COVID-19 Infection Stigma Scale: psychometric properties

Background Stigma has been noticed towards patients with COVID-19 in several regions of the world. This social discrimination has contributed to delay in diagnosis and treatment. Also, it may increase the suffering of the patients leading to poor outcome of the illness. Stigma can be assessed with the use of a valid and reliable instrument developed and adapted to our culture. Our objective was to analyze the psychometric properties of COVID-19 Infection Stigma Scale (CISS) for measuring the social stigma among patients with COVID-19 in Egypt. A cross-sectional study that included 182 COVID-19 patients was carried out. The reliability, the convergent validity, and the external and internal consistency of the scale were measured. Factor analysis was used to exclude the weak items. Results The mean of the COVID-19 Infection Stigma Scale scores was 34.97±10.35 which was higher than 50% of the score. Absence of the floor and ceiling effects was observed. Cronbach’s alpha coefficient for scale reliability ranged from 0.75 to 0.94 with 0.82 for the total score. The convergent validity coefficients ranged from 0.36 to 0.63. Test-retest validity Pearson’s correlation coefficients ranged from 0.72 to 0.92 with 0.89 for the total score. The split half correlation coefficient was 0.86, and the reliability coefficient was 0.92. Both were acceptable correlation coefficients for internal consistency of the scale. Factor analysis showed two factors had latent root greater than 1. The rotated component matrix of the 2 factors revealed that all questions had r value more than 0.30, which means that no need to exclude any of them. Conclusion The results showed that the COVID-19 Infection Stigma Scale is a valid and reliable instrument for the Egyptian people.

A Quantitative Index to Assess the Influence of Joint Fit-Up on Pipeline Weld Root Discontinuities

Field experience on pipelines suggests that under the unique conditions of tie-in welding, a high-low offset at the inner pipe wall is related to an increased occurrence of latent root weld discontinuities such as cold cracking. Codes and standards offer conflicting and unclear guidelines regarding acceptance criteria for high-low offset. This study presents a numerical index to quantify the influence of non-ideal joint geometry on the latent discontinuity susceptibility of the root pass for circumferential pipeline welds. The index is based on the stress concentration at the root and the angular distortion associated with plastic strains produced during welding. This index relates geometric considerations such as pipe diameter, wall thickness, and the cross section of the root pass with welding procedure variables and the mechanical properties of the pipeline material. Although this study is meant for steel pipelines, the conclusions obtained are also applicable to other materials. The index presented is a contribution towards an objective criterion for acceptance of high-low offset during field welding, ranking the susceptibility to latent discontinuities as a function of variables available to practitioners during field welding.

Latent root regression dalam mengatasi multikolinearitas

Salah satu metode yang dapat digunakan untuk mengatasi masalah multikolinearitas pada model regresi adalah latent root regression. Latent root regression merupakan perluasan dari principal component regression. Tujuan penelitian ini adalah untuk melakukan analisis latent root regression dalam mengatasi multikolinearitas yang diterapkan pada faktor-faktor yang mempengaruhi IHSG di Bursa Efek Indonesia. Variabel-variabel yang digunakan pada penelitian ini adalah IHSG, jumlah uang beredar, kurs rupiah terhadap dolar AS, harga emas dunia dan Indeks Dow Jones. Hasil penelitian yang diperoleh adalah faktor jumlah uang beredar, kurs rupiah terhadap dolar AS, harga emas dunia dan Indeks Dow Jones berpengaruh terhadap IHSG, namun terjadi multikolinearitas diantara faktor-faktor tersebut sehingga diselesaikan dengan latent root regression. Kemudian analisis latent root regression tersebut dibandingkan dengan analisis principal component regression pada faktor-faktor yang mempengaruhi IHSG di Bursa Efek Indonesia yang hasilnya adalah latent root regression lebih baik daripada principal component regression karena lebih tinggi dan asumsi regresi lebih banyak dipenuhi pada latent root regression.Kata Kunci: latent root regression, multikolinearitas, IHSG. Latent Root Regression to Solve Multicolinearity AbstractOne of methods can be used to overcome the problem of multicollinearuty in a regression model is latent root regression. Latent root regression is an extension of principal component regression. The purpose of this research is to perfom a latent root regression analysis in solving multicollinearity on the factors that affect JSX Composite in Indonesia Stock Exchange. The variables used in this research are JSX Composite, money supply, rupiah exchange rate against the US dollar, gold price and DJI. The research result obtained are the factors of money supply, rupiah exchange rate against the US dollar, gold price and DJI affect JSX Composite, but multicollinearity occur among these factors thus solved by latent root regression. Then the latent root regression analysis is compared with principal component regression on the factors that affect JSX Composite in Indonesia Stock Exchange that the result is better than latent root regression of principal component regression because is higher and regression assumptions more fulfilled in latent root regression.Keywords: latent root regression, multicollinearity, JSX composite

Analysis of a Predator–Prey Model with Sparssing Effect

In this paper we formulated and analyzed a predator-prey model with sparssing effect, analysis of the existing conditions of equilibrium point, and the sufficient condition of the local asymptotical stability of the equilibrium was studied with the method of latent root, and furthermore, by constructing a Liapunov function to get the boundary equilibrium and the positive equilibrium sufficient conditions for the globally asymptotical stability.

Analysis of a Eco-Epidemiological Model with Disease in the Predator

In this paper we formulated and analyzed a eco-epidemiological model with disease in the predator, analysis of the existing conditions of equilibrium point, the sufficient condition of the local asymptotical stability of the equilibrium was studied with the method of latent root, the global asymptotical stability of two of the boundary equilibriums and the local asymptotical stability of the positive equilibrium is proved by using the Lyapunov function.

PENERAPAN REGRESI AKAR LATEN DALAM MENANGANI MULTIKOLINEARITAS PADA MODEL REGRESI LINIER BERGANDA

Multicollinearity is a problem that often occurs in multiple linear regression. The existence of multicollinearity in the independent variables resulted in a regression model obtained is far from accurate. Latent root regression is an alternative in dealing with the presence of multicollinearity in multiple linear regression. In the latent root regression, multicollinearity was overcome by reducing the original variables into new variables through principal component analysis techniques. In this regression the estimation of parameters is modified least squares method. In this study, the data used are eleven groups of simulated data with varying number of independent variables. Based on the VIF value and the value of correlation, latent root regression is capable of handling multicollinearity completely. On the other hand, a regression model that was obtained by latent root regression has value of 0.99, which indicates that the independent variables can explain the diversity of the response variables accurately.