An Empirical Study of the Nutritional Status of Severe Acute Malnourished (SAM) Children between Pre and Post Admission in Nutrition Rehabilitation Center (NRC)

The aim and objective of this research study was to compare the nutritional status of Severe Acute Malnourished (SAM) Children between Pre and Post admission in Nutrition Rehabilitation Center (NRC). The exploratory as well as descriptive research design was used. The nutritional status was checked by four test variables as Weight-kg, Height-cm, MUAC (Mid-Upper Arm Circumference). The sample size of this study was 211. The normality test was performed using One-Sample Kolmogorov-Smirnov Test. Since the data of four test variable was not normal, hence non-parametric test (Wilcoxon Signed Ranks Test) was used for the comparative study between pre and post condition. The findings concluded that there was a difference of the weight, height, MUAC, of the children in pre and post medical treatment in the NRC for the SAM children.

Productividad científica de los investigadores categorizados del área de Ciencias Médicas y de la Salud: aplicación del modelo matemático de Lotka / Scientific productivity of categorized researchers of the area of medical and health sciences: application of the Lotka's mathematical model

El modelo matemático de Lotka describe la relación entre los autores y su productividad dentro de un área de la ciencia. Este estudio se efectuó con el objetivo de comprobar que el comportamiento de la productividad científica de los investigadores del área de Ciencias Médicas y de la Salud, categorizados al año 2016 en el Programa Nacional de Incentivo a los Investigadores (PRONII), cumple con el modelo matemático de Lotka. Con este fin se consideraron las publicaciones alojadas en la Web of Science en las que éstos figuran como primer autor, aplicándose el criterio de primera autoría tal como lo hizo el formulador del modelo, y con filiación a instituciones paraguayas. Se aplicó el modelo a 236 publicaciones generadas por 77 investigadores, observándose que 21 de ellos tenían una sola publicación. Se comprobó que los datos observados no se ajustaban al modelo propuesto por Lotka. Esto motivó a que de los 77 investigadores analizados inicialmente se seleccionaran a 70. A la productividad de estos 70 investigadores se aplicó la forma general del modelo de Lotka y se comprobó que los datos se ajustaban al modelo. Se observó que de cada 10 investigadores muestreados solo 4 contaban con una única publicación, hecho que podría suponer una limitada existencia de autores ocasionales. Estos hallazgos revisten importancia ya que muestran el comportamiento de la relación entre el investigador y su productividad. Asimismo, mediante el modelo establecido es posible bajo determinadas condiciones realizar predicciones de la cantidad de investigadores con determinado número de publicaciones(AU)

Lotka's mathematical model describes the relationship between authors and their productivity within an area of science. This study was carried out with the aim of verifying that the behavior of the scientific productivity of the researchers in the area of Medical Sciences and Health, categorized to 2016 in the National Program of Incentive to Researchers (PRONII), complies with the model mathematical of Lotka. To this purpose, the publications hosted in the Web of Science were considered in which they appear as the first author, applying the criterion of first authorship as the model's formulator did, and with affiliation to Paraguayan institutions. The model was applied to 236 publications generated by 77 researchers, observing that 21 of them had a single publication. It was found that the observed data did not conform to the model proposed by Lotka. This motivated the selection of 70 of the 77 researchers initially analyzed. The overall form of the Lotka's model was applied to the productivity of these 70 researchers and the data was found to fit the model. It was observed that out of every 10 researchers sampled only four had a single publication, fact that could suppose a limited existence of occasional authors. These findings are important because they show the behavior of the relationship between the researcher and his & her productivity. In addition, through the established model it is possible under certain conditions to make predictions of the number of researchers with a certain number of publications(AU)

Pruebas de bondad de ajuste en distribuciones simétricas, ¿qué estadístico utilizar? / Goodness of Fit Tests for Symmetric Distributions, which Statistical Should I Use?

El uso de pruebas no paramétricas resulta recomendable cuando los datos a analizar no cumplen los supuestos de normalidad y homocedasticidad. Sin embargo, la suposición de la normalidad de los datos o el empleo de pruebas de bondad de ajuste que no son adecuadas para el tamaño muestral empleado son aspectos habituales. Este hecho implica, en muchas ocasiones, el uso de pruebas estadísticas no ajustadas al tipo de distribución real y, consecuentemente, el establecimiento de conclusiones erróneas. Por ello, en el presente estudio se ha analizado el poder de detección de cinco pruebas de bondad de ajuste (Kolmogorov-Smirnov, Kolmogorov-Smirnov-Lilliefors, Shapiro-Wilk, Anderson-Darling y Jarque-Bera) en distribuciones simétricas con seis tamaños muestrales entre 30 y 1000 participantes generados mediante una simulación Monte Carlo. Los resultados muestran una tendencia conservadora generalizada a medida que se incrementa el tamaño muestral. En cuanto a los tamaños muestrales, las pruebas con un mejor poder de detección de la no normalidad son Kolmogorov-Smirnov-Lilliefors y Anderson-Darling para muestra pequeñas, la prueba de Kolmogorov-Smirnov si se emplean tamaños muestrales medios (200 participantes) y la prueba de Shapiro-Wilk cuando se analizan muestras superiores a 500 participantes. Además, la prueba clásica de Kolmogorov-Smirnov se considera absolutamente ineficaz independientemente del tamaño muestral.

The use of nonparametric tests is recommended when the data do not meet the assumptions of normality and homoscedasticity. However, the assumptions of normality of the data or the use of goodness of fit tests that are not appropriate for the assessed sample are common aspects. In many cases, this implies the use of statistical tests unadjusted for the real data distribution and, consequently, the establishment of inaccurate conclusions. Therefore, in this paper the detection power of five tests of goodness of fit (Kolmogorov-Smirnov-Lilliefors, Kolmogorov-Smirnov, Shapiro-Wilk, Anderson-Darling and Jarque -Bera) in symmetric distributions is analysed in six sample sizes between 30 and 1000 participants generated by Monte Carlo simulation. Results show a marked conservative tendency as the sample size becomes larger. Regarding sample sizes to detect non-normality: analysing small samples the best results are provided by Kolmogorov-Smirnov-Lilliefors and Anderson-Darling tests, if the sample is medium-sized (200 participants) the Kolmogorov-Smirnov, and when samples are over 500 participants the Shapiro-Wilk test is recommended. In addition, the classic test of Kolmogorov-Smirnov is considered absolutely ineffective regardless the sample size.

Birth Weight and Future Life-span in Finnish Triplets.

Aims: Interest in the distribution of birth weight arises because of the association between birth weight and the future health of the child. A common statistical result is that the birth weight distribution differs slightly from the Gaussian distribution. Methods: A standard attempt has been done to split the distribution into two components, a predominant Gaussian distribution and an unspecified “residual” distribution. Results: We considered birth weight data among triplets born in Finland in 1905-1959 and compare the birth weight among stillborn and live-born triplets. The stillbirth rates are 119.1 per 1000 births for males, 124.6 for females and 121.8 for all. The sex differences are not significant. The still birth rate for the period 1905-1930 was 119.5 and for the period 1931-1959, 124.2. We identified a strong association between birth weight of the triplets and their survival. The weight distribution for male triplets is described well by the Gaussian curve, while for females a slight deviation from the Gaussian distribution is discernible.

A.N. Kolmogorov's defence of Mendelism

In 1939 N.I. Ermolaeva published the results of an experiment which repeated parts of Mendel's classical experiments. On the basis of her experiment she concluded that Mendel's principle that self-pollination of hybrid plants gave rise to segregation proportions 3:1 was false. The great probability theorist A.N. Kolmogorov reviewed Ermolaeva's data using a test, now referred to as Kolmogorov's, or Kolmogorov-Smirnov, test, which he had proposed in 1933. He found, contrary to Ermolaeva, that her results clearly confirmed Mendel's principle. This paper shows that there were methodological flaws in Kolmogorov's statistical analysis and presents a substantially adjusted approach, which confirms his conclusions. Some historical commentary on the Lysenko-era background is given, to illuminate the relationship of the disciplines of genetics and statistics in the struggle against the prevailing politically-correct pseudoscience in the Soviet Union. There is a Brazilian connection through the person of Th. Dobzhansky.