Carnahan, and Jennifer. Using a grounded theory approach, they try to understand what influences reading comprehension and how meaning is made from text among high-functioning individuals with autism spectrum disorder (ASD). They investigate factors that contribute to reading comprehension, reading intervention for students with disabilities, cognitive style, and construction-integration model of text comprehension. Using grounded theory, a qualitative method to produce theoretical knowledge, they addressed the following research questions: How do high-functioning individuals with asd make meaning from text? What influences comprehension among these individuals? Given the unique cognitive profile of individuals on the spectrum, and given that reading comprehension is a cognitively intensive task, providing researchers with insights into how students on the spectrum interact with text may lead to more effective reading comprehension intervention.
Black hat, usa 2014 Briefings
Positional maps identified the graduate major positions taken and not taken by the student midwives and others. Data for situational maps were drawn from interviews and observational data as well as texts and journal articles. Maps were initially hand drawn and later they were created using nvivo 7 and Ms Notebook programs. The investigation brought to next three findings. These were realization, adaptation, and assimilation. Realization represents the students recognition that the clinical learning environment is highly medicalised and interventionist. Adaptation represents the students responses in the clinical learning environment as the developed strategies to adapt to the situation and fit. Assimilation represents the way in which the students developed competency by integrating and applying the knowledge and skills they had learnt throughout their course. It essay is greatly to the authors credit that his article is comprehensive analysis of applying a contemporary grounded theory methodology. It must be noted that the author believes that the process of creating situational maps allowed for a more in-depth analysis of clinical learning environment experienced by the student midwives being studied. 2) This article was written by pamela williamson, Christina.
They present useful information on the application of a research project exploring the experiences of students studying for midwifery degrees in achieving competency. The author analyzes the experiences of students studying for a degree in midwifery. They used the constant comparative method together with theoretical sampling which is a process in which the researcher engages in ongoing data analysis to identify emerging themes or leads in the data. More to the point they used situational analysis concurrently with coding to provide more in-depth analysis of interview and observational data. The three maps offered by Clarke are situational maps, social world/arenas maps, and positional maps. The author produced situational maps in a creative process drawing on coded interview and observational data, and prior knowledge and research. They were used to identify the major human, non-human discursive and other elements in the student midwives situations, as well as the relationships among the identified elements. The social worlds/arenas maps identified the participants, business non-human elements on the arenas of commitment and discourse in which the student midwives were engaged.
(1991) Practical statistics for medical research. Chapman and father's Hall, london. (1993) Statistika pro biologické a lékařské vědy. (1987) Vícerozměrné statistické metody s aplikacemi. (1994) Statistické business zpracování experimentálních dat. Iowa State University Press. Prentice hall, new Jersey. Abdureim Abdurashytov elt ) The title of the article is Applying a contemporary grounded theory methodology. The author is Licqurish s, and seibold.
Nomograms for design of survival analysis trials. Multivariate analysis of clinical data; introduction into modern methods of analysis of huge data. Principles of multivariate methods and their application to clinical data analysis. Multivariate and univariate data analysis - mutual collaboration or discrepancy? Multivariate data exploration, available tests for multivariate distribution. Multivariate similarity/distance of objects or variables - review of important metrics. Neural networks as possible modelling technique. Data mining and automated analysis of data. Optimalization of experiments; application of multivariate methods in sampling design.
Application and examples of students multivariate regression for prediction of practically important clinical parameters. Logistic regression models - a possible tool for individual prediction of patients. Presentation of predictive models. Kaplan-meier survival analysis and parameter estimates /median survival times /. Range of approaches for comparison of two or more survival curves /Log-rank test, hazard ratio, log rank for trends, confidence intervals for survival probability/. "Cohort life tables" and their analysis of survival.
Modelling of survival, cox regression. Design of studies focused on dissertation survival analysis - quantitative aspects of experimental design, sample size estimation. Survival analysis for stratified clinical trials. Eortc standards for experimental design of survival analysis. Internet and survival analysis: consultation on trials aimed at survival analysis, software for survival analysis.
Goodness of fits test and its application to clinical data. Frequency table analysis - other tests. Basics of correlation and regression. Principles of correlation analysis. Parametric and non-parametric correlation.
Principles of regression analysis. Linear model and its analysis. Application and graphical presentation of correlation and regression. Examples and basics of polynomial and non-linear regression. Principles of multivariate and logistic regression. Multivariate and logistic regression - predictive methods for clinical data. Principles of multivariate regression. Quality of models and possibility of errors.
T-test for dates independent and dependent (paired) data. Analysis of variance (anova) - basic principles of one-way and multi-way anova, tests of contrasts. Non-parametric methods (Mann-Whitney test, wald-Worowitz test, kolmogorov-smirnov two-sample test, Kruskal-Wallis test). Graphical methods for visualization of results of the above-mentioned tests. Univariate statistics - discrete data. Univariate analysis of discrete data. Presentation of percentages and estimates of parameters of data expressed as percentages.
Use and misuse of computers in clinical data analysis. Non-parametric methods - alternative for data which does not fit prerequisites of parametric techniques. Examples of non-parametric techniques. Examples summarizing lessons fashion i - iii. Univariate statistics - continuous data. Univariate analysis of continuous data. One-sample and two-sample tests.
of other parameters. Summary statistics of continuous and discrete data. Examples of summary statistics presentation. Statistics in medical research - basic principles iii. Graphical tools for data visualization - explorative analysis pp plots, qq plots, normal probability plots, box-and-whisker plots, scatter plots, stem and leaf display, histograms, 3D histograms, matrix plots - face plots, contour plots, surface plots. Data transformations in analytical practice.
Application of statistical method is demonstrated in the software Statistica for Windows. Timetable, five afternoon lessons (20 hours in total). Syllabus, statistics in medical research - basic principles. Introduction into the basic principles of statistical data analysis. Concept of probability and plan its presentation, principles of experiment design, principles of hypothesis testing. Nominal, ordinal and continuous data in clinical research and methods of their visualization. Clinical data "specialties" and consequences for analysis. Data description, variability and data centre quantification, distribution of data. Distribution function and its application for graphical representation of data distribution.
Applied Longitudinal Data Analysis: Modeling Change
Analysis of clinical business data, common course for all. Analysis of clinical data, lecturer: associated prof. The course is aimed at intensive education. Students, physicians or other specialists. Participants should learn the basic principles of data analysis, visualization of data, and statistical hypothesis testing. Several specialized lessons provide foundations of multivariate analysis, survival analysis, and predictive modelling of clinical data. Understanding the principles of statistical testing, multivariate analysis, and predictive modelling together with a review of international literature on these topics is the desired output of the course for participants.