Expe Reviewer 1 F

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 (but not its specific content).

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, a control condition in which subjects are treated exactly the same as subjects who are in the experimental group, except for the presence of the actual drug; the prototype of a good control group.

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 of 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 levels of the independent variable.

Two-group design

The simplest experimental design, used when only two treatment conditions are needed.

Two-independent-groups design

An experimental design in which subjects 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.

Factor

An independent variable in a factorial design.

Factorial design

An experimental design in which more than one independent variable is manipulated.

Higher-order interaction

An interaction effect involving more than two independent variables.

Interaction

The effect of one independent variable changes across the levels of another independent variable; can only be detected in factorial designs.

Main effect

The action of a single independent variable in an experiment; the change in the dependent variable produced by the various levels of a single factor.

Shorthand notation

A system that uses numbers to describe the design of a factorial experiment.

Two-factor experiment

The simplest factorial design, having two independent variables.

Across-subjects counterbalancing

A technique for controlling progressive error that pools all subjects’ data together to equalize the effects of progressive error for each condition.

Balanced Latin square

A partial counterbalancing technique for constructing a matrix, or square, of sequences in which each treatment condition (1) appears only once in each position in a sequence and (2) precedes and follows every other condition an equal number of times.

Carryover effect

The persistence of the effect of a treatment condition after the condition ends.

Complete counterbalancing

A technique for controlling progressive error using all possible sequences that can be formed out of the treatment conditions, and using each sequence the same number of times.

Counterbalancing

A technique for controlling order effects by distributing progressive error across the different treatment conditions of the experiment; may also control carryover effects.

Fatigue effects

Changes in performance caused by fatigue, boredom, or irritation.

Latin square counterbalancing

A partial counterbalancing technique in which a matrix, or square, of sequences is constructed so that each treatment appears only once in any order position.

Mixed design

A factorial design that combines within-subjects and between-subjects factors.

Order effects

Change in subjects’ performance that occurs when a condition falls in different positions in a sequence of treatments.

Partial counterbalancing

A technique for controlling progressive error by using some subset of the available sequences of treatment conditions.

Power

The chance of detecting a genuine effect of the independent variable.

Practice effect

Change in subjects’ performance resulting from practice.

Progressive error

Changes in subjects’ responses that are caused by testing in multiple treatment conditions; includes order effects, such as the effects of practice or fatigue.

Randomized partial counterbalancing

The simplest partial counterbalancing procedure in which the experimenter randomly selects as many sequences of treatment conditions as there are subjects for the experiment.

Reverse counterbalancing

A technique for controlling progressive error for each individual subject by presenting all treatment conditions twice, first in one order, then in reverse order.

Subject-by-subject counterbalancing

A technique for controlling progressive error for each subject by presenting all treatment conditions more than once. Ex. Person 1 tastes Cola A, then B, and then Person 2 tastes Cola B, then A.

Within-subjects design

A design in which each subject takes part in more than one condition of the experiment; also called a repeated-measures design.

Within-subjects factorial design

A factorial design in which subjects receive all conditions in the experiment.

AB design

A design in which a baseline condition (A) is measured first, followed by measurements during the experimental intervention (B); there is no return to the baseline condition.

ABA design

A design in which a baseline condition (A) is measured first, followed by measurements during the experimental condition (B), followed by a return to the baseline condition (A) to verify that the change in behavior is linked to the experimental condition; also called a reversal design.

ABAB design

A design in which a baseline condition (A) is measured first, followed by measurements during a treatment condition (B), followed by a return to the baseline condition (A) to verify that the behavior change is linked to the experimental condition, followed by a return to the treatment condition (B).

ABABA design

A design in which a baseline condition (A) is measured first, followed by measurements during a treatment condition (B), followed by a return to the baseline measurement condition (A), followed by a return to the treatment condition (B) and a final baseline measurement condition (A) to verify that the change in behavior is linked to the experimental condition.

Baseline

A measure of behavior as it normally occurs without the experimental manipulation; a control condition that is used to assess the impact of the experimental condition.

Change criterion design

A design used to modify behavior when the behavior cannot be changed all at once; instead, the behavior is modified in increments, and the criterion for success changes as the behavior is modified.

Discrete trials design

A design that relies on presenting and averaging across many, many experimental trials; repeated applications result in a reliable picture of the effects of the independent variable.

Large N design

A design in which the behavior of groups of subjects is compared.

Multiple baseline design

A small N design in which a series of baselines and treatments are compared; once established, however, a treatment is not withdrawn.

Small N design

A design in which just one or a few subjects are used; typically, the experimenter collects baseline data during an initial control condition, applies the experimental treatment, then restraints the original control condition to verify that changes observed in behavior were caused by the experimental intervention.