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15 Cards in this Set
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Categorical measurements (variables)

Reflect qualitative differences rather than quantitative ones (ex. categories such as yes/no, pass/fail, male/female).
The only requirements are those of mutual exclusivity and exhaustiveness. There is no necessary sense in which one category has more or less of a particular quality: they are simply different. 
mutual exclusivity

Each observation (person, case, score) cannot fall into more than one category; one cannot, for example, both pass and fail a test at the same time.

exhaustiveness

Means that your category system should have enough
categories for all the observations. For biological sex there should be no observations (in this case people) who are neither male nor female. 
ordinal measurement

(the assumptions of mutual exclusivity and exhaustiveness apply)
The categories themselves can be rankordered with reference to some external criterion such that being in one category can be regarded as having more or less of some underlying quality than being in another category. The rankings reflect more or less of something but not how much more or less (ex. excellent, fair, poor). *The fundamental unit of measurement is not known. 
Examples of ordinal measurements

Attitudes, intentions, opinions, personality
characteristics, psychological wellbeing, depression, etc. 
interval level measures

Numerically equal distances on the scale reflect equal
differences in the underlying dimension (IQ scores use interval level measures). 
Ratio scale
measurement 
Implies
the existence of a potential absolute zero value (ex. length, time and number of correct answers on a test). 
discrete variables

Measures that can have only discrete, whole number values.

continuous variables

Variables such as height and time, can be divided into ever smaller units of measure, can also be divided up into an infinite number of fractional parts.

approximate value

A single figure recorded when measuring a continuous variable.

descriptive statistics

describes large amounts of data (ex. body weight)

inferential statistics

makes intelligent guess about populations (using sample data)

sample

a set of observations drawn from a population of interest

population

all possible observations about which we'd like to know something

variables

observations of physical, attitudinal, and behavior characteristics that can take on different values
