1. Unit 4 Seminar Causation and Research Design Professor Chris Lim, MA, Ph.D.(ABD) Undergraduate School of Criminal Justice Email: SLim@kaplan.edu Office Hours Thursday from 1pm-3pm EST AIM ID: cj105professor

2. 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

3. 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

4. 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

5. 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

6. 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 time 3. Observed correlation between two variables cannot be explained away by a third variable 4. Causal relationship strengthened by finding causal mechanism 5. Causal relationship should be considered within context

7. 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.

8. 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.

9. 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.

10. 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 !

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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)

20. 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

21. Retrospective Studies Approximates Longitudinal Design Ask subjects to recall past events Can learn timing of events (can answer “which came first?” question)

22. 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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