Research Psychologist — Competency Roadmap
Work towards being a research psychologist: the theoretical foundations across the major areas of psychology, rigorous research methods and statistics, and the practice of designing, running and publishing studies.
This roadmap provides a comprehensive curriculum spanning theoretical psychological sciences, quantitative methodology in R, open science experimental design, and the research publication lifecycle. Structured theory-first to establish core cognitive and methodological mental models before conducting empirical pipelines, it is designed for steady progression at 10 hours per week over an extended academic trajectory. Completing this plan leaves you with a complete preregistered experimental project, an APA-formatted empirical manuscript, an open-science reproducible R codebase, and an academic research portfolio ready for PhD or research assistant applications.
By the end: You will be able to formulate theoretically grounded psychological hypotheses, design robust experiments using R and PsychoPy, execute reproducible statistical models including mixed-effects regressions, and produce peer-review-standard empirical manuscripts adhering to open-science standards.
This is the map — make this roadmap yours
It shows what this journey generally looks like. Tell Kaidoro your version of the goal and it builds the plan around where you are actually starting, what to do first, the hours you really have, and what you have already finished.
Epistemology & the Philosophy of Psychological Science
Establish the foundational principles of scientific inquiry in psychology, the philosophical basis of empiricism, and the historical drivers of the replication crisis. This conceptual grounding is essential before evaluating theoretical literature or designing empirical studies.
- Study falsification and scientific inference in psychology~6hLearn1 resource
Research psychology relies on rigorous deductive inference rather than simple pattern-matching or intuitive assertions.
You'll learn
- Falsificationism — Karl Popper's criterion requiring scientific hypotheses to be capable of being proven false
- Hypothetico-deductive method — The standard scientific cycle of hypothesis formulation, empirical testing, and refinement
- Construct validity — The degree to which a test measures what it claims to measure
Read and critique foundational literature on scientific demarcation, hypothetico-deductive reasoning, and the limits of induction in behavioral science. Contrast Karl Popper's falsificationism with Thomas Kuhn's paradigm shifts to understand how psychological paradigms evolve.
Done when: you have written a 1,000-word critical reflection comparing verificationist versus falsificationist approaches to psychological construct validation.
How to work through it
- Read key readings on Popperian falsification and Kuhnian paradigm shifts in behavioral sciences
- Identify three historically unfalsifiable psychological theories and outline why they failed scientific scrutiny
- Draft a comparative summary defining how modern psychological science formulates testable, falsifiable claims
- Analyze the replication crisis and modern open science reforms~8hLearn1 resource
Modern research psychology demands an acute awareness of methodological vulnerabilities and questionable research practices.
You'll learn
- Replication crisis — Methodological crisis where many published findings failed independent replication
- HARKing — Hypothesizing After Results are Known, a questionable research practice
- Publication bias — Systematic skew toward publishing positive or statistically significant results
Examine the systemic causes of the replication crisis in psychology, including p-hacking, publication bias, and low statistical power. Review key landmark papers from the Open Science Collaboration to understand how the field is systematically reforming research standards.
Done when: you have produced an annotated bibliography of four core replication studies outlining their methodology and key findings.
How to work through it
- Read Open Science Collaboration (2015) 'Estimating the reproducibility of psychological science'
- Deconstruct the mechanisms of p-hacking, HARKing, and the file-drawer effect
- Document the structural reforms implemented across major journals (e.g., Registered Reports, data sharing)
- Audit a published paper for questionable research practices~6hPractice
Critiquing existing literature builds the discernment necessary to prevent methodological errors in your own work.
You'll learn
- Researcher degrees of freedom — Unreported choices during analysis that increase false-positive rates
- p-curve analysis — Statistical inspection of published p-values to assess evidential value
Select an older, classic psychology paper that reported counter-intuitive effects and conduct a methodological autopsy. Identify sample size adequacy, flexibility in data collection rules, and potential post-hoc narrative framing.
Done when: you have completed a written 3-page methodological evaluation report detailing potential design vulnerabilities.
How to work through it
- Select an empirical psychology paper published prior to 2012 featuring high claims with small samples
- Evaluate sample size, effect size claims, reported p-values, and potential researcher degrees of freedom
- Compile your critique into a structured methodological report evaluating replication vulnerability
Core Psychological Foundations — Cognitive & Biological Systems
Develop a theoretical grounding in perception, attention, memory, and neurobiological architecture. This phase provides the mechanistic models of information processing and neural systems that underpin experimental psychology.
- Map human cognitive architecture and memory models~10hLearn
Cognitive psychology provides the foundational behavioral paradigms and terminology used across all experimental research.
You'll learn
- Working memory — Multi-component cognitive system holding information temporarily for manipulation
- Dual-process theory — Conceptual framework separating intuitive (System 1) and deliberative (System 2) processes
- Stroop effect — Demonstration of cognitive interference where reaction times slow during conflicting stimuli
Examine cognitive psychology models detailing sensory perception, working memory, long-term memory encoding and retrieval, and executive function. Compare Baddeley's working memory model against contemporary state-based neurocognitive models.
Done when: you have produced a comparative synthesis chart detailing four major cognitive architecture models and their standard behavioral paradigms.
How to work through it
- Study working memory models, levels of processing, and dual-process cognitive theories
- Identify the standard laboratory tasks used to isolate each cognitive construct (e.g., Stroop, N-back, Sternberg)
- Draft a comprehensive technical summary linking each construct to its empirical measurement tool
- Study functional neuroanatomy and physiological measurement techniques~8hLearn
Biological psychology links cognitive and behavioral observations to physical neural substrates.
You'll learn
- Event-Related Potentials (ERP) — Measured electrophysiological responses time-locked to sensory or cognitive stimuli
- BOLD signal — Blood Oxygenation Level-Dependent signal measured in functional MRI
- Temporal resolution — Precision of a measurement technique with respect to time
Learn the functional organization of the central nervous system, cellular neurobiology, and the physiological basis of psychological measurement. Understand the spatial and temporal trade-offs between EEG, fMRI, TMS, and autonomic measures (e.g., galvanic skin response, pupillometry).
Done when: you have written a guide categorizing 5 neuroimaging/physiological tools by spatial resolution, temporal resolution, and experimental limitations.
How to work through it
- Review functional neuroanatomy: cortical lobes, subcortical structures, and neurotransmitter pathways
- Contrast hemodynamic (fMRI, fNIRS) versus electrophysiological (EEG, ERP) measurement methodologies
- Write a comparative methodological reference guide detailing the trade-offs of each modality
- Synthesize a cognitive-neurobiological case study~6hApply
Integrating biological and cognitive evidence demonstrates your ability to reason across multiple levels of psychological analysis.
You'll learn
- Double dissociation — Proof that two related cognitive functions operate independently via separate neural systems
- Cognitive neuropsychology — Subfield investigating cognitive function via patterns of brain injury
Analyze a classic neuropsychological case study (e.g., patient H.M. or Phineas Gage) alongside modern experimental literature to demonstrate how lesion studies informed cognitive models of memory or executive function.
Done when: you have written a 1,500-word case formulation dissecting double dissociations and what they reveal about cognitive modularity.
How to work through it
- Select a seminal neuropsychological case study with profound cognitive deficits
- Identify modern neuroimaging and behavioral replications assessing the affected cognitive systems
- Write an essay detailing the logic of double dissociation and its contribution to functional localization
Core Psychological Foundations — Social, Developmental & Individual Differences
Explore the social, developmental, and personality domains of psychological theory. This phase can be studied concurrently with quantitative foundations, ensuring comprehensive theoretical breadth.
- Explore social cognition, heuristics, and group dynamics~8hLearn
Social psychology reveals how interpersonal and cultural contexts modify individual cognitive processes.
You'll learn
- Social identity theory — Theory explaining how group membership influences self-concept and intergroup behavior
- Cognitive dissonance — Mental discomfort experienced by holding conflicting beliefs, values, or behaviors
- Attribution theory — Framework describing how individuals explain the causes of behavior and events
Study theories of attribution, social identity, implicit bias, cognitive dissonance, and social influence. Understand how social psychologists operationalize contextual and situational variables in laboratory and field settings.
Done when: you have constructed a theoretical matrix comparing 5 major social psychology theories with their operational definitions and boundary conditions.
How to work through it
- Read foundational and contemporary papers on social identity theory, cognitive dissonance, and attribution
- Map standard experimental paradigms including priming, public goods games, and conformity designs
- Document the known boundary conditions and moderation effects for each theory
- Review developmental trajectories across the lifespan~8hLearn
Developmental psychology provides the temporal lens necessary to understand human behavioral change across time.
You'll learn
- Cohort effect — Variations in characteristics of an area of study over time among individuals who are defined by some shared life experience
- Attachment theory — Model of human development focusing on the psychological dynamics of interpersonal relationships
- Zone of proximal development — The difference between what a learner can do without help and with help
Examine major developmental models including Piagetian constructivism, Vygotsky's sociocultural theory, attachment theory, and life-span developmental stages. Evaluate longitudinal versus cross-sectional research designs in capturing developmental change.
Done when: you have written a comparative critique of cross-sectional versus longitudinal designs in studying cognitive aging.
How to work through it
- Study stage and continuous models of cognitive, social, and emotional development across the lifespan
- Analyze cohort effects, selective attrition, and age-period-cohort confounds in developmental studies
- Draft a methodological comparison outlining how to control for age-period-cohort confounds
- Examine psychometrics and structural models of personality~8hLearn
Individual differences research relies on advanced measurement theory and latent construct validation.
You'll learn
- Five-Factor Model — The dominant taxonomy of personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism)
- Classical Test Theory — Framework positing that an observed score consists of true score plus error
- McDonald's omega — Modern estimate of internal consistency reliability superior to Cronbach's alpha
Investigate the Five-Factor Model (Big Five) and HEXACO framework of personality traits, along with psychometric theory (classical test theory, factor analysis, reliability, and validity). Analyze how latent constructs are modeled statistically.
Done when: you have written a 1,200-word analysis evaluating the psychometric properties of an open-access personality inventory.
How to work through it
- Study the lexical hypothesis and factor-analytic origins of the Five-Factor Model and HEXACO
- Understand psychometric indices: Cronbach's alpha, McDonald's omega, convergent/discriminant validity
- Evaluate a validated personality scale (e.g., BFI-2 or HEXACO-PI) on its psychometric reliability metrics
Research Methodology, Psychometrics & Experimental Design
Learn the mechanics of designing valid psychological experiments and correlational studies. This phase establishes the operational protocols required before writing analysis scripts in R.
- Design experimental and quasi-experimental paradigms~8hLearn
Flawed experimental designs cannot be repaired by statistical analysis after data collection is complete.
You'll learn
- Factorial design — Experimental design with two or more independent variables each tested at multiple levels
- Counterbalancing — Technique to control for order effects in repeated-measures designs
- Internal validity — The rigor with which a study can establish a trustworthy cause-and-effect relationship
Master experimental control, manipulation checks, randomization schemes, counterbalancing, and blinding. Distinguish between within-subject, between-subject, and factorial experimental architectures.
Done when: you have generated experimental architecture blueprints for a 2x2 between-subjects and a 2x2 within-subjects design with counterbalancing logic.
How to work through it
- Study internal vs. external validity threats (selection bias, history, maturation, demand characteristics)
- Define within-subject, between-subject, and mixed factorial designs with Latin-square counterbalancing
- Diagram structural design matrices showing trial sequences, independent variable levels, and dependent measures
- Write operational definitions and construct measurement protocols~6hBuild
Clear operationalization prevents construct drift and measurement unreliability.
You'll learn
- Operational definition — Statement describing the exact variables and procedures used to measure a construct
- Nomological network — Representation of the concepts of interest, their observable manifestations, and interrelationships
Translate abstract psychological theories into concrete, observable, and measurable behavioral indicators or psychometric inventories. Establish explicit operational definitions and measurement scaling.
Done when: you have created an operational measurement protocol for two abstract psychological constructs (e.g., 'academic burnout' and 'attentional focus').
How to work through it
- Select two theoretical constructs from cognitive or social psychology
- Define conceptual definitions and operational indicators (behavioral, self-report, physiological)
- Detail potential measurement errors, floor/ceiling effects, and scale validation metrics
Statistical Computing in R for Behavioral Data
Set up a modern, reproducible R environment for psychological data analysis. Master data cleaning, exploratory data analysis, and foundational inferential statistical testing using the Tidyverse ecosystem.
- Install R, RStudio, and master tidyverse data wrangling~10hBuild1 resource
R and R Markdown/Quarto are the open-science gold standards for fully reproducible psychological research workflows.
You'll learn
- Tidyverse — Opinionated collection of R packages designed for data science and reproducible manipulation
- R Markdown / Quarto — Authoring frameworks that combine executable R code with narrative prose
- Tidy data — Data structure where each variable is a column, each observation is a row, and each observational unit is a table
Install R and RStudio Desktop. Master data import, transformation, reshaping (long vs wide data formats), and visualization using dplyr, tidyr, and ggplot2 on real behavioral datasets.
Done when: you have written an R Markdown document that cleans an untidy raw survey dataset, produces descriptive summary tables, and exports publication-ready ggplot2 figures.
How to work through it
- Install R, RStudio, and tidyverse packages
- Learn dplyr verbs: mutate, select, filter, group_by, summarize
- Practice pivoting raw wide-format survey data into long format using tidyr::pivot_longer
- Build violin, boxplot, and scatterplot visualizations using ggplot2
- Execute univariate inferential tests and assumptions in R~8hPractice
Univariate testing forms the foundation for hypothesis verification across classical psychological experiments.
You'll learn
- Null Hypothesis Significance Testing (NHST) — Method of statistical inference assessing whether observations deviate from null expectations
- Cohen's d — Standardized difference between two means, expressing effect size in standard deviation units
- Homoscedasticity — Assumption that different samples have equal variance
Learn the theory and R implementation of Student's t-tests, paired t-tests, Mann-Whitney U tests, Chi-Square tests, and Pearson/Spearman correlations. Diagnose distributional assumptions (normality, homoscedasticity) and calculate standardized effect sizes (Cohen's d, correlation r).
Done when: you have executed a fully annotated R script running t-tests and correlations across three simulated experimental groups, including assumption checks and APA-style text outputs.
How to work through it
- Study null hypothesis significance testing (NHST), Type I and Type II errors, and p-value interpretations
- Run normality checks (Shapiro-Wilk test, Q-Q plots) and Levene's test for equality of variance in R
- Execute independent-samples and paired t-tests and extract exact test statistics, p-values, and Cohen's d effect sizes
- Generate standard APA-style summary statements from R statistical objects
Advanced Quantitative Modeling: GLM, Mixed Models & Power Analysis
Advance beyond basic t-tests to the General Linear Model (ANOVA, regression), Linear Mixed-Effects Models for repeated measures and hierarchical data, and statistical power analysis using simulation.
- Implement Multiple Regression and Factorial ANOVA in R~10hBuild
Factorial ANOVA and multiple regression accommodate complex, multi-factor experimental and observational paradigms.
You'll learn
- General Linear Model (GLM) — Comprehensive statistical model that encompasses multiple regression, ANOVA, and ANCOVA
- Partial eta-squared — Metric of effect size expressing the proportion of variance explained by a specific factor
- Interaction effect — Situation where the effect of one independent variable depends on the level of another
Master the General Linear Model framework. Implement factorial ANOVA, ANCOVA, and multiple linear regression with continuous and categorical predictors, interaction terms, and post-hoc contrasts.
Done when: you have analyzed a 2x3 factorial dataset in R, tested for interaction effects, conducted Tukey HSD post-hoc contrasts, and reported the results with partial eta-squared effect sizes.
How to work through it
- Learn the unifying mathematics linking regression, t-tests, and ANOVA under the General Linear Model
- Fit multiple regression models with main effects and interaction terms using lm() in R
- Perform factorial ANOVA and ANCOVA, examining main effects, simple effects, and interaction plots
- Conduct post-hoc testing with family-wise error rate corrections (Tukey, Bonferroni) via the emmeans package
- Fit Linear Mixed-Effects Models (LMM) for nested and repeated data~10hBuild
Linear Mixed Models are the standard in experimental cognitive and psychological research for handling nested and trial-by-trial data.
You'll learn
- Linear Mixed-Effects Models (LMM) — Statistical model containing both fixed and random effects, ideal for hierarchical/nested data
- Random intercept — Model term allowing baseline performance levels to vary across individual participants or items
- Random slope — Model term allowing the magnitude of experimental effects to vary across individuals
Understand why traditional repeated-measures ANOVA fails with missing data or trial-level cognitive observations. Learn to estimate random intercepts and random slopes using the lme4 and lmerTest packages in R.
Done when: you have fitted an LMM predicting reaction times from trial-level experimental data, modeling random intercepts for participants and items, and evaluated model fit.
How to work through it
- Understand the concepts of fixed effects versus random effects across participants and stimuli
- Fit linear mixed-effects models using lmer() in the lme4 package
- Compare random intercept vs random slope model specifications using Likelihood Ratio Tests (LRT)
- Extract estimated marginal means and interpret model output
- Conduct prospective power analysis and sample size determination~6hPractice
Prospective power calculation is an absolute prerequisite for ethical research design and preregistration acceptance.
You'll learn
- Statistical power — Probability of correctly rejecting a false null hypothesis (standard target: 0.80 or 0.90)
- Effect size estimation — Determining the smallest effect size of interest (SESOI) rather than assuming inflated published values
- Monte Carlo simulation power analysis — Estimating statistical power by simulating thousands of datasets under specified parameters
Learn how underpowered studies inflate false positives and effect size overestimations. Conduct prospective power calculations and simulation-based power analyses in R using the pwr and simr packages.
Done when: you have written a complete power calculation section justifying a target sample size for a planned 2x2 mixed experimental design for a minimum effect of Cohen's f = 0.20.
How to work through it
- Study the relationships between Alpha, Beta (Power = 1 - Beta), sample size N, and population effect size
- Calculate required sample sizes for t-tests and ANOVA using the pwr library in R
- Learn simulation-based power estimation for complex linear mixed models using the simr package
- Draft a standard power justification statement for a funding or preregistration submission
Research Ethics, Institutional Review Boards & Open Science Protocols
Master the ethical codes governing human behavioral research and the modern open-science pipelines required for preregistration, data archiving, and reproducibility.
- Study research ethics codes and write an IRB protocol~8hApply1 resource
No empirical study on human subjects can proceed without formal institutional ethical approval.
You'll learn
- Belmont Report — Landmark ethical guideline protecting human subjects in biomedical and behavioral research
- Informed consent — Process by which a participant voluntarily confirms willingness to participate after being informed
- Deception and debriefing — Ethical mandate to reveal any experimental concealment immediately following task completion
Review the Declaration of Helsinki, the Belmont Report, and APA Ethics Code Standard 8. Draft an Institutional Review Board (IRB) ethics application including informed consent documents, debriefing scripts, risk assessments, and vulnerable population protections.
Done when: you have completed a comprehensive mock IRB protocol with consent forms and debriefing sheets for a study involving mild cognitive deception.
How to work through it
- Examine Belmont Report principles: Respect for Persons, Beneficence, and Justice
- Draft an participant information sheet detailing risks, confidentiality, data storage, and voluntary withdrawal
- Create a debriefing protocol specifically addressing the ethical resolution of experimental deception
- Complete a mock IRB application addressing risk-benefit ratios and data privacy compliance
- Preregister a psychological study on the Open Science Framework (OSF)~8hBuild1 resource
Preregistration prevents researcher bias, p-hacking, and HARKing by fixing the analysis plan prior to observing data.
You'll learn
- Preregistration — Creating a time-stamped, unalterable research plan on a public registry before collecting data
- Registered Report — A journal publishing format where peer review occurs before data collection begins
- Data exclusion rule — Predefined criteria for removing anomalous or invalid observations without biasing results
Learn the theory and structure of formal study preregistration. Draft a complete preregistration detailing explicit directional hypotheses, sampling plans, stopping rules, exclusion criteria, and exact planned statistical models.
Done when: you have generated a complete, formatted OSF-standard preregistration document for an experimental study.
How to work through it
- Study the OSF Preregistration template and Registered Reports publication models
- Define primary and secondary directional hypotheses with explicit operational variables
- Write strict data exclusion protocols (e.g., reaction time cutoffs, attention check failures)
- Specify the exact R model syntax and contrast matrices that will test each primary hypothesis
Experimental Programming & Behavioral Task Construction
Learn to program computerized behavioral experiments and online surveys. This phase equips you to construct real-time psychophysical and cognitive tasks.
- Build a computerized cognitive task using PsychoPy or jsPsych~12hBuild1 resource
Experimental psychologists must be capable of building custom software to present controlled stimuli and record millisecond-accurate responses.
You'll learn
- PsychoPy — Open-source Python application and library for running neuroscientific and psychological experiments
- jsPsych — JavaScript library for running behavioral experiments in a web browser
- Inter-Trial Interval (ITI) — The pause between the end of one trial and the beginning of the next
Program a reaction-time experiment (e.g., Flanker task, Stroop task, or Lexical Decision Task) using PsychoPy (Python) or jsPsych (JavaScript/HTML). Implement stimulus timing, trial randomization, response logging, and feedback loops.
Done when: you have built and successfully executed a local reaction-time cognitive experiment that records participant keypresses and reaction times accurately to a CSV file.
How to work through it
- Install PsychoPy or set up a jsPsych template in a code editor
- Construct the trial loop: fixation cross (500ms), stimulus presentation, response capture window, and inter-trial interval
- Implement counterbalanced condition blocks and stimulus randomization
- Test run the script and inspect the output CSV data structure for timing accuracy
- Construct an online psychometric survey with quality control checks~6hBuild
Modern psychological data collection heavily utilizes online crowdsourced participant pools requiring strict quality screening.
You'll learn
- Instructional Manipulation Check (IMC) — Question designed to verify whether a participant is reading instructions carefully
- Prolific — Leading academic participant recruitment platform with validated demographic screening
- Branching logic — Survey programming that routes participants along different paths based on prior responses
Design an online survey experiment using platforms like Qualtrics or Gorilla. Build attention checks, instructional manipulation checks (IMCs), response timing filters, and participant compensation workflows for recruitment platforms (e.g., Prolific).
Done when: you have configured a complete, tested online research survey containing three validated inventories, logic branching, and two embedded attention filter checks.
How to work through it
- Set up survey blocks with randomized question presentation and forced response logic where appropriate
- Embed Instructional Manipulation Checks (IMCs) to detect inattentive or automated responding
- Implement branching logic for experimental condition assignment
- Configure redirect completion URLs and test end-to-end data export format
Systematic Literature Reviews & Meta-Analytic Synthesis
Learn to conduct rigorous, systematic syntheses of psychological literature using PRISMA standards and understand the fundamentals of meta-analytic effect size pooling.
- Execute a systematic literature search using PRISMA guidelines~8hPractice1 resource
Research psychologists must systematically map existing evidence before claiming empirical novelty for a new study.
You'll learn
- PRISMA — Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline standard
- Boolean search string — Search strategy combining keywords with operators (AND, OR, NOT) and wildcards
- PICO framework — Structured methodology for defining research questions across behavioral and clinical science
Develop a comprehensive Boolean search string for academic databases (PubMed, PsycINFO, Web of Science). Screen titles, abstracts, and full texts according to explicit inclusion and exclusion criteria, documenting the process with a PRISMA flow diagram.
Done when: you have produced a completed PRISMA 2020 flow diagram and systematic screening extraction matrix for a specific research question across 20+ screened papers.
How to work through it
- Formulate a PICO/PECO research question (Population, Exposure/Intervention, Comparison, Outcome)
- Construct standardized Boolean search syntax across PsycINFO and PubMed
- Import references into screening software (e.g., Rayyan or Covidence) and screen titles/abstracts
- Generate a PRISMA flow diagram documenting inclusion, exclusion, and final study counts
- Calculate and pool effect sizes for meta-analysis in R~8hBuild
Meta-analysis provides the mathematical machinery to pool evidence across conflicting individual empirical studies.
You'll learn
- Hedges' g — Variation of Cohen's d that corrects for small sample bias in effect size estimates
- Forest plot — Graphical display of estimated results from a number of scientific studies addressing the same question
- Heterogeneity (I-squared) — Percentage of total variation across studies due to true heterogeneity rather than chance
Understand meta-analytic theory: fixed-effect versus random-effects models, between-study heterogeneity (I-squared, tau-squared), and funnel plot asymmetry for publication bias. Fit a meta-analysis in R using the metafor package.
Done when: you have fitted a random-effects meta-analytic model in R on a sample dataset of 10 studies, producing a forest plot and a funnel plot with Egger's test.
How to work through it
- Extract means, SDs, and sample sizes or odds ratios across a set of empirical papers
- Calculate standardized mean differences (Hedges' g) to correct for small sample biases
- Fit a random-effects meta-analysis model using metafor::rma() in R
- Generate publication-standard Forest plots and Funnel plots evaluating publication bias
Study Execution, APA Scientific Writing & Manuscript Production
Integrate theoretical framing, programmed tasks, and statistical pipelines into a complete, publication-standard empirical manuscript formatted to APA 7th Edition guidelines.
- Write a complete APA 7th edition empirical manuscript~15hBuild1 resource
The manuscript is the primary scientific currency through which research psychologists communicate findings to the scientific community.
You'll learn
- APA Style (7th Edition) — The official style guide of the American Psychological Association for scholarly writing
- Dynamic document generation — Compiling text and statistical code simultaneously to ensure zero transcription errors
- Transparent reporting — Complete disclosure of all experimental manipulations and exclusions
Author an empirical research paper following APA 7 standards: Title Page, Abstract, Introduction (theoretical grounding and hypotheses), Method (Participants, Design, Materials, Procedure), Results (R statistics, tables, figures), and Discussion (implications, limitations, future directions).
Done when: you have produced a complete, formatted 4,000 to 6,000-word empirical manuscript written in R Markdown/Quarto with dynamically generated statistical values and citations.
How to work through it
- Draft the Introduction establishing the theoretical gap and explicit hypotheses
- Write the Method section with sufficient detail to allow exact replication by an independent lab
- Compile the Results section embedding dynamic R code chunks that inject exact statistical values directly into the text
- Draft the Discussion synthesizing findings, theoretical contributions, and explicit boundary limitations
- Simulate the academic peer review and revision cycle~8hPractice1 resource
Navigating the peer-review process is a critical skill for advancing research to final journal publication.
You'll learn
- Peer review — Independent assessment of research papers by experts in the field to maintain scientific quality
- PsyArXiv — Free preprint service for the psychological sciences hosted on OSF
- Response to Reviewers — Detailed formal document accompanying revised manuscript submissions explaining exact changes made
Conduct a formal peer review of a colleague's manuscript or a preprint from PsyArXiv. Write a structured Reviewer Report evaluating theoretical validity, methodological rigor, statistical robustness, and data accessibility. Draft a formal Response to Reviewers letter.
Done when: you have written a 2-page formal Reviewer Report and a point-by-point Response to Reviewers addressing critique on an empirical paper.
How to work through it
- Select an empirical preprint in psychology from PsyArXiv
- Evaluate the paper against standard reviewer rubrics: novelty, design validity, statistics, and conclusions
- Write a constructive, structured peer review detailing major and minor concerns
- Formulate a point-by-point 'Response to Reviewers' revision letter demonstrating how to address skeptical feedback
Academic Pathways, Research Portfolio & Graduate Preparation
Assemble your empirical work, reproducible code, and research vision into a competitive academic research portfolio to pursue research assistantships, lab manager positions, or competitive PhD programs.
- Build a public open-science research portfolio and GitHub repository~10hBuild
A publicly verifiable computational portfolio provides concrete evidence of your methodological and programming competence to prospective supervisors and labs.
You'll learn
- Reproducible research repository — Structured folder containing code, data, and docs enabling full one-click replication
- Zenodo — Open-access repository allowing researchers to mint permanent DOIs for code and datasets
- Computational provenance — Complete recorded history of how data and figures were generated
Package your empirical projects, clean R analysis code, preregistration links, and task scripts into a public, well-documented GitHub repository with clear READMEs, environment configuration files, and open-data archives.
Done when: you have published a personal academic website/GitHub repository hosting your writing sample, preregistrations, and runnable R analysis code.
How to work through it
- Create a clean GitHub repository containing code scripts, raw/anonymized data, and Quarto manuscript source
- Add a thorough README with instructions on environment reproduction and license terms
- Archive materials with a digital object identifier (DOI) via OSF or Zenodo
- Link your portfolio on a clean personal academic landing page
- Draft an academic CV, Statement of Purpose, and research pitch~8hApply
Academic hiring and PhD admissions evaluate candidate fit, technical research capability, and intellectual clarity through these specific documents.
You'll learn
- Academic CV — Comprehensive curriculum vitae formatted specifically for research, publications, grants, and teaching
- Statement of Purpose (SoP) — Formal essay describing research trajectory, scholarly interests, and lab fit
- Research mentor fit — Alignment between an applicant's proposed questions and an investigator's funding and expertise
Construct an academic curriculum vitae (CV) adhering to research norms. Write a focused Statement of Purpose (SoP) detailing your research interests, theoretical frameworks, methodological skills, and alignment with target research labs and faculty mentors.
Done when: you have completed an academic CV and a 2-page Statement of Purpose tailored to a target psychological research lab.
How to work through it
- Draft an academic CV detailing education, manuscripts, presentations, software skills (R, PsychoPy), and open science projects
- Identify 3-5 primary research laboratories aligned with your theoretical interests
- Draft a 2-page Statement of Purpose articulating your past research accomplishments and proposed future investigation questions
- Refine your research pitch to concisely communicate your theoretical orientation in introductory academic outreach
How the plan fits together
11 phases in 8 stages. Anything on the same row can be worked on at the same time.
An arrow points from a phase to the work it unlocks: before starting any phase, every phase with an arrow into it has to be finished first.
Resources
20 in this plan's library, beyond the links on individual tasks.
Books, Courses & Textbooks
Foundational textbooks in psychological science, statistics, and research methods.
- Building Experiments in PsychoPy (2nd Edition)
Instructs researchers on programming computerized cognitive tasks, stimulus timing precision, randomization, and Pavlovia deployment.
uk.sagepub.com · SAGE Publications · Book · Paid textbook (paperback ~$50–$60; eTextbook ~$54). · Intermediate
- Cognitive Neuroscience: The Biology of the Mind (5th Edition)
Provides foundational mechanistic models of human cognition, biological architecture, and modern neuroimaging methods used in experimental psychology.
wwnorton.com · W. W. Norton & Company · Book · Paid textbook (eTextbook and print editions vary between $70–$180). · Intermediate
- Cognitive Psychology: A Student's Handbook
Comprehensive, standard academic reference covering core theoretical models across attention, memory, language, and perception.
Routledge (Michael W. Eysenck & Mark T. Keane) · Book · ~£45 · Intermediate
- Discovering Statistics Using R and RStudio (2nd Edition)
Advances through the General Linear Model, multi-way ANOVA, multiple regression, mediation, and linear mixed-effects models in R.
uk.sagepub.com · SAGE Publications · Book · Paid textbook (e-book from ~$49; print paperback approx. $75–$90). · Advanced
- Doing Meta-Analysis with R: A Hands-On Guide
Provides an end-to-end practical framework for pooling models, diagnosing heterogeneity, and running meta-regressions using R.
bookdown.org · Chapman & Hall/CRC Press · Book · Fully free open-access web version; paid print/e-book published by CRC Press. · Intermediate-to-Advanced
- Ethical Principles of Psychologists and Code of Conduct
The official ethical code detailing institutional approval, consent, deception protocols, debriefing, and publishing integrity for IRB compliance.
apa.org · American Psychological Association (APA) · Ethics Guidelines · Free · Beginner
- Improving Your Statistical Inferences
Addresses core epistemological challenges, statistical inference frameworks, error control, and practical philosophy of science for experimentalists.
coursera.org · Eindhoven University of Technology on Coursera · Online Course · Free to audit all video lectures and readings; optional paid certificate. · Intermediate
- Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners
Teaches R programming alongside foundational descriptive and inferential statistics specifically for behavioral science workflows.
learningstatisticswithr.com · Danielle Navarro · Book · Free online version; print versions available through third parties. · Beginner
- Mitch’s Uncensored Advice for Applying to Graduate School in Psychology
Outlines how to build a competitive research profile, curate portfolios, navigate mentor matching, and write statements of purpose.
mitch.web.unc.edu · Dr. Mitch Prinstein (University of North Carolina at Chapel Hill) · Guide · Free downloadable PDF. · Beginner
- Publication Manual of the American Psychological Association (7th Edition)
The definitive style authority for structuring empirical articles, formatting tables/figures, and reporting statistics in APA format.
apastyle.apa.org · American Psychological Association (APA) · Book · Paid manual (spiral-bound/paperback ~$25–$35). · Intermediate
- R for Data Science (2nd Edition)
The authoritative reference for wrangling, transforming, and visualizing behavioral datasets using modern Tidyverse principles.
r4ds.hadley.nz · Hadley Wickham, Mine Çetinkaya-Rundel, & Garrett Grolemund · Online Book · Free · Beginner to Intermediate
- Research Methods in Psychology: Evaluating a World of Information
The gold-standard textbook for mastering the four experimental validities and core psychological design paradigms.
W. W. Norton & Company (Beth Morling) · Book · ~£65 · Beginner
- Research Methods in Psychology: Evaluating a World of Information (4th Edition)
Breaks down experimental validity, factorial designs, quasi-experiments, correlational structures, and measurement reliability.
wwnorton.com · W. W. Norton & Company · Book · Paid textbook (eTextbook/rental options from ~$45; print purchase ~$90–$120). · Beginner-to-Intermediate
- Seven Deadly Sins of Psychology: A Manifesto for Reforming the Culture of Scientific Practice
Essential critical reading on the replication crisis, publication bias, and questionable research practices in psychological science.
Princeton University Press (Chris Chambers) · Book · ~£18 · Intermediate
- Social Psychology (4th Edition)
Delivers a comprehensive theoretical overview of social cognition, attitudes, group dynamics, and interpersonal processes.
routledge.com · Psychology Press / Routledge · Book · Paid textbook (e-book approx. $65; paperback approx. $90–$110). · Intermediate
- Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference
Introduces metatheoretical frameworks and contrasts orthodox significance testing against Bayesian epistemology before designing empirical studies.
bloomsbury.com · Palgrave Macmillan · Book · Paid print and e-book (approximately $20–$55 depending on retailer and format). · Intermediate
Open Science & Research Tools
Registries, experimental software, and data repositories.
- Open Science Framework (OSF)
Use to preregister study hypotheses and analysis plans, archive open datasets and scripts, and share preprints via PsyArXiv.
osf.io · Center for Open Science (COS) · Research Platform · Free open-access platform. · Beginner
- PsychoPy Software Environment
The standard open-source application for creating stimuli and running precisely timed psychophysics and behavioral experiments.
psychopy.org · Open Science Tools Ltd / Jonathan Peirce · Software · Free and open-source software (GPL v3). · Intermediate
Societies & Research Communities
Academic psychological associations, conferences, and collaborative networks.
- Association for Psychological Science (APS)
The leading international scientific organization devoted to empirical research and scientific dissemination across psychological subdisciplines.
psychologicalscience.org · Association for Psychological Science (APS) · Professional Society · Paid membership (discounted undergraduate and graduate student tiers). · All Levels
- Society for the Improvement of Psychological Science (SIPS)
An international organization established to reform research practices, training, and open scientific infrastructure in psychology.
improvingpsych.org · Society for the Improvement of Psychological Science (SIPS) · Professional Society · Sliding-scale membership fees (free/discounted tiers available for students and unwaged researchers). · All Levels