  In the course of any statistical analysis, it is necessary to consider issues
of data quality and model appropriateness. Value of information methods were
initially put forward in the middle of the twentieth century in order to
provide a framework for choosing between potential sources of information.
However, since their genesis, value of information methods have been largely
neglected by statisticians. In this paper we review and extend existing value
of information methods and recommend the use of three quantities for
identifying influential and outlying data: an influence measure previously
suggested by \cite{kempthorne1986}, a related quantity known as the expected
value of sample information that is used to gauge how much influence we would
expect a portion of the data to have, and the ratio of these two quantities
which serves as a comparison between observed influence and expected influence.
  We study the basic theoretical properties of those quantities and illustrate
our proposed approach using two datasets. A data set containing employment
rates and other economic factors in U.S. first presented by \cite{longley} is
used to provide an example in the case of linear regression. HIV surveillance
data collected from prenatal clinics have been the main source of information
for monitoring the HIV epidemic in low and middle income countries. A data set
providing information about HIV prevalence in Swaziland is used as an example
in the case of generalized linear mixed models.
