Quasi-experimental and developmental research designs

1. Quasi-experimental and developmental research designs

2. Outline Quasi-experiments Distinguishing characteristics Validity Developmental designs Cross-sectional Longitudinal

3. Quasi-experiments Purpose The goal of a quasi-experiment is to show a causal relationship between two variables Used in situations in which it is difficult or impossible to conduct an experiment An unremoveable confound limits causal conclusions that can be made

4. Quasi-experiments Distinguishing characteristics IV is manipulated DV is measured There is no random assignment a nonmanipulated variable (participant or time) defines the groups or conditions being compared

5. Quasi-experiments Distinguishing characteristics There is no random assignment a nonmanipulated variable (participant or time) defines the groups or conditions being compared participant variable: e.g., male, female time variable: e.g., pre-test, post-test

6. Example of quasi-experiment Dr. Rodin is interested in determining the effects of personal control on well-being of nursing home residents. All residents are given a plant. Residents on one floor are told that it is their responsibility to care for the plant. Residents on another floor are told to that the nursing staff will care for the plant. After two months, participation in activities and scores on well-being questionnaires are measured. What is the IV? What are the levels? What is the DV? Why is this a quasi-experiment? What is the confound?

7. Threats to internal validity History Maturation Assignment bias Selective attrition/mortality Testing All threats are potential confounds

8. Threats to internal validity Illustrative example: Examine effectiveness of weight loss program for overweight preteens 40 participants interviewed and weighed before program and 6 months later Eat special foods for 6 months Results: Lower weight at end of 6 months Did the special foods cause the weight loss?

9. Threats to internal validity History (time variable) Events outside context of study can influence study results E.g., media coverage

10. Threats to internal validity Maturation (time variable) Changes due to natural development can influence the results E.g., baby fat

11. Threats to internal validity Assignment bias (participant variable) Method of assigning participants to conditions produces groups with different characteristics E.g., pregnancy prevention program at two different high schools

12. Threats to internal validity Selective attrition/mortality (time variable) Participants who drop out of a study have different characteristics than participants who remain in a study E.g., depressed vs. nondepressed

13. Threats to internal validity Testing (time variable) Testing changes behavior on the DV E.g., initial interview and weight assessment

14. Threats to external validity Selection (participant variable) Who is included in the study e.g., practical or convenient samples may not be representative of the population of interest

15. Developmental research designs Purpose Study changes in behavior that relate to age

16. Developmental research designs Cross-sectional design Compare groups of different ages all at the same time Assesses age differences Longitudinal design Study the same group of individuals at two or more points in time Assesses age changes

17. A researcher is interested in whether intellectual ability changes with age. She gives an IQ test to a sample of 55-year-olds, 65-year-olds, and 75-year-olds. She finds that the 75-year-olds score lower than the other two age groups. She concludes that intellectual ability declines with age. A researcher is interested in whether intellectual ability changes with age. He tests a sample of 55-year-olds on an IQ test. He then tests this same group of people 10 and 20 years later. He finds very little decline in IQ score with age. He concludes that intellectual ability does not change with age. Cross-sectional vs longitudinal design

18. Cross-sectional vs longitudinal design

19. Cross-sectional design

20. Cross-sectional design Strengths Immediate results Inexpensive Can study many age groups at one time Useful first step in determining possible age differences in behavior

21. Cross-sectional design Weakness Age and cohort confound Can�t distinguish between an age effect and a cohort effect Age effect Age differences due to underlying processes (biological, social, or psychological) Cohort effect Age differences due to experiences unique to a particular generation

22. Longitudinal design

23. Longitudinal design Strengths Can measure age changes Each person acts as his/her own control

24. Longitudinal design Weaknesses Age and time-of-measurement confound Age effect Time-of-measurement effect (history) Differences due to social, environmental, historical, or other events at the time the data are obtained Selective attrition/mortality People who complete the study are not the same as the people who drop out Testing and instrumentation Practice effects Can�t change instruments over time Time-consuming and expensive

25. Cut-off design One way of separating the influences of age and experience E.g.: Question: Are children's improvements in mathematical ability more a function of age (cognitive development), or of educational experience? Take advantage of the fact that children starting school range in age about one year. Can compare performance for: same grade, different age same age, different grade