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Using Statistics in the Social and Health Sciences with SPSS and Excel von Abbott, Martin L. (eBook)

  • Erscheinungsdatum: 28.07.2016
  • Verlag: Wiley
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Using Statistics in the Social and Health Sciences with SPSS and Excel

Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS® and Excel® applications This book identifies connections between statistical applications and research design using cases, examples, and discussion of specific topics from the social and health sciences. Researched and class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations for both the novice and professional alike to understand the fundamental statistical practices for organizing, analyzing, and drawing conclusions from research data in their field. The book begins with an introduction to descriptive and inferential statistics and then acquaints readers with important features of statistical applications (SPSS and Excel) that support statistical analysis and decision making. Subsequent chapters treat the procedures commonly employed when working with data across various fields of social science research. Individual chapters are devoted to specific statistical procedures, each ending with lab application exercises that pose research questions, examine the questions through their application in SPSS and Excel, and conclude with a brief research report that outlines key findings drawn from the results. Real-world examples and data from social and health sciences research are used throughout the book, allowing readers to reinforce their comprehension of the material. Using Statistics in the Social and Health Sciences with SPSS® and Excel® includes: - Use of straightforward procedures and examples that help students focus on understanding of analysis and interpretation of findings - Inclusion of a data lab section in each chapter that provides relevant, clear examples - Introduction to advanced statistical procedures in chapter sections (e.g., regression diagnostics) and separate chapters (e.g., multiple linear regression) for greater relevance to real-world research needs Emphasizing applied statistical analyses, this book can serve as the primary text in undergraduate and graduate university courses within departments of sociology, psychology, urban studies, health sciences, and public health, as well as other related departments. It will also be useful to statistics practitioners through extended sections using SPSS® and Excel® for analyzing data. Martin Lee Abbott, PhD, is Professor of Sociology at Seattle Pacific University, where he has served as Executive Director of the Washington School Research Center, an independent research and data analysis center funded by the Bill & Melinda Gates Foundation. Dr. Abbott has held positions in both academia and industry, focusing his consulting and teaching in the areas of statistical procedures, program evaluation, applied sociology, and research methods. He is the author of Understanding Educational Statistics Using Microsoft Excel ® and SPSS®, The Program Evaluation Prism: Using Statistical Methods to Discover Patterns, and Understanding and Applying Research Design, also from Wiley.


    Format: ePUB
    Kopierschutz: AdobeDRM
    Seitenzahl: 600
    Erscheinungsdatum: 28.07.2016
    Sprache: Englisch
    ISBN: 9781119121060
    Verlag: Wiley
    Größe: 35828 kBytes
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Using Statistics in the Social and Health Sciences with SPSS and Excel


The world suddenly has become awash in data! A great many popular books have been written recently that extol "big data" and the information derived for decision makers. These data are considered "big" because a certain "catalog" of data may be so large that traditional ways of managing and analyzing such information cannot easily accommodate it. The data originate from you and me whenever we use certain social media, or make purchases online, or have information derived from us through radio frequency identification (RFID) readers attached to clothing and cars, even implanted in animals, and so on. The result is a massive avalanche of information that exists for businesses leaders, decision makers, and researchers to use for predicting related behaviors and attitudes.
Big Data Analysis

Decision makers are trying to figure out how to manage and use the information available. Typical computer software used for statistical decision making is currently limited to a number of cases far below that which is available for consideration of big data. A traditional approach to address this issue is known as "data mining" in which a number of techniques, including statistics, are used to discover patterns in a large set of data.

Researchers may be overjoyed with the availability of such rich data, but it provides both opportunities and challenges. On the opportunity side, never before have such large amounts of information been available to assist researchers and policy makers understand widespread public thinking and behavior. On the challenge side however are several difficult questions:

How are such data to be examined?
Do current social science methods and processes provide guidance to examining data sets that surpass historical data-gathering capacity?
Are big data representative?
Do data sets so large obviate the need for probability-based research analyses?
Do decision makers understand how to use social science methodology to assist in their analyses of emerging data?
Will the decisions emerging from big data be used ethically, within the context to social science research guidelines?
Will effect size considerations overshadow questions of significance testing?
Social scientists can rely on existing statistical methods to manage and analyze big data, but the way in which the analyses are used for decision making will change . One trend is that prediction may be hailed as a more prominent method for understanding the data than traditional hypothesis testing. We will have more to say about this distinction later in the book, but it is important at this point to see that researchers will need to adapt statistical approaches for analyzing big data.
Visual Data Analysis

Another emerging trend for understanding and managing the swell of data is the use of visuals. Of course, visual descriptions of data have been used for centuries. It is commonly acknowledged that the first "pie chart" was published by Playfair (1801). Playfair's example in Figure 1.1 compares the dynamics of nations over time.

Figure 1.1 William Playfair's pie chart.

Source : Public domain.

Figure 1.1 compared nations using size, color, and orientation over time. Using this method for comparing information has been useful for viewing the patterns in data not readily observable from numerical analysis.

As with numerical methods, however, there are opportunities and challenges in the use of visual analyses:

Can visual means be used to convey complex meaning?
Are there "rules" that will help to insure a standard way of creating, analyzing, and interpreting such visual information?
Will visual

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