How Shall I Count the Ways? A Method for Quantifying the Qualitative Aspects of Unscripted Movement With Laban Movement Analysis - Tsachor, Rachelle Palnick; Shafir, Tal (Frontiers in Psychology, 2019)
Dance Movement Therapy (DMT) has significant clinical evidence showing that creative and expressive movement processes enhance psycho-social well-being. Yet, because movement is a complex phenomenon, statistically validating which aspects of movement change during interventions or lead to significant positive therapeutic outcomes is challenging because movement has multiple, overlapping variables appearing in unique patterns in different individuals and situations. One factor contributing to the therapeutic effects of DMT is the effect of movement on clients' emotional states. Our previous study identified sets of movement variables which, when executed, contribute to the enhancement of specific emotions. In this paper we describe how we selected movement variables used in the statistical analysis, using a multi-stage methodology for identifying, reducing, coding, and quantifying the multitude of variables present in unscripted movement. In our study, we used Laban Movement Analysis (LMA), an internationally-accepted comprehensive system for movement analysis, and a primary DMT clinical assessment tool for describing the movement analysis. We began with Martha Davis’s three-stepped protocol for analyzing movement patterns and identifying the most important variables: 1) We repeatedly observed samples of validated motor emotional expressions to identify prevalent movement variables, eliminating variables appearing minimally or absent. 2) We used the criteria frequency, duration, and emphasis to eliminate additional variables. 3) We analyzed variations of motor expressions of the same emotion to discover how variables cluster: first, observing 10 movement samples from the same emotion to identify variables common to all samples; second, by qualitative analysis of two highly-recognized samples to determine if phrasing, duration or relationship among variables was significant. We added three new steps to this protocol: 4) We created Motifs (LMA symbols) of combinations of the movement variables extracted in steps 1-3; 5) We asked pilot participants to move these combinations and quantify their emotional experience. Based on the results of the pilot study we eliminated more variables; 6) We quantified the prevalence of the remaining variables in each Motif to enable us to use the results of our experiment in a statistical analysis that examined which variables enhanced each emotion. Our method successfully quantified unscripted movement data for statistical analysis.