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Using Multivariate Statistics

The following paper presents current thinking and research on fit indices for structural equation modelling. The
paper presents a selection of fit indices that are widely regarded as the most informative indices available to researchers.
As well as outlining each of these indices, guidelines are presented on their use. The paper also provides reporting
strategies of these indices and concludes with a discussion on the future of fit indices.

/pdf/structural-equation-modelling-guidelines-for-determining-muejvmc1az.pdf

Structural Equation Modelling: Guidelines for Determining Model Fit

Attachment--and loss.

What good is self-control? We incorporated a new measure of individual differences in self-control into two large investigations of a broad spectrum of behaviors. The new scale showed good internal consistency and retest reliability. Higher scores on self-control correlated with a higher grade point average, better adjustment (fewer reports of psychopathology, higher self-esteem), less binge eating and alcohol abuse, better relationships and interpersonal skills, secure attachment, and more optimal emotional responses. Tests for curvilinearity failed to indicate any drawbacks of so-called overcontrol, and the positive effects remained after controlling for social desirability. Low self-control is thus a significant risk factor for a broad range of personal and interpersonal problems.

/pdf/high-self-control-predicts-good-adjustment-less-pathology-19qckrshli.pdf

High self-control predicts good adjustment, less pathology, better grades, and interpersonal success.

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Background: Symptom network analysis has become an essential tool for researchers and clinicians investigating the structure of mental disorders. Two common methods have been used; one relies on partial correlations, and the second relies on zero order correlations with forced-directed algorithm. The combination enables a multi-dimensional view of mental disorders and provide complimentary information. This combination was used to examine symptom connections for ICD-11 Posttraumatic Stress Disorder (PTSD) and Complex PTSD (CPTSD) as the symptoms for these disorders have been known to be organized in a multi-dimensional and hierarchical fashion. We aimed to examine whether networks of ICD-11 CPTSD symptoms reproduced across samples from three African countries. 

Methods: We produced network models based on data from 2,524 participants in Nigeria (n = 1006), Kenya (n = 1018), and Ghana (n = 500). PTSD and CPTSD symptoms were measured using the International Trauma Questionnaire1. 

Outcomes: The CPTSD network analysis using force-directed method alongside partial correlations based on Gaussian Graphical Models (GGM) revealed the multidimensional-hierarchal structure of CPTSD. The within-cluster symptoms of Disturbances in Self Organization (DSO) and PTSD were strongly correlated with each other in all networks, and the cross-cluster symptoms were lower. The most central symptom was ‘feelings of worthlessness’, a symptom of Negative Self-Concept that is part of the CPTSD cluster. The networks were very similar across the three countries. 

Interpretation: Findings support the ICD-11 model of PTSD and CPTSD in three African countries

Funding: An internal research grant was awarded to MBE from Ariel University (grant no.
RA1700000037).

Declaration of Interests: None.

Ethics Approval Statement: The study complied with the Helsinki declaration regarding human experimentation. All procedures involving human subjects were approved by Ariel’s University IRB [AU-MBE-2018-1029].

Comparing the Network Structure of ICD-11 PTSD and Complex PTSD in Three African Countries

OBJECTIVE
Given the highly infectious nature of COVID-19, social distancing practices are key in stemming the spread of the virus. We aimed to assess the complex interplay among psychological factors, socio-demographic characteristics and social distancing behaviours within the framework of the widely used Capability, Opportunity, Motivation-Behaviour (COM-B) model.


DESIGN
The present research employed network psychometrics on data collected during the first UK lockdown in April 2020 as part of the COVID-19 Psychological Research Consortium (C19PRC) Study. Using a network approach, we examined the predictions of psychological and demographic variables onto social distancing practices at two levels of analysis: macro and micro.


RESULTS
Our findings revealed several factors that influenced social distancing behaviour during the first UK lockdown. The COM-B model was successful in predicting particular aspects of social-distancing via the influence of psychological capability and motivation at the macro-and micro-levels, respectively. Notably, demographic variables, such as education, income, and age, were directly and uniquely predictive of certain social distancing behaviours.


CONCLUSION
Our findings reveal psychological factors that are key predictors of social distancing behaviour and also illustrate how demographic variables directly influence such behaviour. Our research has implications for the design of empirically-driven interventions to promote adherence to social distancing practices in this and future pandemics.

https://www.tandfonline.com/doi/pdf/10.1080/08870446.2022.2057497?needAccess=true

A network approach to understanding social distancing behaviour during the first UK lockdown of the COVID-19 pandemic.

The factor structure of PTSD: testing competing DSM-5 models with an ICD-11 alternative across multiple traumatic exposures

The research literature has suggested that longitudinal changes in posttraumatic stress disorder (PTSD) could be adequately described in terms of one universal trajectory, with individual differences in baseline levels (intercept) and rate of change (slope) being negligible. However, not everyone who has experienced a trauma is diagnosed with PTSD, and symptom severity levels differ between individuals exposed to similar traumas. The current study employed the latent growth mixture modeling technique to test for multiple trajectories using data from a sample of Danish rape victims (N = 255). In addition, the analysis aimed to determine whether a number of explanatory variables could differentiate between the trajectories (age, acute stress disorder [ASD], and perceived social support). Results concluded the existence of two PTSD trajectories. ASD was found to be the only significant predictor of one trajectory characterized by high initial levels of PTSD symptomatology. The present findings confirmed the existence of multiple trajectories with regard to PTSD symptomatology in a way that may be useful to clinicians working with this population.

Mark Shevlin

Papers

Comparing the Network Structure of ICD-11 PTSD and Complex PTSD in Three African Countries

A network approach to understanding social distancing behaviour during the first UK lockdown of the COVID-19 pandemic.

The factor structure of PTSD: testing competing DSM-5 models with an ICD-11 alternative across multiple traumatic exposures

A latent growth mixture modelling approach to PTSD symptoms in rape victims

REPORT: Impact of COVID-19 on Young People Aged 13-24 in the UK: Preliminary Findings (Report 1)