RMI Final

Experimental Design

A design that allows you to determine causality by manipulating at least one independent variable, using random assignment, and controlling extraneous variables.

Independent Variable (IV)

The variable that is manipulated by the researcher.

Dependent Variable (DV)

The measured outcome that may be influenced by the IV.

Random Assignment

Assigning participants to conditions by chance to ensure group equivalence.

Matched Groups Design

Participants are matched on a variable and then randomly assigned.

Repeated Measures Design

Each participant experiences all conditions.

Counterbalancing

Controlling for order effects in repeated measures by varying the order of conditions.

Systematic Variance

Variation due to the IV.

Confound Variance

Variation due to uncontrolled extraneous variables.

Error Variance

Random variation due to individual differences, measurement error, or environment.

Experimenter Expectancy Effects

When a researcher's expectations influence participants' behavior.

Demand Characteristics

When participants try to guess the hypothesis and change their behavior.

Placebo Effect

Improvement caused by the participant's belief in treatment rather than the treatment itself.

Double-Blind Design

Both participant and experimenter are unaware of condition assignments.

External Validity

The extent to which results generalize to other settings, people, or times.

Internal Validity

The degree to which a study demonstrates a causal relationship.

One-Way Design

Experimental design with a single IV.

Factorial Design

Experimental design with two or more IVs.

Main Effect

The effect of one IV, averaging across the levels of other IVs.

Interaction Effect

When the effect of one IV depends on the level of another IV.

Mixed Factorial Design (Expericorr)

Design that includes both manipulated and measured variables.

Exploratory Data Analysis

Using graphs and descriptive stats to understand data.

Null Hypothesis Significance Testing (NHST)

Procedure for testing whether an effect exists.

Type I Error

Rejecting a true null hypothesis (false positive).

Type II Error

Failing to reject a false null hypothesis (false negative).

Statistical Power

Probability of correctly rejecting a false null hypothesis.

Effect Size

A measure of the strength or magnitude of a finding.

Confidence Interval (CI)

A range around a sample estimate that likely contains the population value.

t-Test

Statistical test comparing means between two groups.

Independent Samples t-Test

Used for between-subjects comparisons.

Paired Samples t-Test

Used for within-subjects (repeated measures).

ANOVA (Analysis of Variance)

Used to test differences among three or more group means.

F-Ratio

The ratio of between-group variance to within-group variance.

Post Hoc Test

Conducted after a significant ANOVA to determine which groups differ.

MANOVA

Multivariate analysis of variance; used when there are multiple DVs.

Bonferroni Correction

Adjusts p-value to reduce risk of Type I error in multiple comparisons.

Quasi-Experimental Design

Design that lacks random assignment but includes other features of true experiments.

Pretest-Posttest Design

Measure DV before and after treatment.

Time Series Design

Measure DV repeatedly before and after intervention.

Comparative Time Series Design

Time series across multiple groups.

Longitudinal Design

Tracks same participants over time.

Cross-Sequential Cohort Design

Combines cross-sectional and longitudinal designs.

Program Evaluation

Applied research to assess effectiveness of programs.

Single-Case Design

Focus on individual participant or case.

ABAB Design

Baseline-treatment-baseline-treatment; used to establish causality.

Multiple Baseline Design

Introduce treatment at different times across behaviors or participants.

Case Study

In-depth investigation of an individual using multiple data sources.

Deontology

Ethics based on rules and duties.

Utilitarianism

Ethics based on outcomes.

Ethical Skepticism

Ethics depend on individual conscience.

APA Principles

Respect, beneficence, and justice.

Informed Consent

Participants must be informed and consent voluntarily.

Privacy

Control over access to personal information.

Coercion

Pressure to participate is unethical.

Risk/Stress

Avoid unnecessary harm.

Deception

Must be justified and followed by debriefing.

Confidentiality

Keep participant data secure and private.

Debriefing

Explain study and ensure participant well-being afterward.

Vulnerable Populations

Require extra protections (e.g., children, prisoners).

Animal Research

Must follow ethical guidelines.

Scientific Misconduct

Includes fabrication, falsification, and plagiarism.

Questionable Research Practices

Includes p-hacking, selective reporting.

Suppression of Findings

Withholding results violates scientific integrity.

Ethical Vigilance

Continuous attention to ethical standards and judgment.