A 2×3 experimental study was conducted to examine the degree to which Room Color affects

learning across two different class Topics. The data are provided on the next page. Three room

colors were used (red, green, and blue) and performance was assessed across two class topics

(math and history). Learning was measured by inspecting final exam scores.Part I

Analyze the data as though the design was a 2×3 between-subjects (independent groups)

design in which N = 120. You will find pages 410-414 in your text to be helpful with this.

[After data entry: Analyze → General Linear Model → Univariate → Select dependent variable &

the “fixed factors” (independent variables) to analyze → Under “options” you can get means for the

interaction by checking “descriptive statistics” → CONTINUE → OK]

Part II

Analyze the data as though the design was a 2×3 within-subjects (repeated measures) design

in which N = 20. Again, pages 410-414 of your text will be helpful.

[After data entry: Analyze → General Linear Model → Repeated Measures → Define the

independent variables (enter levels) → Define: Highlight appropriate column [on left] to match

expected label [on right] and press arrow to move it over → Under “options” you can have it display

means for the interaction by checking “descriptive statistics” → CONTINUE → OK]

anova_help.pdf

008_anova_spss.pdf

Unformatted Attachment Preview

ANOVA Between

Data should be entered so that each row represents a unique subject.

There should be one column to represent each unique independent variable.

One column should contain the subjects’ data.

So, for example:

Sbj

F1

F2

DATA

1

1

1

1

7

2

1

7

2

1

2

3

8

2

2

7

3

1

3

5

9

2

3

7

4

1

1

2

10

2

1

8

5

1

2

4

11

2

2

9

6

1

3

6

12

2

3

10

In SPSS:

Analyze → General Linear Model → Univariate

In the Univariate screen/box, you will need to designate which column contains your data (dependent variable)

In the Univariate screen/box, you will need to designate which column contains each Independent Variable

(fixed factors).

OK

Should produce your ANOVA output.

ANOVA Within

Data should be entered so that each row represents a unique subject.

There should be a column to represent data from that subject for each unique condition.

So, for example:

Sbj

A1B1 A1B2 A1B3 A2B1 A2B2 A2B3

1

1

3

5

7

7

7

2

2

4

6

8

9

10

In SPSS:

Analyze → General Linear Model → Repeated Measures

In the Repeated Measures screen/box, you will need to designate the names of your factors and how many

levels of each – be sure to ADD them.

Then click DEFINE so that you can tell SPSS which column represents which combination of factors.

OK

Should produce your ANOVA output (look for your F-values etc. on the lines that say “Sphericity Assumed).

Tests of Between-Subjects Effects

Type III Sum of

Source

Squares

df

Mean Square

F

Sig.

Corrected Model

77.750

a

5

15.550

10.976

.006

Intercept

396.750

1

396.750

280.059

.000

Afactor

60.750

1

60.750

42.882

.001

Bfactor

12.500

2

6.250

4.412

.066

Afactor * Bfactor

4.500

2

2.250

1.588

.280

Error

8.500

6

1.417

Total

483.000

12

86.250

11

Corrected Total

E.g., Main effect of Factor A, F(1,6) = 42.88, p = .001. Main effect of Factor B, F(2,6) = 4.41,

p = .066. Interaction of A with B, F(2,6) = 1.59, p = .280.

Tests of Within-Subjects Effects

Type III Sum of

Source

FactorA

Error(FactorA)

Sphericity Assumed

Greenhouse-Geisser

Huynh-Feldt

Lower-bound

Sphericity Assumed

Squares

60.750

60.750

60.750

60.750

.750

Greenhouse-Geisser

.750

Huynh-Feldt

.750

.

.

Sphericity Assumed

Greenhouse-Geisser

Huynh-Feldt

Lower-bound

Sphericity Assumed

.750

12.500

12.500

12.500

12.500

.500

1.000

2

1.000

.

1.000

2

.750

6.250

12.500

.

12.500

.250

Greenhouse-Geisser

.500

1.000

.500

Huynh-Feldt

.500

.

.

Lower-bound

Sphericity Assumed

Greenhouse-Geisser

Huynh-Feldt

Lower-bound

Sphericity Assumed

.500

4.500

4.500

4.500

4.500

.500

1.000

2

1.000

.

1.000

2

.500

2.250

4.500

.

4.500

.250

Greenhouse-Geisser

.500

1.000

.500

Huynh-Feldt

.500

.

.

Lower-bound

.500

1.000

.500

Lower-bound

FactorB

Error(FactorB)

FactorA * FactorB

Error(FactorA*FactorB)

df

1

1.000

.

1.000

1

Mean Square

60.750

60.750

.

60.750

.750

1.000

.750

F

81.000

81.000

.

81.000

Sig.

.070

.070

.

.070

25.000

25.000

.

25.000

.038

.126

.

.126

9.000

9.000

.

9.000

.100

.205

.

.205

E.g., Main effect of Factor A, F(1,1) = 81.00, p = .070. Main effect of Factor B, F(2,2) = 25.00, p = .038. Interaction of A with B,

F(2,2) = 9.00, p = .100.

SPSS-04

Analysis of Variance (ANOVA)

There’s no limit to how complicated things can get, on account of one thing

always leading to another. – E. B. White

NAME:

A 2×3 experimental study was conducted to examine the degree to which Room Color affects

learning across two different class Topics. The data are provided on the next page. Three room

colors were used (red, green, and blue) and performance was assessed across two class topics

(math and history). Learning was measured by inspecting final exam scores.

Part I

Analyze the data as though the design was a 2×3 between-subjects (independent groups)

design in which N = 120. You will find pages 410-414 in your text to be helpful with this.

[After data entry: Analyze → General Linear Model → Univariate → Select dependent variable &

the “fixed factors” (independent variables) to analyze → Under “options” you can get means for the

interaction by checking “descriptive statistics” → CONTINUE → OK]

Part II

Analyze the data as though the design was a 2×3 within-subjects (repeated measures) design

in which N = 20. Again, pages 410-414 of your text will be helpful.

[After data entry: Analyze → General Linear Model → Repeated Measures → Define the

independent variables (enter levels) → Define: Highlight appropriate column [on left] to match

expected label [on right] and press arrow to move it over → Under “options” you can have it display

means for the interaction by checking “descriptive statistics” → CONTINUE → OK]

REMEMBER: IT IS EXPECTED THAT YOU WILL DO YOUR OWN WORK!

You should be able to (1) Print out a copy of both analysis outputs, and (2) complete the

following summary information from the SPSS outputs:

MEANS

(round to 1

decimal place)

Red

Green

Blue

Math

History

Between-Subjects Analysis

Main effect of Class Topic:

F(

,

) = _________,

p = __________

Main effect of Room Color:

F(

,

) = _________,

p = __________

Interaction effect (Topic x Color):

F(

,

) = _________,

p = __________

Main effect of Class Topic:

F(

,

) = _________,

p = __________

Main effect of Room Color:

F(

,

) = _________,

p = __________

Interaction effect (Topic x Color):

F(

,

) = _________,

p = __________

Within-Subjects Analysis

DUE: ONE WEEK FROM TODAY

…

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