- Slides: 30
Non-Experimental designs: Developmental designs & Small-N designs Psych 231: Research Methods in Psychology
Announcements n Journal Summary 2 due date moved back a week (instead of this week, they are due next week)
Error in survey research n Sampling error - Are there differences in your sample compared to the population as a whole? n Response rate • What proportion of the sample actually responded to the survey? • Hidden costs here - what can you do to increase response rates • Non-response error (bias) • Is there something special about the data that you’re missing? From the people who didn’t respond n Measurement error (validity and reliability) n Are your questions really measuring what you want them to?
Quasi-experiments n What are they? n Almost “true” experiments, but with an inherent confounding variable • Design includes a quasi-independent variable • Examples • An event occurs that the experimenter doesn’t manipulate • Interested in subject variables • Time is used as a variable
Quasi-experiments n What are they? n n Almost “true” experiments, but with an inherent confounding variable Advantages n n Allows applied research when experiments not possible Threats to internal validity can (sometimes) be assessed
Quasi-experiments n What are they? n Almost “true” experiments, but with an inherent confounding variable § Disadvantages § Threats to internal validity may exist (which can not be addressed) § Be careful when making causal claims § Statistical analysis can be difficult § Most statistical analyses assume randomness
Quasi-experiments n What are they? n Almost “true” experiments, but with an inherent confounding variable n Common types n n n Nonequivalent control group designs Program evaluation Interrupted time series designs
Quasi-experiments n Nonequivalent control group designs n With pretest and posttest (most common) Non-Random Assignment Dependent Variable Measure Independent Variable Experimental group Dependent Variable Measure participants Measure Control group Measure – But remember that the results may be compromised because of the nonequivalent control group
Quasi-experiments Program evaluation n – Research on programs that is implemented to achieve some positive effect on a group of individuals. – – e. g. , does abstinence from sex program work in schools Steps in program evaluation: 1. 2. 3. 4. 5. Needs assessment - is there a problem? Program theory assessment - does program address the needs? Process evaluation - does it reach the target population? Is it being run correctly? Outcome evaluation - are the intended outcomes being realized? Efficiency assessment- was it “worth” it? The the benefits worth the costs?
Quasi-experiments n Time series designs n Basic method: Observe a single group multiple times prior to and after a treatment Obs Obs Obs § The pretest observations allow the researcher to look for pre-existing trends § The posttest observations allow the researcher to look for changes in the trends § Is it a temporary change, does it last, etc. ?
Quasi-experiments n Time series designs n A variation of basic time series design • Addition of a nonequivalent no-treatment control group time series Obs Obs Obs treatment Obs Obs Obs
Developmental designs n Used to study changes in behavior that occur as a function of age changes n n Age typically serves as a quasi-independent variable Three major types n n n Cross-sectional Longitudinal Cohort-sequential
Developmental designs n Cross-sectional design n Groups are pre-defined on the basis of a preexisting variable • Study groups of individuals of different ages at the same time • Use age to assign participants to group • Age is subject variable treated as a between-subjects variable
Developmental designs n Cross-sectional design n Advantages: • • Can gather data about different groups (i. e. , ages) at the same time Participants are not required to commit for an extended period of time
Developmental designs n Cross-sectional design n Disavantages: • Individuals are not followed over time • Cohort (or generation) effect: individuals of different ages may be inherently different due to factors in the environment • • Example: are 5 year old different from 13 year olds just because of age, or can factors present in their environment contribute to the differences? Cannot infer causality due to lack of control
Developmental designs n Longitudinal design n Follow the same individual or group over time • Age is treated as a within-subjects variable • • • Rather than comparing groups, the same individuals are compared to themselves at different times Repeated measurements over extended period of time Changes in dependent variable reflect changes due to aging process • Changes in performance are compared on an individual basis and overall
Developmental designs n Longitudinal design n Advantages: • Can see developmental changes clearly • Avoid some cohort effects (participants are all from same generation, so changes are more likely to be due to aging) • Can measure differences within individuals
Developmental designs n Longitudinal design n Disadvantages • Can be very time-consuming • Can have cross-generational effects: • Conclusions based on members of one generation may not apply to other generations • Numerous threats to internal validity: • Attrition/mortality • History • Practice effects • Improved performance over multiple tests may be due to practice taking the test • Cannot determine causality
Developmental designs n Cohort-sequential design n Measure groups of participants as they age • Example: measure a group of 5 year olds, then the same group 5 years later, as well as another group of 5 year olds n Age is both between and within subjects variable • Combines elements of cross-sectional and longitudinal designs • Addresses some of the concerns raised by other designs • For example, allows to evaluate the contribution of generation effects
Developmental designs n Cohort-sequential design n Advantages: • Can measure generation effect • Less time-consuming than longitudinal n Disadvantages: • Still time-consuming • Still cannot make causal claims
Small N designs n What are they? n n Historically, these were the typical kind of design used until 1920’s when there was a shift to using larger sample sizes Even today, in some sub-areas, using small N designs is common place • (e. g. , psychophysics, clinical settings, expertise, etc. )
Small N designs n n One or a few participants Data are not analyzed statistically; rather rely on visual interpretation of the data Observations begin in the absence of treatment (BASELINE) Then treatment is implemented and changes in frequency, magnitude, or intensity of behavior are recorded
Small N designs n Baseline experiments – the basic idea is to show: 1. when the IV occurs, you get the effect 2. when the IV doesn’t occur, you don’t get the effect (reversibility) § § Before introducing treatment (IV), baseline needs to be stable Measure level and trend
Small N designs n Level – how frequent (how intense) is behavior? n n Are all the data points high or low? Trend – does behavior seem to increase (or decrease) n Are data points “flat” or on a slope?
ABA design n ABA design (baseline, treatment, baseline) – The reversibility is necessary, otherwise something else may have caused the effect other than the IV (e. g. , history, maturation, etc. )
Small N designs n Advantages n n n Focus on individual performance, not fooled by group averaging effects Focus is on big effects (small effects typically can’t be seen without using large groups) Avoid some ethical problems – e. g. , with nontreatments Allows to look at unusual (and rare) types of subjects (e. g. , case studies of amnesics, experts vs. novices) Often used to supplement large N studies, with more observations on fewer subjects
Small N designs n Disadvantages n n Effects may be small relative to variability of situation so NEED more observation Some effects are by definition between subjects • Treatment leads to a lasting change, so you don’t get reversals n Difficult to determine how generalizable the effects are
Small N designs n n Some researchers have argued that Small N designs are the best way to go. The goal of psychology is to describe behavior of an individual Looking at data collapsed over groups “looks” in the wrong place Need to look at the data at the level of the individual
Next time n Statistics (Chapter 14)