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What are the developmental research designs?
- cross-sectional design
- longitudinal design
- (cohort) sequential design
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Cross-Sectional Design
- children in different age groups measured at same time
- age = between subjects quasi-experimental IV
- compare memory of 3 groups: 7, 9, and 11 year olds
- Pros: relatively fast and inexpensive to conduct
- Cons: cohort effects: effect of being born in one particular historical context
- cohort: group of people born at same time, exposed to similar culture, historical contexts while growing up (i.e. Baby Boomers)
- no info about development of individuals
- only look at each person one time
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Longitudinal Design
- same group of participants measured at different ages
- age = within subjects quasi-experimental IV
- measured at each age
- compare memory of 1 groups of kids when they are 7, 9, and 11
- Pros: can study development of individual participants, can examine relationships between early and later behavior, no concern about cohort effects
- Cons: cross-generational problem: hard to generalize to groups not part of that cohort; expensive, time-consuming; initial questions, measures may become uninteresting/inadequate; attrition/mortality; testing effects
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(Cohort) Sequential Design
- combines cross-sectional and longitudinal approaches: follows 2 or more cohorts for short longitudinal period
- age = BOTH between AND within subjects IV
- ex: pick 3 groups of kids at beginning, test same groups 2 years later, test same groups 4 years later
- can compare to each other and themselves
- can separate effects due to development, cohort effects, and reduce cross generational problem
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Small n Designs
used to study one or a small sample of participants
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Case Studies
- in depth description, analysis of one person
- often used in clinical settings
- Advantages: provide new hypotheses, chance to test out new treatments, chance to study rare phenomena
- Disadvantages: no causal statements, observer bias may make data less accurate, low external validity
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Baseline Designs
- start by getting a baseline: measuring behavior before treatment to see what typical behavior looks like
- manipulation then given while behavior continues to be measured to see if there's a change
- changes in behavior indicate manipulation may have effect
- problem: can't be sure manipulation causes behavior change
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Reversal Designs
- involve systematically introducing and withdrawing manipulation
- A = without manipulation - getting your baseline
- B = with manipulation - introducing treatment or change of some sort
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ABA Design
- behavior measured to get baseline (A)
- manipulation given while behavior measured again (B)
- manipulation withdrawn and behavior measured final time (A)
- if manipulation caused changes in behavior it's expected that taking it away will return behavior to baseline levels
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Problems with ABA Designs
- still not definite proof that treatment worked or did not work
- not ethical to take away successful treatment
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ABAB Design
- just like ABA, but at the end manipulation is reintroduced
- adds evidence that manipulation is responsible for change
- lets patients continue with successful treatments
- can allow researcher to compare two treatments
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Multiple Baseline Designs
- several different behaviors measured at same time to see if they are affected by manipulation
- so, multiple DVs
- if only behavior of interest changes when manipulation is introduced, gives evidence that manipulation is effective for that specific behavior
- can also measure same behavior in different situations
- offers the strongest test of hypothesis
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