Our goal is to develop a tool and techniques to automate the creation of realistic and representative benchmarks from existing web applications. Worse, they may guide the development of optimizations which are unhelpful for real applications. However, recent studies have shown that these benchmarks fail to accurately represent the dynamic nature of modern JavaScript applications, and so may be poor predictors of real-world performance. Industry benchmarks such as WebKit SunSpider are often cited as a measure of the efficacy of these techniques. Innovative implementation techniques for improving its speed and responsiveness have been developed in recent years. JavaScript is a highly dynamic language for web-based applications. We also show that our adjusted or latent ratings scale is more closely related to actual purchase behavior. We apply our model to a customer satisfaction survey and show that the correlation inferences are much different once proper adjustments are made for the discreteness of the data and scale usage. We model the joint distribution of all ratings scale responses rather than speci c univariate conditional distributions as in the ordinal probit model. To capture scale usage differences, we developed a new model with individual scale and location effects and a discrete outcome variable. These differences in scale usage can impart biases to correlation and regression analyses. Survey research practitioners have long commented that respondent s vary in their usage of the scale: Common patterns include using only the middle of the scale or using the upper or lower end.

Examples in marketing include customer satisfaction measuremen t and purchase intention.

Questions that use a discrete ratings scale are commonplac e in survey research.

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