Research Methods in Psychology
Research Methods in Psychology is a graph-based Study course built from the PsychMethods4E source graph for learners who need to design, critique, analyze, and report psychological research with more discipline than a survey course can provide.
The course follows the 13-chapter structure of KPU's Research Methods in Psychology - 4th American Edition, moving from scientific claims and the scientific method into ethics, measurement, experiments, non-experimental research, surveys, quasi-experiments, factorial designs, single-subject research, reporting, descriptive statistics, and inferential statistics. Each Study page is tied to source nodes in the 1Cademy knowledge graph and keeps paragraph-level source anchors visible, so learners can inspect which claims, examples, methods, and limits support the page.
In Dive In, learners read short methods-focused textbook pages, answer scenario-based unlock questions, and practice the judgment needed to choose research designs, evaluate evidence strength, interpret variables and data patterns, and communicate limitations without overclaiming.
Adaptive
Varies by mastery
11 domains
How the course works
A session is a short study-and-practice checkpoint, not a fixed class meeting. The course can move faster when material is already familiar and slow down when a topic needs more practice.
Study a focused page
Read a small prerequisite-ordered set that gives the context for the next practice step.
Check understanding
Answer linked questions so the system can tell what is already strong and what needs review.
Keep moving
Unlock the next set after the current material is understood, with review scheduled as needed.
Who this course is for
Undergraduate psychology learners, research assistants, and instructors who want a graph-linked path through study design, measurement, ethics, analysis, and reporting.
Objectives
- Explain how psychological science uses systematic evidence to describe, predict, explain, and apply knowledge about behavior and mental processes.
- Develop research questions and hypotheses that connect concepts to observable variables and feasible study designs.
- Apply ethical principles, consent expectations, review processes, and participant safeguards to research scenarios.
- Evaluate measurement choices using reliability, validity, operational definitions, and practical constraints.
- Distinguish experimental, non-experimental, survey, qualitative, quasi-experimental, factorial, and single-subject designs.
- Interpret descriptive and inferential statistics as tools for disciplined uncertainty, not as automatic proof.
- Write and critique research conclusions so the claim matches the design, evidence, sample, and limitations.
- Use source-node anchors to inspect the evidence behind each Study page and transfer that evidence to new research decisions.
Syllabus
This section turns research questions into hypotheses, operational definitions, study designs, data collection plans, and appropriately limited conclusions.
Finding a Research Topic
Move from a broad problem area to a researchable question.
Generating Good Research Questions
Evaluate specificity, feasibility, and evidence needs.
Developing a Hypothesis
Connect predictions to variables and theoretical expectations.
Designing a Research Study
Choose a design that fits the question and practical constraints.
Drawing Conclusions and Reporting Results
State what the study supports without overclaiming.
Learners apply ethical principles and review practices to protect participants while preserving the integrity of psychological research.
Moral Foundations of Ethical Research
Connect respect, beneficence, and justice to study design.
From Moral Principles to Ethics Codes
Translate broad principles into concrete research duties.
Putting Ethics into Practice
Apply consent, confidentiality, deception, debriefing, and review requirements.
Measurement pages focus on turning psychological constructs into observations that can be interpreted reliably and validly.
Understanding Psychological Measurement
Explain variables, scales, operational definitions, and measurement error.
Reliability and Validity of Measurement
Evaluate consistency and whether a measure supports its intended interpretation.
Practical Strategies for Psychological Measurement
Select measures, procedures, and safeguards for concrete studies.
This section teaches the logic of causal inference through manipulation, control, assignment, validity threats, and experimental design choices.
Experiment Basics
Identify manipulation, comparison, assignment, and outcome measurement.
Experimental Design
Compare design structures and select controls that fit the claim.
Experimentation and Validity
Analyze internal, external, construct, and statistical validity concerns.
Practical Considerations
Plan procedures that balance rigor, feasibility, and ethics.
Learners study correlational, observational, archival, and qualitative strategies for questions where experiments are not the right tool.
Overview of Non-Experimental Research
Explain when and why non-experimental methods are useful.
Correlational Research
Interpret associations, direction, strength, and third-variable limits.
Observational Research
Plan structured or naturalistic observation with attention to bias.
Qualitative Research
Use interviews, field notes, and thematic evidence carefully.
Survey pages focus on sampling, question wording, response options, administration, and interpretation of self-report evidence.
Overview of Survey Research
Connect survey goals to populations, samples, and response formats.
Constructing Surveys
Write questions and response options that match the construct.
Conducting Surveys
Plan administration, sampling, and interpretation with limits visible.
Learners analyze study designs that approximate experiments when random assignment is limited or impossible.
Quasi-Experimental Research
Identify when practical or ethical constraints prevent full experiments.
One-Group Designs
Evaluate pretest-posttest and time-based evidence without a strong comparison group.
Non-Equivalent Control Group Designs
Use comparison groups while tracking selection and validity threats.
Factorial-design pages teach learners to reason about multiple independent variables, main effects, interactions, and interpretation.
Factorial Designs
Define factors, levels, cells, and combined experimental conditions.
Setting Up a Factorial Experiment
Plan a design with multiple variables and comparison cells.
Interpreting Factorial Results
Separate main effects from interactions in data patterns.
Learners study intensive designs that use repeated measurement and baseline logic to evaluate change within individuals or small systems.
Overview of Single-Subject Research
Define intensive within-person designs and their use cases.
Single-Subject Research Designs
Interpret baseline, withdrawal, reversal, and multiple-baseline designs.
The Single-Subject versus Group Debate
Compare evidence strengths and limits across design traditions.
This section turns research work into transparent reports, APA-style writing, figures, tables, and claims that match the evidence.
Writing a Research Report
Connect introduction, method, results, and discussion sections.
Expressing Your Results
Use prose, tables, and figures to report evidence clearly.
Other Presentation Formats
Adapt research communication for posters, talks, and shorter formats.
Learners use descriptive statistics to summarize variables and relationships before making broader inferential claims.
Descriptive Statistics
Explain why researchers summarize data before interpreting it.
Describing Single Variables
Use distributions, central tendency, and variability.
Describing Statistical Relationships
Describe association patterns and limits.
The final section introduces uncertainty, null-hypothesis testing, basic tests, analysis planning, and cautious interpretation.
Inferential Statistics
Connect samples, populations, uncertainty, and statistical inference.
Understanding Null Hypothesis Testing
Interpret null hypotheses, p-values, and evidence thresholds carefully.
Some Basic Null Hypothesis Tests
Match common tests to design and data type.
Additional Considerations
Account for assumptions, effect sizes, and interpretation limits.
Analyzing the Data
Choose analyses and write conclusions that fit the study.
References
These are the external source domains cited by nodes tagged for this course. Each source is shown once, with the number of tagged-node citations from that source in parentheses.