• Summary
  • Contents
  • Subject index

A must-have reference resource for qualitative management researchers, this dictionary contains over 90 entries covering the fundamentals of qualitative methodologies; covering both analysis and implementation. Each entry gives an introduction to the topic, lists the key relevant features, gives a worked example, a concise summary and a selection of further reading suggestions. It is suitable for researchers and academics who need a handy and quick point of reference.

Dummy Variable Coding
Dummy variable coding
Introduction

Dummy coding is used to represent categorical variables (e.g. gender, geographic location, ethnicity, company employed in) in a way that enables their use in a number of statistical analyses that require variables to be measured on a continuous (numeric) scale. This requirement is not, unfortunately, met by all social science data. It is possible, however, to include dichotomous, or binary, data in a model if it is appropriately coded. The ability to include dichotomous data enables variables such as male–female, dead–alive, rich–poor, passed–failed, high–low, etc., to be used in statistical models. Multi-category categorical explanatory variables such as drinking levels (high, medium and low), location (Europe, North America, South America, Africa), educational level (unqualified, high school, university), and different experimental treatments ...

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