1.  The basic components of experiments are: 1) taking action 2) observing the consequence of that action  Experimental model is most closely linked to the hard sciences

2.  Experimental models are most closely related to well defined concepts and propositions  They are particularly well-suited for hypothesis testing  Better suited for deductive and/or explanatory research

3. 1) Independent and dependent variables 2) Pre-testing and Post-testing 3) Experimental and Control Groups

4.  Independent variables are the “cause” or the “treatment”  Often independent variables are dichotomous or are measured as treatment and non-treatment  Dependent variables are the outcomes or “effects”  Both independent and dependent variables need to be operationally defined and observable

5.  Subjects are measured in the dependent variable before the treatment  Subjects are then measured in the dependent variable after the treatment  The difference of pre and post-test scores are attributed to the treatment effect or the independent variable

7.  Test – Re-test Effect  Respondents becoming aware of the research question or interest  Hawthorne Effect  Tracking and Matching Subjects pre and post- testing

8.  Experimental Group gets the treatment  Control Group resembles the experiment group as close as possible, except they do not get the treatment  Control group is the most effective away to control for the test-retest effect  Both groups are still suspect to the Hawthorne effect

9.  The Double-Blind Experiment  Controls for the effect of the researcher biasing the experimental or treatment group  Extra attention exaggerates the treatment effect because the researcher wants it to succeed  Placebo effects in subjects  Labelling theory and role expectations Attribution process or expectations-states theory  Often used in medical research

10.  Usually social science experiments are done on undergraduates – not generalizeable to the population  On general social processes and patterns generalizability is less important  More important is the similarity of the control and experimental groups

11.  Probability Sampling  Difficult and expensive to do and seldom used  Two separate samples one for the control group and one for the experimental group Both samples are representative of the population Need a relatively large sample to represent the population (100 or more) Usually experiments are done with small groups

12.  Random assignment of control and experimental groups  Randomization may be more important than sampling because it is more important for the experimental design for the groups to be alike then representative of a larger population

13.  Matching is similar to quota sampling  Randomization is better in most cases  You may not know the important variables to match on  Most statistics are dependent on an assumption of randomization

16. Internal validity means the conclusions from the experiment may not reflect what happened in the experiment 1) History 2) Maturation 3) Testing 4) Instrumentation (different T1 and T2 measures) 5) Statistical Regression (high or low initial scores) 6) Selection Bias

17. 7) Mortality 8) Causal Time Order 9) Diffusion or Imitation of treatments 10) Compensation 11) Compensatory rivalry 12) Demoralization

19.  External Validity deals with the generalizability to the real world  In the classic experimental design the impact of test-retest is not controlled  The Solomon Four-Group Design is effective in this

21.  Often rely upon matching for control group  The treatment is often an unplanned event  Weakest of all designs – but often the most interesting of human events