Academic literature on the topic 'Pearson correlation coefficient (r)'

AbstractObjective: The research assumes that there is a correlation between personality dimensions Extraversion and Conscientiousness which mean autonomy, ambition and consistency of purpose, self-efficacy and determination for young people who want to aspire to a medical career.Methods: In this study’s target group were included 130 students aspiring to a medical career, that participated in the summer school organized by UMF Tirgu-Mures in 2016, of which 25 (19%) male and 106 (81%) female, average overall age group being 17.23.Results: Within the female group, the Pearson cross-correlation coefficient of Openness and Conscientiousness is r. Pearson (105) = − .20, p <0.05. The Pearson coefficient of cross-correlation between Extraversion and Conscientiousness is = − .20, p <0.05. The Pearson coefficient of cross-correlation between emotional stability and conscientiousness is r. Pearson (105) = .36, p <.001. In the male group, the Pearson cross-correlation coefficient between the Extraversion and Conscientiousness personality dimensions is r. Pearson (N = 25) = −.39 where p <0.05.Conclusions: The Extraversion dimension of personality is a dynamic factor and motivator only when it is correlated with the Conscientiousness personality dimension. This indicates dynamism and autonomy, persistence in achieving goals, ambition, high motivation, interpersonal social intelligence and emotional stability, resulting a personality profile that matches one of the aspirants to a medical career.

The aim of the study was to assess the value of multiparametric ultrasound in atherosclerotic cardiovascular disease risk screening of the carotid artery. We performed ultrasonography of carotid arteries in 96 adults in 3 age groups: senior (age ≥65 years, n = 44), middle age (age 45-64 years, n = 31), and young adults (age 20-44 years, n = 21). The senior group was then divided into subgroups: athletes (n = 21) and non-athletes (n = 23). Ultrasound parameters included carotid intima-media thickness, distensibility coefficient, and presence of plaque(s). Statistical analyses included one-way analysis of variance with post hoc to analyze the differences in ultrasound parameters among the age groups; unpaired t-test to examine differences between hypertensive and normotensive participants, between seniors with and without plaque(s), and between senior athletes and non-athletes; Pearson correlation coefficient to analyze correlations of ultrasound parameters to age and blood pressure; and intraclass correlation coefficient to test intra- and inter-observer reliability in performing multiparametric ultrasound. Carotid intima-media thickness and distensibility coefficient significantly differed among the 3 age groups and between athletes and non-athletes ( P < .001). Senior athletes had greater distensibility than non-athletes (all Ps < .05). Carotid intima-media thickness was increased and distensibility was reduced with age and hypertension. Age and blood pressure positively correlated with carotid intima-media thickness (Pearson correlation coefficient, r > .21) and negatively with distensibility coefficient (Pearson correlation coefficient, r < –.48). Inter- and intra-observer reliability in performing multiparametric ultrasound was good (intraclass correlation coefficient, r > .81). Multiparametric ultrasound is a useful tool to assess carotid artery function and morphology comprehensively.

Studying the relationship between two quantitative variables is a standard topic in statistics. In high school and introductory college-level statistics, Pearson's correlation coefficient, r, is typically included as a measure of the direction and strength of the linear relationship between two quantitative variables. This article describes an activity that aids an intuitive development of the formula for r and its interpretation. Exploring scatter plots allows students to develop an intermediate measure of association called the quadrant count ratio (QCR), investigate properties of the QCR, and develop Pearson's r to address some of the QCR's shortcomings.

Studying the relationship between two quantitative variables is a standard topic in statistics. In high school and introductory college-level statistics, Pearson's correlation coefficient, r, is typically included as a measure of the direction and strength of the linear relationship between two quantitative variables. This article describes an activity that aids an intuitive development of the formula for r and its interpretation. Exploring scatter plots allows students to develop an intermediate measure of association called the quadrant count ratio (QCR), investigate properties of the QCR, and develop Pearson's r to address some of the QCR's shortcomings.

SUMMARY Detecting dependence between two random variables is a fundamental problem. Although the Pearson correlation coefficient is effective for capturing linear dependence, it can be entirely powerless for detecting nonlinear and/or heteroscedastic patterns. We introduce a new measure, G-squared, to test whether two univariate random variables are independent and to measure the strength of their relationship. The G-squared statistic is almost identical to the square of the Pearson correlation coefficient, R-squared, for linear relationships with constant error variance, and has the intuitive meaning of the piecewise R-squared between the variables. It is particularly effective in handling nonlinearity and heteroscedastic errors. We propose two estimators of G-squared and show their consistency. Simulations demonstrate that G-squared estimators are among the most powerful test statistics compared with several state-of-the-art methods.

The Motor Status Scale (MSS) measures shoulder, elbow (maximum score = 40), wrist, hand, and finger movements (maximum score = 42), and expands the measurement of upper extremity impairment and disability provided by the Fugl-Meyer (FM) score. This work examines the interrater reliability and criterion validity of the MSS performed in patients admitted to a rehabilitation hospital 21 ± 4 days after stroke. Using the MSS and the FM, 7 occupational therapists masked to each other’s judgments, evaluated 12 consecutive patients with stroke. Two therapists evaluated 6 additional patients on consecutive days. Intraclass correlation coefficients were significant for each group of raters for the shoulder/elbow and for the wrist/hand (P < 0.0001); test-retest measures were also significant for the shoulder/elbow (Pearson correlation coefficient r = 0.99, P < 0.004) and for the wrist/hand (Pearson correlation coefficient r = 0.99, P < 0.003). The internal item consistency for the overall MSS was significant (Cronbach alpha = 0.98, P < 0.0001). Finally the correlation between the MSS and the FM (R 2 = 0.964) was significant (P < 0.0001). The MSS affords a reliable and valid assessment of upper limb impairment and disability following stroke.

Purpose: To summarize data on graduation of the effect size on the base of Hill’s first causality criterion ‘strength of association’ on the magnitude of the correlation coefficient (mainly Pearson r).
 Material and methods: Survey of published sources: monographs, handbooks, papers, educational material on statistics in various disciplines (including on-line), etc. (121 references; of which more than 20 textbooks on statistical methods and statistics in psychology and 8 textbooks on epidemiology).
 Results: Estimation of the strength of association by the correlation size is most common in psycho-sociological disciplines and is almost never used in epidemiology (since the establishment of a fact of statistically significant association/correlation in epidemiology is only the initial stage of evidence, unlike the experimental and named disciplines). A number of known scales for r were obtained: the Chaddok scale (R.E. Chaddock) from 1925, which is now apparently not used abroad, but widely represented in the countries of the former USSR, the Cohen scale (J. Cohen) from 1969–1988, reflecting the ‘soft’ criteria of causality in psychology, D.E. Hinkle with co-authors scale (1979–2003) and the Evans scale (J.D. Evans) from 1996. A number of other graduations, published in the singular, are also given. A total of at least 16 different scales of varying degrees were collected for the correlation coefficient r (1925–2019). The information about the value of r for correlations, which should be neglected was presented. Depending on the source, this is r <0.1; r <0.2 or r <0.3. The data on the possibility of transferring graduations from the Pearson coefficient r to the Spearman correlation coefficient and other parameters of the effect size are given. 
 The question of the difference between estimation of strength of association in epidemiology and medicine and in psycho-sociological disciplines is considered. Unlike the second, in epidemiology and medicine a small value of the correlation coefficient does not necessarily mean a small effect size.
 Conclusions: To estimate the value of r one should use the most common and officially established scales, with the exception of the strongly ‘soft’ Cohen scale. The present study can be used as a reference guide on the graduations of effect size on r for a wide variety of observation disciplines.

Objective: This study assessed the validity of a Food Frequency Questionnaire used to assess food intake in adolescents in Salvador, Bahia, Brazil. Methods: Seventy adolescents enrolled in public schools aged 11 to 17 years participated in this study. The dietary intake of the adolescents was assessed using the Food Frequency Questionnaire and the mean value of three-day food record, used as the reference method. The mean (and standard deviation) energy and nutrient intakes estimated from the Food Frequency Questionnaire and food records were calculated. The paired Student's t test was used to determine differences between the data. Results: Pearson's correlation coefficient and Kappa statistics were used to measure the strength of the association between the two instruments. There was variation in the crude unadjusted (r=0.61-0.73) and deattenuated (r=0.33-0.99) Pearson correlation coefficients. After adjusting for energy, the crude and deattenuated coefficients ranged from r=0.53-0.81 and r=0.27-0.99, respectively. The intake of energy and most nutrients were found to be adequate, except for fiber (r=0.27) and calcium (r=0.33), which showed a weak correlation. Kappa statistics showed good correlation between all values varying from r=0.47 to 0.73. Conclusion: It is considered that an average r value greater than 0.40 indicates a good correlation, the results obtained indicate the good ability of the Food Frequency Questionnaire to assess individuals according to their usual intake of most nutrients.

12107 Background: Symptoms are common among patients enrolled in phase I trials. To integrate the patient perspective, the National Cancer Institute developed a patient-reported outcomes version of the CTCAE (PRO-CTCAE) to capture symptomatic adverse events (AEs) directly from patients; however, the tool has not been used often in early phase trials or in palliative care studies. Our overall objective was to assess the validity of PRO-CTCAE items to previously validated assessments of quality of life (FACT-G) and psychological distress (Distress Thermometer). We utilized data from a randomized trial testing a palliative care intervention for patients with cancer enrolled on phase I trials. Methods: Patients (n = 481) were accrued to the parent study prior to initiating a Phase I clinical trial with data collected at baseline, 4, and 12 weeks. We determined the correlation of PRO-CTCAE with Distress Level, FACT-G total and subscale domain scores. Aggregate scores using PRO-CTCAE were calculated to explore the effect of overall symptom frequency, severity, and interference by calculating the total of all scored items classified within each of those domains. We used these metrics to identify associations between this and other validated tools. Results: Patients were predominantly female (56.8%), over age 60, and 30.7% were minority populations. Correlations between PRO-CTCAE items and corresponding FACT-G (total and subscales) and Distress levels reached statistical significance for all items (p < 0.001). Importantly, many of symptoms captured would have been missed using HRQOL assessment tools. Some of these symptoms affected nearly 50% of patients and were frequently rated as severe or very severe. The correlation coefficient for Distress Level for all PRO-CTCAE items was small to moderate (Pearson r = 0.33 to 0.46). Pearson’s correlation coefficient for FACT-G total was moderate ( r = -0.45 to -0.69). Mood items of the PRO-CTCAE had stronger associations (Pearson r > 0.5). PRO-CTCAE symptom interference scores had the strongest correlation with Distress (Pearson r = 0.46) and FACT-G Total (Pearson r = -0.69). Conclusions: Patients entering Phase I trials are willing to report on symptoms they experience as a result of advancing disease and adverse effects from experimental treatment. Evidence demonstrates favorable validity of PRO-CTCAE in a heterogeneous US sample of patients undergoing cancer treatment on phase I trials. The granular assessment of symptomatic AEs may be on increasing importance as we enter a new therapeutic era in oncology. Clinical trial information: NCT01828775 .

This study assesses risk in road transportation in Ekiti State, Nigeria through a number of factors as well as the various ways by which these factors are perceived through Pearson correlation analysis. Six roads were identified as case studies from which information about how the users perceived risk was obtained mainly by administering questionnaires. The responses to the 23 questions which were developed based on the risk factors were analyzed statistically to obtain their relationships in terms of their correlation coefficients and R-squared values. The most dominant risk factors with respect to the roads considered as case studies were identified and the relationships between them were determined. The results showed that the most correlated risk factors are "drinking and driving (DAD)", "using a mobile phone (hands free) and driving (UMP(hf)AD)", "taking medicine and driving (TMAD)" and "using a mobile phone (hands free) and driving (UMP(hf)AD)" with Pearson correlation coefficient of 0.9772 and R-squared value of 0.9549.