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008			120329s2012 flua sb 001 0 eng d
020			_a9781439880524 (ebook : PDF)
040			_aBD-DhSAU _cBD-DhSAU
090			_aBF39 _b.H625 2012
092			_a300.727 _bH719
100	1		_aHoijtink, Herbert.
245	1	0	_aInformative hypotheses _h[electronic resource] : _btheory and practice for behavioral and social scientists / _cHerbert Hoijtink.
260			_aBoca Raton : _bCRC, _cc2012.
300			_axiv, 227 p. : _bill.
490	1		_aChapman & Hall/CRC statistics in the social and behavioral sciences series
504			_aIncludes bibliographical references (p. 217-224) and index.
505	0		_a1. Introduction -- 2. Bayesian evaluation of informative hypotheses -- 3. Other models, other approaches, and software -- 4. Statistical foundations.
520			_a"When scientists formulate their theories, expectations, and hypotheses, they often use statements like: "I expect mean A to be bigger than means B and C"; "I expect that the relation between Y and both X1 and X2 is positive"; and "I expect the relation between Y and X1 to be stronger than the relation between Y and X2". Stated otherwise, they formulate their expectations in terms of inequality constraints among the parameters in which they are interested, that is, they formulate Informative Hypotheses.There is currently a sound theoretical foundation for the evaluation of informative hypotheses using Bayes factors, p-values and the generalized order restricted information criterion. Furthermore, software that is often free is available to enable researchers to evaluate the informative hypotheses using their own data. The road is open to challenge the dominance of the null hypothesis for contemporary research in behavioral, social, and other sciences"-- _cProvided by publisher.
520			_a"Preface Providing advise to behavioral and social scientists is the most interesting and challenging part of my work as a statistician. It is an opportunity to apply statistics in situations that usually have no resemblance to the clear cut examples discussed in most text books on statistics. A fortiori, it is not unusual that scientists have questions to which I do not have a straightforward answer, either because the question has not yet been considered by statisticians, or, because existing statistical theory can not easily be applied because there is no software with which it can be implemented. An example of the latter are Informative Hypotheses. When I question scientists with respect to their theories, expectations and hypotheses, they often respond with statements like: I expect mean A to be bigger than means B and C"; I expect that the relation between Y and both X1 and X2 is positive"; and I expect the relation between Y and X1 to be stronger than the relation between Y and X2". Stated otherwise, they formulate their expectations in terms of inequality constraints among the parameters in which they are interested, that is, they formulate Informative Hypotheses. In this book the evaluation of informative hypotheses is introduced for behavioral and social scientists. Chapters 1 and 2 introduce the univariate and multivariate normal lin- ear models and the informative hypotheses that can be formulated in the context of these models. An accessible account of Bayesian evaluation of informative hypotheses is provided in Chapters 3 through 7. There is also an account of the non-Bayesian approaches for the evaluation of informative hypotheses for which software with which these approaches can be implemented is available (Chapter 8)"-- _cProvided by publisher.
530			_aAlso available in print edition.
538			_aMode of access: World Wide Web.
650		0	_aPsychology _xStatistical methods.
650		0	_aSocial sciences _xStatistical methods.
650		0	_aHypothesis.
655		7	_aElectronic books. _2lcsh
776	1		_z9781439880517 (hardback)
830		0	_aStatistics in the social and behavioral sciences series.
856	4	0	_uhttp://marc.crcnetbase.com/isbn/9781439880524 _qapplication/PDF
999			_c11840 _d11839