Methodology

Hypothesis Testing and Model Selection in the Social by David L. Weakliem PhD

By David L. Weakliem PhD

Examining the foremost ways to speculation trying out and version choice, this ebook blends statistical idea with ideas for perform, illustrated with real-world social technological know-how examples. It systematically compares classical (frequentist) and Bayesian techniques, displaying how they're utilized, exploring how one can reconcile the diversities among them, and comparing key controversies and criticisms. The publication additionally addresses the position of speculation checking out within the overview of theories, the connection among speculation exams and self assurance periods, and the function of previous wisdom in Bayesian estimation and Bayesian speculation trying out. simply calculated choices to straightforward speculation checks are mentioned intensive: the Akaike details criterion (AIC) and Bayesian info criterion (BIC). The better half site ([ital]www.guilford.com/weakliem-materials[/ital]) offers info and syntax records for the book's examples.

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2, if the null hypothesis is Hypothesis Testing: Criticisms and Alternatives 23 rejected the investigator looks at the size of the parameter estimate. At this stage, some statistically significant parameter estimates may be set aside as being too small to be worth further discussion. Researchers who omitted the hypothesis test and immediately looked at parameter estimates would put themselves at risk of “explaining” effects that do not actually exist. As Wallis and Roberts (1956, p. ” The connection between sample size and statistical significance does raise a more serious problem, however.

257), “Some call a statistical result ‘significant’ if it would arise by chance only once in 100 times.  . Our point, three standard errors, errs if at all in requiring too much before the possibility of chance is ruled out. ” A debate between Ross (1933) and Peters (1933) in the American Journal of Sociology involves the use of 3 standard errors as the standard for significance and anticipates some of the points made in later debates over the interpretation of “nonsignificant” results. A somewhat later textbook (Hagood and Price, 1952, pp.

One could say that the SES model should be preferred on the grounds that, in a test against the full model, the class model can be rejected at the 10% level and the SES model would not, but it is difficult to say how much evidence this amounts to. As this example illustrates, conventional hypothesis tests do not produce a complete ranking of potential models. A model that cannot be rejected against any of the others can be regarded as acceptable, but often there will be more than one acceptable model.

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