Statistics and Research Methodology I

1. Statistics and Research Methodology I Seminar 1

2. About me… Kai Qin (“Kai Chin”) Professor Sir Teacher

3. Structure of SRM I • According to the Course Manual… Midterm test: 20%Data analysis project: 40%Final Exam: 40% • Required JASP (https://jasp-stats.org/)

4. Data analysis project Indian dataset of the World Values Survey 2015

5. Introduction: The Scientific Method Part 1

6. Why take SRM?

7. How many teeth does a horse have?

8. Ways of Knowing • Intuition (“gut” feeling) • Experience • Authority • Rationalism (reasoning, logic) • Science (empiricism + rationalism)

9. The Scientific Method A set of rules with assumptions, attributes, goals, and procedures for asking and answering questions

10. The Scientific Method Assumptions • Whatever we are studying has causes or can be predicted(determinism) • Whatever we are studying can be understood • Whatever we are studying can be measured

11. The Scientific Method Attributes • Empirical use of observation/experimentation to know data-driven • Systematic step-by-step clear definitions and procedures (operational definitions) reliable valid

12. The Scientific Method Attributes • Parsimonious (Occam’s Razor) simplicity preferred; eliminate the superfluous • Objective bias-free (confirmatory bias) verifiable replicable • Tentative not proven “nearer the truth”

13. The Scientific Method Goals • Describe(what is happening?) • Predict(what goes with what?) • Explain(what causes what?) avoid circular/tautological explanations • Control/Change applied vs. basic research

14. The Scientific Method Procedures (Categories) • Descriptive methods • Relational methods • Experimental (Explanatory) methods Exploratory vs. confirmatory approaches Use of theory, hypothesis

15. Theory Characteristics • Comprises a set of propositions involving constructs • Specifies interrelations among constructs • Explains phenomenon of interest (and allows prediction)

16. Theory Functions • Organize empirical findings X Y1 X  Y2 X Y3 • Guide further research (via formulation of hypothesis) • What about X  Y4?

17. Hypothesis A conjectural statement or tentative proposition (prediction) about relations among variables Declarative e.g., XY Directional vs. non-directional e.g., if X +  Y+ Directs what to study and how to study it e.g., children who are physically punished are more aggressive than children who aren’t

18. Hypothesis Attributes • Falsifiable(can be tested) e.g., if ~X  ~Y • Precise(clearly defined components) • Rational(consistent with what is known)

19. Hypothesis Types • Estimation of population characteristics • Relationship among variables • Differences among populations • Cause and effect descriptive explanatory

20. Theory, Hypothesis, & Research Theory (incorporating previous research) Hypothesis Research

22. Basic concepts & terminologies Part 2

23. CONCEPTS & TERMINOLOGIES • POPULATION vs. SAMPLE Population: entire group to which results are to be applied Sample: subset of population yielding results (hopefully, representative sample, but often not)

24. Back to the question… How many teeth does a horse have? How many horses do you need to sample to know the “truth”?

25. CONCEPTS & TERMINOLOGIES • DESCRIPTIVE vs. INFERENTIAL STATISTICS • Descriptive: • Describe a set of data • Inferential: • Extending conclusions from samples to populations

26. CONCEPTS & TERMINOLOGIES • CONSTRUCT vs. VARIABLE Construct: abstract term (concept) incorporating behaviors with shared attributes Variable: measurable aspect of construct; can take on different values (i.e., they vary)

27. CONCEPTS & TERMINOLOGIES • OPERATIONALIZATION • process of “translating” a construct into one or more variables Operational definition definition of variable in terms of method (operations) used to measure it e.g., amount of money donated; likelihood of helping someonein trouble

28. CONCEPTS & TERMINOLOGIES Refer to Tutorial 1 What are the constructs? What are the possible operational definitions? • Ability to concentrate on a task deteriorates when people feel crowded. • Good bowlers improve their performance in the presence of an audience, while average bowlers do worse.

29. CONCEPTS & TERMINOLOGIES • RANDOMIZATION method of sampling or assigning participants such that each participant has an equalprobability of being sampled or assigned to a particular group Random sampling – unbiased/representative sample Random assignment– equivalent groups dependent on sample size

30. Visualizing the difference Q: When do you need to perform each of them?

31. CONCEPTS & TERMINOLOGIES • ERROR Nothing can be measured perfectly (What is the length of your thumb?) Random error -- extraneous variables Systematic error -- confounding variables Bias participant bias (e.g., demand characteristics) experimenter bias (e.g., expectancies)

32. CONCEPTS & TERMINOLOGIES • RELIABILITY consistency of measurement Measurement error events that increase/decrease an individual score for each measurement

33. CONCEPTS & TERMINOLOGIES • RELIABILITY Sources of error(that reduce reliability) • Instrument (e.g., instrument poorly constructed) • Participant(e.g., fatigue, misunderstandings) • Researcher(e.g., entry errors, inconsistent instructions) • Environment(e.g., distractions)

34. CONCEPTS & TERMINOLOGIES • VALIDITY (Reliability a first step to validity) ≈ degree of appropriateness ≈ degree of accuracy

35. CONCEPTS & TERMINOLOGIES • VALIDITY Construct validity • appropriateness of variable(s) as indicator(s) of construct • accuracy in measuring construct of interest e.g., speed  ________________ helping behavior  ________________

36. CONCEPTS & TERMINOLOGIES • VALIDITY Internal validity • appropriateness/accuracy of conclusion cause established? relationship established? • ruling out alternative explanations confounds? extraneous variables?

37. CONCEPTS & TERMINOLOGIES • VALIDITY External validity • generalizable to population(s)? • representativeness of sample

38. Scales of measurement Part 3

39. Scales of measurement Nominal • (arbitrary) numbers indicate categories • used for labeling • mathematical operations meaningless e.g., female = 1, male = 2 Buddhist = 1, Christian = 2, Hindu = 3, Muslim = 4

40. Scales of measurement Ordinal • numbers indicate ranks • used for ordering • distance between ranks unknown e.g., first, second, third, etc. in race short, shorter, shortest

41. Scales of measurement Interval • numbers indicate quantity or amount • “equal” distance between numbers • arbitrary zero (add/subtract possible) • allows for most statistical procedures to be performed strongly disagree = 1, disagree = 2 neither disagree nor agree = 3 agree = 4 strongly agree = 5

42. Scales of measurement Ratio • numbers indicate quantity • equidistant intervals • absolute zero(add/subtract, multiply/divide possible) e.g., height of 3m vs. height of 2m vs. height of 1m

43. Question • A researcher measures room temperature of this classroom. • What is the scale of measurement of this classroom “temperature”? • More philosophically, what can “temperature” actually measure?

44. Summary • Empiricism is a core component of the scientific method • Measurable concepts have different properties. • These properties have implications on the statistical tests (later in the course) Now, refer to Tutorial 1, Qn 3-5

45. Tasks:1. Identify the variables of interest2. Their scale of measurement3. Will any conclusions be valid? A physiological psychologist develops a drug that she thinks will revolutionize the world of horse racing. She names the drug hp (for horsepower), and it is her contention that hp will lead horses to run much faster than they do now. She forms two groups of horses and gives one group injections of hp once a week for 4 weeks. Because hp has some negative effects on horses’ digestive systems, those horses given hp are given a special high-protein diet. Those horses not given hp are maintained on their regular diet. After the 4-week period, all the horses are timed in a 2-mile race.

46. Tasks:1. Identify the variables of interest2. Their scale of measurement3. Will any conclusions be valid? In a study of developmental psycholinguistics, 2-, 3-, and 4-year-old children are shown dolls and asked to act out scenes to determine if they can use certain grammatical rules. Sometimes each child is asked to act out a scene presented in the active voice (Ernie hit Bert); at other times, the same child acts out a scene presented in the passive voice (Bert was hit by Ernie). Children are judged by whether or not they act out the scene accurately (two possible scores: yes/no) and by how quickly they begin acting out the scene (in seconds).

47. Tasks:1. Identify the variables of interest2. Their scale of measurement3. Will any conclusions be valid? A social psychologist is interested in the link between dressing and helping behavior. He happens to have two male graduate students who are happy to assist and who fit his purposes. The first student (Ned) is generally well-dressed, but the second (Ted) doesn’t care much about appearances. An experiment is designed in which passers-by in a mall are approached (in the same way) either by well-dressed Ned or shabbily-dressed Ted. So as not to overtax his students, the psychologist assigns Ned to work on Monday and Ted to work on Friday. On these days Ned or Ted will approach a shopper and ask for a dollar for a cup of coffee. Nearby, the psychologist records whether or not people give the money requested for.