Statistics Resources for Psychology

Resources for every level

  • Research methodology

    • Research methodology
    • Descriptive statistics
    • One and two-tailed tests
    • Type I and II errors
    • Standard error
    • Hypothesis testing
    • Independent and paired-samples t-tests
    Workbook 1a

    Introduction to inferential statistics

    • Nonparametric statistics
    • Chi-square
    • Assigning ranks
    • Wilcoxon t-test
    • Mann-Whitney U test
    • Correlation
    • Coefficient of determination
    • Linear regression
    • Validity and reliability
    Workbook 1b

  • Introduction to Excel and JASP: t-tests

    • Sampling theory
    • Computing descriptive statistics using Excel and JASP
    • Running, interpreting and reporting the three varieties of t-test in Excel and JASP
    Practical 2 worksheet Practical 2 data

    One-way ANOVA

    • Between and within-subjects one-way ANOVA
    • Conducting one-way ANOVAs on Excel and Jasp
    • Conducting non-parametric alternatives to one-way ANOVA in JASP
    • Pairwise comparisons, planned comparisons, and post hoc tests
    • F statistics and F distribution
    Practical 3 worksheet Practical 3 data

    Factorial ANOVA

    • Conduct and interpret between-subjects factorial ANOVA on Excel and JASP
    • Conduct and interpret within-subjects factorial ANOVA on Excel and JASP
    • Revise conducting and interpreting t-tests and one-way ANOVAs in JASP
    Practical 4 worksheet Practical 4a data Practical 4b data

    Mixed factorial ANOVA

    • Conduct a mixed factorial ANOVA in JASP
    • Main effects and interactions
    Practical 5 worksheet Practical 5 data

  • Qualitative research methodology

    • Initial Coding: Techniques for descriptive, linguistic, and conceptual coding.
    • Thematic Analysis: Identifying and developing emergent themes.
    • Content Analysis: Systematic categorization and interpretation of data.
    • Practical Application: Applying qualitative methods to real-world studies.
    Worksheet 6 Worksheet 7 (with example analysis)

    Simple linear (bivariate) regression

    • Understanding the regression equation and variable relationships.
    • Interpreting slope coefficients in context.
    • Evaluating residuals and model accuracy.
    • Analysing regression outputs and identifying outliers using SPSS.
    Practical 8 Practical 8 data

    Multiple Regression

    • Conduct simple and multiple regression analyses in SPSS.
    • Interpret slope coefficients and intercept values.
    • Identify and address outliers in regression analysis.
    • Perform hierarchical multiple regression.
    • Understand regression assumptions and predictor variance.
    Practical 9 Practical 9 data

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