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Qualitative data statistics are one of two large groups of data researchers use to make inferences about a larger population. Many researchers use samples from a larger population to gather specific statistics. Qualitative data statistics typically approximate or characterize the data gathered from the sample. Data types in this statistics group include nominal, ordinal, interval, and ratio variables, all of which have specific use in a study. Researchers can manipulate gathered data to show specific information about the sample — and thus the population — in order to support or not support a hypothesis.
The above qualitative data statistics groups are commonly referred to as variables. The two types of variables that occur in a study are independent and dependent. Independent variables may be those items experimentally manipulated or those that affect the dependent variable. The dependent variable is measured in a study to determine how the independent — and other possible variables — affect it. The identification of variables can be a somewhat tedious process.
Nominal variables are qualitative data statistics that have no order or sequential ranking. In short, the moniker demands this data be organized or separated by name only. For example, answers like yes or no to a question or the sex of participants — male or female — are among the most common nominal data. Researchers may need the information to simply define basic characteristics about the individuals in the study.
Ordinal variables represent data that fall into an ordered series. This data may arise when a researcher asks a question that requires a range of answers. For example, answers that range from poor or fair to good or excellent are ordinal. Some studies may place numbers on these answers, such as one, two, three, and four. This allows the researcher to rank the data for the study.
Interval variables have an equal space between the numbers in qualitative data statistics. Temperature or age are examples that may appear in the gathered data. The key for this data type is that zero is not an option. The information here may also not fall under specific rules, such as mathematical differences between data. For example, 10 may not represent five times two in the data set.
The final qualitative data statistics group is ratio variables. These figures have equal spaces between the data and also have a true zero point. Partial numbers, such as 2.1 or 3.3, may be possible in this group as well. Researchers must be careful to correctly identify ratio data from interval in their studies.