Morpheme Studies

Brown (Reference Brown1973) examined the L1 English acquisition of 14 grammatical morphemes by three children and found that the developmental patterns were similar across the three children. Following Brown’s study, similar investigations emerged in SLA research to establish whether L1 and L2 acquisition show similar patterns. Dulay and Burt (Reference Dulay and Burt1973) investigated the acquisition order of eight grammatical morphemes (present progressive -ing, plural -s, irregular past tense, possessive ’s, articles, third-person -s, copula be, and auxiliary be) by three groups of children learning English as a L2: 95 Mexican-Americans, 26 Spanish, and 30 Puerto Ricans. The researchers predicted that the three groups would yield the same order, but that the order would be different from the order observed in L1 acquisition, because L2 learners already possess semantic distinctions that would affect L2 acquisition of mappings of semantic functions to morphemes.

Central to morpheme studies is their focus on accuracy of use as a measure of acquisition. ^{Footnote 1} To measure accuracy, Dulay and Burt (Reference Dulay and Burt1973) calculated the percentage of correct forms in the contexts in which each morpheme was obligatory. Many subsequent studies followed this practice and adopted the related assumption that accuracy reflects the degree of acquisition (cf. de Villiers & de Villiers, Reference De Villiers and de Villiers1973). Dulay and Burt (Reference Dulay and Burt1973) found that the L2 order of acquisition was different from the acquisition order reported in Brown (Reference Brown1973) for L1 learners. But at the same time they found that all the three groups of L2 learners they investigated showed the same order, a result that led Dulay and Burt to conclude that L2 acquisition is guided by universal strategies giving rise to a universal order of L2 morpheme acquisition.

To confirm the hypothesis of the universal order of morpheme acquisition, Dulay and Burt (Reference Dulay and Burt1974) investigated L1 influence on acquisition order. They compared 60 six- to eight-year-old L1 Spanish children with 55 L1 Chinese children of the same age learning English, and they tested the acquisition order of 11 morphemes. They found a high correlation between the two groups of learners, from which they concluded that there is a consistent acquisition order of grammatical morphemes across L2 learners, despite different L1 backgrounds. Subsequent studies further confirmed the existence of a universal order of acquisition in adult L2 learners (Bailey, Madden, & Krashen, Reference Bailey, Madden and Krashen1974), in both ESL (English as a second language) and EFL (English as a foreign language) learners (Pica, Reference Pica1983a), and in both instructed and noninstructed learners (Larsen-Freeman, Reference Larsen-Freeman1975).

An early criticism of morpheme studies was that the order of acquisition conceals the relative distance in accuracy. A 1% difference in accuracy between two morphemes can result in the same ranking as a difference of 50%. To address this criticism, Krashen (Reference Krashen, Brown, Yorio and Crymes1977) reviewed the literature and grouped up in a single rank morphemes with similar accuracy scores. Based on his analysis, he proposed the “natural order” shown in Figure 1, which is believed to be universally followed by L2 learners of English. The universality of a fixed natural order has since been widely accepted (Luk & Shirai, Reference Luk and Shirai2009) among researchers of varying theoretical perspectives (Meisel, Reference Meisel2011; Ortega, Reference Ortega2009) and is presented as a basic finding in SLA textbooks (Luk & Shirai, Reference Luk and Shirai2009). ^{Footnote 2}

Figure 1. The natural order of acquisition proposed by Krashen (Reference Krashen, Brown, Yorio and Crymes1977).

In the 1980s, focus shifted to explanation, and a number of factors (e.g., complexity and frequency) were proposed to account for the observed order (Larsen-Freeman, Reference Larsen-Freeman1976; VanPatten, Reference VanPatten, Eckman, Bell and Nelson1984; Zobl & Liceras, Reference Zobl and Liceras1994). Given the belief in a universal natural order, L1 has been neglected as a determinant of the order of acquisition. This view, however, is beginning to change.

L1 Influence in Morpheme Studies

The universality of the natural order was consistent with the view that morphological crosslinguistic influence is weak, at least in comparison to phonology or lexis (Jarvis & Pavlenko, Reference Jarvis and Pavlenko2007). At the same time though, research on the acquisition of the English article has shown clear L1 effects (e.g., J. A. Hawkins & Buttery, Reference Hawkins and Buttery2010; Jarvis, Castañeda Jiménez, & Nielsen, Reference Jarvis, Castañeda Jiménez, Nielsen, Jarvis and Crossley2012; Snape, Reference Snape2005, Reference Snape2008; Zdorenko & Paradis, Reference Zdorenko and Paradis2012).

Luk and Shirai (Reference Luk and Shirai2009) reviewed the literature to investigate whether Japanese, Korean, Chinese, and Spanish learners of English acquire grammatical morphemes following the natural order. They found some clear L1 effects. For instance, Japanese, Korean, and Chinese learners acquire the possessive ’s earlier than predicted by the natural order and show later acquisition of the plural -s. However, Luk and Shirai (Reference Luk and Shirai2009) is a review paper drawing from a very diverse set of studies varying in length (longitudinal vs. cross-sectional), criteria of acquisition (accuracy order vs. the threshold of 80% or 90% correct), and mode (oral vs. written production), while its scope is limited to three grammatical morphemes.

Building on Luk and Shirai’s findings, the goal of our article is to investigate L1 effects in a more systematic way. First, we extend our investigation to six morphemes. We hypothesize that the lack of the equivalent feature in the L1 leads to low accuracy, a hypothesis confirmed for the acquisition of the article (e.g., J. A. Hawkins & Buttery, Reference Hawkins and Buttery2010; Ionin & Montrul, Reference Ionin and Montrul2010) but not investigated in a comparative way for six morphemes.

The second question we investigate is whether all of the six morphemes are equally vulnerable to L1 influence—whether, for example, the acquisition of the article and plural -s is equally affected when there are no corresponding morphemes in the L1. This question has not yet been investigated empirically within morpheme studies, and to the best of our knowledge no theoretical account makes explicit predictions regarding this question for the set of morphemes investigated here. We can, however, extract some predictions from existing proposals discussing related issues. Some SLA researchers signal the mapping of semantic/functional features to their morphological realization in the L2 as particularly challenging for learners (Slabakova, Reference Slabakova2014), with the article and aspectual distinctions among the hardest such mappings (DeKeyser, Reference DeKeyser2005). At the same time, other theoretical accounts predict that it is the morphological features generally lacking semantic content (i.e., “uninterpretable” features) that are harder to acquire in the L2 (this is known as the “interpretability hypothesis”; see Tsimpli & Dimitrakopoulou, Reference Tsimpli and Dimitrakopoulou2007).

Slobin (Reference Slobin, Gumperz and Levinson1996) offers a more explicit discussion of the question of which kind of morphemes are more susceptible to L1 influence. He draws a distinction between grammatical categories that correspond to language-specific concepts—categories of “thinking for speaking”—and grammatical categories corresponding to general semantic concepts—categories of thought. Definiteness and aspect are examples of language-specific concepts, because neither definiteness nor perfect or progressive aspect are properties of the world that can be experienced independently of language, through our senses and perceptual system. Rather, they are language-specific concepts that are learned through language use. Plurality, on the other hand, is a language-independent concept, a category of thought (notwithstanding the typological variation in the way languages express plurality and number crosslinguistically). He then predicts that language-specific categories of thinking for speaking like definiteness, aspect, and voice will be more vulnerable to L1 influence, whereas language-independent concepts like plurality will be less influenced.

With the caveat that neither Slobin’s proposal nor the interpretability hypothesis have explicitly discussed the question of L1 vulnerability for the six morphemes of our study, we can attempt to extract some predictions for our study by combining insights from the two approaches. Slobin predicts differential vulnerability to the L1—in particular, that articles and aspectual morphemes ought to be more sensitive to L1 influence than, for instance, plural -s. No specific predictions can be made for an agreement morpheme like third-person -s that does not require acquisition of a new concept (e.g., definiteness) and does not correspond to a language-independent concept, as number marking does. However, if the interpretability hypothesis is correct, we would expect this purely syntactic feature to be harder to acquire than all other features encoding some semantic function. We would therefore expect the third-person agreement -s to be most vulnerable to the L1, followed by the articles and aspectual/tense morphemes, followed by number morphemes.

The Methodological Challenges of Transfer Studies

Jarvis (Reference Jarvis2000) proposes three empirical criteria for demonstrating L1 influence or transfer on L2 acquisition: (a) intragroup homogeneity, (b) intergroup heterogeneity, and (c) crosslinguistic performance congruity. Learners with the same L1 should show the same acquisition order among them (intragroup homogeneity), whereas learners from different L1 backgrounds should exhibit different acquisition orders (intergroup heterogeneity). Intergroup heterogeneity then needs to be linked to the existence of a corresponding morpheme in the L1 performing in a similar manner with the L2. For instance, early acquisition of possessive ’s by Japanese learners of English can be linked to the Japanese particle -no, which expresses possession. It is crucial that intragroup homogeneity and intergroup heterogeneity are investigated in a single research design because they are inherently relative, relying on a comparison of how similar or dissimilar groups of learners are from each other (Jarvis and Pavlenko, Reference Jarvis and Pavlenko2007). However, there are few such studies, a gap addressed in this article.

Aims and Organization of the Article

Adopting the empirical criteria set by Jarvis (Reference Jarvis2000), our main goal is to investigate L1 influence on morpheme acquisition. Our research questions are summarized below.

1. 1. Does L1 affect the accuracy order of L2 English grammatical morphemes?

   1. a. Is the accuracy order consistent within each L1 group?

   2. b. Is the accuracy order different among L1 groups?

   3. c. Can we attribute differences in accuracy order among L1s to specific properties of the L1?

2. 2. How strong is L1 influence in determining the accuracy of English grammatical morphemes compared to other factors such as general proficiency?

3. 3. Are grammatical morphemes equally or differentially affected by the L1?

4. 4. Can we link L1 influence to the absence or presence of congruent morphemes in the L1 in a systematic way?

An important feature of our investigation is that it involves learner groups across proficiency levels, therefore allowing us to ask if, within a given L1 group, the accuracy order remains the same across proficiency. If accuracy order varies as learners progress, then the idea of operationalizing the order of acquisition as the order of accuracy is in doubt.

The structure of the article is as follows. The next section introduces the Cambridge Learner Corpus, the data source of our study, and elaborates on the specific subset of data used in the study and the way information was extracted from the data. We then analyze the data to answer the preceding research questions. As we will see, strong L1 influence was observed in the acquisition order. We then grouped L1s into those with corresponding morphemes and those without and modeled accuracy based on the typological difference in L1 and general proficiency so as to investigate the strength of L1 influence and morpheme-specific L1 effect. We finally discuss the findings with reference to previous morpheme studies and to the predictions extracted from Slobin’s approach and the interpretability hypothesis.

Descriptive Data

We begin with the SOC and TLU scores of the target grammatical morphemes, shown in Figure 3. To facilitate graph inspection, we only show SOC and TLU scores between 0.60 and 1.00. The first observation is that the scores are high overall; some groups achieve a TLU score above 0.90 from the KET level on (e.g., past tense -ed in L1 Japanese or plural -s in L1 Spanish). This suggests that we must watch for ceiling effects in the analysis and interpretation of data. The second observation is the striking differences in the TLU scores of individual morphemes among different L1s, despite an overall trend for TLU scores to increase with proficiency. For instance, articles are consistently the least accurate morpheme among L1 Japanese learners but exhibit high accuracy among L1 Spanish learners. By contrast, past tense -ed exhibits high accuracy across proficiency in L1 Japanese but is a low-accuracy morpheme in L1 Spanish learners. This simple graph then suggests differences in the accuracy order among different L1 groups.

Figure 3. Accuracy of each morpheme in each L1 group.

The number of obligatory contexts was small in some cases, leading to unreliable data points (e.g., five in the case of the possessive ’s in L1 Korean KET or nine in the case of the past tense -ed in L1 German KET). These data points were, nevertheless, included in the correlation analysis to allow comparisons with previous morpheme studies that did not always have a large number of obligatory contexts. Data points unreliable due to size were explicitly marked in the clustering approach and were excluded from the regression analysis.

Similarity in the Accuracy Order within and across L1 Groups

Let us begin by looking at how similar the acquisition order is across the different L1 groups, investigating the consistency of the accuracy order within each L1 group across proficiency and the differences among L1 groups. We replicate prior morpheme studies and adopt Spearman’s rank order, a commonly adopted technique for comparing acquisition orders. The Spearman’s rank-order correlation was based on the SOC scores for each L1 and proficiency level. If two orders were significantly correlated, then the groups from which the orders were obtained were assumed to show the same accuracy order. Following Jarvis’s empirical criteria (Jarvis, Reference Jarvis2000), we compared within-L1 orders (i.e., accuracy orders of learners with the same L1 but different proficiency levels) to between-L1 orders (i.e., accuracy orders of different L1 groups). If within-L1 orders are more consistent (i.e., within-L1 pairs show more similarities in the order) than between-L1 orders, then L1 is likely to affect accuracy order.

One accuracy order per L1 group per proficiency level was obtained from the SOC scores. The total number of orders per L1 per proficiency level was 35 (7 L1s × 5 proficiency levels). The total number of possible comparisons between two orders among them was 595 (₃₅C₂ = ${{35!} \over {2!\, \times \left( {35 - 2} \right)!}}$ = 595). Of the 595 order pairs, 70 were within-L1 pairs (10 within-L1, between-proficiency pairs × 7 L1s), and the rest (525) were between-L1 pairs. The Spearman’s rank correlation coefficient was calculated for each. The result showed that 269 out of the 525 between-L1 pairs (51.2%) were significantly correlated at p < .05, whereas 57 of 70 within-L1 correlations (81.4%) were significant. The difference between the two (51.2% vs. 81.4%) was statistically significant (χ ²(1) = 22.727, p < .001, φ = 0.195), indicating that within-L1 pairs show more similar accuracy orders than between-L1 pairs. This means that accuracy orders are more similar within L1 groups than between them, satisfying the first two requirements for the identification of L1 transfer by Jarvis (Reference Jarvis2000).

Specific Differences in the Accuracy Order

One weakness of the correlation analysis presented in the preceding section is that small differences in accuracy count as heavily as large differences. The analysis also does not reveal which morpheme differs in order between groups. To address these shortcomings, we clustered together morphemes with similar TLU scores within each L1 proficiency group. Our approach is similar in spirit to that of Krashen (Reference Krashen, Brown, Yorio and Crymes1977) in that it aims to group similar morphemes in one rank in the acquisition order but deviates in the technical implementation. In particular, we defined as “similar” morphemes with TLU scores that do not differ significantly from one another. Thus, similar morphemes were grouped together in one rank, whereas only morphemes with TLU scores reaching statistically significant difference were treated in different ranks in the acquisition order. One complication of testing statistical differences among TLU scores for individual morphemes is that each morpheme in a given proficiency level in an L1 group only has one TLU score. To overcome this difficulty we employed a statistical technique called bootstrapping (Larson-Hall & Herrington, Reference Larson-Hall and Herrington2010; Mooney & Duval, Reference Mooney and Duval1993; Reference Plonsky, Egbert and LaflairPlonsky, Egbert, & Laflair, in press). Bootstrapping is a resampling technique used to gain insights into the population of a given sample. Intuitively, it increases the number of data points by repeatedly drawing random exam scripts and calculating a TLU score from each sample. The following list describes the technique using L1 Japanese CPE data as an example.

1. 1. One hundred and sixty-three scripts were randomly selected from the Japanese CPE data. Single scripts could be selected multiple times.

2. 2. A TLU score was calculated from the sample obtained in the first step.

3. 3. Steps 1 and 2 were repeated 10,000 times, resulting in 10,000 TLU scores.

After bootstrapping, we identified all the morpheme pairs with a statistical difference in the TLU score of the morphemes of the pair. For each pair, the 10,000 TLU scores of one morpheme were subtracted from the 10,000 TLU scores of the other morpheme, resulting in 10,000 differences in the TLU scores between the two morphemes. We then tested whether the 95% range of the differences included zero. If it did, the accuracy difference between the two morphemes was considered nonsignificant, and they were clustered together.

Intuitively, bootstrapping allows us to evaluate if the difference between two TLU scores is due to sampling variability or it is likely to reflect a pattern. To test this, we obtained 10,000 differences from assumedly independent samples and analyzed whether the difference is likely to be non-zero. If the great majority of the differences have the same sign (positive or negative), then the difference in the population is unlikely to be zero.

The orders obtained through clustering morphemes after bootstrapping are shown in Table 3. The morphemes in Cluster 1 marked higher TLU scores than those in Cluster 2, which in turn scored higher than those in Cluster 3, and so forth. For example, at the L1 French PET level, articles received the highest TLU score, followed by three morphemes (past tense -ed, plural -s, and progressive -ing) with similar accuracy levels. These were followed by third-person -s, with possessive ’s as the least accurate morpheme.

Table 3. Clustered order of morphemes based on TLU scores

We clustered in the highest rank all morphemes with TLU scores above 0.90 at p < .05 (marked with asterisks (*)) and any morphemes with lower scores but not statistically significantly lower based on the bootstrapping. The 0.90 threshold has been common in morpheme studies, albeit in those that use SOC scores (Hakuta, Reference Hakuta1976). Crossed-out morphemes are unreliable due to their small sample size (number of obligatory contexts < 100). Underlined morphemes did not show a significant difference with any other morpheme. Such morphemes were clustered together with morphemes of closest accuracy. Table 4 shows the natural order of acquisition (Krashen, Reference Krashen, Brown, Yorio and Crymes1977) limited to the target morphemes of the present study.

Table 4. Clustered natural order of acquisition of English grammatical morphemes

Summary of the Between-L1 Differences

Table 5 summarizes cross-L1 differences in the accuracy order. The L1 groups on the left marked a higher accuracy rank of the morpheme than those on the right. These differences were determined using the following method: If it was certain that a morpheme marked a higher accuracy rank in an L1 group than in another, then the L1 group was placed on the left. For instance, in the case of the FCE level, it was considered that there was no difference in the order of plural -s between the L1 German and L1 Turkish groups because in L1 German the accuracy of plural -s was the first, second, or third from the top, whereas in L1 Turkish it was either the third or fourth. In other words, there is a possibility that in both L1 groups plural -s was the third most accurate morpheme, in which case there is no difference in the order of accuracy. In L1 Spanish or Russian, however, plural -s was either the most or the second most accurate morpheme; thus, there could not be an overlap with its order in the L1 Turkish group. Therefore, the accuracy order of plural -s was considered higher in L1 Spanish and Russian than in L1 Turkish. The KET level was removed from the analysis because the data were mostly too small and were thus unreliable.

Table 5. Between-L1 differences in clustered orders

Note. J = L1 Japanese; K = L1 Korean; S = L1 Spanish; R = L1 Russian; T = L1 Turkish; G = L1 German; and F = L1 French. L1 groups on the left mark higher accuracy ranks on the concerned morpheme than those on the right.

The accuracy order varies across L1 groups with respect to all the concerned morphemes. We can make the following observations for the data shown in Tables 3 and 5:

• • Articles consistently rank low in Japanese, Korean, Russian, and Turkish learners, a finding that partially confirms the results of Luk and Shirai’s (Reference Luk and Shirai2009) survey while also running counter to the natural order. In the other L1 groups (Spanish, German, and French), articles tend to be in the upper half.

• • Past tense -ed is consistently in the highest cluster in Korean and Turkish learners, with Japanese, Russian, German, and French learners showing a similar trend. This again does not support the natural order, in which this morpheme is ranked low.

• • Plural -s tends to appear in higher clusters in Spanish, Russian, and German learners, whereas it tends to be located somewhere in the middle in the other L1 groups.

• • Possessive ’s tends to mark a lower accuracy rank in Spanish, Turkish, German, and French learners than in Japanese and Korean learners. Overall, however, it typically ranks low and is in this sense consistent with the natural order.

• • Progressive -ing is in the highest cluster in all L1 groups except for German and French learners. Interestingly, in these two groups it is one of the two morphemes that fail to reach 90% of the TLU score even at the highest proficiency level, CPE.

• • Third-person -s fluctuates even within L1s over proficiency but tends to stay in the lower half for Spanish learners of English.

Summary of the Within-L1 Differences

We have established that the order of acquisition differs across L1 groups. Let us now consider to what extent it is consistent within L1 groups across the proficiency spectrum. Table 6 shows within-L1 differences in the accuracy order, formatted in the same manner as in Table 5. Few within-L1 differences could be observed in the accuracy order, indicating a consistent order across proficiency levels. There was no within-L1, between-proficiency difference in past tense -ed, plural -s, and possessive ’s, and only one in articles. From these two tables, it is clear that the order of accuracy tends to differ among L1 groups but not within them. The clustering analysis therefore confirms both intragroup homogeneity and intergroup heterogeneity.

Table 6. Within-L1 differences in clustered orders

Note. Cp = CPE; Ca = CAE; F = FCE; and P = PET. Test takers mark higher accuracy order on the exam on the left than that on the right.

Comparison with the Natural Order

To directly analyze whether the idea of the natural order is tenable, the orders in the present data were compared with the natural order, shown in Table 7. As in Table 5, when L1 groups are on the left of the natural order (NO), this indicates that the accuracy rank of the morpheme was higher in the L1 groups than the expectation of the natural order. It is clear that our orders often deviate from the natural order. It is worth noting the absence of difference between the natural order and the order of L1 Spanish learners.

Table 7. Differences between the observed order and the natural order based on clustered data

Note. J = L1 Japanese; K = L1 Korean; R = L1 Russian; T = L1 Turkish; G = L1 German; F = L1 French; and NO = natural order. L1 groups on the left show that they mark higher accuracy order on the morpheme concerned than predicted by the NO. L1 groups on the right show the opposite.

Quantifying L1 Influence and Examining Its Varying Effects across Morphemes

Overview of the Approach

So far we have established that accuracy order varies across L1 groups but is consistent within each group across proficiency. We still need to obtain evidence for Jarvis’s last criterion—crosslinguistic performance congruity—in order to demonstrate L1 influence. More specific questions regarding L1 influence also arise: How strong is L1 influence (Research Question 2)? Does its strength vary across individual morphemes (Research Question 3)? To answer these questions we adopted regression modeling. In particular, we regressed accuracy (TLU score) against L1 influence, L1, exam level, morpheme, and their significant two-way interactions. If L1 influence is significant in determining accuracy, then Jarvis’s crosslinguistic performance congruity criterion is satisfied because it indicates that L1 features are related to L2 use. But how can we capture L1 influence? Jarvis suggests comparing the use of L2 features with that of corresponding L1 features to investigate performance congruity. Due to the range of L1s and morphemes considered here, we take a simpler approach—namely, we consider only presence vs. absence of the L2 feature in the L1 and investigate whether such variation in the L1 can affect accuracy in the L2.

We implemented the idea in the regression model through a dichotomous variable coding the L1 effect as 0 if the morpheme is absent or optional in the L1 and as 1 if it is obligatory. For example, the article in L1 Japanese was coded as 0 because Japanese is generally considered an article-less language. On the other hand, a 1 was assigned to past tense -ed in L1 Japanese because a Japanese morpheme, -ta, roughly corresponds to past tense -ed in English, and it is difficult to express pastness without the use of the morpheme in Japanese. Hence, the past tense marker (-ta) was considered obligatory in Japanese, and a 1 was assigned. Although past tense -ed encodes both tense and aspect, the decision was made on the basis of tense. In the remainder, we refer to this dichotomous variable as L1 type.

Table 8 shows all the values of the variable used in the study, (some of) the corresponding features if the value is 1, and the references that support the decision. Admittedly, this is a rather crude and oversimplified modeling of L1 influence. A corresponding morpheme in the L1, for instance, may have multiple meanings, some of which are not covered by the English morpheme (e.g., the Japanese tei, which also refers to a resultative state; Shirai, Reference Shirai1998b). As we shall see, however, it is a useful way of capturing the effect of the L1.

Table 8. L1 type in target L1s × Morphemes

Note. If the morpheme is obligatorily marked in the language, it is given a score of 1; otherwise 0 is given. ø = no corresponding feature. AV = Azoulay & Vicente (Reference Azoulay and Vicente1996); Ch = Choi (Reference Choi2005); Ci = Cinque (Reference Crawley2001); D = Durrell (Reference Durrell2011); E = Ekmekci (Reference Ekmekci1982); FS = Filosofova & Spöring (Reference Filosofova and Spöring2012); HT = R. Hawkins & Towell (Reference Hawkins and Towell2001); Hw = R. Hawkins (Reference Hawkins1981); I6 = Ionin (Reference Ionin2006); I8 = Ionin (Reference Ionin, Haznedar and Gavruseva2008); J = Jelinek (Reference Jelinek1984); L = Lewis (Reference Lewis2000); Le = Lee (Reference Lee2006); Li = Lindauer (Reference Lindauer, Alexiadou and Wilder1998); LS = Luk & Shirai (Reference Luk and Shirai2009); M = Müller (Reference Müller, Arnaudova, Browne, Rivero and Stojanovic2004); Sh-a = Shirai (Reference Shirai1998a); Sh-b = Shirai (Reference Shirai1998b); and Sl = Slobin (Reference Slobin, Gumperz and Levinson1996).

^a Turkish verbs inflect for person with the third-person form as the base form. We coded third-person -s as 0 for Turkish, following Ekmekci (Reference Ekmekci1982). The coding of third-person -s, however, makes little difference in our findings because it turns out to be the morpheme that is fairly immune to L1 influence.

Graphical Analysis

Figure 4 contrasts the TLU scores of the morphemes with and without equivalent forms in learners’ L1s. Unreliable data points (number of obligatory contexts < 100) were removed (34/210 = 16.2%) from the subsequent analysis. In addition, the L1 French KET progressive -ing TLU score was excluded from the graphical representation (but was included in the mean calculation and other statistical analyses), as it was much lower than other reliable data (0.415).

Figure 4. Distribution of TLU scores × L1 type.

The data points in Figure 4 were plotted according to three factors: L1 type, exam level, and morpheme. The scores of the morphemes with no equivalent form in the learners’ L1 (hereafter ABSENT) are shown on the left half, whereas the TLU scores of the morphemes with congruent forms in the learners’ L1 (PRESENT) are shown on the right. Solid lines and plus signs (+) show the mean TLU scores aggregated over morphemes, and the dashed line indicates the threshold TLU score (0.90). Standard deviations of the TLU scores of each group are shown inside parentheses below the exam level, with mean TLU scores for PRESENT and ABSENT in square brackets at the bottom of the figure.

First, we observe that the PRESENT morphemes are used more accurately than the ABSENT ones. The difference in the average TLU scores is 0.084, a large value considering that the overall performance is approaching the ceiling. ^{Footnote 7} More strikingly, the (mean) TLU scores of the ABSENT morphemes at the CPE level are only as high as those of the PRESENT morphemes at the KET level, the difference being a mere 0.007. Few ABSENT morphemes scored above 0.90 in TLU.

Second, we observe that morphemes vary in how much they are affected by L1 influence. The emerging picture, summarized in the following list, further confirms the findings of the correlation and clustering analysis while highlighting the differential L1 influence on individual morphemes. ^{Footnote 8}

• • Articles receive low scores in the ABSENT group but above-average scores in the PRESENT group.

• • Plural -s does not show strong L1 influence until the CAE level, when the difference between ABSENT and PRESENT increases.

• • Possessive ’s shows weak L1 influence, with scores generally low for both ABSENT and PRESENT groups.

• • Progressive -ing shows a clear influence from the L1, with scores generally high in both groups.

• • Third-person -s shows L1 influence at various proficiency levels, but the direction of influence is not consistent across proficiency. The advantage for the PRESENT group only shows up at the advanced levels, whereas it is the ABSENT group that has an advantage at lower levels.

The Logistic Regression Model

To isolate the effect of L1 type from other factors and examine its strength, a logistic regression model was fit to TLU scores.

Model specification and model selection

The number of correct suppliances was entered as the number of successes, and that of errors (i.e., obligatory contexts − correct suppliances + overgeneralization errors) was entered as the number of failures. The predictors included L1Type (two levels: ABSENT or PRESENT), L1 (originally seven levels, one for each L1 group, but see below), ExamLevel (the FCE-centered continuous variable for exam levels), and Morpheme (six levels, one for each target morpheme). The quadratic and cubic terms of ExamLevel were also entered to capture its potentially nonlinear effect. The model further included two-way interactions among the variables. Dummy variables with treatment contrasts were employed for factors. Although two-way interactions were generally put in the model initially, the L1-Morpheme interaction was not entered because (a) it consumes a large number of degrees of freedom and makes the resulting model unstable and (b) the interaction, which tests whether any L1 effect is observed in addition to the effect of L1Type (PRESENT/ABSENT), is not our primary concern. Furthermore, the interactions between L1 and L1Type and those among ExamLevel variables (e.g., the interaction between ExamLevel and ExamLevel ^{Footnote 2} ) were also excluded because their substantial interpretation is difficult.

The interaction terms were reduced in a backward-elimination manner, dropping the one with the highest p value calculated with the Type II sum of squares and comparing the model with and without the interaction by F-tests until the model fit became significantly worse at p < 0.05. With respect to individual L1s, several levels were conflated for statistical parsimony (Crawley, Reference Crawley2007). We performed this step by looking at the estimates of each level, collapsing the two L1 groups with the closest estimates and inspecting whether the resulting model was significantly worse than that without the conflation. The procedure was repeated until the model became significantly worse than the previous model. The model presented subsequently is the one with as few L1 groups as possible without losing fit. Consequently, the L1 Japanese, Korean, and Spanish groups were clustered together, and so were the L1 Turkish and French groups. This deletion procedure was not applied to Morpheme because conflating its levels would make it difficult to interpret the interaction terms it participates in. The reference level of L1Type was the ABSENT group, that of the L1 was the L1 Turkish/L1 French group, and that of Morpheme was articles. Due to overdispersion (residual deviance = 1,463.6 on 120 d.f.), quasibinomial rather than binomial distribution was assumed (Crawley, Reference Crawley2007).

Summary of the model

Table 9 displays the estimates of each parameter and their 95% confidence intervals. Due to space limitations, we focus on the variable of our interest, L1 type. The variable is highly significant: Learners whose L1s have articles (reference-level morpheme) achieved higher accuracy in their use than those whose L1s do not. The significant impact of L1Type on articles is also reflected in the relatively large estimate, the size (1.152) of which is much larger than the difference between the highest and lowest exam levels (CPE − KET = (0.032 × 2² + 0.179 × 2) − (0.032 × (−2)² + 0.179 × (−2)) = 0.714). Thus, although the effect varies across morphemes, as is demonstrated subsequently, L1 influence can outweigh general proficiency differences in morpheme accuracy. Crucially, this analysis satisfies crosslinguistic performance congruity (Jarvis, Reference Jarvis2000), as it shows an association between the presence or absence of an equivalent form in the L1 (L1 performance) and the accuracy of the morpheme in the L2 (L2 performance).

Table 9. Summary of the logistic regression model fitted to TLU scores (n = 176)

Note. CI = confidence interval; UL = upper limit; and LL = lower limit. Asterisks indicate p value: *** = p < .001; ** = p < .01; * = p < .05; and no asterisks = p < .1.

The varying strength of L1 influence across morphemes

The model also confirms that the strength of L1 influence on different morphemes varies, as the model with the interaction between Morpheme and L1Type had a significantly better fit than that without the interaction term [F(5, 150) = 18.173, p < 0.001]. Judging from the size of the coefficients, L1Type was the strongest in articles (interaction estimate of 0), followed by progressive -ing (−0.193), plural -s (−0.492), possessive ’s (−0.904), and third-person -s (−0.938), which was least affected by the L1. For third-person -s, the accuracy difference between the ABSENT and PRESENT groups became smaller than the difference stemming from one exam level (PRESENT-ABSENT difference = 1.152 − 0.938 = 0.214; one exam level difference = (0.032 − 0.039) × 1² + (0.217 + 0.179) × 1 = 0.389).