Data science

This online source is a chapter from the online book titled "A contemporary approach to research and statistics in Psychology" by Klaire Somoray. It provides information about covariation and correlation, hypothesis testing for correlations, correlation and causation, linear regression, and regression to the mean.

This online resource provides several videos, examples and explanations about what a correlation analysis is, correlation and causality, how to interpret correlation, scatter plot and correlation, how to test correlation for significance, information about Pearson and Spearman correlation and point biserial correlation.

This online source is from a "Statistical comparisons using R" workshop provided by QCIF. It provides some examples of continuous variables and R scripts for assessing relationships with scatter plots and degree of association.

This resource is a chapter from the online "Data Science Textbook" by TIBCO Software Inc., and provides information about correlations, including Pearson correlation.

This online resource provides information about Pearson's correlation, the assumptions and how to carry out the test in SPSS. The site also contains a link to point biserial correlation.

This online source provides information about Spearman's correlation, including the assumptions and how to carry out the test in SPSS. This site also contains links to other potential alternative tests.

This online source is from a "Statistical comparisons using R" workshop provided by QCIF. It provides some information about the different tests for comparison of multiple groups and how to carry out those tests in R.

This online resource provides information on when to use parametric and non-parametric tests (including a video). It also includes a table that shows which type of parametric / non-parametric test to use for different sample types (e.g. one sample, two dependent samples, ...).

This online resource provides information about ANOVA, including the different types of ANOVA (one-factor ANOVA, two-factors ANOVA, one-factor ANOVA with repeated measurements, and two-factors ANOVA with repeated measurements). It also contains videos and examples to aid in understanding.

This online source provides information about the Friedman test, which is the non-parametric alternative to the ANOVA with repeated measures. The site also provides examples and a video explaining the Friedman test.

This online resource provides information on the Kruskal-Wallis test, which is the non-parametric alternative to the one-way ANOVA. The site also contains examples and a video explaining the Kruskal-Wallis test.

This online resource provides information about the one-way ANOVA, with a link to ANCOVA (analysis of covariance) if needed. The site details the assumptions for the one-way ANOVA and explains how to carry out the test in SPSS.

This online resource provides information about the two-way ANOVA, the assumptions for the test as well as how to carry out the test in SPSS. The site also contains a link to a three-way ANOVA and a two-way ANCOVA.

This online resource provides information about the ANOVA with repeated measures, the assumptions for the test as well as how to carry out the test in SPSS. This site also contains a link to the two-way repeated measures ANOVA.

This online resource provides information about the Friedman test, which is the non-parametric alternative to the one-way ANOVA with repeated measures. The site includes the assumptions for the test and how to carry out the test in SPSS.

This online resource provides information about the Kruskal-Wallis H test, which is the non-parametric alternative to the one-way ANOVA test. This site outlines the assumptions for the test and shows how the test is carried out in SPSS.

This online resource provides information about the one-way MANOVA but also has links to the two-way MANOVA, one-way MANCOVA and the one-way repeated measures MANOVA. This site outlines the assumptions for using a one-way MANOVA and shows how this test is carried out in SPSS.

This is a chapter from an online book titled "Process of science companion: data analysis, statistics and experimental design" by Michelle Harris, Rick Nordheim & Janet Batzli. It provides information on the one-way and two-way ANOVA along with some examples.

This online resource provides tutorials about ANOVA.

This online source is from a "Statistical comparisons using R" workshop provided by QCIF. It provides some information about categorical data and R scripts to visualise categorical data, as well as how to carry out statistical tests for categorical variables in R.

This online resource provides information about the chi-square test, including through a video and examples.

This online source provides general information about the chi-square test, including the assumptions for the test and how to carry out the test in SPSS.

This online resource provides information and videos about chi-square tests.

This is a chapter from an online book titled "Process of science companion: data analysis, statistics and experimental design" by Michelle Harris, Rick Nordheim & Janet Batzli. It provides information on the chi-square test, including examples.

This online resource provides information about the McNemar test, describes the assumptions that need to be met for this test and explains how the test is carried out in SPSS.

This is an article titled "Statistical notes for clinical researchers: Chi-squared test and Fisher's exact test" by Kim HY. It provides a short explanation about both, chi-squared test and Fisher's exact test with an example for each test.

This online resource provides information about Fisher's exact test, using an example. This site also provides some links to how this test can be performed in R, excel, stata, SPSS and python.

This online resource provides information about regression analysis.

This online source provides information about regression analysis and when to use it, including videos and examples. It also includes information about linear and logistic (aka binomial) regression.

This online resource provides information about linear regression as well as a link to multiple regression (two or more independent variables). This page explains the assumptions for linear regression and shows how to carry out the test in SPSS.

This online resource provides information about simple and multiple regression.

This online resource provides information about binomial logistic regression, the assumptions and how to carry out the test in SPSS.

This online tutorial by Violet A. Brown provides an introduction to mixed-effects modeling and how to implement it in R. Some basic statistics knowledge is assumed.

This online resource provides an introduction to mixed effects models, starting with some information about when you might want to use a mixed effects model, how the data is prepared and how the model is tested in R.

This online resource offers several videos about mixed effects models, such as: "What is a mixed effects model?", "Repeated measures done the wrong way", "Repeated measures using mixed effects I", "Repeated measures using mixed effects II", "Split-plot using mixed effects", and "Using anova() to compare models", focusing on using the R function lme().

This online resource provides information about mixed models with some worked examples in R.

This resource is a chapter from the online book titled "Experimental design and analysis" by Howard J. Seltman and provides an overview of mixed models using examples. It also contains information on how to set up a model in SPSS and how to interpret the results.

This article titled "Beyond t-test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research" by Yu et al. (2022) provides information on linear and generalised mixed-effects models and when and how to use them.

This article titled "A brief introduction to mixed effects modelling and multi-model inference in ecology" by Harrison et al. (2018) provides an overview of linear mixed effects models and addresses model selection.

This is a chapter from an online book titled "Process of science companion: data analysis, statistics and experimental design" by Michelle Harris, Rick Nordheim & Janet Batzli. It provides information on design considerations and the independent two-sample and paired two-sample t-tests along with examples.

This online source is from a "Statistical comparisons using R" workshop provided by QCIF. It provides information about how to choose the relevant test for the comparison of two sample groups and how to carry out those tests in R.

This online resource provides information about the different t-tests, their assumptions, purpose and how to calculate them. The site also provides a video that explains t-tests.

This online resource provides information on when to use parametric and non-parametric tests (including a video). It also includes a table that shows which type of parametric / non-parametric test to use for different sample types (e.g. one sample, two dependent samples, ...).

This online resource provides information about the non-parametric Mann-Whitney U test along with examples and a video.

This online resource provides information about the non-parametric Wilcoxon test, along with examples and a video.

This online resource provides information about the independent t-test, including a link to the non-parametric Mann-Whitney U test.

This online resource provides information about the dependent t-test (also called paired-sample t-test), the assumptions and how to carry out the test in SPSS. This page also contains a link to the one-sample t-test.

This online resource provides information about the non-parametric Wilcoxon signed-rank test, the assumptions and how to carry out the test in SPSS.

This resource is a chapter from the online "Data Science Textbook" by TIBCO Software Inc., and provides drop-down menus for the following: t-test for independent samples, t-test for dependent samples and t-test for single means.

This online book, titled "Bayesian data analysis", by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari & Donald B. Rubin covers a wide range of topics from the fundamentals of Bayesian inference to regression models.

This free online course consists of three sections and provides information on estimating probability, Bayes' theorem, and Bayesian inference.

This is a chapter from the online book titled "Learning statistics with R: a tutorial for psychology students and other beginners. (Version 0.6.1)" by Danielle Navarro. This chapter provides information about Bayesian statistics and how Bayesian versions of chi-square tests, t-tests, regression and ANOVA can be carried out.

This online resource is a chapter from the IBM SPSS statistics 29 documentation and provides information on a range of topics, e.g. Bayesian one sample inference (normal, binomial and Poisson), Bayesian inference about Pearson correlation, Bayesian one-way ANOVA and more.