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Contents
  1. About This Book

    1. Licensing and Copyright
  2. Acknowledgements

  3. Statistical Software Used in this Book

    1. Accessing SPSS Through Your School
    2. Downloading SPSS
  4. University of Saskatchewan: Software Access

    1. On-Campus Lab Access
    2. Remote / Off-Campus Access
    3. USask ICT Help
  5. Data Sets

  6. 1. Background and Motivation
    1. 1.1 Overview

      1. 1.1.1 Textbook Layout, * and ** Symbols Explained
      2. 1.1.2 Intro to Univariate Statistics
    2. 1.2 Basic Definitions

      1. 1.2.1  Types of Data (important!)
      2. 1.2.2 Measurement Scales (avoid this!)
      3. 1.2.3 Kinds of Sampling and Studies
    3. 1.3 Summation Convention

  7. 2. Descriptive Statistics: Frequency Data (Counting)
    1. 2.1 Frequency Tables

    2. 2.2 Plotting Frequency Data

      1. 2.2.1 Stem and Leaf Plots
    3. 2.3 SPSS Lesson 1: Getting Started with SPSS

  8. 3. Descriptive Statistics: Central Tendency and Dispersion
    1. 3.1 Central Tendency: Mean, Median, Mode

      1. 3.1.1 Mean
      2. 3.1.2 Median
      3. 3.1.3 Mode
      4. 3.1.4 Midrange
      5. 3.1.5 Mean, Median and Mode in Histograms: Skewness
      6. 3.1.6 Mean, Median and Mode in Distributions: Geometric Aspects
    2. 3.2 Dispersion: Variance and Standard Deviation

    3. 3.3 z-score / z-transformation

    4. 3.4 SPSS Lesson 2: Combining variables and recoding

  9. 4. Probability and the Binomial Distributions
    1. 4.1 Probability

    2. 4.2 Binomial Distribution

      1. 4.2.1 Practical Binomial Distribution Examples
    3. 4.3 SPSS Lesson 3: Combining variables - advanced

  10. 5. The Normal Distributions
    1. 5.1 Discrete versus Continuous Distributions

    2. 5.2 **The Normal Distribution as a Limit of Binomial Distributions

    3. 5.3 Normal Distribution

      1. 5.3.1 Computing Areas (Probabilities) under the standard normal curve
  11. 6. Percentiles and Quartiles
    1. 6.1 Discrete Data Percentiles and Quartiles

    2. 6.2 Finding Outliers Using Quartiles

    3. 6.3 Box Plots

    4. 6.4 Robust Statistics

    5. 6.5 SPSS Lesson 4: Percentiles

  12. 7. The Central Limit Theorem
    1. 7.1 Using the Normal Distribution to Approximate the Binomial Distribution

    2. 7.2 The Central Limit Theorem

  13. 8. Confidence Intervals
    1. 8.1 Confidence Intervals Using the z-Distribution

    2. 8.2 **Bayesian Statistics

    3. 8.3 The t-Distributions

    4. 8.4 Proportions and Confidence Intervals for Proportions

    5. 8.5 Chi Squared Distribution

  14. 9. Hypothesis Testing
    1. 9.1 Hypothesis Testing Problem Solving Steps

    2. 9.2 z-Test for a Mean

      1. 9.2.1 What p-value is significant?
    3. 9.3 t-Test for Means

    4. 9.4 z-Test for Proportions

    5. 9.5 Chi Squared Test for Variance or Standard Deviation

    6. 9.6 SPSS Lesson 5: Single Sample t-Test

  15. 10. Comparing Two Population Means
    1. 10.1 Unpaired z-Test

    2. 10.2 Confidence Interval for Difference of Means (Large Samples)

    3. 10.3 Difference between Two Variances - the F Distributions

    4. 10.4 Unpaired or Independent Sample t-Test

      1. 10.4.1 General form of the t test statistic
      2. 10.4.2 Two step procedure for the independent samples t test
    5. 10.5 Confidence Intervals for the Difference of Two Means

    6. 10.6 SPSS Lesson 6: Independent Sample t-Test

    7. 10.8 Paired t-Test

    8. 10.9 Confidence Intervals for Paired t-Tests

    9. 10.10 SPSS Lesson 7: Paired Sample t-Test

  16. 11. Comparing Proportions
    1. 11.1 z-Test for Comparing Proportions

    2. 11.2 Confidence Interval for the Difference between Two Proportions

  17. 12. ANOVA
    1. 12.1 One-way ANOVA

    2. 12.2 Post hoc Comparisons

      1. 12.2.1 Scheffé test
      2. 12.2.2 Tukey Test
      3. 12.2.3 Bonferroni correction
    3. 12.3 SPSS Lesson 8: One-way ANOVA

    4. 12.5 Two-way ANOVA

    5. 12.6 SPSS Lesson 9: Two-way ANOVA

    6. 12.8 Higher Factorial ANOVA

      1. 12.8.1 3-way ANOVA
    7. 12.9 Between and Within Factors

      1. 12.9.1 *One-way ANOVA with between factors
    8. 12.10 *Contrasts

  18. 13. Power
    1. 13.1 Power

      1. Using observed power
  19. 14. Correlation and Regression
    1. 14.1 Scatter Plots

    2. 14.2 Correlation

    3. 14.3 SPSS Lesson 10: Scatterplots and Correlation

    4. 14.5 Linear Regression

      1. 14.5.1: Relationship between correlation and slope
    5. 14.6 r² and the Standard Error of the Estimate of y′

      1. 14.6.1: **Details: from deviations to variances
    6. 14.7 Confidence Interval for y′ at a Given x

    7. 14.8 SPSS Lesson 11: Linear Regression

    8. 14.10 Multiple Regression

      1. 14.10.1: Multiple regression coefficient, r
      2. 14.10.2: Significance of r
      3. 14.10.3: Other descriptions of correlation
    9. 14.11 SPSS Lesson 12: Multiple Regression

  20. 15. Chi Squared: Goodness of Fit and Contingency Tables
    1. 15.1 Goodness of Fit

      1. 15.1.1: Test of Normality using the $\chi^{2}$ Goodness of Fit Test
    2. 15.2 Contingency Tables

      1. 15.2.1 Homogeneity of proportions $\chi^{2}$ test
    3. 15.3 SPSS Lesson 13: Proportions, Goodness of Fit, and Contingency Tables

      1. 15.3.1 Binomial test
      2. 15.3.2. $\chi^{2}$ goodness of fit test
      3. 15.3.3. Contingency tables: $\chi^{2}$ test of independence
  21. 16. Non-parametric Tests
    1. 16.1 How to Rank Data

    2. 16.2 Median Sign Test

    3. 16.3 Paired Sample Sign Test

    4. 16.4 Two Sample Wilcoxon Rank Sum Test (Mann-Whitney U Test)

    5. 16.5 Paired Wilcoxon Signed Rank Test

    6. 16.6 Kruskal-Wallis Test (H Test)

    7. 16.7 Spearman Rank Correlation Coefficient

    8. 16.8 SPSS Lesson 14: Non-parametric Tests

      1. 16.8.1 Mann Whitney/Wilcoxson Rank Sum
      2. 16.8.2 Paired Wilcoxon Signed Rank Test and Paired Sign Test
      3. 16.8.3 Kruskal-Wallis Test
    9. 16.10 Runs Test

  22. 17. Overview of the General Linear Model
    1. 17.1 Linear Algebra Basics

      1. 17.1.1 Vector Spaces 
      2. 17.1.2 Linear Transformations or Linear Maps
      3. 17.1.3 Transpose of Matrices
      4. 17.1.4 Matrix Multiplication
      5. 17.1.5 Linearly Independent Vectors
      6. 17.1.6 Rank of a Matrix
      7. 17.1.7 The Inverse of a Matrix
      8. 17.1.8 Solving Systems of Equations
    2. 17.2 The General Linear Model (GLM) for Univariate Statistics

      1. 17.2.1 Linear Regression in GLM Format 
      2. 17.2.2 Multiple Linear Regression in GLM Format
      3. 17.2.3 One-Way ANOVA in GLM Format
      4. 17.2.4 Test Statistics in GLM Format
  23. Appendix: Tables

Introduction to Applied Statistics for Psychology Students

Data Sets

The dataset files listed here, which are used in the SPSS Lessons of this book, were created by Osama Bataineh. They are released with a CC BY-NC-SA 4.0 license.

HyperactiveChildren.sav

Caregiver.sav

HeightLatency.sav

AgeSmoker.sav

HeadCircum.sav

pHLevel.sav

Methadone.sav

BoneStrength.sav

Relief.sav

Hypertension.sav

Cancer.sav

CancerRecovery.sav

CancerRecoveryAge.sav

RetinalAnatomyData.sav

MigraineTriggeringData.sav

CancerTumourReduction.sav

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Previous: University of Saskatchewan: Software Access
Next: 1.1 Overview

License

Icon for the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License

Introduction to Applied Statistics for Psychology Students by Gordon E. Sarty is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.

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