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or kurtosis. Replay our recorded expectations. I guess we should write the actual implementing code so we can prove all of this works. One thing I want to point out that PowerMock helped me see. Something that has become a fundimental piece of the language (all you need to do is look at the Apache Commons project to see that) is so bad that it must be avoided at all costs in the name of testing. Begin analyzing your Likert scale data with descriptive statistics. Time to get a new toolset. Now let's focus specifically on the test method.
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Ok, check out the method that implements this test below: public class urlgenerator public String generateURL return "http tLocalHostname myapplication/images/f I never said it would be pretty. From Wikipedia, the free encyclopedia. Now there is an issue with this. Again, not every set of Likert scaled items food packaging essay pte can be used for Rasch measurement. Further, if the item is accompanied by a visual analog scale, where equal spacing of response levels is clearly indicated, the argument for treating it as interval-level data is even stronger. Likert scales may be subject to distortion from several causes. To record our expectations we do exactly as we normally would. In our case, we will be mocking one class, so we pass in only one. If we have multiple, it can take an array of classes as well. One of the biggest points about PowerMock is that the API is designed to mesh well with EasyMock. Consensus based assessment (CBA) can be used to create an objective standard for Likert scales in domains where no generally accepted standard or objective standard exists. I would analyze the data both ways - with chi-square and with anova, and see how it turns out - if the outcomes are the same, you're all set.