Assessing the Psychometric Qualities of the Data-Informed School Leadership Survey

Purpose: There is little consensus in the literature regarding a) what it means for a school leader to lead with data, and b) how to measure data-informed leadership in a reliable and valid way. This study examines the psychometric properties of an operational measure intended to assess the extent to which a school leader is a ‘data-informed school leader. The measurement invariance, reliabilities and construct and predictive validities of the Data-Informed School Leadership Survey (DISL Survey) are assessed using various psychometric statistical techniques. Methods: Using data collected from 155 teachers from 7 public schools in a southern state, the following psychometric statistics used to address our purpose: the Many-Facet Rasch (MFR) Model, Bayesian second-order Confirmatory Factor Analysis (CFA), Bayesian Structural Equation Modeling- Multiple Indicators, Multiple Causes analysis (Bayesian SEM-MIMIC), and reliability analysis. Findings: Results show an adequate fit from all MFR, Bayesian CFA, and MIMIC models and a high reliability (Cronbach α = .98). The DISL Survey instrument exhibits sound psychometric properties. Results likewise confirm the value of using MFR modeling and Bayesian methods to examine the psychometric properties of DISL Survey as a means of improving educational leadership measures. Implications for Research and Practice: Data from this study confirm the validity and reliability of the Data-Informed School Leadership Survey (DISL Survey) as an instrument to assess the strengths and weaknesses of Data-Informed School Leadership (DISL) and as a means for providing feedback for improving such leadership. Heretofore a measure for assessing this leadership was non-existent.