CRIM 220 Lecture Notes - Lecture 9: Regression Analysis, Causal Inference, Internal Validity

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25 Aug 2016
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Chapter 9: Eliminating Rival Plausible Explanations: The Experiment
How can we take advantage of the existing structure of these data so that our inferences about what’s going
on are both reasonable and justifiable?
Three research approaches
1. Classic experiment
2. Quasi-experimentation
3. Case study analysis
The three share a common underlying logic involving eliminating rival plausible explanations to make
reasonable inferences about causes and other processes
Each differ in the degree that they emphasize manipulative control or analytic control
oManipulative Control: The active and intentional manipulation of the setting by the researcher in
order to manimize clarity of inference by ontrolling rival plausible explanations
Experimenter exerts control over every aspect of the setting
oAnalytic Control: One of the two general approaches to research that atttempts to make
inferencees about causes
Isolating Causes: The Controlled Experiment
Superstitious behavior comes from causal attributions we make that may make us feel better but are
unlikely to have any real causal effect
oLucky ring
Before we could say that one event or person caused some other effect, we had to demonstrate three criteria
1. Temporal Precedence: Show that the thing we think is a cause occurred prior to any changes or
differences that we think might have been produced by it
a. Causes always come before effects
2. Association: Changes in the putative cause co-varies in some reliable way with its alleged effect
a. The effect must occur often
b. Often: More frequently than you would expect on the basis of chance or coincidence alone
3. Elimination of Rival Plausible Explanations: Putative cause per se that is responsible for changes in the
dependent measure, rather than related variables, nuisance variables, artifacts, or any myriad other
potential causal agents that might have been present
The Terminology and Logic of Experimentation
Experimentation begins when we recognize, create a situation that includes the phenomenon of interest to
us
Does not need to exist in the real world as the lab affords us the ability to deal with possibilities or realities
Correlation does not necessarily equal causation
So many different variables intervene between cause and effect
Independent and Dependent Variables
Independent Variable or the Treatment Variable:
Dependent Variable or Outcome Variable:
Internal Validity
Internal Validity: The extent to which differences observed in the study can be attributed to the
experimental treatment itself, rather than to other factors
Possible Threats to Internal Validity
1. History: Specific events occurring between the first and second measurement in addition to the independent
variables
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oWhat other events might have occurred between the pretest and post-test that could account for the
results?
2. Maturation: Processes within the research participants themselves that change as a function of time
oGrowing older, more tired, hungrier, so on
oBiological processes happen over time to cause change
3. Testing: The effects of taking a test on scores in the second testing
oHaving taken a test, you may become sensitized to the issue involved in a way that you wouldn’t
have been otherwise
oPretest Sensitization:
oPractice effects also threaten internal validity
oUnsure if one has improved purely because of the practice the pretest gave you or because of the
independent variable had imposed on you
Regression Toward the Mean
Different threat to internal validity is known as statistical regression or regression toward the mean
oStatistical Regression or Regression Toward the Mean: The propensity of extreme scores on the
first testing to score closer to the mean or average of the group on the second testing
This occurs because chance events are unlikely ever to stack up to precisely the same degree on two
successive occasions
The change is more illusory than real
Any measuring we do is subject to a certain amount of random error, no matter how many precautions we
take to minimize it
These positive and negative chance influences will be distributed equally across a group, so that average
scores will be a good indication of their true score
When chance events stack up to a positive or negative direction, statistical regression may occur
The tendency of extreme scores to move or regress closer to the mean on a subsequent testing is known as
statistical regression or regression toward the mean
oThreatens internal validity whenever a group is picked because of the extremity of their scores on
a pretest
Statistical regression
1. Operates to increase obtained pretest-post-test gain scores among low pretest scores since this groups
pretest scores are more likely to have been depressed by error
2. Operates to decrease obtained change scores among persons with high pretest scores since their pretest
scores are likely to have been inflated by error
3. Does not effect obtained change scores among scores at the center of the pretest distribution since the
group is likely to contain as many units whose pretest scores are inflated by error as units whose
pretest scores are deflated by it
Selection biases and instrumentation changes also threaten interval validity
oSelection:
oInstrumentation:
Controlling for Rival Plausible Explanations
Most common way of controlling rival plausible explanations is to incorporate a control group or
comparison group
Starts off the same as our first group and is treated identically except the control group doesn’t receive the
independent variable
This gives us the pretest/post-test control group design
Any change in the experimental group that wasn’t observed in the control group must have been a function
of the one element on which they differed
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