Measurement and Hypotheses Lesson 3

Variables and Attributes  Attributes: characteristics/qualities that describe some object or person; categories that make up variables.  Variables: logical groupings of attributes  Variable = gender; Attributes = male and female  Variable = occupation; Attributes = dentist, cop, truck driver

Variables and Attributes  THEORY  Variable (attributes)  causation  Variable (attributes)  Self-control theory  Parenting (lack of supervision, lack of attachment)  causation  Low self control  Delinquency (drug use, vandalism, truancy)

Dependent Variable  Depends on/caused by the independent variable  Example: Sentencing = DV; Defense attorney/Judge = IV  Cause and Effect: IV = cause; DV = effect

Hypothesis  Hypothesis: an expectation about the nature of things derived from theory/empirical research.  Null Hypothesis (H0): hypothesis of no difference  Alternate Hypothesis (H1): our research hypothesis  H1: Teenagers who listen to rap music will commit more violent acts than teenagers who listen to Yanni.  H0: There will be no difference in the number of violent acts committed by teenagers who listen to rap music and teenagers who listen to Yanni.

Conceptualization  Concepts: words or symbols in language that we use to represent mental images.  Example: “Crime”…we all have different conceptions of different kinds of behaviors that are considered criminal.  Conceptualization: the mental process whereby imprecise notions are made more specific and precise.

Operationalization  Operationalization: involves spelling out precisely how a concept will be measured; the process of developing precise definitions.

Example  Topic  Juvenile Justice  Research question: what causes delinquency?  Theory: social bonding (Hirschi)  Concept: involvement  Conceptual definition: involvement in socially accepted/organized activities  Operational definition: participation in church, school, sports, hobbies  Measurement of involvement: number of hours per week in church activities, school activities, sports teams, socially accepted hobbies

Levels of Measurement Nominal measures  Lowest form categorically  No particular order; names or labels for characteristics  Examples: states, cars, gender, race, college, SS #, marital status Ordinal measures  Logically rank ordered, some differences between categories but not even or observable  Examples: military rank, occupational status, fear of crime

Levels of Measurement Interval measures  Equal and logical distances between categories  Examples: IQ tests ( is some interval as ) Ratio measures  Usually very similar to intervals but also based on a true zero point.  Example: age, dollar value, # of prior arrests, BAC, etc.

Validity and Reliability  Validity: measuring what you’re supposed to measure.  Does our cause produce the effect?  Example: you’re studying delinquency and you survey adults  Validity error = misleading conclusions/no generalizations

Threats to Validity History effect  Example: UCR includes more behaviors than it used to (arson) Maturation effect  Example: aging out (adults versus teens) Testing effect  Example: giving people a test, repeat several times, can cause the change (shooting targets)

Reliability  Reliability: whether a particular measurement technique, applied repeatedly to the same object, will yield the same result each time.  Example: Cop issuing speeding tickets  Officer’s judgment versus radar gun (more reliable)  Reliability is a concern every time a single observer is the source of data because we have no way to guard against that observer’s subjectivity.