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 youve ever done

2
Small Group Questions

• Name, where youre from
• Best class ever why
• Stupidest thing youve 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 cant get enough
• Chocolate
• Schlitz Malt Liquor
• Diet Pepsi
• Diet Coke
• Diet Schlitz Malt Liquor
• Prune Juice
• Red Bull

7
Pop Quiz 1

• 5. Your instructors 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
Stats Basics 1st Week Overview

• Course Tips
• Types of Data
• Graphing Distributions
• The Normal Curve
• Graphing Sample Means
• Practicing with SPSS

9
Secret Course Tips

• Bulldog Tactics
• Note-taking
• Write Process
• Ask questions!
• Slow me down!
• Homework
• Studying
• Often
• Active
• Self-Explanation
• Practicing SPSS
• Laugh at my jokes!!

• Syllabus
• Office hours
• Engagement Attendance
• Quizzes
• Request for leniency
• Notebook
• Course Packs
• Organization
• Homework, Labs, Reading
• Class time
• Set-up first
• Please avoid surfing

10
Make Friends Quickly!!

• 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

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 cant be ranked
• gender, political affiliation, major, car maker
• Check
• Gender
• region
• weight
• depression
• steps
• Social Security Number
• Letter Grade A, B, C, D

13
Scales of measurement

• 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 moms love list
• height
• IQ
• weight
• Self-esteem (7 point Likert Scale)
• SAT score

• 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
• Sx
• Sx2
• (Sx)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
Training Study Outcomes

• Version 1

• Version 2

35
Measures of Central Tendency

• 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

36
Choosing Measures of Central Tendency

• Data of close friends

• Whats best for A?
• Whats best for B?
• Whats best for C?

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 cant 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 sum and average
  divide sum by 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
• Sowe take the square root of the variance
• Note, subscript x is optional

note that s is no longer squared
45
SD Bridge Building Example

• 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?

• Calculation Tip
• Think anal retentive!!

46
SD Bridge Building II

• So wed 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 s Standard Deviation
  as a population estimate

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
SD Bridge Building Revisited

• So
• s 0.9682
• s 1.0351
• SD calculated as estimate will always be larger.

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.

51
Practice
Problem Calculate s for 4,2,3
52
Practice Calculations I
53
Practice Calculations II
54
Confidence Intervals

• Combines mean with standard deviation
• 68 CI M 1SD
• We can be 68 certain that a given score will
  fall between one SD below the mean to one SD
  above.
• Example
• Bob took the history test after the rest of the
  class. The class scored 70 on average (µ 70)
  with a standard deviation of 10 (s). What score
  do you expect Bob to get?
• 68 CI M 1SD
• 68 CI 70 10
• 68 CI 60, 80
• That is, wed expect Bob to get between 60 and
  80. Well be right about 68 of the time.

55
Error Bars

• Graph in SPSS
• Shows mean 1 SD.