Posted: August 4th, 2022

# Mod 5 – Graphing homework

2

2

Module

3

– Two-way A

N

OVA

Martha Ramsey

Saint Leo University

Research Methods II: PSY

5

35

Instructor Keith Burton

July 24,

20

22

Module 3 – Two-way ANOVA

In this assignment, a two-way ANOVA test was conducted to study the effects of the perceived importance of a task and

interference

/distraction on memory. The test was conducted using SPSS.

The two-way ANOVA results showed there was a significant main effect of interference,

F

(

1

,

16

) =

36.779

, p <

.001

, ηp2 =

.697

, such that interference (M = 59.30, SD = 5.36) had a lower memory score than

no interference

(M = 80.40, SD = 15.87). This indicates the effect of interference on lowering the memory of the topic being addressed to the participant. There was also a significant main effect of importance, F (1,16) =

69.955

, p < .001, ηp2 =

.814

, such that

not important

(M = 55.30, SD = 14.41) had a lower memory score than

highly important

(M = 84.40, SD = 12.14). This indicates the effect of importance on the memory of the topic being addressed to the participant. The interaction effect was not significant F (1,16) = .001, p =

.977

, ηp2 =

.000

, indicating there was no significant interaction between the effects of perceived importance of a task and interference/distraction on memory.

5

not important

5

highly important

5

Total

10

Total

not important

10

highly important

10

Total

 Descriptive Statistics Dependent Variable: Memory Interference Importance Mean Std. Deviation N interference not important 44.8000 12.75539 5 highly important 73.8000 6.76018 Total 59.3000 18.06 19 3 10 no interference 65.8000 5.35724 95.0000 2.23607 80.4000 15.86891 55.3000 14.40717 84.4000 12.13992 69.8500 19.77312 20

Dependent Variable: Memory

97580.450

.000

Interference

1

2226.050

.000

Importance

1

4234.050

.000

1

.050

.000

Total

20

 Tests of Between-Subjects Effects Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Corrected Model 6460.150a 3 2153.383 35.578 .000 .870 Intercept 97580.450 1 1612.234 .990 2226 .050 36.779 .697 4234.050 69.955 .814 Interference * Importance .050 .001 .977 Error 968.400 16 60.525 105009.000 Corrected Total 7428.550 19 a. R Squared = .870 (Adjusted R Squared = .845)

2

2

Module 3 – Two-way ANOVA

Martha Ramsey

Saint Leo University

Research Methods II: PSY 535

Instructor Keith Burton

July 24, 2022

Your temporary usage period for IBM SPSS Statistics will expire in 4913 days.

/COMPRESSED.

UNIANOVA Memory BY Interference Importance

/METHOD=SSTYPE(3)

/INTERCEPT=INCLUDE

/EMMEANS=TABLES(OVERALL)

/EMMEANS=TABLES(Interference*Importance)

/PRINT ETASQ DESCRIPTIVE HOMOGENEITY

/CRITERIA=ALPHA(.05)

/DESIGN=Interference Importance Interference*Importance.

Univariate Analysis of Variance

Notes
Output Created
19-JUL-2022 18:54:00
Input
Data
Active Dataset
DataSet0
Filter

Weight

Split File

N of Rows in Working Data File
21
Missing Value Handling
Definition of Missing
User-defined missing values are treated as missing.
Cases Used
Statistics are based on all cases with valid data for all variables in the model.
Syntax
UNIANOVA Memory BY Interference Importance
/METHOD=SSTYPE(3)
/INTERCEPT=INCLUDE
/EMMEANS=TABLES(OVERALL)
/EMMEANS=TABLES(Interference*Importance)
/PRINT ETASQ DESCRIPTIVE HOMOGENEITY
/CRITERIA=ALPHA(.05)
/DESIGN=Interference Importance Interference*Importance.
Resources
Processor Time
00:00:00.02
Elapsed Time
00:00:00.04

Between-Subjects Factors

Value Label
N
Interference
1.00
interference
10
2.00
no interference
10
Importance
1.00
not important
10
2.00
highly important
10

Descriptive Statistics
Dependent Variable: Memory
Interference
Importance
Mean
Std. Deviation
N
interference
not important
44.8000
12.75539
5
highly important
73.8000
6.76018
5
Total
59.3000
18.06193
10
no interference
not important
65.8000
5.35724
5
highly important
95.0000
2.23607
5
Total
80.4000
15.86891
10
Total
not important
55.3000
14.40717
10
highly important
84.4000
12.13992
10
Total
69.8500
19.77312
20

Levene’s Test of Equality of Error Variances
a,b

Levene Statistic
df1
df2
Sig.
Memory
Based on Mean
11.196
3
16
.000
Based on Median
1.790
3
16
.190
Based on Median and with adjusted df
1.790
3
7.790
.229
Based on trimmed mean
10.552
3
16
.000
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.a,b
a. Dependent variable: Memory
b. Design: Intercept + Interference + Importance + Interference * Importance

Tests of Between-Subjects Effects
Dependent Variable: Memory
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Corrected Model
6460.150a
3
2153.383
35.578
.000
Intercept
97580.450
1
97580.450
1612.234
.000
Interference
2226.050
1
2226.050
36.779
.000
Importance
4234.050
1
4234.050
69.955
.000
Interference * Importance
.050
1
.050
.001
.977
Error
968.400
16
60.525

Total
105009.000
20

Corrected Total
7428.550
19

Tests of Between-Subjects Effects
Dependent Variable: Memory
Source
Partial Eta Squared
Corrected Model
.870
Intercept
.990
Interference
.697
Importance
.814
Interference * Importance
.000
Error
Total
Corrected Total

a. R Squared = .870 (Adjusted R Squared = .845)

Estimated Marginal Means

1. Grand Mean
Dependent Variable: Memory
Mean
Std. Error
95% Confidence Interval

Lower Bound
Upper Bound
69.850
1.740
66.162
73.538

2. Interference

Estimates
Dependent Variable: Memory
Interference
Mean
Std. Error
95% Confidence Interval

Lower Bound
Upper Bound
interference
59.300
2.460
54.085
64.515
no interference
80.400
2.460
75.185
85.615

Pairwise Comparisons
Dependent Variable: Memory
(I) Interference
(J) Interference
Mean Difference (I-J)
Std. Error
Sig.b
95% Confidence Interval for Differenceb

Lower Bound
interference
no interference
-21.100*
3.479
.000
-28.476
no interference
interference
21.100*
3.479
.000
13.724

Pairwise Comparisons
Dependent Variable: Memory
(I) Interference
(J) Interference
95% Confidence Interval for Difference

Upper Bound
interference
no interference
-13.724
no interference
interference
28.476
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).

Univariate Tests
Dependent Variable: Memory
Sum of Squares
df
Mean Square
F
Sig.
Partial Eta Squared
Contrast
2226.050
1
2226.050
36.779
.000
.697
Error
968.400
16
60.525

The F tests the effect of Interference. This test is based on the linearly independent pairwise comparisons among the estimated marginal means.

3. Importance

Estimates
Dependent Variable: Memory
Importance
Mean
Std. Error
95% Confidence Interval

Lower Bound
Upper Bound
not important
55.300
2.460
50.085
60.515
highly important
84.400
2.460
79.185
89.615

Pairwise Comparisons
Dependent Variable: Memory
(I) Importance
(J) Importance
Mean Difference (I-J)
Std. Error
Sig.b
95% Confidence Interval for Differenceb

Lower Bound
not important
highly important
-29.100*
3.479
.000
-36.476
highly important
not important
29.100*
3.479
.000
21.724

Pairwise Comparisons
Dependent Variable: Memory
(I) Importance
(J) Importance
95% Confidence Interval for Difference

Upper Bound
not important
highly important
-21.724
highly important
not important
36.476
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).

Univariate Tests
Dependent Variable: Memory
Sum of Squares
df
Mean Square
F
Sig.
Partial Eta Squared
Contrast
4234.050
1
4234.050
69.955
.000
.814
Error
968.400
16
60.525

The F tests the effect of Importance. This test is based on the linearly independent pairwise comparisons among the estimated marginal means.

4. Interference * Importance
Dependent Variable: Memory
Interference
Importance
Mean
Std. Error
95% Confidence Interval

Lower Bound
Upper Bound
interference
not important
44.800
3.479
37.424
52.176
highly important
73.800
3.479
66.424
81.176
no interference
not important
65.800
3.479
58.424
73.176
highly important
95.000
3.479
87.624
102.376

2

2

Module 4 – Mixed A

N

OVA

Martha Ra

m

sey

Saint Leo University

Research Methods II: PSY 535

Instructor Keith Burton

July 3

1

, 2022

Module 4 – Mixed ANOVA

In this assignment, a mixed ANOVA test was conducted to study male/

f

emale stimulus smile detection accuracy. The test was conducted using SPSS.

The descriptive statistics results showed that males accurately detected male smiles (M = 9.96, SD = 2.82) better than females accurately detected male smiles (M = 8.69, SD = 2.63). On the other hand, females accurately detected female smiles (M = 6.

23

, SD = 2.78) better than males accurately detected female smiles (M = 5.22, SD = 2.00).

The mixed ANOVA results showed there was a significant main effect of smiles,

F

(1,

47

) = 38.18, p < .001, η2 = .45. This indicates the detection accuracy of smiles were significantly different irrespective of the gender of the participant. The main effect of gender participant was not significant, F (1,47) = .08, p = .79, η2 =

.002

. This indicates if we ignore the smiles, male and female participants did not significantly differ in their detection accuracy. The interaction effect between smiles and gender participant was not significant F (1,47) = 3.82, p = .06, η2 = .08, indicating if we ignore whether the smile is from a male or female, male and female participants did not significantly differ in their detection accuracy of male smiles or female smiles.

f

26

m

23

Total

49

 Descriptive Statistics gender_participant Mean Std. Deviation N male_smiles f 8.6923 2.63468 26 m 9.9565 2.82003 23 Total 9.2857 2.76887 49 female_smiles 6.2308 2.77572 5.2174 1.99901 5.7551 2.47092

316.389

38.183

316.389

1.000

316.389

38.183

.000

.448

38.183

1.000

316.389

1.000

316.389

38.183

.000

.448

38.183

1.000

316.389

1.000

316.389

38.183

.000

.448

38.183

1.000

Sphericity Assumed

1

31.654

3.820

Greenhouse-Geisser

31.654

1.000

31.654

3.820

.057

.075

3.820

.482

Huynh-Feldt

31.654

1.000

31.654

3.820

.057

.075

3.820

.482

Lower-bound

31.654

1.000

31.654

3.820

.057

.075

3.820

.482

Sphericity Assumed

Greenhouse-Geisser

389.448

8.286

Huynh-Feldt

389.448

47.000

8.286

Lower-bound

389.448

47.000

8.286

 Tests of Within-Subjects Effects Measure: MEASURE_1 Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Noncent. Parameter Observed Powera Smiles Sphericity Assumed 316.389 1 38.183 .000 .448 1.000 Greenhouse-Geisser Huynh-Feldt Lower-bound Smiles * gender_participant 31.654 3.820 .057 .075 .482 Error (Smiles) 389.448 47 8.286 47.000 a. Computed using alpha = .05

Measure: MEASURE_1

Source

Type III Sum of Squares

df

Mean Square

F

Sig.

Partial Eta Squared

Noncent. Parameter

Observed Powera

1

5527.404

.000

1084.369

1.000

gender_participant

1

.384

.075

.075

47

a. Computed using alpha = .05

 Tests of Between-Subjects Effects Transformed Variable: Average Intercept 5527.404 1084.369 .958 .384 .785 .002 .058 Error 239.575 5.097

2

2

Module 4 – Mixed ANOVA

Martha Ramsey

Saint Leo University
Research Methods II: PSY 535
Instructor Keith Burton
July 31, 2022

GLM male_smiles female_smiles BY gender_participant
/WSFACTOR=Smiles 2 Polynomial
/METHOD=SSTYPE(3)
/POSTHOC=gender_participant(TUKEY)
/PLOT=PROFILE(Smiles*gender_participant) TYPE=LINE ERRORBAR=NO MEANREFERENCE=NO YAXIS=AUTO
/EMMEANS=TABLES(gender_participant*Smiles)
/PRINT=DESCRIPTIVE ETASQ OPOWER HOMOGENEITY
/CRITERIA=ALPHA(.05)
/WSDESIGN=Smiles
/DESIGN=gender_participant.

General Linear Model

Warnings
Post hoc tests are not performed for gender_participant because there are fewer than three groups.

Within-Subjects Factors
Measure: MEASURE_1
Smiles
Dependent Variable
1
male_smiles
2
female_smiles

Between-Subjects Factors

N
gender_participant
f
26
m
23

Descriptive Statistics

gender_participant
Mean
Std. Deviation
N
male_smiles
f
8.6923
2.63468
26
m
9.9565
2.82003
23
Total
9.2857
2.76887
49
female_smiles
f
6.2308
2.77572
26
m
5.2174
1.99901
23
Total
5.7551
2.47092
49

Box’s Test of Equality of Covariance Matrices
a
Box’s M
10.215
F
3.247
df1
3
df2
818359.675
Sig.
.021
Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups.a
a. Design: Intercept + gender_participant
Within Subjects Design: Smiles

Multivariate Tests
a
Effect
Value
F
Hypothesis df
Error df
Smiles
Pillai’s Trace
.448
38.183b
1.000
47.000
Wilks’ Lambda
.552
38.183b
1.000
47.000
Hotelling’s Trace
.812
38.183b
1.000
47.000
Roy’s Largest Root
.812
38.183b
1.000
47.000
Smiles * gender_participant
Pillai’s Trace
.075
3.820b
1.000
47.000
Wilks’ Lambda
.925
3.820b
1.000
47.000
Hotelling’s Trace
.081
3.820b
1.000
47.000
Roy’s Largest Root
.081
3.820b
1.000
47.000

Multivariate Tests
a
Effect
Sig.
Partial Eta Squared
Noncent. Parameter
Smiles
Pillai’s Trace
.000
.448
38.183
Wilks’ Lambda
.000
.448
38.183
Hotelling’s Trace
.000
.448
38.183
Roy’s Largest Root
.000
.448
38.183
Smiles * gender_participant
Pillai’s Trace
.057
.075
3.820
Wilks’ Lambda
.057
.075
3.820
Hotelling’s Trace
.057
.075
3.820
Roy’s Largest Root
.057
.075
3.820

Multivariate Tests
a
Effect
Observed Powerc
Smiles
Pillai’s Trace
1.000
Wilks’ Lambda
1.000
Hotelling’s Trace
1.000
Roy’s Largest Root
1.000
Smiles * gender_participant
Pillai’s Trace
.482
Wilks’ Lambda
.482
Hotelling’s Trace
.482
Roy’s Largest Root
.482
a. Design: Intercept + gender_participant
Within Subjects Design: Smiles
b. Exact statistic
c. Computed using alpha = .05

Mauchly’s Test of Sphericity
a
Measure: MEASURE_1
Within Subjects Effect
Mauchly’s W
Approx. Chi-Square
df
Sig.
Epsilonb

Greenhouse-Geisser
Smiles
1.000
.000
0
.
1.000

Mauchly’s Test of Sphericity
a
Measure: MEASURE_1
Within Subjects Effect
Epsilon
Huynh-Feldt
Lower-bound
Smiles
1.000
1.000
Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix.a
a. Design: Intercept + gender_participant
Within Subjects Design: Smiles
b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table.

Tests of Within-Subjects Effects
Measure: MEASURE_1
Source
Type III Sum of Squares
df
Mean Square
Smiles
Sphericity Assumed
316.389
1
316.389
Greenhouse-Geisser
316.389
1.000
316.389
Huynh-Feldt
316.389
1.000
316.389
Lower-bound
316.389
1.000
316.389
Smiles * gender_participant
Sphericity Assumed
31.654
1
31.654
Greenhouse-Geisser
31.654
1.000
31.654
Huynh-Feldt
31.654
1.000
31.654
Lower-bound
31.654
1.000
31.654
Error(Smiles)
Sphericity Assumed
389.448
47
8.286
Greenhouse-Geisser
389.448
47.000
8.286
Huynh-Feldt
389.448
47.000
8.286
Lower-bound
389.448
47.000
8.286

Tests of Within-Subjects Effects
Measure: MEASURE_1
Source
F
Sig.
Partial Eta Squared
Smiles
Sphericity Assumed
38.183
.000
.448
Greenhouse-Geisser
38.183
.000
.448
Huynh-Feldt
38.183
.000
.448
Lower-bound
38.183
.000
.448
Smiles * gender_participant
Sphericity Assumed
3.820
.057
.075
Greenhouse-Geisser
3.820
.057
.075
Huynh-Feldt
3.820
.057
.075
Lower-bound
3.820
.057
.075
Error(Smiles)
Sphericity Assumed

Greenhouse-Geisser

Huynh-Feldt

Lower-bound

Tests of Within-Subjects Effects
Measure: MEASURE_1
Source
Noncent. Parameter
Observed Powera
Smiles
Sphericity Assumed
38.183
1.000
Greenhouse-Geisser
38.183
1.000
Huynh-Feldt
38.183
1.000
Lower-bound
38.183
1.000
Smiles * gender_participant
Sphericity Assumed
3.820
.482
Greenhouse-Geisser
3.820
.482
Huynh-Feldt
3.820
.482
Lower-bound
3.820
.482
Error(Smiles)
Sphericity Assumed

Greenhouse-Geisser

Huynh-Feldt

Lower-bound

a. Computed using alpha = .05

Tests of Within-Subjects Contrasts
Measure: MEASURE_1
Source
Smiles
Type III Sum of Squares
df
Mean Square
F
Sig.
Smiles
Linear
316.389
1
316.389
38.183
.000
Smiles * gender_participant
Linear
31.654
1
31.654
3.820
.057
Error(Smiles)
Linear
389.448
47
8.286

Tests of Within-Subjects Contrasts
Measure: MEASURE_1
Source
Smiles
Partial Eta Squared
Noncent. Parameter
Observed Powera
Smiles
Linear
.448
38.183
1.000
Smiles * gender_participant
Linear
.075
3.820
.482
Error(Smiles)
Linear

a. Computed using alpha = .05

Levene’s Test of Equality of Error Variances
a

Levene Statistic
df1
df2
Sig.
male_smiles
Based on Mean
.274
1
47
.603
Based on Median
.257
1
47
.614
Based on Median and with adjusted df
.257
1
46.984
.614
Based on trimmed mean
.264
1
47
.610
female_smiles
Based on Mean
1.113
1
47
.297
Based on Median
.525
1
47
.472
Based on Median and with adjusted df
.525
1
37.991
.473
Based on trimmed mean
.874
1
47
.355
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.a
a. Design: Intercept + gender_participant
Within Subjects Design: Smiles

Tests of Between-Subjects Effects
Measure: MEASURE_1
Transformed Variable: Average
Source
Type III Sum of Squares
df
Mean Square
F
Sig.
Intercept
5527.404
1
5527.404
1084.369
.000
gender_participant
.384
1
.384
.075
.785
Error
239.575
47
5.097

Tests of Between-Subjects Effects
Measure: MEASURE_1
Transformed Variable: Average
Source
Partial Eta Squared
Noncent. Parameter
Observed Powera
Intercept
.958
1084.369
1.000
gender_participant
.002
.075
.058
Error

a. Computed using alpha = .05

Estimated Marginal Means

1. Smiles

Estimates
Measure: MEASURE_1
Smiles
Mean
Std. Error
95% Confidence Interval

Lower Bound
Upper Bound
1
9.324
.390
8.540
10.108
2
5.724
.350
5.021
6.428

Pairwise Comparisons
Measure: MEASURE_1
(I) Smiles
(J) Smiles
Mean Difference (I-J)
Std. Error
Sig.b
95% Confidence Interval for Differenceb

Lower Bound
Upper Bound
1
2
3.600*
.583
.000
2.428
4.772
2
1
-3.600*
.583
.000
-4.772
-2.428
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).

Multivariate Tests

Value
F
Hypothesis df
Error df
Sig.
Partial Eta Squared
Pillai’s trace
.448
38.183a
1.000
47.000
.000
.448
Wilks’ lambda
.552
38.183a
1.000
47.000
.000
.448
Hotelling’s trace
.812
38.183a
1.000
47.000
.000
.448
Roy’s largest root
.812
38.183a
1.000
47.000
.000
.448

Multivariate Tests

Noncent. Parameter
Observed Powerb
Pillai’s trace
38.183
1.000
Wilks’ lambda
38.183
1.000
Hotelling’s trace
38.183
1.000
Roy’s largest root
38.183
1.000
Each F tests the multivariate effect of Smiles. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means.
a. Exact statistic
b. Computed using alpha = .05

2. gender_participant * Smiles
Measure: MEASURE_1
gender_participant
Smiles
Mean
Std. Error
95% Confidence Interval

Lower Bound
Upper Bound
f
1
8.692
.534
7.618
9.767
2
6.231
.479
5.267
7.195
m
1
9.957
.568
8.814
11.099
2
5.217
.509
4.193
6.242

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