7- XP- Between- and Within- Subjects Experimental Designs

Between-subjects design

it compares different groups of individuals

Between-subjects design

General goal is to determine whether differences exist between two or more treatment conditions

Between-subjects design

allows only one score for each. participant. Every individual score represents a separate, unique participant

Between-subjects design

requires a separate, independent group of individuals for each treatment condition

Independent-measures experimental design

Because each participant is measured only once, the researcher can be reasonably confident that the resulting measurement is relatively clean and uncontaminated by other treatment factors. For this reason, a between-subjects experimental design is often called an -

Individual differences

personal characteristics that differ from one ­ participant to another.

1. Individual differences

2. Environmental variables

two major sources of confounding that exist in a between-subjects design

1. Created equally

2. Treated equally

3. Composed of equivalent individuals

In order for the groups to be equivalent, they must be:

Created equally

The process used to obtain participants should be as similar as possible for all of the groups.

Treated equally

Except for the treatment conditions that are deliberately varied between groups, the groups of participants should receive exactly the same experiences.

Composed of equivalent individuals

The characteristics of the participants in any one group should be as similar as possible to the characteristics of the participants in every other group.

Restricted random assignment

the group assignment process is limited to ensure predetermined characteristics (such as equal size) for the separate groups.

Matching

involves assigning individuals to groups so that a specific participant. variable is balanced across the groups. The intent is to create groups that are equivalent (or nearly equivalent) with respect to the variable matched.

Differential attrition

refers to differences in attrition rates from one group to another and can threaten the internal validity of a between-subjects experiment

Diffusion

refers to the spread of the treatment from the experimental group to the control group, which tends to reduce the difference between the two conditions.

Compensatory equalization

Another risk is that an untreated group learns about the treatment being received by the other group and demands the same or equal treatment.

Compensatory rivalry

One possibility is that the untreated group works extra hard to show that they can perform just as well as the individuals receiving the special treatment.

Resentful demoralization

It is also possible that the participants in an untreated group simply

give up when they learn that another group is receiving special treatment.

Single-factor two-group design/ two-group design

The simplest version of a between-subjects experimental design involves comparing only two groups of participants: The researcher manipulates one independent variable with only two levels. This design is often referred to as the

Single-factor multiple-group design

research questions often require more than two groups to evaluate the functional relation between an independent and a dependent variable or to include several different control groups in a single study

Within-subjects experimental design

The defining characteristic of a - is that it uses a single group of participants and tests or observes each individual in all of the different treatments being compared.

repeated-measures design

In the context of statistical analysis, a within-subjects design is often called a - because the research study repeats measurements of the same individuals under different conditions.

within-subjects experimental design/ repeated-measures ­ experimental design,

A - compares two or more different treatment conditions (or compares a treatment and a control) by observing or measuring the same group of individuals in all of the treatment conditions being compared.

1. Environmental variables

2. Time-related variables

two major sources of potential confounding for a within-subjects design

History

refers to environmental events other than the treatment that change over time and may affect the scores in one treatment differently than in another treatment

Maturation

Any systematic changes in participants’ physiology or psychology that occur during a research study and affect the participants’ scores are referred to as

Instrumentation/ instrumental bias/ instrumental decay

refers to changes in the measuring instrument that occur during a research study in which participants are measured in a series of treatment conditions.

Regression to the mean/ statistical regression

is a mathematical phenomenon in which extreme scores (high or low) on one measurement tend to be less extreme on a second measurement.

Order effect

Any possible change in performance caused by participation in a previous treatment is called an -

Fatigue effects

progressive decline in performance as a participant works through a series of treatment conditions

Practice effects

progressive improvement in performance as a participant gains experience through the series of treatment condition

Carry-over effects

occur when one treatment condition produces a change in the participants that affects their scores in subsequent treatment conditions.

Contrast effect

the subjective perception of a treatment condition is influenced by its contrast with the previous treatment

Progressive error

refers to changes in a participant’s behavior or performance that are related to general experience in a research study but not related to a specific treatment or treatments

Counterbalancing

For a within-subjects design, - is defined as changing the order in which treatment conditions are administered from one participant to another so that the treatment conditions are matched with respect to time

Partial counterbalancing

Instead of every possible sequence, - simply uses enough different orderings to ensure that each treatment condition occurs first in the sequence for one group of participants, occurs second for another group, third for another group, and so on.

Participant attrition

Another potential problem for a within-subjects design with different treatments administered at different times is -. In simple terms, some of the individuals who start the research study may be gone before the study is completed.

Matched-subjects design

researchers attempt to approximate the advantages of within- and between-subjects designs by using a technique known as a

Matched-subjects design

uses a separate group for each treatment condition, but each individual in one group is matched one-to-one with an individual in every other group.

repeated-measures t or a single-factor ANOVA (repeated measures)

With two treatment conditions, a - can be used to evaluate the statistical significance of the mean difference

Two-treatment within-subjects design

The simplest application of a within-subjects design is to evaluate the difference between two treatment conditions

Repeated-measures ANOVA

appropriate hypothesis test for a within-subjects design comparing mean differences for three treatment conditions