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6

"Dichotomous Predictor Variables", there are two ways to code dichotomous predictors: using the contrast 0,1 or the contrast 1,-1. This is factually wrong. There is no limit to the number of ways they can be coded. Those two are merely the most common (indeed between them, almost ubiquitous), and probably the easiest to deal with. I kind of ...

5

I assume that PreferA = 1 when one prefered A and 0 otherwise and that ControlFALSE = 1 when treated and 0 when control. The odds of preffering A when a person did not do so previously and did not receive a treatment (ControlFALSE=0 and PreferA=0) is $\exp(3.135)= 23$, i.e. there are 23 such persons who prefer A for every such person that prefers B. So A is ...

4

I upvoted simply because the downvote was uncalled-for. To answer the question: Nobody can tell you how to analyze anything unless they have some idea of why you want to analyze it. That said: If you just want to display the data, make a bunch of bar charts. People can see who put what, and how frequently. Done. Do you want to say how two variables ...

3

Things like the predictions, residuals, full-reduced model tests, etc. will not be affected by the change that you propose, but what does change is the interpretation and tests on the individual terms. Most regression routines will provide an automatic test of whether a term is 0 or not. This is meaningful when a term represents the difference between two ...

2

If the order of the answers does not matter, you could count the number of common items, so that e.g. d=dist(P and A, H and A) = 1. (this measures the similarity rather then the distance, you may invert it , i.e. 1/d, to get the opposite direction). If the order matters (e.g. if it is a prioritized list), you could use something like the Hamming distance, or ...

1

@GregSnow is right that this change doesn't really matter. Let me add a few details to extend that. What you are talking about is sometimes called cell means coding, whereas the default coding scheme is called reference cell coding. Note that there are many possible valid coding schemes. If you have a categorical variable with only two levels, then the ...

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

auto.arima can select the order of differencing automatically, and make appropriate forecasts taking account of these differences. It can also include regressors via the xreg argument, and select the appropriate model order taking account of the regressors. If you include regressors, these will be differenced along with the response variable as part of the ...

1

Depends on what you're trying to model. In general you're losing information by binning a continuous predictor, but there are situations where it makes sense, e.g. as a proxy for 'minor' vs 'of legal age' or "of working age" vs "retired". It can sometimes also be a useful way to informally check the predictor's relation to the response in logistic ...

1

A simple way to analyze this data set would be to average response times in each condition across all blocks and trials, as you usually do. It is less than ideal and you will loose power but it would still provide a test of your hypothesis. Alternatively, you could focus only on the last block. You would be throwing away a lot of data but 50 trials should be ...

1

Following up on points 3 and 4 by ACD: I would suggest that you take a look at Latent Class Analysis which handles properly your categorial data, assuming that your grouping of questions represents different latent constructs. In case you are interested in connections (e.g. correlated error terms) or even causal links between your "groups", there are also ...

1

To elaborate on Peter Flom's point: People often centre moderator variables (i.e., subtract the mean from the variable) before forming the interaction term in order to reduce multicollinearity between the component moderator variables and the interaction term. The step of centring will not change the r-square change you get in a hierarchical regression ...

1

This kind of pattern can happen when the effect of B on A is positive in one group of C and negative in the other. If you do not include the interaction term between B and C then these two effect cancel out and you'll find an effect close to 0 (or equivalently an odds ratio close to 1). So yes, B could be non-siginificant in a model without the interaction ...

1

Your question suggests that the variable(s) at hand are your independent variables. If not (or if one of them is the dependent variable), you might want to take up Clark's advice and do Poisson regression. For the independent variables: assuming you're looking at association, you can simply test whether it is necessary to treat the variable as categorical. ...

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