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

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

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

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

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

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

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

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

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

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

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

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

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