Analyzing Survey Research Data



B = Basic
M = Intermediate
A = Advanced

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Further and more detailed information, including the schedule, can be found in the current course tables in the syllabus of the respective course, if the course is offered in the next sessions. The following text serves as information on what can be expected in terms of content in the course.

This course is aimed at demonstrating to students how to complete 3 critical tasks with survey data: 1) combine several survey items into a more reliable and powerful scale, 2) assess the dimensionality of a set of attitudes, 3) produce geometric maps of attitudes and preferences, so that the fundamental structure of people’s beliefs can be more readily interpreted. More generally, this course is aimed at aiding researchers in better measuring the phenomena they are interested in. Though researchers of all sorts recognize measurement as a fundamental and crucial step of the scientific process, the topic is rarely given formal attention in core graduate courses beyond a cursory treatment of the concepts of reliability and validity. The course will cover a variety of strategies for producing quantitative (usually interval-level) variables from qualitative survey responses (which are usually believed to be measured at the nominal or ordinal level). We will begin with a discussion of measurement theory, giving detailed consideration to such concepts as measurement level and measurement accuracy. This will lead us to optimal scaling strategies, for assigning numbers to objects. Following that, we will cover a variety of methods for combining multiple survey responses in order to produce higher-quality summary measures. These include: summated rating (or “Likert”) scales and reliability of measurement; principal components analysis; item response theory; factor analysis; multidimensional scaling; the vector model for profile data; and correspondence analysis. Each of these methods applies a measurement model to empirical data in order to generate a quantitative representation of the observations and survey items. The results provide new variables that can be employed as input to subsequent statistical models. These methods are not just “mere” measurement tools; in addition to quantifying observations, they often provide useful new insights about the systematic structure that exists within those observations. And, from a practical perspective, consideration of measurement theory and scaling methods can guide researchers to construct more powerful batteries of survey questions.