Data Types

The data you collect from your samples can be one of several data types.
These data types can have a large influence on your analysis. Some
data type limit the type of analyzes that are suitable. Here we will
cover some of the major data types, how they are usually analyzed and assumptions
often associated with the data type.

Ratio Scale Data  These are data that have a constant interval
size and a true zero point. These include:

weights (mg, lb, etc.),

volumes (cc, cu ft, bd ft, etc.),

capacities (ml, qt, gal. etc.),

rates (cm/sec, mph, mg/min, etc.) and

lengths of time (hr, yr., etc.).

Proportional Data  A special case of Ratio Scale Data is
Proportional data. This is data that is divided by the largest value
in the data set or by some theoretical maximum value. This type of
data is assumed to be binomially distributed. Binomial distributions
can be converted to normal distributions by an arc sine transformation
( arcsin(x) ).

Interval Scale Data  These are data that have a constant interval
but not a true zero point. Examples include:

temperature scales such as Celsius (C) or Fahrenheit (F),

time of day, or

compass azimuth.

Ordinal Scale Data  These data only convey information on the order
and relative magnitude of the data. An example is:

letter grades (A, B, C, D, and F ) where an A is better than a B,
but in this system the amount is undetermined.

Nominal Scale Data  These data are descriptive categories.
Examples are:

animal eye color, (Blue, brown, green, red, etc.),

sex (male, female),

organism status (dead, alive).
Ratio, internal and ordinal data can be measured in two ways:

Continuous Data  These are data that could take on almost
any value within the observed range. Obviously we don't have instruments
that can measure beyond some minimum resolution but theoretically the data
could take on any value.

Discrete Data  These are data can take on only specific values
within the range of observed. Example of this is counts such as the number
of eggs, number of individuals, etc.
Also See:
Chapter 1  Introduction pages 16 in:
Zar, J. H. 2007. Biostatistical Analysis. PrenticeHall, Inc. Englewood
Cliffs, New Jersey. 718 pp.
Schuster, E. G. and H. R. Zuuring. 1986. Quantifying the Unquantifiable. J. For. 84(4):2530.
