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A criticism of quantitative coding approaches is that such coding sorts qualitative data into predefined categories that are reflective of the categories found in objective science. The variety, richness, and individual characteristics of the qualitative data are reduced or, even, lost.
A categorical variable that can take on exactly two values is termed a binary variable or a dichotomous variable; an important special case is the Bernoulli variable. Categorical variables with more than two possible values are called polytomous variables ; categorical variables are often assumed to be polytomous unless otherwise specified.
Qualitative methods might be used to understand the meaning of the conclusions produced by quantitative methods. Using quantitative methods, it is possible to give precise and testable expression to qualitative ideas. This combination of quantitative and qualitative data gathering is often referred to as mixed-methods research. [14]
For example, qualitative data analysis often involves a fairly structured approach to coding raw data into systematic information and quantifying intercoder reliability. [2] There is often a more complex relationship between "qualitative" and "quantitative" approaches than would be suggested by drawing a simple distinction between them.
In mathematics and statistics, a quantitative variable may be continuous or discrete if it is typically obtained by measuring or counting, respectively. [1] If it can take on two particular real values such that it can also take on all real values between them (including values that are arbitrarily or infinitesimally close together), the variable is continuous in that interval. [2]
Human factors, human work capital is important issue that deals with qualitative properties. Some common aspects are work, motivation, general participation, etc. Although all of these aspects are not measurable in terms of quantitative criteria, the general overview of them could be summarized as a quantitative property.
If not, the null hypothesis is supported (or, more accurately, not rejected), meaning no effect of the independent variable(s) was observed on the dependent variable(s). The result of empirical research using statistical hypothesis testing is never proof. It can only support a hypothesis, reject it, or do neither. These methods yield only ...
The ease of quantification is one of the features used to distinguish hard and soft sciences from each other. Scientists often consider hard sciences to be more scientific or rigorous, but this is disputed by social scientists who maintain that appropriate rigor includes the qualitative evaluation of the broader contexts of qualitative data.