Statistics: Rowan University Library Resources

Chapter 1: Introduction to Social Science Research Principles and Terminology
Chapter 2: Measures of Central Tendency
Chapter 3: Measures of Variability
Chapter 4: The Normal Distribution
Chapter 5: Standardization and z Scores
Chapter 6: Standard Errors
Chapter 7: Statistical Significance, Effect Size, and Confidence Intervals
Chapter 8: Correlation
Chapter 9: t Tests
Chapter 10: One-way Analysis of Variance
Chapter 11: Factorial Analysis of Variance
Chapter 12: Repeated-Measures Analysis of Variance
Chapter 13: Regression
Chapter 14: The Chi-Square Test of Independence

This book presents statistical concepts and techniques in simple, everyday language to help readers gain a better understanding of how they work and how to interpret them correctly. Each self-contained chapter features a description of the statistic including how it is used and the information it provides, how to calculate the formula, the strengths and weaknesses of each technique, the conditions needed for its use, and an example that uses and interprets the statistic.

Chapter 1: The Beginning The Question
Chapter 2: Ambiguity Statistics
Chapter 3: Fodder-Data
Chapter 4: Data-Measurement
Chapter 5: Data Structure: levels of Measurement
5A: Nominal
5B: Ordinal
5C: Interval
5D: Ratio
Chapter 6: Simpliying Groups and Clusters
Chapter 7: Counts and Frequencies
Chapter 8: Pictures and Graphs
Chapter 9: Scatterings and Distributions
Chapter 10: Bell Shaped-The Normal Curve
Chapter 11: Lopsidenesses-Skewness
Chapter 12: Averages-Central Tendencies
12A: Mean
12B: Median
12C: Mode
Chapter 13: Two Types-Distributive and Inferential
Chapter 14: Foundations-Assumptions
Chapter 15: Murkiness-Missing Data
Chapter 16: Leeway-Robustness
Chapter 17: Consistency-Reliability
Chapter 18: Truth-Validity
Chapter 19: Unpredictability-Randomness
Chapter 20: Representativeness-Samples
Chapter 21: Mistakes-Errors
Chapter 22: Real or Not-Outliers
Chapter 23: Impediments-Confounds
Chapter 24: Nuisances-Covariates
Chapter 25: Background-Independent Variables
Chapter 26: Targets-Dependent Variables
Chapter 27: Inequality-Standard Deviations and Variance
Chapter 28: Prove-No, Falsify
Chapter 29: The Difference-The Null Hypothesis
Chapter 30: Reductionism-Models
Chapter 31: Risk-Probability
Chapter 32: Uncertainty-p values
Chapter 33: Expectations-Chi Square
Chapter 34: Importance vs. Difference—Substantive vs. Statistical
Chapter 35: Strength-Power
Chapter 36: Impact-Effect Sizes
Chapter 37: Likely Range—Confidence Intervals
Chapter 38: Association-Correlation
Chapter 39: Predictions-Multiple Regressions
Chapter 40: Abundance—Multivariate Analysis
Chapter 41: Differences—t Tests and Analysis of Variance
41A: ANOVA
41B: ANCOVA
41C: MANOVA
41D: MANCOVA
Chapter 42: Differences That Matter—Discriminant Analysis
Chapter 43: Both Sides Loaded—Canonical Covariance Analysis
Chapter 44: Nesting—Hierarchical Models
Chapter 45: Cohesion—Factor Analysis
Chapter 46: Ordered Events—Path Analysis
Chapter 47: Digging Deeper—Structural Equation Models
Chapter 48: Abundance-Big Data
Chapter 49: Scarcity-Small Data
Chapter 50: Fiddling—Modifications and New Techniques

This Second Edition of The Tao of Statistics: A Path to Understanding (With No Math) provides a reader-friendly approach to statistics in plain English. Unlike other statistics books, this text explains what statistics mean and how they are used, rather than how to calculate them. The book walks readers through basic concepts as well as some of the most complex statistical models in use. The Second Edition adds coverage of big data to better address its impact on p-values and other key concepts; material on small data to show readers how to handle data with fewer data points than optimal; and other new topics like missing data and effect sizes. The book's two characters (a high school principal and a director of public health) return in the revised edition, with their examples expanded and updated with reference to contemporary concerns in the fields of education and health.

Chapter 1 Some introductory concepts;
Chapter 2 Measurement theory;
Chapter 3 Descriptive statistics;
Chapter 4 Patterns in data and presentation of data;
Chapter 5 'None of the above': the role of knowledge representation;
Chapter 6 The sixfold paths: some observations on probability theory;
Chapter 7 Inferential statistics: some introductory thoughts;
Chapter 8 Non-parametric statistics;
Chapter 9 Correlations;
Chapter 10 Parametric statistics;
Chapter 11 Multidimensional statistics
Chapter 12 Some general consideration

Written in an informal style, this book guides the reader gently through the field from the simplest descriptive statistics to multidimensional approaches. It's written in an accessible way, with few calculations and fewer equations, for readers from a broad set of academic disciplines ranging from archaeology to zoology.

If you use statistics and need easy access to simple, reliable definitions and explanations of modern statistical concepts, then look no further than this dictionary. Over 3600 terms are defined, covering medical, survey, theoretical, and applied statistics, including computational aspects.

Chapter 1: Surrounded by Statistics
Chapter 2: Simple Descriptions
Chapter 3: Collecting Good Data
Chapter 4: Probability
Chapter 5: Estimation and Inference
Chapter 6: Statistical Models and Methods
Chapter 7: Statistical Computing

Aimed at readers with no prior mathematical knowledge, this Very Short Introduction explores and explains how statistics work, and how we can decipher them.