Empirical Musicology: Aims, Methods, Prospects

By Eric Clarke; Nicholas Cook | Go to book overview

CHAPTER 9
Data Collection, Experimental Design,
and Statistics in Musical Research

W. Luke Windsor


Introduction

This chapter provides a brief introduction to the ways in which musical research has drawn upon the quantitative methods of the empirical social sciences. The past 25 years have seen increasing moves toward the use of such methods in musical research, especially in the domain that has become variously known as “music psychology,” “psychology of music,” “music cognition,” “music perception,” or even “psychomusicology.” Although these methods can be applied directly to musical data derived from a score, this chapter focuses upon quantitative analytical techniques that can be applied to musical events that involve either listeners or performers. Research on music perception and performance can be carried out using quite standard statistical and experimental methods, but often requires novel approaches to their application.

In carrying out an empirical study, hypotheses, or at least some concrete research questions, must be generated before comparing, describing, coding, or collecting data. This is because it is only in the light of such hypotheses that you can decide precisely what data are relevant, and how irrelevant factors are to be excluded: to this extent the approach must be top-down, rather than bottom-up. Hence, although the first practical step in doing empirical research is observation, a prior conceptual step should be an informed decision about what to observe, how to quantify it, and how to analyze the resulting data. It is pointless collecting data that turn out to be inappropriate for analysis, or which fail to provide evidence that can be used to support or challenge the relevant arguments.

However, not all quantitative research need be experimental in this classical sense. It is perfectly acceptable to collect data in a more exploratory manner as long as it is recognized that it may be hard to understand the relationship between different variables. The “real world” is a complex place, and laboratory researchers often pay a price for ensuring that their experimental results are easy to interpret. This price is loss of “realism” or “ecological validity,” and can result in findings that only hold under extremely unusual and constrained circumstances (such as those within a laboratory). It may be convenient for analytical purposes to take into account only certain things, such as, for example, the duration and pitch-class of events in melodic sequences, but there is a danger of finding out too late that some

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