Experimental Psychology

• In a between-subjects design, each participant only experiences one level of an independent variable.
• Convenience sample: all your subjects come from a single class or location
• Small sample can lead to erroneous results
• Between-subjects design: A design in which different subjects take part in each condition of the experiment.
• Block randomization: A process of randomization that first creates treatment blocks containing one random order of the conditions in the experiment; subjects are then assigned to fill each successive treatment block.
• Control condition: A condition in which subjects receive a zero value of the independent variable.
• Control group: The subjects in a control condition.
• Effect size: A statistical estimate of the size or magnitude of the treatment effect(s)
• Experimental condition: A treatment condition in which the researcher applies a particular value of an independent variable to subjects and then measures the dependent variable.
• Experimental design: The general structure of an experiment.
• Experimental group: The subjects in an experimental condition.
• Multiple-groups design: A between-subjects design with one independent variable, in which there are more than two treatment conditions.
• Multiple-independent-groups design: The most commonly used multiple-groups design in which the subjects are assigned to the different treatment conditions at random.
• Pilot study: A mini-experiment using only a few subjects to pretest selected levels of an independent variable before conducting the actual experiment.
• Placebo group: In drug testing, subjects in the control condition are treated exactly the same as the subjects in experimental condition, except for the presence of the actual drug; the prototype of a good control goup.
• Precision matching: Creating pairs whose subjects have identical scores on the matching variable.
• Random assignment: The technique of assigning subjects to treatments so that each subject has an equal chance of being assigned to each treatment condition.
• Range matching: creating pairs if subjects whose scores on the matching variable fall within a previously specified range of scores.
• Rank-ordered matching: Creating matched pairs by placing subjects in order of their scores on the matching variable; subjects with adjacent scores become pairs.
• Two-experimental-groups design: A design in which two groups of subjects are exposed to different level of the independent variable.
• Two-groups design: The simplest experimental design, used when only two treatment conditions are needed.
• Two-independent-groups design: An experimental design in which subject are placed in each of two treatment conditions through random assignment.
• Two-matched-groups design: An experimental design with two treatment conditions and with subjects who are matched on a subject variable thought to be highly related to the dependent variable.
• The between-subjects factorial design is used to investigate the effects of two or more independent variables on dependent variable(s).
• Factorial design: two or more independent variables at the same time.
• Factors: The independent variables in factorial design.
• Two-factor experiment: The simplest factorial design, only has two factors.
• Main effect: The action of a single independent variable in an experiment.
• Main effect: A change in behavior associated with a change in the value of a single independent variable within the experiment.
• Interaction: When the effects of one factor depend on another factor.
• Interaction: it is present if the effect of one independent variable changes across the levels of another independent variable.
• Interactions qualify the main effects.
• High-order interactions: an interaction involving more than two independent variables.
• As with main effects, we measure interactions quantitively through statistical test-we evaluate their significance.
• The importance of a factorial experiment is that it is more efficient to do one experiment rather than two.
• Additionally, you can know whether these independent variables can interact, which can not be seen when you.
• Design matrix: If you can translate your thinking about an experiment into a simple diagram.
• We cannot use a design matrix to describe a two-factor experiment in a report.
• Shorthand notation: a system that uses numbers to describe the design of a factorial experiment.
• Crossover interaction: effects of each factor completely reverse at each level of other factor.