Some Potential Confounds: All Experimental & Quasi-Experimental Designs (QEDs) 1. Experimenter Bias (i.e., E expectancy effects)

Transcription

1 Some Potential Confounds: All Experimental & Quasi-Experimental Designs (QEDs) 1. Experimenter Bias (i.e., E expectancy effects) 2. Demand Characteristics 3. Hawthorne Effect 4. Uncontrolled Extraneous Variables 5. Treatment Confounds 6. Attrition (i.e., Subject Loss)

2 1. History 2. Maturation 3. Testing QEDs with One Group Some Additional Potential Confounds 4. Instrumentation 5. Statistical Regression to the Mean

3 Shape Up! Dr. Adams believes that enhancing people's physical fitness will increase their self-esteem. She conducts a study. In week one of spring quarter, 300 Psych 101 students complete a psychological test that measures self-esteem. Based on their scores, Dr. Adams asks the 25 students who have the lowest self-esteem scores whether they would participate in an 8-week study on "Physical Fitness". (Students receive extra credit for participation.). Most students (20) say "yes". Their physical fitness is measured and then, for 8 weeks, this group meets twice a week at the IMA. After stretching, they do aerobic activities for an hour (e.g., treadmill), and then play a team sport (e.g., volleyball, basketball). Participants' physical fitness and self-esteem are measured again at the end of the program. The data analysis reveals a significant mean increase both in participants' fitness and self-esteem. Should we conclude from this study that increased physical fitness caused an increase participants' self-esteem?

4 1. What are the independent and dependent variables? 2. Is this a true experiment or a quasi-experiment? Why? 3. What type of experimental or QED design was used? 4. Are there any potential confounding factors? If so, what are they? a. Possible confounds from the "all experiments/qeds" list? b. Possible confounds from the "one-group QEDs" list? 5. How can the design of this study be improved? What confounds would your improved design eliminate or at least reduce?

5 Nightmares A week ago, Dr. Tamuri and his large research team finished conducting a sleep survey using a randomly selected sample of 200 adults in the Los Angeles area. Each adult was interviewed by telephone, and it only took 3 days to collect the data from all participants. Today, by chance, a massive earthquake hits the L.A. area. Dr. Tamuri receives permission from his university's Institutional Review Board to re-interview the participants in order to find out how people's sleep changes following a major traumatic event. Starting a week after the earthquake, the same participants are interviewed again (just once each) over a period of two or three days. Of the original 200 participants, 120 agree to complete the interview again. Among these 120, the findings reveal many statistically significant changes. For example, after the earthquake, the frequency of participants' nightmares increased and, overall, they reported having more trouble falling asleep after turning in for the night.

6 Back to School Before the start of a school year, a company donates 100 computers to Pine Elementary School with the hope that this will improve students' learning. Prior to this, the school which is located in a lightly populated and economically impoverished region did not have student computers. Dr. Lopez, an educational psychologist, is asked to assess whether the computers produce an educational benefit. She develops a set of math and verbal tests and administers them to 5 th and 6 th graders at Pine at the start and end of the school year. She also gets permission from Starbird Elementary School to the give the same tests to its 5 th and 6 th graders at the start and end of the school year. Starbird is the closest school to Pine, although it is still 40 miles away and located in a region that consists mostly of middle-income families. The results show that the children at Pine, despite having computers, showed overall math and verbal gains similar to those at Starbird. Should we conclude that the computers failed to improve students' learning?

7 Definitions history: events to which participants are exposed, independent of their participation in a study. maturation: ways in which people naturally change over time, independent of their participation in a study. testing: the influence that taking a test on one occasion has on a person s test score on a later occasion. instrumentation: changes that occur in a measuring instrument during the course of data collection regression to the mean: statistically, when a group is selected because of their above or below average scores on a measure, the group s average score on a retest will regress toward the population mean. attrition: a loss of participants from the study.