Multilevel Modelling Multilevel Models (MLMs) contain variables that are measured at different levels. These levels are nested within each other and form a hierarchy. MLMs explicitly model the hierarchical nature of data at each individual level within the hierarchy, starting with the lowest level. MLMs can also be used to analyse repeated measures data where the data from each individual is analysed (in this case over the 11 weeks) on the individual level and then incorporated into a higher level analysis (in this case intervention group). MLMs fit a straight line to each individual’s data from the dataset based on a simple linear regression, allowing for the intercepts to vary dependent on the individual, but assume that these intercepts fall within a normal distribution (Kreft & de Leeuw, 1998). Rationale for using the model In the current research it is clear that the data is grouped or nested in a hierarchical structure. Variation at the individual level is likely to be nested within the intervention level. Thus the random component (individual level) needs to be modelled within the fixed component (intervention level), making the MLM framework highly applicable. Additionally, this type of analysis has also been used in previous research using diary methods when exploring the efficacy of yoga for patients with cancer, which highlight its potential use in this research (Carson et al., 2007). Analysis of the Data GAM, GLS and MLM analyses were conducted separately on visual analogue raw scores and change scores. The first set of analyses was conducted using the visual analogue scale scores over the 11 weeks. The raw scores were used for this analysis allowing us to account for actual variation between the two intervention groups. Change scores were computed by subtracting the week 0 score from the week in question, so for week 4, the equation was (VAS week 4 score – VAS week 0 score = Week 4 change score). Use of the change in VAS scores relative to week 0 (as week 0 was a baseline control) allowed for observation of any trends/relationships between the intervention and time, and the effect it had on the change in QoL from week 0. This analysis allowed us to see if there were any differences between the groups taking part in the intervention independent of their starting QoL.
Multilevel Modelling Multilevel Models (MLMs) contain variables that are measured at different levels. These levels are nested within each other and form a hierarchy. MLMs explicitly model the hierarchical nature of data at each individual level within the hierarchy, starting with the lowest level. MLMs can also be used to analyse repeated measures data where the data from each individual is analysed (in this case over the 11 weeks) on the individual level and then incorporated into a higher level analysis (in this case intervention group). MLMs fit a straight line to each individual’s data from the dataset based on a simple linear regression, allowing for the intercepts to vary dependent on the individual, but assume that these intercepts fall within a normal distribution (Kreft & de Leeuw, 1998). Rationale for using the model In the current research it is clear that the data is grouped or nested in a hierarchical structure. Variation at the individual level is likely to be nested within the intervention level. Thus the random component (individual level) needs to be modelled within the fixed component (intervention level), making the MLM framework highly applicable. Additionally, this type of analysis has also been used in previous research using diary methods when exploring the efficacy of yoga for patients with cancer, which highlight its potential use in this research (Carson et al., 2007). Analysis of the Data GAM, GLS and MLM analyses were conducted separately on visual analogue raw scores and change scores. The first set of analyses was conducted using the visual analogue scale scores over the 11 weeks. The raw scores were used for this analysis allowing us to account for actual variation between the two intervention groups. Change scores were computed by subtracting the week 0 score from the week in question, so for week 4, the equation was (VAS week 4 score – VAS week 0 score = Week 4 change score). Use of the change in VAS scores relative to week 0 (as week 0 was a baseline control) allowed for observation of any trends/relationships between the intervention and time, and the effect it had on the change in QoL from week 0. This analysis allowed us to see if there were any differences between the groups taking part in the intervention independent of their starting QoL.
Final+Thesis+for+UDORA.docx