In the case of repeated-measures designs, visualization of only summary statistics hides whether individuals who score high (low) in one condition also score high (low) in another condition (e.g., aerobic running capacity with vs. For example, they do neither conceal clearly whether the raw data follow a symmetric and unimodal distribution nor whether they include outliers ( Weissgerber et al., 2015, 2019).įor small sample studies (i.e., up to 20–30 participants), which are sometimes inevitable in sports science research when, for example, considering elite athletes as participants, visualization of raw data is recommended over dispersion measures to better indicate inter-individual variation ( Weissgerber et al., 2019). However, dispersion measures are of limited value because they do not reveal the actual raw data distribution underlying a measure of central tendency. Dispersion measures are considered as an integral part of the visualization of continuous data to indicate, in the case of SD, the “average” variation of individual data points around the observed arithmetic mean or to indicate, in the case of standard error of the mean (SEM) and CI, the precision in the estimation of an unknown population parameter reflected in the observed arithmetic mean. Continuous quantitative data are often visualized in the form of summary statistics, with a measure of central tendency (e.g., arithmetic mean) being displayed together with a measure of dispersion (e.g., SD, CI). 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.ĭata visualization is an important means to communicate scientific results ( Anscombe, 1973 Tufte, 2001 Duke et al., 2015 Taamneh et al., 2016). A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The free-to-use syntax can also be modified to match with individual needs. Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). 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). 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. 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. Transparency in data visualization is an essential ingredient for scientific communication.
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