Methamphetamine is the most common amphetamine used and, along with 3,4-methylenedioxymethamphetamine (MDMA, Ecstasy), is considered part of a worldwide drug epidemic. Monitoring metham-phetamine levels in the body is important for purposes of drug screening for employment, criminal investigations, and therapeutic drug monitoring. While methamphetamine is suitable for detection using immunoassay techniques, these methods tend to have significant cross reactivity with other compounds. Over the last decade, more than eighty different quantitative, confirmatory analytical methods for measuring methamphetamine in biological samples have been published in the scientific literature. Analytical instrumentation used in these methods includes gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), capillary electrophoresis (CE), among others. These assays are capable of quantifying methamphetamine concentrations in a variety of biological matrices, including blood, plasma, urine, hair, and fingernails. Some of these techniques can achieve detection as low as 0.1 ng/mL (1 ppb) concentra-tions. The strengths and limitations of these methodologies will be discussed in the context of methamphetamine analysis. Additionally, methods that can simultaneously measure methamphetamine levels as well as metabolites and other drugs of abuse will be highlighted.

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The aim of this graduate thesis is proposing an optimization of the likely voter model parameter values utilized by Median (research agency) based on secondary analysis of data from the third wave and post-election follow-up of the Czech Household Panel Study 2017 and the Median omnibus survey. The theoretical chapter presents selected aspects of the analyzed likely voter model parameters. Secondary data analysis confirms hypotheses regarding the relationships of: 1) voter turnout, prior voting behavior and the intent to vote, 2) pre-election voting preferences and actual voting behavior, 3) reported prior voting behavior and time elapsed since the prior election. Hypotheses are confirmed, and analysis results are utilized in construction of an optimized likely voter model. This model's results are then compared to the results of four currently or formerly published likely voter models (MEDIAN, CVVM2017, CVVM2018, KANTAR), all computed using an identical dataset (September/October 2017 Median omnibus survey). Based on prior-set comparison criteria, the proposed model has the highest ranking out of all the compared models. Areas of future research proposed, namely exploring the relationship between prior voting behavior misreporting and voting preference trends, in accordance with cognitive...