Research Designs

1. Research Designs

2. Review -- research • General types of research • Descriptive (“what”) • Exploratory (find out enoughto ask “why”) • Explanatory (“why”) • Unit of analysis: “object, entityor process” under study • Contains the variablesbeing measured • Case: A single instanceof a unit of analysis

3. Review - variable • Any characteristic of a unit of analysis that is not fixed, meaning it can differ or change • How were officers accidentally killed • During which period (pre- or post-Ceasefire) were Boston youths shot dead • Any concept that can be divided into subcategories or values • Only limitation is that a variable must be able to have different values, scores or levels • Must be conceptually free to change • Number of officers accidentally killed each year • How officers were killed • Mean number of Boston youth shot dead each month • Period when Boston youth were shot dead (pre- or post-Ceasefire) • Coding • Assigning a measurement to a variable

4. Review - distributions • An arrangement of cases in a sample or population according to their values or scores on one or more variables • Statistics – mean, median, mode, range, standard deviation – summarize distributions

5. Review - association and causation • Associationmeans that thevalues of two or more variableschange together • In Boston, the number ofyouth shot dead appearsto be associated with thestudy period • After invoking Ceasefirethe mean number of youths slain by gunfiredrops • Causation means that changesin one variable cause corresponding changes in another variable. • The causal variable is called the “independent” variable (here it’s the time period) • The effect variable is called the “dependent” variable (here it’s the mean number of monthly deaths) • So, did Ceasefire cause the reduction?

6. Research designs –non-experimental

7. Non-experimental designs Principles of non-experimental designs • Collect data, or use existing data • Field observation • Surveys • Official sources (public records, census, etc.) • Common non-experimental designs • Cross-sectional • Split independent variable into subgroups (M/F) • Look for differences between subgroup statistics, such as the mean • Panels • Normally means a group of persons • Longitudinal: Repeatedly measure persons over time • T1 … T2 … T3 • Multiple group trend: Measure subsets of the same group at different times • Subgroup 1: T1 • Subgroup 2: T2 • Subgroup 3: T3

8. Non-experimental designs Field observations • Research question: do police officers take harsher legal measures if youths display a bad attitude? • Hypothesis: worse demeanor  harsher disposition • Researchers rode along with cops to observe their interactions with youths • Researchers did NOT intervene -- they let things be • Researchers coded... • Independent variable: youth demeanor • Dependent variable officer disposition • At a later time they used statistical techniques to determine if there is an association between the variables • Depending on the strength of the association they might conclude cause-and-effect

9. Non-experimental designs Surveys Panel 1 Panel 4 Panel 6

10. Non-experimental designs Official sources Panel 6 Panel 9 Panel 10

11. Non-experimental designs Mixed approaches Panel 2 Panel 3

12. Non-experimental designs Issues with non-experimental designs Poverty  Crime or Crime  Poverty? • Causal order: Did the change in the independent variable precede the change in the dependent variable? Poverty  Crime ? Crime  Poverty ? • Intervening variables: Could lack of education or living in a violent area be the true cause of crime? Poverty  poor education  crime • Spurious relationships • Lunar cycles and homicide

13. Research designs –experimental

14. Experimental designs Principles of experimental designs Poverty  Crime orEducation  Crime? • Purposes • Eliminate other possible “causes” (e.g., education instead of poverty) • Assure causal order (e.g., it’s not crime  poverty) • Method • Randomly assign cases to two or more groups • Pre-measure independent and dependent variables • Designate one or more groups as “experimental” and one or more as “control” • Intervene (e.g., introduce independent variable or adjust level of existing independent variable in the hypothesized direction) • Post-measure dependent variable. Any substantial difference between the experimental and control groups can be attributed to the intervention • Simple experiment ( X ) DVt1…IV….DVt2 (intervention) ( C ) DVt1…..……DVt2 (no intervention)

15. Experimental designs Hypothesis: SOCP reduces recidivism • Independent (causal) variable: SOCP (yes/no) (categorical/nominal) • Dependent (effect) variable: recidivism (rearrest rate, continuous) • Randomly assign youths being released to either X or C • Random assignment makes them about equal overall for background factors such as age, criminal record, disciplinary history, etc. • Give X (experimental) group special intensive supervision • This is called an “intervention” • Give C (control) group regular supervision • Wait two years • Compare recidivism (rearrest) rates for both groups • Does the X group have a significantly lower rate?

16. Experimental designs 1973 Kansas City Patrol Experiment • Does routine patrol deter crime? • Area randomly divided into 15 beats • Five C beats (Control - same patrol as before) • Five R beats (Reactive - no random patrol) • Five P beats (Proactive - more patrol) Hypothesis: Random patrol reduces crime • Independent (causal) variable: Patrol (categorical/three values: same amount of patrol as before/less/more) • Dependent (effect) variable: crime rate(continuous) • Randomly divide an area into 15 beats • Randomly assign each a different value of the independent variable (same amount of patrol as before/less/more) • Can be interpreted as ten X groups and five C groups • Five C beats: same patrol as before • Five X 1 beats: More patrol • Five X 2 beats: Less patrol • After one year compare crime rates

17. Experimental designs Experimental designs (cont’d) • Solomon four-group design (checks for effects of premeasure)X1 DVt1 … IV … DVt2C1 DVt1 …………. DVt2X2 ……….... IV … DVt2C2 ……………….... DVt2 • Issues with experimental designs • Practical constraints • How can we test poverty  crime experimentally? • Ethical constraints • Should we test poverty  crime experimentally?

18. Research designs –QUASI-experimental

19. Quasi-experimental designs Quasi-experimental designs • Experiment that lacks random assignment to groups • Groups might differ in an important respect (“matching” often used to try to make up for this) • Experiment without a control group • An extraneous event might be the true cause of the change in the dependent variable • A non-experimental design thatmimics an experiment • A known intervention did takeplace (e.g., it’s known that thelevel of the independent variabledid change at a certain time) • Measures of the dependent variable are available for theperiods before and after theintervention

20. Class assignment • Research question: can harsh sentencing reduce crime? • How would you test the hypothesis that it does experimentally? • Discuss it quietly so that other teams don’t overhear • Write down a brief, non-technical answer -- you’ll get a chance to explain it in more detail in a few minutes (one slip of paper per team) • Remember you must . . . • Randomly assign cases to two or more groups • Pre-measure independent and dependent variables • Designate one or more groups as “experimental” and one or more as “control” • Intervene (e.g., introduce independent variable or adjust level of existing independent variable in the hypothesized direction) • Post-measure dependent variable. Any substantial difference between the experimental and control groups can be attributed to the intervention