Statistical Foundations for Psychological Research



Curriculum: Statistical Foundations for Psychological Research

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Chapter 1: Introduction — Why Study Statistics?

Understanding the Role of Statistics in Psychology
	•	What is statistics, and why is it essential for psychology?
	•	The importance of statistical literacy in interpreting human behavior and mental processes
	•	Learning to “become free through statistics” — not just applying formulas, but critically thinking about data
	•	Advice for learners: combining theory, self-study, and practical exercises

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Chapter 2: Psychology and Statistics — Bridging Concepts

From Theories of Mind to Testable Hypotheses
	•	Explaining psychological phenomena through causal vs. correlational relationships
	•	Empirical research and the role of validity in establishing psychological explanations
	•	Understanding populations vs. samples in generalizing findings
	•	Sampling and the generalizability of psychological studies
	•	Data collection methods: experiments, questionnaires, and observations
	•	The research process: from conceptual framework to statistical testing

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Chapter 3: Organizing and Summarizing Data

Quantifying the Mind — From Raw Scores to Meaning
	•	Types of measurement scales: nominal, ordinal, interval, ratio
	•	Descriptive statistics for quantitative data: mean, variance, standard deviation, z-scores
	•	Descriptive statistics for categorical data: proportions, contingency tables, association coefficients
	•	Correlation analysis: covariance and Pearson’s r in psychological trait analysis

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Chapter 4: Research Methods in Psychology

Designing Studies to Measure Psychological Constructs
	•	Experimental methods: identifying factors, variables, and control procedures
	•	Experimental designs: between-subjects, within-subjects, and mixed designs
	•	Questionnaire methodology: scale development, construct validity, and reliability
	•	Item analysis and Cronbach’s alpha coefficient for scale precision

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Chapter 5: Probability and Probability Distributions

Modeling Uncertainty in Human Behavior
	•	Definitions of probability: classical, frequentist, subjective
	•	Random variables in psychology: modeling responses as probabilistic outcomes
	•	Examples of probability distributions:
	•	Discrete: Bernoulli, Binomial (e.g., binary decision tasks)
	•	Continuous: Normal distribution (e.g., IQ, reaction time)
	•	Foundations of statistical inference: point estimation and confidence intervals

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Chapter 6: Fundamentals of Hypothesis Testing

Making Inferences About Psychological Effects
	•	Null and alternative hypotheses: distinguishing real effects from chance
	•	Type I and Type II errors, statistical power, and effect sizes
	•	Z-tests and t-tests (independent samples, paired samples, unequal variances)
	•	The entire hypothesis testing workflow: framing questions and interpreting results

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Chapter 7: Analytical Methods and Statistical Models

Modeling Complex Psychological Phenomena
	•	Multiple regression: partial regression coefficients, R², testing overall model fit
	•	ANOVA:
	•	One-way ANOVA (between/within subjects)
	•	Two-way ANOVA and interaction effects
	•	ANCOVA: adjusting for covariates in behavioral data
	•	PCA (Principal Component Analysis): reducing psychological dimensions
	•	Factor analysis: latent variables and psychological construct validation
	•	SEM (Structural Equation Modeling): modeling causal structures among observed and latent variables

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Chapter 8: Analyzing Categorical Data

Understanding Discrete Psychological Variables
	•	Chi-square tests:
	•	Goodness-of-fit (how well observed frequencies match expectations)
	•	Test of independence (relationship between two categorical variables)
	•	Contingency table analysis and introduction to log-linear models
	•	Foundations of multivariate categorical analysis in psychological contexts

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