Experiment Basics: Designs Psych 231: Research Methods in Psychology

Between vs. Within Subjects Designs Within-subjects designs All participants participate in all of the conditions of the experiment. participants Colored words BW words Test participants Colored words BW words Test Between-subjects designs Each participant participates in one and only one condition of the experiment.

Between subjects designs Advantages Independence of groups (levels of the IV) Disadvantages Individual differences between the people in the groups Excessive variability Non-Equivalent groups participants Colored words BW words Test Clock Chair Cab Clock Chair Cab Clock Chair Cab Clock Chair Cab

Within-subjects designs All participants participate in all of the conditions of the experiment. participants Colored words BW words Test participants Colored words BW words Test Between-subjects designs Each participant participates in one and only one condition of the experiment. Between vs. Within Subjects Designs

Within subjects designs Advantages: Don’t have to worry about individual differences Fewer participants are required Disadvantages Range effects Order effects: Carry-over effects Progressive error

Within subjects designs Range effects – (context effects) can cause a problem The range of values for your levels may impact performance (typically best performance in middle of range). Since all the participants get the full range of possible values, they may “adapt” their performance (the DV) to this range.

test Condition 2Condition 1 test Order effects Carry-over effects Transfer between conditions is possible Effects may persist from one condition into another e.g. Alcohol vs no alcohol experiment on the effects on hand-eye coordination. Hard to know how long the effects of alcohol may persist. How long do we wait for the effects to wear off?

Order effects Progressive error Practice effects – improvement due to repeated practice Fatigue effects – performance deteriorates as participants get bored, tired, distracted

Dealing with order effects Counterbalancing is probably necessary This is used to control for “order effects” Ideally, use every possible order (n!, e.g., AB = 2! = 2 orders; ABC = 3! = 6 orders, ABCD = 4! = 24 orders, etc ).n! All counterbalancing assumes Symmetrical Transfer The assumption that AB and BA have reverse effects and thus cancel out in a counterbalanced design

Counterbalancing Simple case Two conditions A & B Two counterbalanced orders: AB BA participants Colored words BW words Test Colored words BW words Test Note: this becomes a factorial design

Counterbalancing Often it is not practical to use every possible ordering Partial counterbalancing Latin square designs – a form of partial counterbalancing, so that each group of trials occur in each position an equal number of times

Partial counterbalancing Example: consider four conditions Recall: ABCD = 4! = 24 possible orders 1) Unbalanced Latin square: each condition appears in each position (4 orders) DCBA ADCB BADC CBAD Order 1 Order 2 Order 3 Order 4

Partial counterbalancing 2) Balanced Latin square: each condition appears before and after all others (8 orders) ABDC BCAD CDBA DACB ABCD BCDA CDAB DABC Example: consider four conditions Recall: ABCD = 4! = 24 possible orders

Mixed factorial designs Mixed designs Treat some factors as within-subjects (participants get all levels of that factor) and others as between-subjects (each level of this factor gets a different group of participants). This only works with factorial (multi-factor) designs

Describing your design You need to describe: How many factors How many levels of each factor Whether the factors are within or between groups e.g., 2 (shallow/deep processing) x 2 (abstract/concrete) mixed groups factorial design

Describing your results You need to report: The main effects Depth of processing Word Type The interaction For each report the means (in the case of the main effects, report the marginal means) and the statistical outcomes (the ANOVA results) Depth of processing: F(1,226) = 98.6, p < Word type: F(1,226) = 34.0, p < Interaction: F(1,226) = 5.0, p < Do this with within complete sentences and paragraphs Feel free to supplement the text with a graph if it helps with clarity. abstractconcrete Shallow Deep

Exam 2 Topics (Chpts 4, 6, 11) Relevant stuff from Ex1 Variables types, operationalizing IV: methods of manipulation, getting the right range DV: measurement Validity and Reliability Sampling Control Experimental Designs Vocabulary Single factor designs Between & Within Factorial designs