DataFerrett Video

Types of Data Available in DataFerrett

There are many interpretations to what the types of data are or should be and the DataFerrett Team has strived to portray and deliver to the user our best interpretation possible.

Data types may also be interpreted as kinds of datasets / focus. Demographics, poverty, income, geography, health insurance, etc., are generally included in every dataset to one degree or another.

Below please find the 4 basic types of data that the DataFerrett application software takes into account when processing data for inclusion into the DataFerrett application.

MicroData:

Microdata is data in which every record is at the unit of analysis level and all records must be added up to get the totals for each data item. For example, for surveys of individuals, microdata contain records for each individual interviewed; for surveys of organizations, the microdata contain records for each organization.

Aggregate or Summarized Data:

Aggregate data is data which has already been summarized or added up, usually for specific geographical units or some other unit, such as industry classifications. In this case, each record is a geographical unit and there is no summing needed to get the totals for the geographies.

Longitudinal Datasets:

Longitudinal is a panel data in which many units are observed over multiple time periods. The Bureau of Labor Statistics' National Longitudinal Surveys (NLS) program collects data from a particular age group of people over many years on an annual or biennial basis. The panel data track the same sample of individuals over many time periods.

Time Series Datasets:

Time Series Data is a sequence of observations which are ordered in time (or space). If observations are made on some phenomenon throughout time, it is most sensible to display the data in the order in which they arose, particularly since successive observations will probably be dependent. Time is called the independent variable.

Last update: 1/28/11