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A practical introduction to methods for analyzing longitudinal data in the presence of missing data using a marijuana price survey

A practical introduction to methods for analyzing longitudinal data in the presence of missing... Purpose– In applied psychology research settings, such as criminal psychology, missing data are to be expected. Missing data can cause problems with both biased estimates and lack of statistical power. The paper aims to discuss these issues. Design/methodology/approach– Recently, sophisticated methods for appropriately dealing with missing data, so as to minimize bias and to maximize power have been developed. In this paper the authors use an artificial data set to demonstrate the problems that can arise with missing data, and make naïve attempts to handle data sets where some data are missing. Findings– With the artificial data set, and a data set comprising of the results of a survey investigating prices paid for recreational and medical marijuana, the authors demonstrate the use of multiple imputation and maximum likelihood estimation for obtaining appropriate estimates and standard errors when data are missing. Originality/value– Missing data are ubiquitous in applied research. This paper demonstrates that techniques for handling missing data are accessible and should be employed by researchers. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Criminal Psychology Emerald Publishing

A practical introduction to methods for analyzing longitudinal data in the presence of missing data using a marijuana price survey

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2009-3829
DOI
10.1108/JCP-02-2015-0007
Publisher site
See Article on Publisher Site

Abstract

Purpose– In applied psychology research settings, such as criminal psychology, missing data are to be expected. Missing data can cause problems with both biased estimates and lack of statistical power. The paper aims to discuss these issues. Design/methodology/approach– Recently, sophisticated methods for appropriately dealing with missing data, so as to minimize bias and to maximize power have been developed. In this paper the authors use an artificial data set to demonstrate the problems that can arise with missing data, and make naïve attempts to handle data sets where some data are missing. Findings– With the artificial data set, and a data set comprising of the results of a survey investigating prices paid for recreational and medical marijuana, the authors demonstrate the use of multiple imputation and maximum likelihood estimation for obtaining appropriate estimates and standard errors when data are missing. Originality/value– Missing data are ubiquitous in applied research. This paper demonstrates that techniques for handling missing data are accessible and should be employed by researchers.

Journal

Journal of Criminal PsychologyEmerald Publishing

Published: May 5, 2015

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