Essential First Steps To Data Analysis

The purpose of this book is to provide instruction and guidance on preparing quantitative data sets prior to answering a study's research questions. Preparation may involve data management and manipulation tasks, data organization, structural changes to data files, or conducting preliminary analysis such as examining the scale of a variable, the validity of assumptions or the nature and extent of missing data. The oresultso from these essential first steps can also help guide a researcher in selecting the most appropriate statistical tests for his/her study. The book is intended to serve as a supplemental text in statistics or research courses offered in graduate programs in education, counseling, school psychology, behavioral sciences, and social sciences as well as undergraduate programs that contain a heavy emphasis on statistics. The content and issues covered are also beneficial for faculty and researchers who are knowledgeable about research design and able to use a statistical software package, but are unsure of the first steps to take with their data. Increasingly, faculty are forming partnerships with schools, clinics, and other institutions to help them analyze data in their extensive databases. This book can serve as a reference for helping them get existing data files in an appropriate form to run statistical analysis. This book is not a replacement for a statistics textbook. It assumes that readers have some knowledge of basic statistical concepts and use of statistical software, or that they will be learning these concepts and skills concurrently throughout the course. SPSS was chosen to illustrate the preparation, evaluation, and manipulation of data. However, students or researchers who do not use SPSS will benefit from the content since the overall structure and pedagogical approach of the book focuses heavily on the data issues and decisions to be made.

The purpose of this book is to provide instruction and guidance on preparing quantitative data sets prior to answering a study's research questions. Preparation may involve data management and manipulation tasks, data organization, structural changes to data files, or conducting preliminary analysis such as examining the scale of a variable, the validity of assumptions or the nature and extent of missing data. The oresultso from these essential first steps can also help guide a researcher in selecting the most appropriate statistical tests for his/her study. The book is intended to serve as a supplemental text in statistics or research courses offered in graduate programs in education, counseling, school psychology, behavioral sciences, and social sciences as well as undergraduate programs that contain a heavy emphasis on statistics. The content and issues covered are also beneficial for faculty and researchers who are knowledgeable about research design and able to use a statistical software package, but are unsure of the first steps to take with their data. Increasingly, faculty are forming partnerships with schools, clinics, and other institutions to help them analyze data in their extensive databases. This book can serve as a reference for helping them get existing data files in an appropriate form to run statistical analysis. This book is not a replacement for a statistics textbook. It assumes that readers have some knowledge of basic statistical concepts and use of statistical software, or that they will be learning these concepts and skills concurrently throughout the course. SPSS was chosen to illustrate the preparation, evaluation, and manipulation of data. However, students or researchers who do not use SPSS will benefit from the content since the overall structure and pedagogical approach of the book focuses heavily on the data issues and decisions to be made.

A guide for data managers and analyzers. It shares guidelines for identifying patterns, predicting future outcomes, and presenting findings to others.

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

A friendly and accessible approach to applying statistics in the real world With an emphasis on critical thinking, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics presents fun and unique examples, guides readers through the entire data collection and analysis process, and introduces basic statistical concepts along the way. Leaving proofs and complicated mathematics behind, the author portrays the more engaging side of statistics and emphasizes its role as a problem-solving tool. In addition, light-hearted case studies illustrate the application of statistics to real data analyses, highlighting the strengths and weaknesses of commonly used techniques. Written for the growing academic and industrial population that uses statistics in everyday life, The Art of Data Analysis: How to Answer Almost Any Question Using Basic Statistics highlights important issues that often arise when collecting and sifting through data. Featured concepts include: • Descriptive statistics • Analysis of variance • Probability and sample distributions • Confidence intervals • Hypothesis tests • Regression • Statistical correlation • Data collection • Statistical analysis with graphs Fun and inviting from beginning to end, The Art of Data Analysis is an ideal book for students as well as managers and researchers in industry, medicine, or government who face statistical questions and are in need of an intuitive understanding of basic statistical reasoning.

When you took statistics in school, your instructor gave you specially prepared datasets, told you what analyses to perform, and checked your work to see if it was correct. Once you left the class, though, you were on your own. Did you know how to create and prepare a dataset for analysis? Did you know how to select and generate appropriate graphics and statistics? Did you wonder why you were forced to take the class and when you would ever use what you learned? That's where Stats with Cats can help you out. The book will show you: How to decide what you should put in your dataset and how to arrange the data. How to decide what graphs and statistics to produce for your data. How you can create a statistical model to answer your data analysis questions. The book also provides enough feline support to minimize any stress you may experience. Charles Kufs has been crunching numbers for over thirty years, first as a hydrogeologist, and since the 1990s as a statistician. He is certified as a Six Sigma Green Belt by the American Society for Quality. He currently works as a statistician for the federal government and he is here to help you.

This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.