Stats Topic 3 Flashcards
Measuring Variables in Research Studies & Challenges
- Variables are characteristics or conditions that can change across individuals or groups.
- Some variables are tangible and easily measured (e.g., height, weight).
- Others are abstract constructs (e.g., motivation, self-esteem), requiring operational definitions to be measured.
Operational definition
Specifies how a construct is measured (e.g., intelligence measured via IQ test).
Challenges in measuring variables
- Subjectivity in defining constructs.
- Measurement errors and inconsistencies.
- Observer bias and self-report limitations.
Validity of Measurement
Validity refers to whether a measurement accurately measures what it is supposed to
Internal Validity
Ensures that observed effects in a study are due to the independent variable, not confounding factors.
External Validity
Determines if findings are generalizable beyond the study.
Construct Validity
Assesses whether the measurement tool truly captures the theoretical construct.
Criterion Validity
Evaluates how well one measure predicts an outcome based on another established measure.
Threats to Validity
- Bias in measurement tools.
- Participant responses influenced by expectations (placebo effect).
- Environmental factors affecting responses.
Reliability of Measurement
Reliability refers to the stability and consistency of a measurement.
Test-Retest Reliability
Consistency over time (e.g., IQ test results should remain similar if taken twice).
Inter-Rater Reliability
Agreement among different observers measuring the same phenomenon.
Internal Consistency
Ensures that different items in a test measuring the same construct yield similar results.
Reliability vs. Validity
- A measurement can be reliable but not valid (e.g., a broken scale consistently gives incorrect weight).
- A measurement must be both reliable and valid for scientific accuracy.
