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Statistics for Research in Psychology
A Modern Approach Using Estimation

- Rick Gurnsey - Concordia University - Montréal, Canada

September 2017 | 760 pages | SAGE Publications, Inc

Chapter 1: Basic Concepts

Statistics in Psychology |

Variables, values, scores |

Measurement |

Populations and Samples |

Sampling, Sampling Bias, and Sampling Error |

A preview of what’s ahead |

Appendix 1: Introduction to Excel |

Appendix 2: Introduction to SPSS |

Appendix 3: An introduction to R |

Chapter 2: Distributions of Scores

Introduction |

Distributions of qualitative variables |

Distributions of discrete quantitative variables |

Distributions of continuous variables |

Probability |

Probability Distributions |

Appendix 1: Grouped Frequency Tables and Histograms in Excel |

Appendix 2: Grouped Frequency Tables and Histograms in SPSS |

Chapter 3: Properties of Distributions

Introduction |

Central Tendency |

Dispersion (spread) |

Shape |

Appendix 1: Basic statistics in Excel |

Appendix 2: Basic statistics in SPS |

Chapter 4: Normal Distributions

Introduction |

Normal Distributions |

The Standard Normal Distribution: z-scores |

Area-under-the-curve-problems: approximate solutions |

The z-table |

Area-under-the-curve-problems: exact solutions |

Critical value problems |

Applications |

Appendix 1: NORM.DIST and related functions in Excel |

Chapter 5: Distributions of Statistics

Introduction |

Normal Distributions |

The Standard Normal Distribution: z-scores |

Area-under-the-curve-problems: approximate solutions |

The z-table |

Area-under-the-curve-problems: exact solutions |

Critical value problems |

Applications |

Appendix 1: NORM.DIST and related functions in Excel |

Chapter 6. Estimating the population mean when the population standard deviation is known

Introduction |

An Example |

Point estimates versus interval estimates |

95% Confidence Intervals |

(1-a)100% Confidence Intervals |

Cautions About Interpretation |

Estimating µ when sample size is large |

Assumptions |

Planning a Study |

A word about Jerzy Neyman |

Appendix 1: Computing confidence intervals in Excel |

Chapter 7. Significance Tests

The lady tasting tea |

A scenario: whole language versus phonics |

Significance tests |

Computing exact p-values: directional and non-directional tests |

The alternative hypothesis |

p-values are conditional probabilities |

Statistical significance versus practical significance |

Review of significance tests |

Appendix 1: Conducting significance tests in Excel |

Chapter 8. Decisions, Power, Effect Size, and the Hybrid Model

Introduction |

Statistical Decisions |

Neyman and Pearson |

The determinants of power |

Prospective power analysis: planning experiments |

Interpreting effect size |

The hybrid model: Null Hypothesis Significance Testing (NHST) |

Chapter 9: Significance tests: problems and alternatives

Introduction |

Significance tests under fire |

Criticisms of significance tests |

Confidence intervals |

Estimating µ1-µ0 |

Estimating d = (µ1-µ0)/s |

Estimation versus significance testing |

Chapter 10. Estimating the population mean when the standard deviation is unknown

Introduction |

t-scores: sm versus sm |

t-distributions |

Confidence Intervals: estimating µ |

An Example |

Estimating the difference between two population means |

Estimating d |

Significance tests |

Appendix 1: Excel Functions related to t-distributions |

Appendix 2: Confidence intervals and significance tests in SPSS |

Chapter 11: Estimating the difference between the means of independent populations

Introduction |

The two independent groups design |

An example |

Theoretical foundations for the (1-a)100% confidence interval for µ1-µ2 |

Effect size d |

Significance testing |

Interpretation of our riddle study |

Partitioning Variance |

Meta-analysis |

Appendix 1: Excel calculations |

Appendix 2: Estimation and significance tests in SPSS |

Chapter 12: Estimating the difference between the means of dependent populations

Introduction |

Dependent vs Independent Populations |

The distributions of D and mD |

Repeated measures and matched samples |

Estimating d for dependent populations |

Significance testing |

Partitioning variance |

Appendix 1: Excel Calculations |

Appendix 2: Estimation and significance tests in SPSS |

Chapter 13: Introduction to Correlation and Regression

Introduction |

Associations between two scale variables |

Correlation and Regression |

The correlation coefficient |

The regression equation |

Many bivariate distributions have the same statistics |

Random variables, experiments, and causation |

Appendix 1: Using Excel for Correlation and Regression |

Chapter 14: Inferential Statistics for Simple Linear Regression

Introduction |

Regression when values of x are fixed: theory |

Regression when x values are fixed: an example |

Regression when x is a random variable |

Regression when x is a random variable: an example |

Estimating the expected value of y: E(y|x) |

Prediction Intervals |

Appendix 1: Using Excel for Regression |

Appendix 2: Using SPSS for Regression |

Chapter 15: Inferential statistics for correlation

An Example |

The sampling distribution of r |

Significance Tests |

What is a big correlation and what is the practical significance of r? |

The correlation coefficient is a standardized effect size: meta-analysis |

The Generality of Correlation |

Appendix 1: Functions related to correlation in Excel |

Appendix 2: Correlation Analysis in SPSS |

Chapter 16: Introduction to Multiple Regression

Introduction |

An Example |

Parameters and Statistics in Multiple Regression |

Significance Tests |

Using SPSS to conduct multiple regression |

Degrees of freedom |

Comparing regression models |

Confidence intervals for y and prediction intervals for ynext |

Discussion of our example: to add TIE or not to add TIE |

Appendix 1: Bootstrapped confidence intervals for ?R2 |

Chapter 17: Applying Multiple Regression

Introduction |

The regression coefficients |

Statistical control |

Mediation |

Moderation |

Appendix 1: Installing the PROCESS macro in SPSS |

Chapter 18: Analysis of Variance: One-Factor Between-Subjects

Introduction |

The one-factor, between-subjects ANOVA |

Planned contrasts |

Sources of variance |

Trend analysis |

Corrections for multiple contrasts |

Power |

Regression and ANOVA are the same thing |

Chapter 19: Analysis of Variance: One-Factor Within-Subjects

Introduction |

An example: the Posner cuing task |

The omnibus analysis |

Confidence intervals and significance tests for contrasts |

Conducting the one-factor within-subjects ANOVA in SPSS |

Chapter 20: Two-Factor ANOVA: Omnibus Effects

Two-factor designs: main effects and interactions |

Main effects and interactions in a 3 x 4 design |

Partitioning variability among means: orthogonal decomposition |

An example: the texture discrimination task |

The two-factor, between-subjects design |

The two-factor, within-subjects design |

The two-factor mixed design |

Unequal sample sizes and missing data |

Why bother with main effects and interactions? |

Chapter 21: Contrasts in Two-Factor Designs

Introduction |

The two-factor, between-subjects design |

The two-factor, within-subjects design |

The two-factor mixed design |

HEOA Compliance

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