Spectral and cross-spectral analysis-A tutorial for psychologists and social scientists.

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).

Answering the ethical call of the other: A test of the Strong Relationality Model of Relationship Flourishing.

The philosophical framework of strong relationality has gained greater attention in scholarship and yet empirically testing models built on this important framework are rare. The present study tests predictions made by the Strong Relationality Model of Relationship Flourishing (SRM), which centers on the role of Ethical Responsiveness for relationship health. In doing so, we introduce common fate modeling as a methodological approach for strong relationality research. We used longitudinal data from 1512 couples collected as part of the German longitudinal panel study of families. Results support the Strong Relationality Model's prediction that Ethical Responsiveness (as measured by perceived partner support) positively alters the impact of stress on Gratitude-Recognition (elements of the Responsible Action domain of the SRM), which then increases couples' intimacy (an element of the Relational-Connectivity domain of the SRM). Recommendations for clinical assessment and intervention are given as well as recommendations for future research on the Strong Relationality Model.

A Review of Key Properties of Thermoelectric Composites of Polymers and Inorganic Materials.

This review focusses on the development of thermoelectric composites made of oxide or conventional inorganic materials, and polymers, with specific emphasis on those containing oxides. Discussion of the current state-of-the-art thermoelectric materials, including the individual constituent materials, i.e., conventional materials, oxides and polymers, is firstly presented to provide the reader with a comparison of the top-performing thermoelectric materials. Then, individual materials used in the inorganic/polymer composites are discussed to provide a comparison of the performance of the composites themselves. Finally, the addition of carbon-based compounds is discussed as a route to improving the thermoelectric performance. For each topic discussed, key thermoelectric properties are tabulated and comparative figures are presented for a wide array of materials.

Using spectral and cross-spectral analysis to identify patterns and synchrony in couples' sexual desire.

Sexual desire discrepancy is one of the most frequently reported sexual concerns for individuals and couples and has been shown to be negatively associated with sexual and relationship satisfaction. Sexual desire has increasingly been examined as a state-like construct that ebbs and flows, but little is known about whether there are patterns in the fluctuation of sexual desire. Utilizing spectral and cross-spectral analysis, we transformed 30 days of dyadic daily diary data for perceived levels of sexual desire for a non-clinical sample of 133 couples (266 individuals) into the frequency domain to identify shared periodic state fluctuations in sexual desire. Spectral analysis is a technique commonly used in physics and engineering that allows time series data to be analyzed for the presence of regular cycles of fluctuation. Cross-spectral analysis allows for dyadic data to be analyzed for shared rates of fluctuation between partners as well as the degree of (a)synchrony (or phase shift) between these fluctuations. Men and women were found to exhibit fluctuations in sexual desire at various frequencies including rates of once and twice per month, and to have sexual desire that was unlikely to fluctuate over periods of three days or less and therefore exhibited persistence. Similar patterns of fluctuation were exhibited within couples and these patterns were found to be largely synchronous. While instances of desire discrepancy may arise due to differences in rates of sexual desire fluctuation and random fluctuations, such instances may be normal for romantic relationships. The results have important implications for researchers, clinicians, and educators in that they corroborate the supposition that sexual desire ebbs and flows and suggest that it does so with predictable regularity.

Oxygen-mediated Polymerization Initiated by Oltipraz-derived Thiones.

A pyrrolopyrazine-thione derived from oltipraz, a compound that has been investigated as a chemopreventive agent, affords radicals in the presence of thiols and oxygen via a redox cycle, an attribute that suggests its suitability as an initiator for oxygen-mediated polymerization. Here, we explore the utilization of this pyrrolopyrazine-thione, generated in situ from a precursor, as an initiator for the radical-mediated thiol-ene polymerization. While the pyrrolopyrazine-thione was shown to be capable of generating radicals in the presence of atmospheric oxygen and thiol groups, the reaction extents achievable were lower than desired owing to the presence of unwanted side reactions that would quench radical production and, subsequently, suppress polymerization. Moreover, we found that complex interactions between the pyrrolopyrazine-thione, its precursor, oxygen, and thiol groups determine whether or not the quenching reaction dominates over those favorable to polymerization.

Spatial Statistics for Dyadic Data: Analyzing the Relationship Landscape.

Spatial statistics has a rich tradition in earth, economic, and epidemiological sciences and has potential to affect the study of couples as well. When applied to couple data, spatial statistics can model within- and between-couple differences with results that are readily accessible for researchers and clinicians. This article offers a primer in using spatial statistics as a methodological tool for analyzing dyadic data. The article will introduce spatial approaches, review data structure required for spatial analysis, available software, and examples of data output.

Combining Parallel and Exploratory Factor Analysis in Identifying Relationship Scales in Secondary Data.

Common methods used in the literature to identify factors within exploratory factor analysis has been shown to be potentially problematic. This brief report illustrates a state of the art approach in identifying factor structure by adding parallel analysis prior to exploratory factor analysis. Parallel analysis enables researchers to have a high degree of confidence of the number of factors to extract prior to exploratory factor analysis. The procedure is illustrated by using items from the National Survey of Families and Households (NSFH) that were used to identify relationship scales.

Location, location, location: applying spatial statistics to the relationship landscape.

The desire to understand relationships is a passion shared by professionals in research, clinical, and educational settings. Questionnaires are frequently used in each of these settings for a multitude of purposes-such as screening, assessment, program evaluation, or establishing therapeutic effectiveness. However, clinical issues arise when a couple's answers on questionnaires do not match clinical judgment or lack clinical utility, while statistical problems arise when data from both partners are put into analyses. This article introduces the use of geospatial statistics to analyze couple data plotted on a two-dimensional "relational map." Relationship maps can increase assessment sensitivity, track treatment progress, and remove statistical issues typically associated with couple data. This article briefly introduces core assumptions of spatial models, illustrates the use of spatial models in creating a relational landscape of divorce, offers suggestions for the use of relational maps in a clinical setting, and explores future research ideas.