Using Balanced Scales to Address Acquiescent Response Style

Measurement scales are widely used for collecting survey data about latent constructs in the social sciences. These scales are composed of multiple items that measure a single latent construct through rating response scales. Ideally, higher scores derived from ratings of these items indicate higher locations on a continuum of the latent construct. Nonetheless, errors stemming from how respondents choose their responses may complicate this measurement and lead to erroneous conclusions. Rating response scales are particularly vulnerable to Acquiescent Response Style (ARS), respondents’ tendency to choose “agree” responses regardless of the content of the items. Even though ARS has been studied for over half a century, there is still little agreement on how to address it. Balanced scales, formed by mixing items written in opposite directions of a given latent construct, are a well-known method used to measure and correct for ARS. However, concerns have been raised about the measurement properties of balanced scales, making their use controversial. The goal of this dissertation was to provide an in-depth insight into the capability of balanced scales to not only measure ARS but also to correct for it. For this goal, this dissertation combined three studies. The first study investigated the effects of scale balancing under ARS on construct and convergent validity, reliability, and factor structure. The second study compared statistical methods to correct for ARS in computing scores of latent constructs using balanced scales. The third study empirically examined the differences in measurement properties of two wording strategies for drafting reverse-worded items for balanced scales. Findings from this research suggest that scale balancing alone is insufficient to mitigate ARS-associated error and that statistical correction methods also need to be applied. However, these findings also imply that simple correction methods, such as Ordinary Least Squares regression and Confirmatory Factor Analysis, that use balanced scales may reduce the effects of ARS on scale scores. Furthermore, this study indicates that wording strategies used to generate balanced scales resulted in similar measurement properties. While the best practice for balanced scales design is still to be confirmed, this dissertation suggests that balanced scales may be a useful tool to control for ARS in surveys.PHDSurvey and Data ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/171372/1/mleiton_1.pd