SPSS output

SPSS Output

In SPSS, all the outputs report notes relevant to the procedure such as the data file used and the text syntax for the particular procedure. These notes will not be shown here, but it is sometimes helpful to view them for logistical purposes.

Descriptive Statistics:

The convenience of the "Explore" option in SPSS is that it gives the summary statistics, the box plots and the Normal probability plots all in one option:

how this was done.

Explore

Case Processing Summary

Cases

Valid Missing Total

N Percent N Percent N Percent

AN

32

100.0%

0

.0%

32

100.0%

AW

32

100.0%

0

.0%

32

100.0%

CXEN

32

100.0%

0

.0%

32

100.0%

CXEW

32

100.0%

0

.0%

32

100.0%

	Cases
Valid	Missing	Total
N	Percent	N	Percent	N	Percent
AN	32	100.0%	0	.0%	32	100.0%
AW	32	100.0%	0	.0%	32	100.0%
CXEN	32	100.0%	0	.0%	32	100.0%
CXEW	32	100.0%	0	.0%	32	100.0%

Descriptives

Statistic Std. Error

AN

Mean

41.6395

.9055

95% Confidence Interval for Mean

Lower Bound

39.7929

Upper Bound

43.4862

5% Trimmed Mean

41.4581

Median

42.0833

Variance

26.235

Std. Deviation

5.1220

Minimum

31.12

Maximum

54.39

Range

23.27

Interquartile Range

5.7947

Skewness

.494

.414

Kurtosis

1.062

.809

AW

Mean

40.9184

.9342

95% Confidence Interval for Mean

Lower Bound

39.0132

Upper Bound

42.8237

5% Trimmed Mean

40.9703

Median

41.3928

Variance

27.926

Std. Deviation

5.2845

Minimum

28.56

Maximum

52.95

Range

24.40

Interquartile Range

5.5208

Skewness

-.263

.414

Kurtosis

.556

.809

CXEN

Mean

41.3356

1.0764

95% Confidence Interval for Mean

Lower Bound

39.1402

Upper Bound

43.5311

5% Trimmed Mean

41.3389

Median

42.6035

Variance

37.079

Std. Deviation

6.0892

Minimum

26.83

Maximum

55.45

Range

28.61

Interquartile Range

4.9768

Skewness

-.191

.414

Kurtosis

1.044

.809

CXEW

Mean

41.4576

.9778

95% Confidence Interval for Mean

Lower Bound

39.4635

Upper Bound

43.4518

5% Trimmed Mean

41.4713

Median

42.6907

Variance

30.592

Std. Deviation

5.5310

Minimum

28.89

Maximum

54.92

Range

26.04

Interquartile Range

6.0087

Skewness

-.105

.414

Kurtosis

.751

.809

	Statistic	Std. Error
AN	Mean	41.6395	.9055
95% Confidence Interval for Mean	Lower Bound	39.7929
Upper Bound	43.4862
5% Trimmed Mean	41.4581
Median	42.0833
Variance	26.235
Std. Deviation	5.1220
Minimum	31.12
Maximum	54.39
Range	23.27
Interquartile Range	5.7947
Skewness	.494	.414
Kurtosis	1.062	.809
AW	Mean	40.9184	.9342
95% Confidence Interval for Mean	Lower Bound	39.0132
Upper Bound	42.8237
5% Trimmed Mean	40.9703
Median	41.3928
Variance	27.926
Std. Deviation	5.2845
Minimum	28.56
Maximum	52.95
Range	24.40
Interquartile Range	5.5208
Skewness	-.263	.414
Kurtosis	.556	.809
CXEN	Mean	41.3356	1.0764
95% Confidence Interval for Mean	Lower Bound	39.1402
Upper Bound	43.5311
5% Trimmed Mean	41.3389
Median	42.6035
Variance	37.079
Std. Deviation	6.0892
Minimum	26.83
Maximum	55.45
Range	28.61
Interquartile Range	4.9768
Skewness	-.191	.414
Kurtosis	1.044	.809
CXEW	Mean	41.4576	.9778
95% Confidence Interval for Mean	Lower Bound	39.4635
Upper Bound	43.4518
5% Trimmed Mean	41.4713
Median	42.6907
Variance	30.592
Std. Deviation	5.5310
Minimum	28.89
Maximum	54.92
Range	26.04
Interquartile Range	6.0087
Skewness	-.105	.414
Kurtosis	.751	.809

Normal Q-Q Plots

AW Cxen Cxew Boxplot

Comparing the different conditions:

There are several different ways to compare the different conditions. The first method used, the Within-Subjects Anova, is the method employed by the original experimenters. The second method, a one-tailed, paired t-test, is just as valid but requires simple manipulation of the data.

General Linear Model

how this was done

Within-Subjects Factors

PRIME

WORDTYPE
Dependent Variable

1

1
AW

2
CXEW

2

1
AN

2
CXEN

PRIME

WORDTYPE

PRIME	WORDTYPE	Dependent Variable
1	1	AW
2	CXEW
2	1	AN
2	CXEN

The following Multivariate Tests several different corrections for nonspherical data. For a more detailed description see the Inferential Statistics section of this case study.

Multivariate Tests

Effect
Value F Hypothesis df Error df Sig. Noncent. Parameter Observed Power(a)

PRIME

Pillai's Trace

.060

1.971(b)

1.000

31.000

.170

1.971

.275

Wilks' Lambda

.940

1.971(b)

1.000

31.000

.170

1.971

.275

Hotelling's Trace

.064

1.971(b)

1.000

31.000

.170

1.971

.275

Roy's Largest Root

.064

1.971(b)

1.000

31.000

.170

1.971

.275

WORDTYPE

Pillai's Trace

.007

.214(b)

1.000

31.000

.647

.214

.073

Wilks' Lambda

.993

.214(b)

1.000

31.000

.647

.214

.073

Hotelling's Trace

.007

.214(b)

1.000

31.000

.647

.214

.073

Roy's Largest Root

.007

.214(b)

1.000

31.000

.647

.214

.073

PRIME * WORDTYPE

Pillai's Trace

.132

4.719(b)

1.000

31.000

.038

4.719

.558

Wilks' Lambda

.868

4.719(b)

1.000

31.000

.038

4.719

.558

Hotelling's Trace

.152

4.719(b)

1.000

31.000

.038

4.719

.558

Roy's Largest Root

.152

4.719(b)

1.000

31.000

.038

4.719

.558

a Computed using alpha = .05

b Exact statistic

c Design: Intercept
Within Subjects Design: PRIME+WORDTYPE+PRIME*WORDTYPE

Effect	Value	F	Hypothesis df	Error df	Sig.	Noncent. Parameter	Observed Power(a)
PRIME	Pillai's Trace	.060	1.971(b)	1.000	31.000	.170	1.971	.275
Wilks' Lambda	.940	1.971(b)	1.000	31.000	.170	1.971	.275
Hotelling's Trace	.064	1.971(b)	1.000	31.000	.170	1.971	.275
Roy's Largest Root	.064	1.971(b)	1.000	31.000	.170	1.971	.275
WORDTYPE	Pillai's Trace	.007	.214(b)	1.000	31.000	.647	.214	.073
Wilks' Lambda	.993	.214(b)	1.000	31.000	.647	.214	.073
Hotelling's Trace	.007	.214(b)	1.000	31.000	.647	.214	.073
Roy's Largest Root	.007	.214(b)	1.000	31.000	.647	.214	.073
PRIME * WORDTYPE	Pillai's Trace	.132	4.719(b)	1.000	31.000	.038	4.719	.558
Wilks' Lambda	.868	4.719(b)	1.000	31.000	.038	4.719	.558
Hotelling's Trace	.152	4.719(b)	1.000	31.000	.038	4.719	.558
Roy's Largest Root	.152	4.719(b)	1.000	31.000	.038	4.719	.558
a Computed using alpha = .05
b Exact statistic
c Design: Intercept Within Subjects Design: PRIME+WORDTYPE+PRIME*WORDTYPE

Mauchly's Test of Sphericity

	Mauchly's W	Approx. Chi-Square	df	Sig.	Epsilon(a)
Within Subjects Effect	Mauchly's W	Approx. Chi-Square	df	Sig.	Greenhouse-Geisser	Huynh-Feldt	Lower-bound
PRIME	1.000	.000	0	.	1.000	1.000	1.000
WORDTYPE	1.000	.000	0	.	1.000	1.000	1.000
PRIME * WORDTYPE	1.000	.000	0	.	1.000	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 May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the layers (by default) of the Tests of Within Subjects Effects table.
b Design: Intercept Within Subjects Design: PRIME+WORDTYPE+PRIME*WORDTYPE

Tests of Within-Subjects Effects

Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Noncent. Parameter	Observed Power(a)
PRIME	2.872	1	2.872	1.971	.170	1.971	.275
Error(PRIME)	45.178	31	1.457
WORDTYPE	.443	1	.443	.214	.647	.214	.073
Error(WORDTYPE)	64.123	31	2.068
PRIME * WORDTYPE	5.686	1	5.686	4.719	.038	4.719	.558
Error(PRIME*WORDTYPE)	37.351	31	1.205
a Computed using alpha = .05

Tests of Within-Subjects Contrasts

Source	Transformed Variable	Type III Sum of Squares	df	Mean Square	F	Sig.	Noncent. Parameter	Observed Power(a)
PRIME	PRIME_1	2.872	1	2.872	1.971	.170	1.971	.275
Error(PRIME)	PRIME_1	45.178	31	1.457
WORDTYPE	WORDTYPE_1	.443	1	.443	.214	.647	.214	.073
Error(WORDTYPE)	WORDTYPE_1	64.123	31	2.068
PRIME * WORDTYPE	PRIME_1*WORDTYPE_1	5.686	1	5.686	4.719	.038	4.719	.558
Error(PRIME*WORDTYPE)	PRIME_1*WORDTYPE_1	37.351	31	1.205
a Computed using alpha = .05

Tests of Between-Subjects Effects

Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Noncent. Parameter	Observed Power(a)
Intercept	218728.237	1	218728.237	1867.847	.000	1867.847	1.000
Error	3630.156	31	117.102
a Computed using alpha = .05

Profile Plots

Prime * wordtype

Prime * wordtype

NOTE: The profile Plots help us visually see the interactions between the two main effects.

t-Test

how this was done

One-Sample Statistics

N Mean Std. Deviation Std. Error Mean

DIFF

32

.8431

2.1953

.3881

	N	Mean	Std. Deviation	Std. Error Mean
DIFF	32	.8431	2.1953	.3881

One-Sample Test

	Test Value = 0
	t	df	Sig. (2-tailed)	Mean Difference	95% Confidence Interval of the Difference
	t	df	Sig. (2-tailed)	Mean Difference	Lower	Upper
DIFF	2.172	31	.038	.8431	5.156E-02	1.6346

Between-Within Subjects ANOVA

To check if Gender makes a difference in our results, we use a between-Within Subjects ANOVA.

General Linear Model

how this was done.

Within-Subjects Factors

PRIME

WORDTYPE
Dependent Variable

1

1
AW

2
CXEW

2

1
AN

2
CXEN

PRIME

WORDTYPE

PRIME	WORDTYPE	Dependent Variable
1	1	AW
2	CXEW
2	1	AN
2	CXEN

Between-Subjects Factors

		Value Label	N
SEX	F		15
SEX	M		17

Multivariate Tests

Effect		Value	F	Hypothesis df	Error df	Sig.	Noncent. Parameter	Observed Power(a)
PRIME	Pillai's Trace	.059	1.869(b)	1.000	30.000	.182	1.869	.263
	Wilks' Lambda	.941	1.869(b)	1.000	30.000	.182	1.869	.263
	Hotelling's Trace	.062	1.869(b)	1.000	30.000	.182	1.869	.263
	Roy's Largest Root	.062	1.869(b)	1.000	30.000	.182	1.869	.263
PRIME * SEX	Pillai's Trace	.001	.036(b)	1.000	30.000	.851	.036	.054
	Wilks' Lambda	.999	.036(b)	1.000	30.000	.851	.036	.054
	Hotelling's Trace	.001	.036(b)	1.000	30.000	.851	.036	.054
	Roy's Largest Root	.001	.036(b)	1.000	30.000	.851	.036	.054
WORDTYPE	Pillai's Trace	.007	.196(b)	1.000	30.000	.661	.196	.071
	Wilks' Lambda	.993	.196(b)	1.000	30.000	.661	.196	.071
	Hotelling's Trace	.007	.196(b)	1.000	30.000	.661	.196	.071
	Roy's Largest Root	.007	.196(b)	1.000	30.000	.661	.196	.071
WORDTYPE * SEX	Pillai's Trace	.001	.033(b)	1.000	30.000	.857	.033	.054
	Wilks' Lambda	.999	.033(b)	1.000	30.000	.857	.033	.054
	Hotelling's Trace	.001	.033(b)	1.000	30.000	.857	.033	.054
	Roy's Largest Root	.001	.033(b)	1.000	30.000	.857	.033	.054
PRIME * WORDTYPE	Pillai's Trace	.130	4.483(b)	1.000	30.000	.043	4.483	.536
	Wilks' Lambda	.870	4.483(b)	1.000	30.000	.043	4.483	.536
	Hotelling's Trace	.149	4.483(b)	1.000	30.000	.043	4.483	.536
	Roy's Largest Root	.149	4.483(b)	1.000	30.000	.043	4.483	.536
PRIME * WORDTYPE * SEX	Pillai's Trace	.003	.089(b)	1.000	30.000	.768	.089	.060
	Wilks' Lambda	.997	.089(b)	1.000	30.000	.768	.089	.060
	Hotelling's Trace	.003	.089(b)	1.000	30.000	.768	.089	.060
	Roy's Largest Root	.003	.089(b)	1.000	30.000	.768	.089	.060
a Computed using alpha = .05
b Exact statistic
c Design: Intercept+SEX Within Subjects Design: PRIME+WORDTYPE+PRIME*WORDTYPE

Mauchly's Test of Sphericity

	Mauchly's W	Approx. Chi-Square	df	Sig.	Epsilon(a)
Within Subjects Effect	Mauchly's W	Approx. Chi-Square	df	Sig.	Greenhouse-Geisser	Huynh-Feldt	Lower-bound
PRIME	1.000	.000	0	.	1.000	1.000	1.000
WORDTYPE	1.000	.000	0	.	1.000	1.000	1.000
PRIME * WORDTYPE	1.000	.000	0	.	1.000	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 May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the layers (by default) of the Tests of Within Subjects Effects table.
b Design: Intercept+SEX Within Subjects Design: PRIME+WORDTYPE+PRIME*WORDTYPE

Tests of Within-Subjects Effects

Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Noncent. Parameter	Observed Power(a)
PRIME	2.812	1	2.812	1.869	.182	1.869	.263
PRIME * SEX	5.408E-02	1	5.408E-02	.036	.851	.036	.054
Error(PRIME)	45.124	30	1.504
WORDTYPE	.419	1	.419	.196	.661	.196	.071
WORDTYPE * SEX	7.037E-02	1	7.037E-02	.033	.857	.033	.054
Error(WORDTYPE)	64.053	30	2.135
PRIME * WORDTYPE	5.566	1	5.566	4.483	.043	4.483	.536
PRIME * WORDTYPE * SEX	.110	1	.110	.089	.768	.089	.060
Error(PRIME*WORDTYPE)	37.241	30	1.241
a Computed using alpha = .05

Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Noncent. Parameter	Observed Power(a)
PRIME	2.812	1.000	2.812	1.869	.182	1.869	.263
PRIME * SEX	5.408E-02	1.000	5.408E-02	.036	.851	.036	.054
Error(PRIME)	45.124	30.000	1.504
WORDTYPE	.419	1.000	.419	.196	.661	.196	.071
WORDTYPE * SEX	7.037E-02	1.000	7.037E-02	.033	.857	.033	.054
Error(WORDTYPE)	64.053	30.000	2.135
PRIME * WORDTYPE	5.566	1.000	5.566	4.483	.043	4.483	.536
PRIME * WORDTYPE * SEX	.110	1.000	.110	.089	.768	.089	.060
Error(PRIME*WORDTYPE)	37.241	30.000	1.241
a Computed using alpha = .05

Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Noncent. Parameter	Observed Power(a)
PRIME	2.812	1.000	2.812	1.869	.182	1.869	.263
PRIME * SEX	5.408E-02	1.000	5.408E-02	.036	.851	.036	.054
Error(PRIME)	45.124	30.000	1.504
WORDTYPE	.419	1.000	.419	.196	.661	.196	.071
WORDTYPE * SEX	7.037E-02	1.000	7.037E-02	.033	.857	.033	.054
Error(WORDTYPE)	64.053	30.000	2.135
PRIME * WORDTYPE	5.566	1.000	5.566	4.483	.043	4.483	.536
PRIME * WORDTYPE * SEX	.110	1.000	.110	.089	.768	.089	.060
Error(PRIME*WORDTYPE)	37.241	30.000	1.241
a Computed using alpha = .05

Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Noncent. Parameter	Observed Power(a)
PRIME	2.812	1.000	2.812	1.869	.182	1.869	.263
PRIME * SEX	5.408E-02	1.000	5.408E-02	.036	.851	.036	.054
Error(PRIME)	45.124	30.000	1.504
WORDTYPE	.419	1.000	.419	.196	.661	.196	.071
WORDTYPE * SEX	7.037E-02	1.000	7.037E-02	.033	.857	.033	.054
Error(WORDTYPE)	64.053	30.000	2.135
PRIME * WORDTYPE	5.566	1.000	5.566	4.483	.043	4.483	.536
PRIME * WORDTYPE * SEX	.110	1.000	.110	.089	.768	.089	.060
Error(PRIME*WORDTYPE)	37.241	30.000	1.241
a Computed using alpha = .05

Tests of Within-Subjects Contrasts

Source	Transformed Variable	Type III Sum of Squares	df	Mean Square	F	Sig.	Noncent. Parameter	Observed Power(a)
PRIME	PRIME_1	2.812	1	2.812	1.869	.182	1.869	.263
PRIME * SEX	PRIME_1	5.408E-02	1	5.408E-02	.036	.851	.036	.054
Error(PRIME)	PRIME_1	45.124	30	1.504
WORDTYPE	WORDTYPE_1	.419	1	.419	.196	.661	.196	.071
WORDTYPE * SEX	WORDTYPE_1	7.037E-02	1	7.037E-02	.033	.857	.033	.054
Error(WORDTYPE)	WORDTYPE_1	64.053	30	2.135
PRIME * WORDTYPE	PRIME_1*WORDTYPE_1	5.566	1	5.566	4.483	.043	4.483	.536
PRIME * WORDTYPE * SEX	PRIME_1*WORDTYPE_1	.110	1	.110	.089	.768	.089	.060
Error(PRIME*WORDTYPE)	PRIME_1*WORDTYPE_1	37.241	30	1.241
a Computed using alpha = .05

Tests of Between-Subjects Effects

Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Noncent. Parameter	Observed Power(a)
Intercept	217461.675	1	217461.675	1822.198	.000	1822.198	1.000
SEX	49.947	1	49.947	.419	.523	.419	.096
Error	3580.210	30	119.340
a Computed using alpha = .05

Profile Plots

PRIME * WORDTYPE * SEX

Sex = F

Sex = M