Non-Experimental designsPsych 231: Research Methods in Psychology

Exam 2It was a difficult exam (I have adjusted scores by 5%, an error and a bad question were tossed out)Mean = 70.6Max = 100Min = 47.5

Exam 2Most common area of errors between/within manipulationsinteractions/main effectsvalidity/reliability

If youd like to go over your exam, contact me and well set up a time to go through it

Estimating your current gradeHow do I estimate my current grade?Take your current score for each assignment type (or average) and multiply it by the percentage that it is worth

Example:Ex 1: 87 87 * 15% = 13.05Ex 2: 72 72 * 15% = 10.80JS 1: 89 89 * 5% = 4.45CPdft1: 92 92 * 5% = 4.60LabEx*: 100, 100, 85, 0, 79 avg = 72.8 * 15% = 10.92GrpP*: 100, 95, 100 avg = 98.3 * 15% = 14.75 70% 58.57

So estimate of current grade = 58.57/70 = 83.7% which is a B* doesnt include participation grade, which will be weighted differently

Non-Experimental designsSometimes you just cant perform a fully controlled experimentBecause of the issue of interest Limited resources (not enough subjects, observations are too costly, etc). SurveysCorrelationalQuasi-ExperimentsDevelopmental designsSmall-N designs This does NOT imply that they are bad designsJust remember the advantages and disadvantages of each

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.?ObsObsObsObsObsObsQuasi-experimentsTime series designsBasic method: Observe a single group multiple times prior to and after a treatmenttreatment

Quasi-experimentsA variation of basic time series designAddition of a nonequivalent no-treatment control group time seriesTime series designs

Developmental designsUsed to study changes in behavior that occur as a function of age changesAge typically serves as a quasi-independent variableThree major typesCross-sectional LongitudinalCohort-sequential

Developmental designsCross-sectional designGroups are pre-defined on the basis of a pre-existing variable Study groups of individuals of different ages at the same timeUse age to assign participants to groupAge is subject variable treated as a between-subjects variable

Cross-sectional designDevelopmental designsAdvantages:Can gather data about different groups (i.e., ages) at the same timeParticipants are not required to commit for an extended period of time

Cross-sectional designDevelopmental designs

Longitudinal designDevelopmental designsFollow the same individual or group over timeAge is treated as a within-subjects variableRather than comparing groups, the same individuals are compared to themselves at different times Changes in dependent variable likely to reflect changes due to aging processChanges in performance are compared on an individual basis and overall

Longitudinal DesignsExampleWisconsin Longitudinal Study (WLS)Began in 1957 and is still on-going (50 years)10,317 men and women who graduated from Wisconsin high schools in 1957Originally studied plans for college after graduationNow it can be used as a test of aging and maturation

Longitudinal designDevelopmental designsAdvantages:Can see developmental changes clearlyCan measure differences within individualsAvoid some cohort effects (participants are all from same generation, so changes are more likely to be due to aging)

Longitudinal designDevelopmental designsDisadvantagesCan be very time-consumingCan have cross-generational effects:Conclusions based on members of one generation may not apply to other generationsNumerous threats to internal validity:Attrition/mortalityHistory Practice effectsImproved performance over multiple tests may be due to practice taking the testCannot determine causality

Developmental designsMeasure groups of participants as they ageExample: measure a group of 5 year olds, then the same group 10 years later, as well as another group of 5 year oldsAge is both between and within subjects variable Combines elements of cross-sectional and longitudinal designsAddresses some of the concerns raised by other designsFor example, allows to evaluate the contribution of cohort effectsCohort-sequential design

Developmental designsCohort-sequential designTime of measurement197519851995Cohort ACohort BCohort C1970s1980s1990s

Developmental designsAdvantages:Get more informationCan track developmental changes to individualsCan compare different ages at a single timeCan measure generation effectLess time-consuming than longitudinal (maybe)Disadvantages:Still time-consumingNeed lots of groups of participantsStill cannot make causal claimsCohort-sequential design

Small N designsWhat are they?Historically, these were the typical kind of design used until 1920s 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 designsOne or a few participantsData are typically not analyzed statistically; rather rely on visual interpretation of the dataObservations begin in the absence of treatment (BASELINE)Then treatment is implemented and changes in frequency, magnitude, or intensity of behavior are recorded

Small N designsBaseline experiments the basic idea is to show:when the IV occurs, you get the effectwhen the IV doesnt occur, you dont get the effect (reversibility)Before introducing treatment (IV), baseline needs to be stableMeasure level and trend

Small N designsLevel how frequent (how intense) is behavior?Are all the data points high or low?Trend does behavior seem to increase (or decrease)Are data points flat or on a slope?

ABA designABA 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.)

A

B

A

Steady state (baseline) | Transition steady state | Reversibility

Small N designsAdvantagesFocus on individual performance, not fooled by group averaging effectsFocus is on big effects (small effects typically cant be seen without using large groups)Avoid some ethical problems e.g., with non-treatmentsAllows 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 designsDisadvantagesEffects may be small relative to variability of situation so NEED more observationSome effects are by definition between subjectsTreatment leads to a lasting change, so you dont get reversalsDifficult to determine how generalizable the effects are

Small N designsSome 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 placeNeed to look at the data at the level of the individual

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