PSYCHOLOGICAL STATISTICS REVIEWER 1

Psychological Statistics Overview

1. Research Methodologies

A. Descriptive Methods

• Focus on observing and detailing behaviors as they naturally occur.

• Strengths:

  • Provides rich qualitative data.

  • Versatile across various settings.

  • Non-intrusive; does not manipulate variables.

• Limitations:

  • Lack of control makes causality hard to determine.

  • Subjectivity in data interpretation.

  • Generalizability issues due to specificity.

B. Correlational Methods

• Examines relationships between two variables without implying causation.

• Strengths:

  • Useful for discovering associations.

  • Conducted in natural settings for ecological validity.

  • Ethical flexibility allows studying non-manipulable variables.

• Limitations:

  • Does not establish causation.

  • Potential confounding variables.

  • Directionality problems complicate understanding.

C. Experimental Methods

• Manipulates variables to determine cause-and-effect relationships.

• Strengths:

  • Establishes cause-and-effect relationships.

  • High control reduces extraneous factors.

  • Results can be replicated for reliability.

• Limitations:

  • Often conducted in artificial settings.

  • Some variables cannot be ethically manipulated.

  • Limited scope may overlook broader context.

2. Ethical Considerations in Field Research

A. Informed Consent

• Ensures participants fully understand the research process.

• Cultural Sensitivity:

  • Address language barriers and literacy.

• Voluntary Participation:

  • Emphasize no negative consequences for opting out.

B. Respect for Local Customs

• Community Involvement:

  • Engage local leaders to align research with cultural norms.

  • Conduct community meetings to explain research goals.

C. Confidentiality and Privacy

• Protect identities in close-knit communities.

• Handle sensitive topics with care.

3. Research Design Overview

A. Phase 1: Objective, Method, and Outcomes

• Descriptive Method: Collect baseline data; gather qualitative and quantitative insights.

• Correlational Analysis: Use correlational analysis for relationships among variables.

• Experimental Intervention: Test effects of interventions and manipulations.

4. Statistical Methods

A. Quantitative Methods

• Generate numerical data to identify patterns.

• Strengths:

  • Provides precise evidence; easier to replicate.

• Limitations:

  • May oversimplify complex phenomena; lacks contextual depth.

B. Qualitative Methods

• Capture complex human experiences.

• Strengths:

  • Provides deep understanding of context.

• Limitations:

  • More difficult to replicate; time-consuming.

5. Statistical Concepts

A. Descriptive Statistics

• Summarizes data using measures like mean, median, mode, standard deviation, and range.

B. Inferential Statistics

• Use sample data to infer population characteristics and test hypotheses.

6. Validity and Reliability

A. Reliability Types

• Cronbach’s Alpha: Measures internal consistency.

• Test-Retest Reliability: Evaluates stability over time.

• Inter-Rater Reliability: Assesses agreement between different raters.

B. Validity Types

• Content Validity: Ensures comprehensive coverage of concepts.

• Construct Validity: Assesses theoretical accuracy.

• Criterion-Related Validity: Examines correlation with established measures.

7. Understanding and Analyzing Data

A. Probability Concepts

• Evaluates uncertainty and makes predictions about human behavior.

• Key concepts include random events, probability distributions, and significance testing.

B. Important Statistical Applications

• Hypothesis testing, risk assessment, and generalization of findings from samples to populations.

8. Common Statistical Errors

A. Type I Error (False Positive)

• Incorrectly rejects the null hypothesis, leading to false assumptions of effect.

B. Type II Error (False Negative)

• Fails to reject the null hypothesis, missing real effects.

C. Sampling and Measurement Errors

• Sampling errors affect generalizability; measurement errors compromise data accuracy.

9. Statistical Representations

• Descriptive statistics include mean, median, mode, and variance.

• Understanding quartiles, z-scores, and confidence intervals facilitates data interpretation.

10. Practical Implications of Statistics in Psychology

• Statistics is essential for making evidence-based decisions, validating findings, and enhancing research quality.