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Statistics Brian J. Piper, Ph.D. ψ

Introductory Psychology: Statistics

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lecture 2 from a college level introduction to psychology course taught Fall 2011 by Brian J. Piper, Ph.D. ([email protected]) at Willamette University, crash course in descriptive and inferential statistics, includes scatterplots, correlation, mean, SD/SEM, effect size

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Page 2: Introductory Psychology: Statistics

Mark Twain (?)

• There are three types of lies, lies, damn lies, and statistics.

http://en.wikipedia.org/wiki/Lies,_damned_lies,_and_statistics

Page 3: Introductory Psychology: Statistics

Goals

• Levels of measurement• Group comparisons (t)• Association (scatterplot/r)• Effect-size (d)

Page 4: Introductory Psychology: Statistics

Levels of Measurement

• Nominal: categorical, example: sex• Ratio: quantitative, example: age• Ordinal: ranking, example: self-report

– Strongly disagree = 1– Disagree = 2– Neutral = 3– Agree = 4– Strongly agree = 5– S.D.-------------------------------------------------------------------------------------------S.A.

)

Page 5: Introductory Psychology: Statistics

What do these countries have in common?

• Liberia• Burma• United States

Page 6: Introductory Psychology: Statistics

Metric SystemUnit Symbol Factor

tera T 1 x 1012

giga G 1 x 109

kilo K 1 x 103

--- -- 1

centi c 1 x 10-2

milli m 1 x 10-3

micro μ 1 x 10-6

Page 7: Introductory Psychology: Statistics

DataSex Height

(m)Weight

(kg)

Sex Height (m)

Weight

(kg)

Female 2.0 60 Male 2.5 80

Female 1.9 58 Male 2.3 76

Female 1.8 56 Male 2.1 74

Female 1.7 54 Male 2.0 73

Female 1.6 52 Male 1.8 72

Female 1.5 50 Male 1.7 70

Female 1.4 48 Male 1.6 68

Female 1.3 46 Male 1.5 65

Female 1.6 57 Male 2 50

Page 8: Introductory Psychology: Statistics

Sex Height (m)

Weight

(kg)

Sex Height (m)

Weight

(kg)

Female 2.0 60 Male 2.5 80

Female 1.9 58 Male 2.3 76

Female 1.8 56 Male 2.1 74

Female 1.7 54 Male 2.0 73

Female 1.6 52 Male 1.8 72

Female 1.5 50 Male 1.7 70

Female 1.4 48 Male 1.6 68

Female 1.3 46 Male 1.5 65

Female 1.6 57 Male 2 50Average 1.64 53.4 1.94 69.8

Mean (or average) = Sum (X) /N where N is the # of scores

Page 9: Introductory Psychology: Statistics

Variability

• Variability: how much scores differ, on average, from mean– Variance = Sum (X – Mean)2 /N– Standard Deviation (SD) = √Variance– Standard Error of Mean (SEM) = SD / √ N

Page 10: Introductory Psychology: Statistics

Group Comparisons I

• Are women lighter then men?– P = probability value

if p < .05 therefore statistically “significant”– t test = (MeanMales - MeanFemales) / SEM

– t = 4.97, p = .0001

malefemale

SEX_

40

50

60

70

80

90

WE

IGH

T

0123456789Count

0 1 2 3 4 5 6 7 8 9Count

←←

Page 11: Introductory Psychology: Statistics

Group Comparisons II

• Do men have a higher IQ then women?• T is the measure of variability (e.g. S.E.M.)

A. Sample Size = 40

Men (N = 20) Women (N=20)0

25

50

75

100

125

IQ

B. Sample Size = 4,000

Men (N = 2000) Women (N=2000)0

25

50

75

100

125

IQ

C. Sample Size = 4,000 ( * p < .05).

Men (N = 2000) Women (N=2000)90

95

100

105

*

IQ

A finding with a * refers to a “statistically significant” finding, e.g. men > women

Page 12: Introductory Psychology: Statistics

Error Bars Example 2

Batterham et al. New England Journal of Medicine, 349, 941-948.

Page 13: Introductory Psychology: Statistics

1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0HEIGHT_F

44

48

52

56

60

64

WE

IGH

T_F

Scatterplots

1.4 1.6 1.8 2.0 2.2 2.4 2.6HEIGHT_M

40

50

60

70

80

90

WE

IGH

T_M

ALEOutlier? ->

Outlier? ->

Page 14: Introductory Psychology: Statistics

Positive Association

Study Hours Score

3 80

5 90

2 75

6 80

7 90

1 50

2 65

7 85

1 40

7 100

Page 15: Introductory Psychology: Statistics

Negative Association

Variable A

Variable B

Page 16: Introductory Psychology: Statistics
Page 17: Introductory Psychology: Statistics

Standardized “Z” Scores

• Z is a #• Z = 0 therefore average• Z > 0 therefore above average• Z < 0 therefore below average

• Z = (X – Mean) / SD• Z = (600 – 500) / 100

= 1.0

3.1

Page 18: Introductory Psychology: Statistics

• r: quantifies relationship between two variables (e.g. x & y)• No association: r = 0.00 (C)• Positive association: r > 0.00 (A B)• Negative association: r < 0.00 (DE)• Strong association: A E, Weak association: B D

A B C

DE

r = Sum(Zx * Zy)/ N

3.6

Page 19: Introductory Psychology: Statistics

ProbabilityFrequency Blue Brown

+ 2 10

- 999,998 999,990

Probability

Blue Brown

+ .000002 .000010

- .999998 .99999BrainCancer

Eyes Eyes

Page 20: Introductory Psychology: Statistics

Risk

• Absolute Risk: Rate of condition/total population studied, e.g. .000002 or .000010

• Relative Risk: Rate of condition among group A divided by rate of condition among group B– .000010 = 5.0

________.0000002

.0002% or .0010%

3.2

Page 21: Introductory Psychology: Statistics

Effect-Size

• Procedure used to summarize the magnitude of group differences.– Cohen’s d = (MeanA – MeanB) / SD

• d = 0.20 small effect size• d = 0.50 medium effect size• d = 0.80 large effect size

4.3

Can be averaged for multiple studies (meta-analysis).

Page 22: Introductory Psychology: Statistics

SummaryGoal Intuition Test

Difference in means

Bar Graphs with SEM

“t-test”

Relationship between variables (ratio x ratio)

Scatterplot Correlation “r”

Summarize many studies

Read papers Effect size “Cohen’s d”