This chapter is about methods for managing and analyzing qualitative data. By qualitative data the authors mean text: newspapers, movies, sitcoms, e-mail traffic, folktales, life histories. They also mean narratives--narratives about getting divorced, about being sick, about surviving hand-to-hand combat, about selling sex, about trying to quit smoking. In fact, most of the archaeologically recoverable information about human thought and human behavior is text, the good stuff of social science.

Data Management and Analysis Methods

The study of the web as a graph is not only fascinating in its own right, but also yields valuable insight into web algorithms for crawling, searching and community discovery, and the sociological phenomena which characterize its evolution. We report on experiments on local and global properties of the web graph using two Altavista crawls each with over 200 million pages and 1.5 billion links. Our study indicates that the macroscopic structure of the web is considerably more intricate than suggested by earlier experiments on a smaller scale.

/pdf/graph-structure-in-the-web-9h7qd5q9nt.pdf

Graph structure in the Web

It is the purpose of this paper to analyse a class of distribution functions that appears in a wide range of empirical data-particularly data describing sociological, biological and economic phenomena. Its appearance is so frequent, and the phenomena in which it appears so diverse, that one is led to the conjecture that if these phenomena have any property in common it can only be a similarity in the structure of the underlying probability mechanisms. The empirical distributions to which we shall refer specifically are: (A) distributions of words in prose samples by their frequency of occurrence, (B) distributions of scientists by number of papers published, (C) distributions of cities by population, (D) distributions of incomes by size, and (E) distributions of biological genera by number of species. No one supposes that there is any connexion between horse-kicks suffered by soldiers in the German army and blood cells on a microscope slide other than that the same urn scheme provides a satisfactory abstract model of both phenomena. It is in the same direction that we shall look for an explanation of the observed close similarities among the five classes of distributions listed above. The observed distributions have the following characteristics in common: (a) They are J-shaped, or at least highly skewed, with very long upper tails. The tails can generally be approximated closely by a function of the form

https://snap.stanford.edu/class/cs224w-readings/Simon55Skewdistribution.pdf

On a class of skew distribution functions

Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data This textbook provides a straightforward introduction to the statistical analysis of language Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data

Analyzing linguistic data : a practical introduction to statistics using R

been proposed to describe talkers and listeners to describe the users of language rather than the language itself. As m was pointed out at the beginning of Chapter 12, our language is not merely the collection of our linguistic responses, habits, or dispositions, just as our knowledge of arithmetic is not merely the collection of our arithmetic responses, habits, or dispositions. We must respect this distinction between the person's knowledge and his actual or even potential behavior; a formal characterization of some language is not simultaneously a model of the users of that

Finitary models of language users

A NEW book by Mr. Udny Yule is a statistical event which, though of great rarity, is all the more welcome when it occurs. This, however, is not a formal treatment of a subject which has already shaken down into definite lines of development, or even a systematic introduction to such a subject. Rather is it an account in volume form of Mr. Yule's incursion into a novel and interesting field of research. The Statistical Study of Literary Vocabulary By G. Udny Yule. Pp. ix + 306. (Cambridge: At the University Press, 1944.) 25s. net.

M. G. Kendall

Papers

The Statistical Study of Literary Vocabulary