Welcome

1. Introduction to Statistics for the Social SciencesSBS200, COMM200, GEOG200, PA200, POL200, or SOC200Lecture Section 001, Spring, 2014Room 120 Integrated Learning Center (ILC)10:00 - 10:50 Mondays, Wednesdays & Fridays. Welcome http://www.youtube.com/watch?v=oSQJP40PcGI

2. Please click in My last name starts with a letter somewhere between A. A – D B. E – L C. M – R D. S – Z

3. Use this as your study guide By the end of lecture today1/24/14 Constructs versus measurement Operational definitions Validity of operational definitionsReliability of measurements Independent and dependent variables Control versus treatment True experimental versus quasi-experimental methodology

4. Notetakers Name Major email phone #

5. Schedule of readings Before next exam (February 14th) Please read chapters 1 - 4 in Ha & Ha textbook Please read Appendix D, E & F onlineOn syllabus this is referred to as online readings 1, 2 & 3 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

6. Lab sessions Everyone will want to be enrolled in one of the lab sessions Labs will start again on Monday

7. Homework due – Monday (January 27th) On class website: please print and complete homework worksheet #2 Please note: This assignment is not due until Monday. This will give us time to cover the material required “Question 5” Please double check – Allcell phones other electronic devices are turned off and stowed away

8. In nearly every class we will use clickers to: • answer questions in class and participate in • interactive class demonstrations We’ll register them on our webpage- then use ‘em! student.turningtechnologies.com (Please note there is no “www”) Complete this in the next week and receive extra credit! (By January 29th 2014)

9. 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

10. 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

11. 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”

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

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

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

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. 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

29. So far, Continuous vs Discrete variables Quantitative vs qualitative variables Levels of measurement: Nominal, Ordinal, Interval and Ratio

30. Thank you! See you next time!!