# One Way Repeated Anova

1186 Words May 7th, 2013 5 Pages
One-way repeated measure ANOVA
In a one-way repeated measures ANOVA design,each subject is exposed to two or more different conditions, or measured on the continuous scale onthree or more occasions. It can also be used to compare respondents’ responses to two or more questions or items. These questions, hiwever, must be meausred using the same scale.( Likert scale)
Example of research question: Is there a change in confidence scores over the three time periods?
What you need: One group of participants measured on the same scale on three different occasions or under three different conditions, or each person measured on three different questions or items ( using the same scale). This involves teo variables:
● one indepenedet variable
Chi-Square | df | Sig. | Epsilona | | | | | | Greenhouse-Geisser | Huynh-Feldt | Lower-bound | Time | .342 | 30.071 | 2 | .000 | .603 | .615 | .500 | 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 Tests of Within-Subjects Effects table.b. Design: Intercept Within Subjects Design: Time |

Tests of Within-Subjects Effects | Measure:MEASURE_1 | Source | Type III Sum of Squares | df | Mean Square | F | Sig. | Partial Eta Squared | Time | Sphericity Assumed | 365.867 | 2 | 182.933 | 41.424 | .000 | .588 | | Greenhouse-Geisser | 365.867 | 1.206 | 303.368 | 41.424 | .000 | .588 | | Huynh-Feldt | 365.867 | 1.230 | 297.506 | 41.424 | .000 | .588 | | Lower-bound | 365.867 | 1.000 | 365.867 | 41.424 | .000 | .588 | Error(Time) | Sphericity Assumed | 256.133 | 58 | 4.416 | | | | | Greenhouse-Geisser | 256.133 | 34.974 | 7.323 | | | | | Huynh-Feldt | 256.133 | 35.664 | 7.182 | | | | | Lower-bound | 256.133 | 29.000 | 8.832 | | | |

Tests of Within-Subjects Contrasts | Measure:MEASURE_1 | Source | Time | Type III Sum of Squares | df | Mean Square | F