The Written Questionnaire in Social Dialectology ~[172]~
Methods of linguistic data collection are among the most central aspects in empirical linguistics. While written questionnaires have only played a minor role in the field of social dialectology, the study of regional and social variation, the last decade has seen a methodological revival. This book is the first monograph-length account on written questionnaires in more than 60 years. It reconnects - for the newcomer and the more seasoned empirical linguist alike - the older questionnaire tradition, last given serious treatment in the 1950s, with the more recent instantiations, reincarnations and new developments in an up-to-date, near-comprehensive account. A disciplinary history of the method sets the scene for a discussion of essential theoretical aspects in dialectology and sociolinguistics. The book is rounded off by a step-by-step practical guide - from study idea to data analysis and statistics - that includes hands-on sections on Excel and the statistical suite R for the novice.This book has a companion website: http://dx.doi.org/10.1075/impact.40.website
Hands-On Exploratory Data Analysis with Python ~[172]~
Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key Features Understand the fundamental concepts of exploratory data analysis using Python Find missing values in your data and identify the correlation between different variables Practice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python package Book Description Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You'll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You'll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you'll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you'll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you'll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learn Import, clean, and explore data to perform preliminary analysis using powerful Python packages Identify and transform erroneous data using different data wrangling techniques Explore the use of multiple regression to describe non-linear relationships Discover hypothesis testing and explore techniques of time-series analysis Understand and interpret results obtained from graphical analysis Build, train, and optimize predictive models to estimate results Perform complex EDA techniques on open source datasets Who this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.
How Qualitative Data Analysis Happens by
How is qualitative data actually collected, analyzed, and accomplished? Real stories of How Qualitative Data Analysis Occurs: Moving Beyond "Themes Emerged" offers an in-depth look into how qualitative social science researchers studying family issues and dynamics approach their data analyses. It moves beyond the usual vague statement of "themes emerged from the data" to show readers how researchers actively and consciously arrive at their themes and conclusions, revealing the complexity and time involved in making sense of thousands of pages of interview data, multiple data sources, and diverse types of data. How Qualitative Data Analysis Occurs focuses on a diversity of topics in family research across the life course. The various authors provide detailed narratives into how they analyzed their data from previous publications, and what methodologies they used, ranging from arts-based research, autoethnography, community-based participatory research, ethnography, grounded theory, to narrative analysis. Supplemental figures, images, and screenshots which are referred to in the chapters, are included in an accompanying eResource, as well as links to the previously published work on which the chapters are based. This book is an invaluable resource for experienced and novice qualitative researchers throughout the social sciences.
Survey Methodology and Missing Data by
This book focuses on quantitative survey methodology, data collection and cleaning methods. Providing starting tools for using and analyzing a file once a survey has been conducted, it addresses fields as diverse as advanced weighting, editing, and imputation, which are not well-covered in corresponding survey books. Moreover, it presents numerous empirical examples from the author's extensive research experience, particularly real data sets from multinational surveys.