Podcasts about Bivariate

Bivariate statistics help to understand the statistical relation between the independent and the dependent variable. Once you test a hypothesis you always need this kind of statistics. All slides to the entire series can be downloaded for free here: https://armintrost.de/en/professor/digital/social-research-methods/

Audio Only Version of Introduction to Bivariate Data and Scatterplots For Video: https://www.youtube.com/channel/UCHVyc1NJuYvzpoom-L3nBpg/ For More info: https://blogs.lt.vt.edu/jmrussell/topics/ --- Support this podcast: https://anchor.fm/john-russell10/support

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.16.207001v1?rss=1 Authors: Strahnen, D., Kapanaiah, S. K. T., Bygrave, A. M., Kaetzel, D. Abstract: Communication between brain areas has been implicated in a wide range of cognitive and emotive functions and is impaired in numerous mental disorders. In rodent models, various functional connectivity metrics have been used to quantify inter-regional neuronal communication. However, in individual studies, typically only very few measures of coupling are reported and, hence, redundancy across such indicators is implicitly assumed. In order to test this assumption, we here comparatively assessed a broad range of directional and non-directional metrics like coherence, weighted Phase-Lag-Index (wPLI), Granger causality (GC), spike-phase coupling (SPC), cross-regional phase-amplitude coupling, amplitude cross-correlations, and others. We applied these analyses to simultaneous field recordings from the prefrontal cortex and the ventral and dorsal hippocampus in the schizophrenia-related Gria1-knockout mouse model which displays a robust novelty-induced hyperconnectivity phenotype. We find that across such measures there is a considerable lack of functional redundancy. While coherence and GC yielded similar results, other measures, especially wPLI and SPC, often produced deviating conclusions. Bivariate correlations within animals revealed that virtually none of the metrics consistently co-varied with any of the other measures across the three connections and two genotypes analysed. Parametric GC showed the qualitatively highest degree of redundancy with other metrics and would be most suitable for connectivity analysis. We conclude that analysis of multiple metrics is necessary to characterise functional connectivity. Copy rights belong to original authors. Visit the link for more info

 Approximately one third of older people over 65 years fall each year. Home modifications may decrease occurrence of falls. This study aims to determine the risk factors of falls for frail older persons and to evaluate the impact of home modifications by an occupational therapist on the occurrence of falls. We conducted a longitudinal study using a quasiexperimental design to examine occurrence of falls. All participants 65 years of age and older and were assessed at baseline and 6 months after the intervention. Bivariate analysis and logistic regression models were used to study the risk factors of falls and the effect of home modifications on the incidence of falls.

Statistics II - Methodology of econometrics; Regression analysis; OLS regressions; Minimizing ordinary least squares; Bivariate and multivariate statistics; Regression analysis in practice; Event studies; Cumulated average abnormal returns; Survey data and validity; Stratified random sampling; Misleading averages: Gesell's norms; Percentages and percentage points; How to tune up a graph; Statistical misinterpretation; Marketing with statistics; Suspicious wording; Statistical Don'ts.

This lecture puts the Great East Japan Earthquake into perspective by analysing it in the context of other major disasters. Using micro- and neighborhood-level data from four disasters in three nations over the 20th and 21st centuries, this talk will investigate standard theories of recovery and resilience. Bivariate, time series cross sectional, and matching analyses show that more than factors such as individual or personal wealth, aid from the government, or damage from the disaster, the depth of social capital best predicts recovery. Social capital works through three main mechanisms: elevating voice and suppressing exit, overcoming collective action barriers, and providing informal insurance. Should social networks prove the critical engines before, during, and after disaster, this suggests a new approach to disaster mitigation for NGOs, individuals, and governments. Daniel P. Aldrich is an Associate Professor of Political Science at Purdue University on leave for the academic year 2012  ̶  2013 as a Fulbright research professor at Tokyo University. He received his Ph.D. and M.A. in political science from Harvard University, an M.A. from the University of California at Berkeley, and his B.A. from the University of North Carolina at Chapel Hill. He has published two books (Site fights and Building Resilience) and more than 80 peer reviewed articles, book chapters, reviews, and OpEds in locations such as the New York Times, CNN, and the Asahi Shinbun.

Background: Self stigma among people with mental illness results from multiple cognitive and environmental factors and processes. It can negatively affect adherence to psychiatric services, self esteem, hope, social integration and quality of life of people with mental illness. The purpose of this study was to measure the level of self stigma and its correlates among people with mental illness at Jimma University Specialized Hospital, Psychiatry clinic in southwest Ethiopia. Methods: Facility based cross-sectional study was conducted on 422 consecutive samples of people with mental illness using interviewer administered and pretested internalized stigma of mental illness (ISMI) scale. Data was entered using EPI-DATA and analysis was done using STATA software. Bivariate and multivariate linear regressions were done to identify correlates of self stigma. Results: On a scale ranging from 1 to 4, the mean self stigma score was 2.32 (SD = 0.30). Females had higher self stigma (std. beta = 0.11, P < 0.05) than males. Patients with a history of traditional treatment had higher self stigma (std. beta = 0.11, P < 0.05). There was an inverse relationship between level of education and self-stigma (std. beta = -0.17, P < 0.01). Perceived signs (std. beta = 0.13, P < 0.05) and supernatural causes of mental illness ( std. beta = 0.16, P < 0.01) were positively correlated with self stigma. Higher number of drug side effects were positively correlated (std. beta = 0.15, P < 0.05) while higher self esteem was negatively correlated (std. beta = -0.14, P < 0.01) with self stigma. Conclusions: High feeling of inferiority (alienation) but less agreement with common stereotypes (stereotype endorsement) was found. Female showed higher self stigma than male. History of traditional treatment and higher perceived supernatural explanation of mental illness were associated with higher self stigma. Drug side effects and perceived signs of mental illness were correlated with increased self stigma while education and self esteem decreased self stigma among people with mental illness. Patient empowerment psychosocial interventions and strategies to reduce drug side effects can be helpful in reducing self stigma among people with mental illnesses.

Background: Unemployment is associated with reduced physical and psychological well-being. Perceived health is an important factor influencing health outcomes as well as successful returns to work. This study aims to determine the extent to which perceived health correlates with mental health, various health risk characteristics and socio-demographic characteristics in a setting-selected sample of long-term unemployed persons. Methods: Using SF-12, 365 long-term unemployed persons were assessed for self-perceived health and various socio-demographic and health characteristics. Perceived health data of the sample was compared to the German SF-12 reference population. Bivariate analyses and multiple linear regression models were applied to identify those variables significantly associated with perceived health. Results: The study population reported poorer perceived health compared with the general population. Analyses showed that perceived mental health was significantly worse in women, among persons with heightened depression and anxiety scores, and in participants reporting reduced levels of physical activity. Perceived physical health was significantly lower among older persons, participants with a higher BMI, and participants with heightened depression and anxiety scores. Both mental and physical health were worse among the unemployed assigned to an employment center as compared to those engaged in the secondary labor market. In total, 36% of the variance in the SF-12 mental score and 20% of the variance in the SF-12 physical score were explained by the factors included in the final multiple linear regression models. Conclusions: Perceived health among a select group of long-term unemployed is reduced to a clinically relevant extent compared to the general population. The preliminary findings underline an association between mental health and perceived health. Negative self-perceptions of health were also associated with the labor market setting and some of the socio-demographic and health behavior variables. Further research is needed to determine risk factors leading to reduced perceived health in the unemployed. The strong association between mental health and perceived health suggests interventions targeting mental health are urgently needed to positively influence perceived health, a key determinant of individuals' chances to successfully return to work.

Data Analysis - Herkenhoff

Nonignorable nonresponse is a common problem in bivariate or multivariate data. Here a selection model for bivariate normal distributed data (Y1 ; Y2) is proposed. The missingness of Y2 is supposed to depend on its own values. The model for missingness describes the probability of nonresponse in dependency of Y2 itself and it is chosen nonparametrically to allow exible patterns. We try to get a reasonable estimate for the expectation and especially for the variance of Y2 . Estimation is done by data augmentation and computation by common sampling methods.