W11 longitudinal designs

Question 1

Determinations of causation are based on three criteria

Answer 1

• The constructs need to covary
• There must be temporal precedence
• No confounding factors (i.e. alternative explanations need to be ruled out)

Question 2

Temporal precedence

Answer 2

• in cross-sectional research, temporal precedence can never be directly empirically tested
• the ‘causal’ direction is inferred by allocating a variable as the criterion (DV), and others as the predictors (IVs). This suggests the IVs ‘cause’ the DV
• theory and previous empirical; findings are used to justify the direction of the statistical analysis

Question 3

Panel designs

Answer 3

• panel designs are the gold-standard technique in longitudinal research
• in a panel design, the same participants complete the same questionnaire over multiple time points
• this ensures that both the predictor and criterion variable are measured at multiple time points

Question 4

Stability and change

Answer 4

Stability:
the degree of consistency in scores, means, or rank orders from one time point to another

Change:
the degree of fluctuation in scores, means, or rank orders from one time point to another

In what ways and how much do things stay the same over time versus how much do they change. Often tested as baseline (the initial measurement) to re-test

Question 5

Uni directional relationship of association

Answer 5

Uni

• there is a clear direction in the relationship between the predictor and criterion variable
• a uni-directional relationship in a well-designed longitudinal study provides support for temporal precedence

Question 6

Bi-directional relationship of association

Answer 6

• occurs when the predictor variable is related to the criterion variable, and the criterion variable is related to the predictor variable
• in this instance, it is not possible to conclude that one variable occurred prior to the other, so temporal precedence cannot be determined. Rather both variables ‘cause’ one another

Question 7

Simplex designs

Answer 7

• simplex designs, also known as autoregressive designs, involve regressing a variable on itself across time
• in other words, the measurement of a variable at time 1 should predict time 2 (i.e. stability)
• this type of model is called autoregressive as the values of a scale are automatically regressed onto the same scale
• this type of analysis allows researchers to explore the stability and change in one construct

Question 8

Simplex designs - stable association

Answer 8

• a perfect association between the two time-points indicates that individual’s relative standings on the construct have not changed
• the rank-order of the participants remains the same. Participants with high T1 scores, will have high T2 scores (i.e. there is a high degree of stability from T1 to T2)

Question 9

Simplex designs: stability

Answer 9

• a large autoregressive coefficient can mean one of two things (in this course)
  1. individuals do not change over time
  2. individuals uniformly increase or decrease over time
• across each explanation, the rank-order of the participants have not changed, therefore on a between-participant level (inter-individual) there is stability

Question 10

Simplex designs - changing relative standings

Answer 10

• a small or zero association between the two time-points indicate that individual’s relative standings on the construct have changed dramatically
• with a low autoregressive association, it indicates a rank-order change in the participants scores from T1 to T2, thus there is change between individuals

Question 11

Simplex designs - temporal stability v change

Answer 11

• look up image

- blue is T1 red T2

Question 12

Longitudinal correlations

Answer 12

• longitudinal correlations examine the relationship between the IV at T1 and DV at T2
• similar to the correlations and bivariate regression techniques covered in this course, only two variables are analysed
• if there is a sig relationship between IV1 and DV2 but no sig relationship between DV1 and IV2, it can be argued that temporal precedence has ben found
• > if both sig, may indicate bi-directional relationship

Question 13

Longitudinal correlations - weaknesses

Answer 13

• this analysis does not account for correlations between variables at each time point
• does not account for the stability in a construct
• > there is no indication of the stability or change in the construct over time
• the combination of these limitations ensures researchers cannot rule out that the relationship between T1 and T2 is simply due to a cross-sectional relationship

Question 14

Residualised longitudinal regression

Answer 14

• to address the limitations of longitudinal correlations, researchers can ‘residualise’ the DV
• this is done by entering the score of the DV at T1 into the analysis
• as a result, the correlation between the DV across two time points is considered (i.e. stability), then statistically removed from the analysis
• this process allows researchers to ‘predict change’
• by entering the DV at T1, the unique variance remaining in the DV at T2, reflects the change in the construct over time
• look up image

Question 15

Strengths and weaknesses residualised longitudinal regression

Answer 15

Strengths

• The correlations between variables at T1 statistically controlled for.
• The stability of the DV is accounted for.
• Consequentially, this analytical technique allows a researcher to predict change (in other words, find a longitudinal effect).

Weaknesses
- Temporal precedence is still not identifiable because there is no test for bi-directional relationships.

Question 16

Cross-lagged models

Answer 16

• cross-lagged models combine two residualised longitudinal regressions in the same analysis
• the stability of the construct is statistically removed, allowing each variable to predict the change in the other variable
• in cross-lagged models, the two measures serve as both the independent and dependent variable
• in this analysis, there is more than one DV, allowing for investigation of bi-directional effects

Question 17

Cross-lagged model steps

Answer 17

• correlations between variables at T1 and then at T2
• stability of variables each over time
• ‘cross-lag path’ 1 = V2 at T1 predicting change in V1
• ‘cross-lag path’ 2 = V1 at T1 predicting change in V2

Question 18

Overview of longitudinal regression

Answer 18

*look up image

Question 19

Assumptions of longitudinal analysis - inter-individual stability

Answer 19

• these techniques examine stability and change in the sample over time
• thus, it is assumed that there are no systematic differences in the stability and change between participants
• this assumption is violated when one group of participants changes faster or slower relative to the other participants

Question 20

Assumptions of longitudinal analysis - consistent measurement

Answer 20

• it is assumed that the measurement is the same when using repeated measures
• in practical terms, this means that the items administered to participants need to be exactly the same across time periods
• in conceptual terms, participants need to read and interpret the questions in exactly the same way across time periods

Question 21

Assumptions of longitudinal analysis - synchronicity

Answer 21

• it is assumed that the administration of questionnaire occurs with the same interval between time periods for all participants

Question 22

Considerations of longitudinal analysis - timeframe

Answer 22

• in order to find a longitudinal effect, the length of time between measurements need to be considered
• the timeframe may be too short for a variable to impact another, and so you won’t find an effect
• the timeframe may be too long and the impact of one variable on the other may have dissipated

Question 23

Considerations of longitudinal analysis - other variables

Answer 23

• it is important to ensure that important variables are not omitted
• this idea is related to a causal process or pathway
• for this reason, it is easier to say that a variable at T1 influences or impacts something, rather than causing it