is age qualitative or quantitative?
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In our medical example, age is an example of a quantitative variable because it can take on multiple numerical values. It also makes sense to think about it in numerical form; that is, a person can be 18 years old or 80 years old. Weight and height are also examples of quantitative variables.
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There are two types of quantitative data, which is also referred to as numeric data: continuous and discrete. As a general rule, counts are discrete and measurements are continuous.
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When we do qualitative analysis, we are exploring how we describe something. Very often, we cannot use numbers or numerical expressions to describe those things. When we do qualitative work, we work with descriptions. ... When we do quantitative analysis, we are exploring facts, measures, numbers and percentages.
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Qualitative analysis is the detection or identification of the constituent elements in the sample, semiquantitative analysis is the estimation of their approximate concentrations, and quantitative analysis is the accurate determination of their concentrations.
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Quantitative: Numerical or statistical information (data), which often comes from surveys, surveillance or from administration records. ... Qualitative evidence provides richer, deeper and broader information based on a few individuals or case examples.
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Quantitative data is numerical data, whereas qualitative data has no numbers attached to it.
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Quantitative Versus Qualitative Analysis Qualitative analysis tells 'what' is in a sample, while quantitative analysis is used to tell 'how much' is in a sample. The two types of analysis are often used together and are considered examples of analytical chemistry.
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What are quantitative and qualitative data? Quantitative data are measures of values or counts and are expressed as numbers. Quantitative data are data about numeric variables (e.g. how many; how much; or how often). Qualitative data are measures of 'types' and may be represented by a name, symbol, or a number code.
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There are two broad categories of research: quantitative and qualitative.
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Qualitative data analysis is non-statistical, its methodological approach is primarily guided by the concrete material at hand. In quantitative research, the sole approach to data is statistical and takes places in the form of tabulations.
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Qualitative data are descriptions of the dependent variable, such as color, or sound. Qualitative data can also be a simple “yes-or-no” observation about whether something happens, such as whether a plant grows. • Quantitative data are numerical measurements of the dependent variable.
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Quantitative measurement involves running statistical analysis on data that has numerical values. Qualitative measurement looks for patterns in non-numerical data.
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Quantitative data is numerical data, whereas qualitative data has no numbers attached to it. The gender of respondents in a study, dividing light bulbs into categories like "very bright," "somewhat bright" and "dim," or the type of pizza a customer prefers are all examples of qualitative data.
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This quantitative data tends to be more concrete and precise (e.g. traffic, number of clicks, behaviour). It therefore involves numerical results. Alternatively, the qualitative data is more descriptive and explanatory (e.g. open comments, suggestions, complaints).
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Consider the variable age. Age is frequently collected as ratio data, but can also be collected as ordinal data.
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Time is (usually) a continuous interval variable, so quantitative. Year can be a discretization of time. For example, you might have data for a child's height on January 1 of years from 2010 to 2018. It's meaningful to ask for height at (say) 2013.5, that would just be on June 30, 2018.
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There are two types of data. Qualitative data is descriptive information about characteristics that are difficult to define or measure or cannot be expressed numerically. Quantitative data is numerical information that can be measured or counted.