DOI: 10.3886/e164981
title: Data and codebook for SSLA article: "A closer look at a marginalized test method: Self-assessment as a measure of speaking proficiency"

description: Second language (L2) teachers may shy away from self-assessments because of warnings that students are not accurate self-assessors. This information stems from meta-analyses (Ross, 1998, Li & Zhang, 2021) in which self-assessment scores on average did not correlate highly with proficiency test results. However, researchers mostly used Pearson correlations, when polyserial could be used. Furthermore, self-assessments today can be computer-adaptive. With them, nonlinear statistics are needed to investigate their relationship with other measurements. We wondered, if we explored the relationship between self-assessment and proficiency test scores using more robust measurements (polyserial correlation, continuation ration modeling), would we find different results? We had 807 L2-Spanish learners take a computer-adaptive, L2-speaking self-assessment and the ACTFL Oral Proficiency Interview – computer (OPIc). The scores correlated at .61 (polyserial). Using continuation ratio modeling, we found each unit of increase on the OPIc scale was associated with a 130% increase in the odds of passing the self-assessment thresholds. In other words, a student was more likely to move on to higher self-assessment subsections if they had a higher OPIc rating. We found computer-adaptive self-assessments appropriate for low-stakes L2-proficiency measurements, especially because they are cost effective, make intuitive sense to learners, and promote learner agency.
description: NA
description: Response Rates: The students who received interpretable OPIc scores were enrolled in first-year (100-level; N = 131), second-year (200-level; N = 251), third-year (300-level; N = 346), and fourth-year (400-level; N = 79) Spanish courses within the four-year program, for a total of 807 students in this study. <br>
description: Presence of Common Scales: Please see the codebook, as there is a different scale for each variable.
description: The sample size was not determined via a priori power analysis. It was determined by the number of students who enrolled in the Spanish courses during the study period.
description: College-level Spanish language learners. Smallest Geographic Unit: NA
description: computer-assisted self interview (CASI); web-based survey; The data in this study is a subset of the data collected for the Language Proficiency Flagship project at Michigan State University. For the project, a sample of intact Chinese, French, Russian, Spanish classes at Michigan State University were pseudo-randomly selected to have their proficiency measured on five occasions over the course of three academic years (fall 2014 through spring 2017). At each time of testing, the sampled classes were brought by their language instructors to a computer lab to take a background survey, a self-assessment of oral skills, and a computerized oral proficiency interview test from Language Testing International (a test officially known as ACTFL’s OPIc). For the current study, we focus on the Spanish students who were tested in spring 2017, and we use their oral proficiency interview test scores and their self-assessment outcomes.