U1 ALL

• Confounding variable


  A variable that is not the independent variable (IV) but can influence the dependent variable (DV), making it difficult to determine whether changes in the DV are due to the IV or the confounding variable
• Control


  1. Random assignment

  2. Ensuring the confounding variable is evenly distributed across groups
• Random Assignment


  • Ensures that participant characteristics are evenly distributed across groups
• Standardization


  • Keeping procedures consistent for all participants
• Matching


  • Pairing participants with similar characteristics across different groups
• Control Group


  • A group that does not receive the experimental treatment, used for comparison
• Controlled Experiment


  Manipulates one or more variables to determine their effect on an outcome
• Controlled Experiment


  • High internal validity, ability to establish cause-and-effect relationships
  • May lack external validity, artificial settings can influence behavior
• Case Study


  In-depth analysis of an individual, group, or event
• Case Study


  • Detailed and comprehensive data, useful for rare phenomena
  • Limited generalizability, potential for researcher bias
• Simulation


  Imitation of real-world processes in a controlled environment
• Simulation


  • Allows study of complex systems, high control over variables
  • May not fully replicate real-world conditions, potential for artificiality
• Correlational Study


  Examines the relationship between two or more variables without manipulation
• Correlational Study


  • Can identify relationships between variables, useful for prediction
  • Cannot establish causation, potential for third-variable problem
• Population


  The entire group of individuals of interest in a study
• Sample


  A subset of the population selected for the study
• Random Sampling


  Every member of the population has an equal chance of being selected
• Random Sampling


  • Reduces selection bias, increases generalizability
  • Can be time-consuming and costly, requires a complete list of the population
• Convenience Sampling


  Participants are selected based on availability and willingness to take part
• Convenience Sampling


  • Easy and quick to implement, cost-effective
  • Prone to bias, limited generalizability
• Stratified Sampling


  Population is divided into subgroups (strata) and samples are drawn from each
• Stratified Sampling


  • Ensures representation of key subgroups, increases generalizability
  • More complex and time-consuming, requires detailed knowledge of the population
• Representative Sample


  A sample that accurately reflects the characteristics of the population
• Biased Sample


  A sample that does not accurately reflect the population
• Primary Data


  Data collected directly by the researcher for the specific study
• Primary Data


  • Relevant and specific to the study's objectives
  • Time-consuming and costly to collect
• Secondary Data


  Data previously collected for another purpose
• Secondary Data


  • Readily available, cost-effective
  • May not be perfectly relevant or specific to the current study
• Subjective Data


  Data based on personal opinions, interpretations, or feelings
• Subjective Data


  • Provides in-depth insights into participants' perspectives
  • Prone to bias, less reliable
• Objective Data


  Data based on measurable and observable facts
• Objective Data


  • More reliable and replicable, less prone to bias
  • May miss nuanced insights into participants' experiences
• Quantitative Data


  Numerical data that can be measured and analyzed statistically
• Quantitative Data


  • Allows for precise measurement and analysis, facilitates comparisons
  • May overlook the depth and complexity of the subject matter
• Qualitative Data


  Descriptive data that provides insights into participants' experiences and perspectives
• Qualitative Data


  • Rich and detailed information, captures complexity and context
  • More difficult to analyze and generalize, time-consuming to collect
• Tabulating Data


  Organizing data into tables for clarity and ease of analysis
• Tabulating Data


  • Simplifies data presentation, facilitates comparisons
  • Can become unwieldy with large datasets
• Graphing Data


  Visual representation of data using charts or graphs
• Graphing Data


  • Makes data patterns and trends easily visible
  • Can be misleading if not constructed accurately