Quasi-experiments (not quite experiments). Definition: Experiments lacking random assignment of subjects to treatment conditions and the absence of an independent variable manipulated by the experimenter (e.g., automobile deindividuation study). Confounded Variables.
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Quasi-experiments(not quite experiments) Definition: Experiments lacking random assignment of subjects to treatment conditions and the absence of an independent variable manipulated by the experimenter (e.g., automobile deindividuation study).
Confounded Variables Definition: unintended/uncontrolled variation that may explain the obtained pattern of results rather than the expected relationship between independent and dependent variables.
Problems with the One Group Pretest-Posttest Design • History: any event not part of the experimental manipulation that occurs between the pre- and posttest (e.g., a celebrity dies of lung cancer, The Insider debuts in theatres and gets extensive media coverage). • Maturation of subjects: refers to systematic changes to the subjects as a function of time (e.g., age, subjects get more concerned about their health as they get older, maybe people get more responsible once they have children, boredom and hunger also increase over time).
Problems with the One Group Pretest-Posttest Design • Testing: refers to the problem of the pretest influencing or changing behaviour (e.g., if subjects were asked to keep a smoking diary, merely logging their cigarette consumption may make them realize that they smoke much more than they previously thought). • Instrument decay: refers to a change in the accuracy of the data recording over the course of the research. If a smoking diary were to be kept, possibly by Week 4, subjects are less motivated, or are bored or tired and fail to record all cigarettes actually smoked.
Problems with the One Group Pretest-Posttest Design • Statistical regression: subjects selected on the basis of extreme scores, upon retesting have decreased (or increased if their selection was based on their low score), the extremity of their scores and now appear closer to the mean score. Possibly smokers selected on the basis of extremely high smoking frequency were for some strange reason smoking at a level way above what they might normally do (they got a supply of duty-free cigarettes, smuggled smokes, etc.), so that upon retesting at a later time, they appear to decrease their cigarette consumption as a result of the experimental manipulation (relaxation training).
Problems with the One Group Pretest-Posttest Design Statistical regression (con’t.) Statistical regression often occurs when unreliable measures are used. See the Sports Illustrated effect for batters and pitchers and the example of the best and worst performing Canadian mutual funds.
Statistical Regression: Canadian Mutual Funds(from Report on Business, January 2000, p. 74)Top 5 Canadian Equity Funds (1989) Average Return in 1989 31.9% Average Annual Return over next 10 yrs 11.7%Bottom 5 Canadian Equity Funds (1989)Average Return in 1989 7.1% Average Annual Return over next 10 yrs 8.1%
Regression to the Mean This phenomenon is observed when there is a less than perfect correlation between two measures. The more extreme the selection of scores, the greater the regression to the mean. It occurs in all types of measurement situations (e.g., Sports Illustrated effect, parental heights, I.Qs.). As with most statistical phenomenoa, regression to the mean is true of groups of observations and is probabilistic (i.e., it does not happen every time). Remember the effect is for groups of scores rather than the score(s) of individual group members.
Regression to the meanInitial test mean Subgroup mean Subgroup mean of group for retest9587603530
Mortality or Experimental Mortality Mortality refers to subjects dropping out of an experiment. It is a problem with longitudinal designs or longer experimental time commitments from the subjects. There is a danger of differential dropout in certain conditions which would then cause the different experimental groups to be nonequivalent. Example: heavy smokers may be sicker over the course of the study and have a higher dropout rate, so that by the time the study is over, only the light smokers remain.
Mortality or Experimental Mortality This is the reason for using a pretest. Otherwise, if the sample size is reasonable, the notion of randomization should handle the problem of one group having a different type of subject. Pretests can be awkward, or worse, they sensitize the subjects, tipping them off to the demand characteristics of the experiment.
Well-designed Experiments • Since random assignment was used, maturation and history affect both groups the same way. • What other forms of assignment of subjects to conditions could be performed? What are the advantages or disadvantages of this approach? • If pretests were to be used, what problems might exist (e.g., demand characteristics, experimental mortality).
Experimental Design Issues • What are the defining characteristics of experiments? • Can all possible variables affecting experimental subjects be controlled for or assessed? If not, is experimental work possible? How is this problem/issue handled? • When do we use correlational designs? • In operant conditioning work ABAB designs are frequently used. Each subject is his/her own control. What advantages/disadvantages does this design pose for those working in the real world? Policy issues?
Nonequivalent Control Group Design Unlike the one group pretest-posttest design, here a control group is added. But there is still a problem with confounding because the subjects are not equivalent in both the treatment (experimental) group and the control group. This may be due to selection differences—although all subjects are smokers, perhaps a motivational difference exists such that more motivated, heavy smokers have volunteered to receive relaxation training, while nonvolunteering smokers are in the control group. Or it may be that the relaxation training group smokers are recruited from the maintenance department of a large manufacturing corporation, while the controls are recruited from the electrical engineering department. Here social class, educational variables, intelligence, etc., may vary and smoking may be correlated with those variables
Ex Post Facto Study (after the fact study) Group Selected Naturally Occuring Measure Event—no direct manipulation Do the British Columbia forest fire victims experience post-traumatic stress disorder? No causal statements are possible since rival hypotheses cannot be eliminated since many confounding variables could also produce the same pattern of results. “All sex offenders reported prior exposure to pornography”.
Experimental Design Between-subjects design: a research design with two or more groups with subjects assigned to only one group. Within-subjects design: all subjects receive the same treatments—each subject serves as his/her own control. There is a problem found in within-subjects designs termed order or sequence effects. Here the order of treatments makes a difference in the dependent variable(s). The participant’s behaviour in later parts of the experiment may be influenced by what was presented to the subject earlier in the experiment.
Dealing with Order Effects: Counterbalancing Solution: Counterbalance order of treatments (examples of drivers experiencing either high or low congestion first and then experiencing the other condition to rule out fatigue or the mere passage of time producing stress). Latin square designs may also be used.
Statistical Issues Null hypothesis: the hypothesis that there is no relationship between two or more variables. The null hypothesis may be rejected (which is the researcher’s hope), but never accepted. Power: the ability to find differences (or to avoid Type 2 errors). It is the ability to find significant differences when differences truly exist. Type 1 error: rejecting the null hypothesis (no difference between the treatment groups) when it is true. Declaring a difference between the treatment groups statistically significant when it is really due to chance or random variation. Type 2 error: failure to reject the null hypothesis when it is false. Failing to find a difference between the treatment groups (independent variable) when there really is a difference (relationship between them.