{"product_id":"applied-statistics-i-international-student-edition-9781071807491","title":"Applied Statistics I  International Student","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cstrong\u003eApplied Statistics I:Basic Bivariate Techniques\u003c\/strong\u003e has been created from the first half of Rebecca M. Warner's popular \u003cem\u003eApplied Statistics: From Bivariate Through Multivariate Techniques\u003c\/em\u003e. The author's contemporary approach differs from some of the well-worn texts in the market, and reflects current thinking in the field. It spends less time on statistical significance testing, and moves in the direction of the new statistics by focusing more on confidence intervals and effect size. Instructors of upper undergraduate or beginning graduate level courses will find that the greater focus on basic concepts such as partition of variance and effect size is more useful to students, particularly as preparation for more advanced courses. Spending less time on statistical significance testing allows for more time to be devoted to more interesting and useful statistics that students will see in journal articles (such as correlation and regression). This introductory statistic\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Evaluating Numeric Information    Introduction    Guidelines for Numeracy    Source Credibility    Message Content    Evaluating Generalizability    Making Causal Claims    Quality Control Mechanisms in Science    Biases of Information Consumers    Ethical Issues in Data Collection and Analysis    Lying with Graphs and Statistics    Degrees of Belief    Summary 2. Basic Research Concepts    Introduction    Types of Variables    Independent and Dependent Variables    Typical Research Questions    Conditions for Causal Inference    Experimental Research Design    Non-experimental Research Design    Quasi- Experimental Designs    Other Issues in Design and Analysis    Choice of Statistical Analysis (Preview)    Populations and Samples: Ideal Versus Actual Situations    Common Problems in Interpretation of Results    Appendix 2 A: More About Levels of Measurement    Appendix 2 B: Justification for Use of Likert and Other Rating Scales as  Quantitative Variables (In Some Situations) 3. Frequency Distribution Tables    Introduction    Use of Frequency Tables for Data Screening    Frequency Tables for Categorical Variables    Elements of Frequency Tables    Using SPSS to Obtain a Frequency Table    Mode, Impossible Score Values, and Missing Values    Reporting Data Screening for Categorical Variables    Frequency Tables for Quantitative Variables    Frequency Tables for Categorical Versus Quantitative Variables    Reporting Data Screening for Quantitative Variables    What We Hope to See in Frequency Tables for Categorical Variables    What We Hope to See in Frequency Tables for Quantitative Variables    Summary    Appendix 3 A:  Getting Started in IBM SPSS ® version 25    Appendix 3 B: Missing Values in Frequency Tables    Appendix 3 C: Dividing Scores into Groups or Bins 4. Descriptive Statistics    Introduction    Questions about Quantitative Variables    Notation    Sample Median    Sample Mean (M)    An Important Characteristic of M: Sum of Deviations from M = 0    Disadvantage of M: It is Not Robust Against Influence of Extreme Scores    Behavior of Mean, Median and Mode in Common Real-World Situations    Choosing Among Mean, Median, and Mode    Using SPSS to Obtain Descriptive Statistics for a Quantitative Variable    Minimum, Maximum, and Range: Variation among Scores    The Sample Variance s2    Sample Standard Deviation (s or SD)    How a Standard Deviation Describes Variation Among Scores in a Frequency Table    Why Is There Variance?    Reports of Descriptive Statistics in Journal Articles    Additional Issues in Reporting Descriptive Statistics    Summary    Appendix 4 A  Order of Arithmetic Operations    Appendix 4 B  Rounding 5. Graphs: Bar Charts, Histograms, and Box Plots    Introduction    Pie Charts for Categorical Variables    Bar Charts for Frequencies of Categorical Variables    Good Practice for Construction of Bar Charts    Deceptive Bar Graphs    Histograms for Quantitative Variables    Obtaining a Histogram Using SPSS    Describing and Sketching Bell-Shaped Distributions    Good Practices in Setting up Histograms    Box Plot (Box and Whiskers Plot)    Telling Stories About Distributions    Uses of Graphs in Actual Research    Data Screening: Separate Bar Charts or Histograms for Groups    Use of Bar Charts to Represent Group Means    Other Examples    Summary 6. The Normal Distribution and z Scores    Introduction    Locations of Individual Scores in Normal Distributions    Standardized or “z” Scores    Converting z Scores Back into Original Units of X    Understanding Values of z    Qualitative Description of Normal Distribution Shape    More Precise Description of Normal Distribution Shape    Reading Tables of Areas for the Standard Normal Distribution    Dividing the Normal Distribution Into Three Regions: Lower Tail, Middle, Upper Tail    Outliers Relative to a Normal Distribution    Summary of First Part of Chapter    Why We Assess Distribution Shape    Departure from Normality: Skewness    Another Departure from Normality: Kurtosis    Overall Normality    Practical Recommendations    Reporting Information About Distribution Shape, Missing Values, Outliers, and Descriptive Statistics for Quantitative Variables    Summary    Appendix 6 A: The Mathematics of the Normal Distribution    Appendix 6 B: How to Select and Remove Outliers in SPSS    Appendix 6 C: Quantitative Assessments of Departure from Normality    Appendix 6 D: Why Are Some Real-World Variables Approximately Normally Distributed? 7. Sampling Error and Confidence Intervals    Descriptive Versus Inferential Uses of Statistics    Notations for Samples Versus Populations    Sampling Error and the Sampling Distribution for Values of M    Prediction Error    Sample Versus Population (Revisited)    The Central Limit Theorem: Characteristics of the Sampling Distribution of M    Factors that Influence Population Standard Error    Effect of N on Value of the Population Standard Error    Describing the Location of a Single Outcome for M Relative to a Population Sampling Distribution (Setting Up a z Ratio)    What We Do When ?? Is Unknown    The Family of t Distributions    Tables for t Distributions    Using Sampling Error to Set Up a Confidence Interval    How to Interpret a Confidence Interval    Empirical Example: Confidence Interval for Body Temperature    Other Applications for CIs    Error Bars in Graphs of Group Means    Summary 8. The One-Sample t test: Introduction to Statistical Significance Tests    Introduction    Significance Tests as Yes\/No Questions About Proposed Values of Population Means    Stating a Null Hypothesis    Selecting an Alternative Hypothesis    The One-Sample t Test    Choosing an Alpha (?) Level    Specifying Reject Regions Based on ?, Halt and df    Questions for the One-Sample t Test    Assumptions for the Use of the One-Sample t Test    Rules for the Use of NHST    First Example: Mean Driving Speed (Nondirectional Test)    SPSS Analysis: One Sample t Test for Mean Driving Speed    “Exact” p Values    Reporting Results for a Two-tailed One-Sample t Test    The Driving Speed Data Reconsidered Using a One-Tailed Test    Reporting Results for a One-tailed One-Sample t Test:    Advantages\/ Disadvantages of One Tailed Tests    Traditional NHST Versus New Statistics Recommendations    Things You Should Not Say About p Values    Summary 9. Issues in Significance Tests: Effect Size, Statistical Power, and Decision Errors    Beyond p Values    Cohen’s d: An Effect Size Index    Factors that Affect the Size of t Ratios    Statistical Significance Versus Practical Importance    Statistical Power    Type I and Type II Decision Errors    Meanings of “Error”    Use of NHST in Exploratory Versus Confirmatory Research    Inflated Risk of Type I Error From Multiple Tests Interpretation of Null Outcomes    Interpretation of Null Outcomes    Interpretation of Statistically Significant Outcomes    Understanding Past Research    Planning Future Research    Guidelines for Reporting Results    What You Cannot Say    Summary    Appendix 9 A Further Explanation of Statistical Power 10. Bivariate Pearson Correlation    Research Situations Where Pearson r Is Used    Correlation and Causal Inference    How Sign and Magnitude of r Describe an X, Y Relationship    Setting Up Scatter Plots With Examples of Perfect Linearity    Most Associations Are Not Perfect    Different Situations In Which r = 0    Assumptions for Use of Pearson r    Preliminary Data Screening for Pearson r    Effect of Extreme Bivariate Outliers    Research Example    Data Screening for Research Example    Computation of Pearson r    How Computation for Correlation Is Related to Pattern of Data Points in the Scatter Plot    Testing the Hypothesis That ?0 = 0    Reporting Many Correlations and Inflated Risk of Type I Error    Obtaining CIs for Correlations    Pearson’s r and r2 as Effect-Size Indexes and Partition of Variance    Statistical Power and Sample Size for Correlation Studies    Interpretation of Outcomes for Pearson’s r    SPSS Example    Results Sections for One and Several Pearson r Values    Reasons to Be Skeptical of Correlations    Summary    Appendix 10 A:  Nonparametric Alternatives to Pearson r    Appendix 10 B: Setting Up a 95% CI for Pearson r    Appendix 10 C:  Testing Significance of Differences Between Correlations    Appendix 10 D: Factors That Artifactually Influence the Magnitude of Pearson’s r    Appendix 10 E: Analysis of Non Linear Relationships 11. Bivariate Regression    Research Situations Where Bivariate Regression is Used    New Information Provided by Regression    Regression Equations and Lines    Two Versions of Regression Equations    Steps in Regression Analysis    Preliminary Data Screening    Formulas for Bivariate Regression Coefficients    Statistical Significance Tests for Bivariate Regression    Confidence Intervals for Regression Coefficients    Effect Size and Statistical Power    Empirical Example Using SPSS: Salary Data    SPSS Output: Salary Data    Plotting the Regression Line: Salary Data    Results Section: Salary Data    Using Regression Equation to Predict Score for Individual: Joe’s Hr Data    Partition of SS in Bivariate Regression: Joe’s Hr Data    Issues in Planning a Bivariate Regression Study    Plotting Residuals    Standard Error of the  Estimate, sy.x    Summary    Appendix 11 A OLS Derivation of Equation for Regression Coefficients    Appendix 11 B Fully Worked Example for SS values: Joe’s HR Data 12. The Independent Samples t Test    Research Situations Where the Independent Samples t Test is Used    Hypothetical Research Example    Assumptions for Use of the Independent Samples t Test    Preliminary Data Screening: Evaluating Violations of Assumptions and Getting to Know Your Data    Computation of Independent Samples t Test    Statistical Significance of Independent Samples t Test    Confidence Interval Around (M1 – M2)    SPSS Commands for Independent Samples t Test    SPSS Output for Independent Samples t Test    Effect-Size Indexes for t    Factors that Influence the Size of t    Results Section    Graphing Results: Means and CIs    Decisions About Sample Size for the Independent Samples t Test    Issues in Designing a Study    Summary    Appendix 12 A: A Nonparametric Alternative to the Independent Samples t Test 13. One-Way Between-S Analysis of Variance    Research Situations Where Between-S One-Way ANOVA is Used    Questions in One-Way Between S ANOVA    Hypothetical Research Example    Assumptions and Data Screening for One-Way ANOVA    Computations for One-Way Between-S ANOVA    Patterns of Scores and Magnitudes of SSbetween and SSwithin    Confidence Intervals (CIs) For Group Means    Effect Sizes for One-Way Between-S ANOVA    Statistical Power Analysis for One-Way Between-S ANOVA    Planned Contrasts    Post Hoc or “Protected” Tests    One Way Between S ANOVA  Procedure in SPSS    Output from SPSS for One Way Between S ANOVA    Reporting Results from One Way Between S ANOVA    Issues in Planning a Study    Summary    Appendix A ANOVA Model and Division of Scores Into Components    Appendix B Expected Value of F When H0 is True    Appendix C Comparison of ANOVA to t Test    Appendix D Nonparametric Alternative to One Way Between S ANOVA 14. Paired Samples t-Test    Independent Versus Paired Samples Designs    Between-S and Within-S or Paired Groups Designs    Types of Paired Samples    Hypothetical Study: Effects of Stress on Heart Rate    Review: Data Organization for Independent Samples    New: Data Organization for Paired Samples    A First Look at Repeated Measures Data    Calculation of Difference (d) Scores    Null Hypothesis for Paired Samples t Test    Assumptions for Paired Samples t Test    Formulas for Paired Samples t Test    SPSS Paired Samples t Test Procedure    Comparison of Results For Independent Samples t and Paired Samples t Tests    Effect Size and Power    Some Design Problems in Repeated Measures Designs    Results for Paired Samples t-Test: Stress and HR    Further Evaluation of Assumptions for Larger Dataset    Summary    Appendix A  Nonparametric Alternative to Paired Samples t: Wilcoxon Signed Rank Test 15. One Way Repeated Measures ANOVA    Introduction    Null Hypothesis for Repeated Measures ANOVA    Preliminary Assessment of Repeated Measures Data    Computations for One-Way Repeated Measures ANOVA    Use of SPSS Reliability Procedure for One Way Repeated Measures ANOVA    Partition of SS in Between-S Versus Within-S ANOVA    Assumptions for Repeated Measures ANOVA    Choices of Contrasts in GLM Repeated Measures    SPSS GLM Procedure for Repeated Measures ANOVA    Output for GLM Repeated Measures ANOVA    Paired Samples t Tests as Follow Up    Results    Effect Size    Statistical Power    Counterbalancing in Repeated Measures Studies    More Complex Designs    Summary    Appendix 15 A Test for Person by Treatment Interaction 16. Factorial Analysis of Variance (Between – S)    Research Situations Where Factorial Design Is Used    Questions in Factorial ANOVA    Null Hypotheses in Factorial ANOVA    Screening for Violations of Assumptions    Hypothetical Research Situation    Computations for Between-S Factorial ANOVA    Computation of SS,  df, and MS in Two Way Factorial    Effect Size Estimates for Factorial ANOVA    Statistical Power    Follow-Up Tests    Factorial ANOVA Using the SPSS GLM Procedure    SPSS Output    Results    Design Decisions and Magnitudes of SS Terms    Summary    Appendix 16 A: Unequal Cell ns in Factorial ANOVA    Appendix 16 B: Weighted Versus Unweighted Means    Appendix 16 C: Model for Factorial ANOVA    Appendix 16 D: Fixed Versus Random Factors 17. Chi Square Analysis of Contingency Tables    Evaluating Association Between Two Categorical Variables    First Example: Contingency Tables for Titanic Data    What is Contingency?    Conditional and Unconditional Probabilities    Null Hypothesis for Contingency Table Analysis    Second Empirical Example: Dog Ownership Data    Preliminary Examination of Dog Ownership Data    Expected Cell Frequencies If H0 True    Computation of Chi Squared Significance Test    Evaluation of Statistical Significance of ?2.    Effect Sizes for Chi Squared    Chi Squared Example Using SPSS    Output from Crosstabs Procedure    Reporting Results    Assumptions and Data Screening For Contingency Tables    Other Measures of Association for Contingency Tables    Summary    Appendix 17 A: Margin of Error For Percentages in Surveys    Appendix 17 B: Contingency Tables With Repeated Measures: McNemar Test    Appendix 17 C: Fisher Exact Test    Appendix 17 D: How Marginal Distributions for X and Y Constrain Maximum Value of ??    Appendix 17 E: Other Uses of ?2 18. Selection of Bivariate Analyses and Review of Key Concepts    Selecting Appropriate Bivariate Analyses    Types of Independent and Dependent Variables (Categorical Versus Quantitative)    Parametric Versus Nonparametric Analyses    Comparisons of Means or Medians Across Groups (Categorical IV and Quantitative DV)    Problems with Selective Reporting of Evidence and Analyses    Limitations of Statistical Significance Tests and p Values    Statistical Versus Practical Significance    Generalizability Issues    Causal Inference    Results Sections    Beyond Bivariate Analyses: Adding Variables    Some Multivariable or Multivariate Analyses    Degrees of Belief","brand":"SAGE Publications Inc","offers":[{"title":"Default Title","offer_id":48738195636567,"sku":"9781071807491","price":105.62,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781071807491.jpg?v=1723811809","url":"https:\/\/bookcurl.com\/products\/applied-statistics-i-international-student-edition-9781071807491","provider":"Book Curl","version":"1.0","type":"link"}