Experimental and Quasi-Experimental Designs Chapter 5 Experimental and Quasi-Experimental Designs

Introduction Experimentation is an approach to research best suited for explanation and evaluation An experiment is “a process of observation, to be carried out in a situation expressly brought about for that purpose” Experiments involve: Taking action Observing the consequences of that action Especially suited for hypothesis testing

The Classical Experiment Variables, time order, measures, and groups are the central features of the classical experiment Involves three major pairs of components: Independent and dependent variables Pretesting and posttesting Experimental and control groups

PRE-EXPERIMENTAL RESEARCH DESIGN One-shot case study - single group of subjects is measured on a variable following experimental stimulus One-group pretest-posttest design - adds a pre-test for the group, but lacks a control group Static-group comparison - includes experimental and control group, but no pre-test

One-Shot Case Study A man who exercises is observed to be in trim shape

One-Group Pretest-Posttest Design An overweight man who exercises is later observed to be in trim shape

Static-Group Comparison A man who exercises is observed to be in trim shape while one who doesn’t is observed to be overweight

Independent Variables 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”

Dependent Variables 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”

Pretesting and Posttesting 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

Experimental and Control Groups 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

DIAGRAM OF BASIC EXPERIMENTAL DESIGN

Double-Blind Experiment 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

Selecting Subjects 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

SELECTING SUBJECTS: OPEN MATRIX ILLUSTRATION Three Requirements of Selecting Subjects: Probability sampling Randomization Matching

Random assignment “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”

EVALUATION OF EXPERIMENTAL METHOD Strengths: Isolation of the experimental variable over time Experiments can be replicated several times using different groups of subjects Weaknesses: Artificiality of laboratory setting Social processes that occur in a lab might not occur in a more natural social setting

Experiments and Causal Inference 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

Threats to Internal Validity Conclusions drawn from experimental results may not reflect what went on in experiment History: External events may occur during the course of the experiment Maturation: People constantly are growing 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 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

Threats to Construct Validity 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

Threats to External Validity 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

Threats to Statistical Conclusion Validity 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

Variations in the classical experimental design Four basic building blocks present in experimental designs: The number of experimental & control groups The number & variation of experimental stimuli The number of pretest & posttest measurements The procedures used to select subjects and assign them to groups Variations on the classical experiment can be produced by manipulating the building blocks of experiments

Quasi-Experimental Designs When randomization isn’t 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

Nonequivalent-Groups Designs 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

Cohort Designs 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

Time-Series Designs 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

Variable-Oriented Research and scientific realism 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