Undergraduate School of Criminal Justice
Unit 4 Seminar
Causation and Research Design
Professor
Chris Lim, MA, Ph.D.(ABD)
Email: [email protected]
Office Hours
Thursday from 1pm-3pm EST
AIM ID: cj105professor
What is a ‘cause’?
A cause is an explanation for some
characteristic, attitude, or behavior of
groups, individuals, or other entities (such
as families, gangs, police departments) or
for events.
 Types of causes

 Nomothetic
 Idiographic
Nomothetic Causal Explanation
Involves the belief that variation in an
independent variable will be followed by
variation in the dependent variable, when
all other things are equal (ceteris paribus)
 The situation as it would have been in the
absence of variation in the independent
variable is termed the counterfactual

Idiographic Causal Explanation

… the concrete, individual sequence of events,
thoughts, or actions that resulted in a particular
outcome for a particular individual or that led to
a particular event (Hage & Meeker 1988).
 Sometimes

called “narrative reasoning”
This is the meaning of the term cause that we
use very often in everyday conversation
Idiographic Causal Explanation Example


Includes statements of initial conditions and then relates a
series of events at different times that led to the outcome, or
causal effect
 “When I was a kid, I played the video game, ‘Grand Theft
Auto’ all the time!”
 “My favorite movie was ‘Fight Club’ when I was in high
school.”
 “In college, I spent all my free time watching hockey on
T.V.”
 “Eventually, I started getting into fights at bars.”
 “One night, I hit a guy so hard, I broke his nose.”
 “Eventually, I got arrested for battery.”
Pays close attention to time order and causal mechanisms, but
it is difficult to make a convincing case that one particular
causal narrative should be chosen over an alternative narrative
Criteria for Causation
1.
2.
3.
4.
5.
Two variables must be empirically correlated
with one another for a causal relationship to
exist
Cause must precede effect in time
Observed correlation between two variables
cannot be explained away by a third variable
Causal relationship strengthened by finding
causal mechanism
Causal relationship should be considered
within context
Empirical Association
Before we can search for a causal
relationship between two factors, there
must be evidence that they are somehow
related
 Relationship must be observable – cannot
be only assumed or believed
 The independent variable and the
dependent variable must vary together.

Cause precedes effect in time
•
The change in X must occur before the
change in Y
•
It is often difficult to establish cause-effect
relationships in social research, because it
can be difficult to determine which came
first.
Nonspuriousness
•
Just because two factors/variables are related,
and one thing comes before the other, the
relationship is not necessarily causal !!! One
thing does not necessarily cause the other.
•
We say that a relationship between two
variables is spurious when it is due to variation
in a third variable; so what appears to be a direct
connection is in fact not.
Causal Mechanism

Process that creates the connection
between variation in an independent
variable and the variation in the dependent
variable it is hypothesized to cause
 In
other words, it’s the reason why the
relationship is causal

Not necessary for demonstrating a causal
relationship, but it helps !
Context
Context = set of circumstances
surrounding an event or situation
 No cause has its effect apart from some
larger context involving other variables

 When,
for whom, and in what conditions does
this effect occur?

A cause is really one among a set of
interrelated factors required for the effect
Research Design and Causality

Experiments
(Classical) – the “gold standard” for testing
causal hypotheses
 Quasi-Experiments
 True

Nonexperimental Designs
 Cross-sectional
 Longitudinal

Unit of Analysis
 Individuals
 Groups
What do Experiments do?




Examine the effect of independent variable on
dependent variable
 Independent variable – usually a stimulus (or
intervention) that is either present or absent
 Dependent variable – must be able to measure before
and after experiment
Find out whether stimulus (intervention) made any
difference
Most common CJ applications are program evaluation
and policy analysis
Experiments are best suited to…
 Well-defined and precisely measured concepts
 Testing specific hypotheses
 Well-controlled setting
True (Classical) Experiments



Assignment of study groups
 Study groups must be from same population
 Assign to
 Experimental group (the group that gets the
stimulus/intervention)
 Control group (the group that gets nothing) or is
exposed to different treatment/intervention from
experimental group
Random assignment to groups
Pretest and posttest
 Must be able to have before/after measures to see if the
 stimulus is associated with hypothesized response
 intervention is association with hypothesized outcome
Causality and True Experimental Designs





Association
 Random assignment to treatment and control groups
assures that the only difference between 2 groups is the
intervention/experimental stimulus
 Control group provides information on what would have
happened without the intervention, ceteris paribus
Time order
 Pretest and posttests take care of this requirement
Nonspurious relationships
 Random assignment eliminates many extraneous influences
that can create spurious relationships
Mechanism
 Experimental designs cannot directly address this factor
Context in which change takes place
 Difficult to control context in field (‘real world’) experiments
Nonexperimental Designs and Causation


Cross-sectional Designs
 Snapshot
 Observations are made at one time point - cannot
determine causal order
 Sometimes can infer timing if information exists
 Person must be able to remember which came first
Longitudinal
 Repeated Cross-Sectional Designs (Trend)
 Fixed-Sample Panel Designs
 Event- or Cohort-Based Designs
Cross-Sectional Designs that Enhance
Ability to Identify Causal Relationships

Independent variable is fixed at a time point
earlier than variation in dependent variable
 E.g.,

demographic characteristics
Respondents can give reliable information on
events, thoughts, feelings at earlier point in time
 Retrospective


studies
Measures come from records that contain
information from earlier time periods
Know that value of dependent variable was
similar for all cases prior to the treatment
Repeated Cross-Sectional
(Trend) Designs


Data are collected at 2 or more points in time
from different sample selected from the same
population
General changes in population
 usually

not detailed information
Several snapshots strung together
 Slideshow
 Like
year
example on previous slide: acquittal rate for each
Fixed-Sample Panel Designs

Study same group of people at several intervals
 Collect
data from sample at time 1
 Collect data from same people at time 2, etc.


Very expensive
Rarely done
 Expensive
 Attrition,
especially in long studies
 Subject fatigue (drop out or don’t provide valid
information)
Event-Based Designs

Group of individual units who enter or
leave defined population during specified
time period
 Common

starting point
Study this group over some period of time
Retrospective Studies
Approximates Longitudinal Design
 Ask subjects to recall past events
 Can learn timing of events (can answer
“which came first?” question)

Causality in Experimental vs.
Nonexperimental Designs




Issues
 How well can we meet criteria for causality?
 Does one type of design do better than another?
Time order
 Experimental - YES
 Nonexperimental – Maybe
Correlation
 Both equally capable of showing correlation
Spuriousness
 Experimental - YES, because the only difference is the intervention
 Nonexperimental – use statistical control
 Hold variable(s) constant so relationship between two or more
other variables can be examined apart from influence of ‘control’
variable(s)
 Intervening variables
Unlocking the Keys to Success!
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
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