Chapter 8 Experimental Design
Causality Review Remember from Chapter 4 that in order to make a causal inference, you need to satisfy three requirements: 1. Covariation 2. Temporal order 3. Eliminate alternative explanations (3 rd variable problem)
Confounding Third variables which are not controlled for are called confounding variables, or simply confounds.
Experimental Design
Basic experiments As we already know, experiments address confounding variables through: Experimental control Randomization Experiments establish temporal order by manipulating the independent variable and measuring its effects on the dependent variable. Thus, the most basic experiment will have only two variables, one IV and one DV, each one with 2 levels.
Basic Experiments Example: What is the effect of whining on the sympathy of a romantic partner? Whining (yes/no) Sympathy (1-10) To test this hypothesis in an experiment, there are three steps: 1. Assign participants to conditions 2. Conduct manipulation 3. Measure the dependent variable
Step 1: Assigning Participants Independent Groups (Between subjects) Assign different participants to each experimental condition Repeated Measures (Within groups) Every participant is assigned to every condition of the independent variable
Repeated Measures design Advantages Fewer participants needed Extremely sensitive to effects of the independent variable Why?
Repeated Measures design Disadvantages Order effects 1. Practice effect 2. Fatigue effect 3. Contrast effect
Developmental Designs Developmental designs explore how individuals change as a function of age. Cross-sectional Longitudinal Sequential
Chapter 9: Conducting the Experiment
Step 2: Conducting the Manipulation There are 2 types of manipulation: Straightforward manipulation Independent variable is manipulated through written, verbal, or visual materials Staged manipulation Event is artificially created by the researcher to: 1. Create a psychological state in participants 2. Increase fidelity to real world
Strength of Manipulation Within ethical and financial limits, a stronger manipulation is generally better. Remember, if the manipulation is too weak, then it isn t possible to detect any differences between groups.
Step 3: Measuring the DV Types of Measures: Self-report Behavioral Physiological
Measuring the DV Back to our example: Whining (yes/no) Sympathy (1-10) This design is called a posttest-only design because the participants were only tested once, after the manipulation (hence, posttest). Contrast this design with the pretest-posttest design (Cozby, p. 141)
Pretest-Posttest Design However, sometimes it is helpful to measure the participants on the DV both before and after the manipulation: Small sample size (less than ~25 participants per cell) Participant selection Risk of mortality Unfortunately, pretesting is more time consuming and costly. Plus, pretesting risks sensitizing the participant.
Interpreting Experimental Results Once you have measured the participants on the DV, you can compare the scores of the two groups: Sympathy 9 8 7 6 5 4 3 2 1 0 Whining Control
Expanding the Experiment...
Expanding the experiment Psychological phenomena are way too complicated to ever be explained with this basic design. Usually, experiments will increase: the number of levels of the IV the number of IVs.
Increasing # of Levels of the IV Increasing the # of levels allows us to: 1. Detect curvilinear relationships: Whining (control, low, high) Sympathy (1-10) 2. Compare more than two groups: Persuasion (none, whining, screaming, begging, asking) Sympathy (1-10)
Increasing the number of IVs Designs with more than one IV are called factorial designs. Suppose we want to know how the frustration level of the partner interacts with different persuasion techniques Persuasion (whining, screaming, asking) Partner s Frustration (low, high) Sympathy (1-10)
Factorial designs Terminology Each IV is called a factor, and of course each will have at least 2 levels An experimental design can be described by the number of IVs and the number of levels of each: Number of levels (IV1) X Number of levels (IV2)...
Factorial designs Thus, our study would be a 3 X 2 This creates 6 conditions, or cells, that participants will be assigned to. Each cell of a design requires ~25 participants.
Interpreting Factorial designs Factorial designs yield two kinds of info: 1. Main effects Information about each of the IVs taken by itself. 2. Interactions Information about each of the IVs across levels of the other IV(s).
Main Effects Remember the results from our first, basic design: 9 8 7 6 5 4 3 2 1 0 Whining Control For all practical purposes, this is a main effect for whining
Main Effects Now we can combine persuasion with frustration: 12 10 8 6 4 2 0 Whining Asking Persuasion technique Screaming Hi Frustration Lo Frustration
Main Effects & Interactions Three ways to present information: 1. Bar graph 2. Line graph 3. Table See p. 185
Expanding some more... Longitudinal designs are a special kind of expansion, in which the same measures are repeated at least twice, in order to assess how people change over time. Thus, Time can be thought of as an additional IV, and the number of repetitions (Waves) are the levels of the variable.
Questions?