Circumplex Models with Multivariate Time Series: An Idiographic Approach
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| Title: | Circumplex Models with Multivariate Time Series: An Idiographic Approach |
|---|---|
| Language: | English |
| Authors: | Dayoung Lee (ORCID |
| Source: | Structural Equation Modeling: A Multidisciplinary Journal. 2024 31(3):498-510. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Research Methodology, Affective Measures, Family Relationship, Multivariate Analysis, Time Management, Individual Testing |
| DOI: | 10.1080/10705511.2023.2259105 |
| ISSN: | 1070-5511 1532-8007 |
| Abstract: | The circumplex model posits a circular representation of affect and some personality traits. There is an increasing need to examine the viability of the circumplex model with multivariate time series data collected on the same individuals due to the development of new data collection methods such as smartphone applications and wearable sensors. Estimating the circumplex model with time series data is more complex than with cross-sectional data because scores at nearby time points tend to be correlated. We adapt Browne's circumplex model to accommodate time series data. We illustrate the proposed method with an empirical data set of daily affect ratings of an individual over 70 days. We conducted a simulation study to explore the statistical properties of the proposed method. The results show that the method provides more satisfactory confidence intervals and test statistics than a method that treats time series data as if they were cross-sectional data. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1431593 |
| Database: | ERIC |
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| Abstract: | The circumplex model posits a circular representation of affect and some personality traits. There is an increasing need to examine the viability of the circumplex model with multivariate time series data collected on the same individuals due to the development of new data collection methods such as smartphone applications and wearable sensors. Estimating the circumplex model with time series data is more complex than with cross-sectional data because scores at nearby time points tend to be correlated. We adapt Browne's circumplex model to accommodate time series data. We illustrate the proposed method with an empirical data set of daily affect ratings of an individual over 70 days. We conducted a simulation study to explore the statistical properties of the proposed method. The results show that the method provides more satisfactory confidence intervals and test statistics than a method that treats time series data as if they were cross-sectional data. |
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| ISSN: | 1070-5511 1532-8007 |
| DOI: | 10.1080/10705511.2023.2259105 |
