is age categorical or quantitative data?
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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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Quantitative data includes anything described numerically: revenue, task time, rating scales—anything that can be counted. We can break down quantitative data into two groups: discrete and continuous.
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"Quantitative" data deals, literally, with quantities—that is, things that can be measured. Conversely, "qualitative" data deals with qualities—that is, things that can be observed and described, but not directly measured. In sociology, quantitative data is often collected by amassing a large body of qualitative data.
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Consider the variable age. Age is frequently collected as ratio data, but can also be collected as ordinal data. ... However, many variables that get captured as ordinal have a similar variable that can be captured as interval or ratio data, if you so choose.
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When working with statistics, it's important to recognize the different types of data: numerical (discrete and continuous), categorical, and ordinal. Data are the actual pieces of information that you collect through your study. ... Most data fall into one of two groups: numerical or categorical. Numerical data.
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Age can be both nominal and ordinal data depending on the question types. I.e "How old are you" is a used to collect nominal data while "Are you the first born or What position are you in your family" is used to collect ordinal data. Age becomes ordinal data when there's some sort of order to it.
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Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.
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Explanation: A categorical variable is a category or type. For example, hair color is a categorical value or hometown is a categorical variable. ... A numerical variable is a variable where the measurement or number has a numerical meaning.
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Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age). Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
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Data at the nominal level of measurement are qualitative only. Data at this level are categorized using​ names, labels, or qualities. No mathematical computations can be made at this level. Data at the ordinal level of measurement are qualitative or quantitative.
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By Saul McLeod, updated 2019. Download Article as PDF. There exists a fundamental distinction between two types of data: Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.
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Qualitative data is of two types, namely; nominal data and ordinal data. Qualitative data sometimes takes up numeric values but doesn't have numeric properties. This is a common case in ordinal data. Ordinal data have a scale and order to it.
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['At the highest level, two kinds of data exist: quantitative and qualitative.', 'There are two types of quantitative data, which is also referred to as numeric data: continuous and discrete.']
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Qualitative (categorical) data deals with descriptions with words, such as gender or nationality. Paired and unpaired t-tests and z-tests are just some of the statistical tests that can be used to test quantitative data.
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Age is frequently collected as ratio data, but can also be collected as ordinal data. This happens on surveys when they ask, “What age group do you fall in?” There, you wouldn't have data on your respondent's individual ages – you'd only know how many were between 18-24, 25-34, etc.
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Quantitative data is defined as the value of data in the form of counts or numbers where each data-set has an unique numerical value associated with it. ... Quantitative data is usually collected for statistical analysis using surveys, polls or questionnaires sent across to a specific section of a population.
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Quantitative data is a bit like a countable noun. They both refer to things that can be counted, even if it seems like it'd take a lifetime to measure. Here are some example of quantitative data: A jug of milk holds one gallon.