1. Variation, Validity, & Variables Lesson 3

2. Research Methods & Statistics n Integral relationship l Must consider both during planning n Research Methods l How data are collected l What kind of data n Statistics l Analysis & interpretation depends on data & how it is collected ~

3. Scientific Validity n Scientific conclusions l About relationships b/n variables n Validity l Soundness, legitimacy, truth n Internal validity l About cause & effect n External (ecological) validity l About broad applicability ~

4. How are data collected? n 2 scientific approaches l Same or similar statistical analysis l NOT same confidence in conclusions n Observational methods l Observe co-occurrence of variables l Naturalistic observation, case studies, archival research, surveys, etc. n Experimental method l Manipulate a variable  observe effect on another variable ~

5. The Experimental Method n At least 2 variables: l Independent (IV) & Dependent (DV) n At least 2 groups (levels of IV) l control group - no treatment l experimental - receives treatment l random assignment to groups n Control extraneous variables l Which might also affect DV l Weakens internal validity ~

6. Experimental Variables n Independent (IV) l Predictor (or cause) l Manipulated n Dependent (DV) l Outcome (or effect) l Measured n Extraneous variables l Or confounding l Might also affect outcome (DV) ~

7. Variation within an Experiment n Systematic l Variation due to manipulation of IV l Difference between groups n Unsystematic l Individual differences l Variation due to random or uncontrolled variables l Potentially confounding variables ~

8. Variation within an Experiment

9. Internal Validity n Legitimacy of conclusions l about cause & effect n High internal validity l Confident that only changes in IV cause change in DV n Low internal validity l Confounding variables influence outcome ~

10. Randomization n Important for validity l Helps avoid bias n Random sampling (or selection) l Selection of participants for study l Representative sample from population l  external validity n Random assignment to condition (groups) l Minimize biasing of groups l  internal validity ~

11. Observational vs. Experimental n Internal vs External validity l Inverse relationship based on control n Observational? l  internal vs  external l Cannot determine causality n Experimental l  internal vs  external l Establishes cause & effect relationships n For useful conclusions need both ~

12. Observational vs. Experimental: Statistical Methods n Misperception l Observational  only correlational l Experiment  hypothesis tests l Method not sole determinant of analysis n Strength of cause & effect conclusions l Observational  weaker l Experiment  stronger ~

13. Planning Research n Observational or experimental research n Research design l Between-groups or within-subjects n Operational definition of variables l Data categorical or quantitative n Statistical analysis l Depends on all of the above ~

14. What are data? n Information from measurement l datum = single observation n Variables l Dimensions that can take on different values u IQ, height, shoe size, hair color l Is not the same for all individuals being measured ~

15. Measuring Variables n Operational definitions l Variables often abstract l Intelligence, anxiety, fitness, etc. l Need to objectively measure n Hypothesis: Exercise increases fitness n Independent: Exercise l Operational definition? n Dependent: fitness l Operational definition? ~

16. Levels of Measurement n Limits type of statistical analysis possible n Qualitative l Categorical l Frequency data l Discrete: only whole numbers n Quantitative l Continuous or discrete l represents magnitude l infinite # intermediate values ~

17. Levels of Measurement: Categorical n Nominal scale l categorical l order NOT meaningful l can assign arbitrary values n Ordinal scale l Categorical + meaningful order l No info about magnitude of differences l If assign numerical value, must reflect order ~

18. Levels of Measurement: Quantitative n Interval scale (numbers) l Continuous or discrete l Equal intervals  equal differences n Ratio scale l same characteristics as interval l Ratios of values must be meaningful for magnitude l scale must have true zero point n Most statistics: interval/ratio treated the same ~

19. Levels of Measurement: SPSS n Variable view tab l Formatting of variable l Measure n Nominal scale n Ordinal scale n Scale l Interval & ratio n Reminder: IV must be nominal for most statistical tests ~

20. Measurement Error n Discrepancy l between actual value of observation and the reported value n Sources of measurement error l Sensitivity of measuring instrument l Conscientiousness of observer l Surveys: inaccurate or untruthful l Low reliability of instrument n  unsystematic variation ~

21. Reliability & Validity n Accurate measurement requires both n Reliability l Consistency of measurement n Criterion validity l Extent instrument actually measures what it claims to measure l Score on IQ test measures intelligence? l  pulse rate a measure of fear? n Important for internal & external validity ~