With repeated measures ANOVA you can compare over times. With SPSS click analyze, then general linear model, then repeated measures. Your independent variable is time (5 in this case) . Click add.  Click then define: tumor size time 1, tumor size1, tumor size time 2, tumor size2, tumor size time 3, tumor size3, tumor size time 4, tumor size4, tumor size time 5, tumor size5,  move them into the Within  subjects variables

click option box

tick the descriptive statistics and estimates of effect size in display. Select time in the factor and factor interactions and move them into Display means for box and you get the post-hoc tests. Tick Compare main effects. In Confidence interval adjustment take Bonferroni (the second option).

Click continue and OK. (or paste to save to syntax editor) .

Hi Shahar,

You can compare tumor growth over time periods by doing a two-way anova comparison. You can compare each timepoint and not only the final measurements between groups.

I hope that it helped.

With repeated measures ANOVA you can compare over times. With SPSS click analyze, then general linear model, then repeated measures. Your independent variable is time (5 in this case) . Click add.  Click then define: tumor size time 1, tumor size1, tumor size time 2, tumor size2, tumor size time 3, tumor size3, tumor size time 4, tumor size4, tumor size time 5, tumor size5,  move them into the Within  subjects variables

click option box

tick the descriptive statistics and estimates of effect size in display. Select time in the factor and factor interactions and move them into Display means for box and you get the post-hoc tests. Tick Compare main effects. In Confidence interval adjustment take Bonferroni (the second option).

Click continue and OK. (or paste to save to syntax editor) .

Thanks a lot!

Béatrice Marianne Ewalds-Kvist gave you a sound advice. Use it.

you can also use the Graph pad prism software. In this case select a grouped analysis with 5 replicas per group. inser your data (put each time point in a row) and click on analyze, grouped analysis, two-ways anova. Select the third option; each row represent a different time point and you will get your results.

Best

Shahar,

Without getting into the weeds of SPSS, which I don't use, a multiple-group approach to trajectory modeling should give you what you need. In a mixed-regression model, which I think is what Béatrice is describing, you can model a within-subjects effect of time (and/or nonlinear effects of time) and use dummy or effect codes to predict the time effects--in other words, group differences in trajectories.

In a latent variable approach, you can do essentially the same thing with a multiple-group trajectory model, with the added flexibility (and complexity) of moving to latent-variable software.

Pat

Same question, but does anyone know how to do this in Sigmaplot?

I have data of the same variable in two time periods. i.e 2001 and then 2016. (respondents were asked their health status or education level in 2001 and then in 2016). which test to use to compare the difference?

I have done the analysis as described by Beatrice but the problem is that, even though I have got significance in the ANOVA, when I do the post-hoc test I can not find significative differences between groups. For this reason I have been criticised by reviewers. Any explanation or bibliographic reference to explain this? 

There are no significances, your SD are too big and overlap.

Would it be better to use repeated mesures two-way ANOVA? If after 2-way ANOVA you use Bonferroni posthoc test, you can see which treatments and at which exact time they induce a significant change of tumor volume. So you can see the effect of the time and the treatment on your tumors. I am not sure if I am right, could you give me some advice?

I have 50 variables that have been measures over 13 years (once a year). Each variable has 13 numeric values representing each year.

Which methods would help me to extract tendencies of the variables over years and compare them, e.g., which variables increased or decreased, which has similarities, or wich evolved differently?

Huge thanks !!

You can perform a growth curve model to evaluate the tendencies of variables over years. There is also a similar approach, named Individual Growth Curve Models. The problems you proposed can be well addressed by such mixed-effects models. So many relevant references of the operational syntax for these statistical approaches can be found in Google scholar.@Agnis Stibe

@Felipe Rettore Andreis, This link is usefull when using R: https://datascienceplus.com/two-way-anova-with-repeated-measures/

When you take sample divide it into two and treat the two sub samples and differently ( to check effect of treatment method).

Then each treated sample is packed in many different bottles, so that the bottles can to be analyzed at different days of incubation. Can I use repeated measures to analyze the changes in the bottle overtime and the effect of the treatment? Or is repeated measures only applicable when it the sample is coming from exactly the same bottle ?

you can use the ANOVA test. In this case, two-way is a better option.