Guidelines for determining nonprobabilistic sample sizes are virtually nonexistent. Purposive samples are the most commonly used form of nonprobabilistic sampling, and their size typically relies on the concept of “saturation,” or the point at which no new information or themes are observed in the data. Although the idea of saturation is helpful at the conceptual level, it provides little practical guidance for estimating sample sizes, prior to data collection, necessary for conducting quality research. Using data from a study involving sixty in-depth interviews with women in two West African countries, the authors systematically document the degree of data saturation and variability over the course of thematic analysis. They operationalize saturation and make evidence-based recommendations regarding nonprobabilistic sample sizes for interviews. Based on the data set, they found that saturation occurred within the first twelve interviews, although basic elements for metathemes were present as early as six...

https://www.rincondepaco.com.mx/rincon/Inicio/Seminario/Documentos/Met_otros/Docs_Dra_Fleiz/Lecturas_sugeridas/Guest_data_saturation_interviews.pdf

How Many Interviews Are Enough?: An Experiment with Data Saturation and Variability

From the Publisher:
Written by the author of the best-selling HyperText & HyperMedia, this book provides an excellent guide to the methods of usability engineering. Special features: emphasizes cost-effective methods that will help developers improve their user interfaces immediately, shows you how to avoid the four most frequently listed reasons for delay in software projects, provides step-by-step information about which methods to use at various stages during the development life cycle, and offers information on the unique issues relating to informational usability. You do not need to have previous knowledge of usability to implement the methods provided, yet all of the latest research is covered.

Usability Engineering

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

/pdf/evaluating-collaborative-filtering-recommender-systems-au0a9p6z94.pdf

Evaluating collaborative filtering recommender systems

Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual‐usage meaning of words by statistical computations applied to a large corpus of text (Landauer & Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSA's reflection of human knowledge has been established in a variety of ways. For example, its scores overlap those of humans on standard vocabulary and subject matter tests; it mimics human word sorting and category judgments; it simulates word‐word and passage‐word lexical priming data; and, as reported in 3 following articles in this issue, it accurately estimates passage coherence, learnability of passages by individual students, and the quality and quantity of knowledge contained in an essay.

/pdf/an-introduction-to-latent-semantic-analysis-2hdkkxwv7r.pdf

An introduction to latent semantic analysis

Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set. LPP should be seen as an alternative to Principal Component Analysis (PCA) – a classical linear technique that projects the data along the directions of maximal variance. When the high dimensional data lies on a low dimensional manifold embedded in the ambient space, the Locality Preserving Projections are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold. As a result, LPP shares many of the data representation properties of nonlinear techniques such as Laplacian Eigenmaps or Locally Linear Embedding. Yet LPP is linear and more crucially is defined everywhere in ambient space rather than just on the training data points. This is borne out by illustrative examples on some high dimensional data sets.

/pdf/locality-preserving-projections-3upb6d2qui.pdf

Locality Preserving Projections

Heuristic evaluation is an informal method of usability analysis where a number of evaluators are presented with an interface design and asked to comment on it. Four experiments showed that individual evaluators were mostly quite bad at doing such heuristic evaluations and that they only found between 20 and 51% of the usability problems in the interfaces they evaluated. On the other hand, we could aggregate the evaluations from several evaluators to a single evaluation and such aggregates do rather well, even when they consist of only three to five people.

Heuristic evaluation of user interfaces

Usability inspection is the generic name for a set of costeffective ways of evaluating user interfaces to find usability problems. They are fairly informal methods and easy to use.

Usability inspection methods

From the Publisher:
Can you afford to be less knowledgable than your competitors? Over 100,000 Internet professionals around the world have already bought this book. Users experience the usability of a site before they have committed to using it and before they have spent any money on potential purchases. The web is the ultimate environment for empowerment, and he or she who clicks the mouse decides everything. Jakob Nielson, shares with you the full weight of his wisdom and experience. From content and page design to designing for ease of navigation and users with disabilities, Nielsen delivers complete direction on how to connect with any web user, in any situation. Nielsen has arrived at a series of principles that work in support of his findings: 1. that web users want to find what they're after quickly, and 2. if they don't know what they're after, they nevertheless want to browse quickly and access information they come across in a logical manner.

Designing Web Usability: The Practice of Simplicity

• Heuristic Evaluation – Nielsen & Molich (1990) CHI Proceedings – Based upon empirical article Molich & Nielsen (1990) (in readings) – Inspection of a prototype or finished system to identify all changes necessary to optimize human performance and preference – Evaluators use a set of guidelines or general principle • hence term: “heuristics” • Distinctions not always made clear in studies and criticisms of heuristic evaluation: – Use of heuristics (guidelines) or not – Experience level of reviewers • experts vs. non-experts using just heuristics) – Review by lone individual or joint review by group • Research shows it makes a difference – Use of prescribed tasks versus self-guided evaluation

Jakob Nielsen

Papers

Usability Engineering

Heuristic evaluation of user interfaces

Usability inspection methods

Designing Web Usability: The Practice of Simplicity

Heuristic evaluation