Value Sensitive Design is a theoretically grounded approach to the design of technology that accounts for human values in a principled and comprehensive manner throughout the design process. It employs an integrative and iterative tripartite methodology, consisting of conceptual, empirical, and technical investigations. We explicate Value Sensitive Design by drawing on three case studies. The first study concerns information and control of web browser cookies, implicating the value of informed consent. The second study concerns using high-definition plasma displays in an office environment to provide a “window” to the outside world, implicating the values of physical and psychological well-being and privacy in public spaces. The third study concerns an integrated land use, transportation, and environmental simulation system to support public deliberation and debate on major land use and transportation decisions, implicating the values of fairness, accountability, and support for the democratic process, as well as a highly diverse range of values that might be held by different stakeholders, such as environmental sustainability, opportunities for business expansion, or walkable neighborhoods. We conclude with direct and practical suggestions for how to engage in Value Sensitive Design.

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Value Sensitive Design and Information Systems

Foundations. J.J. Hox, E.D. de Leeuw, D. Dillman, The Cornerstones of Survey Research. N. Schwarz, B. Knauper, D. Oyserman, C. Stich, The Psychology of Asking Questions. P. Lynn, The Problem of Nonresponse. J. A. Harkness, Comparative Survey Research: Goal and Challenges. E. Singer, Ethical Issues in Surveys. Design. S.L. Lohr, Coverage and Sampling. E.D. de Leeuw, Choosing the Method of Data Collection. F.J. Fowler, C. Cosenza, Writing Effective Questions. D.A. Dillman, The Logic and Psychology of Constructing Questionnaires. P. Campanelli, Testing Survey Questions. Implementation. G. Loosveldt, Face-to-Face interviews. C. Steeh, Telephone Surveys. E.D. de Leeuw, J.J. Hox, Self-Administered Questionnaires: Mail Surveys and Other Applications. K.L. Manfreda, V. Vehovar, Internet Surveys. D.M. Steiger, B. Conroy, IVR: Interactive Voice Response. E.D. de Leeuw, D.A. Dillman, J.J. Hox, Mixed Mode Surveys: When and Why. Data Analysis. P.P. Biemer, S.L. Christ, Weighting Survey Data. L.M. Stapleton, Analysis of Data from Complex Surveys. S. Rassler, D.B. Rubin, N. Schenker, Incomplete Data: Diagnosis, imputation, and estimation. J.J. Hox, Accommodating Measurement Errors. Special Issues. P. Mohler, B. Pennell, F. Hubbard, Survey documentation: Towards Professional Knowledge Management in Sample Surveys. L.E. Lyberg, P.P. Biemer, Quality Assurance and Quality Control in Surveys. J.T. Lessler, J. Eyerman, K. Wang, Interviewer Training. G. Lensvelt-Mulders, Surveying Sensitive Topics. D. Sikkel, A. Hoogendoorn, Panel Surveys. J. Betlehem, Surveys Without Questions.

International handbook of survey methodology.

Examples are discussed to show the differences among discriminant analysis, logistic regression, and multiple regression. Chapter 6, “Multivariate Analysis of Variance,” presents advantages of multivariate analysis of variance (MANOVA) over univariate analysis of variance (ANOVA), discusses assumptions of MANOVA, and assesses validations of MANOVA assumptions and model estimation. The authors also discuss post hoc tests of MANOVA and multivariate analysis of covariance. Chapter 7, “Conjoint Analysis,” explains what conjoint analysis does and how it is different from other multivariate techniques. Guidelines of selecting attributes, models, and methods of data collection are presented. Chapter 8, “Cluster Analysis,” studies objectives, roles, and limitations of cluster analysis. Two basic concepts: similarity and distance are discussed. The authors also discuss details of five most popular hierarchical algorithms (singlelinkage, complete-linkage, average-linkage, centroid method, Ward’s method) and three nonhierarchical algorithms (the sequential threshold method, the parallel threshold method, and the optimizing procedure). Profiles of clusters and guidelines for cluster validation are studied as well. Chapter 9, “Multidimensional Scaling and Correspondence Analysis,” introduces two interdependence techniques to display the relationships in the data. The book describes clearly and intuitively the differences between the two techniques and how these two techniques are performed. Chapters 10–12 cover topics in SEM. Chapter 10, “Structural Equation Modeling: An Introduction,” introduces SEM and related concepts such as exogenous, endogenous constructs, and so on, points out the differences between SEM and other multivariate techniques, overviews the decision process of SEM. Chapter 11, “Confirmatory Factor Analysis,” explains the differences between exploratory and confirmatory factor analysis, discusses how to construct, validate, and assess the goodness of fit of a measurement model in SEM by confirmatory factor analysis. Chapter 12, “Testing a Structural Model,” presents some methods of SEM in examining the relationships between latent constructs. The book is an excellent book for people in management and marketing. For the Technometrics audience, this book does not have much flavor of physical, chemical, and engineering sciences. For example, partial least squares, a very popular method in Chemometrics, is discussed but not as detailed as other techniques in the book. Furthermore, due to the amount of materials covered in the book, it might be inappropriate for someone who is new to multivariate analysis.

The Grammar of Graphics

The digital age has not simply changed the nature of resources and information; it has transformed several basic social and economic enterprises. Contemporary society—the settings where we live, work, and learn—has likewise changed dramatically. Both the amount of information and access to it have grown exponentially; a significant potential for using varied resources in numerous ways for instruction and learning has emerged. However, several issues related to the educational uses of varied resources (e.g., people, place, things, ideas) must be addressed if we are successfully to implement resource-based learning environments. In this paper, we trace the changing nature of resources and perspectives in their use for learning in the digital age, describe the overarching structures of resource-based learning environments, and identify key challenges to be addressed.

Teaching and Learning in Digital Environments: The Resurgence of Resource-Based Learning

Human values and ethical considerations no longer stand apart
from the human-computer interaction (HCI) communityperhaps in some separate field called “computer ethics”—but
are fundamentally part of our practice.

Human values, ethics, and design

Social informatics (SI) refers to a multidisciplinary research field that examines the design, uses, and implications of information and communication technologies (ICTs) in ways that account for their interactions with institutional and cultural contexts. This Special Issue presents seven articles that illustrate the range and depth of research in SI. This essay outlines the concerns of SI researchers and briefly describes the articles that follow. © 1998 John Wiley & Sons, Inc.

Social informatics in information science: an introduction

Information Seeking Behavior on Statistical Websites: Theoretical and Design Implications

Usability of Web sites has become an increasingly important area of research as Web sites proliferate and problems with use are noted. Generally, aspects of Web sites that have been investigated focus on such areas as overall design and navigation. The exploratory study reported on here investigates one specific component of a Web site-the index structure. By employing index usability metrics developed by Liddy and Jorgensen (1993; Jorgensen & Liddy, 1996) and modified to accommodate a hypertext environment, the study compared the effectiveness and efficiency of 20 subjects who used one existing index (the A-Z index on the FedStats Web site at http://www.fedstats.gov) and three experimental variants to complete five researcher-generated tasks. User satisfaction with the indexes was also evaluated. The findings indicate that a hypertext index with multiple access points for each concept, all linked to the same resource, led to greater effectiveness and efficiency of retrieval on almost all measures. Satisfaction measures were more variable. The study offers insight into potential improvements in the design of Web-based indexes and provides preliminary assessment of the validity of the measures employed.

A usability assessment of online indexing structures in the networked environment

This paper argues that training is a critical success factor for the implementation of the Internet in organizations. Given the Internet′s potential for transforming organizations, it is imperative that we develop training that goes beyond the teaching of Internet skills and instead enables trainees to become active participants in the strategic planning process. This perspective on training is termed the learning organization perspective. A number of conceptual areas are suggested as relevant to the development of the necessary training and the associated theoretical perspective. It is hoped that this initial articulation of the critical role of training will provide the impetus for others to expand on the perspective and its actual implementation in organizations

A learning organization perspective on training: Critical success factors for Internet implementation

This paper describes an architecture for a statistical knowledge network that depends on user interfaces as the glue among the components. These interfaces aim to serve non-expert users with diverse needs and statistical and computing experiences. Such interfaces are crucially dependent on different indexing schemes and good metadata.

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Carol A. Hert

Papers

Social informatics in information science: an introduction

Information Seeking Behavior on Statistical Websites: Theoretical and Design Implications

A usability assessment of online indexing structures in the networked environment

A learning organization perspective on training: Critical success factors for Internet implementation

Toward a statistical knowledge network