Machine generated contents note: pt. I Preliminary Information: "First Things First" -- 1.Math Review, Vocabulary, and Symbols -- Getting Started -- Vocabulary and Symbols -- Some Rules and Procedures -- More Rules and Procedures -- 2.Measurement Scales -- What is Measurement? -- Scales of Measurement -- Nominal Scale -- Ordinal Scale -- Interval Scale -- Ratio Scale -- Continuous Versus Discrete Variables -- Real Limits -- pt. II Tables and Graphs: "One Display" -- 3.Frequency and Percentile Tables -- Why Use Tables? -- Frequency Tables -- Relative Frequency or Percentage Tables -- Grouped Frequency Tables -- Percentile and Percentile Rank Tables -- SPSS Connection -- 4.Graphs and Plots -- Why Use Graphs? -- Graphing Continuous Data -- Stem-and-Leaf Displays -- Principles of Graph Construction -- Histogram -- Frequency Curve or Line Graph -- Symmetry, Skew, and Kurtosis -- Graphing Discrete Data -- Bar Graph -- Pie Graph -- SPSS Connection --

Contents note continued: pt. III Central Tendency: "Bull's-Eye" -- 5.Mode, Median, and Mean -- What is Central Tendency? -- Mode -- Median -- Mean -- Skew and Central Tendency -- SPSS Connection -- pt. IV Dispersion: "From Here to Eternity" -- 6.Range, Variance, and Standard Deviation -- What is Dispersion? -- Range -- Variance -- Standard Deviation -- Average Absolute Deviation -- Controversy: N Versus n - 1 -- SPSS Connection -- pt. V The Normal Curve and Standard Scores: "What's the Score?" -- 7.Percent Area and the Normal Curve -- What is a Normal Curve? -- History of the Normal Curve -- Uses of the Normal Curve -- Looking Ahead -- 8.z Scores -- What is a Standard Score? -- Benefits of Standard Scores -- Calculating z Scores -- Comparing Scores Across Different Tests -- SPSS Connection -- 9.Score Transformations and Their Effects -- Why Transform Scores? -- Effects on Central Tendency -- Effects on Dispersion -- A Graphic Look at Transformations --

Contents note continued: Summary of Transformation Effects -- Some Common Transformed Scores -- Looking Ahead -- pt. VI Probability: "Odds Are" -- 10.Probability Definitions and Theorems -- Why Study Probability? -- Probability as a Proportion -- Equally Likely Model -- Mutually Exclusive Outcomes -- Addition Theorem -- Independent Outcomes -- Multiplication Theorem -- A Brief Review -- Probability and Inference -- 11.The Binomial Distribution -- What are Dichotomous Events? -- Finding Probabilities by Listing and Counting -- Finding Probabilities by the Binomial Formula -- Finding Probabilities by the Binomial Table -- Probaility and Experimentation -- Looking Ahead -- Nonnormal Data -- pt. VII Inferential Theory: "Of Truth and Relativity" -- 12.Sampling, Variables, and Hypotheses -- From Description to Inference -- Sampling -- Variables -- Hypotheses -- 13.Errors and Significance -- Random Sampling Revisited -- Sampling Error -- Significant Difference -- The Decision Table --

Contents note continued: Type 1 Error -- Type 2 Error -- 14.The z Score as a Hypothesis Test -- Inferential Logic and the z Score -- Constructing a Hypothesis Test for a z Score -- Looking Ahead -- pt. VIII The One-Sample Test: "Are They From Our Part of Town?" -- 15.Standard Error of the Mean -- Central Limit Theorem -- Sampling Distribution of the Mean -- Calculating the Standard Error of the Mean -- Sample Size and the Standard Error of the Mean -- Looking Ahead -- 16.Normal Deviate Z Test -- Prototype Logic and the Z Test -- Calculating a Normal Deviate Z Test -- Examples of Normal Deviate Z Tests -- Decision Making With a Normal Deviate Z Test -- Looking Ahead -- 17.One-Sample t Test -- Z Test Versus t Test -- Comparison of Z-Test Versus t-Test Formulas -- Degrees of Freedom -- Biased and Unbiased Estimates -- When do We Reject the Null Hypothesis? -- One-Tailed versus Two-Tailed Tests -- The t Distribution Versus the Normal Distribution --

Contents note continued: The t Table Versus the Normal Curve Table -- Calculating a One-Sample t Test -- Interpreting a One-Sample t Test -- Looking Ahead -- SPSS Connection -- 18.Interpreting and Reporting One-Sample t: Error, Confidence, and Parameter Estimates -- What is Confidence? -- Refining Error and Confidence -- Decision Making With a One-Sample t Test -- Dichotomous Decisions Versus Reports of Actual p -- Parameter Estimation: Point and Interval -- SPSS Connection -- pt. IX The Two-Sample Test: "Ours is Better than Yours" -- 19.Standard Error of the Difference Between the Means -- One-Sample Versus Two-Sample Studies -- Sampling Distribution of the Difference Between the Means -- Calculating the Standard Error of the Difference Between the Means -- Importance of the Size of the Standard Error of the Difference Between the Means -- Looking Ahead -- 20.t Test Wind Independent Samples and Equal Sample Sizes -- A Two-Sample Study --

Contents note continued: Inferential Logic and the Two-Sample t Test -- Calculating a Two-Sample t Test -- Interpreting a Two-Sample t Test -- Looking Ahead -- SPSS Connection -- 21.t Test With Unequal Sample Sizes -- What Makes Sample Sizes Unequal? -- Comparison of Special-Case and Generalized Formulas -- More Clarification of the Underlying Logic -- Calculating a t Test With Unequal Sample Sizes -- Interpreting a t Test With Unequal Sample Sizes -- SPSS Connection -- 22.t Test With Related Samples -- What Makes Samples Related? -- Comparison of Special-Case and Related-Samples Formulas -- Advantage and Disadvantage of Related Samples -- Computational Formula -- Calculating a t Test With Related Samples -- Interpreting a t Test With Related Samples -- SPSS Connection -- 23.Interpreting and Reporting Two-Sample t: Error, Confidence, and Parameter Estimates -- What is Confidence? -- Refining Error and Confidence -- Decision Making With a Two-Sample t Test --

Contents note continued: Dichotomous Decisions Versus Reports of Actual p -- Parameter Estimation: Point and Interval -- SPSS Connection -- pt. X The Multisample Test: "Ours is Better than Yours or Theirs" -- 24.ANOVA Logic: Sums of Squares, Partitioning, and Mean Squares -- When do we use ANOVA? -- ANOVA Assumptions -- Partitioning of Deviation Scores -- From Deviation Scores to Variances -- From Variances to Mean Squares -- From Mean Squares to F -- Looking Ahead -- 25.One-Way ANOVA: Independent Samples and Equal Sample Sizes -- What is a One-Way ANOVA? -- Inferential Logic and ANOVA -- Sums of Squares Formulas: Deviation Score Method -- Calculating Sums of Squares: Deviation Score Method -- Sums of Squares Formulas: Raw Score Method -- Calculating Sums of Squares: Raw Score Method -- Remaining Steps: Mean Squares and F -- Interpreting a One-Way ANOVA -- The ANOVA Summary Table -- SPSS Connection -- pt. XI Post Hoc Tests: "So Who's Responsible?" -- 26.Tukey HSD Test --

Contents note continued: Why Do WE Need a Post Hoc Test? -- Calculating the Tukey HSD -- Interpreting the Tukey HSD -- SPSS Connection -- 27.Scheffe Test -- Why do we Need a Post Hoc Test? -- Calculating the Scheffe -- Interpreting the Scheffe -- SPSS Connection -- pt. XII More than One Independent Variable: "Double Dutch Jump Rope" -- 28.Main Effects and Interaction Effects -- What is a Factorial ANOVA? -- Factorial ANOVA Designs -- Number and Type of Hypotheses -- Main Effects -- Interaction Effects -- Looking Ahead -- 29.Factorial ANOVA -- Review of Factorial ANOVA Designs -- Data Setup and Preliminary Expectations -- Sums of Squares Formulas -- Calculating Factorial ANOVA Sums of Squares: Raw Score Method -- Factorial Mean Squares and Fs -- Interpreting a Factorial F Test -- The Factorial ANOVA Summary Table -- SPSS Connection -- pt. XIII Nonparametric Statistics: "Without Form or Void" -- 30.One-Variable Chi-Square: Goodness of Fit -- What is a Nonparametric Test? --

Contents note continued: Chi-Square as a Goodness-of-Fit Test -- Formula for Chi-Square -- Inferential Logic and Chi-Square -- Calculating a χ2 Goodness of Fit -- Interpreting a χ2 Goodness of Fit -- Looking Ahead -- SPSS Connection -- 31.Two-Variable Chi-Square: Test of Independence -- Chi-Square as a Test of Independence -- Prerequisites for a Chi-Square Test of Independence -- Formula for a Chi-Square -- Finding Expected Frequencies -- Calculating a Chi-Square Test of Independence -- Interpreting a Chi-Square Test of Independence -- SPSS Connection -- pt. XIV Effect Size and Power: "How Much is Enough?" -- 32.Measure of Effect Size -- What is Effect Size? -- For Two-Sample t Tests -- For ANOVA F Tests -- For Chi-Square Tests -- For a Goodness-of-Fit Test -- For a Test of Independence -- 33.Power and Factors Affecting It -- What is Power? -- Factors Affecting Power -- Size of Type 1 Error -- Directionality of the Alternative Hypothesis --

Contents note continued: Size of the Actual Difference Between the Means -- Amount of Error Variance -- Sample Size -- Putting It Together: Alpha, Power, Effect Size, and Sample Size -- Looking Ahead -- pt. XV Correlation: "Whither Thou Goest, I Will Go" -- 34.Relationship Strength and Direction -- Experimental Versus Correlational Studies -- Plotting Correlation Data -- Relationship Strength -- Relationship Direction -- Linear and Nonlinear Relationships -- Outliers and Their Effects -- Looking Ahead -- SPSS Connection -- 35.Pearson r -- What is a Correlation Coefficient? -- Formulas for Pearson r -- z-Score Scatterplots and r -- Calculating Pearson r: Raw Score Method -- Interpreting a Pearson r Coefficient -- Looking Ahead -- SPSS Connection -- 36.Correlation Pitfalls -- Effect of Sample Size on Statistical Significance -- Statistical Significance Versus Practical Importance -- Effect of Restriction in Range -- Effect of Sample Heterogeneity or Homogeneity --

Contents note continued: Effect of Unreliability in the Measurement Instrument -- Correlation Versus Common Variance -- Correlation Versus Causation -- pt. XVI Linear Prediction: "You're So Predictable" -- 37.Linear Prediction -- Correlation Permits Prediction -- Logic of a Prediction Line -- Concept of Best-Fitting Line -- Equation for Best-Fitting Line -- Using a Prediction Equation to Predict Scores on Y -- Another Calculation Example -- SPSS Connection -- 38.Standard Error of Prediction -- What is a Confidence Interval? -- Correlation and Prediction Error -- Distribution of Prediction Error -- Calculating the Standard Error of Prediction -- Using the Standard Error of Prediction to Calculate Confidence Intervals -- Factors Influencing the Standard Error of Prediction -- Another Calculation Example -- 39.Introduction to Multiple Regression -- What is Regression? -- Prediction Error, Revisited -- Why Multiple Regression? -- The Multiple Regression Equation --

Contents note continued: Multiple Regression and Predicted Variance -- Hypothesis Testing in Multiple Regression -- An Example -- The General Linear Model -- SPSS Connection -- pt. XVII Review: "Say It Again, Sam" -- 40.Selecting the Appropriate Analysis -- Review of Descriptive Methods -- Tables and Graphs -- Descriptive Statistics -- Review of Inferential Methods -- Parametric Test Statistics -- Nonparametric Test Statistics -- Effect Size and Power.