Applied statistics I. Basic bivariate techniques / (2020)

• Record Type: Book
• Other Titles: Basic bivariate techniques
• Further Information: Note: Rebecca M. Warner.
• Authors: Warner, Rebecca M
• Contents: 1. 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 S
• Edition: Third edition
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 519.535; Social sciences -- Statistical methods; Psychology -- Statistical methods; Multivariate analysis
• Languages: English
• ISBNs: 9781506352824
• Related ISBNs: 9781506352800
• Notes: Note: Description based on CIP data; resource not viewed.
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