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NVivo is intended to help users organize and analyze non-numerical or unstructured data.Its developers state that it helps qualitative researchers to organize, analyze and find insights in unstructured or qualitative data like interviews, open-ended survey responses, journal articles, social media and web content, where deep levels of analysis on small or large volumes of data are required.
Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text -heavy, but may contain data such as dates, numbers, and facts as well.
This category is hidden on its member pages—unless the corresponding user preference (Appearance → Show hidden categories) is set.; These categories can be used to track, build and organize lists of pages needing "attention en masse" (for example, pages using deprecated syntax), or that may need to be edited at someone's earliest convenience.
Dummy variables are useful in various cases. For example, in econometric time series analysis, dummy variables may be used to indicate the occurrence of wars, or major strikes. It could thus be thought of as a Boolean, i.e., a truth value represented as the numerical value 0 or 1 (as is sometimes done in computer programming).
Burden of all infectious diseases, worldwide in 2004, measured in disability-adjusted life years Burden of non-communicable diseases, worldwide in 2004, measured in disability-adjusted life years Disease burden is the impact of a health problem as measured by financial cost , mortality , morbidity , or other indicators.
Numeric subscripts (including floating-point numbers) are stored from lowest to highest. All non-numeric subscripts are stored in alphabetical order following the numbers. In MUMPS terminology, this is canonical order. By using only non-negative integer subscripts, the MUMPS programmer can emulate the arrays data type
Qualitative research is a type of research that aims to gather and analyse non-numerical (descriptive) data in order to gain an understanding of individuals' social reality, including understanding their attitudes, beliefs, and motivation.
For example, for diabetes, HEDIS selects an eligible population based on age (18–75 years), continuous enrollment with a certain health insurer and certain "Events/diagnosis" from pharmacy data (electronic), insurance claims data (electronic) or from medical records. Pharmacy data is based on a list of medications prescribed for diabetes.