Research Methods and Statistics in Psychology
Course materials for PS203 and PS303. Page numbers refer to the textbooks specific to PS203 and PS303. An interactive R console, powered by webR, is available below to test any provided code.
Only USP students can access the lectures with their student ID.
PS203: Research Methods in Psychology
- How do we acquire knowledge?
- Differentiating science from pseudoscience
- Science aims to describe, predict and ultimately explain
- The merits of common sense?
- Intuition & confirmation bias
- Varieties of Psychologists
- What are the steps involved in the scientific method?
- How do you identify a suitable research topic?
- Literature search strategies
- Experimental and non-experimental approaches
- Defining Type-1 and Type-2 errors
- Presenting your results in APA format
- Ethical considerations during research design
- Weighing risks and benefits
- Treatment of participant groups
- Minimizing deception and discomfort
- Ethics in research practice
- Psychological constructs
- Assessment tools
- Variable categories
- Reliability and validity
- Four steps for identifying a research topic
- Cause-and-effect relations
- Independent and dependent variables
- More on validity
- Sampling strategies
- Controlling for confounds
- Correlational approaches
- Cross-sectional and longitudinal designs
- Internal or external validity?
- Qualitative or quantitative approaches
- Observational methods
- Item-order effects
- Sampling context
- Open-ended and closed-ended questions
- BRUSO model of questionnaire construction
- Probabilistic & non-probabilistic sampling strategies
- APA styling
- Scoring surveys
- Normal and non-normal distributions
- Descriptives
- Point and range estimates
- Difference scores
- Hypotheses in research versus statistics
- Null Hypothesis Significance Testing (NHST)
- Testing assumptions
- Statistical versus practical significance
- Common frequentist tests
- Types of correlations
- Calculating a correlation coefficient
- NHST and correlational analyses
- Single-sample tests
- Independent two-sample tests
- Paired/dependent two-sample tests
- One-sided versus two-sided hypotheses
- Running a 1-way ANOVA
- Preparing a data frame in R
- Post-hoc contrasts and effect sizes
- Boxplots
- Installing packages
- Testing assumptions
PS303: Statistical Methods in Psychology
- Course expectations
- Installing R and R-studio
- Basic arithmetic operations
- Central tendency statistics
- Lecture Slides
- Vectors and data frames
- Factorial and numerical variables
- Value indexing
- Central tendency point and range estimates
- Lecture Slides
- Frequentist definitions of probability
- Binomial data
- Normal distributions and the Central Limit Theorem
- Standard error and Confidence Intervals
- Lecture Slides
- Research and statistical hypotheses
- Null and alternative hypotheses
- Type-1 and Type-2 errors
- Interpreting the p-value
- Reporting your results in APA format
- Lecture Slides
- Analyzing k-categories using chi-square tests
- Assumption tests and alternatives
- and Cramer's V
- Lecture Slides
- Testing materials and resources for the research report are available on Moodle.
- Lecture Slides
- One-sample and two-sample tests
- Welch's method and bias-corrected effects
- Other nonparametric alternatives
- Reporting results alongside outcomes of assumption checks
- Lecture Slides
- ANOVAs for analyzing k>2 groups
- Assumptions for ANOVA
- Relating the F-ratio to the Sum of Squares
- Post-hoc contrasts with Holm corrections
- Lecture Slides
- Prediction versus correlation
- Multiple predictors and regression lines
- Understanding residuals
- Hypothesis tests between regression models
- Assumptions
- Regression diagnostics: Strategies for selecting the 'best' model
- Lecture Slides [Part 1]
- Lecture Slides [Part 2]
- Considering multiple predictors
- Interactions and main effects
- Alternate effect sizes and range estimates
- Assumptions for parametric designs
- Contrasting models using F-ratios
- Lecture Slides [Part 1]
- Lecture Slides [Part 2]
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