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...

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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

Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.

Learning the parts of objects by non-negative matrix factorization

Learning parts of objects by non-negative matrix factorization

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched.

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A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.

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.

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An introduction to latent semantic analysis

For 11 studies, we find that the detection of usability problems as a function of number of users tested or heuristic evaluators employed is well modeled as a Poisson process. The model can be used to plan the amount of evaluation required to achieve desired levels of thoroughness or benefits. Results of early tests can provide estimates of the number of problems left to be found and the number of additional evaluations needed to find a given fraction. With quantitative evaluation costs and detection values, the model can estimate the numbers of evaluations at which optimal cost/benefit ratios are obtained and at which marginal utility vanishes. For a “medium” example, we estimate that 16 evaluations would be worth their cost, with maximum benefit/cost ratio at four.

/pdf/a-mathematical-model-of-the-finding-of-usability-problems-nnnq0clxe1.pdf

A mathematical model of the finding of usability problems

Contents: Part I: Introduction to LSA: Theory and Methods. T.K. Landauer, LSA as a Theory of Meaning. D. Martin, M. Berry, Mathematical Foundations Behind Latent Semantic Analysis. S. Dennis, How to Use the LSA Website. J. Quesada, Creating Your Own LSA Spaces. Part II: LSA in Cognitive Theory. W. Kintsch, Meaning in Context. M. Louwerse, Symbolic or Embodied Representations: A Case for Symbol Interdependency. M.W. Howard, K. Addis, B. Jing, M.K. Kahana, Semantic Structure and Episodic Memory. G. DenhiSre, B. Lemaire, C. Bellissens, S. Jhean-Larose, A Semantic Space for Modeling Children's Semantic Memory. P. Foltz, Discourse Coherence and LSA. J. Quesada, Spaces for Problem Solving. Part III: LSA in Educational Applications. K. Millis, J. Magliano, K. Wiemer-Hastings, S. Todaro, D.S. McNamara, Assessing and Improving Comprehension With Latent Semantic Analysis. D.S. McNamara, C. Boonthum, I. Levinstein, K. Millis, Evaluating Self-Explanations in iSTART: Comparing Word-Based and LSA Algorithms. A. Graesser, P. Penumatsa, M. Ventura, Z. Cai, X. Hu, Using LSA in AutoTutor: Learning Through Mixed-Initiative Dialog in Natural Language. E. Kintsch, D. Caccamise, M. Franzke, N. Johnson, S. Dooley, Summary Streetr: Computer-Guided Summary Writing. L. Streeter, K. Lochbaum, N. LaVoie, J.E. Psotka, Automated Tools for Collaborative Learning Environments. Part IV: Information Retrieval and HCI Applications of LSA. S.T. Dumais, LSA and Information Retrieval: Getting Back to Basics. P.K. Foltz, T.K. Landauer, Helping People Find and Learn From Documents: Exploiting Synergies Between Human and Computer Retrieval With SuperManual. M.H. Blackmon, M. Kitajima, D.R. Mandalia, P.G. Polson, Automating Usability Evaluation Cognitive Walkthrough for the Web Puts LSA to Work on Real-World HCI Design Problems. Part V: Extensions to LSA. D.S. McNamara, Z. Cai, M.M. Louwerse, Optimizing LSA Measures of Cohesion. X. Hu, Z. Cai, P. Wiemer-Hastings, A.C. Graesser, D.S. McNamara, Strength, Weakness, and Extensions of LSA. M. Steyvers, T. Griffiths, Probabilistic Topic Models. S. Dennis, Introducing Word Order: Within the LSA Framework. Part VI: Conclusion. W. Kintsch, D.S. McNamara, S. Dennis, T.K. Landauer, LSA and Meaning: In Theory and Application.

Handbook of latent semantic analysis

Latent Semantic Analysis (LSA) is used as a technique for measuring the coherence of texts. By comparing the vectors for 2 adjoining segments of text in a high‐dimensional semantic space, the method provides a characterization of the degree of semantic relatedness between the segments. We illustrate the approach for predicting coherence through reanalyzing sets of texts from 2 studies that manipulated the coherence of texts and assessed readers’ comprehension. The results indicate that the method is able to predict the effect of text coherence on comprehension and is more effective than simple term‐term overlap measures. In this manner, LSA can be applied as an automated method that produces coherence predictions similar to propositional modeling. We describe additional studies investigating the application of LSA to analyzing discourse structure and examine the potential of LSA as a psychological model of coherence effects in text comprehension.

Thomas K. Landauer

Papers

A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.

An introduction to latent semantic analysis

A mathematical model of the finding of usability problems

Handbook of latent semantic analysis

The Measurement of Textual Coherence with Latent Semantic Analysis.