Introduction to Design and Analysis of Scientific Studies provides a modern framework for classical experimental design and observational studies, using R to implement designs and analyze data. It emphasizes ethics, communication, and decision-making in design, data analysis, and statistical thinking.

Format: Hardback
Length: 292 pages
Publication date: 22 March 2022
Publisher: Taylor & Francis Ltd

Introduction to Design and Analysis of Scientific Studies

This comprehensive textbook offers a thorough introduction to the principles and methodologies of design and analysis of scientific studies. It caters to undergraduate and graduate students seeking to gain a solid foundation in classical experimental design and observational studies, presented within a modern framework: The Rubin Causal Model.

A causal inference framework plays a vital role in the design, data collection, and analysis processes. It provides investigators with a structured approach to evaluate study limitations and draw appropriate conclusions. R, a powerful programming language, is utilized to implement designs and analyze the data collected.

Key Features:

Classical Experimental Design: The textbook delves into classical experimental design principles, emphasizing computation using tidyverse packages in R. It covers various design types, such as randomized controlled trials, factorial designs, and crossover designs.

Applications of Experimental Design: The book showcases real-world applications of experimental design to clinical trials, A/B testing, and other modern examples. It demonstrates how experimental design techniques can be used to answer research questions and make informed decisions.

Discussion of the Link between Classical Experimental Design and Causal Inference: The textbook explores the connection between classical experimental design and causal inference. It discusses the importance of identifying and controlling confounding variables, estimating causal effects, and drawing valid conclusions based on empirical evidence.

The Role of Randomization in Experimental Design and Sampling in the Big Data Era: The book emphasizes the role of randomization in experimental design and sampling in the era of big data. It discusses the challenges of analyzing large datasets and the strategies employed to address them, including randomization techniques, sample size considerations, and data analysis methods.

Exercises with Solutions: The textbook includes a comprehensive set of exercises with detailed solutions. These exercises help students apply the concepts learned and reinforce their understanding of the topics covered.

Instructor Slides in RMarkdown: An additional resource will be developed, consisting of instructor slides in RMarkdown format. These slides will be used in conjunction with the textbook and will provide additional guidance and insights for instructors teaching the course

Bookdown Version: The textbook will be freely available in a bookdown version, allowing for easy access and reading on various devices. This version will also include additional features such as interactive code snippets and links to relevant resources.

Ethics, Communication, and Decision Making: The book emphasizes the ethical considerations, communication skills, and decision-making aspects that are integral to the design, data analysis, and statistical thinking processes. It encourages students to develop a critical mindset and apply ethical principles to their research endeavors.

By providing a comprehensive and up-to-date introduction to design and analysis of scientific studies, this textbook aims to equip students with the necessary skills and knowledge to conduct rigorous and effective research in various scientific disciplines.

Weight: 700g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367456856