Understanding What the Data Says
Our team of evaluators use a variety of statistical techniques, from descriptive statistics to advanced multivariate analysis.
The Evaluation Group uses descriptive statistics (i.e., means, standard deviations, frequencies, and percentages) to summarize features of the grant performance data in the evaluation study which can help enable comparisons across target schools, sites, subgroups, and years. This helps to simplify large grant data sets in an easy-to-understand manner when presenting and understanding quantitative data.
The Evaluation Group uses multivariate analysis (i.e., hierarchical linear modeling, time series, MANCOVAs, MANOVAs, factor analysis, etc.) to show the relationship between different variables being studied for exploratory and confirmatory data analysis. This is used when there are multiple variables or measurements and the relationship among and between these measurements is important to the evaluation study.
Data analysis plays a key role in providing timely and relevant evaluation information to inform continuous program improvement, answer evaluation and research questions, and support grant sustainability. It involves cleaning, transforming, and modeling data using statistical applications. The Evaluation Group’s team of evaluators are master’s and PhD level professionals trained in the use of Statistical Package for the Social Sciences (SPSS), R, Access, Google Suite, MySQL, Excel, MPlus, and SAS for statistical analysis, data management, and data documentation.