Explore chapters and articles related to this topic
Correlation
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
Based on this sample, there appears to be a moderately strong linear relationship between the percentage of children immunized against dpt in a specified country and its under-5 mortality rate. Since r is negative, mortality rate decreases in magnitude as percent immunization increases. Care must be taken when interpreting this relationship, however. An effective immunization program might be the primary reason for the decrease in mortality, or it might be a ramification of a successful comprehensive health care system that is itself the cause of the decrease. The correlation coefficient merely tells us that a linear relationship exists between two variables; it does not specify whether the relationship is cause-and-effect.
Work stress induced psychological disorders in construction
Published in Imriyas Kamardeen, Work Stress Induced Chronic Diseases in Construction, 2021
Table 2.14 illustrates the correlations between organisational stress interventions and psychological strains and job outcomes among construction industry professionals. The correlation coefficient for a given association can range from −1.0 to +1.0 with (−) sign denoting a negative correlation and (+) sign for a positive correlation between the concerned variables. The strength of the correlation is determined by the coefficient value. Nardi (2014) suggested that coefficients below .30 are considered weak, those between .30 to .70 are moderate and those above .70 are strong. The interventions have statistically significant negative correlations with psychological strains such as work stress, burnout,anxiety and depression. However, the strengths of correlations are in the weak range. Statistically significant positive correlations are discernible between the interventions and job satisfaction, and the strengths of the correlations are moderate. Similarly, the interventions appear to have statistically significant positive correlations with job performance, but the strength of association is weak. These findings partially support the stress model shown in Figure 2.1 about the role of organisational interventions in minimising work stress and its consequences.
Clinical Workflows Supported by Patient Care Device Data
Published in John R. Zaleski, Clinical Surveillance, 2020
Note that in computing correlation, the parameter that is computed is the correlation coefficient which ranges between -1 and 1. A correlation coefficient of -1 between two parameters indicates a relationship between the two in which as one parameter increases, the correlated parameter decreases in direct proportion to the first parameter. In contrast, a correlation coefficient of 1 indicates a direct relationship in which an increase in the first parameter results in a direct increase in the correlated parameter. These two instances are referred to as perfect inverse correlation (i.e., correlation coefficient of -1) and perfect direct correlation (i.e., correlation coefficient of 1), respectively. When the correlation coefficient is computed to be 0, this means no direct relationship or association between any two parameters.
Cortisol levels versus self-report stress measures during pregnancy as predictors of adverse infant outcomes: a systematic review
Published in Stress, 2022
Rafael A. Caparros-Gonzalez, Fiona Lynn, Fiona Alderdice, Maria Isabel Peralta-Ramirez
We planned to pool relevant data from studies to perform meta-regression analyses and assess the strength of associations between the stress measures and outcomes reported by pooling the mean change in an outcome given a 1 unit shift in stress. However, this was not feasible, as there was a high level of heterogeneity across the studies in the stress measures used and outcomes recorded, the timing of assessments and/or differences in the studies’ samples. Subsequently, a narrative review was presented. In addressing the primary aim of evaluate the association between self-report stress measures and cortisol levels during pregnancy, we extracted and synthesized data from studies that reported the correlation coefficient. While there are limitations in the correlation coefficient as a test for association between a dependent and independent variable, it is useful as an indicator for association between two independent variables that represent alternative approaches to measuring a concept, such as stress.
Turkish validity and reliability of the sexual complaints screener for men
Published in Psychiatry and Clinical Psychopharmacology, 2019
Anıl Gündüz, Sencan Sertçelik, İbrahim Gündoğmuş, Meliha Zengin Eroğlu, Rayka Kumru Bayazit, Hatice Gönül, Alişan Burak Yaşar, Mehmet Zihni Sungur
The negative correlation coefficient indicated an inverse relationship between the variables. A negative correlation was found between SCS-M’s and SF-36’s subscales. This meant an increase in sexual complaints, an adverse effect on the quality of life, and thus a consistent reflection of the literature [17,18]. Although studies for urogenital pain in men found pain in the genital area and sexual dysfunction to coexist [17], no correlation between sexual complaints and the general pain was found in the current study. The two questions on pain and social functioning included in the SF-36 scale assesses pain in general terms and lacks an assessment of urogenital or sexual pain. This finding was in alignment with the findings of the current study which found no statistically significant correlation between sexual complaints and pain.
A proposed definition of remission from chronic pain, based on retrospective evaluation of 24-month outcomes with spinal cord stimulation
Published in Postgraduate Medicine, 2019
Kasra Amirdelfan, Bradford E Gliner, Leonardo Kapural, B. Todd Sitzman, Ricardo Vallejo, Cong Yu, David Caraway, Anand Rotte, Rose Province-Azalde, Elliot Krames
To further confirm the correlation between VAS and functional outcomes and to identify the VAS cut-off that had a greater association with all the functional outcomes, correlation of pre-defined VAS cut-offs (2.0 cm, 2.5 cm, 3.0 cm, and 3.5 cm) were tested with outcomes at 24-month assessment using Pearson correlation analysis. Higher correlation coefficient values indicate better correlation, and negative values indicate inverse relationship. As listed in Table 2, the correlation coefficient values for ODI, GIC, and satisfaction increased as the cut-off value increased from 2.0 cm to 3.0 cm and decreased as the cut-off further increased to 3.5 cm. The odds of predicting lower disability and higher satisfaction using pre-defined VAS cut-offs was tested using the ‘odds ratio’ values. Similar to the correlation coefficient values, VAS 3.0 cm cut-off had the best odds ratio value to predict disability and patient satisfaction (Table 3).