Ch07 Experimental & Quasi-Experimental Designs


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Maxfield, Michael G. & Babbie, Earl R. (2011). Research Methods for Criminal Justice and Criminology, 6th Edition. Belmont, CA: Wadsworth Publishing.

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Ch07 Experimental & Quasi-Experimental Designs

  1. 1. 1Experimental andQuasi-ExperimentalDesigns
  2. 2. OUTLINE Introduction The Classical Experiment Experiments and Causal Inference Variations in the Classical Experimental Design Quasi-Experimental Designs
  3. 3. 3•Experimentation is an approach to researchbest suited for explanation and evaluation•An experiment is “a process of observation,to be carried out in a situation expresslybrought about for that purpose”•Experiments involve: •Taking action •Observing the consequences of that action•Especially suited for hypothesis testing
  4. 4. 4•Variables, time order, measures, and groupsare the central features of the classicalexperiment•Involves three major pairs of components: •Independent and dependent variables •Pretesting and posttesting •Experimental and control groups
  5. 5. 5• The Independent Variable takes the form of a dichotomous stimulus that is either present or absent• It varies (i.e., is independent) in our experimental process• “The Cause”
  6. 6. 6• The outcome, the effect we expect to see• Depends on the Independent Variable• Might be physical conditions, social behavior, attitudes, feelings, or beliefs• “The Effect”
  7. 7. 7• Subjects are initially measured in terms of the Dependent Variable prior to association with the Independent Variable (pretested)• Then, they are exposed to the Independent Variable• Then, they are re-measured in terms of the Dependent Variable (posttested)• Differences noted between the measurements on the Dependent Variable are attributed to influence of the Independent Variable
  8. 8. 8• Experimental group – Exposed to whatever treatment, policy, initiative we are testing• Control group – Very similar to experimental group, except that they are NOT exposed• 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
  9. 9. 9• Pointed to the necessity of control groups• Independent Variable: improved working conditions (better lighting)• Dependent Variable: improvement in employee satisfaction and productivity• Workers were responding more to the attention than to the improved working conditions
  10. 10. 10• We often don’t want people to know if they are receiving treatment or not• We expose our control group to a “dummy” Independent Variable just so we are treating everyone the same• Medical research: Participants don’t know what they are taking• Ensures that changes in Dependent Variable actually result from Independent Variable and are not psychologically based
  11. 11. 11• Experimenters may be more likely to “observe” improvements among those who received drug• 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
  12. 12. 12• First, must decide on target population – the group to which the results of your experiment will apply• Second, must decide how to select particular members from that group for your experiment• Cardinal rule – ensure that Experimental and Control groups are as similar as possible• Randomization purposes towards this
  13. 13. 13• “Randomization”• Central feature of the classical experiment • Produces experimental and control groups that are statistically equivalent• 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”• “All Other Things are Equal”
  14. 14. 14• Experiments potentially control for many threats to the validity of causal inference• 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
  15. 15. 15• Conclusions drawn from experimental results may not reflect what went on in experiment3. History: External events may occur during the course of the experiment4. Maturation: People constantly are growing5. Testing: The process of testing and retesting
  16. 16. 164. Instrumentation: Changes in the measurement process5. Statistical regression: Extreme scores regress to the mean6. Selection biases: The way in which subjects are chosen (use random assignment)7. Experimental mortality: Subjects may drop out prior to completion of experiment
  17. 17. 178. Causal time order: Ambiguity about order of stimulus and Dependent Variable – which caused which?9. Diffusion/Imitation of treatments: Experimental group may pass on elements to Control group when communicating10. Compensatory treatment: Cgroup is deprived of something considered to be of value
  18. 18. 1811. Compensatory Rivalry: Control group deprived of the stimulus may try to compensate by working harder12. Demoralization: Feelings of deprivation among control group result in subjects giving up
  19. 19. 19• 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• Two dimensions of generalizability: • Construct Validity • External Validity
  20. 20. 20• Concerned with generalizing from experiment to actual causal processes in the real world• Link construct and measures to theory• Clearly indicate what constructs are represented by what measures• Decide how much treatment is required to produce change in Dependent Variable
  21. 21. 21• Significant for experiments conducted under carefully controlled conditions rather than more natural conditions• Reduces internal validity threats• John Eck (2002): "diabolical dilemma."• Suggestion: • explanatory studies  internal validity • applied studies  external validity
  22. 22. 22• Becomes an issue when findings are based on small samples• More cases allows you to reliably detect small differences; less cases result in detection of only large differences• 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
  23. 23. 23• Four basic building blocks present in experimental designs:2.The number of experimental & control groups3.The number & variation of experimental stimuli4.The number of pretest & posttest measurements5.The procedures used to select subjects and assign them to groups• Variations on the classical experiment can be produced by manipulating the building blocks
  24. 24. 24• When randomization is not possible for legal or ethical reasons• Renders them subject to Internal Validity threats• Quasi = “to a certain degree”• Two categories: • nonequivalent-groups designs • time series designs
  25. 25. 25• When we cannot randomize, we cannot assume equivalency; hence the name• We take steps to make groups as comparable as possible• Match subjects in Experimental and Control groups using important variables likely related to Dependent Variable under study• Aggregate matching – comparable average characteristics
  26. 26. 26• 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• Necessary to ensure that two cohorts being examined against one another are actually comparable
  27. 27. 27• Longitudinal Studies • Examine a series of observations over time• Interrupted – Observations compared before and after some intervention (used in cause-and-effect studies)• 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
  28. 28. 28• A large number of variables are studied for a small number of cases or subjects• Case-oriented research: Many cases are examined to understand a small number of variables (Boston Gun Project)• 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