Score Statistics Research Articles

Computationally efficient familywise error rate control in genome‐wide association studies using score tests for generalized linear models

AbstractIn genetic association studies, detecting phenotype–genotype association is a primary goal. We assume that the relationship between the data—phenotype, genetic markers and environmental covariates—can be modeled by a generalized linear model. The number of markers is allowed to be far greater than the number of individuals of the study. A multivariate score statistic is used to test each marker for association with a phenotype. We assume that the test statistics asymptotically follow a multivariate normal distribution under the complete null hypothesis of no phenotype–genotype association. We present the familywise error rate order k approximation method to find a local significance level (alternatively, an adjusted p‐value) for each test such that the familywise error rate is controlled. The special case k=1 gives the Šidák method. As a by‐product, an effective number of independent tests can be defined. Furthermore, if environmental covariates and genetic markers are uncorrelated, or no environmental covariates are present, we show that covariances between score statistics depend on genetic markers alone. This not only leads to more efficient calculations but also to a local significance level that is determined only by the collection of markers used, independent of the phenotypes and environmental covariates of the experiment at hand.

Score tests in GMM: Why use implied probabilities?

While simple to implement and thus attractive in practice, the GMM score test of Newey and West (1987) often displays upward size distortion under common scenarios involving skewed moment vectors or models with weak identification. Inference based on the Generalized Empirical Likelihood (GEL) is seen as a general solution to this problem. Kleibergen (2005) and, more generally, Guggenberger and Smith (2005) devised an elegant theory for the GEL score tests. However, strictly speaking, the GEL score tests do not nest the Newey–West score test. Our paper provides a unified framework for score tests in GMM that nests all of the above as special cases and helps us to understand the precise mechanism by which the standard first order asymptotic theory on size and power well approximates the finite sample behavior of some score tests (namely, a subset of the GEL score tests) but not others. Special attention is paid to models with weak identification. We also argue that the apparent computational burden of GEL can be overcome in practice by recognizing the fundamental common role played by the GEL implied probabilities under all special cases of our framework. In particular, we show that all the GEL implied probabilities are asymptotically equivalent at a higher order – both under the null and under appropriate sequences of alternatives – and thus are exchangeable across computationally burdensome (e.g. Empirical Likelihood) and easy (e.g. Euclidean Empirical Likelihood) GEL score tests without affecting the first order asymptotics. Extensive simulation evidence is provided to corroborate our theoretical results. The simulation results also support a simple and yet important insight on the power of the tests: the use of implied probabilities to efficiently estimate the components of the score statistic, namely, the Jacobian and the asymptotic variance of the moment vector, can significantly improve the power of the score test in finite samples.

Using Prior Expansions for Prior-Data Conflict Checking

Any Bayesian analysis involves combining information represented through different model components, and when different sources of information are in conflict it is important to detect this. Here we consider checking for prior-data conflict in Bayesian models by expanding the prior used for the analysis into a larger family of priors, and considering a marginal likelihood score statistic for the expansion parameter. Consideration of different expansions can be informative about the nature of any conflict, and an appropriate choice of expansion can provide more sensitive checks for conflicts of certain types. Extensions to hierarchically specified priors and connections with other approaches to prior-data conflict checking are considered, and implementation in complex situations is illustrated with two applications. The first concerns testing for the appropriateness of a LASSO penalty in shrinkage estimation of coefficients in linear regression. Our method is compared with a recent suggestion in the literature designed to be powerful against alternatives in the exponential power family, and we use this family as the prior expansion for constructing our check. A second application concerns a problem in quantum state estimation, where a multinomial model is considered with physical constraints on the model parameters. In this example, the usefulness of different prior expansions is demonstrated for obtaining checks which are sensitive to different aspects of the prior.

Modulation of oxidative stress/antioxidative defence in human serum treated by four different tyrosine kinase inhibitors.

Recent findings implied the significance of reactive oxygen species (ROS) as a part of tyrosine kinase inhibitors (TKIs) pharmacological activity. Evidences also suggested that toxic effects of TKIs were related to ROS production. The results regarding benefits of vitamin E usage alongside with prescribed TKIs therapy are ambiguous. We aimed to examine oxidative stress and antioxidative defense in human serum treated with four different TKIs and their possible interactions with hydrosoluble vitamin E analog (Trolox). An in-vitro experiment with serum pool as a substitute model was performed. Different parameters of oxidative stress and antioxidative defense were measured in serum pool with and without addition of TKIs (axitinib, crizotinib, nilotinib, and imatinib), before and after addition of Trolox. Z score statistic was used for calculation of Prooxidative and Antioxidative scores. The highest oxidative potential was recorded for samples incubated with imatinib and nilotinib, while the lowest damaging scores were observed for crizotinib and axitinib (nilotinib vs. imatinib, P < 0.05; axitinib vs. imatinib, P < 0.01; crizotinib vs. imatinib, P < 0.001). The best capability for antioxidative protection was seen in samples with nilotinib, then with imatinib, while the lowest level was obtained in samples with crizotinib and axitinib (imatinib and axitinib vs. nilotinib, P < 0.05 for both; crizotinib vs. nilotinib, P < 0.01; axitinib vs. imatinib, P < 0.05, crizotinib vs. imatinib, P < 0.01). Our results demonstrated the opposite effects of Trolox in combination with imatinib and nilotinib. Usage of antioxidant in combination with TKIs should be carefully evaluated in each specific case.

The score test for the two‐sample occupancy model

SummaryThe score test statistic from the observed information is easy to compute numerically. Its large sample distribution under the null hypothesis is well known and is equivalent to that of the score test based on the expected information, the likelihood‐ratio test and the Wald test. However, several authors have noted that under the alternative hypothesis this no longer holds and in particular the score statistic from the observed information can take negative values. We extend the anthology on the score test to a problem of interest in ecology when studying species occurrence. This is the comparison of two zero‐inflated binomial random variables from two independent samples under imperfect detection. An analysis of eigenvalues associated with the score test in this setting assists in understanding why using the observed information matrix in the score test can be problematic. We demonstrate through a combination of simulations and theoretical analysis that the power of the score test calculated under the observed information decreases as the populations being compared become more dissimilar. In particular, the score test based on the observed information is inconsistent. Finally, we propose a modified rule that rejects the null hypothesis when the score statistic is computed using the observed information is negative or is larger than the usual chi‐square cut‐off. In simulations in our setting this has power that is comparable to the Wald and likelihood ratio tests and consistency is largely restored. Our new test is easy to use and inference is possible. Supplementary material for this article is available online as per journal instructions.

Simultaneous confidence interval for assessing non-inferiority with assay sensitivity in a three-arm trial with binary endpoints.

A three-arm trial including an experimental treatment, an active reference treatment and a placebo is often used to assess the non-inferiority (NI) with assay sensitivity of an experimental treatment. Various hypothesis-test-based approaches via a fraction or pre-specified margin have been proposed to assess the NI with assay sensitivity in a three-arm trial. There is little work done on confidence interval in a three-arm trial. This paper develops a hybrid approach to construct simultaneous confidence interval for assessing NI and assay sensitivity in a three-arm trial. For comparison, we present normal-approximation-based and bootstrap-resampling-based simultaneous confidence intervals. Simulation studies evidence that the hybrid approach with the Wilson score statistic performs better than other approaches in terms of empirical coverage probability and mesial-non-coverage probability. An example is used to illustrate the proposed approaches.

Admission diagnosis and mortality risk prediction in a contemporary cardiac intensive care unit population

Critical care risk scores can stratify mortality risk among cardiac intensive care unit (CICU) patients, yet risk score performance across common CICU admission diagnoses remains uncertain. We evaluated performance of the Acute Physiology and Chronic Health Evaluation (APACHE)-III, APACHE-IV, Sequential Organ Failure Assessment (SOFA) and Oxford Acute Severity of Illness Score (OASIS) scores at the time of CICU admission in common CICU admission diagnoses. Using a database of 9,898 unique CICU patients admitted between 2007 and 2015, we compared the discrimination (c-statistic) and calibration (Hosmer-Lemeshow statistic) of each risk score in patients with selected admission diagnoses. Overall hospital mortality was 9.2%. The 3182 (32%) patients with a critical care diagnosis such as cardiac arrest, shock, respiratory failure, or sepsis accounted for >85% of all hospital deaths. Mortality discrimination by each risk score was comparable in each admission diagnosis (c-statistic 95% CI values were generally overlapping for all scores), although calibration was variable and best with APACHE-III. The c-statistic values for each score were 0.85-0.86 among patients with acute coronary syndromes, and 0.76-0.79 among patients with heart failure. Discrimination for each risk score was lower in patients with critical care diagnoses (c-statistic range 0.68-0.78) compared to non-critical cardiac diagnoses (c-statistic range 0.76-0.86). The tested risk scores demonstrated inconsistent performance for mortality risk stratification across admission diagnoses in this CICU population, emphasizing the need to develop improved tools for mortality risk prediction among critically-ill CICU patients.

Comprehensive assessment of knee joint synovitis at 7\u2009T MRI using contrast-enhanced and non-enhanced sequences

BackgroundSeven T ultra-high field MRI systems have recently been approved for clinical use by the U.S. and European regulatory agencies. These systems are now being used clinically and will likely be more widely available in the near future. One of the applications of 7 T systems is musculoskeletal disease and particularly peripheral arthritis imaging. Since the introduction of potent anti-rheumatic therapies over the last two decades MRI has gained increasing importance particularly for assessment of disease activity in early stages of several rheumatic disorders. Commonly gadolinium-based contrast agents are used for assessment of synovitis. Due to potential side-effects of gadolinium non-enhanced techniques are desirable that enable visualization of inflammatory disease manifestations. The feasibility of 7 T MRI for evaluation of peripheral arthritis has not been shown up to now. Aim of our study was to evaluate the feasibility of contrast-enhanced (CE) and non-enhanced MRI at 7 T for the assessment of knee joint synovitis.MethodSeven T MRI was acquired for 10 patients with an established diagnosis of psoriatic or rheumatoid arthritis. The study pulse sequence protocol was comprised of a sagittal intermediate-weighted fat-suppressed (FS), axial fluid-attenuated inversion recovery (FLAIR) FS, sagittal 3D T1-weighted dynamic contrast enhanced (DCE) and an axial static 2D T1-weighted FS contrast-enhanced sequence (T1-FS CE). Ordinal scoring on non-enhanced (Hoffa- and effusion-synovitis) and enhanced MRI (11-point synovitis score), and comparison of FLAIR-FS with static T1-FS CE MRI using semiquantitative (SQ) grading and volume assessment was performed. For inter- and intra-reader reliability assessment weighted kappa statistics for ordinal scores and intraclass correlation coefficients (ICC) for continuous variables were used.ResultsThe total length of study protocol was 15 min 38 s. Different amounts of synovitis were observed in all patients (mild: n = 3; moderate: n = 5; severe: n = 2). Consistently, SQ assessment yielded significantly lower peripatellar summed synovitis scores for the FLAIR-FS sequence compared to the CE T1-FS sequence (p < 0.01). FLAIR-FS showed significantly lower peripatellar synovial volumes (p < 0.01) compared to CE T1-FS imaging with an average percentage difference of 18.6 ± 9.5%. Inter- and intra-reader reliability for ordinal SQ scoring ranged from 0.21 (inter-reader Hoffa-synovitis) to 1.00 (inter-reader effusion-synovitis). Inter- and intra-observer reliability of SQ 3D-DCE parameters ranged from 0.86 to 0.99.ConclusionsSeven T FLAIR-FS ultra-high field MRI is a potential non-enhanced imaging method able to visualize synovial inflammation with high conspicuity and holds promise for further application in research endeavors and clinical routine by trained readers.

Application of Brain-Based Teaching Strategies on Academic Performance of Children with Attention Deficit Hyperactivity Disorder (ADHD) In Mathematics

This study examined application of Brain-based Teaching Strategies on academic performance of children with ADHD in Mathematics. Gender and Mathematics anxiety level were introduced as moderator variables. A pre-test, post-test, control group experimental design was employed for this study. Two groups were involved (experimental and control groups). The experimental group was exposed to the application of Brain-based Teaching Strategies while the control group was exposed to the conventional method. Two instruments were used (i) Achievement Test in Mathematics (ATM) (r = 0.83) and (ii) Mathematics Anxiety Rating Scale (MARS) (r = 0.80). Data obtained were analyzed using descriptive statistics of means scores and standard deviations to explain and compare pretest and posttest mean scores of the experimental and control groups in all the criteria measured. Inferential statistics of Analysis of covariance (ANCOVA) was used to test the hypotheses and estimate the impacts of various factors on the dependent variables. Treatment was more effective at improving children with ADHD attitude to mathematics than the conventional method. Findings indicated significant improvement on children’s attention span resulting from taking cognizance of “prime times” in the teaching-learning episode. Also, tension that is normally associated with the teaching-learning process of mathematics was significantly reduced.

Improved score tests for exponential family nonlinear models

This paper focuses on the corrections to the score test statistic under the exponential family nonlinear model. We use Monte Carlo simulations to compare the corrected statistics and their uncorrected versions and to examine the impact of the number of nuisance parameters on their finite-sample behaviors for the normal nonlinear regression model. The numerical results have shown that the corrected score statistic performs better than the uncorrected version. Finally, we perform a statistical analysis with real data by using the approach proposed in the article.

Using Cox regression to develop linear rank tests with zero-inflated clustered data.

Zero-inflated data arise in many fields of study. When comparing zero-inflated data between two groups with independent subjects, a two degree-of-freedom test has been developed, which is the sum of a 1 degree-of-freedom Pearson chi-square test for the 2×2 table of group vs dichotomized outcome (0,> 0) and a 1 degree-of-freedom Wilcoxon rank-sum test for the values of the outcome > 0. Here, we extend this 2 degree-of-freedom test to clustered data settings. We first propose using an estimating equations score statistic from a time-varying weighted Cox regression model under naive independence, with a robust sandwich variance estimator to account for clustering. Since our proposed test statistics can be put in the framework of a Cox model, to gain efficiency over naive independence, we apply a generalized estimating equations (GEE) Cox model with a non-independence 'working correlation' between observations in a cluster. The proposed methods are applied to a General Social Survey study of days with mental health problems in a month, in which 52.3% of subjects report they have no days with problems, a zero-inflated outcome. A simulation study is used to compare our proposed test statistics to previously proposed zero-inflated test statistics.

Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation

In this article, we develop a new estimation and valid inference method for single or low-dimensional regression coefficients in high-dimensional generalized linear models. The number of the predictors is allowed to grow exponentially fast with respect to the sample size. The proposed estimator is computed by solving a score function. We recursively conduct model selection to reduce the dimensionality from high to a moderate scale and construct the score equation based on the selected variables. The proposed confidence interval (CI) achieves valid coverage without assuming consistency of the model selection procedure. When the selection consistency is achieved, we show the length of the proposed CI is asymptotically the same as the CI of the “oracle” method which works as well as if the support of the control variables were known. In addition, we prove the proposed CI is asymptotically narrower than the CIs constructed based on the desparsified Lasso estimator and the decorrelated score statistic. Simulation studies and real data applications are presented to back up our theoretical findings. Supplementary materials for this article are available online.

A revisit to testing the equality of means for several lognormal distributions

In this paper, we propose a new test statistic for testing the equality of several lognormal means based on Score statistic. Besides the traditional chi-square approximation to Score statistic, we use a parametric bootstrap method called computational approach test (CAT). We compare the proposed tests in terms of type I error rate and power under different number of groups and sample sizes to evaluate efficiency of these tests against the existing tests in the literature. Simulation results indicate that powers of these tests are outstandingly higher compared to their competitors under all the considered cases. Although the performance of chi-square approximation of Score statistic is insufficient in small sample cases for many problems, this approximation performs quite well for our problem even in some small sample sizes. Besides, we illustrate the considered methods using both numerical example and a real data example.

Relationship between Metacognitive Awareness and Reflective Learning of Medical Students at the Faculty of Medicine, Suez Canal University

Aim: To examine the relationship between reflective thinking and metacognitive awareness to help medical students to be independent learners who can control their learning and improve their professional performance.&#x0D; Study Design: It is a cross-sectional correlational study.&#x0D; Place and Duration of the Study: This study was conducted at the Faculty of Medicine-Suez Canal University in August 2018.&#x0D; Methodology: This is a cross-sectional correlational study; the study population included the undergraduate medical students in all study years at the Faculty of Medicine, Suez Canal University. The sample was 264 randomly selected students by using a cluster sampling technique. The instruments that were used for data collection were the Metacognitive Awareness Inventory (MAI) to measure students’ metacognitive awareness levels, Reflection-in-Learning Scale (RLS) to measure the students’ reflective learning levels.&#x0D; Results: The descriptive statistics of both MAI and RLS total scores of students in the six study years revealed that students have mean total MAI total scores = 178 ± 26 and have mean total RLS total scores = 60 ± 13.&#x0D; The Spearman's correlation between the metacognitive awareness and the reflective learning skills of medical students revealed that there was a statistically significant high positive correlation between the metacognitive awareness and the reflection in the learning of FOM-SCU students (p= 0.699, p-value&lt;0.0001).&#x0D; Multiple regression analysis revealed that the weighted combination of the predictor variables explained approximately 50% of the variance of reflection in learning.&#x0D; Conclusion: It is concluded from this study that the students at the Faculty of Medicine, Suez Canal University have fair to good metacognitive awareness and partial to ample reflective abilities. There is a significantly high positive relationship between metacognitive awareness and the reflective learning skills of medical students.

The New Freezing of Gait Questionnaire: Unsuitable as an Outcome in Clinical Trials?

Freezing of gait (FOG) is a common gait deficit in Parkinson's disease. The New Freezing of Gait Questionnaire (NFOG-Q) is a widely used and valid tool to quantify freezing of gait severity. However, its test-retest reliability and minimal detectable change remain unknown. To determine the test-retest reliability and responsiveness of the NFOG-Q. Two groups of freezers, involved in 2 previous rehabilitation trials, completed the NFOG-Q at 2 time points (T1 and T2), separated by a 6-week control period without active intervention. Sample 1 (N = 57) was measured in ON and sample 2 (N = 14) in OFF. We calculated various reliability statistics for the NFOG-Q scores between T1 and T2 as well as correlation coefficients with clinical descriptors to explain the variability between time points. In sample 1 the NFOG-Q showed modest reliability (intraclass correlation coefficient = 0.68 [0.52-0.80]) without differences between T1 and T2. However, a minimal detectable change of 9.95 (7.90-12.27) points emerged for the total score (range 28 points, relative minimal detectable change of 35.5%). Sample 2 showed largely similar results. We found no associations between cognitive-related or disease severity-related outcomes and variability in NFOG-Q scores. We conclude that the NFOG-Q is insufficiently reliable or responsive to detect small effect sizes, as changes need to go beyond 35% to surpass measurement error. Therefore, we warrant caution in using the NFOG-Q as a primary outcome in clinical trials. These results emphasize the need for robust and objective freezing of gait outcome measures.

324 Shock Wave Erectile Enhancement Trial (SWEET) Study: A 41 Site Study on the Use of Shock Wave Therapy for the Treatment of Erectile Dysfunction

Shock wave therapy is becoming more prevalent in the treatment of erectile dysfunction (ED). Currently, there are no standard treatment protocols. The Shock Wave Erectile Enhancement Trial (SWEET) is a multi-site observational study on the use of Low-Intensity Shock Wave Therapy (Li-SWT) in the treatment of ED in North America. Adult men engaging in Li-SWT for the treatment of ED at participating study sites will be assessed for eligibility. The primary outcome will be erectile function measured as a continuous variable via the Sexual Health Inventory for Men (SHIM) score. A study questionnaire containing these items as well as items on pertinent demographic and comorbid characteristics will be administered to participants prior to Li-SWT, after treatment, and every three months after completing Li-SWT. Analysis will consist of descriptive statistics of SHIM score, demographic, and comorbid variables at baseline. Change in SHIM score over time will be explored using a multivariate mixed effects model and marginal estimates.

Effect of sublingual immunotherapy on patients with allergic rhinitis

Objective:Study the efficacy of sublingual immunotherapy （SLIT） for monosensitized and polysensitized allergic rhinitis patients of different severity. Method:According to the results of intradermal experiments, 186 patients with AR who were treated with standardized dust mites SLIT for 2 years. These patients have been divided into mild monosensitized group and mild polysensitized group, moderate monosensitized group and moderate polysensitized group, severe monosensitized group and severe polysensitized group. Statistics and analysis of total nasal symptom score （TNSS）, total medication score （TMS） , visual analog scale （VAS） about before and after SLIT treatment. Result:Before SLIT treatment and after 2 years, the TNSS: Mild monosensitized group （5. 58±1. 68）（0. 58±0. 75）, mild polysensitized group（5. 34±1. 49）（0. 93±0. 75）;moderate monosensitized group （4. 68±1. 55）（0. 32±0. 63）, moderate polysensitized group（6. 26±1. 46）（0. 61±0. 67）,which have showed a statistically significant difference（P<0.05）,between the two groups have no significant difference （P>0.05）. Severe monosensitized group（6. 26±0. 73）（1. 16±1. 13）, severe polysensitized group（6. 49±1. 04）（1. 89±1. 07）, which have showed a statistically significant difference（P<0.05）, between the two groups have a statistically significant difference（P<0.05）. The VAS:Mild monosensitized group （6. 09±1. 23）（1. 52±1. 25）, mild polysensitized group（6. 21±0. 90）（1. 97±1. 18）,moderate monosensitized group（6. 12±0. 53）（1. 16±1. 03）, moderate polysensitized group（6. 65±1. 08）（1. 65±0. 98）,which have showed a statistically significant difference（P<0.05）,between the two groups have no significant difference （P>0.05）. Severe monosensitized group（6. 26±0. 73）（1. 16±1. 13）, severe polysensitized group（6. 49±1. 04）（1. 89±1. 07）, which have showed a statistically significant difference（P<0.05）, between the two groups have a statistically significant difference（P<0.05）. The TMS:These have showed a statistically significant difference（P<0.05）with same group,between all groups have no significant difference （P>0.05）. Conclusion:Dust mites under the SLIT are effective for monosensitized and polysensitized allergic patients. For patients with severe polysensitized , it is necessary to extend the treatment time （>2 years）.

Local power of some likelihood‐based tests in multivariate Student‐t regression models

The Student‐ distribution has proved to be a useful alternative to the traditional normal distribution, mainly to deal with heavy tails and when robust estimation is desired. We consider the multivariate Student‐ regression model and derive the nonnull asymptotic expansions (under a sequence of Pitman alternatives) of the distribution functions of the likelihood ratio, Wald, Rao score, and gradient test statistics for testing a subset of regression parameters in this class of multivariate regression models. On the basis of the nonnull asymptotic expansions derived, the power of all four tests, which are equivalent to the first order, are compared. We provide conditions in which one test can be more locally powerful than the other one in this class of multivariate regression models and, hence, one can choose the most powerful test based on the general conditions.

Assessment on the Implementation of Process Approach in Teaching Writing Skill: The Case of Preparatory Schools in Hadiya Zone

The main purpose of this study was to assess the implementation of process approach in writing skills in preparatory schools of Hadiya zone which is found in southern part of Ethiopia 230kms far away from Addis Ababa (the Capital). The subjects of the study were ten (10) English teachers and 500 students of the grade levels. Simple random sampling was used for students and purposive sampling was used for teachers. The data were collected through four instruments; namely written test, questionnaire, classroom observation and document analysis. The data were analyzed using Z score, achievement test score and descriptive statistics for the written test, simple descriptive statistics for close ended questionnaire and classroom observation and thematic and inductive analysis techniques for open ended questionnaire and document analysis. The findings of this study revealed that most of the teachers and students (80%) were not implementing process approach in writing skill due to shortage of time, lack of exposure, low background and lack of interest. Therefore, it was suggested that writing skills should be taught according to its principles in process approach at preparatory schools because they are facing many challenges at college courses, teachers should expose learners for different writing activities and the textbooks should be revisited in line with process approach in writing skills.

Emotional Intelligence and Academic Achievement Among Management and Science University’s (MSU) School of Education and Social Sciences (SESS) students

This study aims to look at the existing level of emotional intelligence among Management and Science University’s (MSU) School of Education and Social Sciences (SESS) students. It will also look at the relationship between intrapersonal and interpersonal level among SESS students. The sample is 100 students that divide by 50 males and 50 females. In this research, it used a survey tool which Emotional Competencies Inventories (ECI). In this research it used inventory with 40 items in it. It is divided to two parts, intrapersonal and interpersonal abilities. First is to recognize emotions categories of self. Second is emotional self-management and lastly personal motivation. Interpersonal part is consists of two categories, which is the ability to recognize others feelings and the ability to handle relationships effectively. This researcher analyzes the data using the Statistical Package for the Social Science (SPSS) version 17.0. The reliability for this instrument (Emotional Intelligent Inventory) is .791. The data was process by using the descriptive statistics of the mean score and the standard deviation. Pearson correlation coefficient is used to measure the relationship between the construct of intrapersonal and interpersonal relationships among the students. The results showed that each of the questions and objectives of the study have been answered. The results of the study also illustrate the level of emotional intelligence among SESS students are at moderate level. This study also discusses the results of the study, future research recommendations and limitations of the study