experimental and quasi-experimental designs. o experimentation is an approach to research best...
TRANSCRIPT
CHAPTER 5
Experimental and Quasi-Experimental Designs
INTRODUCTION
o Experimentation is an approach to research best suited for explanation and evaluation
o An experiment is “a process of observation, to be carried out in a situation expressly brought about for that purpose”
o Experiments involve:
o Taking action
o Observing the consequences of that action
o Especially suited for hypothesis testing
THE CLASSICAL EXPERIMENT
o Variables, time order, measures, and groups are the central features of the classical experiment
o Involves three major pairs of components:
Independent and dependent variables
Pretesting and posttesting
Experimental and control groups
PRE-EXPERIMENTAL RESEARCH DESIGN
o One-shot case study - single group of subjects is measured on a variable following experimental stimulus
o One-group pretest-posttest design - adds a pre-test for the group, but lacks a control group
o Static-group comparison - includes experimental and control group, but no pre-test
One-Shot Case Study
o A man who exercises is observed to be in trim shape
One-Group Pretest-Posttest Design
o An overweight man who exercises is later observed to be in trim shape
Static-Group Comparison
o A man who exercises is observed to be in trim shape while one who doesn’t is observed to be overweight
INDEPENDENT VARIABLES
o The Independent Variable takes the form of a dichotomous stimulus that is either present or absent
o It varies (i.e., is independent) in our experimental process
o “The Cause”
DEPENDENT VARIABLES
o The outcome, the effect we expect to see
o Depends on the Independent Variable
o Might be physical conditions, social behavior, attitudes, feelings, or beliefs
o “The Effect”
PRETESTING AND POSTTESTING
o Subjects are initially measured in terms of the Dependent Variable prior to association with the Independent Variable (pretested)
o Then, they are exposed to the Independent Variable
o Then, they are re-measured in terms of the Dependent Variable (posttested)
o Differences noted between the measurements on the Dependent Variable are attributed to influence of the Independent Variable
EXPERIMENTAL AND CONTROL GROUPS
o Experimental group – Exposed to whatever treatment, policy, initiative we are testing
o Control group – Very similar to experimental group, except that they are NOT exposed
o If we see a difference, we want to make sure it is due to the Independent Variable, and not to a difference between the two groups
DIAGRAM OF BASIC EXPERIMENTAL DESIGN
DOUBLE-BLIND EXPERIMENT
o Experimenters may be more likely to “observe” improvements among those who received drug
o In a Double-Blind experiment, neither the subjects nor the experimenters know which is the experimental group and which is the control group
Broward County Florida and Portland, Oregon domestic violence policing units study: “keeping safe” strategies
SELECTING SUBJECTS
o First, must decide on target population – the group to which the results of your experiment will apply
o Second, must decide how to select particular members from that group for your experiment
o Cardinal rule – ensure that Experimental and Control groups are as similar as possible
o Randomization purposes towards this
SELECTING SUBJECTS: OPEN MATRIX ILLUSTRATION
o Three Requirements of Selecting Subjects:
Probability sampling
Randomization
Matching
RANDOM ASSIGNMENT
o “Randomization”
o Central feature of the classical experiment
Produces experimental and control groups that are statistically equivalent
o Farrington and associates:
“Randomization insures that the average unit in the treatment group is approx. equivalent to the average unit in another group before the treatment is applied”
o “All Other Things are Equal”
EVALUATION OF EXPERIMENTAL METHOD
Strengths:
o Isolation of the experimental variable over time
o Experiments can be replicated several times using different groups of subjects
Weaknesses:
o Artificiality of laboratory setting
o Social processes that occur in a lab might not occur in a more natural social setting
EXPERIMENTS AND CAUSAL INFERENCE
o Experiments potentially control for many threats to the validity of causal inference
o Experimental design ensures:
Cause precedes effect via taking posttest
Empirical correlation exists via comparing pretest to posttest
No spurious 3rd variable influencing correlation via posttest comparison between experimental and control groups, and via randomization
THREATS TO INTERNAL VALIDITY
o Conclusions drawn from experimental results may not reflect what went on in experiment
1. History: External events may occur during the course of the experiment
2. Maturation: People constantly are growing
3. Testing: The process of testing and retesting
THREATS TO INTERNAL VALIDITY
4. Instrumentation: Changes in the measurement process
5. Statistical regression: Extreme scores regress to the mean
6. Selection biases: The way in which subjects are chosen (use random assignment)
7. Experimental mortality: Subjects may drop out prior to completion of experiment
GENERALIZABILITY AND THREATS TO VALIDITY
o Potential threats to internal validity are only some of the complications faced by experimenters; they also have the problem of generalizing from experimental findings to the real world
o Two dimensions of generalizability: Construct Validity
External Validity
THREATS TO CONSTRUCT VALIDITY
o Concerned with generalizing from experiment to actual causal processes in the real world
o Link construct and measures to theory
o Clearly indicate what constructs are represented by what measures
o Decide how much treatment is required to produce change in Dependent Variable
THREATS TO EXTERNAL VALIDITY
o Significant for experiments conducted under carefully controlled conditions rather than more natural conditions
o Reduces internal validity threats
o John Eck (2002): "diabolical dilemma."
o Suggestion:
explanatory studies internal validity
applied studies external validity
THREATS TO STATISTICAL CONCLUSION VALIDITY
o Becomes an issue when findings are based on small samples
o More cases allows you to reliably detect small differences; less cases result in detection of only large differences
o Finding cause-and-effect relationships through experiments depends on two related factors: Number of Subjects
Magnitude of posttest differences between the experimental and control groups
VARIATIONS IN THE CLASSICAL EXPERIMENTAL DESIGN
o Four basic building blocks present in experimental designs:
1. The number of experimental & control groups
2. The number & variation of experimental stimuli
3. The number of pretest & posttest measurements
4. The procedures used to select subjects and assign them to groups
o Variations on the classical experiment can be produced by manipulating the building blocks of experiments
QUASI-EXPERIMENTAL DESIGNS
o When randomization isn’t possible for legal or ethical reasons
o Renders them subject to Internal Validity threats
o Quasi = “to a certain degree”
o Two categories:
Nonequivalent-groups designs
Time series designs
NONEQUIVALENT-GROUPS DESIGNS
o When we cannot randomize, we cannot assume equivalency; hence the name
o We take steps to make groups as comparable as possible
o Match subjects in Experimental and Control groups using important variables likely related to Dependent Variable under study
o Aggregate matching – comparable average characteristics
COHORT DESIGNS
o Cohort – Group of subjects who enter or leave an institution at the same time Ex: A class of police officers who graduate from a
training academy at the same time, All persons who were sentenced to probation in May
o Necessary to ensure that two cohorts being examined against one another are actually comparable
TIME-SERIES DESIGNS
o Longitudinal Studies Examine a series of observations over time
o Interrupted – Observations compared before and after some intervention Used in cause-and-effect studies
o Instrumentation threat to internal validity is likely because changes in measurements may occur over a long period of time Often use measures produced by CJ organizations
VARIABLE-ORIENTED RESEARCH AND SCIENTIFIC REALISM
o A large number of variables are studied for a small number of cases or subjects
o Case-oriented research: Many cases are examined to understand a small number of variables (Boston Gun Project)
o Variable-oriented research: A large number of variables are studied for a small number of cases or subjects Case Study Design: Centered on an in-depth examination
of one or a few cases on many dimensions