review 2

  • Sample Bias and Generalization

    • Question of whether results from a biased sample can be generalized to the population.

    • Conclusion: Confidence in generalizability decreases if the sample is not representative.

  • Sampling Methods

    • Differentiation between Probability Sampling and Non-Probability Sampling Methods:

    • Probability Sampling: Each member of the population has an equal chance of being included in the sample.

      • Random Sampling: Regarded as one of the best probability sampling methods, but requires a complete list of the population for proper execution.

    • Non-Probability Sampling: Samples are selected based on non-random criteria.

      • Convenience Sampling: Usage of participants who are readily available, e.g., a psychology subject pool which may not be representative of the broader student population.

  • Stratified Sampling

    • Involves dividing the population into strata based on characteristics like gender or educational level to ensure that the sample reflects these proportions.

  • Operational vs. Conceptual Definitions

    • Operational Definition: Describes how a concept or variable is measured or manipulated in a study; e.g., using heart rate to measure stress.

    • Conceptual Definition: A more abstract explanation of what a concept means, often derived from previous research.

  • Reliability and Validity

    • Reliability:

    • The consistency of a measure, e.g., a scale giving the same weight reading if used repeatedly under identical conditions.

    • Key Principle: High reliability requires minimizing measurement error.

    • Validity:

    • The extent to which a measure accurately reflects the construct it is intended to measure.

    • A measure can be reliable but not valid if it consistently gives the same wrong answer.

  • Types of Reliability

    • Internal Consistency Reliability:

    • Assesses how consistent results are across items within a test; measured by methods like Cronbach's Alpha, which aims for a value between 0 (no consistency) and 1 (perfect consistency).

    • Test-Retest Reliability: Measures stability of responses over time by testing the same participants under similar conditions after a period.

    • Inter-rater Reliability: Evaluates consistency between different raters observing the same phenomenon.

  • Types of Validity

    • Content Validity:

    • Ensures that the measure represents all facets of a given construct; evaluated by experts familiar with the topic.

    • Construct Validity:

    • Evaluates whether a measure behaves as expected, correlating with other relevant measures (Convergent Validity) and not correlating with unrelated constructs (Discriminant Validity).

    • External Validity:

    • Reflects how generalizable the study findings are across different populations or environments.

    • Statistical Conclusion Validity:

    • Focuses on whether the conclusions drawn from statistical analyses are justified and based on sound statistical practices (e.g., appropriate use of statistical tests).

  • Measurement Scales

    • Characteristics of different measurement scales include:

    • Nominal: Categories without implied order (e.g., gender).

    • Ordinal: Categories with order but unequal intervals (e.g., race rankings).

    • Interval: Equal intervals between values but no true zero (e.g., temperature).

    • Ratio: Equal intervals with a meaningful zero point (e.g., height, weight).

  • Effect Size and Confidence Intervals

    • Effect Size: Provides a quantitative measure of the magnitude of a phenomenon; Cohen's d and Pearson's r are common effect size indices.

    • Confidence Interval: A range around a point estimate that likely contains the population parameter; helps interpret the precision of the estimate.

  • Independent and Dependent Variables

    • Independent Variable: The manipulated factor in a study that is hypothesized to affect another variable.

    • Dependent Variable: The outcome variable that is measured to assess the effect of the independent variable.

    • Subject Variable: Variables that characterize the subjects (e.g., age, gender) that are not controlled by the experimenter.

  • Research Designs and Methodologies

    • Random Assignment: Ensures each participant has an equal chance to be assigned to any condition, reducing bias. Alternatives include blocked and matched random assignment.

    • Single Blind vs. Double Blind:

    • Single Blind: Participants do not know which treatment they receive to reduce bias.

    • Double Blind: Both participants and researchers are unaware of treatment assignments, further reducing bias.

  • Developmental Research

    • Focuses on changes over time, studying cognitive, social and emotional development.

    • Cross-Sectional, Longitudinal, and Cohort Sequential Designs:

    • Cross-Sectional: Observes different age groups at one point in time, risk of historical confounding.

    • Longitudinal: Tracks the same individuals over time to see changes and stability, controls for historical differences.

    • Cohort Sequential: Combines longitudinal and cross-sectional designs; observes multiple cohorts over time to assess developmental change while managing historical factors.