The familiar classical test is on Analyze > Compare Means > Independent Samples t test, and the Bayesian equivalent is on Analyze > Bayesian Statistics > Independent Samples Normal. I am not going to add to the many commentaries available on the web or in textbooks on Bayesian methods, but I will illustrate the approach and compare it with classical methods for the independent samples test for equality of means. Reference priors are available in the appropriate procedures and are generally the default.Ī Classical Versus Bayesian Test Procedure The mathematics is beyond the scope of this note, but the important thing is that they allow us to have objective Bayes estimates. Newer methods, referred to as reference priors provide a solution. There are, however, difficulties with a uniform prior over an infinite space. As the amount of data increases, the impact of the prior on the posterior distribution, which incorporates both the prior and the data, is dominated by the data, so the prior choice becomes increasingly unimportant as long as the prior has positive probability for the relevant parameter space. But if we don’t, we might use a noninformative prior such as that all values are equally probable. The procedures give a variety of choices, and that information will be combined with the data in the posterior distribution. If we do have prior information, it can be valuable. The commands are BAYES ANOVA, BAYES CORRELATION, BAYES INDEPENDENT, BAYES LOGLINEAR, BAYES ONESAMPLE, BAYES REGRESION, and BAYES RELATED.īefore we dive into the procedures, we need to address the second problem above where we don’t have a firm basis for selecting a prior. The Analze > Bayesian Statistics submenu lists the procedures. They are included in the Statistics Standard Edition. They have the familiar Statistics user interface style, have traditional Statistics syntax, and, like other procedures, produce tables and charts in the Viewer. IBM SPSS Statistics version 25, though, introduces seven native Bayesian procedures in nine dialog boxes. Bayesian results show the whole distribution of the parameters rather than just point estimates.Īmong the last things I did before retiring from IBM at the end of 2015 was to create four Bayesian extension commands which are available via the Extension Hub from within Statistics 24 or later or via Utilities in older versions.They allow us to talk about results in intuitive ways that are not strictly correct with classical methods. Bayesian results are easier to interpret than p values and confidence intervals.Bayesian methods provide exact inferences without resorting to asymptotic approximations.Bayesian methods provide a rigorous way to include prior information when available compared to hunches or suspicions that cannot be systematically included in classical methods. Well, one nontheoretical reason is that Bayesian methods are hot right now! You might want to jump on the bandwagon. Why should you be interested in Bayesian methods? After all, Bayesian methods often give similar results to classical methods. #Ibm spss statistics 21 tutorial softwareThird, and perhaps most important, there has been a dearth of efficient, easy to use, mainstream statistical software for Bayesian analysis.While these methods work very well when there is prior information, often in applied work there is little such information available. Second, Bayesian methods require a prior distribution to be specified for the parameters to be estimated.First, and possibly due to the next two reasons, applied statistics courses have mostly not spent much time on Bayesian approaches.There are, I believe, three main reasons for this. Applied statisticians, though, have until recently been overwhelmingly in the frequentist camp. (But see also this link for a vigorous debate on this: What's wrong with this?). Googling Bayesian versus frequentist produces a vast collection of items on this topic. For decades, statistical theorists have debated the merits of the classical or frequentist approach versus the Bayesian approach.
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