Posted: August 4th, 2022

Please assist with assignment

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

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

Interference1

2226.050

.000

Importance1

4234.050

.000

1

.050

.000

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.

SAVE OUTFILE=’C:\Users\HP 840 G3\Downloads\Homework Module 3.sav’

/COMPRESSED.

UNIANOVA Memory BY Interference Importance

/METHOD=SSTYPE(3)

/INTERCEPT=INCLUDE

/EMMEANS=TABLES(OVERALL)

/EMMEANS=TABLES(Interference) COMPARE ADJ(LSD)

/EMMEANS=TABLES(Importance) COMPARE ADJ(LSD)

/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

Comments

Input

Data

C:\Users\HP 840 G3\Downloads\Homework Module 3.sav

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) COMPARE ADJ(LSD)

/EMMEANS=TABLES(Importance) COMPARE ADJ(LSD)

/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

[DataSet0] C:\Users\HP 840 G3\Downloads\Homework Module 3.sav

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

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(Smiles) COMPARE ADJ(LSD)

/EMMEANS=TABLES(gender_participant*Smiles)

/PRINT=DESCRIPTIVE ETASQ OPOWER HOMOGENEITY

/CRITERIA=ALPHA(.05)

/WSDESIGN=Smiles

/DESIGN=gender_participant.

General Linear Model

[DataSet0] C:\Users\HP 840 G3\Downloads\Smile data.sav

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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