Raw Data Visualization for Common Factorial Designs Using SPSS: A Syntax Collection and Tutorial - Loffing, Florian (Frontiers in Psychology, 2022)
Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e. measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g. three-factorial mixed-designs). Depending on graph type (e.g. pure dot plot, box plot, line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean, median) and dispersion (95% confidence interval, standard deviation). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers and others working with SPSS a valuable tool to move towards more transparency in data visualization.