Data Analytical and Statistical Framework

This section will illustrate in two ways how the learners’ data was analysed: The first one has to do with the methodological approach – the analytical procedure – adopted in examining and comparing the data of the participants. The second has to do with the statistical software tool used in analysing the quantitative data and the various statistical techniques utilised to make the necessary reports, comparisons, and contrasts.

Given the complex nature of the interlanguage research questions stated in 4.2.2, it was important for the researcher to adopt a reliable and valid grammatical-error comparison approach to examine learners’ L2 use in order to generate

convincing and generalizable answers to the study questions, such as the renewed version of the traditional contrastive analysis (CA) approach referred to by Granger (1996, 1998) as the contrastive interlanguage analysis (CIA).¹⁰³ Instead of comparing the native and the target languages of learners, the CIA compares and contrasts

“what non-native and native speakers of a language do in a comparable situation”

(Granger, 1998, p. 12, after Pery-Woodley, 1990, p. 143). This comparative model involves basically two major types of comparison: a comparison between native speakers and L2 learners (native language vs. interlanguage) to “uncover the patterns of use distinguishing learner data from native data” (Granger, 2003, p. 541), and a comparison between L2 learners of the same language with different L1 backgrounds (ILs vs. ILs) “to establish whether the differences uncovered are developmental or transfer related” (Granger, 2003, p. 541). The former type may also involve comparing L1 child data with adult L2 learner data to uncover the similar and/or different patterns of acquisition between these different learner groups. The latter may also involve comparing data of L2 learners of the same language who share the same L1 background but are at different stages of L2 development in order to identify the characteristics of different IL stages.

It can be claimed, on the basis of such different systematic analyses of L2 learners’ data, that this method of comparison is very reliable when conducting interlanguage research. It has not only deepened our understanding of the nature of interlanguage by answering many fundamental questions in SLA research, but it also has opened up various unexpected avenues of enquiry into the field of study.

Granger (2009) points out that such “L1–L2 comparisons are extremely powerful heuristic techniques which help bring to light features of learner language which have not been focused on before, and which, once uncovered, can be analysed from a strictly L2 perspective” (p. 18).

To make such necessary complex analytical comparisons, a relatively new but appropriately flexible statistical tool was applied in the data analysis – the R Statistical

103 For a detailed historical review of the origin of the CIA, which was originally referred to as a “new type of CA” by Selinker (1989, p. 14), see Gilquin (2001).

Programme.¹⁰⁴ Like SPSS, R can execute sophisticated statistical analyses by providing a wide variety of statistical procedures. Not only is it a completely free and easy-to-learn open-source program, but one of R’s strengths over SPSS and many other famous statistical packages is its graphing capabilities; it provides hundreds of effective ways to present the data in very beautiful and reader-friendly data depictions. Over the past few years, it has become the statistical programme of choice for many researchers in a variety of fields including linguistics.¹⁰⁵

Prior to running the statistical analyses, however, the refined Excel data sets obtained from the participants’ performance on both tasks discussed in 4.3.6 were imported into R. Then, for the purpose of descriptive statistics, each subgroup’s results were reported including information about the mean, minimum, maximum, standard deviation, and percentage of acceptance or omission for each variable under investigation. These quantitative values were calculated out of the total number of the included accepted ungrammatical items in the GJ task and of the included omitted target form in the translation task, except for the percentages of acceptance and omission, which are calculated by dividing each sum of acceptances or omissions by the total number of the included responses.¹⁰⁶ After that, to pave the way for running the appropriate statistical inferential procedures, the sample’s normality of distribution was checked using the Shapiro test. Not all of the subgroups’

results in the two tasks were normally distributed (p > .05) for all variables.¹⁰⁷

104 R was initially developed by Robert Gentleman and Ross Ihaka at Auckland University. To obtain more comprehensive information about R, visit its official website:

http://www.r-project.org/

105 For more information about how to use R to process linguistic or psycholinguistic data, refer to Baayen (2008) and Gries (2009).

106 Refer back to subsections 4.3.6.1 and 4.3.6.2 to see how the included responses and omissions were calculated in both of the elicitation tasks.

107 Because of the large number of the participants’ subgroups and the large number of investigated variables in both tasks, and since normality tests are applied only to select the appropriate parametric or nonparametric statistical techniques to be used with the data, the results of such tests will not be presented or discussed in this chapter. A list of

Therefore, both parametric and nonparametric procedures were used. Accordingly, seven types of statistical tests were applied to test for significant differences between and within the subgroups of learners and to examine the strength and direction of the relationship between variables. If the data are normally distributed, (a) paired samples t-tests are used when two variables are compared within the same subgroup, (b) independent samples t-tests are used when the results of two subgroups for the same variable are compared against one another, and (c) analysis of variance (ANOVA) followed by a post hoc test (Tukey HSD) are used when the results of more than two subgroups for the same variable are compared against one another. If the data is not normally distributed, however, nonparametric tests are used instead of the parametric ones; for example, (d) the Wilcoxon test, which in R is called Wilcox.test, is the nonparametric alternative test to both the paired samples t-test and the independent samples t-test (but used in two different ways), and (e) the Kruskal-Wallis test, in R called Kruskal.test, is the nonparametric alternative test to the ANOVA.¹⁰⁸ As for the correlation analysis, (f) the Pearson product–moment correlation is run with both normally and not normally distributed data to compute the correlation coefficient between two variables; it is abbreviated as r.¹⁰⁹ As for the interaction analysis, (g) the generalised linear mixed effects model (GLMM) was used to investigate whether the performance on the variables under investigation differ depending on the learners’ L1 and proficiency levels. The alpha level was set at p <

0.05 for all of these tests; in other words, a result is considered significant if p < 0.05.

the results of a total of 126 normality tests applied for all variables and samples is available in appendix 8.

108 Unlike the R Statistical Programme, SPSS uses two different nonparametric tests instead of the two types of t-tests: the Mann-Whitney U test is the alternative to the independent samples t-test, and the Wilcoxon signed-rank test is the alternative to the paired samples t-test. Refer to Dörnyei (2007) for the SPSS and to Baayen (2008), Gries (2009), and Race (2012) for R.

109 The Spearman’s rank order correlation is designed to compute r for those data that do not satisfy the distribution normality. Nevertheless, it was not used here because its result is less powerful than Pearson’s, which can also be used for the data not normally distributed (see Dörnyei, 2007; Race, 2012).

The next chapter illustrates precisely how these descriptive and inferential statistical procedures are put into practice.

Results and Discussion

5.1 Introduction

The primary goals of this chapter are (1) to present the empirical data that emerged from the data elicitation tools, (2) to interpret and discuss these results in light of the theoretical assumptions outlined in the previous chapters, and (3) to connect the findings to those of previous studies in this field.¹¹⁰ However, because the research questions and hypotheses formulated in the previous methodology chapter revolve around the issue of transfer of the L1 value of the null subject parameter, and in order for transfer to be unambiguously established and understood, only the group results from non-target-like performance relating to subject pronouns as well as missing verbal agreement are presented, examined, compared, and discussed here. That is to say, the data analysis and discussion of the results will concentrate on the participants’

non-well-formed translated items and on their acceptance of sentences that are ungrammatical with respect to the syntactic properties being investigated.¹¹¹ The only exception is when the participants’ strength of preference for overt pronouns over null forms needs to be measured; in Subsection 5.2.3.2, I will look at the participants’

rejection of these ungrammatical items.

Note, as briefly mentioned above, I will only analyse, report, and discuss the group results in this chapter; the individual results will not be analysed here, except in one context when discussing the notion of parameter resetting in Subsection

110 Because the analysis and the discussion are closely connected, a decision has been made to have them in one chapter against the norm, which is two separate chapters.

Such a procedure helps the author to discuss the results in a cohesive way without making many references throughout this chapter to the findings.

111 It’s clear that the other percentage representing their performance is what they got right/target-like.

5.3.2.2, to illustrate the fact that the group results cannot be applied to all the individuals in each group. However, due to the large number of participants and space limitations, only the descriptive results from the advanced Finnish-speaking participants are presented at the individual level to demonstrate the problem of generalisation in L2 development.

This chapter comprises four sections to address the research questions listed in 4.2.2. Section 5.2 investigates the results of the analysis of the data and seeks to answer the first and second research questions, namely if null subject transfers in L2A equally affect grammatical intuitions and oral production and if parameters can be reset in L2A. Section 5.3 investigates the data to answer the research question regarding whether the results of the subgroups diverge similarly or differently across the different syntactic formations; it also reconsiders the second research question, namely if the L1 value of the null subject parameter can be reset in L2A.

Section 5.4 tries to answer the third research question; it seeks to investigate the mechanisms by which the presence of a null subject is licensed in the learner’s IL grammars.