System

Supplementary material on emotion vocab across textbooks examined in the research.

This dataset corresponds to a study of authenticity in L2 oral-performance language testing conducted in 2018.

Frequency rankings in the textbook and COCA corpora used for this study and raw scores of all the participants for three tests conducted in this study are included in the file.

Supplementary Materials: Excel sheet showing focal vocabulary items and data These materials consist of conventional multiword expressions (MWEs) and various associated measures. All these MWEs are composed of two words. The most common structures are N-N, Adj-N, and V-_. There are essentially two sets of MWEs, although the MWEs within them are mostly the same. The first set figured in the study relating to emotional valence; the second set figured in the study relating to arousal. The whole MWE ratings of valence and arousal were crowd-sourced using Amazon Mechanical Turk (AMT) (https://www.mturk.com/), The corresponding ratings of the constituent words stem from a list compiled by Warriner, Kuperman, and Brysbaert (2013). The list itself is available at: http://crr.ugent.be/archives/1003. See the main article for further details. Associated with each set of MWEs is a smaller set of MWEs having both AMT ratings and ratings from Warriner et al. These matched, ‘overlapping’ ratings were used to assess the reliability of the new AMT ratings. In the main lists of MWEs these overlappers are given in red italics. Additionally, there are lists of regression residuals. The closer a residual is to zero, the more accurately MWE valence/arousal was predicted by the valence/arousal ratings of the mean of the constituent word ratings. Important abbreviations used in the spreadsheet are: AMT = Amazon Mechanical Turk; C-word = Constituent word; Geo.mean = Geometrical mean; Harm.mean = Harmonic mean; Most valenced = The C-word rating that is the furthest from 5 (i.e., neutral) either toward 1 or toward 9; SD = The standard deviation of the individual AMT ratings obtained for a given MWE; WKB = Warriner et al. (2013); NA = not available. NA was used in place of values (e.g., C-word ratings) that could not be found in the list of WKB. This abbreviation was chosen because the R functions used in the studies can handle datasets that include NA in place of a missing value. For instance, the appropriate calls in base R for calculating Spearman’s and Pearson’s correlations between the variables x and y, when NAs are present, are: cor(x, y, method = "s", use = "pairwise.complete.obs") and cor(x, y, method = "p", use = "pairwise.complete.obs"). The R functions of Wilcox (2012) that were used handle missing values even more automatically. For example, when Wilcox’s R functions are installed (https://dornsife.usc.edu/labs/rwilcox/software/), the call corb(x,y, corfun = spear, nboot = 20000) gives a bootstrap 95% confidence interval for Spearman’s correlation. The call for the median-based linear regression method that is mentioned would be: tsreg(x, y), where x and y are the independent and the dependent variables, respectively. Reference Warriner, A., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45, 1191–11207. https://doi.org/10.3758/s13428-012-0314-x

Uploaded are three items: A Codebook (.pdf), generated from SPSS that lists the variables and descriptions of the variables. A Comma-separated Values (.cvs) file that contains the data. The top row are the variables listed in the Codebook. The SPSS file with the data, from which the Codebook and the .cvs files were created. Abstract of the study based on these data: In this study, we investigated EFL undergraduate students’ motivation to learn English in the context of Pakistan using Dörnyei’s (2009) L2 Motivational Self System as the theoretical framework. We investigated the attitudes of the participants about the status of English as the official language of Pakistan and as the medium of competitive examinations. The participants of this study were undergraduate students in three public universities in Pakistan. We employed mixed methods. The first author collected the data by using a 54-item structured questionnaire and semi-structured interviews, and analyzed the data using descriptive as well as inferential statistics. The interview data was analyzed by thematic analysis. The findings suggest that the participants reported Attitudes to Learning English, Ought-to L2 self, International Posture, Instrumentality-promotion, Milieu, Ideal L2 Self, and Instrumentality-prevention as the most important motivational factors for learning English in Pakistan. The results also indicate that Dörnyei’s (2009) L2 Motivational Self System is valid in the EFL context of Pakistan. The participants also mostly favored English as the official language of Pakistan and as the medium of competitive examinations.

The appendix file includes a simplified coding scheme, a checklist for future reporting practice, and a full list of reference of studies meta-analyzed. The excel spreadsheet shows the forrest plot of the overall effect size analysis.

These are the interview transcriptions for the four case studies (A-D) each numbered (1-6) to represent the different data generation points over the course of an academic year.

This data set includes semantic and syntactic word strings to test L2 Japanese and Italic Indo-European learners of English on their reaction time and accuracy for determining correct and incorrect strings. The syntactic word strings were designed to mirror the semantic strings in terms of the vocabulary used at the 1 thousand frequency band from the British National Corpus.