RMINR at University of Plymouth, School of Psychology
Stage 1
PSYC411 Learning (Semester 1, first four weeks)

Introduction to RStudio. A basic introduction to the software.

Exploring data. Means, medians, and histograms.

More on tibbles. Deeper explanation of ‘tibbles’ in R.

Means and medians. Some slides on the difference between a mean and a median.


Group differences. Means and standard deviations, by group. Filtering data. Effect size.

Evidence. Introduction to p values. Traditional betweensubjects ttest. Bayesian betweensubjects ttest.

More on ttests. Further information on traditional ttests, and confidence intervals.

More on Bayes Factors. A more detailed discussion of Bayes Factors.


Analyzing your project data. Analysing your own data.

Entering data by hand. Entering data into a spreadsheet. Saving data into your RStudio project.
PSYC412 Psychological Science (Semester 1, from 5th week)

PsycEL exercise “Navigation” uses the spatial navigation worksheet. More on bar graphs.

PsycEL exercise “Recognising faces” uses the face recognition worksheet. Means, filtering data, and a bar graph.

PsycEL exercise “Memory from Life” uses the autobiographical memory worksheet. Entering data by hand, histograms.
PSYC413 Debates in Psychology (Semester 1, from 5th week)

PsycEl exercise “Visual illusions 1” uses the visual illusions worksheet. Filtering data, means, violin plot, Bayesian ttest.

PsycEL exercise “Automatic imitation” uses the response compatibility worksheet. Means, filtering data, standard deviations, and density plots.
PSYC414 Relationships (Semester 2, first four weeks)

Interrater reliability. Percentage agreement. Cohen’s kappa.
 More on Cohen’s kappa. A discussion of some potentially surprising outputs from a Cohen’s kaapa calculation.

Relationships. Frequency and contingency tables. Mosaic plots. Traditional chisquare test. Bayesian test.
 More on relationships. Extension material on chisquare calculations, including issues surrounding ordered variables (e.g. age), the interpretation of large contingency tables, and a further explanation of the output of the Bayesian chisquare test.

Relationships, part 2. Density plots. Scatter plots. Correlation coefficient. Bayesian and traditional tests.
 More on relationships, part 2. Spearman’s correlation, Kendall’s tau, onetailed tests, confidence intervals, plus a deeper look at the output of the Bayesian correlation test.

Sample characteristics. How to calculate summary information about your sample, such as number of participants or gender balance, from your data file.

Making reports with R. How to insert an RStudio graph into your wordprocessor document (e.g. Word). Links to RMarkdown as an alternative.

PsycEL exercise “Recognition” Facial attractiveness. Means, standard deviations, interquartile range, and density plots.
PSYC415 Topics in Psychology (Semester 2, from 5th week)

PsycEL exercise “Eye Witness Memory” uses the police lineup worksheet. Contingency table, mosaic plot, Bayesian contingency test, means, density plot, Bayesian ttest

PsycEL exercise “Risk taking” uses the risk taking worksheet. Means, combining data frames, filtering data, and density plots.

A former PsycEL exercise used the creativity and the environment. Preprocessing, means, density plots, effect size, Bayesian ttest.
PSYC416 Connecting Psychology (Semester 2, from 5th week)

PsycEL exercise “Animal Welfare” uses the animal welfare worksheet. Percentage agreement, Cohen’s kappa, contingency tables, bar charts.

A former PsycEL exercise “Political psychology” used the political psychology worksheet. Means, filtering data, summarising data, density plots, effect size, Bayesian ttest, traditional ttest.
Stage 2
PSYC519 Research Methods in Practice 1 (Semester 1)
 Research Methods in Practice: Data handling, fitting lines  scatterplot with best fit line , converting Likert scales from text to numbers, reverse scoring scale items, multiple regression.
PSYC520 Research Methods in Practice 2 (Semester 2)

Revision: A quick recap of key information covered in earlier courses.

Statistical power: How to calculate the statistical power of experiments.
 More on statistical power: A deeper discussion on statistical power, including: (1) relation between statistical power and the replication crisis, (2) better standards for statistical power, (3) how to improve effect size, (4) estimating effect size from previous work.
 Data preprocessing: Getting data from labbased (OpenSesame)
experiments into a format closer to something you can actually analyse, in
five steps: loading, selecting, filtering, summarising, and combining. Also covers combining data frames, renaming columns, and using loops.
 More on preprocessing: A slightly more advanced worksheet, covering adding columns to a data frame, and subsetting strings.

Withinsubject differences: Data preprocessing (pivoting and mutating). Onefactor withinsubject Bayesian ANOVA. Pairwise comparisons, multiple comparisons.

Understanding interactions: Learn what an interaction is, and learn how to do line plots at the same time.
 Factorial differences: Twofactor Bayesian ANOVA (one within, one between), plus advice on: pairwise comparisons, better graphs, reporting Bayesian ANOVA, and ordinal (i.e. ordered) independent variables.
Stage 4
PSYC605 Research Project

Data management
 Data management: Anonymity and privacy, good and bad file types, creating and sharing a private github repository, adding a repository to Rstudio, adding files to github using Rstudio, modifying and updating files, git log as your logbook, branching, recovering an earlier version of a file.

Preprocessing

Data preprocessing for experiments: Deduplicating data, excluding participants, log transform.

Data preprocessing for scales: Handling missing data, calculating scale scores, tidying survey data.


Descriptive statistics

Better tables: correlation matrix, custom table of descriptive statistics.

Better graphs: publicationquality graphs showing both central tendency and variability (or uncertainty) of your data, including: line plots, distribution plots (density, violion, halfviolin), box plots, and confidence intervals. Suggested plots for one and twofactor designs, withinsubject, betweensubject, or mixed designs, and with ordered and unordered variables. Discussion of common bad plots to avoid (bar plots; confusions over confidence intervals). Pairs plot for correlational designs.

Analysing scales: Cronbach’s alpha.


Bayesian inferential statistics

Onesample Bayesian ttest: Comparing a singlegroup sample of data against a population mean.

More on Bayesian ANOVA: More on twofactor Bayesian ANOVA.

More on regression: Multiple regression with more than two predictors, hierarchical regression, evidence for individual predictors.


Traditional inferential statistics

Traditional ANOVA: pvalue based, approach to ANOVA.

Traditional nonparametric tests: MannWhitney U, KruskalWallis H.
