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Poor school connectedness (SC), defined as students’ feelings of belonging, safety, and fairness at school, is a risk factor for negative psychosocial outcomes. Few studies have examined the specific relationship between SC and anxiety. This study examined the relation between SC and anxiety within a group of 114 clinically anxious youth (mean age =10.82; SD = 2.93; 48.2% female; 70.2% White, non-Hispanic); age differences were also examined. Results indicated that SC was significantly negatively associated with age but unrelated to gender, race/ethnicity, socio-economic status, parent education, or presence of a comorbid disorder. Findings generally revealed that low SC was associated with greater total and domain specific anxiety. SC may play a unique role in the maintenance of global and domain specific anxiety symptoms.
Scholars and policy makers need systematic assessments of the validity of the measures produced by V-Dem. In Chapter 6, we present our approach to comparative data validation – the set of steps we take to evaluate the precision, accuracy, and reliability of our measures, both in isolation and compared to extant measures of the same concepts. Our approach assesses the degree to which measures align with shared concepts (content validation), shared rules of translation (data generation assessment), and shared realities (convergent validation). Within convergent validity, we execute two convergent validity tests. First, we examine convergent validity as it is typically conceived – examining convergence between V-Dem measures and extant measures. Second, we evaluate the level of convergence across coders, considering the individual coder and country traits that predict coder convergence. Throughout the chapter, we focus on three indices included in the V-Dem data set: polyarchy, corruption, and core civil society. These three concepts collectively provide a “hard test” for the validity of our data, representing a range of existing measurement approaches, challenges, and solutions.
This chapter sets forth the conceptual scheme for the V–Dem project. We begin by discussing the concept of democracy. Next, we lay out seven principles by which this key concept may be understood – electoral, liberal, majoritarian, consensual, participatory, deliberative, and egalitarian. Each defines a “variety“ of democracy, and together they offer a fairly comprehensive accounting of the concept as used in the world today. Next, we show how this seven-part framework fits into our overall thinking about democracy, including multiple levels of disaggregation – to components, subcomponents, and indicators. The final section of the chapter discusses several important caveats and clarifications pertaining to this ambitious taxonomic exercise.
This chapter recounts how a project of this scale came together and why it has succeeded. Five main factors were responsible for V–Dem’s success: timing, inclusion, deliberation, administrative centralization, and fund–raising. First, planning for V-Dem began at a time when both social scientists and practitioners were realizing that they needed better democracy measures. This made it possible to recruit collaborators and find funding. Second, the leaders of the project were always eager to expand the team to acquire whatever expertise they lacked and share credit with everyone who contributed. Third, the project leaders practiced an intensely deliberative decision–making style to ensure that all points of view were consulted and only decisions that won wide acceptance were adopted. Fourth, centralizing the execution of the agreed–upon tasks helped tremendously by streamlining processes and promoting standardization, documentation, professionalization, and coordination of a large number of intricate steps. Finally, successful fund–raising from a mix of both research foundations and bilateral and multilateral organizations has been critical.
In this chapter we focus on the measurement of five key principles of democracy – electoral, liberal, participatory, deliberative, and egalitarian. For each principle, we discuss (1) the theoretical rationale for the selected indicators, (2) whether these indicators are correlated strongly enough to warrant being collapsed into an index, and (3) the justification of aggregation rules for moving from indicators to components and from components to higher–level indices. In each section we also (4) highlight the top– and bottom–five countries on each principle of democracy in early (1812 or 1912) and late (2012) years of our sample period, as well as the aggregate trend over the whole time period 1789–2017 (where applicable). Finally, we (5) look at how the different principles are intercorrelated in order to assess the trade–offs involved between the conceptual parsimony achieved by aggregating to a few general concepts and the retention of useful variation permitted by aggregating less.
Four characteristics of V-Dem data present distinct opportunities and challenges for explanatory analysis: (1) the large number of democracy indicators (i.e., variables), (2) the measurement of concepts by multiple coders filtered through the V-Dem measurement model, (3) the large number of years in the data set, and 4) the ex ante potential for dependence across countries (generically referred to as spatial dependence). This chapter discusses 3 challenges and 10 opportunities that are implied by these characteristics. At the end of this chapter, we also discuss three assumptions that are implicit in most analyses of observational indicators of macro-features at the national level, which aim to draw conclusions about causal relationships.
Varieties of Democracy is the essential user's guide to The Varieties of Democracy project (V-Dem), one of the most ambitious data collection efforts in comparative politics. This global research collaboration sparked a dramatic change in how we study the nature, causes, and consequences of democracy. This book is ambitious in scope: more than a reference guide, it raises standards for causal inferences in democratization research and introduces new, measurable, concepts of democracy and many political institutions. Varieties of Democracy enables anyone interested in democracy - teachers, students, journalists, activists, researchers and others - to analyze V-Dem data in new and exciting ways. This book creates opportunities for V-Dem data to be used in education, research, news analysis, advocacy, policy work, and elsewhere. V-Dem is rapidly becoming the preferred source for democracy data.
Users of V–Dem data should take care to understand how the data are generated because the data collection strategies have consequences for the validity, reliability, and proper interpretation of the values. Chapters 4 and 5 explain how we process the data after collecting the raw scores and how we aggregate the most specific indicators into more general indices. In this chapter we explain where the raw scores come from. We distinguish among the different types of data that V–Dem reports and describe the processes that produce each type and the infrastructure required to execute these processes.
V-Dem relies on country experts who code a host of ordinal variables, providing subjective ratings of latent – that is, not directly observable – regime characteristics. Sets of around five experts rate each case, and each rater works independently. Our statistical tools model patterns of disagreement between experts, who may offer divergent ratings because of differences of opinion, variation in scale conceptualization, or mistakes. These tools allow us to aggregate ratings into point estimates of latent concepts and quantify our uncertainty around these estimates. This chapter describes item response theory models that can account and adjust for differential item functioning (i.e., differences in how experts apply ordinal scales to cases) and variation in rater reliability (i.e., random error). We also discuss key challenges specific to applying item response theory to expert–coded cross-national panel data, explain how we address them, highlight potential problems with our current framework, and describe long-term plans for improving our models and estimates. Finally, we provide an overview of the end–user–accessible products of the V-Dem measurement model.
We analyzed birth order differences in means and variances of height and body mass index (BMI) in monozygotic (MZ) and dizygotic (DZ) twins from infancy to old age. The data were derived from the international CODATwins database. The total number of height and BMI measures from 0.5 to 79.5 years of age was 397,466. As expected, first-born twins had greater birth weight than second-born twins. With respect to height, first-born twins were slightly taller than second-born twins in childhood. After adjusting the results for birth weight, the birth order differences decreased and were no longer statistically significant. First-born twins had greater BMI than the second-born twins over childhood and adolescence. After adjusting the results for birth weight, birth order was still associated with BMI until 12 years of age. No interaction effect between birth order and zygosity was found. Only limited evidence was found that birth order influenced variances of height or BMI. The results were similar among boys and girls and also in MZ and DZ twins. Overall, the differences in height and BMI between first- and second-born twins were modest even in early childhood, while adjustment for birth weight reduced the birth order differences but did not remove them for BMI.
A nationwide initiative was implemented in February 2014 to decrease Clostridium difficile infections (CDI) in Veterans Affairs (VA) long-term care facilities. We report a baseline of national CDI data collected during the 2 years before the Initiative.
Personnel at each of 122 reporting sites entered monthly retrospective CDI case data from February 2012 through January 2014 into a national database using case definitions similar to those used in the National Healthcare Safety Network Multidrug-Resistant Organism/CDI module. The data were evaluated using Poisson regression models to examine infection occurrences over time while accounting for admission prevalence and type of diagnostic test.
During the 24-month analysis period, there were 100,800 admissions, 6,976,121 resident days, and 1,558 CDI cases. The pooled CDI admission prevalence rate (including recurrent cases) was 0.38 per 100 admissions, and the pooled nonduplicate/nonrecurrent community-onset rate was 0.17 per 100 admissions. The pooled long-term care facility–onset rate and the clinically confirmed (ie, diarrhea or evidence of pseudomembranous colitis) long-term care facility–onset rate were 1.98 and 1.78 per 10,000 resident days, respectively. Accounting for diagnostic test type, the long-term care facility–onset rate declined significantly (P=.05), but the clinically confirmed long-term care facility–onset rate did not.
VA long-term care facility CDI rates were comparable to those in recent reports from other long-term care facilities. The significant decline in the long-term care facility-onset rate but not in the clinically confirmed long-term care facility–onset rate may have been due to less testing of asymptomatic patients. Efforts to decrease CDI rates in long-term care facilities are necessary as part of a coordinated approach to decrease healthcare-associated infections.
Infect. Control Hosp. Epidemiol. 2016;37(3):295–300
A trend toward greater body size in dizygotic (DZ) than in monozygotic (MZ) twins has been suggested by some but not all studies, and this difference may also vary by age. We analyzed zygosity differences in mean values and variances of height and body mass index (BMI) among male and female twins from infancy to old age. Data were derived from an international database of 54 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins), and included 842,951 height and BMI measurements from twins aged 1 to 102 years. The results showed that DZ twins were consistently taller than MZ twins, with differences of up to 2.0 cm in childhood and adolescence and up to 0.9 cm in adulthood. Similarly, a greater mean BMI of up to 0.3 kg/m2 in childhood and adolescence and up to 0.2 kg/m2 in adulthood was observed in DZ twins, although the pattern was less consistent. DZ twins presented up to 1.7% greater height and 1.9% greater BMI than MZ twins; these percentage differences were largest in middle and late childhood and decreased with age in both sexes. The variance of height was similar in MZ and DZ twins at most ages. In contrast, the variance of BMI was significantly higher in DZ than in MZ twins, particularly in childhood. In conclusion, DZ twins were generally taller and had greater BMI than MZ twins, but the differences decreased with age in both sexes.