Statistical Testing with jamovi Psychology: SECOND EDITION

Höfundur: Cole Davis (Útgáfa: 2)
Statistical Testing with jamovi Psychology: SECOND EDITION

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Nánar um bókina

Útgefandi
Vor Press
ISBN
9781915500175
Print ISBN
9781915500168
Format
Page Fidelity (PDF)
Útgáfa
2
Höfundar
Cole Davis
Tungumál
English
Útgefið
05/2023
Prent takmörkun á líftíma
100
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Title page
  • Copyright
  • Contents
  • Part 1 – Background knowledge
  • Chapter 1 – Introduction
  • Chapter 2 – Research design
  • Experiments, control groups, variables and other terms
  • Chapter 3 – Descriptive statistics
  • Central tendency
  • Dispersion
  • Assumptions for parametric tests
  • Testing for distribution
  • Chapter 4 – Null hypothesis significance testing
  • One-tailed and two-tailed hypotheses
  • One Sample T-Test – does a sample belong to a population?
  • The two-tailed hypothesis revisited
  • Part 2 – Statistical testing fundamentals
  • Chapter 5 – Tests of differences
  • Design considerations for the analysis of differences
  • Some research design terminology applied
  • Tests for same subjects
  • Paired Samples t test: a parametric test for two conditions, same subjects
  • The Wilcoxon test: a non-parametric test for two conditions, same subjects
  • Repeated Measures one-way ANOVA: a parametric test for more than two conditions, same subjects (also
  • Friedman: a non-parametric test for more than two conditions, same subjects
  • Tests for different subjects
  • Independent Samples T-Test: a parametric test for two conditions, different subjects
  • The Mann-Whitney test: a non-parametric test for two conditions, different subjects
  • Between-subjects one-way ANOVA: a parametric test for more than two conditions, different subjects
  • Kruskal-Wallis: a non-parametric test for more than two conditions, different subjects
  • Chapter 6 – Tests of relationships
  • Correlations
  • Correlations and effect sizes
  • The Pearson test: a parametric correlational test
  • The Spearman and Kendall's tau-b tests: non-parametric correlational tests
  • Multiple correlations – parametric - using Pearson's test
  • Multiple correlations – non-parametric - using Spearman/Kendall's tau-b
  • Regression
  • Simple linear regression (two conditions) – parametric
  • Standard (simultaneous) multiple regression – multiple predictors against one dependent variable (
  • Hierarchical regression
  • Which type of multiple regression should I use?
  • Chapter 7 – Categorical analyses
  • Introduction
  • The binomial test: a frequency test for dichotomies (either/or)
  • The multinomial test: a frequency test for more than two categories
  • The chi squared Test of Association: a frequency test for two variables
  • Log-linear regression: modeling three or more categorical variables
  • The McNemar test: correlated dichotomies (linked pairs)
  • Chapter 8 – Exercises
  • Questions
  • Answers
  • Chapter 9 – Reporting research
  • Data – absolute or averages?
  • Different audiences
  • Graphics
  • To a live audience!
  • Part 3 – ANOVA extended
  • Chapter 10 – Factorial ANOVA and multiple comparisons
  • Typical case studies
  • Effect sizes for factorial ANOVA
  • Repeated Measures Two-Way ANOVA
  • Repeated Measures Three-Way ANOVA
  • Between-Subjects ANOVA
  • Mixed ANOVA
  • Multiple comparisons
  • Chapter 11 – ANCOVA considered
  • Chapter 12 – MANOVA
  • Part 4 – Relationships, broad and narrow
  • Chapter 13 – PCA and factor analysis
  • Introduction
  • PCA and EFA, compared and contrasted
  • Assumptions for data
  • The effectiveness of parallel analysis
  • Principal components analysis in action
  • Traditional principal components analysis
  • Exploratory factor analysis
  • Controversies
  • Controversy 1 – Deciding on component and factor numbers
  • Controversy 2 – PCA versus EFA techniques for factor analysis
  • Controversy 3 – rotation methods
  • Beyond the technicalities
  • Chapter 14 – Logistic regression
  • Assumptions
  • Suitable data set structures
  • Binomial logistic regression
  • Releveling as preparation for logistic regression
  • Basic reporting
  • Interpreting the coefficients
  • Pseudo R-squared statistics
  • Prediction
  • Multinomial logistic regression
  • Ordinal logistic regression
  • Chapter 15 - Partial and semi-partial (‘part’) correlations
  • Partial correlations
  • Semi-partial correlations (also known as part correlations)
  • Part 5 – Bayesian statistics introduced
  • Chapter 16 – Theory: the minister, the prior and the post
  • Classical statistics – a brief preparatory overview
  • Bayesian statistics as the antithesis of classical statistics
  • Bayesian statistics introduced, via conditional probability
  • A brief history
  • How are Bayesian statistics used to test hypotheses?
  • And now, even better news!
  • Chapter 17 – Application: Jeffreys and Jamovi
  • Reporting Bayesian results
  • Installing the jsq module in Jamovi
  • A practical example using the paired t test
  • Which tests to use, classical or Bayesian?
  • Part 6 – Visual exploration
  • Chapter 18 – Survival analysis: the Kaplan-Meier curve
  • Introduction
  • Statistical assumptions
  • The Kaplan-Meier survival function
  • Chapter 19 – Cluster analysis
  • Introduction
  • Data preparation
  • Hierarchical cluster analysis
  • Distance measures
  • Clustering methods
  • k-means clustering
  • References
  • Index
  • Back cover