Experimental Designs Leedy and Ormrod, Ch. 10

Introduction Experiments are part of the traditional science model Involve taking “action” and observing consequences of this action Can collect data using rigorous control A good example of a sophisticated experiemntal design experiment is Albert Bandura’s Bobo doll study: “Transmission of Aggression Through Imitation of Aggressive Models” (1961)  Go to this link to read this classic paper:

Introduction (cont.) Excellent for hypothesis testing and establishing causality For explanatory research Small group interaction Two types:  Laboratory settings (traditional) Used in psychology  Natural settings (field experiments) More typical in sociology

The classical experiment Has three pairs of components:  Independent and dependent variables IV is manipulated DV is observed for change  Pre-testing and post-testing To compare variation in DV before and after treatment  Experimental and control groups Experimental group receives “treatment” and is compared to control group (no treatment)

Experimental Subjects Probability sampling if possible Random assignment essential to ensure no difference between experimental and control groups Sometimes “matching” used  Individuals are matched on important characteristics Problem of “volunteer” subjects

“Pre-experimental” Designs No random assignment used A. One shot case study: T x (IV) Obs (DV) B. One group pre-test-post-test design: Obs 1 T x Obs 2 C. Static group comparison: Exp. Grp.TxObs Ctrl. Grp.Obs

“True Experimental” Designs Use random assignment D. Pretest-Post-test control group design (classical experimental design): Exp.Obs 1 T x Obs 2 Ctrl. Obs 1 Obs 2 E. Post-test only control group design Exp. T x Obs Ctrl. Obs

“True Experimental” Designs (cont.) F. Solomon Four-Group design (Bandura’s Design): Exp.Obs 1 T x Obs 2 Ctrl. Obs 1 Obs 2 Exp. T x Obs 2 Ctrl. Obs 2

Problems of Internal Validity Is change in DV actually caused by IV? A. Problems concerning Subjects  1. Selection bias  2. Experimental Mortality  3. Rivalry  4. Demoralization

Problems (cont.) B. Problems with experimental procedure  1. Testing  2. Instrumentation  3. Causal time order  4. Diffusion  5. Compensation to control group C. Problems with time  History  Maturation

The Double-Blind Experiment Neither researchers or subjects know who is experimental group To reduce experimental bias

The Hawthorne Effect: The Hawthorne effect – refers to change in behaviour produced by the “stimulus” of being singled out and made to feel important during the experimental process. First observed at Hawthorne plant in Illinois during series of experiments on effects of lighting on worker productivity Explanation for increase provided by Professor Elton Mayo (“founder” of human relations theory)

The Hawthorne Experiments Four experiments:  A. Used Three experimental groups and no control group. Found productivity increased when lighting improved.  B. Experimental and control group used. Experimental group had increased lighting. Both groups showed increased productivity.  C. Experimental and control group used. Experimental group had decreased lighting. Both groups showed increased productivity.  D. Two women studied under varying lighting conditions. Subjects increased productivity according to which lighting conditions (more or less) researchers said were “good”

Other Problems: External validity or generalizability of experimental research Modelling Effect Sample problems  Volunteers vs non-volunteers have different characterisitics

Strengths and Weaknesses of Experimental Design Strengths  Establishing causality  Control  Longitudinal research  Replication Weaknesses  Artificiality  Experimenter effect  Sample size

The Field Experiment Used in sociology Natural setting Uses statistical control For an excellent example of a field experiment, read “The Impact of Television: A Longitudinal Canadian Study” by Tannis Macbeth Williams  To read a summary of this work, go to:

Strengths and Weakness of Field Experiments Strengths  Takes place in “real world”  Very good generalizability Weaknesses  Lack of internal validity  Difficult to establish causality  Time consuming  Subject to researcher bias