Psyc 242 Lecture 7.2 Repeated Measures Design PDF

Summary

This lecture covers repeated measures designs in psychology, focusing on methodological issues and advantages/disadvantages. It explores different types of designs (complete versus incomplete), discusses counterbalancing techniques (Latin square design), and explains data analysis methods. The lecture provides practical examples and exercises.

Full Transcript

Psyc.242 Lecture 7.2 Methodological issues with within-subject design Advantage of repeated measures designs  No need to balance individual differences across conditions of experiment  Fewer participants needed  Convenient and efficient  More sensitive design Sensitivity  A sen...

Psyc.242 Lecture 7.2 Methodological issues with within-subject design Advantage of repeated measures designs  No need to balance individual differences across conditions of experiment  Fewer participants needed  Convenient and efficient  More sensitive design Sensitivity  A sensitive experiment  Can detect effects of IV even when IV has small effect  “Error variance” is reduced  Same people participate in each condition  Variability due to individual differences eliminated Disadvantage of repeated measures design: practice effect  People change as they are tested repeatedly.  Performance may improve over time.  People may become bored or tired as number of “trials” increases.  Practice effects become a potential confounding variable if not controlled. Practice Effects, example  Suppose a researcher compares two different study methods, A and B.  Condition A: Participants use a highlighter while reading a text, then take a test on the material.  Condition B: Participants read a text and make up sample test questions and answers, then take a test on the material.  Suppose  All participants first experience Condition A and then Condition B  Results: test score A > test score B  Potential Confounding variable: participants are bored when they work on Condition B  What if the result is A < B? Solution to practice effect  Balance practice effects across conditions.  Counterbalance the order of conditions  Half of the participants do Condition A, then B  The remaining participants do Condition B, then A  Random Selection presentation.  Randomly select the conditions and present to participants.  Can be applied when there are many conditions Techniques with Counterbalancing  Complete Balance design  Incomplete Balance design  All possible order  Select order (Latin square design) Complete Balance Design  Balance practice effects within each participant.  Each participant experiences each order of condition, at least once – everyone will experience each condition several times.  Each participant forms a “complete experiment.”  Use different orders each time  Use when each condition is brief, and the number of conditions are not too many  e.g., simple judgments about stimuli For example  1. There are two conditions: A and B. Participants will go though the conditions many times, in different orders. Such as: ABBAABBAABABBABA, etc..  2. There are three conditions: ABC. Participants will go through each order of conditions at least once. Such as: ABC, ACB, BAC, BCA, CAB, CBA Incomplete Balance Design  Each participant experiences each condition once (not each order of conditions).  Balance practice effects across participants.  General rule for balancing practice effects  Each condition must appear in each ordinal position (1st, 2nd, 3rd, etc.) equally often. Incomplete Balance Design: all possible order  All possible orders  Use with limited number of IV conditions (3 or 4)  3 conditions (A, B, C) → 6 possible orders  Randomly assign participants to one of the six orders  4 conditions (A, B, C, D) → 24 possible orders  Randomly assign participants to one of the 24 orders.  Need at least 1 participant randomly assigned to each order Incomplete Balance Design: select order (Latin square)  Selected orders  Select particular orders of conditions to balance practice effects  Each condition appears in each ordinal position exactly once.  Randomly assign each participant to one of the orders of conditions.  This is applicable to situations when the conditions are longer or more complicated to execute Latin square example: for conditions ABC (remember for three conditions, the total possible combination is 6) Ordinal position Participants 1st 2nd 3rd #1 A B C #2 B C A #3 C A B Latin square design example  https://www.youtube.com/watch?v=2FQZnybGrTY &t=7s In class exercise: can you create a Latin Square design for 4 participants over 4 conditions ABCD? Order Participants 1 2 3 4 #1 #2 #3 #4 Comparing the different designs  Complete Balance design vs. Incomplete Balance design ?  Counterbalance design vs. random selection presentation design? Complete vs. Incomplete balance design  Here, “Complete” means whether each participant goes through ALL conditions and ALL orders of the conditions.  In a Complete Balance design, the participant goes through ALL conditions and ALL orders of the conditions.  In an Incomplete Balance design, the participant still goes through ALL conditions, but just one order  All possible order vs. select order: whether all possible orders of the conditions will be administered When to use the two designs?  Complete balance design: applicable when the conditions are very brief  Incomplete balance design: applicable when the conditions are longer or more complicated to administer. (Especially the Select order design – conditions might be very complicated)  Number of conditions should not exceed 4 Counterbalance vs. Random selection presentation  In Counterbalance design, each participants goes through ALL conditions  In Random Selection presentation, each participant goes through some randomly chosen conditions – NOT necessarily ALL conditions  When there are more than 5 conditions, usually only the Random selection presentation design is appropriate.  When there are 4 or less conditions, either a counterbalance design or a random selection presentation can be used. Data analysis  Repeated measures ANOVA (the conditions are entered as the “repeated measure”) Hypothesis of repeated measure ANOVA  Null hypothesis: all of the conditions are equal  Research hypothesis: at least one of the conditions are different from the others 23 Results reported by a graph  Note that the columns 30 show the difference S 25 among the conditions, e T 20 NOT the order of testing a i 15 r m c e 10 h 5 0 10 15 20 Number of Distracters Assignments  Connect_Chapter 7  Quiz 7 available on Blackboard now.

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