DAKS: An R Package for Data Analysis Methods in Knowledge Space Theory

Knowledge space theory is part of psychometrics and provides a theoretical framework for the modeling, assessment, and training of knowledge. It utilizes the idea that some pieces of knowledge may imply others, and is based on order and set theory. We introduce the R package DAKS for performing basic and advanced operations in knowledge space theory. This package implements three inductive item tree analysis algorithms for deriving quasi orders from binary data, the original, corrected, and minimized corrected algorithms, in sample as well as population quantities. It provides functions for computing population and estimated asymptotic variances of and one and two sample Z tests for the diff fit measures, and for switching between test item and knowledge state representations. Other features are a function for computing response pattern and knowledge state frequencies, a data (based on a finite mixture latent variable model) and quasi order simulation tool, and a Hasse diagram drawing device. We describe the functions of the package and demonstrate their usage by real and simulated data examples.

Fechnerian Scaling in R: The Package fechner

Fechnerian scaling is a procedure for constructing a metric on a set of objects (e.g., colors, symbols, X-ray films, or even statistical models) to represent dissimilarities among the objects "from the point of view" of a system (e.g., person, technical device, or even computational algorithm) "perceiving" these objects. This metric, called Fechnerian, is computed from a data matrix of pairwise discrimination probabilities or any other pairwise measure which can be interpreted as the degree with which two objects within the set are discriminated from each other. This paper presents the package fechner for performing Fechnerian scaling of object sets in R. We describe the functions of the package. Fechnerian scaling then is demonstrated on real and artificial data sets accompanying the package.

DAKS: An R Package for Data Analysis Methods in Knowledge Space Theory

Knowledge space theory is part of psychometrics and provides a theoretical framework for the modeling, assessment, and training of knowledge. It utilizes the idea that some pieces of knowledge may imply others, and is based on order and set theory. We introduce the R package DAKS for performing basic and advanced operations in knowledge space theory. This package implements three inductive item tree analysis algorithms for deriving quasi orders from binary data, the original, corrected, and minimized corrected algorithms, in sample as well as population quantities. It provides functions for computing population and estimated asymptotic variances of and one and two sample Z tests for the diff fit measures, and for switching between test item and knowledge state representations. Other features are a function for computing response pattern and knowledge state frequencies, a data (based on a finite mixture latent variable model) and quasi order simulation tool, and a Hasse diagram drawing device. We describe the functions of the package and demonstrate their usage by real and simulated data examples

Techniques for Sampling Quasi-orders

In educational theories, e.g., learning spaces, mastery dependencies between test items are represented as reflexive and transitive binary relations, i.e., quasi-orders, on the item set of a knowledge domain. Item dependencies can be used for efficient adaptive knowledge assessment and derived through exploratory data analysis, for example by algorithms of item tree analysis. To compare item tree analysis methods, typically large-scale simulation studies are employed, with samples of randomly generated quasi-orders at their basis and assumed to underlie the data. In this context, a serious problem is the fact that all of the algorithms are sensitive to the underlying quasi-order structure. Thus, it is crucial to base any simulation study that aims at comparing the algorithms in a reliable manner on representative samples, meaning that each quasi-order in the population is equally likely to be selected as part of a sample. Suboptimal sampling strategies were considered in previous studies leading to biased conclusions. In this paper, we discuss sampling techniques that allow us to generate representative, or close to representative, random quasi-orders. The item tree analysis methods are compared on ten items with a representative, large sample of quasi-orders, thereby supporting their invariant ordering

Interactive Graphics: Exemplified with Real Data Applications

Graphics are widely used in modern applied statistics because they are easy to create, convenient to use, and they can present information effectively. Static plots do not allow interacting with graphics. User interaction, on the other hand, is crucial in exploring data. It gives flexibility and control. One can experiment with the data and the displays. One can investigate the data from different perspectives to produce views that are easily interpretable and informative. In this paper, we try to explain interactive graphics and advocate their use as a practical tool. The benefits and strengths of interactive graphics for data exploration and data quality analyses are illustrated systematically with three complex real datasets

Patients' approach to medicines in COVID-19

Several different guidelines and therapeutic recommendations have been reported for the treatment of COVID-19 since the announcement of the pandemic. In our study, the attitudes and approaches of patients with a medical indication for COVID-19 who were given drugs towards drug usage were evaluated. We aimed to present our data on the drug usage characteristics of patients to contribute to the literature. A total of 399 patients were included in the study. In the study, 51.1% of the patients were female, and 48.9% were male. The highest number of the patients were in the 18-30 age group (27.6%), the lowest number of the patients were 65 years old or older (9.8%). Twenty-five questions prepared by the researchers were asked to the patients to evaluate "their knowledge and attitudes on drug usage and disease prevention in COVID-19." Of the patients, 75.7% were not smokers. No history of chronic disease was present in 65.5% of the patients. It was determined that no drug was recommended for 9.8% of the patients, and hydroxychloroquine and favipiravir were recommended together in 49.9%. The rate of the use of chloroquine alone was 4.8%, and the rate of using only favipiravir was 32.8%. Eighty-two percent of the patients reported that they regularly used the drugs that were recommended. Among the patients, 11.5% either never used the recommended drugs or did not use them at the recommended dose and time. Of the 46 (11.5%) patients who did not use the prescribed drugs regularly, none died. In other words, improvement was observed in the patients who did not use the drugs that were recommended to them. Our aim in this study was to determine the rate and characteristics of the drugs prescribed by physicians in diagnosed patients. In this cross-sectional sample of Turkey, it was determined that the rate of recommended drug usage was sufficient with the data of the city where the study was carried out. © 2022 Ondokuz Mayis Universitesi. All rights reserved

THE ACUTE EFFECTS OF PROPRIOCEPTIVE NEUROMUSCULAR FACILITATION(PNF) STRETCHING ON DYNAMIC BALANCE PERFORMANCE IN ELITE WRESTLERS

The aim of this study was to investigate the effect of Proprioceptive Neuromuscular Facilitation (PNF) stretch techniques on dynamic standing balance performances using Biodex Balance System (BBS). Seven wrestlers from the Turkish National Team between the ages of 18 and 25 were tested before and immediately after PNF stretching interventions. The stretching protocol involved a 5-min warm-up at at at 70 rpm with 1-kp resistance on a stationary cycle ergometer followed by four PNF stretching exercises to stretch the leg extensor muscles of the dominant and nondominant limbs according to the procedures of a previous study. Balance was measured in two conditions; dominant and nondominant limbs over a period of 20s and Medial–lateral stability index (MLSI), Anterior–posterior stability index (APSI) and an Overall stability index (OSI) were recorded before and after PNF interventions. One way repeated-measures analysis of variance (ANOVA) was used to analyze the differences between pretest and posttest BBS values. There were no significant balance index differences after the PNF interventions for dominant leg (p>0.05). However, a significant increase was found in MLSI values for non-dominant leg. Even though the PNF stretching intervention reduced MLSI dynamic balance performance for the non-dominant leg in our findings, other BBS index scores showed that dynamic balance performance was not affected by the acute bout of PNF stretching

A STUDY BASED ON USING PEARSON DISTRIBUTION FAMILY ON RELIABILITY ANALYSIS

Günlük hayatımızda kullandığımız tüm ürünler veya sistemler zaman içinde yıpranmakta ve bunun sonucunda da bozulmaktadır. Üreticiler açısından bu olası yıpranma ve bozulmaların sebeplerinin önceden bilinmesi hayati önem taşımaktadır. Bu bakış açısıyla ürünlerin potansiyel yaşamlarının belirlenmesi amacına yönelik güvenilirlik analizi çalışmaları yapılmaktadır. Güvenilirlik analizinin temelinde hata sürelerinin dağılımı vardır. Uygun dağılım belirlenirken çeşitli istatistiksel araçlardan yararlanılabilir. Güvenilirlik analizinde genellikle kümülatif dağılım fonksiyonu, güvenilirlik fonksiyonu, hazard fonksiyonu, ortamla artık yaşam fonksiyonu ve artık yaşam varyansı bu dağılımı belirlemede kullanılan en yaygın araçlardır. Aynı zamanda hata dağılışları bu fonksiyonlar arasındaki ilişkilerden yararlanılarak karakterize edilebilmektedir. Pearson diferansiyel denklem sistemi, güvenilirlik analizinde kullanılan birçok dağılışı içerisinde barındırmaktadır. Bu nedenle güvenilirlik analizinde önemli bir yeri vardır. Bu çalışmada Pearson diferansiyel denklem sisteminin, asimetrik dağılım türeten kübik paydalı bir yapısı ele alınacaktır. Daha sonra bu yapı için koşullu momentler ile asimetri ölçüleri incelenecektir All products or systems that we use in daily life, degrade in time so, they ultimately fail. It is very crucial for manufacturers to forecast the reasons of the failures before. With that perspective reliability analysis is carried out to determine the potential lifetime of products. Failure time's distribution is the basis of the reliability analysis. While determining the proper distribution, some statistical methods can be used. Cumulative distribution, reliability function, hazard function, mean residual life, variance residual life are most common tools to determine proper distribution in reliability analysis. At the same time failure distributions can be characterized by using relations between these functions. Pearson Differantial Equation System includes many distributions which are also used in reliability analysis commonly. Because of this it plays a very important role in reliability analysis. In this study, Pearson Differantial Equation System's cubic denominator structure which derives asymmetric distribution will be handled. Then conditional moments and asymmetry measures will be analysed for that structur