21 - Repeated Assessments

Question 1

Benefits of Repeated Assessments

Answer 1

• Examines within-person change to evaluate causality

- Examine time effects (development, sensitive periods)

Question 2

Possible Reasons for X-Y Correlation

Answer 2

1. X causes Y
2. Y causes X
3. Confound: a third variable influences both X, Y
4. Spurious: false

Question 3

Benefit of Longitudinal Design

Answer 3

• Whether predictor predicts outcome

- Uses person as their own control (within-person)

Question 4

Internal Validity

Answer 4

Extent to which a causal inference is justified from the research

Question 5

Longitudinal Approaches, Ranked Worst to Best

Answer 5

1. Cross-Sectional: one timepoint, cannot establish causality
2. Lagged Association: X at T1 predicts Y at T2
3. LA Controlling for Prior Levels: X at T1 predicts Y at T2 controlling for Y at T1 (predicts change)
4. LACfPL, Testing Both Direction of Effects Simultaneously: demonstrates chicken/egg and directionality (if bidirectional, magnitude of each)

Question 6

Test of Mediation

Answer 6

• 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

Question 7

Use if you want to observe growth over time

Answer 7

RAW SCORES (not tranformed/normed scores)

Question 8

Score Tranformations/Norms

Answer 8

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

Question 9

Strengthening Inference of Change

Answer 9

• 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)

Question 10

Difference Scores

Answer 10

-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

Question 11

Nomothetic vs Idiographic

Answer 11

Nomothetic: population approach; assuming homogeneity (more accurate, more generalizable)

Idiographic: individual approach

Question 12

Structural Equation Modeling

Answer 12

Allows for multiple dependent variables to help assess causality

Question 13

Cross-Sectional Design

Answer 13

• Multiple participants at one timepoint
• Interest: age differences
• Confounds: cohort differences
• Limitation: does not show change over time

Question 14

Cross-Sectional Sequence Design

Answer 14

• Successive studies of different participants at different times
• Interest: age differences
• Confounds: cohort differences, time-of-measurement

Question 15

Time-Lag Design

Answer 15

• Participants from different cohorts assessed at same AGE
• Interest: cohort differences
• Confounds: time-of-measurement

Question 16

Longitudinal Design

Answer 16

• Same participants measured at multiple timepoints
• Interest: age differences
• Confounds: cohort effects and time-of-measurement

Question 17

Longitudinal Sequences Design

Answer 17

• Following multiple cohorts across time
  1. Time sequential: multiple ages assessed at multiple times
  2. Cross sequential: multiple cohorts assessed at multiple times
  3. Cohort sequential: multiple cohorts assessed at multiple ages

*Remember the triangle!

Question 18

Heterotypic, Homotypic, Phenotypic Continuity & Discontonuity

Answer 18

• Homotypic: same process, same behavior across time
• Heterotypic: same process, different behavior
• Phenotypic: different process, same behavior
• Discontinuity: different process, different behavior

Question 19

Identifying Heterotypic Continuity

Answer 19

1. Rank order stability: changes in degree of stability
2. Content level on the construct (IRT-difficulty, SEM-intercept)
3. How strongly the content reflects the construct (IRT-discrimination, SEM-factor loading)

Question 20

Assessing a construct across development

Answer 20

1. All possible content
   
   • Pros: comprehensive, allows you to examine change in each facet
   • Cons: Inefficient, intrusions
2. Only the common content
   
   • Pros: efficient, may exclude inappropriate content, same measure for easy interpretation
   • Cons: fewer items, loss of info, gaps
3. Only the construct-valid content
   -Pros: efficient, retains construct & content validity
   -Cons: time-intensive, uses different measures (so harder to compare)
   RECOMMENDED!

Question 21

Ensuring Statistical Equivalence

Answer 21

• AKA: same mathematical metric
• Use Developmental scaling: measures that differ in difficulty and discrimination are on same scale with all content (age-common and age-different) to estimate each person’s score on that scale

Developmental Scaling Approaches:

• SEM: allows estimation of latent variable with different content across time
• IRT: links measures’ scales based on difficulty and discrimination of common content

Question 22

Ensuring Theoretical Equivalence

Answer 22

• Construct validity invariance = content reflects the construct at each age
• Should show content validity (sample all aspects of the construct)
• Test-retest reliability in short term
• Convergent and discriminant validity of measures
• Similar factor structure across time based on factor analysis
• High internal consistency reliability

Question 23

Confounds of Change

Answer 23

1. Practice effects
2. Cohort effects
3. Time of measurement

Question 24

Inferring Development

Answer 24

Would need to do all 3 longitudinal sequential designs and see that age-related differences were stronger than cohort- and time-of-measurement differences

Establish longitudinal measurement invariance:

1. SEM: same intercepts and factor loadings across ages
2. IRT: same difficulty and discrimination across ages