{"product_id":"introduction-to-real-world-statistics-9781138292307","title":"Introduction to Real World Statistics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003eIntroduction to Real World Statistics\u003c\/em\u003e provides students with the basic concepts and practices of applied statistics, including data management and preparation; an introduction to the concept of probability; data screening and descriptive statistics; various inferential analysis techniques; and a series of exercises that are designed to integrate core statistical concepts. The author's systematic approach, which assumes no prior knowledge of the subject,\u003cb\u003e \u003c\/b\u003eequips student practitioners with a fundamental understanding of applied statistics that can be deployed across a wide variety of disciplines and professions. \u003c\/p\u003e\u003cp\u003eNotable features include:\u003c\/p\u003e\u003cul\u003e\n\u003cul\u003e\u003cp\u003e\u003c\/p\u003e\u003c\/ul\u003e\n\u003cli\u003eshort, digestible chapters that build and integrate statistical skills with real-world applications, demonstrating the flexible usage of statistics for evidence-based decision-making\u003c\/li\u003e\n\u003cul\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003c\/ul\u003e\n\u003cli\u003estatistical procedures presented in a practical context with less emphasis on technical ja\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cem\u003eThis book serves students being introduced to quantitative research as well as research professionals seeking to add to their statistical analysis and quantitative reasoning skills. Its emphasis on providing the reasons and prerequisites for using a statistical procedure make it valuable as a book-shelf reference as well as a textbook. Integration of relevant exercises to be carried out with SPSS enhances understanding of general statistical concepts with a body of hands-on experience, and that combination results in a very valuable skill set that will serve the reader well for years.\u003c\/em\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eJames H. Watt, Professor Emeritus, University of Connecticut\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eProfessor Vieira combines a straightforward approach to applied statistics with the most accessible statistical package, SPSS. His non-technical, clear, and concise writing style makes\u003c\/em\u003e Introduction to Real World Statistics \u003cem\u003ea valuable handbook and reference for students and practitioners as well as an effective text for introductory statistics courses.\u003c\/em\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eJohn Lowe, Associate Dean for Undergraduate Programs, Simmons College\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eThis is a textbook that attempts to bridge areas of content that are traditionally addressed independently: a the conceptual knowledge of technical information; b. the real world application of such technical knowledge and c. a leading software tool that assists a researcher in applying the conceptual knowledge of technical information to a real world context. Vieira seems to have used the feedback he received from his students over several decades to successfully build a bridge across these three content areas. This textbook and its approach are a welcome addition for making the process of learning statistics in the social sciences a lot smoother and a lot more relevant to students. \u003c\/em\u003e\u003cstrong\u003e \u003c\/strong\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eMichael G. Elasmar, Ph.D., Associate Professor and Director of the Marketing Communication Research graduate program, Boston University\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eDr. Vieira’s book is comprehensive, clear, and has great examples to illustrate the concepts. I particularly liked the section on Sampling. I envision that this book would be suitable for a variety of audiences and levels.\u003c\/em\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eClayton W. Barrows, University of New Hampshire\u003c\/strong\u003e\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface\u003c\/p\u003e\n\u003cp\u003eWhy Read This Book?\u003c\/p\u003e\n\u003cp\u003eNotable Features\u003c\/p\u003e\n\u003cp\u003eAssumes No Prior Knowledge of Statistics\u003c\/p\u003e\n\u003cp\u003eShort Digestible Chapters That Build and Integrate Real World Statistical Skills\u003c\/p\u003e\n\u003cp\u003eAn Alternative to the Traditional Hypothesis Testing Approach\u003c\/p\u003e\n\u003cp\u003eInterdisciplinary Applications\u003c\/p\u003e\n\u003cp\u003eSPSS Step-by-Step Detailed Instructions with Screenshots\u003c\/p\u003e\n\u003cp\u003eChapter PowerPoints and Test Bank\u003c\/p\u003e\n\u003cp\u003eA Systematic Approach to Teaching Statistics\u003c\/p\u003e\n\u003cp\u003eBook Organization\u003c\/p\u003e\n\u003cp\u003eAcknowledgments\u003c\/p\u003e\n\u003cp\u003ePART I: GETTING STARTED\u003c\/p\u003e\n\u003cp\u003e1 Introduction to Real World Statistics \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e1.1 What Is Statistics?\u003c\/p\u003e\n\u003cp\u003eSample Data vs. Census Data \u003c\/p\u003e\n\u003cp\u003e1.2 Reification \u003c\/p\u003e\n\u003cp\u003e1.3 Naïve Science: The Deception of Common Sense \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e1.4 Importance of Statistics \u003c\/p\u003e\n\u003cp\u003eStatistical Assumptions \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eIntroductory Applied Exercises \u003c\/p\u003e\n\u003cp\u003e2 Statistics: Descriptive, Correlation, and Inferential \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e2.1 Introduction to Descriptive, Correlation, and Inferential Statistics \u003c\/p\u003e\n\u003cp\u003e2.2 Descriptive Statistics \u003c\/p\u003e\n\u003cp\u003eMeasures of Variation \u003c\/p\u003e\n\u003cp\u003e2.3 Correlation Statistics \u003c\/p\u003e\n\u003cp\u003e2.4 Inferential Statistics\u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e2.5 Descriptive, Correlation, and Inferential Statistics \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eDescriptive, Correlation, and Inferential Statistics Applied Exercises \u003c\/p\u003e\n\u003cp\u003e3 Data and Types of Variables \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e3.1 Introduction to Variables \u003c\/p\u003e\n\u003cp\u003e3.2 Kinds of Variables \u003c\/p\u003e\n\u003cp\u003e3.3 Variables by Type of Data \u003c\/p\u003e\n\u003cp\u003eCategorical Data \u003c\/p\u003e\n\u003cp\u003eBinary-Level Data (Variable) \u003c\/p\u003e\n\u003cp\u003eNominal-Level Data (Variable) \u003c\/p\u003e\n\u003cp\u003eOrdinal-Level Data (Variable) \u003c\/p\u003e\n\u003cp\u003eNumeric Data \u003c\/p\u003e\n\u003cp\u003eRatio-Level Data (Variable) \u003c\/p\u003e\n\u003cp\u003eInterval-Level Data (Variable) \u003c\/p\u003e\n\u003cp\u003eScale Response Formatted Variables: A Special Case \u003c\/p\u003e\n\u003cp\u003eAppropriate Analysis for Variable (Data) Type \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e3.4 Variables by Influence \u003c\/p\u003e\n\u003cp\u003eIndependent Variables (Predictors) \u003c\/p\u003e\n\u003cp\u003eDependent Variables (Outcomes) \u003c\/p\u003e\n\u003cp\u003eControl Variables \u003c\/p\u003e\n\u003cp\u003eInteraction Variables \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eVariables Applied Exercises \u003c\/p\u003e\n\u003cp\u003e4 SPSS Statistics Data Management Basics: Preparing Data for Analysis \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e4.1 Introduction to SPSS and Data, Output, and Syntax Files\u003c\/p\u003e\n\u003cp\u003e4.2 Setting up the Data File \u003c\/p\u003e\n\u003cp\u003e4.3 Key SPSS Data Management Tools \u003c\/p\u003e\n\u003cp\u003e4.4 Opening SPSS \u003c\/p\u003e\n\u003cp\u003e4.5 Formatting the Variables’ Data \u003c\/p\u003e\n\u003cp\u003eName \u003c\/p\u003e\n\u003cp\u003eType \u003c\/p\u003e\n\u003cp\u003eWidth \u003c\/p\u003e\n\u003cp\u003eDecimals \u003c\/p\u003e\n\u003cp\u003eLabel \u003c\/p\u003e\n\u003cp\u003eValues \u003c\/p\u003e\n\u003cp\u003eMissing\u003c\/p\u003e\n\u003cp\u003eColumn\u003c\/p\u003e\n\u003cp\u003eAlign \u003c\/p\u003e\n\u003cp\u003eMeasure \u003c\/p\u003e\n\u003cp\u003eRole \u003c\/p\u003e\n\u003cp\u003e4.6 The SPSS Data File \u003c\/p\u003e\n\u003cp\u003eData Access \u003c\/p\u003e\n\u003cp\u003eManual Entry \u003c\/p\u003e\n\u003cp\u003eOpening an Existing SPSS Data File \u003c\/p\u003e\n\u003cp\u003eOpening Other Formatted Spreadsheet Data Files \u003c\/p\u003e\n\u003cp\u003eText Files \u003c\/p\u003e\n\u003cp\u003eCut and Paste \u003c\/p\u003e\n\u003cp\u003eSaving the Data File \u003c\/p\u003e\n\u003cp\u003eSaving Data in SPSS \u003c\/p\u003e\n\u003cp\u003eSaving Data in Other Spreadsheet Formats \u003c\/p\u003e\n\u003cp\u003e4.7 The SPSS Output File \u003c\/p\u003e\n\u003cp\u003eCreating a New SPSS Output File \u003c\/p\u003e\n\u003cp\u003eOpening an Existing SPSS Output File \u003c\/p\u003e\n\u003cp\u003eDisplaying the Full P-Value in the Output File \u003c\/p\u003e\n\u003cp\u003eSaving an SPSS Output File \u003c\/p\u003e\n\u003cp\u003eSaving Output for the SPSS Output Viewer \u003c\/p\u003e\n\u003cp\u003eSaving SPSS Output in Another Format \u003c\/p\u003e\n\u003cp\u003e4.8 A Brief Review of the Syntax File \u003c\/p\u003e\n\u003cp\u003e4.9 Creating a Codebook \u003c\/p\u003e\n\u003cp\u003eCreating a Codebook from Scratch \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003eThe SPSS Codebook \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eData Management Applied Exercises \u003c\/p\u003e\n\u003cp\u003ePART II: SAMPLING CONSIDERATIONS\u003c\/p\u003e\n\u003cp\u003e5 Sampling Strategies \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e5.1 Introduction to the Sampling Process \u003c\/p\u003e\n\u003cp\u003e5.2 Probability Sampling \u003c\/p\u003e\n\u003cp\u003eRandom vs. Representative Sampling \u003c\/p\u003e\n\u003cp\u003eRandom Sampling and the Shape of Data Distribution \u003c\/p\u003e\n\u003cp\u003eSimple Random Sampling \u003c\/p\u003e\n\u003cp\u003eSystematic Random Sampling \u003c\/p\u003e\n\u003cp\u003eCluster (Random) Sampling \u003c\/p\u003e\n\u003cp\u003eStratified Random Sampling \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e5.3 Nonprobability Sampling \u003c\/p\u003e\n\u003cp\u003eConvenience Sampling \u003c\/p\u003e\n\u003cp\u003eExpert Sampling \u003c\/p\u003e\n\u003cp\u003eQuota Sampling \u003c\/p\u003e\n\u003cp\u003eSnowball Sampling \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eSampling Applied Exercises \u003c\/p\u003e\n\u003cp\u003e6 Sample Size \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e6.1 Introduction to Sample Size \u003c\/p\u003e\n\u003cp\u003eNumeric Data \u003c\/p\u003e\n\u003cp\u003eCentral Limit Theorem \u003c\/p\u003e\n\u003cp\u003eCategorical Data \u003c\/p\u003e\n\u003cp\u003eOther Considerations \u003c\/p\u003e\n\u003cp\u003e6.2 Power Analysis and Sample Size \u003c\/p\u003e\n\u003cp\u003eFinite Population Correction \u003c\/p\u003e\n\u003cp\u003eInformed Power Analysis \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003eComparison of Unequal Sample Sizes \u003c\/p\u003e\n\u003cp\u003eData Assumptions \u003c\/p\u003e\n\u003cp\u003e6.3 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 6.1: Means: One-sample t-test that mean = specific value \u003c\/p\u003e\n\u003cp\u003eInterpretation \u003c\/p\u003e\n\u003cp\u003eExample 6.2: Means: Paired t-test that mean = 0 \u003c\/p\u003e\n\u003cp\u003eInterpretation \u003c\/p\u003e\n\u003cp\u003eExample 6.3: Proportions: One-sample test that proportion = \".50\" \u003c\/p\u003e\n\u003cp\u003eInterpretation \u003c\/p\u003e\n\u003cp\u003eExample 6.4: Proportions: 2 × 2 for independent samples (chi-square or Fisher’s exact test) \u003c\/p\u003e\n\u003cp\u003eInterpretation \u003c\/p\u003e\n\u003cp\u003eExample 6.5: Correlations: One-sample test that correlation = 0 \u003c\/p\u003e\n\u003cp\u003eInterpretation \u003c\/p\u003e\n\u003cp\u003eExample 6.6: ANOVA: One-way analysis of variance \u003c\/p\u003e\n\u003cp\u003eInterpretation \u003c\/p\u003e\n\u003cp\u003eExample 6.7: Regression: One set of predictors \u003c\/p\u003e\n\u003cp\u003eInterpretation \u003c\/p\u003e\n\u003cp\u003eExample 6.8: Clustering \u003c\/p\u003e\n\u003cp\u003eInterpretation \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eSample Size Applied Exercises \u003c\/p\u003e\n\u003cp\u003e7 Sources and Types of Statistical Error \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e7.1 Introduction to Sources of Statistical Error \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e7.2 Sampling Error \u003c\/p\u003e\n\u003cp\u003eSampling Random Error \u003c\/p\u003e\n\u003cp\u003eSampling Systematic Error \u003c\/p\u003e\n\u003cp\u003e7.3 Nonsampling Error \u003c\/p\u003e\n\u003cp\u003eNonsampling Random Error \u003c\/p\u003e\n\u003cp\u003eNonsampling Systematic Error \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eStatistical Error Applied Exercises \u003c\/p\u003e\n\u003cp\u003e8 Missing Data \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e8.1 Introduction to Missing Data \u003c\/p\u003e\n\u003cp\u003e8.2 Missing Value (Data) Analysis \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e8.3 Methods for Replacing Missing Values \u003c\/p\u003e\n\u003cp\u003eListwise (Casewise) \u003c\/p\u003e\n\u003cp\u003ePairwise \u003c\/p\u003e\n\u003cp\u003eSeries Mean \u003c\/p\u003e\n\u003cp\u003eMean of Nearby Points \u003c\/p\u003e\n\u003cp\u003eMedian of Nearby Points \u003c\/p\u003e\n\u003cp\u003eLinear Interpolation \u003c\/p\u003e\n\u003cp\u003eLinear Trend at Point \u003c\/p\u003e\n\u003cp\u003e8.4 New Data Replacement Methods \u003c\/p\u003e\n\u003cp\u003eExpectation Maximization \u003c\/p\u003e\n\u003cp\u003eMultiple Imputation \u003c\/p\u003e\n\u003cp\u003e8.5 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 8.1: The MCAR Case \u003c\/p\u003e\n\u003cp\u003eInterpretation \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 8.2: The NMAR Case \u003c\/p\u003e\n\u003cp\u003eInterpretation \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eMissing Data Applied Exercises \u003c\/p\u003e\n\u003cp\u003ePART III: DATA SCREENING, DESCRIBING, AND PROBABILITIES\u003c\/p\u003e\n\u003cp\u003e9 Describing Categorical Variables \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e9.1 Introduction to Describing Categorical Variables \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e9.2 Charting Categorical Variables \u003c\/p\u003e\n\u003cp\u003ePie Chart \u003c\/p\u003e\n\u003cp\u003eDichotomous (Two-Category) Pie Chart \u003c\/p\u003e\n\u003cp\u003eExample 9.1: A Pie Chart with Two Categories \u003c\/p\u003e\n\u003cp\u003eDescribing and Reporting \u003c\/p\u003e\n\u003cp\u003eMultiple Category Pie Chart \u003c\/p\u003e\n\u003cp\u003eExample 9.2: A Pie Chart with More Than Two Categories \u003c\/p\u003e\n\u003cp\u003eDescribing and Reporting \u003c\/p\u003e\n\u003cp\u003eBar Chart \u003c\/p\u003e\n\u003cp\u003eExample 9.3: A Bar Chart with More Than Two Categories \u003c\/p\u003e\n\u003cp\u003eDescribing and Reporting \u003c\/p\u003e\n\u003cp\u003e9.3 Categorical Variable Tables \u003c\/p\u003e\n\u003cp\u003eSingle Variable Tables \u003c\/p\u003e\n\u003cp\u003eExample 9.4: A Single Categorical Variable with Two Categories Table \u003c\/p\u003e\n\u003cp\u003eDescribing and Reporting \u003c\/p\u003e\n\u003cp\u003eExample 9.5: A Single Categorical Variable with More Than Two Categories Table \u003c\/p\u003e\n\u003cp\u003eDescribing and Reporting \u003c\/p\u003e\n\u003cp\u003eMultiple Variable Tables \u003c\/p\u003e\n\u003cp\u003eTwo Variables \u003c\/p\u003e\n\u003cp\u003eExample 9.6: Two Categorical Variables Each with Two or More Categories Table \u003c\/p\u003e\n\u003cp\u003eDescribing and Reporting \u003c\/p\u003e\n\u003cp\u003eThree Variables \u003c\/p\u003e\n\u003cp\u003eExample 9.7: Three Categorical Variables Each with Two or More Categories Table \u003c\/p\u003e\n\u003cp\u003eDescribing and Reporting \u003c\/p\u003e\n\u003cp\u003eSummary of Concepts \u003c\/p\u003e\n\u003cp\u003eDescribing Categorical Variables Applied Exercises \u003c\/p\u003e\n\u003cp\u003e10 Basic Probabilities for Categorical Variables \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e10.1 Introduction to Basic Probability \u003c\/p\u003e\n\u003cp\u003e10.2 Assumptions \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e10.3 Simple (Marginal) Probability \u003c\/p\u003e\n\u003cp\u003e10.4 Joint Probability \u003c\/p\u003e\n\u003cp\u003e10.5 Conditional Probability \u003c\/p\u003e\n\u003cp\u003e10.6 Tables \u003c\/p\u003e\n\u003cp\u003e10.7 Multiplication Rule in Probability \u003c\/p\u003e\n\u003cp\u003e10.8 Addition Rule in Probability \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eCategorical Data Probability Applied Exercises \u003c\/p\u003e\n\u003cp\u003e11 The Concepts of Data Distribution, Probability Values, and Significance Testing \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e11.1 Introduction to Data Distribution and Probability \u003c\/p\u003e\n\u003cp\u003e11.2 Numerical Data Distribution \u003c\/p\u003e\n\u003cp\u003eStandard Deviation and the Normal Distribution \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003eZ-Distribution and Z-Scores \u003c\/p\u003e\n\u003cp\u003eT-Distribution \u003c\/p\u003e\n\u003cp\u003eProbability Based on the Normal Distribution \u003c\/p\u003e\n\u003cp\u003eProbability Value (P-Values) \u003c\/p\u003e\n\u003cp\u003eLevel of Significance (Alpha) and Significance Testing \u003c\/p\u003e\n\u003cp\u003e11.3 Categorical Data Distribution \u003c\/p\u003e\n\u003cp\u003eThe Chi-Square Significance Test \u003c\/p\u003e\n\u003cp\u003eDegrees of Freedom \u003c\/p\u003e\n\u003cp\u003eTwo Types of Expected Observations \u003c\/p\u003e\n\u003cp\u003eProbability (P-Values) Based on the Chi-Square Distribution \u003c\/p\u003e\n\u003cp\u003e11.4 Confidence Intervals \u003c\/p\u003e\n\u003cp\u003e11.5 Conclusion \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eDistribution and Significance Testing Applied Exercises \u003c\/p\u003e\n\u003cp\u003e12 Numeric Variables: Data Screening and Removing Outliers \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e12.1 Introduction to Numeric Data Screening and Removing Outliers \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e12.2 Measuring Central Tendency \u003c\/p\u003e\n\u003cp\u003eMean \u003c\/p\u003e\n\u003cp\u003eMedian \u003c\/p\u003e\n\u003cp\u003eMode \u003c\/p\u003e\n\u003cp\u003eCoefficient of Skewness \u003c\/p\u003e\n\u003cp\u003e12.3 Measuring Dispersion \u003c\/p\u003e\n\u003cp\u003eRange \u003c\/p\u003e\n\u003cp\u003eVariance \u003c\/p\u003e\n\u003cp\u003eStandard Deviation \u003c\/p\u003e\n\u003cp\u003eCoefficient of Variation \u003c\/p\u003e\n\u003cp\u003eCoefficient of Kurtosis \u003c\/p\u003e\n\u003cp\u003e12.4 Screening Data: Identifying and Removing Outliers \u003c\/p\u003e\n\u003cp\u003eOutliers \u003c\/p\u003e\n\u003cp\u003eVisual Assessment \u003c\/p\u003e\n\u003cp\u003eStatistical Measures \u003c\/p\u003e\n\u003cp\u003eMethods for Identifying and Removing Outliers \u003c\/p\u003e\n\u003cp\u003eSimple Outlier Removal \u003c\/p\u003e\n\u003cp\u003eStandard Deviation Rule \u003c\/p\u003e\n\u003cp\u003eTrimming or Truncating \u003c\/p\u003e\n\u003cp\u003eWinsorizing \u003c\/p\u003e\n\u003cp\u003eOutlier Labeling Rule \u003c\/p\u003e\n\u003cp\u003eData Removal and Analysis \u003c\/p\u003e\n\u003cp\u003eData Screening and the Removal of Outliers Assumptions \u003c\/p\u003e\n\u003cp\u003e12.5 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 12.1: Simple Outlier Removal \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eExample 12.2: Outlier Labeling Rule Removal \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003e12.6 Other Remedies for Non-Normal Data Distribution \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eNumeric Data Screening and Removing Outliers Applied Exercises \u003c\/p\u003e\n\u003cp\u003ePART IV: STATISTICAL ANALYSIS\u003c\/p\u003e\n\u003cp\u003eCategorical Variables\u003c\/p\u003e\n\u003cp\u003e13 Chi-Square Goodness of Fit Test: Comparing Counts in a Single Variable with Two or More Categories \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e13.1 Introduction to the Chi-Square Goodness of Fit Test \u003c\/p\u003e\n\u003cp\u003e13.2 Calculating and Understanding the Chi-Square Statistic \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e13.3 Data Assumptions \u003c\/p\u003e\n\u003cp\u003e13.4 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 13.1: Equal Expected Counts: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eChi-Square Goodness of Fit Test Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 13.2: Equal Expected Counts: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eChi-Square Goodness of Fit Test Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 13.3: Specified Expected Counts: Both Cases \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eChi-Square Goodness of Fit Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Significant Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eReporting Nonsignificant Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eChi-Square Goodness of Fit Test Applied Exercises \u003c\/p\u003e\n\u003cp\u003e14 Chi-Square Test of Independence: Comparing Counts between Two Variables Each with Two or More Categories \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e14.1 Introduction to the Chi-Square Test of Independence \u003c\/p\u003e\n\u003cp\u003e14.2 Calculating and Understanding the Chi-Square Test of Independence \u003c\/p\u003e\n\u003cp\u003e14.3 Data Assumptions \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e14.4 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 14.1: A 2 × 2 Chi-Square Test of Independence: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eChi-Square Test of Independence Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 14.2: A 2 × 2 Chi-Square Test of Independence: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eChi-Square Test of Independence Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 14.3: A 3 × 5 Chi-Square Test of Independence: Both Cases \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eChi-Square Test of Independence Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Significant Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eReporting Nonsignificant Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eChi-Square Test of Independence Applied Exercises \u003c\/p\u003e\n\u003cp\u003e15 Chi-Square Test of the Same Sample: Comparing Counts of the Same Sample Measured Twice Using a Categorical Variable \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e15.1 Introduction to the Same Sample Measured Twice Using a Categorical Variable \u003c\/p\u003e\n\u003cp\u003e15.2 Data Assumptions \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e15.3 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 15.1: A Crosstabs 2 × 2 Repeated Measures McNemar Test: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening\u003c\/p\u003e\n\u003cp\u003eChi-Square Test for Repeated Counts Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 15.2: A Crosstabs 2 × 2 Repeated Measures McNemar Test: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eChi-Square Test for Repeated Counts Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 15.3: A Crosstabs 4 × 2 Repeated Measures McNemar-Bowker Test: Both Cases \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eChi-Square Test for Repeated Counts Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Significant Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eReporting Nonsignificant Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eChi-Square Test of Two Related Samples Measured Twice Applied Exercises \u003c\/p\u003e\n\u003cp\u003eNumeric Variables\u003c\/p\u003e\n\u003cp\u003e16 T-Test: Comparing a Single Sample Mean to a Specific Value \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e16.1 Introduction to the Single Sample T-Test \u003c\/p\u003e\n\u003cp\u003e16.2 Confidence Interval for a Single Sample T-Test \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e16.3 Data Assumptions \u003c\/p\u003e\n\u003cp\u003e16.4 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 16.1: Single Sample T-Tests: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eSingle Sample T\u003ci\u003e-Test Analysis \u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 16.2: Single Sample T-Tests: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eSingle Sample T\u003ci\u003e-Test Analysis \u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003e16.5 Single Sample T-Test Applied Exercises \u003c\/p\u003e\n\u003cp\u003e17 T-Test: Comparing Two Independent Samples’ Variable Means \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e17.1 Introduction to the Two Independent Samples T-Test \u003c\/p\u003e\n\u003cp\u003e17.2 Equality of Variance \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003ePooled or Separate Two Independent Samples T-Test \u003c\/p\u003e\n\u003cp\u003e17.3 Data Assumptions \u003c\/p\u003e\n\u003cp\u003e17.4 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 17.1: Two Independent Samples T-Tests: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eTwo Independent Samples T\u003ci\u003e-Test Analysis \u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 17.2: Two Independent Samples T-Tests: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eTwo Independent Samples T\u003ci\u003e-Test Analysis \u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eTwo Independent Samples T-Test Applied Exercises \u003c\/p\u003e\n\u003cp\u003e18 Analysis of Variance (ANOVA): Comparing More Than Two Independent Samples’ Means to Test for Differences among Them by One Type of Classification \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e18.1 Introduction to One-Way ANOVA \u003c\/p\u003e\n\u003cp\u003e18.2 Variance \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e18.3 Data Assumptions \u003c\/p\u003e\n\u003cp\u003e18.4 Strategies for Addressing Violations of Assumptions \u003c\/p\u003e\n\u003cp\u003e18.5 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 18.1: ANOVA F-Test: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eANOVA (Analysis) \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 18.2: ANOVA F-Test: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eANOVA (Analysis) \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eOne-Way ANOVA F-Test Applied Exercises \u003c\/p\u003e\n\u003cp\u003e19 Paired T-Test: Comparing the Means of the Same Sample Measured Twice Using a Numeric Variable \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e19.1 Introduction to the Paired-Sample T-Test \u003c\/p\u003e\n\u003cp\u003e19.2 Paired T-Test Calculations \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e19.3 Data Assumptions \u003c\/p\u003e\n\u003cp\u003e19.4 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 19.1: Paired-Sample T-Tests: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003ePaired-Sample T\u003ci\u003e-Test Analysis \u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 19.2: Single Sample T-Tests: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003ePaired-Sample T\u003ci\u003e-Test Analysis \u003c\/i\u003e\u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003ePaired-Samples T-Test Applied Exercises \u003c\/p\u003e\n\u003cp\u003e20 General Linear Model Repeated Measures: Comparing Means of the Same Sample Measured More Than Twice Using a Numeric Variable \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e20.1 Introduction to General Linear Model Repeated Measures \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e20.2 Data Assumptions \u003c\/p\u003e\n\u003cp\u003e20.3 Strategies for Addressing Violations of Assumptions \u003c\/p\u003e\n\u003cp\u003e20.4 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 20.1: General Linear Model Repeated Measures: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eGeneral Linear Model Repeated Measures Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 20.2: General Linear Model Repeated Measures: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eGeneral Linear Model Repeated Measures Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eGeneral Linear Model Repeated Measures Applied Exercises \u003c\/p\u003e\n\u003cp\u003e21 Correlation Analysis: Looking for an Association between Two Variables \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e21.1 Introduction to Pearson, Spearman, and Partial Bivariate Correlations \u003c\/p\u003e\n\u003cp\u003eExplained Variance (r2) \u003c\/p\u003e\n\u003cp\u003e21.2 Strength and Directionality of Correlations \u003c\/p\u003e\n\u003cp\u003eCorrelation Strength \u003c\/p\u003e\n\u003cp\u003eCorrelation Directionality \u003c\/p\u003e\n\u003cp\u003eLinear Correlation Strength and Directionality Together \u003c\/p\u003e\n\u003cp\u003e21.3 Calculating a Correlation for Numeric Data \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e21.4 Types of Correlations \u003c\/p\u003e\n\u003cp\u003e21.5 General Data Assumptions \u003c\/p\u003e\n\u003cp\u003e21.6 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 21.1: Pearson Correlation: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003ePearson Correlation Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 21.2: Pearson Correlation: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003ePearson Correlation Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 21.3: Spearman Correlation: Both Cases \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eSpearman Correlation Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Significant Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eReporting Nonsignificant Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 21.4: Partial Correlation: Both Cases \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003ePartial Correlation Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Nonsignificant Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eReporting Significant Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eCorrelation Analysis Applied Exercises \u003c\/p\u003e\n\u003cp\u003e22 Single Linear Regression \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e22.1 Introduction to Single Linear Regression \u003c\/p\u003e\n\u003cp\u003ePrediction vs. Cause and Effect \u003c\/p\u003e\n\u003cp\u003e22.2 Prediction Model \u003c\/p\u003e\n\u003cp\u003eApplying the Prediction Model \u003c\/p\u003e\n\u003cp\u003eStandardized Regression Coefficients \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e22.3 Data Assumptions \u003c\/p\u003e\n\u003cp\u003eTesting Data Assumptions \u003c\/p\u003e\n\u003cp\u003e22.4 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 22.1: Single Linear Regression: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eSingle Linear Regression Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 22.2: Single Linear Regression: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eSingle Linear Regression Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eSingle Linear Regression Applied Exercises \u003c\/p\u003e\n\u003cp\u003e23 Multiple Linear Regression \u003c\/p\u003e\n\u003cp\u003eLearning Objectives \u003c\/p\u003e\n\u003cp\u003e23.1 Introduction to Multiple Linear Regression \u003c\/p\u003e\n\u003cp\u003e23.2 Prediction Model \u003c\/p\u003e\n\u003cp\u003eR-Square and Adjusted R-Square \u003c\/p\u003e\n\u003cp\u003eReal World Snapshot \u003c\/p\u003e\n\u003cp\u003e23.3 Data Assumptions \u003c\/p\u003e\n\u003cp\u003e23.4 Examples Using SPSS: Step-by-Step Instructions \u003c\/p\u003e\n\u003cp\u003eExample 23.1: Multiple Linear Regression: The Significant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eMultiple Linear Regression Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eExample 23.2: Multiple Linear Regression: The Nonsignificant Case \u003c\/p\u003e\n\u003cp\u003eSPSS Output Interpretation \u003c\/p\u003e\n\u003cp\u003eData Screening \u003c\/p\u003e\n\u003cp\u003eMultiple Linear Regression Analysis \u003c\/p\u003e\n\u003cp\u003eReporting Results \u003c\/p\u003e\n\u003cp\u003eWrite-Up \u003c\/p\u003e\n\u003cp\u003eSummary of Key Concepts \u003c\/p\u003e\n\u003cp\u003eMultiple Linear Regression Applied Exercises \u003c\/p\u003e\n\u003cp\u003eAPPENDICES\u003c\/p\u003e\n\u003cp\u003eAppendix A: Glossary \u003c\/p\u003e\n\u003cp\u003eAppendix B: Chapter Statistical Exercise Solutions \u003c\/p\u003e\n\u003cp\u003eB.1 Chapter 1 \u003c\/p\u003e\n\u003cp\u003eB.2 Chapter 2 \u003c\/p\u003e\n\u003cp\u003eB.3 Chapter 3 \u003c\/p\u003e\n\u003cp\u003eB.4 Chapter 4 \u003c\/p\u003e\n\u003cp\u003eB.5 Chapter 5 \u003c\/p\u003e\n\u003cp\u003eB.6 Chapter 6 \u003c\/p\u003e\n\u003cp\u003eB.7 Chapter 7 \u003c\/p\u003e\n\u003cp\u003eB.8 Chapter 8 \u003c\/p\u003e\n\u003cp\u003eB.9 Chapter 9 \u003c\/p\u003e\n\u003cp\u003eB.10 Chapter 10 \u003c\/p\u003e\n\u003cp\u003eB.11 Chapter 11 \u003c\/p\u003e\n\u003cp\u003eB.12 Chapter 12 \u003c\/p\u003e\n\u003cp\u003eB.13 Chapter 13 \u003c\/p\u003e\n\u003cp\u003eB.14 Chapter 14 \u003c\/p\u003e\n\u003cp\u003eB.15 Chapter 15 \u003c\/p\u003e\n\u003cp\u003eB.16 Chapter 16 \u003c\/p\u003e\n\u003cp\u003eB.17 Chapter 17 \u003c\/p\u003e\n\u003cp\u003eB.18 Chapter 18 \u003c\/p\u003e\n\u003cp\u003eB.19 Chapter 19 \u003c\/p\u003e\n\u003cp\u003eB.20 Chapter 20 \u003c\/p\u003e\n\u003cp\u003eB.21 Chapter 21 \u003c\/p\u003e\n\u003cp\u003eB.22 Chapter 22 \u003c\/p\u003e\n\u003cp\u003eB.23 Chapter 23 \u003c\/p\u003e\n\u003cp\u003eAppendix C: Case Studies and Solutions \u003c\/p\u003e\n\u003cp\u003eC.1 Case Study Questions \u003c\/p\u003e\n\u003cp\u003eFinancial Attributes \u003c\/p\u003e\n\u003cp\u003eGift Shop Customers \u003c\/p\u003e\n\u003cp\u003eHealth Issues \u003c\/p\u003e\n\u003cp\u003eMoving Services \u003c\/p\u003e\n\u003cp\u003eSample Size Matters \u003c\/p\u003e\n\u003cp\u003eViolent Crime Recidivism \u003c\/p\u003e\n\u003cp\u003eWhat Motivates Students to Perform \u003c\/p\u003e\n\u003cp\u003ePsychological Effects of the Workplace \u003c\/p\u003e\n\u003cp\u003eC.2 Case Study Solutions \u003c\/p\u003e\n\u003cp\u003eFinancial Attributes \u003c\/p\u003e\n\u003cp\u003eGift Shop Customers \u003c\/p\u003e\n\u003cp\u003eHealth Issues \u003c\/p\u003e\n\u003cp\u003eMoving Services \u003c\/p\u003e\n\u003cp\u003eSample Size Matters \u003c\/p\u003e\n\u003cp\u003eViolent Crime Recidivism \u003c\/p\u003e\n\u003cp\u003eWhat Motivates Students to Perform \u003c\/p\u003e\n\u003cp\u003ePsychological Effects of the Workplace \u003c\/p\u003e\n\u003cp\u003eAppendix D: Research Goal and Objectives \u003c\/p\u003e\n\u003cp\u003eD.1 Research Goal \u003c\/p\u003e\n\u003cp\u003eE.2 Research Objectives \u003c\/p\u003e\n\u003cp\u003eDeveloping and Testing Research Statements \u003c\/p\u003e\n\u003cp\u003eDeveloping and Answering Research Questions \u003c\/p\u003e\n\u003cp\u003eD.3 The Interconnected Parts of Research Goals and Objectives \u003c\/p\u003e\n\u003cp\u003eAppendix E: Types of Research Design \u003c\/p\u003e\n\u003cp\u003eE.1 Introduction to Research Designs \u003c\/p\u003e\n\u003cp\u003eE.2 Survey or Self-Report Research Design \u003c\/p\u003e\n\u003cp\u003ePerson-to-Person Administered Survey \u003c\/p\u003e\n\u003cp\u003eSelf-Administered Survey \u003c\/p\u003e\n\u003cp\u003eE.3 Experimental Research Design \u003c\/p\u003e\n\u003cp\u003eCause and Effect Relationship \u003c\/p\u003e\n\u003cp\u003eLab Experiment \u003c\/p\u003e\n\u003cp\u003eField Experiment \u003c\/p\u003e\n\u003cp\u003eManipulation Check \u003c\/p\u003e\n\u003cp\u003eExternal Influences \u003c\/p\u003e\n\u003cp\u003eManaging the Effects of Unaccounted for Extraneous Variables \u003c\/p\u003e\n\u003cp\u003eThe Experimental Design Process Model \u003c\/p\u003e\n\u003cp\u003eE.4 Observational Research Design \u003c\/p\u003e\n\u003cp\u003ePersonal Observation \u003c\/p\u003e\n\u003cp\u003eMechanical Observation \u003c\/p\u003e\n\u003cp\u003eContent Analysis \u003c\/p\u003e\n\u003cp\u003eE.5 Other Research Designs \u003c\/p\u003e\n\u003cp\u003eSingle Time vs. Repeated Measures Designs \u003c\/p\u003e\n\u003cp\u003eCross-Sectional Design \u003c\/p\u003e\n\u003cp\u003eLongitudinal Design \u003c\/p\u003e\n\u003cp\u003eMixed Research Designs \u003c\/p\u003e\n\u003cp\u003eAppendix F: Comparing Counts of the Same Sample Measured More Than\u003c\/p\u003e\n\u003cp\u003eTwice Using a Categorical Variable \u003c\/p\u003e\n\u003cp\u003eF.1 A Categorical Variable Measured More Than Twice Using the Same Sample \u003c\/p\u003e\n\u003cp\u003eF.2 Data Assumptions \u003c\/p\u003e\n\u003cp\u003eAppendix G: More on Linear Regression \u003c\/p\u003e\n\u003cp\u003eG.1 Introduction to Other Tools in Regression Analysis \u003c\/p\u003e\n\u003cp\u003eG.2 The Influence of Outliers on Linear Regression Results \u003c\/p\u003e\n\u003cp\u003eG.3 Linear Regression Methods \u003c\/p\u003e\n\u003cp\u003eStepwise \u003c\/p\u003e\n\u003cp\u003eHierarchical \u003c\/p\u003e\n\u003cp\u003eG.4 Dummy Coding \u003c\/p\u003e\n\u003cp\u003eG.5 Interaction Terms (Variables) \u003c\/p\u003e\n\u003cp\u003eG.6 Residual Analysis \u003c\/p\u003e\n\u003cp\u003eG.7 Multicollinearity \u003c\/p\u003e\n\u003cp\u003eAppendix H: Statistics Flow Chart \u003c\/p\u003e\n\u003cp\u003eReferences \u003c\/p\u003e\n\u003cp\u003eIndex \u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default 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