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Statistics in Language Research gives a non-technical but more or
less complete treatment of Analysis of Variance (ANOVA) for
language researchers. ANOVA is the most frequently used technique
when handling the outcomes of research designs with more than two
treatments or groups. This technique is used in all parts of
linguistics which deal with observations obtained in survey studies
and in (quasi-)experimental research, like applied linguistics,
psycholinguistics, sociolinguistics, language and speech pathology
and phonetics. Most statistical textbooks in the social sciences
take examples typical of their own field and, in addition, omit
subjects which are particularly relevant for language researchers,
like power analysis, quasi F, F1, F2 and minF'. This book offers a
thorough introduction to the basic principles of analysis of
variance, based on examples taken from language research, and goes
beyond the conventional topics treated in introductory textbooks,
as it covers topics like 'violations of assumptions', 'missing
data', 'problems in repeated measures designs', 'alternatives to
analysis of variance' (such as randomization tests and multilevel
analysis). Each chapter consists of four sections: treatment of the
subject under discussion, a summary of relevant terms and concepts,
a section devoted to reporting statistics, and finally an exercise
section. After the first introductory chapter, in which fundamental
concepts like 'variables', 'cases' and SPSS data formats are
presented, the book continues with two 'refreshment' chapters, in
which the principles of statistical testing are revised, focusing
on the well-known t test. These chapters also deal with the
essential, but often neglected concepts of 'statistical power' and
'sample size'. In every chapter examples of SPSS input and output
are given.
A textbook on statistical techniques covering an unusually wide
range of techniques and subjects, from classic techniques like
analysis of variance and multiple regression to reliability and
agreement analysis, matrix algebra, factor analysis, log linear
modeling, and logistic regression. The emphas
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