Data Quality with SPSS

This world-wide only book about data quality with SPSS provides you with a real Swiss Army knife: ● Learn about data quality● Recognize data problems● Understand consequences of data problems● Solve data problems = establish data quality● Communicate data quality This book systematically presents the most important criteria for data quality with SPSS: Completeness, consistency, plausibility and how to deal with duplicates, missings and outliers. And some more. The book contains countless real-world examples, from typical dirty data disasters you find in the daily news to the enlightening role of outliers in research. Chapter 1: The most common problem areas, e.g. completeness, uniformity, duplicates, missings, outliers and plausibility. The DQ Pyramid concept clarifies the interrelationships of these criteria, and the fundamental benefits of data quality. Further criteria for the quality of data, as well as their communication, are presented in chapters 13 and 19. The DQ Pyramid provides also the structure for this book.Chapter 2: Recommendations how to tackle a DQ project, e.g. language, resources, support, structures, priorities and metrics, and considerations about sustainable work. Chapter 3: First options to control the completeness of datasets, the completeness of data sets, cases (rows), variables (columns) and values or missings. Chapter 4: Numerous possibilities to identify inconsistencies or to standardize in numerical values, time units and strings. Chapter 5: Identify, understand and (if necessary) filter multiple values or data rows. Chapter 6: Dealing with missing data. After assessing causes (patterns), consequences, extent and mechanisms, numerous methods of handling are discussed, from imputation to multivariate estimates (MVA). Chapter 7: Identify, understand and handle outliers. The special role of expectation ("frames") is discussed. Chapter 8: Qualitative and quantitative approaches to plausibility testing. The examination of the multivariate quality of data is presented using a qualitative and also a quantitative (anomaly) approach. Chapter 9: Checking several variables and criteria by means of validation rules ("Validation" or SPSS procedure VALIDATEDATA). Chapter 10: Numerous examples for checking several values, rows and columns in a data set at once. The numerous variants of counting variables (counters) presented are likely to be of particular interest. Chapter 11: Numerous other examples of working with several (separate) data sets at once, e.g. using macros to screen, split or merge several data sets.Chapter 12: Time or date-related problems, and how to recognize and solve them. Chapter 13: Further criteria for the quality of data, e.g. quantity, unambiguity, relevance, accuracy or comprehensibility. Other chapters offer an exercise, notes about nodes for data quality and data preparation in IBM SPSS Modeler, and a check list. Chapter 19 provides annotated criteria for communicating the quality of data, surveys and analyses, including the correct interpretation and communication of the concept of significance. A separate chapter highlights the "deadly sins" of professional work. And their not so pretty consequences. By the end of this book, you should know many data quality criteria and the most important features of SPSS to ensure them, be able to define them according to your standard of optimality, and apply them to your data using mouse or syntax controls. This book is important for all those who work with SPSS and who place special value on the quality of data and results. Data quality is not everything, but without data quality everything is nothing.