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Screen. Cabinet. Cabinet. Lecturer’s desk. Table. Computer Storage Cabinet. Row A. 3. 4. 5. 19. 6. 18. 7. 17. 16. 8. 15. 9. 10. 11. 14. 13. 12. Row B. 1. 2. 3. 4. 23. 5. 6. 22. 21. 7. 20. 8. 9. 10. 19. 11. 18. 16. 15. 13. 12. 17. 14. Row C. 1. 2.

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Presentation Transcript
slide1

Screen

Cabinet

Cabinet

Lecturer’s desk

Table

Computer Storage Cabinet

Row A

3

4

5

19

6

18

7

17

16

8

15

9

10

11

14

13

12

Row B

1

2

3

4

23

5

6

22

21

7

20

8

9

10

19

11

18

16

15

13

12

17

14

Row C

1

2

3

24

4

23

5

6

22

21

7

20

8

9

10

19

11

18

16

15

13

12

17

14

Row D

1

2

25

3

24

4

23

5

6

22

21

7

20

8

9

10

19

11

18

16

15

13

12

17

14

Row E

1

26

2

25

3

24

4

23

5

6

22

21

7

20

8

9

10

19

11

18

16

15

13

12

17

14

Row F

27

1

26

2

25

3

24

4

23

5

6

22

21

7

20

8

9

10

19

11

18

16

15

13

12

17

14

28

Row G

27

1

26

2

25

3

24

4

23

5

6

22

21

7

20

8

9

29

10

19

11

18

16

15

13

12

17

14

28

Row H

27

1

26

2

25

3

24

4

23

5

6

22

21

7

20

8

9

10

19

11

18

16

15

13

12

17

14

Row I

1

26

2

25

3

24

4

23

5

6

22

21

7

20

8

9

10

19

11

18

16

15

13

12

17

14

1

Row J

26

2

25

3

24

4

23

5

6

22

21

7

20

8

9

10

19

11

18

16

15

13

12

17

14

28

27

1

Row K

26

2

25

3

24

4

23

5

6

22

21

7

20

8

9

10

19

11

18

16

15

13

12

17

14

Row L

20

1

19

2

18

3

17

4

16

5

15

6

7

14

13

INTEGRATED LEARNING CENTER

ILC 120

9

8

10

12

11

broken

desk

slide2

Guest Lecture – Nick ThorneIntroduction 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

slide3

Please click in

My last name starts with a

letter somewhere between

A. A – D

B. E – L

C. M – R

D. S – Z

slide4

Lab sessions

Labs continue

this week with Project 2

slide8

Create example of t-test

Identify single IV (two levels)

Identify DV (must be numeric)

Graph should have two bars (one for each mean)

Think about

how you might

Study Type 2: t-test

Comparing Two

Means?

Use a t-test

slide9

Schedule of readings

Before next exam (April 11th)

Please read chapters 7 – 11 in Ha & Ha

Please read Chapters 2, 3, and 4 in Plous

Chapter 2: Cognitive Dissonance

Chapter 3: Memory and Hindsight Bias

Chapter 4: Context Dependence

slide10

Homework due – Wednesday (April 2nd)

On class website:

Please print and complete homework worksheet #15

Hypothesis testing

by the end of lecture today 3 28 14

Use this as your

study guide

By the end of lecture today3/28/14

Review what makes a study a t-test

Logic of hypothesis testing

Steps for hypothesis testing

Levels of significance (Levels of alpha)

what does p < 0.05 mean?

what does p < 0.01 mean?

type i or type ii error

Gender

.

Type I or type II error?

Independent

Variable?

Height

Dependent Variable?

IV:

Nominal

IV: Nominal Ordinal Interval or Ratio?

Who is taller men or women?

DV:

Ratio

DV: Nominal Ordinal Interval or Ratio?

IV:

Discrete

IV: Continuous or discrete?

What would null hypothesis be?

DV: Continuous

DV: Continuous or discrete?

No difference in the height between men and women

type i or type ii error1

.

Type I or type II error?

Two –tailed

One-tailed

Or

Two –tailed?

Between

Between

Or within?

Who is taller men or women?

Quasi

Quasi or True?

What would null hypothesis be?

No difference in the height between men and women

type i or type ii error2

.

Type I or type II error?

Who is taller men or women?

What would null hypothesis be?

No difference in the height between men and women

Type I error: Rejecting a true null hypothesis

Type I

Error

Saying that there is a difference in height when in fact there is not (false alarm)

Type II error: Not rejecting a false null hypothesis

Type II

Error

Saying there is no difference in height when

in fact there is a difference (miss)

This is an example of a _____.

a. correlation

b. t-test

c. one-way ANOVA

d. two-way ANOVA

t-test

type i or type ii error3

.

Type I or type II error?

Curly versus straight hair – which is more “dateable”?

What would null hypothesis be?

No difference in the dateability between curly and straight hair

Type I error: Rejecting a true null hypothesis

Saying that there is a difference in dateability when in fact there is not (false alarm)

Type II error: Not rejecting a false null hypothesis

Saying there is no difference in dateability when

in fact there is a difference (miss)

This is an example of a _____.

a. correlation

b. t-test

c. one-way ANOVA

d. two-way ANOVA

t-test

writing assignment
Writing Assignment

Please watch this video describing a series of t-tests

What is the independent variable?

How many different dependent variables did they use?

(They would conduct a different t-test

for every dependent variable)

http://www.everydayresearchmethods.com/2011/09/curly-or-straight-.html

http://www.youtube.com/watch?v=z7kfiA2SXMY

http://www.youtube.com/watch?v=n4WQhJHGQB4

writing assignment worksheet
Writing Assignment Worksheet

Design two t-tests

http://www.youtube.com/watch?v=z7kfiA2SXMY

http://www.youtube.com/watch?v=n4WQhJHGQB4

slide18

Five steps to hypothesis testing

Step 1: Identify the research problem (hypothesis)

Describe the null and alternative hypotheses

How is a t score same as a z score?

How is a t score different than a z score?

Step 2: Decision rule

  • Alpha level? (α= .05 or .01)?
  • Critical statistic (e.g. z or t) value?

Step 3: Calculations

Step 4: Make decision whether or not to reject null hypothesis

If observed z (or t) is bigger then critical z (or t) then

reject null

Population versus sample standard deviation

Population versus sample standard deviation

Step 5: Conclusion - tie findings back in to research problem

slide19

.

.

A note on z scores, and t score:

  • Numerator is always distance between means
  • (how far away the distributions are or “effect size”)
  • Denominator is always measure of variability
    • (how wide or much overlap there is between distributions)

Difference between means

Difference between means

Variability of curve(s)(within group variability)

Variabilityof curve(s)

slide20

.

A note on variability

versus effect size

Difference between means

Difference between means

Variability of curve(s)(within group variability)

Variabilityof curve(s)

slide21

.

A note on variability

versus effect size

Difference between means

Difference between means

.

Variability of curve(s)(within group variability)

Variabilityof curve(s)

effect size is considered relative to variability of distributions

.

Effect size is considered relativeto variability of distributions

1. Larger variance harder to find significant difference

Treatment

Effect

x

Treatment

Effect

2. Smaller variance easier to find significant difference

x

effect size is considered relative to variability of distributions1

.

Effect size is considered relativeto variability of distributions

Treatment

Effect

x

Difference between means

Treatment

Effect

x

Variability of curve(s)(within group variability)

slide24

Thank you!

See you next time!!