Research Methods

Explore how the scientific process distinguishes science from pseudoscience, and how skepticism, errors in reasoning, and biases shape evidence-based inquiry for policy, theory, and personal research.

Apply a systematic method to gather data, analyze it, and draw conclusions, while minimizing biases and errors across descriptive, exploratory, explanatory, and evaluation research.

Explore how to generate focused research questions, guided by theory, literature review, ethics, and generalizability, and design robust studies with clear hypotheses and variables.

Expose the evolution of research ethics through Nazi, Tuskegee, Milgram, and Stanford Prison experiments, and learn the Nuremberg Code, informed consent, and IRB oversight for safeguarding participants.

Conceptualization turns a research question into measurable concepts and indicators by defining concepts, choosing absolute or relative definitions, and using multiple measures via a statement of understanding.

Turn concepts into measurable measures through operationalization, select appropriate measures (UCR, Ncvs), use multiple measures when needed, and define dependent, independent, and control variables to explore correlations and causality.

Examine data through four levels of measurement (nominal, ordinal, interval, ratio) alongside closed and open questions, then assess construct validity with face and criterion validity, and reliability concepts like test-retest.

Learn how to sample a target population, distinguishing census from sampling frames and units of analysis, and understand how sampling error depends on sample size rather than population size.

Explore probability and non-probability sampling methods, from simple random and systematic sampling to stratified and cluster designs, and learn how sampling frames and errors shape results.

Explains causation, nomothetic versus idiographic, and counterfactuals, then outlines true experiments with four requirements: two groups, random assignment, a treatment, and an observation (pretest or post-test), including Solomon four-group design.

Examine quasi experimental designs, including nonequivalent control and before-after designs, and natural experiments, while evaluating causality criteria: correlation, non-spuriousness, and time order, across trend, panel, and cohort studies.

Examine external and internal validity, their generalizability across people, places, and times, and identify internal threats like selection bias, attrition, testing, maturation, history, instrumentation, and Hawthorne and placebo effects.

Discover how surveys offer versatile, efficient data collection and generalizability, with pretests, skip patterns, Likert scales, omnibus and split-ballot designs, and mixed-mode methods.

Design precise survey questions, manage translations, apply interpretive and filter questions, and avoid bias and vagueness while using indexes and weighting to improve reliability.

Explore qualitative research methods, focusing on qualitative data, social context, reflexive design, and meaning-making. Learn ethical considerations including informed consent, confidentiality, and researcher as instrument in data collection and reporting.

Explore qualitative methods, including ethnography, ethnomethodology, and participant observation, while managing reactive effects and covert participation, and employing intensive interviews, focus groups, unobtrusive measures, and archives.

Explore secondary data use, including surveys, official statistics, records, and historical documents. Learn about unit of analysis, sampling, coding, confidentiality, and cultural considerations.