REPLICATION STUDIES

1999). Theoretical positions supporting either text type are weakened because few studies have investigated the linguistic properties of L2 reading texts, but researchers often depend on simplified or authentic texts’ linguistic features, syntax, and discourse structures to justify their use in language learning. This research gap was partially filled when Crossley et al. (2007) conducted an exploratory analysis of the linguistic features found in simplified and authentic texts using the computational tool, COH-METRIX (Graesser, McNamara, Louwerse & Cai 2004). Their study investigated differences in the linguistic structures of beginner level, simplified and authentic reading texts to provide a better understanding of what linguistic features actually comprised these reading texts. The results demonstrated that while many of the theoretical assumptions about the differences between simplified and authentic texts were accurate, many were not. As observed by Crossley et al. (2007), a weakness of their preliminary investigation was the limited size and lack of specificity in the corpus they used. Therefore, the purpose of the present study is to approximately replicate the earlier study using a larger and more specific corpus that takes into consideration not only learner level, but also the language skill being taught. Additionally, the present study will examine how differences between simplified and authentic texts support or do not support assumptions that L2 reading theorists have held about the value of these texts. Lastly, the present study will demonstrate the practical potential of the findings for L2 learning and material development. Because the Crossley et al. paper is a relatively recent study, there has been little critical research in the field since it was published. Thus, this paper will depend on many of the same theoretical premises that they introduced and much of their background information is briefly re-presented below. Readers interested in more than a cursory overview of the problem should refer back to the original Crossley et al. (2007) study.

1.1 Simpliﬁed text

In the field of L2 reading, the simplification of text is common. So common that publishing houses and editors provide material developers with linguistic guidelines to assist in the construction of text (Simensen 1987; Young 1999). The motivation behind simplification is to make text more comprehensible for L2 learners and to help prepare those learners for more advanced, authentic text (Young 1999). It is thought to be beneficial because it excludes unnecessary and distracting, idiosyncratic styles without suffering a loss of valuable communication features and concepts (Allen & Widdowson 1979) and because it contains increased redundancy and amplified explanation (Kuo 1993). However, simplified text is also widely criticized for removing authentic language in favor of more simplified and frequent forms, thus denying learners the opportunity to learn natural forms of language (Long & Ross 1993).

1.2 Authentic text

Authentic text is text written to fulﬁll a social purpose for native speakers within a language community (Lee 1995). Authentic text is thought valuable because it introduces students to natural and contextualized language (Larsen-Freeman 2002) and because of its use of S. A. CROSSLEY & D. S. MCNAMARA: L2 READING TEXTS 411

authentic linguistic features, especially cohesive devices, which are vital for the development of reading comprehension skills and information processing skills (Cowan 1976; Mackay 1979; Graesser, McNamara & Louwerse 2003). Authentic text is also favored because the natural lexical redundancy it is thought to contain is advantageous for reconstructing text and understanding unfamiliar lexicon (Goodman 1986; Johnson 1981). However, authentic text is also criticized for its linguistic difﬁculty, speciﬁcally its lexical and syntactic complexity and its conceptual and cultural density (McLaughlin, Rossman & McLeod 1983; McLaughlin 1987; Shook 1997; Young 1999).

1.3 Linguistic analysis of text types

Overall, authentic text is assumed to provide more natural language and more naturally occurring cohesion than simplified text, while simplified text is criticized as creating discourse that is unnatural and serves to reduce helpful redundancy, thus increasing text readability (Crandall 1995). Simplified text, however, is thought to benefit L2 learners because it is lexically,syntactically,and rhetorically less difficult than authentic text. Until recently,though, none of these theories had been empirically tested for their validity based on the actual linguistic features of the text. Crossley et al. (2007) addressed this research gap when they used the computational tool Coh-Metrix to analyze a general corpus of 105 texts that were taken from seven beginner L2 learning textbooks. The results of this study, while only preliminary, suggested that simplified texts provide L2 learners with more co-referential cohesion and relies on more frequent and familiar words than authentic texts. The results also indicated that simplified texts demonstrated less diversity in their part-of-speech tags, displayed less causality, depended less on complex logical operators, and were more syntactically complex than authentic texts. Lastly,the results suggested that no significant differences existed between simplified and authentic texts in reference to the polysemy and hypernymy values of the words in the texts (see Table 1 for an overview). The results of this initial study provided an important contribution to fields in L2 teaching and learning, not only because they explained how simplified and authentic texts differed in their linguistic construction and how these differences benefited or hindered L2 acquisition, but also because they demonstrated how computational tools could be of assistance to L2 reading researchers, material developers and publishers of L2 teaching materials. However, the authors recognized the limitations of the study and called for further research into the differences between simplified and authentic texts, particularly with regard to larger and more specialized corpora and studies that considered different types of learners, learner genres, and learner levels. This paper attempts to address these earlier limitations by approximately replicating the methods of the Crossley et al. study with an augmented corpus.

1.4 Coh-Metrix

As noted by Crossley et al. (2007), Coh-Metrix (Graesser et al. 2004) connects recent developments in computational linguistics and discourse processing to allow for the investigation of text difﬁculty and comprehension. Coh-Metrix features advanced syntactic 412 REPLICATION STUDIES

Table 1 Findings of Crossley et al. (2007): predicted versus result.

Predicted Result higher in higher in

Causality Authentic Authentic∗ Connectives Authentic No significant difference Logical operators Authentic No significant difference Lexical co-reference Simplified Simplified∗ Latent semantic analysis Simplified Simplified∗ POS tags frequent Simplified Simplified∗ POS tags infrequent Authentic Authentic∗ Polysemy Simplified No significant difference Hypernymy Authentic No significant difference Syntactic complexity Simplified Simplified∗ CELEX word frequency Simplified Simplified∗ Word familiarity Simplified Simplified∗∗ Concreteness Simplified No significant difference Imagability Simplified No significant difference Colorado Meaningfulness Simplified No significant difference Paivio Meaningfulness Simplified No significant difference Age of acquisition Authentic No significant difference ∗Finding was significant at p < .05 ∗∗Finding was significant at p < .001

parsers, part-of-speech taggers, shallow semantic interpreters, distributional models, and psycholinguistic databases. These components are integrated into an automated tool that can generate over 400 indices of language difﬁculty, readability, and cohesion. Many of the variables reported by Coh-Metrix closely match the linguistic features used to support both authentic and simpliﬁed text.

2. Predictions

Based on the close correlation between Coh-Metrix indices, research carried out within the field of material development, and the Crossley et al. (2007) study, seven sets of metrics from Coh-Metrix were selected for this analysis. As with the Crossley et al. study, the chosen metrics were selected on the basis of the linguistic methods used in the simplification process including the deletion of low-frequency vocabulary words, the revision of complex syntax, the shortening of text length, plot simplification, the simplification of difficult phrases (Cripwell & Foley 1984; Young 1999), the control of information, the control of language, and the control of discourse (Simensen 1987). These linguistic features are thought to most influence the text simplification process and are prominent in both the scholarly literature and publisher guidelines. The Coh-Metrix metrics that correspond to these linguistic features include causal cohesion, connectives and logical operators, co-reference measures, density of major parts S. A. CROSSLEY & D. S. MCNAMARA: L2 READING TEXTS 413

of speech measures, polysemy and hypernymy measures, syntactic complexity, and word information and frequency measures. Based on these metrics, predictions were made prior to the actual analysis of the corpus in this study. These predictions were based on expected differences between authentic and simpliﬁed text.

2.1 Causal cohesion

Causal cohesion is relevant to texts that depend on causal relations between events and actions. Additionally, it demonstrates causal relationships between simple clauses at the sentential level (Pearson 1974–75). It is measured in Coh-Metrix by calculating the ratio of causal verbs (make, cause) to causal particles (consequently, as a result). For this study, causality was predicted to be higher in authentic texts based on the accounts of Simensen (1987), who reported that publishing houses suggest that material writers simplify plot structure. This simpliﬁcation should result in a lower ratio of causal verbs and particles and mirror the ﬁndings of Crossley et al. (2007).

2.2 Connectives

Connectives play an important role in the creation of cohesive links between ideas. They are measured by Coh-Metrix in two dimensions: positive versus negative connectives and additive, temporal, and causal connectives. Logical operators are also measured in Coh- Metrix and include variants of ‘or’, ‘and’, ‘not’, and ‘if-then’ combinations, all of which have been shown to relate directly to the density and abstractness of a text and correlate to higher demands on working memory (Costerman & Fayol 1997). In the present study, it was predicted that some differences would be found in the incidence of overall connectives (all the connectives measured by Coh-Metrix conﬂated into one index) and logical operators based both on the idea that publishers’ guidelines call for lexical control and the careful use of connectives in L2 reading text (Simensen 1987) and from the ﬁndings of Crossley et al. (2007), which indicated that authentic texts were likely to have higher incidences of some connectives and logical operators.

2.3 Co-reference

Lexical co-reference has been shown to aid in text comprehension and reading speed (Kintsch & van Dijk 1978). In Coh-Metrix, it is measured in three forms: noun overlap between sentences, argument overlap between sentences, and stem overlap between sentences. Coh-Metrix also measures semantic similarity between parts of text using latent semantic analysis (LSA) at the text, paragraph, and sentence level. Unlike lexical markers of co- referentiality (noun, argument, and stem overlap), LSA provides for the tracking of words that are semantically similar, but may not be related morphologically (Deerwester et al. 1990; Landauer & Dumais 1997; Landauer, Foltz & Laham 1998). In this study,it was predicted that co-referentiality in the form of both LSA scores and co-reference scores would be greater 414 REPLICATION STUDIES

in simplified texts as they are created with considerations for increased clarification and elaboration (Young 1999) and publisher guidelines urge writers of simplified texts to take great care with pronominal reference (Simensen 1987). This prediction was also supported through the findings of the Crossley et al. (2007), who found that simplified texts were more likely to demonstrate greater co-referentiality than authentic texts.

2.4 Part-of-speech density

Material developers and writers often want to know how frequently particular parts of speech (POS) occur in the text in order to control for infrequent parts of speech (e.g., predeterminers, superlative adverbs, wh-determiners). Coh-Metrix tallies incidence scores for specific classes of POS as defined by the Brill (1995) POS tagger. In the present study it was predicted that authentic texts would demonstrate greater part-of-speech density in less frequent lexical categories as a result of publishers’ guidelines urging lexical constraints with regard to part- of-speech types used in simplified texts (Simensen 1987). This prediction was also supported through the findings of Crossley et al. (2007), who found that authentic texts was more likely to include less common parts of speech than simplified texts.

2.5 Syntactic complexity

Syntactic complexity is measured by Coh-Metrix in four major ways. The first index calculates the mean number of modifiers per noun phrase. The second and third indices measure the mean number of high-level constituents, defined as sentences and embedded sentence constituents, per word and per noun phrase. The fourth index tracks the ratio of words classified as pronouns to the incidence of noun phrases in the text. The present study predicted that simplified texts, if modified for sentence length, might exhibit more syntactic complexity because they would depend on shorter sentences with reduced language features and specified grammatical constructions (Cripwell & Foley 1984; Long & Ross 1993). Additionally, if simplified texts at the intermediate level depended on basic parts of speech at the expense of less frequent parts of speech (Cripwell & Foley 1984; Long & Ross 1993), there might be an increase in the number of modifiers per noun phrase and number of words before the main verb of the main clause, thus increasing the syntactic complexity of the texts.

2.6 Polysemy and hypernymy values

Coh-Metrix tracks the ambiguity and abstractness of a text by calculating the polysemy values, which refers to the number of senses a word has, and hypernymy values, which refers to the number of levels a word has in a conceptual, taxonomic hierarchy. The number of senses and the number of levels attributed to a word are measured using WordNet (Miller et al. 1990; Fellbaum 1998). Polysemy and hypernymy values are important as the process of simpliﬁcation consists of trimming words and phrases, deleting low-frequency vocabulary words (Young 1999), and controlling for ambiguity and vocabulary (Simensen 1987). One S. A. CROSSLEY & D. S. MCNAMARA: L2 READING TEXTS 415

possible shortcoming of simplified texts is that lexical simplification could lead to a reliance on more common words in the texts. Because more common words in English are more likely to have multiple meanings than less common words (Zipf 1949; Davies & Widdowson 1974), it has been argued that simplified texts would exhibit higher degrees of polysemy and hypernymy. This was not, however, supported by Crossley et al. (2007), who found no differences.

2.7 Word information

Coh-Metrix calculates word information on five indices: familiarity,concreteness, imagability, meaningfulness, and age of acquisition. These measures come from the MRC (Medical Research Council) Psycholinguistic Database (Coltheart 1981) and are based on the works of Paivio (1965), Toglia & Battig (1978) and Gilhooly & Logie (1980), who used human subjects to rate large collections of words for psychological properties. Coh-Metrix measures word frequency using CELEX (Baayen, Piepenbrock & Gulikers 1995), which contains word frequencies taken from the 1991 version of the COBUILD corpus, a 17.9 million word corpus. Word frequency is important because frequent words are normally read more rapidly and understood better than infrequent words (Just & Carpenter 1980; Haberlandt & Graesser 1985). In this study,it was predicted that simplified texts would display a greater degree of word familiarity, showing lower CELEX word frequency scores. This is because the simplification process consists of trimming words and phrases and deleting low-frequency vocabulary words (Cripwell & Foley 1984; Young 1999). In light of the findings reported by Crossley et al. (2007), no significant differences were expected in the categories of concreteness, meaningfulness, age of acquisition, and imagability.

3. Method: text selection

A total of 224 texts were taken from 11 intermediate L2 reading textbooks. Because this analysis is intended as a more discriminating one than that used by Crossley et al. (2007), a more specialized corpus was produced that reflected a greater restricted audience and purpose. The corpus used by Crossley et al. was a general corpus and not well defined in terms of age level of audience and language skill being taught, and only contained a small, unbalanced sampling of authentic texts. Their corpus was constrained by the realities of beginner L2 reading textbooks, which rarely include authentic reading texts. This forced their analysis to unevenly examine many more simplified texts than authentic texts. Additionally, the lack of access to authentic beginner texts did not allow them to consider age level, with their corpus containing texts marketed for both children and adults, nor the language skill with their corpus containing texts taken from grammar books, reading books, and writing books. These restrictions limited the extendibility of their findings and forced the study to be exploratory in nature. In contrast, the present analysis was designed to benefit from a much larger corpus that was more narrowly angled and used more rigorous selection criteria including ability level, age level, and language skill. To correct for the restrictions found in Crossley et al., a larger sampling of texts was selected. Additionally, all texts for this analysis 416 REPLICATION STUDIES

Table 2 Descriptive corpus comparison.

Crossley et al. (2007) study Restrictive study Simpliﬁed Authentic Simpliﬁed Authentic texts texts texts texts

Intended audience Adults/Children Children Adults Adults Language skill Grammar/Reading/ Reading Reading Reading Writing Number of source 5 274 textbooks Number of reading texts 81 24 123 101 Size of corpus (words) 21,117 15,640 57,961 70,333 were taken from intermediate reading textbooks that were marketed for adult learners in either an ESL or EFL (English as a Foreign Language) environment. While the texts were all from reading textbooks, the genres varied. Common genres found in both the simplified and authentic reading textbooks included history texts, inspirational texts, social science texts, informational texts, biographies, and fiction. As in the earlier study, all reading texts in the selected readers that contained approximately 100 words or more were included in the analysis. Of the 11 selected textbooks, four contained authentic texts (Fellag 2000; Pimsleur 1995; Ryall 2000; Sokolik 1999), while the others included simplified texts (Collins 2004, 2005; Malarcher 2004a, b; Pickett 1991; Smith & Mare 2004; Zukowski/Faust 2002). There was no overlap between the texts. The total size of the two corpora was 128,294 words. The simplified corpus contained 57, 961 words (M = 471, SD = 222) and comprised 123 texts. The authentic corpus contained 70,333 words (M = 696, SD = 555) and contained 101 texts. Descriptive data comparing the corpus used in this study to the corpus used in Crossley et al. (2007) are presented in Table 2. Text size was not considered a factor, particularly because Coh-Metrix either normalizes its findings based on text length or provides normalized ratio scores.

4. Results

4.1 Statistical analysis

This study follows the statistical methodology of Crossley et al. (2007) in that it uses t-tests to distinguish between differences in text types.1 As discussed by Crossley et al., multiple statistical analyses increase the probability of Type 1 errors. However, like Crossley et al., the

1 For data that was non-parametric, both t-tests and Mann-Whitney tests were conducted. If both the parametric and the non-parametric tests yielded signiﬁcant results, then we only report the parametric tests. This approach is used so that mean and standard deviation can be reported. The mean and standard deviation found in t-tests are more informative than the sum of ranks and mean of ranks reported by Mann-Whitney tests. S. A. CROSSLEY & D. S. MCNAMARA: L2 READING TEXTS 417

Table 3 Comparison of authentic and simpliﬁed texts for causal particles and verbs: means (M), standard deviations (SD), t-value, and p-value.

Authentic text Simpliﬁed text

MSDMSDt (224) p Causal particles and verbs 1.31 0.93 1.16 0.66 1.43 0.16

Table 4 Comparison of authentic and simpliﬁed texts for connectives: means (M), standard deviations (SD), t-values, and p-values.

Authentic text Simpliﬁed text

MSDMSDt (224) p Incidence 80.75 15.34 82.50 17.71 −0.78 0.44 connectives Positive additive 37.20 13.12 37.50 12.47 −0.18 0.86 Positive temporal 10.43 6.72 10.16 6.35 0.31 0.76 Positive causal 22.87 8.50 24.46 9.39 1.31 0.19 Negative additive 11.28 7.00 11.71 7.71 −0.43 0.67 Negative temporal 0.72 1.48 0.46 1.19 1.45 0.15

analyses in this study were theoretically motivated and predictions were made a-priori (see ‘Predictions’ section for more information). Thus, conﬁdence levels for the statistical analyses were not lowered.

4.2 Causal cohesion

As predicted and found by Crossley et al. (2007), there was a trend for authentic texts to contain more causal particles and verbs (see Table 3). However, this difference was not statistically signiﬁcant.

4.3 Connectives

The results for connectives were mixed (see Table 4). As predicted, the overall incidence of connectives did not differ significantly between authentic and simplified texts. However, in contrast to Crossley et al. (2007), no significant differences were found in more specific categories of connectives such as positive causal connectives, negative additive connectives, and negative temporal connectives. 418 REPLICATION STUDIES

Table 5 Comparison of authentic and simpliﬁed texts for logical operators: means (M), standard deviations (SD), t-values, and p-values.

Authentic text Simpliﬁed text

MSDMSDt (224) p Density logical 44.78 14.12 40.55 13.59 2.28 0.02 operators Density noun 325.32 25.71 339.20 39.95 −3.02 <.01 phrases Density noun and 0.22 0.13 0.20 0.11 1.19 0.24 pronoun Ratio pronouns to 0.24 0.14 0.21 0.12 0.22 0.14 noun phrases Number of ands 29.04 11.37 27.33 10.90 1.15 0.25 Number of ifs 2.09 3.13 1.80 2.54 0.74 0.46 Number of ors 4.98 5.51 3.77 4.58 1.80 0.07 Number of 2.09 3.14 1.80 2.54 0.74 0.46 conditionals Number of 8.68 7.19 7.66 5.60 1.20 0.23 negations

4.4 Density of logical operators

Contrary to the findings of Crossley et al. (2007) and the prediction that no difference would be found in the use of logical connectors, authentic texts showed a significantly greater density of all logical connectors (see Table 5). When comparing specific instances of logical operators, authentic texts were shown to have a greater density of ors as compared to simplified texts, but this only approached significance. Simplified texts, in contrast, were shown to have a significantly greater density of noun phrases. No significant differences were noted in the other categories of logical operators.

4.5 Lexical co-reference

As predicted and in line with the findings of Crossley et al. (2007), when comparing simplified and authentic texts, significant differences were found in overall co-referentiality (see Table 6), with simplified texts exhibiting greater co-referentiality than authentic texts. In two of the three primary categories of co-reference cohesion, simplified texts displayed significantly greater co-referentiality: stem overlap and noun overlap. Simplified texts also showed a trend toward greater argument overlap than authentic texts, but this finding only approached significance. S. A. CROSSLEY & D. S. MCNAMARA: L2 READING TEXTS 419

Table 6 Comparison of authentic and simpliﬁed texts for lexical co-referentiality: means (M), standard deviations (SD), t-values, and p-values.

Authentic text Simpliﬁed text

MSDMSDt (224) p All co-referential 0.73 0.40 0.90 0.50 −2.61 0.01 Stem overlap 0.25 0.15 0.32 0.19 −2.78 <.01 Noun overlap 0.19 0.13 0.25 0.16 −3.02 <.01 Argument overlap .29 0.14 0.32 0.16 −1.68 0.09

Table 7 Comparison of authentic and simpliﬁed texts for latent semantic analysis (LSA): means (M), standard deviations (SD), t-values, and p-values.

Authentic text Simpliﬁed text

MSDMSDt (224) p LSA overall 0.88 0.32 0.97 0.30 −2.31 0.02 LSA sentence to text 0.29 0.10 0.31 0.08 0.37 0.55 LSA sentence to sentence 0.19 0.07 0.21 0.07 −2.40 0.02 (Adj) LSA sentence to sentence 0.16 0.07 0.18 0.07 −2.13 0.03 (all) LSA sentence to paragraph 0.23 0.10 0.27 0.10 −3.31 <.01

4.6 Latent semantic analysis (LSA)

As predicted and found by Crossley et al. (2007), significant differences were noted between the LSA scores of authentic and simplified texts (see Table 7). Overall, simplified and authentic texts showed significant differences in their LSA scores with simplified texts demonstrating more semantic overlap than authentic texts. Additionally, LSA sentence to sentence comparing adjacent sentences was significantly greater in simplified texts, as was LSA sentence to sentence comparing all sentences, and LSA sentence to paragraph scores than in authentic texts. These results indicate that simplified texts exhibited more semantic similarity within sentences and within paragraphs than did authentic texts. However, no significant difference was found in LSA sentence to text comparisons.

4.7 Incidence of major parts of speech

As predicted, significant differences were found between simplified and authentic texts in relation to their part-of-speech use, but to a much lesser extent than in Crossley et al.’s (2007) exploratory analysis (see Table 8). In this analysis, authentic texts revealed a significantly greater number of infrequent linguistic features such as gerunds, past participles, and 420 REPLICATION STUDIES

Table 8 Comparison of authentic and simpliﬁed texts for parts of speech: means (M), standard deviations (SD), t-values, and p-values.

Authentic text Simpliﬁed text

MSDMSDt (224) p Nouns overall 286.42 52.05 301.80 45.35 2.50 0.02 Pronouns 79.50 47.30 73.27 39.60 1.26 0.21 Verb incidence 172.81 27.57 172.21 24.20 0.17 0.86 Comparative adverbs 0.86 1.49 1.02 1.81 −0.71 0.48 Gerunds 17.60 9.42 14.71 8.82 2.36 0.02 Interjections 0.34 1.06 0.22 0.98 0.88 0.37 Modals 12.71 9.00 10.93 7.48 1.62 0.10 Particle incidence 0.40 0.93 0.30 0.91 0.74 0.46 Past participles 23.52 12.03 17.58 10.82 4.30 <.01 Singular proper nouns 49.21 37.57 55.01 38.73 −1.13 0.26 Superlative adverbs 1.88 3.73 1.42 2.23 1.14 0.25 Wh-determiners 3.71 3.67 3.28 3.40 0.92 0.36 Wh-pronouns 5.00 4.48 3.58 3.88 2.01 0.01 Past tense verbs 46.30 35.67 57.10 35.77 2.25 0.03 Adjectives 71.80 27.52 65.83 22.15 1.80 0.07 Subordinating conjunctions 121.70 20.40 116.37 23.09 1.80 0.07 and prepositions wh-pronouns. Authentic texts also displayed a tendency toward having a higher incidence of adjectives and subordinating conjunctions and prepositions, but these findings only approached significance. Simplified texts, on the other hand, showed significantly greater incidence of nouns, and past tense verbs. Unlike the exploratory analysis, no significant differences were noted between simplified and authentic texts in other categories.

4.8 Polysemy and hypernymy

Contrary to the findings of Crossley et al. (2007), where no differences were found in polysemy and hypernymy scores, significant differences were noted in their values when comparing the simplified and authentic texts in the present analysis (see Table 9). Verb hypernymy was significantly greater in simplified texts,2 while polysemy values were significantly greater in simplified texts. No significant difference, however, was noted in noun hypernymy.

2 Conversely, this is demonstrated by a lower hypernymy score. This is because the hypernymy scale is reversed so that subordinates (hyponyms) of the superordinate (hypernymy) can be measured by increasing intervals. If hypernymy scores were measured using a normal hierarchical scale with hypernyms above hyponyms, the amount of hyponyms possible would be ﬁxed. S. A. CROSSLEY & D. S. MCNAMARA: L2 READING TEXTS 421

Table 9 Comparison of authentic and simpliﬁed texts for polysemy and hypernymy: means (M), standard deviations (SD), t-values, and p-values.

Authentic text Simpliﬁed text

MSDMSDt (224) p Polysemy 6.50 1.12 6.86 1.03 −2.50 <.01 Verb hypernymy 1.91 0.15 1.84 0.16 3.58 <.01 Noun hypernymy 4.99 0.41 5.03 0.36 −0.88 0.36

Table 10 Comparison of authentic and simpliﬁed texts for syntactic complexity: means (M), standard deviations (SD), t-values, and p-values.

Authentic text Simpliﬁed text

MSDMSDt (224) p Syntactic complexity (all) 7.47 1.62 6.67 1.76 3.51 <.01 Number constituents per 3.10 0.77 2.60 0.76 4.92 <.01 sentence Number constituents per 0.19 0.04 0.19 0.03 −0.34 0.73 word Syntactic logic 44.87 14.11 40.56 13.59 2.28 0.02 Modiﬁers per noun phrase 0.79 0.16 0.75 0.16 1.90 0.06 Number of words before 3.58 1.20 3.33 1.13 1.62 0.10 main verb of main clause

4.9 Syntactic complexity

Contrary to prediction that syntactic complexity would be greater in simplified texts and to the findings of Crossley et al. (2007), the results of this study indicated that authentic texts are significantly more syntactically complex than simplified texts (see Table 10). Additionally, authentic texts showed more higher-level constituents per sentence and higher syntactic logic scores than simplified texts. Authentic texts also demonstrated a propensity to have more modifiers per noun phrase, but this finding only approached significance. For higher-level constituents per word and number of words before the main verb of a clause, no significant differences were noted.

4.10 Word information

As predicted and in line with Crossley et al. (2007), this investigation showed that authentic texts and simplified texts differ significantly in the word information categories of familiarity and frequency (see Table 11). With regard to familiarity, simplified texts were significantly more likely to contain words that were more familiar than those in authentic texts, while 422 REPLICATION STUDIES

Table 11 Comparison of authentic and simpliﬁed texts for word information: means (M), standard deviations (SD), t-values, and p-values.

Authentic text Simpliﬁed text

MSDMSDt (224) p Familiarity 576.09 7.99 579.54 7.52 −3.32 <.01 CELEX word frequency 2.27 0.18 2.34 0.14 −3.15 <.01 Concreteness 385.40 24.08 388.68 23.12 −1.03 0.30 Imagability 418.80 22.22 422.64 20.40 −1.35 0.18 Colorado Meaningfulness 432.24 13.38 434.94 12.72 −1.54 0.12 Paivio Meaningfulness 634.83 28.95 645.17 34.86 2.38 0.02 Age of acquisition 332.17 40.62 318.68 35.51 2.65 <.01 word frequency comparisons demonstrated that simplified texts contained significantly more frequent words than authentic texts. Additionally, this study demonstrated that simplified texts showed significantly lower age of acquisition scores and higher Paivio Meaningfulness scores. In the word information categories of concreteness, Colorado Meaningfulness, and imagability, no significant differences were noted.3

5. Discussion

Using the computational tool Coh-Metrix, this study has demonstrated that many properties of both simplified and authentic texts that were examined in Crossley et al.’s. (2007) exploratory analysis held true in this larger and more specific analysis. While differences between the two analyses were evident, these differences are explainable and are likely a consequence of the linguistic difficulty of the text being modified to suit the learning needs of the advancing student. Additionally, it is possible that because the corpus constructed for this analysis was much larger than the corpus used by Crossley et al. it allowed for greater coverage of lexical items such as connectives, logical operators, part-of-speech tags, polysemy and hypernymy values, and word information measures. The findings reported here thus allow for an expansion of the criteria that can generally be used to classify the language features found in authentic and simplified texts (see Table 12 for an overview) and should be seen as a progression of the Crossley et al. study. This study, as an improvement on the Crossley et al. study, is important as it provides material developers, reading researchers, and classroom teachers with more explicit information about the value of reading texts.

3 Colorado Meaningfulness scores are based on the work of Toglia & Battig (1978), while Paivio Meaningfulness scores are based on the work of Paivio, Yuille & Madigan (1968). Meaningfulness scores refer to how strongly a word is associated with other words. In both studies, subjects were directed to rate words on a scale of 1 to 7 according to how strongly they felt the words were associated in meaning to other words. In the MRC Psycholinguistic Database, the two scores were not merged because the scores did not correlate highly as the result of different instructions given to the participants of each study. S. A. CROSSLEY & D. S. MCNAMARA: L2 READING TEXTS 423

Table 12 Restrictive study ﬁndings: predicted versus result.

Results from Predicted Crossley et al. higher in Result higher in (2007) study

Causality Authentic No significant Authentic∗ difference Connectives Authentic No significant No significant difference difference Logical operators Authentic Authentic∗ No significant difference Lexical co-reference Simplified Simplified∗ Simplified∗ Latent semantic analysis Simplified Simplified∗ Simplified∗ POS tags frequent Simplified No significant Simplified∗ difference POS tags infrequent Authentic No significant Authentic∗ difference Polysemy Simplified Simplified∗ No significant difference Hypernymy Simplified Simplified∗∗ No significant difference Syntactic complexity Simplified Authentic∗∗ Simplified∗ CELEX word frequency Simplified Simplified∗ Simplified∗ Word familiarity Simplified Simplified∗∗ Simplified∗∗ Concreteness Simplified No significant No significant difference difference Imagability Simplified No significant No significant difference difference Colorado Meaningfulness Simplified No significant No significant difference difference Paivio Meaningfulness Simplified Simplified∗ No significant difference Age of acquisition Authentic Authentic∗ No significant difference ∗Finding was significant at p < .05 ∗∗Finding was significant at p < .001

This analysis has demonstrated that intermediate texts, unlike the beginner texts used by Crossley et al. (2007), exhibit no significant difference in the use of causal particles and verbs. While this finding contradicts the finding of the exploratory study, it is quite likely that the amount of causality displayed in simplified texts advances as learners advance. The present study also demonstrates that the use of connectives is similar to the use of connectives in Crossley et al., with the exception of positive causal, negative additive, and negative temporal connectives, which were higher in the exploratory analysis. This finding should likely be taken to mean that simplified texts at the intermediate level have begun to develop and link ideas with more complex connectives than had simplified texts at the beginner 424 REPLICATION STUDIES

level. However, unlike the findings reported by Crossley et al., this study demonstrates that authentic texts are more likely to employ logical connectors than is simplified texts. Logical operators are important for discussing hypothetical situations and logically linking phrases and their comparative absence from simplified texts might limit the discourse structure of the texts. In support of Crossley et al. (2007), this study indicates that authentic texts exhibit less overall lexical and semantic co-referential cohesion than do simplified texts and provides further evidence that simplified texts, with their dependency on noun phrases, avoidance of pronominal reference, and simple syntactic structure, provide greater co-referentiality. This study also finds that authentic texts at the intermediate level reveal greater diversity of word types than simplified texts, especially in more complex part-of-speech types. However, when compared to the beginner level analysis of Crossley et al. (2007), the differences between authentic and simplified texts are not as great. With regard to syntax, this study demonstrates that simplified texts at the intermediate level do not show a tendency to have more complex syntactic constructions than authentic texts. These findings are likely the result of simplified texts at the intermediate level moving toward more natural syntactic constructions and moving away from short sentences that rely on simple sentence constructions and elaboration. When considering word information and frequency, the findings of this analysis were similar to those of Crossley et al. (2007) in that authentic texts used significantly less familiar words than simplified texts and used significantly less frequent words than simplified texts. Additionally, unlike the exploratory analysis of Crossley et al., simplified texts showed a significantly lower age of acquisition score and significantly higher Paivio Meaningfulness scores. These results should be viewed as indications of the strength of simplified texts to provide more developmental and substantial language to L2 learners. In polysemy and hypernymy categories, some key differences were noted that were not apparent in the earlier study. Most importantly, with regard to polysemy and hypernymy values, significant differences between simplified and authentic texts were discovered. In this larger and more specific analysis, verb hypernymy was significantly greater in simplified texts leading to the conclusion that simplified texts uses more abstract verbs than authentic texts. Also, polysemy values were significantly greater in simplified texts, leading to the conclusion that simplified texts provide more ambiguous lexical items than authentic texts. These findings help to further substantiate how authentic and simplified texts differ in their use of linguistic features and provide an important foundation for what these differences are. However, in the absence of transfer potential, the results are little more than descriptive. Nevertheless, this study, when considered in tandem with Crossley et al. (2007), opens important avenues for discussion about the benefits of reading texts types and how these texts can be better designed to suit the needs of readers based on language processing theories. For instance, knowing that logical operators are important lexical features that constitute cohesive bonds between sections of texts (Halliday 1985), it would be beneficial to avoid the minimalization of such bonds when writing simplified texts. This is because reducing the amount of logical operators in a texts could lead to texts that do not elaborate, extend, and enhance the ideas of the texts as much as might be found in authentic texts. Thus, this may create texts that are more difficult for L2 learners to process (Johnson 1981). A tool such as S. A. CROSSLEY & D. S. MCNAMARA: L2 READING TEXTS 425

Coh-Metrix would be invaluable in measuring this lexical feature. Another major criticism of simplified texts has been their dependence on lexical simplification, which could mean more lexically ambiguous texts resulting from more complex and precise words being replaced with simpler and more frequent words (Davies & Widdowson 1974). This criticism appears to be accurate, but is addressable. Specifically, by tracking the polysemy and hypernymy scores of simplified texts using Wordnet, material developers would be able to select lexical items that are frequent but not necessarily ambiguous or abstract. This could produce texts that are easier to process as well as produce texts that would promote greater language acquisition. Texts controlled for frequency in this way would allow for quicker lexical processing and thus could be an advantage for L2 readers with regard to automaticity and word recognition (Woodinsky & Nation 1988; Carrell & Grabe 2002). Lastly, simplified texts appear to not provide as robust causal relations as authentic texts. Using a computational tool such as Coh-Metrix would allow material developers to measure causal relationships in simplified text and produce texts that better assist the reader in relating events to actions by integrating stronger causal relationships between clauses (Pearson 1974–75). Authentic texts, however, are far from optimal, but computational tools such as Coh- Metrix can also be a valuable resource in aiding material developers with selecting authentic texts that allow maximum potential for language acquisition and textual processing. For instance, the co-referentiality found in simplified L2 texts is considered important in providing helpful redundancy (Swaffar 1981; Crandall 1995; Nuttall 2005; Day & Bamford 1998). Co- referentiality has also been shown to assist readers in understanding the message and intention of a text and is an important means by which readers can connect to a text (Haber & Haber 1981; Smith 1988). However, authentic texts lack the same degree of co-referentiality as simplified texts. Measuring the lexical and semantic co-referentiality of authentic texts prior to adoption would allow for the selection of authentic texts that best provide the reader with helpful redundancy. Additionally, authentic texts could be compared and selected on the basis of multiple measures of syntactic complexity beyond the length of the sentence. These more robust indices of syntactic complexity would help in better matching the text to that of the reader’s abilities, allowing for greater language comprehension and intake.

6. Conclusion

The aim of this study was to provide an approximate replication study of the Crossley et al. (2007) analysis of L2 reading texts. To improve upon this original analysis, the present study looked at a larger corpus of simplified and authentic texts that were selected under more rigid parameters including audience and purpose. Generally, the results of this study replicate those of Crossley et al. and provide strong empirical evidence as to the type and extent of the differences between the two most prominent forms of L2 reading material: authentic and simplified texts. There are also possible limitations to the study. The assumption that texts taken from different learner levels can be compared mutually raises theoretical questions about how well beginner and intermediate texts represent similar genres. While this study would have benefited from a selection of beginner texts that were different from those used by Crossley 426 REPLICATION STUDIES

et al. (2007), this was not a practical alternative as authentic texts at the beginner level are rare and often marketed for children. Moreover, considering that the Crossley et al. study was exploratory, this study only attempted to replicate the findings. Any differences between the two studies were explained partially based on linguistic advancements that mirrored the progression of the texts’ learning level. Additionally, the corpora used in this study, while larger than those used in Crossley et al., do not likely reach a level of representativeness. If feasible, future replication studies should consider even larger corpora. One interesting by-product of this analysis, when viewed in light of Crossley et al.’s (2007) exploratory study,is that some measurements differed between the study of beginner texts and the present study of intermediate texts. To discern whether these differences are the result of the size of the two corpora (with the larger corpus in this analysis allowing for more coverage of lexical items) or of advancing linguistic features between beginner and intermediate texts, a comparison needs to be made between simplified beginner texts and simplified intermediate texts. The same needs to be done for authentic texts used at the beginner and intermediate levels. Such analyses would allow for a comparison of linguistic features and provide answers regarding whether the differences noted between these two studies are a result of controlled linguistic changes within the texts themselves, their advancement from the beginner level to the intermediate level, or the result of differences in coverage. Such analyses would also prove helpful in investigating how well Coh-Metrix can measure texts for linguistic difficulty between levels and perhaps provide support for the notion that Coh-Metrix provides an alternative to traditional readability formulas. Moreover, an analysis comparing authentic or simplified texts between beginner and intermediate levels could provide telling information about how efficient the linguistic strategies used by material developers are in terms of selecting authentic texts or writing simplified texts. This analysis, while providing evidence of the differences between simplified and authentic texts, also demonstrates that modifying texts according to pedagogical and theoretical principles can cause unintended consequences to the natural structure of the discourse. These modifications can affect how a text is processed, comprehended, and understood. In the case of L2 learners, this may have important ramifications. The recognition of how these modifications interact with language processing can allow for important changes to be made in L2 reading texts. Progress such as this would allow for more readable texts and greater access to comprehensible input. This would have important implications for the development of L2 reading material, L2 acquisition, and L2 reading theory.

Acknowledgments

The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant IES R3056020018-02 to the University of Memphis. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education. The authors would like to thank Drs. Charles Hall and Max Louwerse for their help with the early forms of this paper. The authors would also like to express their appreciation to Heinle, a part of Cengage Learning, for their help in ﬁnding and providing many of the texts analyzed in this study. The authors are deeply S. A. CROSSLEY & D. S. MCNAMARA: L2 READING TEXTS 427

indebted to the anonymous reviewers whose comments and ideas were crucial in producing the ﬁnal version of this article.

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SCOTT CROSSLEY is an Assistant Professor and director of the TESOL program at Mississippi State University. His interests include computational linguistics, corpus linguistics, discourse processing, and discourse analysis. He has published articles in genre analysis, multi-dimensional analysis, discourse processing, speech act classiﬁcation, cognitive science, and text linguistics.

DANIELLE MCNAMARA is a cognitive scientist and director of the Cognitive Program at the University of Memphis. Her work involves the theoretical study of cognitive processes as well as the application of cognitive principles to educational practice. She has published widely in cognitive science with a recent emphasis on text comprehension and the effects of text coherence.