Between Subjects Experimental Designs

Between Subjects Experimental Designs

Overview

  • Source: Gravetter | Forzano, Research Methods Behavioral Sciences, 6th Edition, © 2023 Cengage

  • Goal: To demonstrate a cause-and-effect relationship between two variables.

  • Requirements:

    • Manipulation of one variable (Independent Variable, IV)

    • Measurement of a second variable (Dependent Variable, DV)

    • Comparison of scores between treatments

    • Control of all other variables

Characteristics of Between-Subjects Designs

  • An experimental research strategy where:

    • A researcher manipulates an IV.

    • Measures the DV for each participant.

  • Objective: To determine whether differences exist between two or more treatment conditions.

  • Requirement: Only one score is obtained for each participant.

  • Participants are separated into different groups for various treatment conditions.

The Structure of a Between-Subjects Experiment

  • Individual scores are independent, meaning the score from one individual does not impact another.

Advantages of Between-Subjects Designs

  • Independence of Scores:

    • Each individual score is independent from all others, eliminating potential biases.

  • Impact of Treatments:

    • Individual’s score is not influenced by:

    • Practice or experience gained in other treatments.

    • Fatigue or boredom from participating in multiple treatments.

    • Contrast effects that arise from direct comparisons among treatments.

Disadvantages of Between-Subjects Designs

  • Participant Demand:

    • Requires a relatively large number of participants.

    • Example: For three different treatment conditions with 30 scores each, a total of 90 participants are required.

  • Individual Differences: Each score is obtained from a unique individual, introducing variability into the experiment.

Effects of Individual Differences in Groups

  • Importance of Equivalence:

    • Groups in a between-subjects experiment must be as similar as possible, except for the IV distinguishing them.

  • Confounding Variables: Individual differences that are not controlled can skew results, making it difficult to draw conclusions about treatments.

Confounding Variables

  • Age as a Confounding Variable:

    • Age can influence outcomes if not controlled.

  • Other Sources:

    • Environmental variables can also confound results.

    • Example: Testing one group in a large room and another in a smaller room can lead to differential results.

Equivalent Groups

  • Researcher’s Control:

    • The researcher must create groups that are:

    • Created equally

    • Treated equally

    • Composed of equivalent individuals

Limiting Confounding Variables

  • Techniques:

    1. Random Assignment (Randomization): Assign participants to groups randomly.

    2. Matching Groups (Matched Assignment): Match individuals based on specific variables of interest.

    3. Holding Variables Constant: Restrict certain variables from playing a role in the experiment, e.g., eliminating gender as a variable if suspected to be confounding.

Effects of Variability

  • Influence of Variability:

    • Large differences between individuals lead to high variance within treatment scores.

    • Variability affects statistical interpretation; significant differences suggest a true effect rather than random variance.

Differences Between Treatments and Variance Within Treatments

  • Good Differences:

    • Large differences between treatments indicate differential treatment effects, supporting research hypotheses.

  • Bad Variance:

    • Variance within treatments should be minimized to avoid obscuring patterns in data.

  • Minimization Goal:

    • Researchers aim to increase differences between treatments and reduce variance within treatments.

Minimizing Variance Within Treatments

  • Procedural Standardization:

    • Standardizing research procedures and treatment settings can reduce variability.

  • Limiting Individual Differences: Strategies like random assignments and matching can limit variability.

  • Sample Size Consideration: A larger sample size generally leads to reduced variance.

Threats to Internal Validity

  • Differential Attrition:

    • Participant dropout can skew results if it occurs unevenly across groups.

  • Communication Between Groups:

    • Types of Diffusion:

    • Treatment spreading to the control group.

    • Compensatory Equalization: Control group changes behavior to match experimental group treatments.

    • Compensatory Rivalry: Control group may exert extra effort due to awareness of the experimental group's treatment.

    • Resentful Demoralization: Control group may become demotivated due to perceived unfairness.

Applications and Statistical Analyses

  • Two-Group Mean Difference:

    • Simplest form of between-subjects experimental design, involving two participant groups.

    • Researcher manipulates one IV with two levels.

  • Statistical Test:

    • Independent-measures t-test to evaluate if there is a significant difference between group means.

Advantages and Disadvantages of Two-Group Design

  • Advantages:

    • Simplicity of design facilitates clear comparisons between treatment conditions.

    • Researchers can select extreme values for the IV for maximal effect.

  • Disadvantages:

    • Limited scope, yielding only two data points for comparison.

    • Circumstances may restrict the ability to include more than two groups in study.

Comparing Means for More than Two Groups

  • Single-Factor Multiple-Group Design:

    • Compares means across three or more groups under a single IV.

  • Statistical Method:

    • Analyzed using a single-factor analysis of variance (ANOVA), providing robust evidence for causal relationships in experiments involving multiple treatments.