I would NOT recommend choosing a p-value < .05 and reporting it. @David Morse

knows more about this than I and hopefully he will see it and comment. If not I recommend you contact him on RG and ask. Best, David Booth

One may ask what is your experience in statistical analysis? You may need to ask those with an experience in statistical analysis to solve your problem. They may need to see your data in order to select proper analysis method.

Dear

Wilks' lambda statistic look to multivarite response which is relation between them or no or we can say it test the effect of factors on mult-response ; but comparison look to compare between level of each factore.

you can download my artical in my Account.

Thank

Hello Başak,

I agree with Abjulmuhsin that you might be better served by connecting with a local statistician to assist with understanding your specific research questions and recommending the most suitable analysis and interpretation thereof.

That said, I can give you some general suggestions and observations based on what you present. However, without your research questions and all the relevant output, I'm somewhat at a disadvantage!

1. The no significant main effect of time (for the repeated measures analysis) indicates that combined scores across groups do not differ on average for Time 1 vs. Time 2 vs. Time 3.

2. The no significant main effect of group (for the repeated measures analysis) indicates that combined time scores (Time1 + Time 2 + Time 3) do not differ on average among groups (Group 1 vs. Group 2 vs. Group 3).

3. Note that these first two tests are seldom the most informative, since they ignore or collapse data across levels of the other factor.

4. The no significant group x time interaction indicates that the differences in times (e.g., Time 1 vs. Time 3) are consistent across groups. As well, the differences in groups (e.g., Group 2 vs. Group 3) are consistent across times. This test is usually the more informative one for one-between, one-within (aka, split-plot/mixed/RM anova). So, profiles of mean performance across the times do not differ by group.

5. The multivariate test (given when you run a RM analysis in SPSS, whether it makes sense to do so or not) evaluates a different hypothesis from those that the one-between, one-within (repeated measures) address. The multivariate (null) hypothesis is that there is no linear combination of measures on which the groups differ. In RM, the main effect of group evaluates differences for the summed time scores. In manova, the main effect of group evaluates equality of all possible linear combinations of the DVs (which, for your data, would be the time scores).

6. If time scores for your study are, in fact, the same measure/variable, then RM is arguably the better framework compared to manova.

7. I'm not clear as to what specific multivariate table you might be referring to, but if it's the default manova from within the RM analysis (Analyze/General Linear Model/Repeated measures...), then note #5 above is likely the explanation. I presume you meant to say, for the main effect of group, instead of "for the experimental group." If you in fact ran a separate manova with the three time scores for a single group, that tests the null hypothesis that the three DV (time) means are zero for that group. Usually, that test would not be very informative, unless scores were scaled such that zero was a meaningful value.

8. So you found one pairwise comparison to be significant for one of your three groups, is that correct? There are 9 such tests possible, and these are typically run as ordinary t-tests, so that the error terms are different from those used for the three general effects in 1B, 1W design. Hence, you could see what appears to be "discrepant" results.

9. For your two significant results: (a) a pairwise test of two times for one treatment group in the RM analysis; and (b) the manova for (apparently, as questioned in #7) a single group, are you certain that these tests are as you described? I'm asking about the single group aspect, not significance; the reason being that such tests do not come automatically from the analysis in spss, even if you ask for "post hoc" and pairwise comparisons on the within-subjects factor via EM Means... option.

Sorry to prattle on so long, but that's about all I can offer without more information.

Good luck with your work!

The wording of these is important. Just taking #1.

" 1. The no significant main effect of time (for the repeated measures analysis) indicates that combined scores across groups do not differ on average for Time 1 vs. Time 2 vs. Time 3. "

The NS effect does not mean that the groups do not differ either in the sample (which you can just look at the means) or in the population. That would be accepting the null hypothesis . I don't mean to get picky here, but I think it is important not to treat whether p

Hello Daniel and anyone else following this thread,

Oh, my. Here's one version of a fully qualified statement:

"Based on a method that, if appropriately repeated an infinite number of times, would yield false positive results no more than (100 * alpha)% and false negative results for my chosen effect size of interest no more than (100 * beta)% of the time, I interpret the results of the statistical comparison to mean that I cannot reject the hypothesis/proposition that any observed difference between/among treatments is due to nothing other than chance. I will therefore behave as if the treatments are comparable in their effect and that the respective data sets have come from the same population, even though in so doing, I could be wrong."

And that's why most people don't fully qualify their results, lol. However, I concur that it's likely useful that everyone understand that the fully qualified statement is implicit in the more terse: "there was no statistically significant effect."

Hope everyone is holding up well in these challenging times.

I think the issue not giving the qualified responses is that students can be led astray. Here, we don't want them to think a ns result tells them that the null hypothesis is true. Even the qualified response "behave as if the treatments are comparable" is dependent on the research question/situation. NHST is tricky. Not qualified responses may make people think NHST is something that it is not.