168 Chapter 7 to calculate SDs of nodes strengths and visualize dynamic network videos described the non-stationarity issue vividly. The main difference with the case study of Chapter 6 is that we there only visualized either raw timeseries or descriptive features of timeseries (e.g., dynamic complexity) without first inferring relationships in the data. Additionally, Chapter 6 employed qualitative methods, whose strengths lie in providing rich descriptions of complex phenomena (Creswell & Clark, 2017). Future research should therefore shift its focus from inferring abstract relations at the idiographic level to applying (or further developing analyses) that describe non-linearity. State space grid analysis and recurrence quantification analysis are examples of other descriptive non-linear analyses that have been applied before to better understand change patterns of people with intellectual disabilities (e.g., Hoekstra et al., 2023; Vink et al., 2019). Practice Take it Personal! Professionals personalize treatment on a daily basis, navigating the unique characteristics and change processes of each person. Take it Personal! offers them the tools to formalize this personalized approach in practice, when preventing substance use problems is the aim. But how personalized is personalized? Take it Personal! is an intervention that consists of four different versions for each of the four personality risk profiles. As such, it is designed to offer a personalized starting point for intervention efforts, tailoring exercises to the personality profile with specific substance use motives. Although Chapter 5 showed that the estimated idiographic personality networks varied considerably within these personality profiles, there are two reasons why this does not unequivocally imply that the classification of profiles in Take it Personal! was inaccurate. First, personality traits are highly context-specific: patterns are theorized to be variable across different situations over time, but stable across similar situations (Mischel & Shoda, 1995), but substance use was not included as a node in the network analyses (Chapter 5). This bars conclusions about personality patterns on days with substance use. Second, items that covary over time will be estimated as significant edges in idiographic network analysis, but covariance does not provide information on absolute levels of a trait. That is, some participants may self-report the two impulsivity items on the upper end of the scale (3 or 4) whereas another participant on the lower end (0 or 1), but when the items covary over time their edge will be estimated as significant. In conclusion,
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