Web Design Responsive Vs Adaptive
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Adaptive Web Design
'Adaptive Web Design' shows you how you can craft interfaces that work for any user and how each and every decision you make in a web project affects your end user. You will learn how content strategy, UX, HTML, CSS, responsive web design, JavaScript, server-side programming, and performance optimisation all come together in the service of users on whatever device they happen to use to access the web
Learning Responsive Web Design
Author: Clarissa Peterson
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2014-06-09
Deliver an optimal user experience to all devices—including tablets, smartphones, feature phones, laptops, and large screens—by learning the basics of responsive web design. In this hands-on guide, UX designer Clarissa Peterson explains how responsive web design works, and takes you through a responsive workflow from project kickoff to site launch. Ideal for anyone involved in the process of creating websites—not just developers—this book teaches you fundamental strategies and techniques for using HTML and CSS to design websites that not only adapt to any screen size, but also use progressive enhancement to provide a better user experience based on device capabilities such as touchscreens and retina displays. Start with content strategy before creating a visual design Learn why your default design should be for the narrowest screens Explore the HTML elements and CSS properties essential for responsive web design Use media queries to display different CSS styles based on a device’s viewport width Handle elements such as images, typography, and navigation Use performance optimization techniques to make your site lighter and faster
Adaptive Survey Design
Adaptive survey designs (ASDs) provide a framework for data-driven tailoring of data collection procedures to different sample members, often for cost and bias reduction. People vary in how likely they are to respond and in how they respond. This variation leads to opportunities to selectively deploy design features in order to control both nonresponse and measurement errors. ASD aims at the optimal matching of design features and the characteristics of respondents given the survey budget. Such a goal is sensible, but ASD requires investment in more advanced technical systems and management infrastructure and asks for the collection of relevant auxiliary data. So what are current best practices in ASD? And is ASD worthwhile when the same auxiliary data are employed in the estimation afterwards? In this book, the authors provide answers to these questions, and much more.