# Tag Info

7

It is very difficult to diagnose without knowing which one is categorical, binary, and continuous. If a categorical variable has k levels, you only need (k-1) dummies to capture the information. If you fit all k of them, SPSS will throw one out. The reason is that if all k are present, the whole set are perfectly collinear, standard errors will then get too ...

6

As you indicated in the comments, SS type I (aov()) vs. III (SPSS) is indeed one reason for the different results. The other reason is that your formula for aov() is incorrect, as the Error() term you used is the for two crossed within factors - whereas you have one between (group) and one within (phase) factor. Here's a reproducible example on how to ...

3

I don't think continuous scale is absolutely essential for regression of the usual kind. There are plenty of examples in which I might use regression on a response (what you call DV) that is a counted variable. But in essence I think you are right. The key word here is ordinal or ordered: your response variable (DV in your notation) is on an ordinal or ...

3

I think that the easiest is to center your dependent variable around the grand mean. Given your example: test$Satisfaction <- scale(test$Satisfaction, center=TRUE) This way, the grand mean is now 0, and the mean for each ethnic group is the deviation from the grand mean. Then you run your regression as usual, but the four tests that you get are whether ...

3

First, the difference between significant and non-significant is not necessarily significant. So if after adding a covariate your interaction p-value changed from .04 to .06, this doesn't mean anything substantial other than you've bounced from one side of a binary .05 decision threshold. However, the other possibility is that the covariate has ...

3

Whether a variable is categorical depends only on the variable, not on any "sharing" of common values. In your case, LAW_FAM is categorical because it has four discrete categories: FRA, SCA, ENG, GER. In particular, LAW_FAM is nominal: the categories have no ordering. You could have several countries which happen to have exactly the same DEP_AVG, but that ...

2

This is of course possible in SPSS in several procedures. As the most handy tool to visually explore importances of variables in separating this or that cluster I recommend you AIM command (available only through syntax). AIM appeared around SPSS version 13. An example: cluster v1 v2 1 .2231 -.5798 1 -.1558 -.3753 ...

2

If your data are balanced, the sum contrasts (see contr.sum under ?contrasts) are explicitly the differences you're asking about, so you may want to look at testing those. More generally, one problem is that "the mean of all races" includes the present race you're comparing it to, so you lose independence, which can make things more complex. However, note ...

2

This comes up frequently on the site. Odour has an effect, colour has an effect, and they interact. Therefore, the degree of odour's effect varies across colour. You know this now. Look at the means and describe the amount of effects. Let's say that you checked the effect of odour for light red and it was significant but it wasn't for colourless. What ...

1

In my opinion, a good way to understand a model is just to plot it. This is as true for logistic regression as for standard linear regression. If you don't have any interactions, you can present each variable independently. (After all, the lack of interactions means the model is assuming the effect of each variable is independent of each other variable.) ...

1

First, whether you focus on the results of the overall ANOVA or the multiple comparisons depends on your research question. The null hypothesis in the overall ANOVA is that all data were sampled from one population with one mean. Multiple comparison tests are pairwise tests of the means of the groups. They are valid to perform even when the overall ANOVA is ...

1

First, why is "propensity to dance" binary? That seems like a mistake. I think it would vary along from people with no propensity to dance (e.g. me) to those who will dance at every opportunity, or even make opportunities). But, if it has to be binary, then .... logistic regression is OK here; like other forms of regression, it assumes that there is a ...

1

General Points There is no statistical difference between your two continuous predictors. It is all just a linear model. Your categorical predictor (unless it is binary) needs to be coded in an appropriate way (e.g., dummy coding) but after that they are all just predictors. That said, I'm assuming that there is a conceptual difference between your ...

1

GLM Repeated Measures If you have no missing data and everyone is measured at each of the 12 time points, then you could use Analyze - GLM - Repeated Measures. This allows you to include a between subjects factor and the 12 time points. Your data would need to be set out in wide format with one SPSS variable for group and 12 SPSS variables for the 12 time ...

1

OK, let me get this straight. In response to your older question here, you're trying to fit a more complicated mixed/multilevel/hierarchical model (yah for terminology). Not having any experience with SPSS, this is going to be more general, along with some guesses at what SPSS is looking for via the screenshots provided (one-eyed leading the blind and all ...

1

Your situation is a bit complicated. We just need to take a step back. In order for us to run this regression we need to know what your research question / hypothesis is? You might not have to use the GLM, but could build a model from the linear regression and use the "test method" (which is not available in the drop menu of SPSS and only in syntax) ...

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