Emotions evoked by the sound of music: Characterization, classification, and measurement.

One reason for the universal appeal of music lies in the emotional rewards that music offers to its listeners. But what makes these rewards so special? The authors addressed this question by progressively characterizing music-induced emotions in 4 interrelated studies. Studies 1 and 2 (n = 354) were conducted to compile a list of music-relevant emotion terms and to study the frequency of both felt and perceived emotions across 5 groups of listeners with distinct music preferences. Emotional responses varied greatly according to musical genre and type of response (felt vs. perceived). Study 3 (n = 801)--a field study carried out during a music festival--examined the structure of music-induced emotions via confirmatory factor analysis of emotion ratings, resulting in a 9-factorial model of music-induced emotions. Study 4 (n = 238) replicated this model and found that it accounted for music-elicited emotions better than the basic emotion and dimensional emotion models. A domain-specific device to measure musically induced emotions is introduced--the Geneva Emotional Music Scale. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Case matching and the reduction of selection bias in quasi-experiments: The relative importance of pretest measures of outcome, of unreliable measurement, and of mode of data analysis.

In this article, we note the many ontological, epistemological, and methodological similarities between how Campbell and Rubin conceptualize causation. We then explore 3 differences in their written emphases about individual case matching in observational studies. We contend that (a) Campbell places greater emphasis than Rubin on the special role of pretest measures of outcome among matching variables; (b) Campbell is more explicitly concerned with unreliability in the covariates; and (c) for analyzing the outcome, only Rubin emphasizes the advantages of using propensity score over regression methods. To explore how well these 3 factors reduce bias, we reanalyze and review within-study comparisons that contrast experimental and statistically adjusted nonexperimental causal estimates from studies with the same target population and treatment content. In this context, the choice of covariates counts most for reducing selection bias, and the pretest usually plays a special role relative to all the other covariates considered singly. Unreliability in the covariates also influences bias reduction but by less. Furthermore, propensity score and regression methods produce comparable degrees of bias reduction, though these within-study comparisons may not have met the theoretically specified conditions most likely to produce differences due to analytic method. (PsycINFO Database Record (c) 2010 APA, all rights reserved)