9.4.1 Different Types of Data
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Knows that There are Different Types of Data: Categorical (Ordinal, Nominal, Dichotomous) and Continuous
In research, data is collected to provide evidence to support or reject hypotheses. It is essential to understand the types of data as they inform the type of statistical analysis that can be conducted. Generally, data can be classified into two main categories: categorical and continuous.
Categorial data:
Categorical data refers to data that can be divided into distinct categories or groups. Ordinal data is categorical data that has an inherent order or rank, such as a rating system. Nominal data, on the other hand, does not have an inherent order, such as gender or ethnicity. Dichotomous data is a special type of nominal data where there are only two categories, for example, true or false, yes or no.
| Type of Categorical Data | Definition | Examples |
| Ordinal | Categories have a natural order or ranking | Level of education (e.g. elementary, high school, college), pain severity (e.g. mild, moderate, severe) |
| Nominal | Categories have no natural order or ranking | Gender (e.g. male, female), race/ethnicity (e.g. White, Black, Hispanic), blood type (e.g. A, B, AB, O) |
| Dichotomous | Only two categories | Yes/no questions (e.g. smoker/non-smoker), presence/absence of a condition (e.g. diabetes, hypertension) |
Continuous data:
Continuous data refers to data that can take any value within a certain range and typically represents measurements or quantities. Examples include age, height, and blood pressure. Continuous data are often further classified into interval or ratio data. Interval data has equal intervals between values, but there is no true zero point. Examples include temperature on the Celsius or Fahrenheit scale. Ratio data, on the other hand, has a true zero point, meaning that a value of zero represents an absence of the measured variable. Examples include weight and time.
Understanding the type of data being collected is crucial for selecting appropriate statistical tests and interpreting the results accurately.
References:
- Rosner, B. (2015). Fundamentals of Biostatistics (8th ed.). Cengage Learning.
- Stevens, J. (2002). Applied multivariate statistics for the social sciences. Lawrence Erlbaum Associates.
- Lohr, S. L. (2019). Sampling: Design and analysis. Cengage Learning.