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 between-subjects t-test. Bayesian between-subjects t-test.

  • More on t-tests. Further information on traditional t-tests, 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 t-test.

• PsycEL exercise “Automatic imitation” uses the response compatibility worksheet. Means, filtering data, standard deviations, and density plots.

PSYC414 Relationships (Semester 2, first four weeks)

• Inter-rater 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 chi-square test. Bayesian test.

  • More on relationships. Extension material on chi-square 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 chi-square test.

• Relationships, part 2. Density plots. Scatter plots. Correlation co-efficient. Bayesian and traditional tests.

  • More on relationships, part 2. Spearman’s correlation, Kendall’s tau, one-tailed 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, inter-quartile 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 t-test

• 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 t-test.

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 t-test, traditional t-test.

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 lab-based (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.

• Within-subject differences: Data preprocessing (pivoting and mutating). One-factor within-subject 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: Two-factor 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: De-duplicating 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: publication-quality graphs showing both central tendency and variability (or uncertainty) of your data, including: line plots, distribution plots (density, violion, half-violin), box plots, and confidence intervals. Suggested plots for one- and two-factor designs, within-subject, between-subject, 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

  • One-sample Bayesian t-test: Comparing a single-group sample of data against a population mean.

  • More on Bayesian ANOVA: More on two-factor Bayesian ANOVA.

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

• Traditional inferential statistics

  • Traditional ANOVA: p-value based, approach to ANOVA.

  • Traditional non-parametric tests: Mann-Whitney U, Kruskal-Wallis H.