1. Associated with quantitative studies
Chapter 6 Measurement
Associated with quantitative studies
Numbers used as a tool for identifying and presenting information
Links the conceptual to the empirical
Necessary to conduct quantitative research

2. Measurement Principles
Numbers measure value, intensity, degree, depth, length, width, distance
Descriptive and evaluative device
Numbers have no value until we provide meaning
Includes everything the researcher does to arrive at a number
Details the operationalization of the variable

3. Levels of Measurement Discrete or continuous
Both representative of communication phenomena
Each produces different kind of data
How data are collected determines how they can be used in statistical analyses

4. Discrete Data The presence or absence of some characteristic
Also known as nominal or categorical data
Categories
Reflect different types not differing amounts
Have no inherent value
Must be mutually exclusive, exhaustive, and equivalent

5. Continuous Level Data Reveals quantity, intensity, or magnitude
Values that differ in degree, amount, or frequency can be ordered on a continuum
Three types
Ordinal data
Interval data
Ratio data

6. Ordinal Data Ranks elements in logical numerical order
Sequence suggests value of data
Ranking positions are relative
Distance between ranks is unknown
Zero does not exist

7. Interval Data Identifies highest, next highest, and so on
Identifies exact difference between and among scores
Acknowledges zero
Allows meaningful comparisons
Likert-type scales
Semantic differential scales

8. Ratio Data All of the characteristics of interval data
Zero is absolute
Indicates complete lack of the variable measured
Provides measure of degree to which something actually exists

9. Validity
Extent to which it measures what you want it to measure and not something else
Validity is a matter of degree
Internal validity
Face validity
Content validity
Criterion-related validity: concurrent or predictive
Construct validity

10. Reliability Degree of consistency among similar items
Reliability coefficient – 0.0 to 1.0
Closer to 1.00, the greater the degree of reliability
Generally, above .70 is acceptable
Internal reliability
Items invoke same response
Reliability between coders
Test-retest reliability
Split-half reliability

11. Relationship between Validity and Reliability
A measurement should be both valid and reliable
Validity and reliability connected in fundamental ways
Reliable measurements can be obtained without validity
When validity is achieved, reliability is presumed

12. Threats to Validity and Reliability
Issues of data collection
Internal validity
Reliability over time
Issues of sample representativeness
External validity
Ecological validity
Alternative explanations

13. Issues of Data Interpretation
Researchers responsible for
Collecting data accurately and ethically
Interpreting and reporting data responsibly
Quality of data interpretation cannot be better than quality of data collected
Measurement is central to quality of outcomes and links to theory