Data Analysis in Survey Research: Contesting ‘Methodological Hegemony’ Through the Choices of
Paul Lazarsfeld
More than 70 years have passed since Paul Lazarsfeld and his colleagues interviewed registered
voters in Erie County, Ohio about decision-making processes during a presidential election campaign.
The results of that study, conducted in 1940 and subsequently published in The People’s Choice
(Lazarsfeld, Berelson & Gaudet, 1948), led to a paradigm shift in mass-communication theory and
research, from the hypodermic-needle model to the two-step flow (Rogers, 1994; Schramm, 1997).
While scholars have questioned the validity of that shift (Chaffee & Hochheimer, 1985), few have
disputed its perceived existence and reification (see Glynn et al., 1999, p. 399; Katz, 1987); political
information, the study suggested, flowed from the media to opinion leaders, and then from opinion
leaders to those less engaged in politics.
The current study is concerned less with the two-step flow than the quantitative research
philosophy of Paul Lazarsfeld. Considering much of his work exploratory, Lazarsfeld did not believe in
applying tests of statistical significance to the data that he and his colleagues gathered through survey
research (Leahey, 2005, p. 18). He conceptualized social research as an avenue to formulate substantive
hypotheses about human behavior (Rogers, 1994, pp. 300-301; Selvin, 1957, p. 527), and his crosstabulations of data reflected that conceptualization (see Lazarsfeld, 1955). Seeking to contribute both
theoretically and methodologically to the study of political communication and public opinion, the
present study applies log-linear analysis to data gathered in the 2008 National Election Studies (NES).
The research demonstrates how analyses of contingency tables can reduce violations of statistical
assumptions while preserving substantive information. Through its methodological approach, the study
aspires to inform analyses of secondary data more generally, providing a framework for testing
relationships among nominal and ordinal variables. Ordinal measures, in particular, are frequently
treated as “quasi-interval” without appropriate checks on randomness, distribution normality and
equality of variance assumptions. But as the proposed study suggests, data gathered in survey research
do not have to be analyzed with techniques that are “close enough” to those that offer an equal or
superior treatment of the data.