A look at the qualitative and quantitative properties of cars

Quantitative analysis uses exact inputs such as profit marginsdebt ratiosearnings multiplesand the like. Of course, for the time being, a human has to write the program that crunches these numbers, and that involves a fair bit of subjective judgment. Qualitative analysis, on the other hand, deals with intangible, inexact concerns that belong to the social and experiential realm rather than the mathematical one.

A look at the qualitative and quantitative properties of cars

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It also one of the most mysterious. Explicit descriptions of theme discovery are rarely described in articles and reports and if so are often regulated to appendices or footnotes. Techniques are shared among small groups of social scientists and are often impeded by disciplinary or epistemological boundaries.

During the proposal-writing phase of a project, investigators struggle to clearly explain and justify plans for discovering themes. These issues are particularly cogent when funding reviewers are unfamiliar with qualitative traditions.

In this article we have outlined a dozen techniques that social scientists have used to discover themes in texts. The techniques are drawn from across epistemological and disciplinary boundaries.

They range from quick word counts to laborious, in-depth, line-by-line scrutiny. Some methods work well for short answers to open-ended questions while others are more appropriate for rich, complex narratives.

Novices and non-native speakers may find some techniques easier than others. No single technique is does it all. To us, these techniques are simply tools to help us do better research.

Russell Bernard is professor of anthropology at the University of Florida. We wish to thank Stephen Borgatti for his helpful suggestions and two anonymous reviewers for their invaluable comments on earlier drafts of this paper.

Introduction At the heart of qualitative data analysis is the task of discovering themes. By themes, we mean abstract, often fuzzy, constructs which investigators identify before, during, and after data collection. Where do these themes come from?

They come from reviewing the literature, of course. Richer literatures produce more themes. They come from the characteristics of the phenomena being studied.

Mostly, though, researchers who consider themselves part of the qualitative tradition in social science induce themes from texts. This is what grounded theorists call open coding, and what classic content analysts call qualitative analysis Berleson or latent coding Shapiro and Markoff There are many variations on these methods.

Unfortunately, however, they are a scattered across journals and books that are read by disparate groups of specialists; and b often entangled in the epistemological wars that have divided the social sciences.

Our goal in this paper is to cross these boundaries and lay out a variety of theme-dredging methods so that all researchers who deal with texts can use them to solve common research problems.

We outline here a dozen helpful techniques for discovering themes in texts. These techniques are based on: The list is by no means exhaustive. Social scientists are an enterprising lot. Over the last century they have invented solutions to all kinds of problems for managing and analyzing texts, and they will continue to do so.

These bursts of methodological creativity, however, are commonly described perfunctorily, or are relegated to footnotes, and get little notice by colleagues across disciplines. The dozen methods we describe here come from across the social sciences and have been used by positivists and interpretivists alike.

A look at the qualitative and quantitative properties of cars

Word repetitions We begin with word-based techniques. Word repetitions, key-indigenous terms, and key-words-in-contexts KWIC all draw on a simple observation—if you want to understand what people are talking about, look at the words they use.

Words that occur a lot are often seen as being salient in the minds of respondents. He observes that "indeed, anyone who has listened to long stretches of talk, whether generated by a friend, spouse, workmate, informant, or patient, knows how frequently people circle through the same network of ideas" Word repetitions can be analyzed formally and informally.

In the informal mode, investigators simply read the text and note words or synonyms that people use a lot. For example, while conducting multiple in-depth interviews with Tony, a retired blue collar worker in Connecticut, Claudia Strauss found that Tony repeatedly referred to ideas associated with greed, money, businessmen, siblings, and "being different.

Strauss displayed the relationships among these ideas by writing the concepts on a page of paper and connecting them with lines and explanations.

Computers can easily generate word-frequency lists from texts and are a quick and easy way to look for themes.

A look at the qualitative and quantitative properties of cars

Ryan and Weisner asked fathers and mothers of adolescents: In your own words, just tell us about them. Mothers were more likely than fathers to use words like friends, creative, time, and honest; fathers were more likely than mothers to use words like school, good, lack, student, enjoys, independent, and extremely.

Ryan and Weisner used this information as clues for themes that they would use later in actually coding the texts.Qualitative analysis is a securities analysis that uses subjective judgment based on nonquantifiable information, such as management expertise, industry cycles, and labor relations.

The earliest instances of what might today be called genetic algorithms appeared in the late s and early s, programmed on computers by evolutionary biologists who were explicitly seeking to model aspects of natural evolution.

Qualitative data is contrasted with quantitative data wherein quantitative data sets have numbers associated with them that evaluate the quantity of an object or objects with shared features.

Oftentimes, quantitative data is used to analyze qualitative data sets. Techniques to Identify Themes in Qualitative Data Gery W. Ryan RAND Main Street P.O.

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