Measuring linguistic complexity in long‐term L2 speakers of English and L1 attriters of German

Abstract

Linguistic complexity is neither easily defined nor measured. The challenge in finding reliable ways to measure linguistic complexity is even more pronounced when the variation of contexts in which complexity is measured is taken into account. This paper therefore aims at finding measures for assessing syntactic and lexical complexity that are sensitive and non‐overlapping even in a less frequently studied context: spontaneous speech of L1 attriters and highly proficient L2 English speakers. To evaluate operationalizations of syntactic and lexical complexity, several previously proposed measures are applied to a corpus of spontaneous speech and a principal component analysis is conducted. The results show which of the measures group together as underlying variables of syntactic and lexical complexity and how this can inform future operationalizations of complexity.

Asymmetric sampling of personality (ASP): A novel approach to understanding validity limits in personality assessment and possible remedies

“…Personality research has accumulated a detailed list of different biases and constructirrelevant response styles in (self-and other-)reports, and researchers continuously advance methods to correct for distortion. An illustrative but non-exhaustive list of prominent examples features memory biases, addressed by specifically cued recall (e.g., Golding & MacLeod, 2013, Mary et al, 2020Mather & Sutherland, 2009;Schwarz, 1999;Wilson & Dunn, 2004), reference standards, controlled by specifying the comparison standards explicitly (e.g., Brenner & DeLamater, 2016;Festinger, 1954;Lehnhausen et al, 2022), errors from linguistic complexity, countered by using plain language and simplifying item wording (e.g., Lahmann et al, 2019;Saris, 2013;Saris & Gallhofer, 2014), intentionally adapted responding like faking, assessed by measuring social desirability and response tendencies (e.g., King & Bruner, 2000;Paulhus, 1991;Zickar & Robie, 1999;Ziegler et al, 2011), and unintentional response styles, taken into account when statistically modelling the items' latent constructs (e.g., Danner et al, 2016;Valentini, 2017). Although considerations of self-report biases are abundant and potent theories exist to categorize and explain them (see e.g., Tourangeau et al, 2000), they are mostly limited to effects on respondents during survey participation, with exceptions for the exploration of memory bias (Golding & MacLeod, 2013;Mary et al, 2020;Wilson & Dunn, 2004).…”

An Examination of the Degree of Complexity of Teacher Questions in English-Medium University Courses

“…Whereas researchers have thoroughly examined complexity in L2 writing research, L2 speaking complexity has been neglected, which is the language mode we intend to focus on. In this paper, we will refer to two of the perspectives in complexity within the CAF (complexity, accuracy, and fluency) triad: the simple view and the holistic view (Lahmann et al, 2019). Pallotti (2015) expounded on the simple perspective, whose focus lies on the complexity of the text (in our case classroom talk) rather than on the learner system and how it is influenced by proficiency, processing costs (difficulty) and developmental dynamics (acquisition).…”

Lexical and morphosyntactic variation in Persian heritage language outcomes

“…Similar to the findings and trends regarding HL vocabulary development, various studies have reported variation in HL morphosyntax as well. While some of these studies used more universal measures of morphosyntactic competence (e.g., grammatical complexity, clausal density, MLU) (see e.g., Gharibi & Boers, 2019;Lahmann, Steinkrauss, & Schmid, 2019;Lloyd-Smith et al, 2020), others looked at more specific linguistic features (e.g., relative clauses, passives, clitics) (e.g., Unsworth, 2013;Montrul, 2016;Montrul & Sánchez-Walker, 2013;Pascual y Cabo, 2020;Pires & Rothman, 2009;Polinsky, 2018).…”