Psychology 3450W: Experimental Psychology Fall, 2017 Professor Delamater

Experimental Designs Outline of Todays Lecture: Single Factor Designs Multi-factor (Factorial) Designs Labeling Scheme Main Effects & Interactions Data Patterns 2-Factor Designs 3-Factor Designs, etc Types of Factorial Designs Between Within Mixed Person x Environment (P x E)

Experimental Designs Single Factor Designs Between vs Within (as we discussed previously) 2 levels or > 2 levels (to assess potential non-linear effects Control Groups: Placebo Control – used in drug research to assess expectancy effects. (e.g., sugar pill vs drug & ADHD) Yoked Control – used to control the frequency and timing of a crucial event in the experiment, and to assess the importance of the “response-reward contingency” in behavioral research. (e.g., lollipop reward & social beh) Wait List Control – used in drug research when some subjects must wait before they can be exposed to the drug treatment. (e.g., new drug & Zika virus…)

Experimental Designs Single Factor Designs Statistical Analysis With 2 levels: ? With > 2 levels: ?

Experimental Designs Single Factor Designs Statistical Analysis With 2 levels: t test With > 2 levels: ANOVA (F test), with follow up post-hoc tests OR planned comparisons. These are used in order to determine exactly where the differences are.

Experimental Designs Single Factor Designs Statistical Analysis With 2 levels: t test With > 2 levels: ANOVA (F test), with follow up post-hoc tests OR planned comparisons. These are used in order to determine exactly where the differences are. Graphing Data DV always on Y axis, IV on X axis Discrete vs continuous variables – Bar/Line graphs

Experimental Designs Single Factor Designs Statistical Analysis With 2 levels: t test With > 2 levels: ANOVA (F test), with follow up post-hoc tests OR planned comparisons. These are used in order to determine exactly where the differences are. Graphing Data DV always on Y axis, IV on X axis Discrete vs continuous variables – Bar/Line graphs e.g., gender on x axis, RT on y axis – bar graph e.g., dose of caffeine on x axis, RT on y axis – line graph

Factorial Designs > 1 Factor Designs (i.e., Factorial Designs) These experimental designs are among the most powerful because they allow the investigator to concurrently assess in a single study the effects of multiple independent variables. I x J Factorial With 2 Factors (i.e., Independent variables) Simplest one has 2 levels of each: 2 x 2 Factorial design Could have many more levels than just 2: e.g., 3 x 5 design this requires 15 experimental conditions, etc.

Factorial Designs > 1 Factor Designs (i.e., Factorial Designs) These experimental designs are among the most powerful because they allow the investigator to concurrently assess in a single study the effects of multiple independent variables. I x J Factorial Labeling Scheme Factor ”J” J1 J2 I1 I2 Factor ”I”

Factorial Designs > 1 Factor Designs (i.e., Factorial Designs) These experimental designs are among the most powerful because they allow the investigator to concurrently assess in a single study the effects of multiple independent variables. I x J Factorial Labeling Scheme Factor ”J” J1 J2 I1 I2 I1J1 I1J2 Factor ”I” I2J1 I2J2

Factorial Designs > 1 Factor Designs (i.e., Factorial Designs) These experimental designs are among the most powerful because they allow the investigator to concurrently assess in a single study the effects of multiple independent variables. I x J Factorial Labeling Scheme Factor ”J” J1 J2 Stroop Study Factor I – Type of task (Color/Word naming) Factor J – Congruency (C/I) DV – RT Notice there are 4 conditions in the 2 x 2 factorial experiment. I1 I2 I1J1 I1J2 Factor ”I” I2J1 I2J2

Factorial Designs Main Effects & Interactions I1 Avg I2 Avg In this experimental design there are 3 basic questions of interest. These include assessing the “main effect” of Factor I, the “main effect” of Factor J, and the “I x J interaction.” Lets see what this means and how the design allows us to answer these questions. Main Effect of I: compare I1 vs I2 by collapsing across the two levels of J. I1J1 + I1J2 vs I2J1 + I2J2 I1 average vs I2 average Factor ”J” J1 J2 I1 I2 I1J1 I1J2 I1 Avg I2 Avg Factor ”I” I2J1 I2J2

Factorial Designs Main Effects & Interactions J1 Avg J2 Avg In this experimental design there are 3 basic questions of interest. These include assessing the “main effect” of Factor I, the “main effect” of Factor J, and the “I x J interaction.” Lets see what this means and how the design allows us to answer these questions. Factor ”J” J1 J2 Main Effect of J: compare J1 vs J2 by collapsing across the two levels of I. I1J1 + I2J1 vs I1J2 + I2J2 J1 average vs J2 average I1 I2 I1J1 I1J2 Factor ”I” I2J1 I2J2 J1 Avg J2 Avg

Factorial Designs Example: Effect of Gender and Other’s Opinions on Aesthetic Judgments Suppose we are interested in how people’s views of art at an art gallery might be influenced by other peoples opinions, and suppose we also wonder whether gender might play a role in this. We go to the art gallery and recruit several people to act as “stooges” who vocalize their like/dislike of different works of art. We then ask the views of nearby random people who can hear these comments…. Opinions of Others Like Dislike Main Effect of Gender: Do Females differ from Males in their aesthetic judgments? Compare Females to Males by collapsing across the two levels of the Opinions of Others IV. Female Like Female Dislike Female Avg Female Male Gender Male Like Male Dislike Male Avg

Factorial Designs Example: Effect of Gender and Other’s Opinions on Aesthetic Judgments Suppose we are interested in how people’s views of art at an art gallery might be influenced by other peoples opinions, and suppose we also wonder whether gender might play a role in this. We go to the art gallery and recruit several people to act as “stooges” who vocalize their like/dislike of different works of art. We then ask the views of nearby random people who can hear these comments…. Opinions of Others Like Dislike Main Effect of Opinions of Others: Are people’s aesthetic judgments affected by the opinions of others? Compare scores when people are exposed to others who like vs dislike the art, but collapse across the Gender IV. Female Like Female Dislike Female Male Gender Male Like Male Dislike “Like” Avg “Dislike” Avg

Factorial Designs I x J Interaction We now wish to know, in addition to the main effect questions, whether the two variables of interest might interact in their impact on the dependent variable. We can ask this question in each of two ways: Is the effect of variable I dependent upon the level of J? OR, Is the effect of variable J dependent upon the level of I? Factor ”J” J1 J2 Interaction Questions: (I1J1 – I2J1) vs (I1J2 – I2J2) OR (I1J1 – I1J2) vs (I2J1 – I2J2) I1 I2 I1J1 I1J2 Factor ”I” I2J1 I2J2 Case 1: Does a difference between I1 and I2 depend upon the level of J? Case 2: Does a difference between J1 and J2 depend upon the level of I?

Factorial Designs Example: Effect of Gender and Other’s Opinions on Aesthetic Judgments Suppose we are interested in how people’s views of art at an art gallery might be influenced by other peoples opinions, and suppose we also wonder whether gender might play a role in this. We go to the art gallery and recruit several people to act as “stooges” who vocalize their like/dislike of different works of art. We then ask the views of nearby random people who can hear these comments…. Gender x Opinions of Others Interaction Question: Is the effect of the Opinions of Others variable dependent upon Gender? Compare scores when people are exposed to others who like vs dislike the art, but Compare this effect in females vs males. Opinions of Others Like Dislike Female Like Female Dislike Female Male Gender Male Like Male Dislike

Factorial Designs Example: Effect of Gender and Other’s Opinions on Aesthetic Judgments Suppose we are interested in how people’s views of art at an art gallery might be influenced by other peoples opinions, and suppose we also wonder whether gender might play a role in this. We go to the art gallery and recruit several people to act as “stooges” who vocalize their like/dislike of different works of art. We then ask the views of nearby random people who can hear these comments…. Gender x Opinions of Others Interaction Question: Is the effect of the Opinions of Others variable dependent upon Gender? Compare scores when people are exposed to others who like vs dislike the art, but Compare this effect in females vs males.

Factorial Designs Example: Effect of Gender and Other’s Opinions on Aesthetic Judgments Suppose we are interested in how people’s views of art at an art gallery might be influenced by other peoples opinions, and suppose we also wonder whether gender might play a role in this. We go to the art gallery and recruit several people to act as “stooges” who vocalize their like/dislike of different works of art. We then ask the views of nearby random people who can hear these comments…. Gender x Opinions of Others Interaction Question: Is the effect of the Opinions of Others variable dependent upon Gender? Compare scores when people are exposed to others who like vs dislike the art, but Compare this effect in females vs males.

Factorial Designs Example: Effect of Gender and Other’s Opinions on Aesthetic Judgments Suppose we are interested in how people’s views of art at an art gallery might be influenced by other peoples opinions, and suppose we also wonder whether gender might play a role in this. We go to the art gallery and recruit several people to act as “stooges” who vocalize their like/dislike of different works of art. We then ask the views of nearby random people who can hear these comments…. Gender x Opinions of Others Interaction Question: Is an effect of Gender dependent upon the Opinions of Others variable? Compare Female to Male scores when exposed to others who like vs dislike the art. Opinions of Others Like Dislike Female Like Female Dislike Female Male Gender Male Like Male Dislike

Factorial Designs Example: Effect of Gender and Other’s Opinions on Aesthetic Judgments Suppose we are interested in how people’s views of art at an art gallery might be influenced by other peoples opinions, and suppose we also wonder whether gender might play a role in this. We go to the art gallery and recruit several people to act as “stooges” who vocalize their like/dislike of different works of art. We then ask the views of nearby random people who can hear these comments…. Gender x Opinions of Others Interaction Question: Is an effect of Gender dependent upon the Opinions of Others variable? Compare Female to Male scores when exposed to others who like vs dislike the art.

Factorial Designs Example: Effect of Gender and Other’s Opinions on Aesthetic Judgments Suppose we are interested in how people’s views of art at an art gallery might be influenced by other peoples opinions, and suppose we also wonder whether gender might play a role in this. We go to the art gallery and recruit several people to act as “stooges” who vocalize their like/dislike of different works of art. We then ask the views of nearby random people who can hear these comments…. Gender x Opinions of Others Interaction Question: Is an effect of Gender dependent upon the Opinions of Others variable? Compare Female to Male scores when exposed to others who like vs dislike the art.

Factorial Designs Main Effects & Interactions In this experimental design there are 3 basic questions of interest. These include assessing the “main effect” of Factor I, the “main effect” of Factor J, and the “I x J interaction.” These 3 questions can be statistically assessed by running a factorial ANOVA and determining if the 3 F scores (one for each of the main effects and one for the interaction) exceed the appropriate criterion F. The null hypotheses are that (1) I1 = I2, (2) J1 = J2, and (3) the effect of one IV does NOT depend upon the level of the other. Factor ”J” J1 J2 I1 I2 I1J1 I1J2 Factor ”I” I2J1 I2J2

Factorial Designs Reading Graphs Now lets get some practice learning how to read graphs from this sort of experimental design. Just by looking at the data we will try to determine if there may be main effects of I, of J, and an I x J interaction in the data. Of course, these will just be our impressions. We will, ultimately, need to conduct a statistical analysis to make a final determination. Nonetheless, it is always best to start by examining the data! There are 8 POSSIBLE patterns of results from a 2-factor factorial design. Lets now consider some of these.

Factorial Designs Pattern 1: Do you think there is a: Main Effect of Factor I? Main Effect of Factor J? I x J Interaction?

Factorial Designs Pattern 2: Do you think there is a: Main Effect of Factor I? Main Effect of Factor J? I x J Interaction?

Factorial Designs Pattern 3: Do you think there is a: Main Effect of Factor I? Main Effect of Factor J? I x J Interaction?

Factorial Designs Pattern 4: Do you think there is a: Main Effect of Factor I? Main Effect of Factor J? I x J Interaction?

Factorial Designs Pattern 5: Do you think there is a: Main Effect of Factor I? Main Effect of Factor J? I x J Interaction?

Factorial Designs Pattern 6: Do you think there is a: Main Effect of Factor I? Main Effect of Factor J? I x J Interaction?

Factorial Designs Pattern 7: Do you think there is a: Main Effect of Factor I? Main Effect of Factor J? I x J Interaction?

Factorial Designs Pattern 8: Do you think there is a: Main Effect of Factor I? Main Effect of Factor J? I x J Interaction?

Factorial Designs Types of Factorial Designs: Between Group Factorials Independent Groups (all different groups, with random assignment) Matched Groups (all are different groups, but matched on some variables) Non-equivalent Groups (all IVs are subject variables)

Factorial Designs Types of Factorial Designs: Between-Group Factorials Independent Groups (all different groups, with random assignment) Matched Groups (all are different groups, but matched on some variables) Non-equivalent Groups (all IVs are subject variables) Within-Group Factorials Repeated Measures Factorial (all subjects exposed to all cells of the design)

Factorial Designs Types of Factorial Designs: Between-Group Factorials Independent Groups (all different groups, with random assignment) Matched Groups (all are different groups, but matched on some variables) Non-equivalent Groups (all IVs are subject variables) Within-Group Factorials Repeated Measures Factorial (all subjects exposed to all cells of the design) Mixed Factorials Both between-group and within-group IVs, but all are manipulated

Factorial Designs Types of Factorial Designs: Between-Group Factorials Independent Groups (all different groups, with random assignment) Matched Groups (all are different groups, but matched on some variables) Non-equivalent Groups (all IVs are subject variables) Within-Group Factorials Repeated Measures Factorial (all subjects exposed to all cells of the design) Mixed Factorials Both between-group and within-group IVs, but all are manipulated Person x Environment Factorial At least one IV is a subject variable and others are manipulated

Factorial Designs I x J x K Factorial Design: The simplest 3-factor design has 2 levels of each variable. It would be characterized as a 2 x 2 x 2 design

Main Effects & Interactions in a 3 Independent Variable Factorial Design I x J x K Factorial Design: The simplest 3-factor design has 2 levels of each variable. It would be characterized as a 2 x 2 x 2 design

Main Effects & Interactions in a 3 Independent Variable Factorial Design I x J x K Factorial Design: The simplest 3-factor design has 2 levels of each variable. It would be characterized as a 2 x 2 x 2 design Example Semantic Priming Experiment (Cognitive Psychology) If you ask subjects to pronounce the name of a target word and this word is preceded by a related “prime” word, then the naming time (reaction time or RT) is faster than if the target word is preceded by an unrelated prime word.

Main Effects & Interactions in a 3 Independent Variable Factorial Design The Task is to Name the Target word, & record the time this takes Prime Target Doctor – Nurse (this is a Related prime-target pair) Guitar – Nurse (this is an Unrelated prime-target pair)

Main Effects & Interactions in a 3 Independent Variable Factorial Design The Task is to Name the Target word, & record the time this takes Prime Target Doctor – Nurse (this is a Related prime-target pair) Guitar – Nurse (this is an Unrelated prime-target pair) * It takes less time to pronounce “Nurse” if it had been primed by the word “Doctor” than by “Guitar” because “Doctor” is related to “Nurse” and related words are encoded more closely together within a “Semantic Memory Network” than are unrelated words. * Further, assume that once a word is activated within this network its activation spreads to its neighbors. If so, then Nurse should be named more quickly when preceded by doctor because doctor should preactivate Nurse before it is actually presented.

Main Effects & Interactions in a 3 Independent Variable Factorial Design The Task is to Name the Target word, & record the time this takes Prime Target Doctor – Nurse (this is a Related prime-target pair) Guitar – Nurse (this is an Unrelated prime-target pair) Related Target RT is Faster than Unrelated Target RT

Main Effects & Interactions in a 3 Independent Variable Factorial Design The Task is to Name the Target word, & record the time this takes Prime Target Doctor – Nurse (this is a Related prime-target pair) Guitar – Nurse (this is an Unrelated prime-target pair) So far, we only have one independent variable (Prime-Target Relatedness).

Main Effects & Interactions in a 3 Independent Variable Factorial Design The Task is to Name the Target word, & record the time this takes Prime Target Doctor – Nurse (this is a Related prime-target pair) Guitar – Nurse (this is an Unrelated prime-target pair) SOA: 200 vs 800 msec So far, we only have one independent variable (Prime-Target Relatedness). Suppose we also were to vary the time between Prime and Target in this hypothetical experiment. Now we have a second independent variable. This variable is sometimes referred to as the Stimulus Onset Asynchrony or SOA for short.

Main Effects & Interactions in a 3 Independent Variable Factorial Design The Task is to Name the Target word, & record the time this takes Prime Target Doctor – Nurse (this is a Related prime-target pair) Guitar – Nurse (this is an Unrelated prime-target pair) SOA: 200 vs 800 msec Now we have a 2x2 Factorial Design with Relatedness & SOA factors. Now, lets add one more independent variable. Suppose we wanted to study priming effects not only among these Types of word pairs (which are Concrete), but also among more Abstract Word pairs.

Main Effects & Interactions in a 3 Independent Variable Factorial Design The Task is to Name the Target word, & record the time this takes Prime Target Doctor – Nurse (this is a Related prime-target pair) Guitar – Nurse (this is an Unrelated prime-target pair) Truth – Justice (this is a Related prime-target pair) Time – Justice (this is an Unrelated prime-target pair) SOA: 200 vs 800 msec Now we have a 2 x 2 x 2 Factorial Design with 3 Factors: Relatedness (R, U), SOA (200, 800), Type of Word (Concrete, Abstract)

Main Effects & Interactions in a 3 Independent Variable Factorial Design I x J x K Factorial Design: How many Main Effects? How many Interactions?

Main Effects & Interactions in a 3 Independent Variable Factorial Design I x J x K Factorial Design: How many Main Effects – 3 (I, J, and K) How many Interactions – 4 (I x J, I x K, J x K, I x J x K)

Main Effects & Interactions in a 3 Independent Variable Factorial Design I x J x K Factorial Design: How many Main Effects – 3 (I, J, and K) How many Interactions – 4 (I x J, I x K, J x K, I x J x K) Thus, there are a total of 7 key questions addressed in any 3-factor Factorial design (it does not matter how many levels there are of each) The ANOVA program will produce F scores for each one of these to determine which of the significant main effects and/or interactions is not likely to have occurred by chance.

Identify the Main Effects & Interactions in this Data set Here is some made up data relevant to our hypothetical 2x2x2 Factorial Design Try to determine what the different main effect and interaction questions are And also try to determine which of these would appear to be significant

Identify the Main Effects & Interactions in this Data set Main Effect Questions: Relatedness (Related vs Unrelated, overall) SOA (200 vs 800, overall) Type of Word (Concrete vs Abstract, overall) Interaction Questions: Rel x SOA Rel x Word SOA x Word Rel x SOA x Word

Identify the Main Effects & Interactions in this Data set Main Effect Questions: Relatedness (Related vs Unrelated, overall) SOA (200 vs 800, overall) Type of Word (Concrete vs Abstract, overall) Interaction Questions: Rel x SOA Rel x Word SOA x Word Rel x SOA x Word Looks to me like the only significant effects would be: Relatedness, Rel x SOA, & Rel x Word

Identify the Main Effects & Interactions in this Data set Main Effect Questions: Relatedness (Related vs Unrelated, overall) SOA (200 vs 800, overall) Type of Word (Concrete vs Abstract, overall) Interaction Questions: Rel x SOA Rel x Word SOA x Word Rel x SOA x Word Looks to me like the only significant effects would be: Relatedness, Rel x SOA, & Rel x Word Of course, this is just our impression and the final word would depend upon what the statistics shows.

Identify the Main Effects & Interactions in this Data set Here’s another possible data pattern. Now, you tell me what you think Is going on in these data. Which Main Effects and Interactions do you think are present in these data?