Unit 4 Seminar

Causation and Research Design

ProfessorProfessor

Chris Lim, MA, Ph.D.(ABD)Chris Lim, MA, Ph.D.(ABD)

Undergraduate School of Criminal Justice

Email: SLim@kaplan.edu

Office Hours Thursday from 1pm-3pm ESTAIM 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 causesNomothetic 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. Two variables must be empirically correlated with one another for a causal relationship to exist

2. Cause must precede effect in time3. Observed correlation between two variables

cannot be explained away by a third variable4. Causal relationship strengthened by finding

causal mechanism5. 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 variablesWhen, 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 True (Classical) – the “gold standard” for testing

causal hypotheses Quasi-Experiments

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 example on previous slide: acquittal rate for each

year

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 periodCommon 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

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