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1. Introduction to Statistics for the Social SciencesSBS200, COMM200, GEOG200, PA200, POL200, SOC200Lecture Section 001, Fall, 2011Room 201 Physics-Atmospheric Sciences (PAS)10:00 - 10:50 Mondays & Wednesdays + Lab Session Welcome http://www.youtube.com/watch?v=oSQJP40PcGI

2. Use this as your study guide By the end of lecture today8/31/11 Independent and dependent variables Within-participant and between-participant design Single blind (placebo) procedure Double blind procedure Continuous vs Discrete Levels of Measurement Nominal, Ordinal, Interval, & Ratio Surveys and questionnaire design Correlational methodology Dot Plots Frequency Distributions - Frequency Histograms Frequency, cumulative frequency Relative frequency, cumulative relative frequency Guidelines for constructing frequency distributions

3. Homework due - (September 7th) On class website: please print and complete homework worksheet #1 Please double check – Allcell phones other electronic devices are turned off and stowed away

4. Schedule of readings • Before next exam: • Please read chapters 1 - 4 & • Appendix D & E in Lind Please read Chapters 1, 5, 6 and 13 in Plous • Chapter 1: Selective Perception Chapter 5: Plasticity Chapter 6: Effects of Question Wording and Framing Chapter 13: Anchoring and Adjustment

5. Random assignment Independent and Dependent Variables How do we decide who gets into each condition? Random assignment of subjects into groups: Any subject had an equal chance of getting assigned to either condition Gender and spending If random assignment then you may have a “true” experiment If no random assignment then you have a “quasi” experiment Sleep and memory Music and plants Cell phones and driving Cars and cool

6. Within - participant (same as within - subject)& Between - participant (same as between - subject) Within-participant design: each subject participates in every level of independent variable (aka repeated measures) Between-participant design: each subject participates in only one level of independent variable Within or between participant design? Music make plants grow? Animals make funnier commercials? Effect of sleep on memory ability

7. Random sampling vs Random assignment Random assignment of participants into groups: Any subject had an equal chance of getting assigned to either condition (related to quasi versus true experiment) Random sampling of participants into experiment: Each person in the population has an equal chance of being selected to be in the sample Population: The entire group of people about whom a researcher wants to learn Sample: The group of people who actually participate in a research study

8. How tall is the average U of A student? Population: University students – 37,000 students Sample: Subset of students – 100 students Biases?

9. Group 1 - studies & tested If you want to know if studying improves test performance in young children Break up group of kids into two groups What is the independent variable? What is the dependent variable? How many levels are there of the IV? Is this a “quasi” or “true” experiment? “Between” or “within” participant design? Group 2 - does not study & tested

10. First test group with placebo drink (sugar pill) If you want to know if “Ginseng drink” is associated with feelings of satisfaction What is the independent variable? What is the dependent variable? How many levels are there of the IV? “Between” or “within” participant design? Placebo Then test same group with “Ginseng drink”

15. Hiram S. Dudson 1930 – 1993Member ,Placebo Group

16. Placebo (single blind) versus double blind procedure • Single blind procedure (example: use of placebo) • Double blind procedure What about experimenter bias?

17. So far, Measurement: observable actions Theoretical constructs: concepts (like “humor” or “satisfaction”) Operational definitions Validity and reliability Independent and dependent variable Random assignment and Random sampling Within-participant and between-participant design Single blind (placebo) and double blind procedures

18. Duration Continuous versus discrete Continuous variable: Variables that can assume any value. There are (in principle) an infinite number of values between any two numbers Discrete variable: Variables that can only assume whole numbers. There are no intermediate values between the whole numbers Distance Number of kids Height Number of eggs in a carton Number of textbooks required for class

19. Categorical versus Numerical data Categorical data (also called qualitative data) - a set of observations where any single observation is a word or a number that represents a class or category Numerical data (also called quantitative data) - a set of observations where any single observation is a number that represents an amount or count

20. Categorical data (also called qualitative data) - a set of observations where any single observation is a word or a number that represents a class or category Numerical data (also called quantitative data) - a set of observations where any single observation is a number that represents an amount or count Handedness - right handed or left handed Family size Hair color Ethnic group GPA Age Yearly salary Breed of dog Gender - male or female Temperature Please note this is a binary variable

21. Categorical data (also called qualitative data) - a set of observations where any single observation is a word or a number that represents a class or category Numerical data (also called quantitative data) - a set of observations where any single observation is a number that represents an amount or count On a the top half of a writing assignment form please generate two examples of categorical data and two examples of numerical data Please note we’ll use the bottom half for something else

22. What are the four “levels of measurement”? Categorical data • Nominal data - classification, differences in kind, names of categories • Ordinal data - order, rankings, differences in degree Numerical data • Interval data - measurable differences in amount, equal intervals • Ratio data - measurable differences in amount with a “true zero”

23. What are the four “levels of measurement”? Categorical data • Nominal data - classification, differences in kind, names of categories • Ordinal data - order, rankings, differences in degree Numerical data • Interval data - measurable differences in amount, equal intervals • Ratio data - measurable differences in amount with a “true zero” Gender - male or female Family size Jersey number Place in a foot race (1st, 2nd, 3rd, etc) Handedness - right handed or left handed

24. What are the four “levels of measurement”? Categorical data • Nominal data - classification, differences in kind, names of categories • Ordinal data - order, rankings, differences in degree Numerical data • Interval data - measurable differences in amount, equal intervals • Ratio data - measurable differences in amount with a “true zero” Age Hair color Telephone number Ethnic group Breed of dog Temperature Yearly salary

25. Please note : page 29 in text

26. What are the four “levels of measurement”? Categorical data • Nominal data - classification, differences in kind, names of categories • Ordinal data - order, rankings, differences in degree Numerical data • Interval data - measurable differences in amount, equal intervals • Ratio data - measurable differences in amount with a “true zero” Look at your examples of qualitative and quantitative data. Which levels of measurement are they?

28. Thank you! See you next time!!