Experiments: Part 2

Overview This section mainly reviews some of the more difficult concepts from the readings What are the defining features that differentiate an experiment from a non-experiment? What are “factors” and “levels,” and how does this have implications for analyses? What are some of the more sophisticated concepts in experimental design?

Review of Experiments Experiment Study where a researcher systematically manipulates one variable in order to examine its effect(s) on one or more other variables Two components Includes two or more groups Participants are randomly assigned by the researcher Random = Equal odds of being in any particular group

Review of Terminology Variables: Independent, Dependent, and Extraneous Designs: Between-group, Within-subject Terms: Factor, Level Single-factor, two-level design Single-factor, multi-level design Two-factor design (coming after Exam 2) Analyses: t-test, ANOVA (F-test) Matching, counterbalancing, and block randomization

Review of Terminology Variables: Independent, Dependent, and Extraneous Designs: Between-group, Within-subject Terms: Factor, Level Single-factor, two-level design Single-factor, multi-level design Two-factor design Analyses: t-test, ANOVA (F-test) Matching, counterbalancing, and block randomization

Review of Terminology Matching Counterbalancing Block randomization What is it? Assigning participants evenly across conditions based on some participant variable Why is it used? Prevent “imbalance,” and thus confounding Counterbalancing What is it? Using varying sequences in within-subject designs Why is it used? Prevent ordering effects Block randomization What is it? Rule that each condition must be completed before the next repetition Why is it used? Keep sample sizes similar across conditions, increasing power (ability to detect a significant effect)

Review of Terminology Variables: Independent, Dependent, and Extraneous Designs: Between-group, Within-subject Terms: Factor, Level Single-factor, two-level design Single-factor, multi-level design Two-factor design Analyses: t-test, ANOVA (F-test) Matching, counterbalancing, and block randomization Final section on experiments (e.g., Goodwin8) is very challenging, so need to get these concepts down. Which experimental concepts are most confusing to date?

Practice #1 In a randomized controlled trial comparing surgery, medication, and placebo treatments for heart disease, a research team examined how well these treatments improves blood pressure. How many factors? One. Treatment type How many levels? Three. Surgery, medication, and placebo What type of analysis would you use? ANOVA. The IV is categorical and has multiple levels, and the DV is continuous.

Practice #2 Participants in a study complete 10 frames of bowling. During half of the frames, participants bowl as quickly as possible. During the other half, participants bowl normally. The DV is number of points (pins knocked down in fast vs. normal condition). How many factors? One. Bowling condition. How many levels? Two. Speed or control. (Doesn’t matter that there are repeats) What type of analysis would you use? t-test. The IV is categorical and has two levels, and the DV is continuous

Practice #3 A researcher has each participant complete a survey measure of “Need for Cognition” and compares these scores to GPA. How many factors? Trick question. No categorical variables. How many levels? What type of analysis? Correlational. Both variables are continuous.

Practice #4 A researcher wants to study memory span. She examines whether caffeine improves memory over a placebo control, and she also examines male/female gender differences in memory span. How many factors? Two. Type of pill and gender. How many levels? Two for pill (caffeine vs. placebo) and two for gender (male vs. female) Type of analysis? ANOVA because multiple categorical IVs are present