Employing Matching to Alleviate Bias in Survey Data

There are two main obstacles that impede the ability of political scientists to evaluate the effect of campaigns and mass communication. First, it is dif-ficult to measure media and campaign exposure reliably. Surveys often rely on self-reporting from a participant, which is unreliable and contributes to the difficulty of measuring exposure to a message. Second, it is difficult to find significant media biases that can act as natural experiments. Public opinion in the aggregate is reasonably stable, even in the short term, and movement of opinion is expected when there is a change in the balance of messages across time or individuals, which is rare (Ladd and Lenz, 2007). Also, as Bartels em-phasizes, media coverage is an equilibrium condition, and opposing messages mask each others effects (1993). Newer surveys incorporate time dimensions, such as rolling cross-sections, in addition to the computer assisted and weighted internet survey designs. Political scientists are hoping to use these improved methods to create causal inferences, which are inherently difficult using obser-vational data, instead of correlation inferences. I examine what techniques are best for analyzing these new designs and whether these methods are sufficient to allow for causal inferences using the 1988 Canadian National Election Studies data as a test case. Employing a Genetic Matching method, I find that in the 1988 election, there was a significant debate effect regarding perceptions of the candidate that had no significant effect on the vote decision of the election.∗† ∗The author would like to thank Jas Sekhon and Henry Brady for advice and guidance throughout this project and Rocio Titiunik for answering every programming and data question encountered.

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