Informative Hypotheses

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A01=Herbert Hoijtink
ANCOVA Model
ANOVA Model
Author_Herbert Hoijtink
bayes
Bayes Factor
bayesian
Bayesian Evaluation
Bayesian hypothesis testing in psychology
behavioral statistics
Category=JMB
Category=KCH
Category=PBT
Class Specific Probabilities
Command Files
Conditional Error Probabilities
Data Sets
distribution
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Equality Constraints
evaluation
factor
Group ANOVA
Group Standard Deviation
Inequality Constraints
Informative Hypotheses
Latent Class Models
linear
model
multivariate
Multivariate Normal Linear Model
normal
Normal Linear Model
Null Hypothesis Significance Testing
order restricted inference
parameter estimation
PLM
Posterior Distribution
Posterior Model Probabilities
prior
Prior Distribution
quantitative research design
Restriction Matrix
Sample Size Determination
social science methodology
statistical modeling techniques
Unconstrained Hypothesis
Van De Schoot

Product details

  • ISBN 9780367382223
  • Weight: 476g
  • Dimensions: 178 x 254mm
  • Publication Date: 23 Sep 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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.

Since 2003, Herbert Hoijtink has been Professor of Applied Bayesian Statistics at Utrecht University. He works in the Faculty of Social Sciences where he does research, teaches and provides statistical advice to behavioral (US spelling)and social scientists. In 2005, he received the prestigious VICI grant from the Netherlands Organisation for Scientific Research. This grant enabled him to establish a research group with the purpose to develop the statistical theory and corresponding software such that behavioral and social scientists will be able to evaluate informative hypotheses. The achievements of this group are presented in this book. Further information about the author can be found at http://tinyurl.com/hoijtink.

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