Qualitative data statistics are used by researchers to make inferences about a larger population. The data types include nominal, ordinal, range, and ratio variables, and can be manipulated to support or not support a hypothesis. Independent and dependent variables are identified, and nominal, ordinal, range, and ratio variables are explained.
Qualitative data statistics is one of two large sets of data that 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 collected from the sample. The data types in this group of statistics include nominal, ordinal, range, and ratio variables, all of which have a specific use in a study. Researchers can manipulate the collected data to show specific information about the sample – and therefore about the population – in order to support or not support a hypothesis.
The above groups of qualitative data statistics are commonly referred to as variables. The two types of variables that occur in a study are independent and dependent. The independent variables can be those items that are 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. Identifying variables can be a rather tedious process.
Nominal variables are qualitative data statistics that have no sequential order or classification. In short, the moniker requires that this data be organized or separated by name only. For example, answers like yes or no to a question or the gender of the participants – male or female – are among the most common nominal data. Researchers may need the information to simply define the basic characteristics of the individuals in the study.
Ordinal variables represent data that falls into an ordered series. This data can arise when a researcher asks a question that requires a series of answers. For example, responses ranging from poor or mediocre 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 categorize the data for the study.
Range variables have equal space between numbers in qualitative data statistics. Temperature or age are examples that may appear in the collected data. The key to this data type is that zero is not an option. The information here might not even fall under specific rules, such as mathematical differences between the data. For example, 10 may not represent five times two in the dataset.
The group of statistics on the final qualitative data is represented by the reporting variables. These digits have equal spaces between the data and also have a true zero point. Partial numbers, such as 2.1 or 3.3, may also be possible in this group. Researchers must be careful to correctly identify range ratio data in their studies.
Protect your devices with Threat Protection by NordVPN
