Dove Medical Press - Open Access Publisher of Medical Journals Interactive Statistical Calculation Pages Critical Appraisal Tools - University of South Australia - quality university study and education in Australia Critical appraisal is an integral process in Evidence Based Practice. Critical appraisal aims to identify methodological flaws in the literature and provide consumers of research evidence the opportunity to make informed decisions about the quality of research evidence. Below is a list of critical appraisal tools, linked to the websites where they were developed. iCAHE staff will update this webpage as new critical appraisal tools are published. Healthcare Improvement Scotland have a tutorial on how to best conduct a critical appraisal for those that feel they need a refresher, and the linked YouTube clip gives an introduction to critical appraisal including how to incorpotate evidence into clinical decisions. Please choose a type of study: Randomised Controlled Trials Validation of the PEDro tool: Maher, C. link to Maher et al. 2003 article (pdf 301KB) ^top Non-Randomised Controlled Trials Other Quantitative Research Case studies Qualitative research Mixed Methods research Systematic Reviews
Determinación del tamaño muestral Todo estudio epidemiológico lleva implícito en la fase de diseño la determinación del tamaño muestral necesario para la ejecución del mismo (1-4). El no realizar dicho proceso, puede llevarnos a dos situaciones diferentes: primera que realicemos el estudio sin el número adecuado de pacientes, con lo cual no podremos ser precisos al estimar los parámetros y además no encontraremos diferencias significativas cuando en la realidad sí existen. La segunda situación es que podríamos estudiar un número innecesario de pacientes, lo cual lleva implícito no solo la pérdida de tiempo e incremento de recursos innecesarios sino que además la calidad del estudio, dado dicho incremento, puede verse afectada en sentido negativo. n = 48 pacientes.
RStats Resources - RStats Institute Statistics Tutoring Undergraduate students who need assistance with statistics homework can receive one-on-one tutoring through Missouri State University's Bear CLAW (Center for Learning and Writing). Click here to access Bear CLAW Statistics Tutoring. Instructional Videos Tables and Calculators Click here to access: Normal Distribution TableT Distribution TableCritical Pearson's r ValuesF Distribution TableChi Square Distribution Table and CalculatorCohen's D Effect Size Calculator Notes from Previous RStats Workshops Information About RStats
How To Determine Sample Size, Determining Sample Size In order to prove that a process has been improved, you must measure the process capability before and after improvements are implemented. This allows you to quantify the process improvement (e.g., defect reduction or productivity increase) and translate the effects into an estimated financial result – something business leaders can understand and appreciate. If data is not readily available for the process, how many members of the population should be selected to ensure that the population is properly represented? If data has been collected, how do you determine if you have enough data? Determining sample size is a very important issue because samples that are too large may waste time, resources and money, while samples that are too small may lead to inaccurate results. In many cases, we can easily determine the minimum sample size needed to estimate a process parameter, such as the population mean When sample data is collected and the sample mean . where: is the sample size. to within . .
R Tutorials--Logistic Regression Preliminaries Model Formulae You will need to know a bit about Model Formulae to understand this tutorial. Odds, Odds Ratios, and Logit When you go to the track, how do you know which horse to bet on? p(one outcome) p(success) p odds = -------------------- = ----------- = ---, where q = 1 - p p(the other outcome) p(failure) q So for Sea Brisket, odds(winning) = (1/9)/(8/9) = 1/8. The natural log of odds is called the logit, or logit transformation, of p: logit(p) = loge(p/q). If odds(success) = 1, then logit(p) = 0. Logistic regression is a method for fitting a regression curve, y = f(x), when y consists of proportions or probabilities, or binary coded (0,1--failure,success) data. y = [exp(b0 + b1x)] / [1 + exp(b0 + b1x)] Logistic regression fits b0 and b1, the regression coefficients (which were 0 and 1, respectively, for the graph above). logit(y) = b0 + b1x Odds ratio might best be illustrated by returning to our horse race. Logistic Regression: One Numerical Predictor I'm impressed!
StatThink - Statistical Thinking Diagrams and Models From: Pfannkuch, M., Regan, M., Wild, C. and Horton, N.J. (2010) Telling Data Stories: Essential Dialogues for Comparative Reasoning.Journal of Statistics Education, 18(1). Looking at data Download as a png or an eps From: Forster, M. and Wild, C. Data analysis cycle Download as a png or an eps From: : Wild, C.J. and Pfannkuch, M. (1999) "Statistical thinking in empirical enquiry" (with discussion). Learning via statistics Download as a png or an eps Investigative Cycle (Statistical investigation cycle/PPDAC cycle) Download as a png or an eps Types of Thinking Download as a png or an eps Interrogative Cycle Download as a png or an eps Dispositions Download as a png or an eps From Inkling to Plan Download as a png or an eps Shuttling between the spheres Download as a png or an eps Using any technique Download as a png or an eps Distillation and Encapsulation Download as a png or an eps Sources of variation in data Download as a png or an eps Practical responses to variation Download as a png or an eps