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\"What we count matters, and in a world where policies and decisions are underpinned by numbers, statistics and data, if you're not counted, you don't count. In this book, Alex Cobham argues that systematic gaps in economic and demographic data not only lead us to understate a wide range of damaging inequalities, but also to actively exacerbate them\"-- Provided by publisher.

In the mid-nineteenth century, American and British governments marched with great fanfare into the marketplace of knowledge and publishing. British royal commissions of inquiry, inspectorates, and parliamentary committees conducted famous social inquiries into child labor, poverty, housing, and factories. The American federal government studied Indian tribes, explored the West, and investigated the condition of the South during and after the Civil War.Performing, printing, and then circulating these studies, government established an economy of exchange with its diverse constituencies. In this medium, which Frankel terms \"print statism,\" not only tangible objects such as reports and books but knowledge itself changed hands. As participants, citizens assumed the standing of informants and readers. Even as policy investigations and official reportage became a distinctive feature of the modern governing process, buttressing the claim of the state to represent its populace, government discovered an unintended consequence: it could exercise only limited control over the process of inquiry, the behavior of its emissaries as investigators or authors, and the fate of official reports once issued and widely circulated.This study contributes to current debates over knowledge, print culture, and the growth of the state as well as the nature and history of the \"public sphere.\" It interweaves innovative, theoretical discussions into meticulous, historical analysis.

\"The United States census provides researchers, students, and the public with some of the richest and broadest information available about the American people. Exploring the U.S. Census by Frank Donnelly gives social science students and researchers alike the tools to understand, extract, process, and analyze data from the decennial census, the American Community Survey, and other data collected by the U.S. Census Bureau. More than just a data collection exercise performed every ten years, the census is a series of datasets updated on an ongoing basis. With all that data comes opportunities and challenges: opportunities to teach students the value of census data for studying communities and answering research questions, and the challenges of navigating and comprehending such a massive data source and transforming it into usable information that students and researchers can analyze with basic skills and software. Just as important as showing what the census can tell social researchers is showing how to ask good questions of census data. Exploring the U.S. Census provides a thorough background on the data collection methods, structures, and potential pitfalls of the census for unfamiliar researchers, collecting information previously available only in widely disparate sources into one handy guide. Hands-on, applied exercises at the end of the chapters help readers dive into the data\"-- Provided by publisher.

Biological Distance Analysis: Forensic and Bioarchaeological Perspectives synthesizes research within the realm of biological distance analysis, highlighting current work within the field and discussing future directions.The book is divided into three main sections.

\"The study of aging is not fundamentally about how old people are. It is about people's capabilities and their disabilities. In the field of population aging, measurements have generally been made with instruments devised many decades ago. Those measurements systems did not take the changing characteristics of older people into account. Using them 65-year-olds with a remaining life expectancy of 5 years could not be distinguished from 65-year-olds with a remaining life expectancy of 25 years. Although, in the past, those instruments did help us see better, it is now clear that there is a great deal that they did not allow us to see. Prospective Longevity provide a new view of who is old, how healthy people are in old age, the gender gap in survival at older ages, differences in patterns of survival across Russian regions and United States, the effects on the pace of population aging of medical breakthroughs that allow people to live much longer lives, and how an intergenerationally equitable pension age should change as life expectancy increases\"-- Provided by publisher.

Wildlife Demography compiles the multitude of available estimation techniques based on sex and age data, and presents these varying techniques in one organized, unified volume. Designed to guide researchers to the most appropriate estimator based upon their particular data set and the desired level of study precision, this book provides quantitative consideration, statistical models, estimator variance, assumptions and examples of use.The authors focus on estimation techniques using sex and age ratios because this data is relatively easy to collect and commonly used by wildlife management * Applicable to a wide array of wildlife species, including game and non-game birds and mammals * Features more than 100 annotated examples illustrating application of statistical methods* Includes more than 640 references of the analysis of nontagging data and the factors that may influence interpretation* Derives historical and ad hoc demographic methods in a modern statistical framework

This volume is a review of up-to-date methods used in human growth research.

The COVID-19 pandemic has revealed significant challenges in disease forecasting and in developing a public health response, emphasizing the need to manage missing data from various sources in making accurate forecasts. We aimed to show how handling missing data can affect estimates of the COVID-19 incidence rate (CIR) in different pandemic situations. This study used data from the COVID-19/SARS-CoV-2 surveillance system at the National Institute of Hygiene and Epidemiology, Vietnam. We separated the available data set into 3 distinct periods: zero COVID-19, transition, and new normal. We randomly removed 5% to 30% of data that were missing completely at random, with a break of 5% at each time point in the variable daily caseload of COVID-19. We selected 7 analytical methods to assess the effects of handling missing data and calculated statistical and epidemiological indices to measure the effectiveness of each method. Our study examined missing data imputation performance across 3 study time periods: zero COVID-19 (n=3149), transition (n=1290), and new normal (n=9288). Imputation analyses showed that K-nearest neighbor (KNN) had the lowest mean absolute percentage change (APC) in CIR across the range (5% to 30%) of missing data. For instance, with 15% missing data, KNN resulted in 10.6%, 10.6%, and 9.7% average bias across the zero COVID-19, transition, and new normal periods, compared to 39.9%, 51.9%, and 289.7% with the maximum likelihood method. The autoregressive integrated moving average model showed the greatest mean APC in the mean number of confirmed cases of COVID-19 during each COVID-19 containment cycle (CCC) when we imputed the missing data in the zero COVID-19 period, rising from 226.3% at the 5% missing level to 6955.7% at the 30% missing level. Imputing missing data with median imputation methods had the lowest bias in the average number of confirmed cases in each CCC at all levels of missing data. In detail, in the 20% missing scenario, while median imputation had an average bias of 16.3% for confirmed cases in each CCC, which was lower than the KNN figure, maximum likelihood imputation showed a bias on average of 92.4% for confirmed cases in each CCC, which was the highest figure. During the new normal period in the 25% and 30% missing data scenarios, KNN imputation had average biases for CIR and confirmed cases in each CCC ranging from 21% to 32% for both, while maximum likelihood and moving average imputation showed biases on average above 250% for both CIR and confirmed cases in each CCC. Our study emphasizes the importance of understanding that the specific imputation method used by investigators should be tailored to the specific epidemiological context and data collection environment to ensure reliable estimates of the CIR.

Spatial analysis assists theoretical understanding and empirical testing in the social sciences, and rapidly expanding applications of geographic information technologies have advanced the spatial data-gathering needed for spatial analysis and model making. This much-needed volume covers outstanding examples of spatial thinking in the social sciences, with each chapter showing some aspect of how certain social processes can be understood by analyzing their spatial context. The audience for this work is as trans-disciplinary as its authorship because it contains approaches and methodologies useful to geography, anthropology, history, political science, economics, criminology, sociology, and statistics.

Accurate population size estimates (PSE) of key populations-those disproportionately affected by HIV-are critical to forecast need and inform HIV prevention and treatment programs, though they can be difficult to ascertain due to low visibility of these groups. In Zambia, reliable estimates on the number of people who inject drugs are limited, inhibiting public health response. We sought to estimate the population size of people who inject drugs in 3 large cities in Zambia, assess how PSEs vary across different estimation methods, and explore the strengths and limitations of each approach. We applied 2-source capture-recapture (2S-CRC), 3-source capture-recapture (3S-CRC), and successive sampling population size estimation (SS-PSE) methods in Lusaka, Livingstone, and Ndola, Zambia. 3S-CRC methods included location-based 2S-CRC in combination with a respondent-driven sampling (RDS) survey. Data were collected from November 2021 to February 2022 and analyzed using a Bayesian nonparametric latent class model. SS-PSEs were produced using the RDS recruitment and network sizes. Kruskal tests and general linear models were used to examine sociodemographic and behavioral factors associated with being captured in 2S-CRC among RDS participants. Final city population estimates, incorporating 3S-CRC and SS-PSE with imputed visibility estimates, were generated using a Bayesian consensus estimator. Bayesian consensus PSEs ranged between 0.5% and 1.8% of the adult male population and were below 1% of the total adult population in each city. Consensus estimates were highest in Lusaka (3700, 95% credible interval [CRI] 1500-7500), followed by Ndola (2200, 95% CRI 1600-2900) and Livingstone (1200, 95% CRI 900-1,900). There was variability in estimates by method, with SS-PSE with imputed visibility generally providing the lowest estimates across cities, excluding Lusaka. Across methods, PSEs and uncertainty bounds (95% confidence interval [CI] or CRI depending on method) ranged from 1510 (95% CRI 1030-2070) to 4350 (95% CI 1410-18,890) in Lusaka, 360 (95% CI 290-530) to 2620 (95% CRI 1510-4680) in Livingstone, and 760 (95% CI 390-3060) to 4030 (95% CRI 960-5480) in Ndola. In all cities, fewer recaptures occurred in capture 3 (RDS) than with location sampling via 2S-CRC. Though results varied across cities, RDS participants captured through 2S-CRC differed from those captured solely through RDS in sociodemographic and behavioral risk factors, including housing, education, injection or needle sharing frequency, time since last injection, receipt of drug treatment, and experience with a peer educator in at least one city. This study used rigorous methods to produce PSEs in Zambia, and is the first to produce these for major geographies in the country. Through RDS, 3S-CRC reached people who inject drugs with distinct characteristics that were less accessible via location-based sampling (2S-CRC), yielding a PSE that may better reflect the population and informing the Bayesian consensus estimate. Findings from this study can guide program planning and future surveillance activities.