This is to increase transparency, facilitates for overseeing and managing the analytic processes and will help with writing-up. Whether working inductively or deductively, it is of high importance to record every step executed in a memo, which functions as a research diary.

The coding applied, forms the basis from where the analysis tools can be used to ask the data questions. The basics of these deductive and inductive approaches can be employed when working exclusively with qualitative fieldwork data, but also when ATLAS.ti is used in mixed method approaches (for example when surveys are imported into ATLAS.ti, or when data is exported to SPSS), when working with literature (for example importing reference manager data) or social media data (importing from Twitter or EverNote), and even when using reflexive approaches such as auto-ethnography (for example by converting memos to documents that can be coded). In ATLAS.ti we would use open coding (creating and naming a new code) or in-vivo coding (the selected segment forms the name of the new code) to create labels or concepts to attach to each data segment. Moreover, if we fragment data systematically, this process helps us to refrain from superimposing our own preconceived ideas on the data and prevents us from becoming so immersed in respondent’s worldviews that we accept the information in large chunks without questioning. This is not only to identify important segments, but also gives us the opportunity to compare data with data. In this case, we would start with reading, observing or listening to our data and fragmenting it. This approach is for example used by an ethnographer who tries to describe how a certain people view human-animal relationships. In inductive reasoning, we begin with specific observations and measures, applying labels until we detect patterns and regularities, formulate tentative hypotheses that we can explore, and finally end up developing some general conclusions or even a theory. Informally, this is sometimes called a “bottom up” approach. A concise guide to the latest version of Stata, A Handbook of Statistical Analyses Using Stata, Fourth Edition illustrates the benefits of using Stata to perform various statistical analyses for both data analysis courses and self-study.Inductive reasoning works the other way around: Starting from our data, we work toward broader generalizations and theories. The chapters conclude with several exercises based on data sets from different disciplines. The core of the book centers on how to use Stata to perform analyses and how to interpret the results. The methods covered include descriptives, simple tests, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and cluster analysis. The authors begin each chapter with descriptions of the data and the statistical techniques to be used. Each chapter describes the analysis appropriate for a particular application, focusing on the medical, social, and behavioral fields. This edition covers many new features of Stata, including a new command for mixed models and a new matrix language. Fulfilling this need, A Handbook of Statistical Analyses Using Stata, Fourth Edition has been fully updated to provide an introduction to Stata version 9. With each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software. Each chapter ends with exercises to consolidate acquired skills. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. The examples emphasize good statistical practice and reproducible research. Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. The book is written as a self-study tutorial and organized around examples. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Data Analysis Using Stata, Third Editionis a comprehensive introduction to both statistical methods and Stata.

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