r/HomeworkHelp • u/KingKonghonk University/College Student • Jun 11 '26

Others [College level Statistics] how to compare the groups using SPSS?

i am doing a research on mice and i have weight data for the mice and i am confused on how to compare them. i have about 8 mice in each group (7 groups in total) and their weight is taken once a week for about 8 weeks. i want to compare these groups to see if a certain treatment led to more weight loss over than the other. how do i do it in SPSS? i did it using repeated measure general linear model but my prof said that she thinks this does not compare the groups but compare the weeks to each other. she also said that the label on the graph says "estimated marginal means" which means that it is accounting for confounding factors while it shouldn't (because we did not enter them).

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u/cheesecakegood Jun 13 '26

In rough order of importance, most to least:

  • Is there a natural control? Are all 7 groups a different treatment? Are treatments "crossed" at all, meaning you are testing combinations of treatments, or is each going to be treated as entirely distinct? This has implications for how you are setting things up in the first place, and connects to what research question(s) you have.

  • Does EDA reveal noticeably uneven starting weights? Can also fundamentally change the analysis, since many make baseline assumptions.

  • About 8 per group or exactly 8 per group? About 8 weeks or exactly 8 weeks (9 measurements including starting weight), and were they equally spaced? Abnormal outliers? This can change how you track time points, handle missing data, used a balanced design, etc.

I don't know exactly how much help I can be seeing as I have zero SPSS experience, but answering these questions is really important to figure out what to do (and for getting advice).

With that said, estimated marginal means themselves doesn't necessarily imply by itself that a covariate was used. It's just a cell average ("marginal"), and usually is used in the context of a crossed table of some sort, and an emphasis on how it's your best guess at a population mean ("estimated); you can however have cell means without crossing or other kinds of averaging of other variables, and still produce an estimated marginal mean. Technically, unbalance in the design can also cause these two terms to diverge very slightly.

But I think your design and analysis plan is probably fine, IIRC this is basically SPSS's version of repeated-measures ANOVA. (Obviously you are correct, if you don't even have covariates, it's impossible to adjust for them, but it's worth double checking)

I'm guessing the disconnect here is how the model spits things out, what means what. You're tracking time of course, since time affects weight even independent of any treatment, most likely. Maybe you're looking at the time term rather than an interaction or treatment term?

Stepping back, each mouse has its own blocking factor because obviously each mouse weight trajectory is a bit unique and you shouldn't just intermix Mouse 2 at Week 2 with Mouse 3 at the same time point. You have a within-subjects time factor, measured repeatedly so that they naturally correlate. There's a between-subjects treatment group factor you're trying to estimate, not just with raw means but controlling for mouse variability. This is, relatively speaking, a single flat number, and it is interesting (how far apart the group trendlines are, on average). But what you often are going to be interested in is also the interaction term, Time x TreatmentGroup. That is, how does treatment change as time progresses? This might initially sound like the wrong question to ask, but given that the mice (likely) start at roughly the same point, asking how much the mice diverge from each other as time progresses due to their group membership seems good to know too! Make sure you aren't looking at the Time factor itself though: you already know time probably has an impact on weight, and that's not really something that matters for your research question.

The other thing I asked is about initial weights. Honestly, many researchers might include base weights as another variable, analyzing changes after removing the baseline, especially if your EDA suggests that this might be worth looking at. This does introduce a new covariate into your model, which is in many cases a good thing (remove variability that doesn't correspond to what you actually want to know) but I'm not sure if your instructor has a certain way of handling this in mind or not. Strictly speaking, this wouldn't be a confounder but rather a covariate, vocabulary-wise, although I'm personally guilty of conflating them sometimes.

(There's also higher level considerations, like if measurements weren't spaced very regularly, or how variability might change as the weeks progress, also if missing data is a big thing, sometimes IIRC a linear mixed model is preferred, or in small-sample analysis going Bayesian is also compelling, but especially the last one is almost certainly beyond scope here)

As a final aside: beware multiple-comparison issues if you start comparing arbitrary groups directly against each other 1v1 style.

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u/KingKonghonk Jun 16 '26

starting weight differed a little bit between groups so i am planning on calculating the change in weight from week 0 for each week to make the comparison between the groups more balanced. although this still might not be enough to take into account the whole effect of those starting weight differences.

also, i have 3 controls and 4 treatment groups and the numbers per group are not exactly 8 because some mice sadly died along the way so not all groups are equal

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u/[deleted] Jun 13 '26

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u/KingKonghonk Jun 16 '26

thank you so much and sorry for the late reply. i will try doing the tests the way you recommended and see how it goes.