DOI: 10.15128/r2m039k495v
title: A genre-specific measure of subjective feeling in music listening: Constructing the Durham Emotion in Qawwālī Scale (DEQS) [dataset]

description: This dataset contains the raw data, R script, and data tables produced by two studies focussing on subjective feeling felt by listeners to the South Asian genre of Sufi music known as qawwali. It specifically focusses on regular listeners to qawwali at the shrine of the Sufi saint Hazrat Nizamuddin Auliya in Delhi. These studies are based on the method used by Zentner and colleagues when they proposed the GEMS dimensions of musical emotion. We suggest that while Zentner's method affords a good data-driven approach to determining the most salient emotion terms for a particular group of listeners listening to a particular genre of music, it cannot be generalised beyond that context. Therefore, we adapt his method to develop a scale of subjective feeling for a group of listeners in a completely different context: qawwali.
description: Both studies to which this dataset relates took the form of online Qualtrics questionnaires utilising likert scales. The first study determines a few emotion words which are most relevant to qawwali listening. The second asks participants to compare how similar or different the words outputted from study one are to each other. The associated R script analyses these data in two ways: using multidimensional scaling to map the conceptual space between the emotion terms, and exploratory factor analysis to develop a scale of a small number of factors explaining most subjective feeling terms for qawwali listeners. This scale is termed the Durham Emotion in Qawwali Scale (DEQS), and consists of virtuous feelings, spiritual love, and trance.
description: Description of methods used for collection/generation of data: Study 1: The questionnaire was run online through Qualtrics. 31 participants were recruited at Hazrat Nizamuddin Dargah by Syed Murshid Nizami, where they completed the questionnaire on his smartphone. All participants were regular attendees of qawwali at the shrine. Participants were presented with 75 emotion terms including Sufi terms and Urdu translations of sixty-six adjectives shown to music festival attendees in the third study of Zentner and colleagues’ GEMS article (2008: 503). Participants were asked to rate how often they feel each of these terms while listening to qawwali on a likert scale of 1-5. Study 2: In study two, the lower number of terms outputted by study one (totalling emotion 20 terms) were used. 32 participants were recruited at Hazrat Nizamuddin Dargah by Syed Murshid Nizami and Thomas A. Graves, where they completed the questionnaire on smartphones. Participants were presented with all possible combinations of two of the twenty emotion terms and asked to rate how similar those two terms feel on a scale of 1-5 (a total of 191 questions). Due to the large number of questions, three attention checks were included, in which participants were asked to select a certain number. Three participants failed at least one attention check so these answers were excluded from analysis. Another participant answered “2” for most questions, so that response was also excluded, leaving a total N of 28 for analysis. Methods for processing the data: Study 1: Means and Standard deviations for each term were calculated, and terms scoring a mean average above 4 were retained for use in study 2. Several theoretically important terms for qawwali were also retained for use in study 2. Study 2: First, the comparisons between emotion terms were organised into a dissimilarity matrix for each participant. Then 2D multidimensional scaling was conducted for each participant individually. After this, an aggregated 2D multidimensional scaling was conducted to combine all of the participants' results. Lines were drawn between the most closely related results on the 2D solution based on reading of the data. Next, 3D SMACOF Multidimensional scaling was conducted. Finally, exploratory factor analysis was conducted on the data, determining how many factors could explain the data, and which emotion terms were loaded onto which factor.