Yo In'nami

A Meta-Analysis of Test Format Effects on Reading and Listening Test Performance: Focus on Multiple-Choice and Open-Ended Formats.

A meta-analysis was conducted on the effects of multiple-choice and open-ended formats on L1 reading, L2 reading, and L2 listening test performance. Fifty-six data sources located in an extensive search of the literature were the basis for the estimates of the mean effect sizes of test format effects. The results using the mixed effects model of meta-analysis indicate that multiple-choice formats are easier than open-ended formats in L1 reading and L2 listening, with the degree of format effect ranging from small to large in L1 reading and medium to large in L2 listening. Overall, format effects in L2 reading are not found, although multiple-choice formats are found to be easier than open-ended formats when any one of the following four conditions is met: the studies involve between-subjects designs, random assignment, stem-equivalent items, or learners with a high L2 proficiency level. Format effects favoring multiple-choice formats across the three domains are consistently observed when studies employ between-subjects designs, random assignment, or stem-equivalent items.

Can Structural Equation Models in Second Language Testing and Learning Research be Successfully Replicated

Because structural equation models are widely used in testing and assessment, investigation into the accuracy of such models may help raise awareness of the value of reanalysis or replication. We focused on second language testing and learning studies and examined: (a) To what extent is information necessary for replication provided by authors? (b) To what extent can the original models be successfully replicated? Regarding (a), we e-mailed authors of 31 articles that did not contain information needed to replicate the study and asked them for the missing information. We obtained data from only four authors. Regarding (b), we succeeded in replicating 89% of the models in the preliminary analysis, 87% to 100% of fit indices, and 94% of parameter estimates. The results suggest that for the most part, structural equation modeling research reported in second language testing and learning research is accurate.