# results section of a research paper

here is exactly what I need done for each of the 10 different tests…..
ANOVAs (both one-way and two-way) are reported like the t test, but there are two degrees-of-freedom numbers to report. First report the between-groups degrees of freedom, then report the within-groups degrees of freedom (separated by a comma). After that report the F statistic (rounded off to two decimal places) and the significance level.

There was a significant main effect for treatment, F(1, 145) = 5.43, p = .02, and a significant interaction, F(2, 145) = 3.24, p = .04.

I have provided all the data all I need you to do is put in on paper. there were 10 questions used a 2 way anova was used for the testing I just need all the stats written, It is strictly the results portion you would find in a research paper. 2×2 factorial design. When reporting non-significant results, the p-value is generally reported as the a posteriori probability of the
test-statistic. For example: t(28) = 1.10, SEM = 28.95, p = .268.1….Reporting an omnibus oneway ANOVA, with post-hoc tests:
The analysis of variances showed that the effect of group significantly influenced anxiety, F(2, 57) =
5.00, MSE = 100.25, p = .009. Post hoc analyses were conducted using Tukey’s post-hoc test. Based on a
Tukey’s value of CD = 2.50, the anxiety in the drug group (M = 5.25, SD = 1.80) was significantly less than in
the placebo group (M = 8.35, SD = 2.68) and the control group (M = 8.10, SD = 1.69). The anxiety in the
placebo group and the drug group did not differ significantly. example of 2 way anova results Test completion times, in minutes, were submitted to a two-way ANOVA with two levels of room noise
(noisy, quiet) and two levels of room temperature (hot, cold). The main effect of room noise was significant,
F(1, 39) = 12.65, MSE = 101.55, p = .001, suggesting that the test completion time in the noisy group (M =
3.55, SD = 1.20) was greater than the quiet group (M = 2.78, SD = 0.92). The main effect of room temperature
was significant, F(1, 39) = 5.85, MSE = 101.55, p = .02, suggesting that the test completion time in the cold
group (M = 4.04, SD = 1.25) was greater than the hot group (M = 3.58, SD = 0.99). The interaction was not
significant, F(1, 39) = 1.12, MSE = 101.55, p = .296.
Reporting results of major tests in factorial ANOVA; non-significant interaction:
Attitude change scores were subjected to a two-way analysis of variance having two levels of message
discrepancy (small, large) and two levels of source expertise (high, low). All effects were statistically significant
at the .05 significance level.
The main effect of message discrepancy yielded an F ratio of F(1, 24) = 44.4, p < .001, indicating that the mean change score was significantly greater for large-discrepancy messages (M = 4.78, SD = 1.99) than for smalldiscrepancy messages (M = 2.17, SD = 1.25). The main effect of source expertise yielded an F ratio of F(1, 24) = 25.4, p < .01, indicating that the mean change score was significantly higher in the high-expertise message source (M = 5.49, SD = 2.25) than in the low-expertise message source (M = 0.88, SD = 1.21). The interaction effect was non-significant, F(1, 24) = 1.22, p > .05
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