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FN Flashcards

1
Q

A design in which different subjects take part in each condition of the experiment.

A

Between-subjects design

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2
Q

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.

A

Block randomization

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3
Q

A condition in which subjects receive a zero value of the independent variable.

A

Control condition

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4
Q

The subjects in a control condition.

A

Control group

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5
Q

A statistical estimate of the size or magnitude of the treatment effect(s).

A

Effect size

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6
Q

A treatment condition in which the researcher applies a particular value of an independent variable to subjects and then measures the dependent variable; in an experimental group–control group design, the group that receives some value of the independent variable.

A

Experimental condition

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7
Q

The general structure of an experiment (but not its specific content).

A

Experimental design

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8
Q

The subjects in an experimental condition.

A

Experimental group

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9
Q

A between-subjects design with one independent variable, in which there are more than two treatment conditions.

A

Multiple-groups design

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10
Q

The most commonly used multiple-groups design in which the subjects are assigned to the different treatment conditions at random.

A

Multiple-independent-groups design

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11
Q

A mini-experiment using only a few subjects to pretest selected levels of an independent variable before conducting the actual experiment.

A

Pilot study

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12
Q

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.

A

Placebo group

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13
Q

Creating pairs whose subjects have identical scores on the matching variable.

A

Precision matching

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14
Q

The technique of assigning subjects to treatments so that each subject has an equal chance of being assigned to each treatment condition.

A

Random assignment

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15
Q

Creating pairs of subjects whose scores on the matching variable fall within a previously specified range of scores.

A

Range matching

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16
Q

Creating matched pairs by placing subjects in order of their scores on the matching variable; subjects with adjacent scores become pairs.

A

Rank-ordered matching

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17
Q

A design in which two groups of subjects are exposed to different levels of the independent variable.

A

Two-experimental-groups design

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18
Q

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

A

Two-group design

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19
Q

An experimental design in which subjects are placed in each of two treatment conditions through random assignment.

A

Two-independent-groups design

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20
Q

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.

A

Two-matched-groups design

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21
Q

An independent variable in a factorial design.

A

Factor

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22
Q

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

A

Factorial design

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23
Q

An interaction effect involving more than two independent variables.

A

Higher-order interaction

24
Q

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

A

Interaction

25
Q

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.

A

Main effect

26
Q

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

A

Shorthand notation

27
Q

The simplest factorial design, having two independent variables.

A

Two-factor experiment

28
Q

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

A

Across-subjects counterbalancing

29
Q

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.

A

Balanced Latin square

30
Q

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.

A

Block randomization

31
Q

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

A

Carryover effect

32
Q

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.

A

Complete counterbalancing

33
Q

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

A

Counterbalancing

34
Q

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

A

Fatigue effects

35
Q

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.

A

Latin square counterbalancing

36
Q

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

A

Mixed design

37
Q

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

A

Order effects

38
Q

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

A

Partial counterbalancing

39
Q

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

A

Power

40
Q

Change in subjects’ performance resulting from practice.

A

Practice effect

41
Q

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

A

Progressive error

42
Q

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

A

Randomized partial counterbalancing

43
Q

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

A

Reverse counterbalancing

44
Q

A technique for controlling progressive error for each individual subject by presenting all treatment conditions more than once.

A

Subject-by-subject counterbalancing

45
Q

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

A

Within-subjects design

46
Q

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

A

Within-subjects factorial design

47
Q

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.

A

AB design

48
Q

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.

A

ABA design

49
Q

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 change in behavior is linked to the experimental condition, followed by a return to the treatment condition (B).

A

ABAB design

50
Q

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.

A

ABABA design

51
Q

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

A

Baseline

52
Q

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.

A

Changing criterion design

53
Q

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.

A

Discrete trials design

54
Q

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

A

Large N design

55
Q

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

A

Multiple baseline design

56
Q

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 reinstates the original control condition to verify that changes observed in behavior were caused by the experimental intervention.

A

Small N design

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