@article{ref1,
title="Longitudinal criminology",
journal="Journal of quantitative criminology",
year="2010",
author="Greenberg, David F.",
volume="26",
number="4",
pages="437-443",
abstract="Virtually every textbook devoted to longitudinal statistical methods begins by touting their advantages over cross-sectional methods: for purposes of studying change it is helpful to have actual data on change. Also, information about temporal ordering helps to establish a relationship between variables as causal. Each of these alleged advantages calls for elaboration.In principle, change can be studied with cross-sectional data--provided one has a properly specified equation representing the relationships among a set of variables and is able to estimate its parameters without bias. However, the researcher cannot always be confident that a posited relationship accurately describes the causal relationships at work, or that its parameters can be estimated consistently. For example, if x and y are mutually dependent, an OLS regression of y on x will mischaracterize the influence of x on y. While an instrumental variable estimation can help, it may require stringent assumptions if implement...<p /> <p>Language: en</p>",
language="en",
issn="0748-4518",
doi="10.1007/s10940-010-9111-9",
url="http://dx.doi.org/10.1007/s10940-010-9111-9"
}