3003PSYmini lecture Residualised Longitudinal Regressions.pdf

3003PSY Survey Design and Analysis in Psychology RESIDUALISED LONGITUDINAL REGRESSIONS  RESIDUALISED LONGITUDINAL REGRESSION To address the limitations of longitudinal correlations, researchers can ‘residualise’ the DV. This is done by entering the score of the DV at time 1 into the analys...

3003PSY Survey Design and Analysis in Psychology RESIDUALISED LONGITUDINAL REGRESSIONS  RESIDUALISED LONGITUDINAL REGRESSION To address the limitations of longitudinal correlations, researchers can ‘residualise’ the DV. This is done by entering the score of the DV at time 1 into the analysis. RESIDUALISED LONGITUDINAL REGRESSION As a result, the correlation between the DV across two time points is considered (i.e. the stability), and then statistically removed from the analysis. This process allows researchers to ‘predict change.’ By entering the DV at time 1, the unique variance remaining in the DV at time 2, reflects the change in the construct over time. SIMPLEX DESIGNS Stability Change LONGITUDINAL CORRELATIONS RESIDUALISED LONGITUDINAL REGRESSION Stability Change Predicting change RESIDUALISED REGRESSION: EXAMPLE 374 high school students were followed over two years (grade 9 and 10). The analysis investigated if connecting with the community during extracurricular activities was related to higher wellbeing. O’Donnell, Pegg, Lala, & Barber. (2018). Diverse Peers, links to the community, and social identity: An investigation of co-participants in organised activities and depressed mood. To be presented at 25th Biennial Meeting of the International Society for the Study of Behavioural Development RESIDUALISED REGRESSION: EXAMPLE Wellbeing is a well-known stable construct. Based upon previous research, they knew that there is a cross- sectional (i.e. contemporaneous) relationship between feeling connected to the community and higher wellbeing. RESIDUALISED REGRESSION: EXAMPLE Theoretically, feeling connected to the community should cause higher wellbeing over time. The stability of wellbeing indicates that it shares the same statistical variance over time. The cross-sectional relationship between the IV and DV indicate that feeling connected to the community shares variance with wellbeing. How do we know that feeling connected with the community predicts change in wellbeing, and not stability? RESIDUALISED REGRESSION: EXAMPLE Controlling for the previous year’s wellbeing, infers that feeling connected with the community predicts the change in adolescent’s wellbeing. By controlling for wellbeing at time 1, the stability in the construct has been statistically removed. As a result, links to the community predicts the change in wellbeing. B =.20, p =.018.001 <.75, p B = RESIDUALISED REGRESSION: EXAMPLE Change of interest RESIDUALISED REGRESSION: EXAMPLE 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.  SUMMARY u Residualised longitudinal regression combines the strengths of the simplex design and the longitudinal correlation uIn this design the DV measure at T1 is entered into the analysis along with the IV to predict the DV measured at T2 uThis partitions the variance in the DV measured at T2 into variance shared with the DV measured at T1 (stability) and unique variance – the “residualised” variance (change) uThe IV can then be used to predict change in the DV uThis approach is limited in testing for temporal precedence as it cannot test for bi-directional relationships