Teachers' judgment accuracy: A replication check by psychometric meta-analysis

Teachers' judgment accuracy is a core competency in their daily business. Due to its importance, several meta-analyses have estimated how accurately teachers judge students' academic achievements by measuring teachers' judgment accuracy (i.e., the correlation between teachers' judgments of students' academic abilities and students' scores on achievement tests). In our study, we considered previous meta-analyses and updated these databases and the analytic combination of data using a psychometric meta-analysis to explain variations in results across studies. Our results demonstrate the importance of considering aggregation and publication bias as well as correcting for the most important artifacts (e.g., sampling and measurement error), but also that most studies fail to report the data needed for conducting a meta-analysis according to current best practices. We find that previous reviews have underestimated teachers' judgment accuracy and overestimated the variance in estimates of teachers' judgment accuracy across studies because at least 10% of this variance may be associated with common artifacts. We conclude that ignoring artifacts, as in classical meta-analysis, may lead one to erroneously conclude that moderator variables, instead of artifacts, explain any variation. We describe how online data repositories could improve the scientific process and the potential for using psychometric meta-analysis to synthesize results and assess replicability.