complex design

Complex Designs

The Typical Research Sample

  • The characteristics of research samples can vary widely and may include:

    • Undergraduate students: Often used as subjects in studies due to ease of access.

    • Weirdest subjects in the world: May refer to atypical sample groups for unique insights.

    • Volunteers and associated biases:

    • Volunteer Bias: Participants who volunteer may differ systematically from those who do not volunteer.

    • Response Bias: The tendency of participants to respond inaccurately or falsely to questions.

    • Selection Bias (locale): Bias introduced when the sample is not representative of the population from which it is drawn.

  • Skewed male/female ratio: An imbalance in gender representation; questions whether this impacts findings.

  • Cultural considerations: The cultural background of participants should be considered to ensure relevance and applicability of findings.

  • Different group sizes: Questions of whether larger or smaller sample sizes impact results and how size alters the reliability of findings.

  • Bottom Line: Specialists should assess when a difference in samples may lead to problematic results.

    • Profound findings warrant greater caution regarding sample quality and representativeness.

    • Carl Sagan popularized the phrase “Extraordinary claims require extraordinary evidence”, rewording Laplace's principle, emphasizing the need for robust evidence proportional to the strangeness of a claim (Gillispie et al., 1999).

Why Complicated Designs are Necessary

  • Moderator Variables: Crucial for understanding behavior dynamics.

    • Definition: A moderator variable influences or changes the strength or direction of the relationship between an independent variable (IV) and a dependent variable (DV).

    • Example Factors: Can include gender, time of day, order of experiments, dose of treatment, etc.

  • Use of Factorial Designs: Allow researchers to unravel complexities hidden within simpler designs by exploring effects of moderators.

Distinction between Mediators and Moderators

  • Mediator Variable: Explains the mechanism or process through which two variables are related.

  • Moderator Variable: Influences the strength and direction of the relationship between IV and DV.

Searching for Moderator Variables

  • The task of researchers is to identify and measure moderator variables to ensure the IV solely influences the DV, minimizing confounding factors

Research Methodologies: Laboratory vs. Field Research

  • Laboratory Research: Offers good experimental control but may produce artificial feelings among participants.

    • Mundane Realism vs. Psychological Realism: Balancing the authenticity of the experimental setting compared to real-world applications.

  • Field Experiments: They introduce distractions that may affect the relationship between IV and DV.

Importance of Replications

  • Replications Types:

    • Exact Replications: Repeating a study under the same conditions to see if results persist.

    • Conceptual Replications: Testing the same hypothesis with different methods or alternate conditions.

  • Strategies for Handling Replication Failures:

    • Conduct an exact replication.

    • Reach out to primary authors for insights.

    • Analyze possible reasons for discrepancies.

    • Consider publishing findings, even if they contradict existing literature, which is generally more challenging.

Some Extra Stuff

  • Literature Reviews: Important for summarizing research landscape, identifying trends, gaps, and future research directions.

  • Qualitative Research: Effective in uncovering general trends and exploring themes deeply.

  • Meta-Analyses: A statistical approach to combine results from multiple studies, enhancing power and generalizability against single study limitations.

  • Overcoming the File-Drawer Problem: Refers to the bias where only positive results are published, leading to a skewed understanding of research outcomes (Rosenthal, 1979).

Negative Results in Research

  • Acknowledgment that many researchers do not reject their null hypothesis; continual failures are common.

  • Improvement through Experience: Research proficiency increases with time and effort in the field.

  • Strategies for Enhancement:

    • Employ manipulation checks and pilot studies before conducting major experiments.

    • Develop a robust statistical analysis plan prior to experiments.

    • Persistence is crucial; face challenges head-on.

  • Inquiry Post-Experiment:

    • Assess whether the research premise or hypothesis was faulty.

    • Evaluate the adequateness of manipulations used.

    • Examine samples and procedures for integrity.

  • Variance Considerations: Assessing within-group variance is essential for establishing outcome reliability.

  • Peer review of similar papers can illuminate discrepancies and inspire fresh ideas.

Complex Experimental Designs

  • Example: Understanding stress impact on cortisol levels using various statistical tests:

    • Between-Subjects T-Test: Analyze cortisol levels at two time points, comparing groups under high and low stress.

    • Within-Subjects T-Test: Calculate difference scores over time for applied stress conditions.

Assumptions of a T-Test

  • Key assumptions to validate using T-tests:

    • The dependent variable (DV) must be continuous.

    • The independent variable (IV) consists of two independent groups.

    • Observations must be independent across groups.

    • No significant outliers should exist in the data.

    • Homogeneity of Variance: Assumes equal variance across groups.

    • Normal Distribution: Data should ideally follow a normal distribution curve.

  • Violations can lead to increased type I errors; non-parametric alternatives may be necessary when violations are problematic.

Non-Parametric Versions of the T-Test

  • Mann-Whitney U Test: A non-parametric alternative for two-group comparisons utilizing median values instead of means.

  • Wilcoxon Signed-Rank Test: Assesses related samples by comparing based on medians.

Increasing Levels of Independent Variables

  • The simplest experimental design features one IV with two levels; however, expanding levels can:

    • Help identify curvilinear relationships or provide granular data.

    • Considerations include:

    • The relationship between IV and DV.

    • Sample size availability.

    • Anticipation of variable interactions.

Increasing Independent Variables

  • In standard designs, usually only one IV is employed.

  • More complex designs, such as factorial designs, frequently incorporate multiple IVs for comprehensive exploration of variables.

  • Example: 2x3 Factorial Design comparisons amongst different anti-anxiety drugs under varying stress conditions.

Factorial Design

  • Key vocabulary for understanding designs:

    • IVs Count: In a 3x3 independent factorial design, there are 2 IVs.

    • Cells Count: Cells are formed through combination of levels of the IVs.

  • Common pitfalls exist, such as complex factorial designs being challenging to interpret due to the number of interactions.

Tests of Between-Subjects Effects

  • Example output from statistical analysis can include summary tables displaying dependent variables and their correlation effects.

    • Significant Variables Identified: Attractive features and commitment levels.

    • Statistical significance coding and relation to overall variance accounted for.

Formulating Interactions and Effects

  • Main Effects: The unique effect of one particular IV on the DV.

  • Interactions: Occur when the effect of one IV varies at different levels of another IV, indicating combined effects.

Interpretation of Factorial Designs

  • Visualization aids, such as graphs, can clarify outcomes and interactions when interpreting data from factorial designs.

  • Observations may reveal significant impacts of certain treatments or conditions across various DV measures.

Repeated Measures Designs

  • Repeated Measures ANOVA: Collects observations from the same group multiple times, often applicable in developmental and longitudinal research.

  • Key assumptions include:

    • Requirement for homogeneity of variance and covariance (Sphericity Assumption).

Mixed Designs

  • Mixed designs combine between-subjects and within-subjects variables, often to account for complex research questions.

  • Pre-Post-Control Design: A prevalent type of mixed design evaluating differences pre- and post-intervention with experimental and control groups.

ANCOVA (Analysis of Covariance)

  • Description: Assessing main and interaction effects of categorical variables on a continuous DV while controlling for selected continuous covariates.

Complex Correlational Designs & Regression

  • Utilizes multiple correlations with the inclusion of covariates, known as partial correlations; emphasizes conditions where regression is desirable.

  • Assumptions of Regression:

    • Must show linear relationships, multivariate normality, minimal multicollinearity, no autocorrelation, and homoscedasticity.

Conclusion

  • A comprehensive understanding of experimental and correlational design remains essential in intricate research settings to draw valid and reliable inferences.