RMINR at University of Plymouth, School of Psychology
Stage 1
PSYC411 Learning (Semester 1, first four weeks)
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Introduction to RStudio. A basic introduction to the software.
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Exploring data. Means, medians, and histograms.
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More on tibbles. Deeper explanation of ‘tibbles’ in R.
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Means and medians. Some slides on the difference between a mean and a median.
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Group differences. Means and standard deviations, by group. Filtering data. Effect size.
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Evidence. Introduction to p values. Traditional between-subjects t-test. Bayesian between-subjects t-test.
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More on t-tests. Further information on traditional t-tests, and confidence intervals.
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More on Bayes Factors. A more detailed discussion of Bayes Factors.
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Analyzing your project data. Analysing your own data.
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Entering data by hand. Entering data into a spreadsheet. Saving data into your RStudio project.
PSYC412 Psychological Science (Semester 1, from 5th week)
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PsycEL exercise “Navigation” uses the spatial navigation worksheet. More on bar graphs.
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PsycEL exercise “Recognising faces” uses the face recognition worksheet. Means, filtering data, and a bar graph.
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PsycEL exercise “Memory from Life” uses the autobiographical memory worksheet. Entering data by hand, histograms.
PSYC413 Debates in Psychology (Semester 1, from 5th week)
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PsycEl exercise “Visual illusions 1” uses the visual illusions worksheet. Filtering data, means, violin plot, Bayesian t-test.
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PsycEL exercise “Automatic imitation” uses the response compatibility worksheet. Means, filtering data, standard deviations, and density plots.
PSYC414 Relationships (Semester 2, first four weeks)
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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.
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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.
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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.
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Sample characteristics. How to calculate summary information about your sample, such as number of participants or gender balance, from your data file.
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Making reports with R. How to insert an RStudio graph into your wordprocessor document (e.g. Word). Links to RMarkdown as an alternative.
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PsycEL exercise “Recognition” Facial attractiveness. Means, standard deviations, inter-quartile range, and density plots.
PSYC415 Topics in Psychology (Semester 2, from 5th week)
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PsycEL exercise “Eye Witness Memory” uses the police lineup worksheet. Contingency table, mosaic plot, Bayesian contingency test, means, density plot, Bayesian t-test
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PsycEL exercise “Risk taking” uses the risk taking worksheet. Means, combining data frames, filtering data, and density plots.
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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)
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PsycEL exercise “Animal Welfare” uses the animal welfare worksheet. Percentage agreement, Cohen’s kappa, contingency tables, bar charts.
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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)
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Revision: A quick recap of key information covered in earlier courses.
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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.
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Within-subject differences: Data preprocessing (pivoting and mutating). One-factor within-subject Bayesian ANOVA. Pairwise comparisons, multiple comparisons.
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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
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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.
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Preprocessing
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Data preprocessing for experiments: De-duplicating data, excluding participants, log transform.
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Data preprocessing for scales: Handling missing data, calculating scale scores, tidying survey data.
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Descriptive statistics
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Better tables: correlation matrix, custom table of descriptive statistics.
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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.
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Analysing scales: Cronbach’s alpha.
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Bayesian inferential statistics
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One-sample Bayesian t-test: Comparing a single-group sample of data against a population mean.
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More on Bayesian ANOVA: More on two-factor Bayesian ANOVA.
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More on regression: Multiple regression with more than two predictors, hierarchical regression, evidence for individual predictors.
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Traditional inferential statistics
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Traditional ANOVA: p-value based, approach to ANOVA.
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Traditional non-parametric tests: Mann-Whitney U, Kruskal-Wallis H.
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