21 - Repeated Assessments Flashcards
Benefits of Repeated Assessments
- Examines within-person change to evaluate causality
- Examine time effects (development, sensitive periods)
Possible Reasons for X-Y Correlation
- X causes Y
- Y causes X
- Confound: a third variable influences both X, Y
- Spurious: false
Benefit of Longitudinal Design
- Whether predictor predicts outcome
- Uses person as their own control (within-person)
Internal Validity
Extent to which a causal inference is justified from the research
Longitudinal Approaches, Ranked Worst to Best
- Cross-Sectional: one timepoint, cannot establish causality
- Lagged Association: X at T1 predicts Y at T2
- LA Controlling for Prior Levels: X at T1 predicts Y at T2 controlling for Y at T1 (predicts change)
- LACfPL, Testing Both Direction of Effects Simultaneously: demonstrates chicken/egg and directionality (if bidirectional, magnitude of each)
Test of Mediation
- Evaluate whether X predicts M at a later time point when controlling for earlier M; and whether X and Y are better explained by M
- Use MEM or SEM
- Challenge: time points on scales may differ
Use if you want to observe growth over time
RAW SCORES (not tranformed/normed scores)
Score Tranformations/Norms
T: mean 50, SD 10
Z: mean 0, SD 1
Standard: mean 100, SD 15
*DON”T ALLOW YOU TO OBSERVE GROWTH: for that, you need raw scores
Strengthening Inference of Change
- Large magnitude between scores
- Measurement error (unreliability) at both timepoints is small (reduce unreliability by combining multiple measures)
- Measurement is invariant across time (same meaning, comparable scale)
- Assess using SEM (intercept, factor loading) or IRT (differential item functioning, or difficulty/discrimination)
-No evidence for confounds of change (practice effects, cohort effects, time-of-measurement effects)
Difference Scores
-Less reliable than each individual measure; more so if they’re correlated
- Depends on:
1. reliability of individual measures
2. if variability of true individual differences is large
Nomothetic vs Idiographic
Nomothetic: population approach; assuming homogeneity (more accurate, more generalizable)
Idiographic: individual approach
Structural Equation Modeling
Allows for multiple dependent variables to help assess causality
Cross-Sectional Design
- Multiple participants at one timepoint
- Interest: age differences
- Confounds: cohort differences
- Limitation: does not show change over time
Cross-Sectional Sequence Design
- Successive studies of different participants at different times
- Interest: age differences
- Confounds: cohort differences, time-of-measurement
Time-Lag Design
- Participants from different cohorts assessed at same AGE
- Interest: cohort differences
- Confounds: time-of-measurement