scholarly journals New Estimates of Over 500 Years of Historic GDP and Population Data

2021 ◽  
Author(s):  
Christopher J Fariss ◽  
Therese Anders ◽  
Jonathan Markowitz ◽  
Miriam Barnum
Keyword(s):  
Latent Variable ◽  
Measurement Theory ◽  
Population Data ◽  
Modeling Framework ◽  
Variable Model ◽  
Conflict Processes ◽  
Multiple Indicators ◽  
Historical Coverage ◽  
The Relationship ◽  
Temporal Coverage

Gross Domestic Product (GDP), GDP per capita, and population are central to the study of politics and economics broadly, and conflict processes in particular. Despite the prominence of these variables in empirical research, existing data lack historical coverage and are assumed to be measured without error. We develop a latent variable modeling framework that expands data coverage (1500 A.D--2018 A.D) and, by making use of multiple indicators for each variable, provides a principled framework to estimate uncertainty for values for all country-year variables relative to one another. Expanded temporal coverage of estimates provides new insights about the relationship between development and democracy, conflict, repression, and health. We also demonstrate how to incorporate uncertainty in observational models. Results show that the relationship between repression and development is weaker than models that do not incorporate uncertainty suggest. Future extensions of the latent variable model can address other forms of systematic measurement error with new data, new measurement theory, or both.

scholarly journals The Origins and Implications of Language Effects in Multilingual Surveys: A MIMIC Approach with Application to Latino Political Attitudes

2011 ◽  
Vol 19 (4) ◽  
pp. 434-454 ◽  
Author(s):  
Efrén O. Pérez
Keyword(s):  
Latent Variable ◽  
Political Attitudes ◽  
Theoretical Work ◽  
Modeling Framework ◽  
Language Differences ◽  
Multiple Indicators ◽  
Item Functioning ◽  
Language Effects ◽  
The Relationship ◽  
Mimic Approach

The study of Latino public opinion has renewed interest in the relationship between language and survey response. However, extant research generally relies on statistical methods that cannot distinguish between two related yet distinct types of language effects in Latino surveys: (1) differences inattitudeand (2) differences inmeasuresof attitude. The former reflects varied levels of a latent attitude between English and Spanish interviewees. The latter—formally known as Differential Item Functioning (DIF)—refers to linguistic differences in the interpretation of survey items, which lead Latino respondents to misreport their level of attitude. This paper proposes Multiple Indicators Multiple Causes models to decouple these two types of language effects. Using this modeling framework, I examine language differences in measures of subjective and factual political attitudes from theLatino National Survey(2006). I find that the language of interview systematically colors Latinos' interpretation of survey items, even after controlling for measurement error and individual differences in the latent variable being assessed. I then show through an applied analysis how ignoring language DIF can yield misleading inferences about hypothesized relationships between variables. Together, these findings highlight a need for greater theoretical work on the psychological origins of language effects in multilingual political surveys.

scholarly journals A Latent Variable Model for Learning Distributional Relation Vectors

2019 ◽  
Author(s):  
Jose Camacho-Collados ◽  
Luis Espinosa-Anke ◽  
Shoaib Jameel ◽  
Steven Schockaert
Keyword(s):  
Latent Variable ◽  
Language Model ◽  
Word Embedding ◽  
Latent Variable Model ◽  
Variable Model ◽  
The Given ◽  
The Relationship ◽  
Unsupervised Approaches ◽  
Target Words

Recently a number of unsupervised approaches have been proposed for learning vectors that capture the relationship between two words. Inspired by word embedding models, these approaches rely on co-occurrence statistics that are obtained from sentences in which the two target words appear. However, the number of such sentences is often quite small, and most of the words that occur in them are not relevant for characterizing the considered relationship. As a result, standard co-occurrence statistics typically lead to noisy relation vectors. To address this issue, we propose a latent variable model that aims to explicitly determine what words from the given sentences best characterize the relationship between the two target words. Relation vectors then correspond to the parameters of a simple unigram language model which is estimated from these words.

A latent variable approach to measuring wartime sexual violence

2020 ◽  
Vol 57 (6) ◽  
pp. 728-739
Author(s):  
Jule Krüger ◽  
Ragnhild Nordås
Keyword(s):  
Sexual Violence ◽  
Latent Variable ◽  
Best Practice ◽  
Temporal Trends ◽  
Credible Interval ◽  
Future Research ◽  
Variable Model ◽  
Multiple Indicators ◽  
Variable Approach ◽  
Security Problem

Conflict-related sexual violence is an international security problem and is sometimes used as a weapon of war. It is also a complex and hard-to-observe phenomenon, constituting perhaps one of the most hidden forms of wartime violence. Latent variable models (LVM) offer a promising avenue to account for differences in observed measures. Three annual human rights sources report on the sexual violence practices of armed conflict actors around the world since 1989 and were coded into ordinal indicators of conflict-year prevalence. Because information diverges significantly across these measures, we currently have a poor scientific understanding with regard to trends and patterns of the problem. In this article, we use an LVM approach to leverage information across multiple indicators of wartime sexual violence to estimate its true extent, to express uncertainty in the form of a credible interval, and to account for temporal trends in the underlying data. We argue that a dynamic LVM parametrization constitutes the best fit in this context. It outperforms a static latent variable model, as well as analysis of observed indicators. Based on our findings, we argue that an LVM approach currently constitutes the best practice for this line of inquiry and conclude with suggestions for future research.

The Relationship Between Transfer Student Status, Student Engagement, and High-Impact Practice Participation

2018 ◽  
Vol 46 (4) ◽  
pp. 368-387 ◽  
Author(s):  
John Zilvinskis ◽  
Amber D. Dumford
Keyword(s):  
Student Engagement ◽  
Service Learning ◽  
Transfer Students ◽  
Latent Variable ◽  
High Impact ◽  
Transfer Student ◽  
Variable Model ◽  
Senior Student ◽  
Faculty Interaction ◽  
The Relationship

Objective: Based on the growing number of transfer students in higher education and the concern that transfer students are not as engaged as their peers, specifically in participation in high-impact practices (HIPs), this research asks, “Is there a significant direct or indirect relationship between transfer status, student engagement, and HIP participation?” Method: The current study employed a general latent variable model to explore the relationship between community college transfer student status, student engagement, and participation in HIPs. Using data from the 2014 administration of the National Survey of Student Engagement, 22,994 senior student responses were examined to measure the association between transfer status (students who transferred from a 2-year to 4-year institution compared with nontransfer students), student engagement (collaborative learning, student–faculty interaction, and supportive campus environment), and HIP participation (learning community, service-learning, research with a faculty member, internship, study abroad, and culminating senior experience). Results: Although each of the student engagement indicators significantly mediated HIP participation for transfer students, only the effect for student–faculty interaction was nontrivial. Contributions: The results from this study indicate the importance of faculty in advocating for and supporting transfer students, while presenting questions about the degree to which these students may need additional institutional support to recognize HIPs in a 4-year context. Implications for enhancing student–faculty interaction among transfer students, as a means to increase HIP participation, are discussed.

Innovations in concepts and measurement for the study of peace and conflict

2020 ◽  
Vol 57 (6) ◽  
pp. 669-678
Author(s):  
Christopher J Fariss ◽  
James Lo
Keyword(s):  
Latent Variable ◽  
Measurement Model ◽  
Theoretical Concept ◽  
Special Issue ◽  
Variable Model ◽  
Conflict Processes ◽  
Substantive Content ◽  
Measurement Models ◽  
Introductory Article ◽  
Peace And Conflict

The observation, measurement, and analysis of violent and contentious processes are essential parts of the scientific study of peace and conflict. However, concepts such as the level of repression, the number of individuals killed during a civil war, or the perception of members of an out-group, are often by definition difficult to observe directly. This is because governments, non-state groups, NGOs, international organizations, monitoring organizations, and other actors are not incentivized to make information about their actions systematically observable to analysts. In this context, latent variable models can play a valuable role by aggregating various behavioral indicators and signals together to help measure latent concepts of interest that would not otherwise be directly observable. Each of the articles in this special issue uses some form of a latent variable model or related measurement model to bring together observable pieces of information and estimate a set of values for the underlying theoretical concept of interest. Each of the articles pays special attention to the processes that make the observation of peace and conflict processes so challenging. As we highlight throughout this introductory article, the unifying framework we present in this special issue is validation. Though the substantive content of each of the articles in this special issue varies, they represent the diversity of substantive interests that span the study of peace and conflict, broadly conceived. Overall, we hope that the special issue becomes a standard reference for scholars interested in developing and validating new measurement models for the study of peace and conflict.

scholarly journals Enhancing sensitivity and controlling false discovery rate in somatic indel discovery using a latent variable model

2017 ◽  
Author(s):  
Louis J. Dijkstra ◽  
Johannes Köster ◽  
Tobias Marschall ◽  
Alexander Schönhuth
Keyword(s):  
False Discovery Rate ◽  
Latent Variable ◽  
State Of The Art ◽  
Genetic Disorder ◽  
Latent Variable Model ◽  
Confounding Factors ◽  
Modeling Framework ◽  
Variable Model ◽  
Link Type ◽  
False Discovery

AbstractCancer is a genetic disorder in the first place. Therefore, next-generation sequencing (NGS) based discovery of somatically acquired genetic variants has gained widespread attention. Computational prediction of somatic variants, however, is affected by a variety of confounding factors. In addition to the uncertainties that one commonly encounters also in germline variation prediction, such as misplaced and/or inaccurate read alignments, cancer heterogeneity and impure samples significantly add to the issues. Overall, this hampers state-of-the-art indel discovery tools to discover somatic indels at operable performance rates, although they perform excellently when calling germline indels. While affecting all size ranges, both common and cancer-specific problems interfere in particularly unfavorable ways in the prediction of somatic midsize (30-150 bp) insertions and deletions.Here, we present a latent variable model that can take the major confounding factors and uncertainties into a unifying account. Using this modeling framework, we first demonstrate how to efficiently compute the probability for a (putative) indel to be somatic, thereby resolving a principled computational runtime bottleneck in Bayesian uncertainty quantification. Second, we show how to reliably estimate the allele frequencies for a given list of indels. Third, we also present an intuitive and effective way to control the false discovery rate, an issue in genetic variant discovery that has been found notoriously hard to deal with. As a tool that implements all methodology developed, we present PROSIC (PROcessing Somatic Indel Calls). PROSIC achieves significant improvements in particular in terms of recall when applied to deletion call sheets, as provided by prevalent state-of-the-art tools, in comparison to their integrated somatic indel calling routines.The software is publicly available at https://prosic.github.io and can be easily installed via https://bioconda.github.io.

A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials

2018 ◽  
Vol 15 (5) ◽  
pp. 429-442 ◽  
Author(s):  
Nishant Verma ◽  
S. Natasha Beretvas ◽  
Belen Pascual ◽  
Joseph C. Masdeu ◽  
Mia K. Markey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Latent Variable ◽  
Brain Regions ◽  
Disease Assessment ◽  
Latent Variable Analysis ◽  
The Relationship ◽  
Selection Of

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease. Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers. Results: Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials. Conclusion: The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer’s disease.

scholarly journals HIV and cardiovascular disease in sub-Saharan Africa: Demographic and Health Survey data for 4 countries

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Leonard E. Egede ◽  
Rebekah J. Walker ◽  
Patricia Monroe ◽  
Joni S. Williams ◽  
Jennifer A. Campbell ◽  
...  
Keyword(s):  
South Africa ◽  
Cardiovascular Disease ◽  
Health Survey ◽  
Survey Design ◽  
Logistic Models ◽  
Population Data ◽  
Demographic And Health Survey ◽  
Sub Saharan Africa ◽  
Sub Saharan ◽  
The Relationship

Abstract Background Investigate the relationship between two common cardiovascular diseases and HIV in adults living in sub-Saharan Africa using population data provided through the Demographic and Health Survey. Methods Data for four sub-Saharan countries were used. All adults asked questions regarding diagnosis of HIV, diabetes, and hypertension were included in the sample totaling 5356 in Lesotho, 3294 in Namibia, 9917 in Senegal, and 1051 in South Africa. Logistic models were run for each country separately, with self-reported diabetes as the first outcome and self-reported hypertension as the second outcome and HIV status as the primary independent variable. Models were adjusted for age, gender, rural/urban residence and BMI. Complex survey design allowed weighting to the population. Results Prevalence of self-reported diabetes ranged from 3.8% in Namibia to 0.5% in Senegal. Prevalence of self-reported hypertension ranged from 22.9% in Namibia to 0.6% in Senegal. In unadjusted models, individuals with HIV in Lesotho were 2 times more likely to have self-reported diabetes (OR = 2.01, 95% CI 1.08–3.73), however the relationship lost significance after adjustment. Individuals with HIV were less likely to have self-reported diabetes after adjustment in Namibia (OR = 0.29, 95% CI 0.12–0.72) and less likely to have self-reported hypertension after adjustment in Lesotho (OR = 0.63, 95% CI 0.47–0.83). Relationships were not significant for Senegal or South Africa. Discussion HIV did not serve as a risk factor for self-reported cardiovascular disease in sub-Saharan Africa during the years included in this study. However, given the growing prevalence of diabetes and hypertension in the region, and the high prevalence of undiagnosed cardiovascular disease, it will be important to continue to track and monitor cardiovascular disease at the population level and in individuals with and without HIV. Conclusions The odds of self-reported diabetes in individuals with HIV was high in Lesotho and low in Namibia, while the odds of self-reported hypertension in individuals with HIV was low across all 4 countries included in this study. Programs are needed to target individuals that need to manage multiple diseases at once and should consider increasing access to cardiovascular disease management programs for older adults, individuals with high BMI, women, and those living in urban settings.

Sign in / Sign up

Export Citation Format

Share Document