Measurement Reliability, Construct Validity, and Transparent Reporting in Original and Replication Psychological Research

While published (replication) studies using measures to assess psychological phenomena need to transparently report information on measurement procedures, validity, and reliability to allow verification and use in future (replication) research, earlier results highlighted common poor reporting of psychological measurement. Here, we investigated the measurement reporting in a sample of 77 measures within 56 Many Labs replications and related original articles (Ebersole et al., 2016, 2020; Klein et al., 2014, 2018) and found that the information relevant for reusing measures was reported in full in around half the replication measures, and only 5.2% in the original studies. We also observed that only around a third of multiple-item measures in original studies and 11.4% in replications reported reliability coefficients, with comparable proportions for reporting any convergent, discrimant, predictive, or factorial validity evidence. We performed checks of the reliability and unidimensionality of multiple-item measures using the openly available Many Labs item response data. We observed that the reliability and unidimensionality of the measures was rarely stable across labs. These results corroborate existing findings that measurement reporting in published research lacks transparency, and that poor measurement reporting often obscure insufficient reliability and validity. We offer suggestions on how to improve measurement reporting practices and increase the use of validated measures.