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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.