Repeated Measures Designs Different form Independent Groups Design Independent groups Compare groups of participants (experimental vs control) Repeated measures Do not assign participants to groups – there is only one group Compare data across conditions (experimental vs control) Each individual participates in each condition of the experiment. after participant finishes one condition they then experiences the next condition of the experiment dependent variable measured for each condition hence, “repeated measures” also called a “within-subject” design, because the entire experiment is conducted “within” each subject.

Examples of Independent and Repeated measures design Groups   Group 1 Group 2 Participant score 1 5.14 13 8.96 2 0.62 14 3.99 3 7.35 15 6.07 4 7.67 16 1.96 5 3.86 17 0.17 6 2.90 18 6.31 7 1.88 19 9.86 8 8.94 20 1.49 9 2.03 21 1.08 10 5.94 22 1.14 11 6.48 23 4.74 12 4.62 24 3.31 Repeated Measures   Cond1 Cond2 Participant score 1 3.04 9.92 2 2.95 1.44 3 3.08 9.47 4 5.41 2.91 5 1.61 8.97 6 5.30 8.75 7 5.29 5.77 8 5.35 1.11 9 5.87 10 7.89 1.76 11 2.66 1.37 12 2.55 3.01

Repeated Measures Designs Advantages of using a repeated measures design (1) conduct an experiment when few participants are available when studying a special population although commonly used when participants are readily available (2) conduct the experiment more efficiently When procedures are short ; show a picture (3) increase the sensitivity of the experiment easier to detect difference in independent variable see next slide (4) study changes in participants’ behavior over time learning research with before manipulation, after manipulation compare across stimuli such as photographs

Sensitivity A “sensitive” experiment is one that can detect the effect of the independent variable, even when that effect is small because “error variation” is reduced From individual differences From variation in procedure In repeated measures design the same people participate in each condition variability in responses due to different people is reduced (compared to an independent groups design).

Box 7.1 Repeated measurements and the Repeated Measures Design Repeated measurements is collecting data from the same person more then once longitudinal survey design when survey researchers administer surveys more than once to the same people test–retest reliability when researchers investigate consistency to establish the reliability of a measure Repeated measures design experiment researchers manipulate an independent variable to compare measures of participants’ behavior in two or more conditions.

The Role of Practice Effects in Repeated Measures Designs FIGURE 7.1 There are both positive and negative effects of practicing a new skill. Repeating the same experience can lead to improvement, but it also can lead to fatigue, a decrease in motivation, and even boredom.

The Role of Practice Effects in Repeated Measures Designs The main disadvantage of repeated measures designs is practice effects. Practice effects arise because people change as they are repeatedly tested. they may get better with practice they may become tired or bored.

Practice effects become a confounding variable if not controlled. Example: Suppose a researcher compares two different study methods, A and B. Condition A: Participants read a text passage and use a highlighter to indicate the important points. Participants then take a test of this material. Condition B: Participants read 10 pages of similar text, and make up sample test questions and answers. Participants then take a test of this material. Participants experience Condition A first then Condition B Participants have higher test score on B Can we conclude that condition B is a better study method than condition A?

Balancing Practice Effects Practice effects must be balanced, or averaged, across the conditions of the experiment. Counterbalancing the order of the conditions makes sure that the practice effects are distributed equally across the conditions of the experiment. Counterbalancing the study conditions: half of the participants do Condition A first, then Condition B, the remaining participants do Condition B first, then Condition A. In this way, both Conditions A and B have the same amount of practice effects. Practice effects can’t be eliminated, but they can be balanced, or averaged, across the conditions of an experiment.

Balancing Practice Effects There are two types of Repeated Measures Designs: Complete and Incomplete. Based on type of counterbalancing Each repeated measures design uses different procedures for balancing practice effects across the conditions of the experiment. Complete balances conditions for each participant Incomplete balances conditions across participants

Counter Balancing Repeated measures design   Cond1 Cond2 Participant score 1 3.04 9.92 2 2.95 1.44 3 3.08 9.47 4 5.41 2.91 5 1.61 8.97 6 5.30 8.75 7 5.29 5.77 8 5.35 1.11 9 5.87 10 7.89 1.76 11 2.66 1.37 12 2.55 3.01 Repeated Measures   Participant Cond1 Cond2 1 Con Exp 2 3 4 5 6 7 8 9 10 11 12

V basketball team _______ V cat _______ L chair _______ L beetle ________ V chair ________ V ant ________ L basketball team ________ L cat ________ V spider ________ V monkey ________ L centipede ________ L desk ________ V caterpillar ________ V table ________ L hammock ________ L tripod ________ V butterfly ________ V man ________ L husky ________ L hamster ________ V tarantula ________ V parakeet ________ L sofa ________ L millipede ________ V chimpanzee ________ V milking stool ________ L snail ________ 1A V beetle _______ L ant _______ L spider _______ V basketball team _______ V cat _______ L chair _______ L table _______ V husky _______ V desk _______ L caterpillar _______ L monkey _______ V hammock _______ V centipede _______ L butterfly _______ L parakeet _______ V sofa _______ V hamster _______ L tarantula _______ L man _______ V tripod _______ V millipede _______ L chimpanzee _______ L milking stool _______ V snail _______

Complete Design Practice effects are balanced within each participant in the complete design. Each participant experiences each condition of the experiment several times (or hundreds of times) using different orders each time. A complete repeated measures design is most often used when each condition is brief (e.g., simple judgments about stimuli).

Complete Design (continued) Two methods for generating orders of the conditions in the complete design are block randomization and ABBA counterbalancing. Block randomization A “block” represents all conditions of the experiment (e.g., 4 conditions, A B C D). A random order of the block is generated (e.g., ACBD) Thus, a participant would first do condition A, then C, then B, then D. A new random order would be generated for each time the participant completes the conditions of the experiment.

First impressions, rate photographs of people on a personality trait such as trustworthiness or likability

photos were presented for either 100 ms, 500 ms, and 1000 ms participants rated 22 faces at each of the three exposure times for a total of 66 face ratings four walk randomization the 66 trials are separated into 22 blocks of three trials each within each block the three conditions are in random order

Complete Design (continued) Block randomization balances practice effects only when the conditions are presented many times. Many administrations of the conditions are needed to balance practice effects across the conditions of the experiment. not useful when the conditions are presented only a few times to each participant. A different method ABBA Counterbalancing is needed when conditions are presented only a few times to each participant.

Complete Design (continued) ABBA Counterbalancing For each participant present the conditions of the experiment in one random sequence with two conditions A, B then opposite sequence B, A with three conditions A, B, C followed by C, B, A with four conditions A, B, C, D followed by D, C, B, A If the conditions are presented again, generate another random order of the conditions followed by the opposite sequence.

Complete Design (continued) ABBA counterbalancing balances practice effects only when practice effects are “linear.” Linear practice effects occur when participants change in the same way following each presentation of a condition. Nonlinear practice effects occur when participants change dramatically following the administration of a condition. This can occur when participants experience an insight (“aha”) regarding how to complete the experimental task during the course of the experiment. They are likely to use this new insight in subsequent conditions.

Example of linear practice effects: Suppose participants gain “one unit” of practice with each administration of a condition (there are zero practice effects with the first administration): Practice effects are balanced because the total practice effects is +5 for each condition: Mod: 0 + 5 Fast: 1 + 4 Slow: 2 + 3 Example of nonlinear practice effects: Suppose a participant figures out a method for completing the task on the 2nd trial and then uses this new method for subsequent administrations: The total practice effects are not the same for each condition: Mod: 0 + 6 = 6 Fast: 6 + 6 = 12 Slow: 6 + 6 = 12

Complete Design (continued) nonlinear practice effects This represents a confounding because differences in scores on the dependent variable may not be caused by the independent variable (conditions A, B, C) but to different amounts of practice effects associated with each condition. ABBA counterbalancing should not be used when practice effects are likely to vary or change during the course of the experiment (i.e., when practice effects are nonlinear). Use block randomization.

Complete Design (continued) ABBA counterbalancing should not be used when anticipation effects can occur. Anticipation effects occur when participants develop expectations about which condition will appear next in a sequence. Participants’ responses may become influenced by their expectations rather than by the conditions of the independent variable. If anticipation effects are likely, use block randomization.