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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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