Statistics for the Behavioral Sciences by Frederick J. Gravetter, Larry B. Wallnau ISBN 10: 1305504917 ISBN 13: 9781305504912

702 Statistics Organizer: Finding the Right Statistics for Your Data
Ratio scales and interval scales produce numerical scores that are compatible with
the full range of mathematical manipulation. Examples include measurements of
height in inches, weight in pounds, the number of errors on a task, and IQ scores.
Ordinal scales consist of ranks or ordered categories. Examples include classifying
cups of coffee as small, medium, and large or ranking job applicants as 1st, 2nd,
and 3rd.
Nominal scales consist of named categories. Examples include gender (male/female),
academic major, or occupation.
Within each category of data, we present examples representing these three measurement
scales and discuss the statistics that apply to each.
■ Category 1: A Single Group of Participants with One
Score per Participant
This type of data often exists in research studies that are conducted simply to describe
individual variables as they exist naturally. For example, a recent news report stated that
half of American teenagers, ages 12–17, send 50 or more text messages a day. To get this
number, the researchers had to measure the number of text messages for each individual
in a large sample of teenagers. The resulting data consist of one score per participant for a
single group.
It is also possible that the data are a portion of the results from a larger study examining
several variables. For example, a college administrator may conduct a survey to
obtain information describing the eating, sleeping, and study habits of the college’s students.
Although several variables are being measured, the intent is to look at them one at a
time. For example, the administrator will look at the number of hours each week that each
student spends studying. These data consist of one score for each individual in a single
group. The administrator will then shift attention to the number of hours per day that each
student spends sleeping. Again, the data consist of one score for each person in a single
group. The identifying feature for this type of research (and this type of data) is that there
is no attempt to examine relationships between different variables. Instead, the goal is to
describe individual variables, one at a time.
Table 1 presents three examples of data in this category. Note that the three data sets
differ in terms of the scale of measurement used to obtain the scores. The first set (a) shows
numerical scores measured on an interval or ratio scale. The second set (b) consists of
ordinal, or rank-ordered categories, and the third set shows nominal measurements. The
statistics used for data in this category are discussed in Section I.
TABLE 1
Three examples of data
with one score per
participant for one group
of participants.
(a) Number of Text Messages
Sent in Past 24 Hours
(b) Rank in Class for High
School Graduation
(c) Got a Flu Shot Last
Season
X X X
6 23rd No
13 18th No
28 5th Yes
11 38th No
9 17th Yes
31 42nd No
18 32nd No