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Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

This paper advocates the use of standard high level programming languages for medical computing. It recommends that U.S. Government agencies having health care missions implement coordinated policies that encourage the use of existing standard languages and the development of new ones, thereby enabling them and the medical computing community at large to share state-of-the-art application programs. Examples are based on a model that characterizes language and language translator influence upon the specification, development, test, evaluation, and transfer of application programs.

Statistical analysis is common in the social sciences, and among the more popular programs is R. This text provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualise, and analyse data. The focus is on how to address substantive questions with data analysis and replicate published findings. The work adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualise data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyse data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares regression, and model assumption diagnostics.

Prior to discussion, members of decision-making groups often possess a certain amount of decision-relevant information that others in the group do not hold. This is referred to as unshared information and stands in contrast to the shared information that all group members possess. One objective of group discussion is to enable members to exchange their unshared information. Research suggests, however, that groups often discuss more oftheirshared than their unshared information overall, and that they discuss their shared information first. A dynamic information sampling model that predicts these results is reviewed. Several empirical tests of this model are summarized, and a computer program that operationalizes the model is described. The computer program provides an efficient means of generating specific predictions for empirical studies andfor exploring the expected impact on group discussion of varying the model's input parameters.

This book serves as a hands-on guide to the "acs" R package for demographers, planners, and other researchers who work with American Community Survey (ACS) data. It gathers the most common problems associated with using ACS data and implements functions as a package in the R statistical programming language. The package defines a new "acs" class object (containing estimates, standard errors, and metadata for tables from the ACS) with methods to deal appropriately with common tasks (e.g., creating and combining subgroups or geographies, automatic fetching of data via the Census API, mathematical operations on estimates, tests of significance, plots of confidence intervals)