10 Single Variable, Independent Groups Designs

Importance of Planning in Independent Groups Designs

  • Planning is comprehensive:

    • Involves all aspects of experimentation.

    • Developing a clear experimental design at the outset is essential.

  • Planning includes:

    • Sample selection.

    • Ethical considerations.

    • Selection and assignment of participants.

    • Observational procedures.

    • Selection of controls.

    • Data analysis.

Carrying Out the Plan

  • Precision in execution:

    • The plan must be implemented precisely and exactly.

  • Variance:

    • Variance is crucial; without it, there is nothing to study or hypothesize.

    • Independent Variable (IV) Variance:

      • Manipulating the IV introduces experimental variance.

      • Variance in the IV leads to variance in the Dependent Variable (DV).

    • Random Assignment:

      • Equates the groups; manipulation of IV may disrupt this equality, causing variations.

    • Extraneous Variance:

      • Can threaten internal validity and create alternative explanations.

Types of Variance

  • Systematic Between Groups Variance:

    • Caused by manipulation of the IV and reflects differences between groups.

    • Significance:

      • Look for significant differences greater than expected by chance or sampling error.

  • Sources of Systematic Effects:

    • Experimental Variance:

      • Introduced deliberately by the researcher.

    • Extraneous Variance:

      • Arises from uncontrolled variables that confound results.

    • Sampling Error:

      • Natural variation occurring when sampling from a population.

Nonsystematic Within Groups Variance

  • Also called error variance:

    • Results from random factors affecting participants within the same group.

    • Characteristics:

      • Normal variability is expected, even if no systematic effects are present.

    • Influence of Random Variance:

      • Variance is affected by scores that are lower or higher than expected, leading to an increase in variance.

Systematic Effects and Error Variance

  • Systematic between groups variance + Error variance = Total variance.

  • An F ratio of around 1.00 indicates only error variance; higher suggests systematic effects are present.

  • Controlling Experimental and Error Variance:

    • To show causal inferences, the experimental variance must be high while controlling the extraneous and error variance.

Maximizing Experimental Variance

  • Ensure that the manipulation of the IV has its intended effects.

  • Use of Manipulation Check:

    • Assesses whether the IV was varied as intended.

    • Example: In a study on emotional arousal, it's crucial to confirm participant perceptions of the study's humor.

Controlling Extraneous Variance

  • Ensuring that groups are similar at the outset is essential.

  • Only the IV should differ between groups.

  • Techniques:

    • Conduct tightly controlled studies and consider participant homogeneity to reduce extraneous variance.

    • Match participants or apply within-subjects designs to manage confounds effectively.

Minimizing Error Variance

  • Individual differences and chance factors contribute to error variance.

  • Sources:

    • Measurement error and variation in participant responses.

    • Strategies:

      • Maintain reliable instruments and control for individual differences.

Nonexperimental Approaches

  • Ex Post Facto Studies:

    • Observing present behavior to relate it to prior experiences, but lacking manipulation raises concerns regarding validity.

    • Example: Observations of patients reporting historical trauma and its relation to psychopathology do not establish causation.

  • Single Group Studies:

    • Offer limited control, risks confounding to various factors such as Placebo effects, History, Maturation, and Regression to Mean.

  • Pretest-Posttest Studies:

    • Improved control, but still vulnerable to uncontrolled factors.

  • Experimental Approaches:

    • Introduce control groups and random assignment to minimize confounds.

Statistical Analyses in Experimental Designs

  • Depending on data level, different statistics are applicable:

    • Nominal Data: Chi-square.

    • Ordinal Data: Mann-Whitney U.

    • Interval/Ratio Data: t-test or ANOVA.

  • ANOVA Assumptions:

    • Data must be normally distributed with homogeneity of variance.

  • Multiple Analysis Techniques:

    • Use t-tests for multiple comparisons post ANOVA to determine where differences lie while managing Type I error rates with controlled methods like Tukey's tests.