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<p>Manuscript published in Clinical Psychological Science (<a href="https://doi.org/10.1177/2167702617744325" rel="nofollow">https://doi.org/10.1177/2167702617744325</a>). </p> <p>Abstract:</p> <p>Recent literature has introduced (1) the network perspective to psychology, and (2) collection of time-series data in order to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intra-individual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time-series data. We explain the importance of partial correlation networks and exemplify the network structures on time-series data of a psychiatric patient. </p>
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