SRM II Review of key concepts
SRM I Scientific method Descriptive statistics Inferential statistics Hypothesis testing T-test, chi-square, ANOVA, regression, correlation Power, effect size, confidence intervals
Q: “How much from SRM I will be in our exams?” A: The minimum basics of SRM I (e.g., mean, SD, t, p, the logic of statistical inference). There will be no questions on regression, power, and confidence intervals in your SRM II exam.
SRM II Questionnaire development Experimental research Control groups Single factor designs Factorial designs Small N, observational, correlational designs Quasi-experimental Lots of data analyses Lots of critiques about other people’s research Your first independent research project!
Questionnaire development What is a psychological construct? How do you operationalize it? How do you measure it? Validity Predictive Discriminant Construct Face Reliability Internal consistency Inter-rater (not covered)
Experimental research What is the defining characteristic of an experiment? Principles of causality (what are they?) Threats to internal validity Biases (what kinds?) Regression to the mean History and maturation
Control problems Between/within-subjects designs each has its own control problems Creating equivalent groups (between-subj) Randomization and counterbalancing (within-subj) Cohort effects in longitudinal design
Control problems Just because the experimenter didn’t investigate factor B in a A Y design, is A a confound? No! B could be a moderator (A*B Y) What exactly is a confound? A confound is an alternative explanation that you don’t want A mediator is an explanation that you want Both are explanatory variables. What does your theory say? What is worth controlling for?
Mediators vs. moderators IV DV Which of these can be MED and/or MOD? Gender Helpfulness Race Frequency of smiling Nationality Moderator IV DV
Single factor designs Distinction between factors and levels Various control groups Placebo Waitlist Nested models (you need to learn how to recognize it when you see one)
Factorial designs Reality is complex Having additional factors help us understand this complexity This complexity can reveal The boundary conditions The processes Fully between, fully within, mixed subjects
Drawing graphs For factorial designs, having graphs is useful To understand general trends To examine which simple effects you can break down To know what you are expecting during the planning stages of a research
Quasi-experimental designs We went through examples of earthquake and epidemiological studies Well-controlled experiments may not always be possible Researchers sometimes need to be opportunistic too.
Small N, observational, correlational designs Small N is useful, though rarely used Lab-based studies can only capture a tiny part of what it means to be human Observational studies are a nice complement
Will there be an SRM III? There are other designs out there: Observational, survey research methods, Big Data, etc. There are other statistics out there: Structural equation modeling, multilevel modeling, mediation, survival analysis, cluster analysis, logistic regression, trend analysis, etc.
The world of possibilities.
Bio Cog Dev Clinical Social Statistics Research Methods
Final report Read the Course Manual Review the Grading Rubrics while writing your report How can I best show that I have mastered the concept?
Expectations Acknowledge important limitations or confounds Grade is not dependent on statistical significance APA style – just adhere to it. The ability to write well is a bonus Not sure how to write well? Read more Psychological Science articles s…l...o...w…l…y My mantra: Be clear, complete, and unambiguous.
Exam Sleep 7-8 hours Don’t panic. Work through information calmly Draw diagrams; doodle; plan your answers Open book: For some questions, the answers can be found in the slides. But you need to revise them well so that you know where to find them! You are allowed to bring laptop (but no internet)