Key Science Skills

Population and Sample

  • Population: The larger group of people that a researcher wishes to study.

  • Sample: A smaller subset of the population chosen to participate in the study.

  • Generalisability: The ability for a sample’s results to be applied to the wider population.

Sampling Techniques

  • Convenience Sampling: Selecting readily available participants without a random or systematic approach.

  • Random Sampling: A technique ensuring every member of the population has an equal chance of being selected.

  • Stratified Sampling: A technique where the population is divided into strata (subgroups), and participants are selected in proportion to their representation.

Variables

  • Extraneous Variable: A variable other than the independent variable that may affect the dependent variable.

  • Confounding Variable: A variable that has directly and systematically affected the dependent variable apart from the independent variable.

Common Sources of Error (POPSEND)

  1. Participant-related variables: Differences in participants (e.g., age, background) that affect results.

  2. Order effects: When the sequence of experimental tasks affects performance.

  3. Placebo effect: When participants respond to an inactive treatment due to expectations.

  4. Situational variables: Environmental factors affecting results (e.g., weather, time of day).

  5. Experimenter effects: Researcher bias influencing results.

  6. Non-standardised instructions and procedures: Differences in how participants are instructed.

  7. Demand characteristics: Participants altering behavior based on perceived study aims.

Preventing Extraneous and Confounding Variables

  • Counterbalancing: Alternating the order of conditions to reduce order effects.

  • Single Blind Procedure: Participants do not know which group they are in.

  • Double Blind Procedure: Both participants and experimenters do not know who is in which group.

  • Standardised Instructions and Procedures: Ensuring uniformity in instructions and processes.

Types of Data

  • Primary Data: Collected first-hand by a researcher.

  • Secondary Data: Data obtained from previous research.

  • Qualitative Data: Non-numerical data (e.g., descriptions, feelings).

  • Quantitative Data: Numerical data (e.g., test scores).

  • Objective Data: Measurable and not influenced by personal bias (e.g., IQ scores).

  • Subjective Data: Based on personal opinions or perceptions (e.g., self-reports).

Descriptive Statistics

  • Mean: The numerical average of a dataset.

  • Median: The middle value in an ordered dataset.

  • Mode: The most frequently occurring value in a dataset.

Measures of Variability

  • Range: The difference between the highest and lowest values in a dataset.

  • Standard Deviation: A measure of how much values deviate from the mean.

Evaluation of Research

  • Accuracy: How close a measurement is to the true value.

  • Precision: How close repeated measurements are to each other.

  • Systematic Error: Consistent error affecting all measurements (e.g., faulty scale).

  • Random Error: Unpredictable errors that vary from measurement to measurement.

Validity

  • Internal Validity: The extent to which a study truly measures what it claims to.

  • External Validity: The extent to which results can be applied to different settings.

Ethical Considerations

  • Ethical Concepts: Broad moral principles guiding research.

  • Ethical Guidelines: Formal rules researchers must follow to ensure ethical standards.