An Array of Qualitative Data Analysis Tools: A Call for Data Analysis Triangulation Nancy L. Leech University of Colorado at Denver and Health Sciences Center Anthony J. Onwuegbuzie Sam Houston State University One of the most important steps in the qualitative research process is analysis of data. The purpose of this article is to provide elements for understanding multiple types of ...
Double-precision floating-point format is a computer number format, usually occupying 64 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point. Floating point is used to represent fractional values, or when a wider range is needed than is provided by fixed point (of the same bit width), even if at the cost of precision.
You have to concern yourself with only two types of integers: the normal integer — the int — and the long integer — the long. The int is a whole-number value, ranging from –32,768 to 32,767. It’s ideally put to use for small numbers without a fractional part.
To describe different types of qualitative data collection methods; To compare and contrast the methods; To recognize which method of data collection is the most appropriate for a given research topic. As discussed in the previous model, there are five common approaches to studying a qualitative research question.
Now, coming to your question, imagine that possible qualitative findings about your phenomenon of interest are data points on a nominal scale: A, B, C, etc. If it is feasible to differentiate between them in your observations, you can postulate your null hypothesis as A (it must be a likely choice based on your prior knowledge of phenomenon, see above).
Oftentimes, quantitative data is used to analyze qualitative data sets. Qualitative Versus Quantitative Data. It's pretty easy to understand the difference between qualitative and quantitative data: the former doesn't include numbers in its definition of traits of an object or group of objects while the latter does.
Generally speaking, qualitative data is higher value than quantitative data but it can't be easily processed by machines. That is to say, that processing qualitative data requires natural language processing using advanced techniques such as artificial intelligence. At present, it is mostly humans that create qualitative data. In future, machines may become equally adept at expressing complex ideas with words. The following are common examples of qualitative data.