Evaluation is increasingly recognized as an essential component of HCI research. However, evaluation itself is a changing research area. In particular, the many variations of qualitative research are emerging as important empirical methods. This half-day tutorial is designed for beginning to intermediate audiences. We will focus on the basic methods for analyzing qualitative data using a mixture of talks and hands-on activities. In particular we will consider closed and open coding as well as clustering and categorizing coded data. After completing this tutorial, attendees will have a richer understanding of the benefits and challenges of qualitative empirical research and, more specifically, how to analyze qualitative data.

Analyzing Qualitative Data

Supersizing the Mind: Embodiment, Action and Cognitive Extension.

Understanding media : the extensions of man

Introducing a new hobby for other people may inspire them to join with you. Reading, as one of mutual hobby, is considered as the very easy hobby to do. But, many people are not interested in this hobby. Why? Boring is the reason of why. However, this feel actually can deal with the book and time of you reading. Yeah, one that we will refer to break the boredom in reading is choosing the myth of the paperless office as the reading material.

The Myth of the Paperless Office

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

Tag clouds are a popular method for visualizing and linking socially-organized information on websites. Tag clouds represent variables of interest (such as popularity) in the visual appearance of the keywords themselves - using text properties such as font size, weight, or colour. Although tag clouds are becoming common, there is still little information about which visual features of tags draw the attention of viewers. As tag clouds attempt to represent a wider range of variables with a wider range of visual properties, it becomes difficult to predict what will appear visually important to a viewer. To investigate this issue, we carried out an exploratory study that asked users to select tags from clouds that manipulated nine visual properties. Our results show that font size and font weight have stronger effects than intensity, number of characters, or tag area; but when several visual properties are manipulated at once, there is no one property that stands out above the others. This study adds to the understanding of how visual properties of text capture the attention of users, indicates general guidelines for designers of tag clouds, and provides a study paradigm and starting points for future studies. In addition, our findings may be applied more generally to the visual presentation of textual hyperlinks as a way to provide more information to web navigators.

Seeing things in the clouds: the effect of visual features on tag cloud selections

We studied the memorability of free-form gesture sets for invoking actions. We compared three types of gesture sets: user-defined gesture sets, gesture sets designed by the authors, and random gesture sets in three studies with 33 participants in total. We found that user-defined gestures are easier to remember, both immediately after creation and on the next day (up to a 24% difference in recall rate compared to pre-designed gestures). We also discovered that the differences between gesture sets are mostly due to association errors (rather than gesture form errors), that participants prefer user-defined sets, and that they think user-defined gestures take less time to learn. Finally, we contribute a qualitative analysis of the tradeoffs involved in gesture type selection and share our data and a video corpus of 66 gestures for replicability and further analysis.

/pdf/memorability-of-pre-designed-and-user-defined-gesture-sets-1ol629m9po.pdf

Memorability of pre-designed and user-defined gesture sets

Recent advances in multi-user collaboration have seen a proliferation of interaction techniques for moving digital objects from one device to another. However, little is known about how these techniques work in realistic situations, or how they compare to one another. We conducted a study to compare the efficiency of six techniques for moving objects from a tablet to a tabletop display. We compared the techniques in four different distance ranges and with three movement directions. We found that techniques like the Radar View and Pick-and-Drop, that have a control-to-display ratio of 1, are significantly faster for object movement than techniques that have smaller control-to-display ratios. We also found that using spatial manipulation of objects was faster than pressure-based manipulation.

A comparison of techniques for multi-display reaching

Ray-pointing techniques are often advocated as a way for people to interact with very large displays from several meters away. We are interested in two factors that can affect ray pointing: the particular technique's control type, and parallax.

Consequently, we tested four ray pointing variants on a wall display that covers a large part of the user's field of view. Tasks included horizontal and vertical targeting, and tracing. Our results show that (a) techniques based on 'rotational control' perform better for targeting tasks, and (b) techniques with low parallax are best for tracing tasks. We also show that a Fitts's law analysis based on angles (as opposed to linear distances) better approximates people's ray pointing performance.

http://grouplab.cpsc.ucalgary.ca/grouplab/uploads/Publications/Publications/2010-RayPointing.GI.pdf

A comparison of ray pointing techniques for very large displays

Multi-display environments and smart meeting rooms are now becoming more common. These environments build a shared display space from variety of devices: tablets, projected surfaces, tabletops, and traditional monitors. Since the different display surfaces are usually not organized in a single plane, traditional schemes for stitching the displays together can cause problems for interaction. However, there is a more natural way to compose display space -- using perspective. In this paper, we develop interaction techniques for multi-display environments that are based on the user's perspective on the room. We designed the Perspective Cursor, a mapping of cursor to display space that appears natural and logical from wherever the user is located. We conducted an experiment to compare two perspective-based techniques, the Perspective Cursor and a beam-based technique, with traditional stitched displays. We found that both perspective techniques were significantly faster for targeting tasks than the traditional technique, and that Perspective Cursor was the most preferred method. Our results show that integrating perspective into the design of multi-display environments can substantially improve performance.

Miguel A. Nacenta

Papers

Seeing things in the clouds: the effect of visual features on tag cloud selections

Memorability of pre-designed and user-defined gesture sets

A comparison of techniques for multi-display reaching

A comparison of ray pointing techniques for very large displays

Perspective cursor: perspective-based interaction for multi-display environments