General Linear Model<\/p>\n
Within-Subjects Factors<\/p>\n
Measure: MEASURE_1 <\/p>\n
LOP<\/p>\n
Dependent Variable<\/p>\n
1<\/p>\n
Shallow<\/p>\n
2<\/p>\n
Deep<\/p>\n
Between-Subjects Factors<\/p>\n
Value Label<\/p>\n
N<\/p>\n
Awareness<\/p>\n
1<\/p>\n
Aware<\/p>\n
32<\/p>\n
2<\/p>\n
Unaware<\/p>\n
32<\/p>\n
Multivariate Testsa<\/p>\n
Effect<\/p>\n
Value<\/p>\n
F<\/p>\n
Hypothesis df<\/p>\n
Error df<\/p>\n
Sig.<\/p>\n
LOP<\/p>\n
Pillai’s Trace<\/p>\n
.459<\/p>\n
52.699b<\/p>\n
1.000<\/p>\n
62.000<\/p>\n
.000<\/p>\n
Wilks’ Lambda<\/p>\n
.541<\/p>\n
52.699b<\/p>\n
1.000<\/p>\n
62.000<\/p>\n
.000<\/p>\n
Hotelling’s Trace<\/p>\n
.850<\/p>\n
52.699b<\/p>\n
1.000<\/p>\n
62.000<\/p>\n
.000<\/p>\n
Roy’s Largest Root<\/p>\n
.850<\/p>\n
52.699b<\/p>\n
1.000<\/p>\n
62.000<\/p>\n
.000<\/p>\n
LOP * Awareness<\/p>\n
Pillai’s Trace<\/p>\n
.003<\/p>\n
.169b<\/p>\n
1.000<\/p>\n
62.000<\/p>\n
.683<\/p>\n
Wilks’ Lambda<\/p>\n
.997<\/p>\n
.169b<\/p>\n
1.000<\/p>\n
62.000<\/p>\n
.683<\/p>\n
Hotelling’s Trace<\/p>\n
.003<\/p>\n
.169b<\/p>\n
1.000<\/p>\n
62.000<\/p>\n
.683<\/p>\n
Roy’s Largest Root<\/p>\n
.003<\/p>\n
.169b<\/p>\n
1.000<\/p>\n
62.000<\/p>\n
.683<\/p>\n
a. Design: Intercept + Awareness <\/p>\n
Within Subjects Design: LOP<\/p>\n
b. Exact statistic<\/p>\n
Mauchly’s Test of Sphericitya<\/p>\n
Measure: MEASURE_1 <\/p>\n
Within Subjects Effect<\/p>\n
Mauchly’s W<\/p>\n
Approx. Chi-Square<\/p>\n
df<\/p>\n
Sig.<\/p>\n
Epsilonb<\/p>\n
Greenhouse-Geisser<\/p>\n
Huynh-Feldt<\/p>\n
Lower-bound<\/p>\n
LOP<\/p>\n
1.000<\/p>\n
.000<\/p>\n
0<\/p>\n
.<\/p>\n
1.000<\/p>\n
1.000<\/p>\n
1.000<\/p>\n
Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix.<\/p>\n
a. Design: Intercept + Awareness <\/p>\n
Within Subjects Design: LOP<\/p>\n
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.<\/p>\n
Tests of Within-Subjects Effects<\/p>\n
Measure: MEASURE_1 <\/p>\n
Source<\/p>\n
Type III Sum of Squares<\/p>\n
df<\/p>\n
Mean Square<\/p>\n
F<\/p>\n
Sig.<\/p>\n
LOP<\/p>\n
Sphericity Assumed<\/p>\n
2.552<\/p>\n
1<\/p>\n
2.552<\/p>\n
52.699<\/p>\n
.000<\/p>\n
Greenhouse-Geisser<\/p>\n
2.552<\/p>\n
1.000<\/p>\n
2.552<\/p>\n
52.699<\/p>\n
.000<\/p>\n
Huynh-Feldt<\/p>\n
2.552<\/p>\n
1.000<\/p>\n
2.552<\/p>\n
52.699<\/p>\n
.000<\/p>\n
Lower-bound<\/p>\n
2.552<\/p>\n
1.000<\/p>\n
2.552<\/p>\n
52.699<\/p>\n
.000<\/p>\n
LOP * Awareness<\/p>\n
Sphericity Assumed<\/p>\n
.008<\/p>\n
1<\/p>\n
.008<\/p>\n
.169<\/p>\n
.683<\/p>\n
Greenhouse-Geisser<\/p>\n
.008<\/p>\n
1.000<\/p>\n
.008<\/p>\n
.169<\/p>\n
.683<\/p>\n
Huynh-Feldt<\/p>\n
.008<\/p>\n
1.000<\/p>\n
.008<\/p>\n
.169<\/p>\n
.683<\/p>\n
Lower-bound<\/p>\n
.008<\/p>\n
1.000<\/p>\n
.008<\/p>\n
.169<\/p>\n
.683<\/p>\n
Error(LOP)<\/p>\n
Sphericity Assumed<\/p>\n
3.003<\/p>\n
62<\/p>\n
.048<\/p>\n
Greenhouse-Geisser<\/p>\n
3.003<\/p>\n
62.000<\/p>\n
.048<\/p>\n
Huynh-Feldt<\/p>\n
3.003<\/p>\n
62.000<\/p>\n
.048<\/p>\n
Lower-bound<\/p>\n
3.003<\/p>\n
62.000<\/p>\n
.048<\/p>\n
Tests of Within-Subjects Contrasts<\/p>\n
Measure: MEASURE_1 <\/p>\n
Source<\/p>\n
LOP<\/p>\n
Type III Sum of Squares<\/p>\n
df<\/p>\n
Mean Square<\/p>\n
F<\/p>\n
Sig.<\/p>\n
LOP<\/p>\n
Linear<\/p>\n
2.552<\/p>\n
1<\/p>\n
2.552<\/p>\n
52.699<\/p>\n
.000<\/p>\n
LOP * Awareness<\/p>\n
Linear<\/p>\n
.008<\/p>\n
1<\/p>\n
.008<\/p>\n
.169<\/p>\n
.683<\/p>\n
Error(LOP)<\/p>\n
Linear<\/p>\n
3.003<\/p>\n
62<\/p>\n
.048<\/p>\n
Tests of Between-Subjects Effects<\/p>\n
Measure: MEASURE_1 <\/p>\n
Source<\/p>\n
Type III Sum of Squares<\/p>\n
df<\/p>\n
Mean Square<\/p>\n
F<\/p>\n
Sig.<\/p>\n
Intercept<\/p>\n
58.646<\/p>\n
1<\/p>\n
58.646<\/p>\n
898.961<\/p>\n
.000<\/p>\n
Awareness<\/p>\n
.188<\/p>\n
1<\/p>\n
.188<\/p>\n
2.879<\/p>\n
.095<\/p>\n
Error<\/p>\n
4.045<\/p>\n
62<\/p>\n
.065<\/p>\n
Estimated Marginal Means<\/p>\n
1. Awareness<\/p>\n
Measure: MEASURE_1 <\/p>\n
Awareness<\/p>\n
Mean<\/p>\n
Std. Error<\/p>\n
95% Confidence Interval<\/p>\n
Lower Bound<\/p>\n
Upper Bound<\/p>\n
Aware<\/p>\n
.715<\/p>\n
.032<\/p>\n
.651<\/p>\n
.779<\/p>\n
Unaware<\/p>\n
.639<\/p>\n
.032<\/p>\n
.575<\/p>\n
.702<\/p>\n
2. LOP<\/p>\n
Measure: MEASURE_1 <\/p>\n
LOP<\/p>\n
Mean<\/p>\n
Std. Error<\/p>\n
95% Confidence Interval<\/p>\n
Lower Bound<\/p>\n
Upper Bound<\/p>\n
Shallow<\/p>\n
.536<\/p>\n
.032<\/p>\n
.473<\/p>\n
.599<\/p>\n
Deep<\/p>\n
.818<\/p>\n
.028<\/p>\n
.762<\/p>\n
.874<\/p>\n
3. Awareness * LOP<\/p>\n
Measure: MEASURE_1 <\/p>\n
Awareness<\/p>\n
LOP<\/p>\n
Mean<\/p>\n
Std. Error<\/p>\n
95% Confidence Interval<\/p>\n
Lower Bound<\/p>\n
Upper Bound<\/p>\n
Aware<\/p>\n
Shallow<\/p>\n
.582<\/p>\n
.045<\/p>\n
.493<\/p>\n
.671<\/p>\n
Deep<\/p>\n
.848<\/p>\n
.040<\/p>\n
.769<\/p>\n
.927<\/p>\n
Unaware<\/p>\n
Shallow<\/p>\n
.489<\/p>\n
.045<\/p>\n
.400<\/p>\n
.579<\/p>\n
Deep<\/p>\n
.788<\/p>\n
.040<\/p>\n
.709<\/p>\n
.867<\/p>\n
Profile Plots<\/p>\n","protected":false},"excerpt":{"rendered":"
General Linear Model Within-Subjects Factors Measure: MEASURE_1 LOP Dependent Variable 1 Shallow 2 Deep Between-Subjects Factors Value Label N Awareness 1 Aware 32 2 Unaware 32 Multivariate Testsa Effect Value F Hypothesis df Error df Sig. LOP Pillai’s Trace .459 52.699b 1.000 62.000 .000 Wilks’ Lambda .541 52.699b 1.000 62.000 .000 Hotelling’s Trace .850 52.699b […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[10],"class_list":["post-78452","post","type-post","status-publish","format-standard","hentry","category-research-paper-writing","tag-writing"],"_links":{"self":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/posts\/78452","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/comments?post=78452"}],"version-history":[{"count":0,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/posts\/78452\/revisions"}],"wp:attachment":[{"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/media?parent=78452"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/categories?post=78452"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/papersspot.com\/blog\/wp-json\/wp\/v2\/tags?post=78452"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}