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# Planned Contrast: Execution (Conceptual) - PowerPoint PPT Presentation

Planned Contrast: Execution (Conceptual) 1. Must predict pattern of interaction before gathering data. Predict that Democratic women will be most opposed to gun instruction in school, compared to Democratic men, Republican men, and Republican women. Post Hoc Tests

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1. Must predict pattern of interaction before gathering data.

Predict that Democratic women will be most opposed to gun instruction in school, compared to Democratic men, Republican men, and Republican women.

Do female democrats differ from other groups?

1 = Male/Republican 5.00

2 = Male/Democrat 4.50

3 = Female/Republican 4.75

4 = Female/Democrat 2.75

Conduct six t tests? NO. Why not?

Will capitalizes on chance.

Solution: Post hoc tests of multiple comparisons.

Post hoc tests consider the inflated likelihood of Type I error

Kent's favorite—Tukey test of multiple comparisons,

which is the most generous.

NOTE: Post hoc tests can be done on any multiple set of means, not only on planned contrasts.

1. Recode data from multiple factors into single factor, as per planned contrast.

2. Run oneway ANOVA statistic

3. Select "posthoc tests" option.

Note: Not necessary to conduct planned contrast to conduct post-hoc test

ONEWAY

gunctrl BY genparty

/CONTRAST= -1 -1 -1 3

/STATISTICS DESCRIPTIVES

/MISSING ANALYSIS

/POSTHOC = TUKEY

ALPHA(.05).

Selected post-hoc test

Setting up data file

Checking accuracy of data

Disposition of data

Why obsess on these details? Murphy's Law

If something can go wrong, it will go wrong,

and at the worst possible time.

Errars Happin!

1. Get survey copy

2. Assign variable names

3. Assign variable values

4. Assign missing values

5. Proof master for accuracy

6. Make spare copy, keep in file drawer

variable values

variable names

Note: Var. values not needed for scales

1. Exercise in delay of gratification

2. Purpose: Reduce random error

3. Improve power of inferential stats.

Note: Are any cases missing data?

Are any “Minimums” too low?

Are any “Maximums” too high?

Do Ns indicate missing data?

Do SDs indicate extreme outliers?

Do variables correlate in the expected manner?

Gender

Oldest

Oldest

Youngest

Youngest

Only Child

Only Child

None

Males

10

4

10

3

6

20

Females

One

3

5

4

15

20

0

More than one

TOTAL

3

15

25

4

40

2

TOTAL

10

10

8

Using Cross Tabs to Check for

Missing or Erroneous Data Entry

Case A: Expect equal cell sizes

Case B: Impossible outcome

Raw Data

1. Hold raw data in secure place

2. File raw data by ID #

3. Hold raw date for at least 5 years post publication, per APA

Automated Data

1. One pristine source, one working file, one syntax file

2. Back up, Back up, Back up

` 3. Use external hard drive as back-up for PC

41-60

61-80

81-100

101-120

File Raw Data Records By ID Number

01-20

COMMENT DATA MANAGEMENT

IF (gender = 1 & party = 1) genparty = 1 .

EXECUTE .

IF (gender = 1 & party = 2) genparty = 2 .

EXECUTE .

IF (gender = 2 & party = 1) genparty = 3 .

EXECUTE .

IF (gender = 2 & party = 2) genparty = 4 .

EXECUTE .

COMMENT ANALYSES

UNIANOVA

gunctrl BY gender party

/METHOD = SSTYPE(3)

/INTERCEPT = INCLUDE

/PRINT = DESCRIPTIVE

/CRITERIA = ALPHA(.05)

/DESIGN = gender party gender*party .

ONEWAY

gunctrl BY genparty

/CONTRAST= -1 -1 -1 3

/STATISTICS DESCRIPTIVES

/MISSING ANALYSIS

/POSTHOC = TUKEY ALPHA(.05).

Save Syntax File!!!

Purpose: All-in-one handy summary of research project

Content: 1. Administrative (timeline, list of staff, etc.)

2. Overview of Research

3. Experiment Materials

* Surveys

* Consents, debriefings

* Manipulations

* Procedures summary/instructions

4. IRB materials

* Application

* Approval

5. Data

* Coding forms

* Syntax file

* Primary outcomes

### Correlation

Class 20

What and why of measures of association

Covariation

Pearson's r correlation coefficient

Partial Correlation

Comparing two correlations

Non-Parametric correlations

Positive

Is teacher pay related to performance?

Is exercise related to illness?

Is CO2 related to global warming?

Is platoon cohesion related to PTSD?

Is TV viewing related to shoe size?

Negative

Positive

Negative

Zero

1. How many times a week do you exercise? _____

2. How many days have you missed school this term due to illness? _____

3. How many hours of sleep do you get each night? ____

[C]

Sleep Hours

[A] Exercise

[B] Illness

A --> B Exercise reduces illness

B --> C Illness reduces exercise

C --> (A & B) Third variable (sleep) affects exercise and illness simultaneously

Regression Line

8

7

6

5

4

3

2

1

0

exercise days

sick days

# Days

Subject Number

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Σ (Xi – X) (Yi – Y)

cov (x,y) =

N – 1

(-3.32) + (0.40) + (-0.46) …+ (-1.02)

cov(exercise, sickness) =

14-1

= -23/13 = -1.77

"To all health and exercise researchers: Please send us your exercise and health covariations."

Team 1: exercise = days per week exercise, covariation = -1.77

Team 2: exercise = hours per week exercise, covariation = -34.00

What if we all we have are the covariations?

How do we compare them?

How would we know, in this case, whether Team 1 showed a larger, smaller, or equal covariation than did Team 2?

Σ (Xi – X) (Yi – Y)

r =

sxsy

(N – 1)

Pearson Correlation Coefficient

covxy

r =

sxsy

Pearson r (“rho”): -1.00 to + 1.00

Using R2 to Interpret Correlation

R2 = r2 = amount of variance shared between correlated variables.

Correl: exercise.hours, sick.days = .613

R2 = .6132 = .376

“About 38% of variability in sick days is explained by variability in exercise hours.”

R2 = .6132 = .376

Exercise hours = .376%

0 2.5 7

Number of Sick Days Last Term

Issue: How much does Variable 1 explain Variable 2, AFTER accounting for the influence of Variable 3?

Sickness and Exercise Study: How much does exercise explain days sick, AFTER accounting for the influence of nightly hours of sleep?

Sick Days

Exercise Days

Sleep Hours

var. explained = .376

var. explained by sleep alone (.17)

var. explained = .277

var. explained by exercise + sleep (.21)

var. explained by exercise alone (.04)

PARTIAL CORR /VARIABLES= sleep.hours exercise.days by sick.days /SIGNIFICANCE=TWOTAIL /MISSING=LISTWISE.

PARTIAL CORR /VARIABLES= sleep.hours sick.days by exercise.days /SIGNIFICANCE=TWOTAIL /MISSING=LISTWISE.

Assumptions of Correlations

1. Normally distributed data

2. Homogeneity of variance

3. Interval data (at least)

What if Assumptions Not Met?

Spearman's rho: Data are ordinal.

Kendall's tau: Data are ordinal, but small sample, and many scores have the same ranking

Assumptions of Correlations

1. Normally distributed data

2. Homogeneity of variance

3. Interval data (at least)

Watch

TV

1 hr

2 hr

3 hr

4 hr

5 hr

Eat Fast Food

1 day

2 day

3 day

4 day

5 day

Var. A

Var. B

What if Assumptions Not Met?

Spearman's rho: Data are ordinal.

Kendall's tau: Data are ordinal, but small sample, and many scores have the same ranking.

Watch

TV

Never

Daily

Weekly

Monthly

Yearly

Eat Fast

Food

Never

Daily

Weekends

Holidays

Leap Years

Var. A

Var. B

Issue: How do we know if one correlation is different from another?

Example: Is the nightly-sleep / sick days correl. different from the TV hours /sick days correl?

Diff. Between 2 Independent correlations

zr1 - zr2

z =

1

1

+

n1 - 3

n2 - 3

Diff. Between 2 dependent = correlations

(n-3) (1 + rxz)

tdifference = (rxy - rzy)

2 (1-r2xy -r2xz - r2zy + 2rxyrxzrzy)

Link to calculator for two ind. samples correlations

http://faculty.vassar.edu/lowry/rdiff.html