Index Cards

1. Index Cards • Name • Major • Favorite Class Ever, & Why • Areas of Interest in Psychology • Unique/Bizarre/Little Know fact about you. • Most exciting event over vacation • Favorite TV show ever • Stupidest thing you’ve ever done

2. Small Group Questions • Name, where you’re from • Best class ever & why • Stupidest thing you’ve ever done • Bizarre facts/tricks you can do

3. Pop Quiz #1 1. Your instructor is from… • Nevada • New York • Nebraska • Minnesota • East-central Tibet

4. Pop Quiz #1 2. Your instructor has taught statistics • Never • About 10 times • About 20 times • About 30 times • About 40 times • Way, way too many times

5. Pop Quiz #1 3. Your instructor was once bitten by a … • Rattle Snake • Polar Bear • South American Malting Meek Mouse • Snapping Turtle • An oversized freshman • His wife, after refusing to mow the lawn

6. Pop Quiz #1 4. Your instructor can’t get enough… • Chocolate • Schlitz Malt Liquor • Diet Pepsi • Diet Coke • Diet Schlitz Malt Liquor • Prune Juice • Red Bull

7. Pop Quiz #1 5. Your instructor’s 2nd favorite TV show is … • Married with Children • The Simpsons • Survivor #5: Downtown Rockhill • The Daily Show • Space Ghost • Seinfeld • NOVA – Deadly Snapping Turtles

8. Course Tips Types of Data Graphing Distributions The Normal Curve Graphing Sample Means Practicing with SPSS Stats Basics: 1st Week Overview

9. Syllabus Office hours Engagement & Attendance Quizzes Request for leniency Notebook Course Packs Organization Homework, Labs, & Reading Class time Set-up first Please avoid surfing Bulldog Tactics Note-taking Write & Process Ask questions! Slow me down! Homework ** Studying ** Often Active Self-Explanation Practicing SPSS Laugh at my jokes!! Secret Course Tips

10. Option A: Solo Every Penguin For Herself! Keep the competition down. Option B: Teamwork!!! Ask questions of peers Answer questions Form study groups Practice explaining Make Friends Quickly!!

11. Terminology: Samples vs. Populations • Samples & Populations • Statistics: refer to characteristics of samples • e.g., xbar or M • always regular alphabet symbols • Parameters: refer to characteristics of population • e.g. μ • always greek symbols • Self-check: • height of several students in class to represent class • height of class to represent height of typical undergraduates

12. Qualitative vs. Quantitative Data • Quantitative: can be ranked • shoe size, height, self-esteem score on scale, airplane lift • Qualitative: can’t be ranked • gender, political affiliation, major, car maker • Check • Gender • region • weight • depression • steps • Social Security Number • Letter Grade: A, B, C, D

13. Nominal – classify data into categories (religion) Ordinal – classify and rank (Olympic Medals) Interval – classify and rank with equal intervals (Celsius) Ratio – classify, rank with equal intervals, true zero (Kelvin) your residence hall batting average your rank on mom’s love list height IQ weight Self-esteem (7 point Likert Scale) SAT score Scales of measurement • Grade: A, B, C, D • distance • gender • gpa • number of close friends • social security number • region of country • level of depression

14. Experimental terms • Empirical Method: Experimental Method • Question: Why do airplanes fly? • Theory: Wings create lift • Operational Definitions • IV: Wing position: (straight, bent up) ‘levels’ • DV: Lift • Gathering data • Careful observation; quantification • Level of measurement – use highest possible • Controlling Extraneous Variables • Drawing Conclusions

15. Experimental terms (2) • Experimental Terminology • Independent Variable: (e.g., Wing Position) • Variable you manipulate; • variable you think will impact DV • Dependent Variable: (e.g., Change in Vertical Position) • Variable that might be affected by IV; • variable you measure • Extraneous Variable: (e.g., drafts, throwing style) • Any fact that affects the DV other than the IV • Sources of “error” – we want to STANDARDIZE conditions to minimize the amount of error • Quasi Experimental Design • No manipulation of IV

16. Experimental terms (3) • Practice • Can fat people eat more bacon than skinny people? • Does B.O. significantly decrease attractiveness? • Do kids who get “hooked on phonics” have more problems with addiction later in life • Do people who study more do better on tests?

17. Frequency Distributions • Definitions • The values taken on by a given variable • All the actual data points you obtained for a given variable • Most basic ways to look at study outcomes • Quantitative Examples: • The SAT scores for all Winthrop students • The reaction times for all study participants • Grades on the first test: #’s of As, Bs, Cs, & Ds • The starting salaries of graduates • Qualitative Examples: • Favorite TV shows of students in this class • Residence halls occupied by students in this class

18. Representing Frequency Distributions • Table: • List possible values, and indicate the number of times each value occurred. • Graphs • X-axis: possible values • Y-axis: # of times that value occured

19. Graphing Distributions • Quantitative Data • Line graphs or Histograms (columns touching) • Qualitative Data • Pie charts & Bar graphs (columns not touching) • See SPSS Guide for examples • Also, you can practice with these datasets on the website… • city sprawl • bogus winthrop data • employee data

20. A Graph of the Normal Curve • Hypothetical Frequency Distribution (Line Graph) • Shows distribution of infinitely large sample (theoretical) • Symmetrical • Shows common and uncommon (extreme) scores • Basis for testing hypotheses • Percentiles Population SAT Scores μ = 500

21. Normal Curve (with raw and standard scores) μ Few Extreme Scores Few Extreme Scores

22. Deviations from Normality • Ways in which distribution can be non-normal • Skew • Positive Skew • Negative Skew • Kurtosis • Platykurtic • Mesokurtic • Leptokurtic • Modality • Unimodal • Bimodal (etc.)

23. Graphing Sample Means • One IV: Typically use bar-graph • Two IV: Typically use line-graph

24. Math Review • Preparation for Calculating Standard Deviation • Learn the differences between… • Σx • Σx2 • (Σx)2

25. Problem #1

26. Problem #1 Answer

27. Problem #2

28. Problem #2: Answer-a

29. Problem #2: Answer-b

30. Problem #3

31. Problem #3: Answer

32. Descriptive Statistics • Measures of Central Tendency • Where does the center of the distribution fall? • Where are most of the scores • Measures of Variability • How spread out is the distribution? • How dispersed are the scores? • Importance: • To determine whether IV affects DV, we consider: • The difference between the means • The amount of variability

33. Imaginary Study with 2 Outcomes • Purpose: See why variability is important • Research Question: • Imagine a business where customers are routinely offended: • comments about their mothers • misc. name calling • Does social skills training for clerks improve customer satisfaction scores. • IV: Social Skills training (training, no training) • DV: Customer Satisfaction • Imagine two worlds where we get two different outcomes

34. Version 1 Version 2 Training Study Outcomes

35. Data: # of close friends Note: Use “frequencies” in SPSS Mean arithmetic mean: all scores divided by “n” Sample: xbar or M Population: μ (“mu”) most arithmetically sophisticated best predictor if no other info available used in deviation score calculation M = 4.36 Median Score at 50th percentile – middle score less influenced by skew Md = 4 Mode most frequent score used with qualitative data Mo = 3 Measures of Central Tendency

36. Data: # of close friends What’s best for A? What’s best for B? What’s best for C? Choosing Measures of Central Tendency

37. SPSS – Setting up Frequencies Analysis

38. SPSS Frequencies Output (partial) • Note: Need to select mean, median, & mode

39. Measures of Variability • What is Variability? • dispersion; spread; distance between scores • “Some people did really well, some did really poorly” • “My tips are always about the same, between $30 and $35” • “Some students study only a few minutes a day, some put in 30 hours per week.” • Range • simplest measure • High Score – Low Score • Problems: • only uses two scores – not good for summarize entire distribution • unduly affected by extreme scores

40. The Big Daddy: Standard Deviation • Standard Deviation • The typical deviation of a score from the mean of the distribution • Most scores (68%) fall between +1 and –1 SD. • Four Steps to Standard Deviation • 1. Deviation Score • 2. Sum of Squares • 3. Variance • 4. Standard Deviation

41. 1. Deviation Scores • idea: • consider deviation of every score and add up • distance from mean of a given score: x – xbar • positive/negative deviation scores fall to the ____ of the mean • problem • why can’t we just add up the deviation scores • consider distribution of : 1, 2, 3

42. 2. Sum of Squares (SS) • Means “Sum of the Squared Deviation Scores” • Square each score, then add up • Conceptual Formula (how we think about it) • Computational Formula (how we calculate by hand) Sum of x Quantity Squared Sum of x Squared • Problem • Biased by sample size – bigger samples have bigger SS

43. 3. Variance • Sum of Squares – no control for size of sample • Think of relation between sumand average – divide sumby n • … with sum of sq.and variance – divide sum of sq. by n • Variance: • Average of the Squared Deviation Scores

44. 4. Standard Deviation • Want measure in metric of raw scores • Remember?? We used Sum of the SQUARED Deviations • So…we take the square root of the variance • Note, subscript “x” is optional note that σ is no longer squared

45. How high should the bridge be? Truck Height: 7,6,8,5,6,5,6,7 average: 6.25 Can we build it 6.25? SD: Bridge Building Example • Calculation Tip: • Think anal retentive!!

46. So we’d expect the truck height to range between about 6.25  .9682 Roughly 5.25 to 7.25. But… What if we missed some extremely tall trucks??? Should actually calculate ŝ – Standard Deviation as a population estimate SD: Bridge Building II

47. SD: Typical Formula • Standard Deviation as a Population Parameter • SD as a Population Parameter Estimate • corrects for bias of smaller samples – missing of extreme scores

48. SD: Different Forms

49. So… σ = 0.9682 ŝ = 1.0351 SD calculated as estimate will always be larger. SD: Bridge Building Revisited

50. What type of Standard Deviation? • A manager wants to know the variability in shift productivity for planning future projects. • A teacher calculates the variability of reading scores for just her class of 25 students, and only applies it to her sample. • The Educational Testing Service calculates the variability among SAT scores for all the students that took the SAT. • A researcher determines the variability in reaction time in a perception study. • Your statistics professor calculates test score variability with 25 students to know how much variability to expect on that sort of test. • A researcher on anxiety collects data from 1000 participants in order to develop norms for a new anxiety instrument.