Analyzing Policy Capturing Data Using Structural Equation Modeling for Within

We present the SEMWISE (structural equation modeling for within-subject experiments) approach for analyzing policy capturing data. Policy capturing entails estimating the weights (or utilities) of experimentally manipulated attributes in predicting a response variable of interest (e.g., the effect of experimentally manipulated market-technology combination characteristics on perceived entrepreneurial opportunity). In the SEMWISE approach, a factor model is specified in which latent weight factors capture individually varying effects of experimentally manipulated attributes on the response variable. We describe the core SEMWISE model and propose several extensions (how to incorporate nonbinary attributes and interactions, model multiple indicators of the response variable, relate the latent weight factors to antecedents and/or consequences, and simultaneously investigate several populations of respondents). The primary advantage of the SEMWISE approach is that it facilitates the integration of individually varying policy capturing weights into a broader nomological network while accounting for measurement error. We illustrate the approach with two empirical examples, compare and contrast the SEMWISE approach with multilevel modeling (MLM), discuss how researchers can choose between SEMWISE and MLM, and provide implementation guidelines