The Dynamics of Self-Esteem and Physical Self: Between Preservation and Adaptation

The Relationship between Body Appreciation and Self-Esteem and Associated Factors among Omani University Students: An Online Cross-Sectional Survey

BACKGROUND

Given the rapid pace of globalization and the fact that the Sultanate of Oman is experiencing a significant impact of social media on specifying appearance norms among youth in the country, research into positive body images and self-esteem among young individuals has become a national priority. Whilst body image has been well studied across cultures, both positive body image and the relationship between positive body image and self-esteem among Omani youth have been neglected. The aim of the study was to investigate the relationship between positive body image and self-esteem and associated sociodemographic factors among Omani university students based on gender.

METHODS

This cross-sectional study used an online survey consisting of the two questionnaires that are Body Appreciation Scale-2 and Rosenberg's Self-Esteem Scale. A total of 237 students were recruited from Sultan Qaboos University's different colleges.

RESULTS

The results indicated that positive body image has a significant relationship with an individual's self-esteem (β = 0.122, t = 2.197, p=0.038), Cumulative Grade Point Average (cGPA) (β = 0.140, t = 2.306, p=0.022), body mass index (BMI) (β = -0.414, t = -6.930, p < 0.001), monthly household income (β = -0.129, t = 2.467, p=0.029), and the number of social media accounts (≥2, β = -0.132, t = -2.232, p=0.027). In addition, an individual's self-esteem was significantly associated with an individual's cGPA (β = 0.231, t = 3.592, p < 0.001) and mothers' educational level (β = -0.130, t = -2.065, p=0.040) besides body appreciation (β = 0.160, t = 2.491, p=0.013).

CONCLUSIONS

The findings of this study shed light on the current status of positive body image among university students of Oman. In light of the new knowledge, we propose health interventions that include strategies such as involvement of family, to maintain and/or promote positive body image perceptions among young individuals and subsequently promote healthy appreciation of the physical appearance and self-esteem.

Origins of 1/f noise in human music performance from short-range autocorrelations related to rhythmic structures

1/f fluctuations have been described in numerous physical and biological processes. This noise structure describes an inverse relationship between the intensity and frequency of events in a time series (for example reflected in power spectra), and is believed to indicate long-range dependence, whereby events at one time point influence events many observations later. 1/f has been identified in rhythmic behaviors, such as music, and is typically attributed to long-range correlations. However short-range dependence in musical performance is a well-established finding and past research has suggested that 1/f can arise from multiple continuing short-range processes. We tested this possibility using simulations and time-series modeling, complemented by traditional analyses using power spectra and detrended fluctuation analysis (as often adopted more recently). Our results show that 1/f-type fluctuations in musical contexts may be explained by short-range models involving multiple time lags, and the temporal ranges in which rhythmic hierarchies are expressed are apt to create these fluctuations through such short-range autocorrelations. We also analyzed gait, heartbeat, and resting-state EEG data, demonstrating the coexistence of multiple short-range processes and 1/f fluctuation in a variety of phenomena. This suggests that 1/f fluctuation might not indicate long-range correlations, and points to its likely origins in musical rhythm and related structures.

Explaining the “How” of Self-Esteem Development: The Self-Organizing Self-Esteem Model

The current article proposes a theoretical model of self-esteem called the Self-Organizing Self-Esteem (SOSE) model. The model provides an integrative framework for conceptualizing and understanding the intrinsic dynamics of self-esteem and the role of the context across 3 levels of development: The macro level, which is the level of trait self-esteem, the meso level, on which we find state self-esteem, and the micro level, which is the level of discrete self experiences. The model applies principles from the complex dynamics systems perspective to self-esteem, and can thus uniquely describe the underlying mechanism of self-esteem development based on self-organizational processes and interacting time scales. We compare the proposed SOSE model with a formalized account of the traditional approach to self-esteem, showing that the SOSE model is especially conducive to the understanding of self-esteem development in a way that the traditional approach is not—namely, in its ability to explain and predict the underlying dynamics of trait and state self-esteem, the meaning of variability, and the role of the context.

Modeling BAS Dysregulation in Bipolar Disorder

Time series analysis is a technique that can be used to analyze the data from a single subject and has great potential to investigate clinically relevant processes like affect regulation. This article uses time series models to investigate the assumed dysregulation of affect that is associated with bipolar disorder. By formulating a number of alternative models that capture different kinds of theoretically predicted dysregulation, and by comparing these in both bipolar patients and controls, we aim to illustrate the heuristic potential this method of analysis has for clinical psychology. We argue that, not only can time series analysis elucidate specific maladaptive dynamics associated with psychopathology, it may also be clinically applied in symptom monitoring and the evaluation of therapeutic interventions.

Deterministic or Stochastic Trend

Time series with deterministic and stochastic trends possess different memory characteristics and exhibit dissimilar long-range development. Trending series are nonstationary and must be transformed to be stabilized. The choice of correct transformation depends on patterns of nonstationarity in the data. Inappropriate transformations are consequential for subsequent analysis and should be omitted. The objectives of this article are (1) to introduce unit root testing procedures, (2) to evaluate the strategies for distinguishing between stochastic and deterministic alternatives by means of Monte Carlo experiments, and (3) to demonstrate their implementation on empirical examples using SAS for Windows.

Model Identification of Integrated ARMA Processes

This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can be applied to either nontransformed or differenced series, so the advantages and drawbacks of both procedures were compared. The best results were 79% of correct identifications for SCAN and 80% for ESACF. For some models and parameterizations, the accuracy of SCAN and ESACF was disappointing. The key finding of the study is that both human experts and automated methods provide inconsistent model identifications. Hence an elaborated strategy for model selection combining different techniques was developed and demonstrated on 2 empirical examples.

Detection of long-range dependence and estimation of fractal exponents through ARFIMA modelling

We evaluate the performance of autoregressive, fractionally integrated, moving average (ARFIMA) modelling for detecting long-range dependence and estimating fractal exponents. More specifically, we test the procedure proposed by Wagenmakers, Farrell, and Ratcliff, and compare the results obtained with the Akaike information criterion (AIC) and the Bayes information criterion (BIC). The present studies show that ARFIMA modelling is able to adequately detect long-range dependence in simulated fractal series. Conversely, this method tends to produce a non-negligible rate of false detections in pure autoregressive and moving average (ARMA) series. Generally, ARFIMA modelling has a bias favouring the detection of long-range dependence. AIC and BIC gave dissimilar results, due to the different weights attributed by the two criteria to accuracy and parsimony. Finally, ARFIMA modelling provides good estimates of fractal exponents, and could adequately complement classical methods, such as spectral analysis, detrended fluctuation analysis or rescaled range analysis.