1. Research Designs

2. REVIEW
Research
Variable
Association
Causation
Distributions

3. Review - research Descriptive research (“what”)
Crime and imprisonment went up together until 1990
After 1990 crime went down while imprisonment kept increasing
Monthly mean youth homicide victims was about 5 until Ceasefire, then about 1 after
Exploratory (find out enough to ask “why”)
Explanatory (“why”)
Imprisonment reduces crime
Ceasefire reduces youth homicides
Unit of analysis: “object, entity or process” under study
Contains the variables being measured
Case: A single instance of a unit of analysis
Value or score of variable
How many at each value/score
Trend line

4. Review - variable
Any characteristic that is (a) measurable, and (b) can take on different values
Incarceration rate (no. of persons sent to prison/100,000 population)
Mean number of Boston youth shot dead each month
Period when Boston youth were shot dead (pre- or post-Ceasefire)
Height, weight, gender
Coding: assigning a measurement to a variable
Types of variables
Categorical
Nominal: mutually exclusive categories (e.g., M/F)
Nominal variables sometimes recoded as “dummy” variables: 0/1
Ordinal: implied ranking (low/medium/high)
Continuous
Use a scale (e.g., height, weight)
Value or score of variable
How many at each value/score
Trend line

5. Review - association and causation
Association means that the values of two or more variables seem to change in sync
Until 1990 imprisonment and violent crime rates went up together
They later moved in opposite directions
In Boston, the mean number of youth shot dead went down when Ceasefire was turned “on”
Causation means that changes in one variable cause corresponding changes in another variable.
The causal variable is called the “independent” variable (in Ceasefire it’s the time period)
The effect variable is called the “dependent” variable (mean number of monthly deaths)
But did Ceasefire cause the reduction?
Value or score of variable
How many at each value/score
Trend line

6. 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

7. Research designs – non-experimental

8. Principles of non-experimental designs
Begin with a hypothesis
Changes in independent variables(s)  changes in dependent variable(s)
Lower income  more crime
Assess the hypothesis by collecting data on variables of interest.
Data usually reflects the values of variables at one point in time
Data can also be collected in “waves,” meaning at succeeding points in time
In non-experimental designs investigators only collect data - they do nothing that might affect the values of the variables
Data sources
Field observations
Surveys
Official sources (public records, census, etc.)

9. Data source: field observations
Non-experimental designs
Data source: field observations
Research question: Do police officers take harsher measures against youths with an “attitude”? Researchers do a literature review and formulate a…
Hypothesis: worse demeanor  harsher disposition
Researchers then do their own study to “test” this hypothesis, riding along with cops and “coding” interactions with youths
Researchers coded...
Independent variable: youth’s demeanor (2 values)
Dependent variable: officer disposition (4 values)
At a later time they used statistical techniques to assess whether youth demeanor was associated with officer disposition in the hypothesized direction (the worse the demeanor, the harsher the disposition)
If so, depending on the strength of this association, they might conclude:
There is a cause-and-effect relationship: hypothesis confirmed
There is an association between variables, in the predicted direction, but it does not go beyond what could be obtained by chance: hypothesis rejected

10. Data source: official sources
Non-experimental designs
Data source: official sources
Panel 4
Panel 6

11. Non-experimental designs
Data source: surveys
Panel 1
Panel 3

12. Data sources: surveys + official sources
Non-experimental designs
Panel 2
Panel 5

13. Issues in non-experimental designs
Causal order: Did the change in the independent variable precede (come before) the change in the dependent variable?
Poverty  crime OR Crime  poverty
Intervening variables: Could lack of education or living in a violent area be the more proximate (closer) cause of crime?
Poverty  poor education  crime
Here poverty is still the cause, but it affects crime through intervening variable education, which is the more proximate cause
Spurious relationship: What seems to be a relationship isn’t - it’s bogus!
Often caused by a strong association between the independent variable of interest (e.g., poverty) and another independent variable (e.g., poor social controls) which turn out to be the real cause
Poor social controls  crime
Poverty

14. Research designs – experimental

15. Principles of experimental designs
Purposes
Eliminate other possible “causes” (e.g., it’s not poverty, it’s education)
Set the causal order (e.g., you are definitely testing poverty  crime)
Why? Data that’s out there which seems to support your hypothesis could really be supporting that crime  poverty
Method
Randomly assign cases to two or more groups. Designate one or more groups as “experimental” (X) and one as “control” (C)
Measure the dependent variable (time 1) for each group. Random assignment insures that the mean values of the independent variable(s) should be about the same for each group.
“Intervene” by adjusting the level of the independent variable in the experimental group. Leave the control group alone.
Post-measure dependent variable (time 2) for each group. If the differences between experimental and control groups are “statistically significant” they can be attributed to the intervention.
X DVt1 ……...IV…….DVt2
C DVt1 ………………DVt2

16. Experimental designs population: 200 patrol officers 150 males (75%)
Hypothesis: officers who complete a special training program will be less cynical
population:
200 patrol officers
150 males (75%)
50 females (25%)
CONTROL
GROUP
Randomly Assign
25 Officers
CONTROL GROUP
EXPERIMENTAL
For each group, pre-measure dependent variable officer cynicism
Apply the intervention (apply the value of the independent variable – the program.)
NO YES YES NO
For each group, post-measure dependent variable officer cynicism
Also compare within-group changes – what do they tell us?

17. Hypothesis: SOCP reduces recidivism
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 equal overall for background factors such as age, criminal record, etc.
X (experimental group) gets intensive supervision (SOCP yes)
C (control group) remains with regular supervision (SOCP no)
Wait two years, compare recidivism
Does the X group have a significantly lower rearrest rate?
Does the X group have significantly lower rates of drug & alcohol use?
Population: youths cited during an 18-month period
EXPERIMENTAL
GROUP
Randomly Assign
264 youths
CONTROL GROUP
Randomly Assign
265 youths
pre-measure arrest record, drug and alcohol use, etc.
Apply the intervention
YES NO
For each group, post-measure dependent variable measures: arrest record, drug and alcohol use, etc.

18. 1973 Kansas City Patrol Experiment
Experimental designs
1973 Kansas City Patrol Experiment
Research question: Does routine patrol deter crime?
Hypothesis: Routine patrol reduces crime
Independent (causal) variable: Patrol (categorical/ordinal - three levels)
Dependent (effect) variable: crime rate (continuous)
Randomly divide an area into 15 beats
Measure crime in each beat
Randomly assign each a different value of the independent variable
Five C (control) beats: same patrol as usual
Five X1 (experimental) beats: no patrol (“R” - reactive - only answer calls for service)
Five X2 experimental beats: more patrol than usual (“P” - proactive - more cars cruising, looking for trouble)
After one year compare crime rates

19. Some issues with experimental designs
According to the Kansas City experimenters, there was no significant difference in crime rates between the experimental and control groups.
Since neither increasing nor decreasing patrol made a difference, the hypothesis that random patrol can reduce crime was rejected.
However, the experiment was later criticized:
Level of the independent variable (amount of patrol) was not sufficiently increased in the proactive beats to be able to demonstrate a statistically significant effect
Due to contamination by other units, level of patrol was not sufficiently reduced in the reactive beats to be able to demonstrate a statistically significant effect
Other constraints
Practicality
Could we experimentally test poverty  crime?
Ethics
Should we experimentally test poverty  crime? Can we make some people poor, then see what happens!

20. Research designs – QUASI-experimental

21. Quasi-experimental designs & issues
Experiment that lacks random assignment to groups
Groups might differ along a key independent variable (“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 that mimics an experiment
A known intervention did take place (e.g., it’s known that the level of the independent variable did change at a certain time)
Measures of the dependent variable are available for the periods before and after the intervention

22. Quasi-experimental designs
Data source: Vignette
“Vignettes” are brief descriptions of actual or realistic events that are administered to elicit responses by test subjects to key issues of interest to researchers
In this example a vignette is used to test the hypothesis that police officers with military experience are more likely, in domestic violence situations, to show leniency to other veterans
Officer’s veteran status  Officer’s disposition
Since everyone was administered the vignette (there was no equivalent “control group”) the possibility exists that independent variables other than those tested could explain why officers acted as they did