Measurement, model testing, and legislative influence in the European Union

Within the last several years, new data have become available to test the various theoretical models of EU decision-making, and, in doing so, to assess actor influence. This article examines the extent to which the recent DEU and DEUII datasets provide sufficient information to distinguish between competing theoretical models of legislative decision-making, and accurately assess the power of the different branches of EU government. It argues that insufficient attention has been paid to measurement error in these data. Once measurement error is accounted for, it becomes clear that these data do not provide sufficient information to distinguish between most models of legislative politics. Moreover, empirical models that fail to account for measurement error are likely to lead researchers to erroneous conclusions about actors’ legislative influence. </jats:p

A Mark in Time Saves Nein

A method for predicting political interactions and policy outcomes based on two political theorems is presented and illustrated with an examination of the decision to merge the two German currencies. Political perceptions and actions are anticipated by combining the substantive knowledge of area experts with the theoretical insights embedded in the median voter theorem and a monotonicity theorem that links expectations to probabilistic statements of action. The proposed model has proven accurate about 90 percent of the time. The proposed forecasting method identifies a sequential strategy that may have been followed by Chancellor Kohl in forging the coalition needed to merge successfully the two German currencies. Using comparative statics, the analysis suggests how subtle and sophisticated Chancellor Kohl had to be to succeed in getting the policy outcome he desired despite stiff opposition.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66924/2/10.1177_019251219201300106.pd

Cohort study on clustering of lifestyle risk factors and understanding its association with stress on health and wellbeing among school teachers in Malaysia (CLUSTer)--a study protocol.

BACKGROUND: The study on Clustering of Lifestyle risk factors and Understanding its association with Stress on health and wellbeing among school Teachers in Malaysia (CLUSTer) is a prospective cohort study which aims to extensively study teachers in Malaysia with respect to clustering of lifestyle risk factors and stress, and subsequently, to follow-up the population for important health outcomes. METHOD/DESIGN: This study is being conducted in six states within Peninsular Malaysia. From each state, schools from each district are randomly selected and invited to participate in the study. Once the schools agree to participate, all teachers who fulfilled the inclusion criteria are invited to participate. Data collection includes a questionnaire survey and health assessment. Information collected in the questionnaire includes socio-demographic characteristics, participants’ medical history and family history of chronic diseases, teaching characteristics and burden, questions on smoking, alcohol consumption and physical activities (IPAQ); a food frequency questionnaire, the job content questionnaire (JCQ); depression, anxiety and stress scale (DASS21); health related quality of life (SF12-V2); Voice Handicap Index 10 on voice disorder, questions on chronic pain, sleep duration and obstetric history for female participants. Following blood drawn for predefined clinical tests, additional blood and urine specimens are collected and stored for future analysis. Active follow up of exposure and health outcomes will be carried out every two years via telephone or face to face contact. Data collection started in March 2013 and as of the end of March 2014 has been completed for four states: Kuala Lumpur, Selangor, Melaka and Penang. Approximately 6580 participants have been recruited. The first round of data collection and blood sampling is expected to be completed by the end of 2014 with an expected 10,000 participants recruited. DISCUSSION: Our study will provide a good basis for exploring the clustering of lifestyle risk factors and stress and its association with major chronic medical conditions such as obesity, hypertension, impaired glucose tolerance, diabetes mellitus, coronary heart diseases, kidney failure and cancers among teachers

The Power to Resist: Mobilization and the Logic of Terrorist Attacks in Civil War

Existing research has argued that terrorism is common in civil war because it is "effective." Surprisingly, however, only some groups use terrorism during civil wars, while many refrain altogether. We also see considerable variation in the use of terrorism over time. This article presents a theory of terrorism as a mobilization strategy in civil war, taking into account benefits, costs, and temporal dynamics. We argue that the choice and the timing of terrorism arise from the interaction between conditions for effective mobilization and battlefield dynamics. Terrorism can mobilize support when it provokes indiscriminate government repression or when it radicalizes rebels' constituency by antagonizing specific societal groups. The timing of attacks, however, is in uenced by battlefield losses, which increase rebels' need to rally civilian support. The analyses of new disaggregated data on rebels' terrorist attacks during con icts (1989-2009) and of ISIS tactics in Iraq and Syria support our theoretical argument

Introducing SpatialGridBuilder: A new system for creating geo-coded datasets

Researchers in the conflict research community have become increasingly aware that we can no longer depend on state-aggregated data. Numerous factors at the substate level affect the nature of human interactions, so if we really want to understand conflict, we need to find more appropriate units of analysis. However, while many conflict researchers have realized this, actually taking the next step and performing data analysis on spatial data grids has remained a rather elusive goal for many because of the difficulty of learning the new techniques to perform such analyses. This paper introduces SpatialGridBuilder, a new, freely available, open-source system with the goal of empowering conflict researchers with no background in GIS methods to start their own spatial analyses. SpatialGridBuilder allows the researcher to: (a) create entirely new spatial datasets, based on the needs of their own research; (b) import their own spatial data; (c) easily add a range of important variables to the datasets, including commonly used conflict variables, plus new variables that have not been presented before; and (d) visualize graphical renderings of this data. Having done this, SpatialGridBuilder will then export the dataset for the researcher to analyse using conventional statistical methods. This article introduces the new program, and demonstrates how it can be used to set up such a statistical analysis. It also shows how different results can be achieved by building grids of different resolutions, thereby encouraging researchers to choose grid resolutions appropriate to their research questions and data. The article also introduces a novel means of determining infrastructure complexity, using Google maps