{"product_id":"the-program-evaluation-prism-isbn-9780470579046","title":"The Program Evaluation Prism","description":"This book is a comprehensive treatment of correlation\/regression techniques and using SPSS for interpretation of findings. Striking a balance between detailed coverage and approachability, this book provides a thorough treatment of the elements of regression and how they can be used with real research problems in program evaluation.   \u003cp\u003eThe author begins with a basic introduction to evaluation methodology, and its ability to recognize embedded patterns of meaning in research data. Subsequent chapters explore the statistical tools that can be applied by researchers and evaluators irrespective of the design that was used to generate this data.\u003c\/p\u003e \u003cp\u003eTopics of coverage include: correlation, single predictor regression, multiple correlation, part and partial correlation, detection of extreme scores, multiple regression, regression with continuous predictors, coding of categorical data, regression with categorical predictors, methods for entering predictors in multiple regression, and interaction in multiple regression.\u003c\/p\u003e \u003cp\u003eEach chapter is presented in the same comprehensive format: an introduction to the topic, followed by a discussion of its primary elements, illustrations of the data through numerous tables and figures, SPSS procedures for designing the analysis, SPSS output of the analysis , and guidance on how to interpret findings from the analyses. \u003ci\u003eDiscover Note\u003c\/i\u003e and \u003ci\u003eResearch Steps\u003c\/i\u003e sections illustrate how using statistical processes can unveil unobserved patterns and assist readers with identifying such patterns in their own data. \u003c\/p\u003e \u003cp\u003eReal-world analyses are used throughout the book, utilizing meaningful social issues as a catalyst for teaching statistical procedures, and a related Web site features additional data sets, solutions, and research projects for readers.\u003c\/p\u003e  CHAPTER ONE: INTRODUCTION.  \u003cp\u003eInitial Considerations.\u003c\/p\u003e \u003cp\u003eBook Plan.\u003c\/p\u003e \u003cp\u003eReal Examples.\u003c\/p\u003e \u003cp\u003eUsing Statistical Programs.\u003c\/p\u003e \u003cp\u003eThe Evaluator’s Journey.\u003c\/p\u003e \u003cp\u003eCHAPTER TWO: THE ELEMENTS OF EVALUATION.\u003c\/p\u003e \u003cp\u003eNature of Evaluation.\u003c\/p\u003e \u003cp\u003eEvaluation Concerns.\u003c\/p\u003e \u003cp\u003eEvaluation Standards.\u003c\/p\u003e \u003cp\u003eMethods used in Evaluation.\u003c\/p\u003e \u003cp\u003eThe Evaluator’s Tools.\u003c\/p\u003e \u003cp\u003eEvaluation Hurdles.\u003c\/p\u003e \u003cp\u003eQuantification.\u003c\/p\u003e \u003cp\u003eResistance to Quantification.\u003c\/p\u003e \u003cp\u003eThe Nature of Quantification.\u003c\/p\u003e \u003cp\u003eQualitative Methods.\u003c\/p\u003e \u003cp\u003eSpecialization.\u003c\/p\u003e \u003cp\u003eStatistical Issues.\u003c\/p\u003e \u003cp\u003eCertainty vs. Probability.\u003c\/p\u003e \u003cp\u003eStatistical Significance.\u003c\/p\u003e \u003cp\u003eEffect Sizes.\u003c\/p\u003e \u003cp\u003eCan We Achieve Certainty?\u003c\/p\u003e \u003cp\u003eDispelling the Mystique of Statistics.\u003c\/p\u003e \u003cp\u003eResearch Literacy.\u003c\/p\u003e \u003cp\u003eThe Discovery Questions.\u003c\/p\u003e \u003cp\u003eSchool Characteristics and Student Learning.\u003c\/p\u003e \u003cp\u003eWorker Participation.\u003c\/p\u003e \u003cp\u003eThe Impact of Technology on the Classroom.\u003c\/p\u003e \u003cp\u003eClassroom Observation Data.\u003c\/p\u003e \u003cp\u003eDiscovery Learning.\u003c\/p\u003e \u003cp\u003eTerms and Concepts.\u003c\/p\u003e \u003cp\u003eCHAPTER THREE: Using SPSS?\u003c\/p\u003e \u003cp\u003eGeneral Features.\u003c\/p\u003e \u003cp\u003eManagement Functions.\u003c\/p\u003e \u003cp\u003eReading and Importing Data.\u003c\/p\u003e \u003cp\u003eSort.\u003c\/p\u003e \u003cp\u003eSplit File.\u003c\/p\u003e \u003cp\u003eTransform\/compute (creating indices).\u003c\/p\u003e \u003cp\u003eMerge.\u003c\/p\u003e \u003cp\u003eAnalysis Functions.\u003c\/p\u003e \u003cp\u003eGraphing Functions.\u003c\/p\u003e \u003cp\u003eCHAPTER FOUR: CORRELATION.\u003c\/p\u003e \u003cp\u003eThe Nature of Correlation.\u003c\/p\u003e \u003cp\u003ePrediction.\u003c\/p\u003e \u003cp\u003eCorrelation is not Causation.\u003c\/p\u003e \u003cp\u003ePearson’s r.\u003c\/p\u003e \u003cp\u003eStrength and Direction.\u003c\/p\u003e \u003cp\u003eA Note on the Nature of the Data.\u003c\/p\u003e \u003cp\u003eInterpreting Pearson’s r.\u003c\/p\u003e \u003cp\u003eTesting the Statistical Significance of a Correlation.\u003c\/p\u003e \u003cp\u003eThe \"Practical Significance\" of r: Effect Sizes.\u003c\/p\u003e \u003cp\u003eAn Evaluation Example of Correlation: The Impact of Technology on Teaching and Learning.\u003c\/p\u003e \u003cp\u003eInfluences on Correlation.\u003c\/p\u003e \u003cp\u003eRestricted Range.\u003c\/p\u003e \u003cp\u003eExtreme (outlier) Scores.\u003c\/p\u003e \u003cp\u003eOther Kinds of Correlation.\u003c\/p\u003e \u003cp\u003eA Research Example of Spearman’s rho Correlation.\u003c\/p\u003e \u003cp\u003eNon Linear Correlation.\u003c\/p\u003e \u003cp\u003e\"Extending\" Correlation to Include Additional Variables.\u003c\/p\u003e \u003cp\u003eCorrelation - Detail for the Curious.\u003c\/p\u003e \u003cp\u003eComputing Pearson’s r.\u003c\/p\u003e \u003cp\u003eAssumptions of Correlation.\u003c\/p\u003e \u003cp\u003eNon-Linear Correlation.\u003c\/p\u003e \u003cp\u003eDiscovery Learning.\u003c\/p\u003e \u003cp\u003eTerms and Concepts.\u003c\/p\u003e \u003cp\u003ePractical Application-Correlation.\u003c\/p\u003e \u003cp\u003eDescription of the Data.\u003c\/p\u003e \u003cp\u003eEvaluation Questions.\u003c\/p\u003e \u003cp\u003eCHAPTER FIVE: REGRESSION.\u003c\/p\u003e \u003cp\u003eThe Regression Line - Line of \"Best Fit\".\u003c\/p\u003e \u003cp\u003eThe Regression Formula.\u003c\/p\u003e \u003cp\u003eStandard Error of Estimate.\u003c\/p\u003e \u003cp\u003eConfidence Interval.\u003c\/p\u003e \u003cp\u003eResiduals.\u003c\/p\u003e \u003cp\u003eRegression Example with Achievement Data.\u003c\/p\u003e \u003cp\u003eThe Results of the Analysis.\u003c\/p\u003e \u003cp\u003eThe Graph of the Results.\u003c\/p\u003e \u003cp\u003eStandard Error of Estimate.\u003c\/p\u003e \u003cp\u003eThe Confidence Interval.\u003c\/p\u003e \u003cp\u003eDetail - for the curious.\u003c\/p\u003e \u003cp\u003eAssumptions of Regression.\u003c\/p\u003e \u003cp\u003eFixed vs. Random Effects Modeling.\u003c\/p\u003e \u003cp\u003eNon-Linear Correlation.\u003c\/p\u003e \u003cp\u003eCalculating the Standard Error of the Estimate.\u003c\/p\u003e \u003cp\u003eDiscovery Note.\u003c\/p\u003e \u003cp\u003eTerms and Concepts.\u003c\/p\u003e \u003cp\u003ePractical Application - Bivariate Regression.\u003c\/p\u003e \u003cp\u003eCHAPTER SIX: CLEANING THE DATA - DETECTING OUTLIERS.\u003c\/p\u003e \u003cp\u003eUnivariate Extreme Scores.\u003c\/p\u003e \u003cp\u003eMultivariate Extreme Scores.\u003c\/p\u003e \u003cp\u003eDistance Statistics.\u003c\/p\u003e \u003cp\u003eInfluence Statistics.\u003c\/p\u003e \u003cp\u003eDiscovery Note.\u003c\/p\u003e \u003cp\u003eTerms and Concepts.\u003c\/p\u003e \u003cp\u003ePractical Application - Extreme Scores.\u003c\/p\u003e \u003cp\u003eCHAPTER SEVEN: MULTIPLE CORRELATION.\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eControl Variables.\u003c\/p\u003e \u003cp\u003eMediator Variables.\u003c\/p\u003e \u003cp\u003eUsing Multiple Correlation to Control Variables: Partial \u0026amp; Semi-partial correlation.\u003c\/p\u003e \u003cp\u003ePartial Correlation.\u003c\/p\u003e \u003cp\u003eSemi-partial (Part) Correlation.\u003c\/p\u003e \u003cp\u003eDiscovery Note.\u003c\/p\u003e \u003cp\u003eTerms and Concepts.\u003c\/p\u003e \u003cp\u003ePractical Application - Partial and Semi-Partial Correlation.\u003c\/p\u003e \u003cp\u003eCHAPTER EIGHT: MULTIPLE REGRESSION.\u003c\/p\u003e \u003cp\u003eMultiple Regression With Two Predictor Variables.\u003c\/p\u003e \u003cp\u003eUses of Multiple Regression.\u003c\/p\u003e \u003cp\u003eMultiple Regression Outcomes.\u003c\/p\u003e \u003cp\u003eOmnibus Findings for the Overall Model.\u003c\/p\u003e \u003cp\u003eIndividual Predictors.\u003c\/p\u003e \u003cp\u003eAdditional SPSS® Results.\u003c\/p\u003e \u003cp\u003eMultiple Regression: How to Enter Predictors.\u003c\/p\u003e \u003cp\u003eStepwise Regression and Other Methods.\u003c\/p\u003e \u003cp\u003eAssumptions of Multiple Regression.\u003c\/p\u003e \u003cp\u003eMulticollinearity.\u003c\/p\u003e \u003cp\u003eCleaning the Database.\u003c\/p\u003e \u003cp\u003eMultiple Regression with More Than Two Predictor Variables: Research Examples.\u003c\/p\u003e \u003cp\u003ePredicting the Impact of School Variables on Teaching and Learning: the TAGLIT Data.\u003c\/p\u003e \u003cp\u003eOmnibus Findings.\u003c\/p\u003e \u003cp\u003eResults of Individual Predictors.\u003c\/p\u003e \u003cp\u003eDiscovery Notes.\u003c\/p\u003e \u003cp\u003eTerms and Concepts.\u003c\/p\u003e \u003cp\u003ePractical Application: Multiple Regression.\u003c\/p\u003e \u003cp\u003eCHAPTER NINE: CODING - USING MLR WITH CATEGORICAL VARIABLES.\u003c\/p\u003e \u003cp\u003eNature of Dummy Variables.\u003c\/p\u003e \u003cp\u003eOne Categorical Variable with Two Groups.\u003c\/p\u003e \u003cp\u003eCreating Dummy Variables.\u003c\/p\u003e \u003cp\u003eCreating Subvariables in SPSS®.\u003c\/p\u003e \u003cp\u003eOne Categorical Variable with More Than Two Groups.\u003c\/p\u003e \u003cp\u003eA Hypothetical Example.\u003c\/p\u003e \u003cp\u003eAn Example from the School Database.\u003c\/p\u003e \u003cp\u003eDiscovery Notes.\u003c\/p\u003e \u003cp\u003eDetail for the Curious.\u003c\/p\u003e \u003cp\u003eFalse Dichotomies.\u003c\/p\u003e \u003cp\u003eDiscovery Note.\u003c\/p\u003e \u003cp\u003eTerms and Concepts.\u003c\/p\u003e \u003cp\u003ePractical Application: Dummy Coding.\u003c\/p\u003e \u003cp\u003eCHAPTER TEN: INTERACTION.\u003c\/p\u003e \u003cp\u003eInteractions with Continuous Variables.\u003c\/p\u003e \u003cp\u003eInteraction with Categorical Variables.\u003c\/p\u003e \u003cp\u003eDiscovery Notes.\u003c\/p\u003e \u003cp\u003eTerms and Concepts.\u003c\/p\u003e \u003cp\u003ePractical Application - Interaction.\u003c\/p\u003e \u003cp\u003eCHAPTER ELEVEN: DISCOVERY LEARNING THROUGH CORRELATION AND REGRESSION.\u003c\/p\u003e \u003cp\u003eOverall Discovery Notes.\u003c\/p\u003e \u003cp\u003eFindings from the Data.\u003c\/p\u003e \u003cp\u003eStudent Academic Achievement.\u003c\/p\u003e \u003cp\u003eWorkplace Participation.\u003c\/p\u003e \u003cp\u003eThe Impact of Technology on Student Learning.\u003c\/p\u003e \u003cp\u003eAdvanced Statistical Techniques.\u003c\/p\u003e \u003cp\u003eHierarchical Linear Regression.\u003c\/p\u003e \u003cp\u003eStructural Equation Modeling and Path Analysis.\u003c\/p\u003e \u003cp\u003eOther Regression Procedures.\u003c\/p\u003e \u003cp\u003eCHAPTER TWELVE: PRACTICAL APPLICATION ANALYSES.\u003c\/p\u003e \u003cp\u003ePractical Application: Correlation.\u003c\/p\u003e \u003cp\u003ePractical Application: Bivariate Regression.\u003c\/p\u003e \u003cp\u003ePractical Application: Extreme Scores.\u003c\/p\u003e \u003cp\u003ePractical Application: Partial and Semi-Partial Correlation.\u003c\/p\u003e \u003cp\u003ePractical Application: Multiple Regression.\u003c\/p\u003e \u003cp\u003ePractical Application: Dummy Coding.\u003c\/p\u003e \u003cp\u003ePractical Application: Interaction.\u003c\/p\u003e \u003cp\u003eREFERENCES.\u003c\/p\u003e \u003cp\u003eMARTIN LEE ABBOTT, PhD, is Professor of Sociology at Seattle Pacific University, where he also serves as Executive Director of the Washington School Research Center, an independent research and data analysis center funded by the Bill and Melinda Gates Foundation. Dr. Abbott has held positions in both academia and industry, focusing his consulting and teaching in the areas of program evaluation, applied sociology, statistics, and research methods.\u003c\/p\u003e   \u003cp\u003eA hands-on treatment of essential statistical tools for data interpretation and evaluation in research\u003c\/p\u003e \u003cp\u003eAcross various disciplines, it is important for researchers and professionals to have an advanced understanding of evaluation methods and procedures in order to identify the most valuable meanings and results from gathered data. The Program Evaluation Prism presents a thorough yet accessible guide to the use of regression and correlation methods for formulating questions and dissecting data in order to achieve effective problem solving in evaluation research. Providing detailed coverage in an accessible manner, this book presents the statistical tools necessary to find unobserved patterns that affect daily life, while also guiding readers to discover the dynamics hidden in their own research.\u003c\/p\u003e \u003cp\u003eThe book begins with a basic introduction to evaluation methodology and its ability to recognize embedded patterns of meaning in research data. Subsequent chapters explore the statistical tools that can be applied irrespective of the design used to generate this data, such as correlation, regression, multiple regression, and detection of extreme scores. The author also provides in-depth coverage of techniques that are not extensively covered in the current literature on the topic, including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ecoding categorical data\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003econducting interaction analyses\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eorder-of-entry methods\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003edata cleaning\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003epart and partial correlation\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eReal-world analyses incorporate meaningful social issues into the discussed techniques, and a final chapter discusses the Practical Application problems found throughout the book. Discover Note and Research Steps sections illustrate how using statistical processes can unveil unobserved patterns and assist readers with identifying such trends in their own data. SPSS® output is included throughout, and a related FTP site features large and small data sets for application of the book's procedures, SPSS® lab exercises, and access to modules and materials from the author's own coursework.\u003c\/p\u003e \u003cp\u003eThe Program Evaluation Prism serves as an excellent book for courses on program evaluation and research methods at the upper-undergraduate and graduate levels. It is also a valuable reference for practitioners, consultants, and researchers who conduct data analysis in the fields of education, psychology, business, and public health.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990322036965,"sku":"NP9780470579046","price":122.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470579046.jpg?v=1761787350","url":"https:\/\/k12savings.com\/products\/the-program-evaluation-prism-isbn-9780470579046","provider":"K12savings","version":"1.0","type":"link"}