references.bib

@article{haqiqatkhah_2024_DailyDynamicsWeekly,
  title = {Daily Dynamics and Weekly Rhythms: {{A}} Tutorial on Seasonal {{ARMA}} Models Combined with Day-of-Week Effects},
  shorttitle = {Daily Dynamics and Weekly Rhythms},
  author = {Haqiqatkhah, MohammadHossein Manuel and Hamaker, Ellen},
  year = {2024},
  month = feb,
  publisher = {{OSF}},
  doi = {10.31234/osf.io/duvqh},
  urldate = {2024-02-20},
  abstract = {Daily diary data of emotional experiences are typically modeled with a first-order autoregressive model to account for possible day-to-day dynamics. However, our emotional experiences are likely to be affected by the weekly rhythm of our activities, which may be reflected by: (a) day-of-week effects (DOWEs), where different days of the week are characterized by different means; and (b) week-to-week dynamics, where weekday-specific activities and experiences have a delayed effect on the emotions that we experience on the same weekday a week later. While DOWEs have been studied occasionally, week-to-week dynamics have been largely ignored in psychological research. To gain more insight in the various regularities that may exist in daily diary data, we begin with presenting a set of complementary visualization techniques that can help to detect and characterize weekly rhythms and day-to-day dynamics in time series data. Subsequently, we introduce the family of seasonal autoregressive--moving average (SARMA) models from the econometrics literature, and extend this with models for the DOWEs. We illustrate how the different model components show up in the various visualizations of the time series data. We then provide a tutorial on fitting these models in R, discussing model fit and model selection, and apply this to a daily diary dataset consisting of 56-101 daily measures from 98 individuals. The results suggests that most individuals in the sample are characterized by patterns and dynamics that the current practices in psychological research cannot capture adequately. We discuss the implications of our findings for current psychological research practices.}
}

@article{wright_2015_DailyInterpersonalAffective,
  title = {Daily {{Interpersonal}} and {{Affective Dynamics}} in {{Personality Disorder}}},
  author = {Wright, Aidan G. C. and Hopwood, Christopher J. and Simms, Leonard J.},
  year = {2015},
  month = aug,
  journal = {Journal of Personality Disorders},
  volume = {29},
  number = {4},
  pages = {503--525},
  publisher = {{Guilford Publications Inc.}},
  issn = {0885-579X},
  doi = {10.1521/pedi.2015.29.4.503},
  urldate = {2023-05-11},
  abstract = {In this naturalistic study, the authors adopt the lens of interpersonal theory to examine between- and within-person differences in dynamic processes of daily affect and interpersonal behaviors among individuals (N = 101) previously diagnosed with personality disorders who completed daily diaries over the course of 100 days. Dispositional ratings of interpersonal problems and measures of daily stress were used as predictors of daily shifts in interpersonal behavior and affect in multilevel models. Results indicate that {$\sim$}40\%{\textendash}50\% of the variance in interpersonal behavior and affect is due to daily fluctuations, which are modestly related to dispositional measures of interpersonal problems but strongly related to daily stress. The findings support conceptions of personality disorders as a dynamic form of psychopathology involving the individuals interacting with and regulating in response to the contextual features of their environment.}
}

@misc{lund_2023_CircularCircularStatistics,
  title = {Circular: {{Circular Statistics}}},
  shorttitle = {Circular},
  author = {Lund, Ulric and Agostinelli, Claudio and Arai, Hiroyoshi and Gagliardi, Alessando and {Garc{\'i}a-Portugu{\'e}s}, Eduardo and Giunchi, Dimitri and Irisson, Jean-Olivier and Pocernich, Matthew and Rotolo, Federico},
  year = {2023},
  month = sep,
  urldate = {2024-02-15},
  abstract = {Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.},
  copyright = {GPL-2}
}

@article{cremers_2018_OneDirectionTutorial,
  title = {One {{Direction}}? {{A Tutorial}} for {{Circular Data Analysis Using R With Examples}} in {{Cognitive Psychology}}},
  shorttitle = {One {{Direction}}?},
  author = {Cremers, Jolien and Klugkist, Irene},
  year = {2018},
  journal = {Frontiers in Psychology},
  volume = {9},
  issn = {1664-1078},
  urldate = {2024-02-16},
  abstract = {Circular data is data that is measured on a circle in degrees or radians. It is fundamentally different from linear data due to its periodic nature (0{$^\circ$} = 360{$^\circ$}). Circular data arises in a large variety of research fields. Among others in ecology, the medical sciences, personality measurement, educational science, sociology, and political science circular data is collected. The most direct examples of circular data within the social sciences arise in cognitive and experimental psychology. However, despite numerous examples of circular data being collected in different areas of cognitive and experimental psychology, the knowledge of this type of data is not well-spread and literature in which these types of data are analyzed using methods for circular data is relatively scarce. This paper therefore aims to give a tutorial in working with and analyzing circular data to researchers in cognitive psychology and the social sciences in general. It will do so by focusing on data inspection, model fit, estimation and hypothesis testing for two specific models for circular data using packages from the statistical programming language R.}
}