Vowels Matthew J, Vowels Laura M, Wood Nathan D
Department of Electrical and Electronic Engineering, Center for Computer Vision, Speech and Signal Processing, University of Surrey.
Department of Psychology, University of Southampton.
Psychol Methods. 2023 Jun;28(3):631-650. doi: 10.1037/met0000399. Epub 2021 Jul 22.
Social scientists have become increasingly interested in using intensive longitudinal methods to study social phenomena that change over time. Many of these phenomena are expected to exhibit cycling fluctuations (e.g., sleep, mood, sexual desire). However, researchers typically employ analytical methods which are unable to model such patterns. We present spectral and cross-spectral analysis as means to address this limitation. Spectral analysis provides a means to interrogate time series from a different, frequency domain perspective, and to understand how the time series may be decomposed into their constituent periodic components. Cross-spectral extends this to dyadic data and allows for synchrony and time offsets to be identified. The techniques are commonly used in the physical and engineering sciences, and we discuss how to apply these popular analytical techniques to the social sciences while also demonstrating how to undertake estimations of significance and effect size. In this tutorial we begin by introducing spectral and cross-spectral analysis, before demonstrating its application to simulated univariate and bivariate individual- and group-level data. We employ cross-power spectral density techniques to understand synchrony between the individual time series in a dyadic time series, and circular statistics and polar plots to understand phase offsets between constituent periodic components. Finally, we present a means to undertake nonparameteric bootstrapping in order to estimate the significance, and derive a proxy for effect size. A Jupyter Notebook (Python 3.6) is provided as supplementary material to aid researchers who intend to apply these techniques. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
社会科学家们越来越热衷于运用密集纵向研究方法来探究随时间变化的社会现象。其中许多现象预计会呈现周期性波动（例如，睡眠、情绪、性欲）。然而，研究人员通常采用的分析方法无法对这类模式进行建模。我们提出频谱分析和互谱分析，作为解决这一局限性问题的方法。频谱分析提供了一种从不同的频域视角审视时间序列的手段，有助于理解时间序列如何分解为其组成的周期性成分。互谱分析将此扩展到二元数据，并能够识别同步性和时间偏移。这些技术在物理和工程科学中常用，我们将讨论如何把这些流行的分析技术应用于社会科学，同时展示如何进行显著性和效应量的估计。在本教程中，我们首先介绍频谱分析和互谱分析，然后展示其在模拟的单变量和双变量个体及组水平数据中的应用。我们运用交叉功率谱密度技术来理解二元时间序列中各个时间序列之间的同步性，并使用循环统计和极坐标图来理解组成周期性成分之间的相位偏移。最后，我们提出一种进行非参数自抽样的方法，以估计显著性，并推导出效应量的代理指标。提供了一个Jupyter Notebook（Python 3.6）作为补充材料，以帮助有意应用这些技术的研究人员。（PsycInfo数据库记录（c）2023美国心理学会，保留所有权利）
