The use of statistics in second language acquisition (SLA) research has increased over the past 30-40 years (Brown, 2004; Loewen & Gass, 2009). Further, several methodological syntheses (e.g., Plonsky, 2011; Plonsky & Gonulal, 2015; Winke, 2014) revealed that researchers in the field have begun to use more sophisticated and novel statistical methods (e.g., factor analysis, mixed models/mixed regression analyses, structural equation modeling, Bayesian statistics) even if common inferential statistics (e.g., t tests, ANOVAs, and correlations) are still dominating quantitative second language research (Plonsky, 2013, 2015). However, the increased use of a larger variety of statistical methods does not necessarily translate to high methodological quality. In fact, several SLA researchers have accentuated the state of statistical literacy and statistical training in the field of SLA (e.g., Godfroid & Spino, 2015; Loewen et al., 2014; Norris, Ross & Schoonen, 2015; Plonsky, 2011, 2013, 2015, Plonsky & Gonulal, 2015). Indeed, statistical literacy appears to be critical to SLA researchers’ ability to advance L2 theory and practice. While some studies on statistical literacy in the field have been published, it appears that no studies exist that measure SLA researchers’ statistical knowledge, which is also an important piece of the puzzle. In this dissertation, I focus on SLA doctoral students—an important part of academia— and attempt to investigate their statistical training and knowledge of statistics. To this end, I used two primary instruments: the SLA for SLA (that is, the Statistical Literacy Assessment for Second Language Acquisition) survey, and semi-structured interviews). One hundred and twenty SLA doctoral students in North America took the SLA for SLA survey, and 16 of them participated in follow-up interviews. The participants were from 30 different SLA programs across North America. The results of this study show that doctoral students are well trained in basic descriptive statistics, while their training in inferential statistics, particularly advanced statistics, is limited. Further, it appears that self-training in statistics is not very common among SLA doctoral students. The results also point out that more in-house statistics courses, particularly intermediate and advanced statistics, are needed. When looking at their statistical knowledge, the results indicate that SLA doctoral students are good at understanding descriptive and inferential statistics, but they find it hard to interpret statistical analyses related to inferential statistics that are commonly encountered in SLA research. Another important finding is that as might be expected, the number of statistics courses taken, self-training in statistics and quantitative research orientation are predictive of statistical literacy, whereas surprisingly years spent in the doctoral program are significant components of statistical literacy. Based on the findings of this study, I make some suggestions directed toward improving statistical literacy in the field of SLA.
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