# Tag Info

11

Here is a quick attempt at heat map, I have used black cell borders to break up the cells, but perhaps the tiles should be separated more as in Glen_b's answer. library(ggplot2) runningcounts.df <- as.data.frame(table(importance, often)) ggplot(runningcounts.df, aes(importance, often)) + geom_tile(aes(fill = Freq), colour = "black") + ...

9

The way I've done this is a bit of a fudge, but it could be fixed up easily enough. This is a modified version of the jittering approach. Removing the axes reduces the temptation to interpret the scale as continuous; drawing boxes around the jittered combinations emphasizes there's something like a "scale break" - that the intervals aren't necessarily ...

8

Here's an example of what a spineplot of the data would look like. I did this in Stata pretty quickly, but there's an R implementation. I think in R it should be just: spineplot(factor(often)~factor(importance)) The spineplot actually seems to be the default if you give R categorical variables: plot(factor(often)~factor(importance)) The fractional ...

7

Lets start with the good news: The proportional odds assumptions does not require that the distances between categories are the same. So what does the proportional odds assumption imply? Say we have three ordered outcomes (1, 2, 3), then we could model the choice of 1 versus 2 or 3 and the choice of 2 versus 3. The proportional odds assumption says that ...

5

A spineplot (mosaic plot) works well for the example data here, but can be difficult to read or interpret if some combinations of categories are rare or don't exist. Naturally it's reasonable, and expected, that a low frequency is represented by a small tile, and zero by no tile at all, but the psychological difficulty can remain. It's also natural that ...

5

The short answer is that this is contentious. Contrary to the advice you mention, people in many fields do take means of ordinal scales and are often happy that means do what they want. Grade-point averages or the equivalent in many educational systems are one example. However, ordinal data not being normally distributed is not a valid reason, because the ...

4

The word "greater" here is poorly defined. The coefficient will tell you the effect of a unit change in the explanatory variable on the linear predictor, $x'\beta$ part of your model. However, if $X_1$ takes values 0 and 1, and $X_2$ varies from -10 to +200, a unit change in the first one is A LOT, and a unit change in the other is minuscule compared to the ...

4

Stevens' classification scheme can be useful, but it is by no means complete nor perfect. I wrote about this on Yahoo Voices. Using Stevens' scheme you can only call your variable ordinal, since he doesn't a name for what you've got. I don't know of any particular name for a variable like yours. I propose "semi-interval" :-) By the way, an interval level ...

4

The answer mainly depends on what you want to use those models for and how well these models fit your data. So I would just start by thinking what you want to do with the results once the computer program spits them out at you. This often helps in narrowing down the options. After that, just fit the remaining models and stare at them till you understand each ...

4

The discreteness is not an issue, so much as the ordinal (ordered, graded) scale used for your assessment from normal to severe. That indeed implies something different from standard linear regression, namely some ordinal regression method such as ordered logit or ordered probit. Note incidentally that multivariate regression is not the same as multiple ...

4

Regarding your second question, article is categorical so that R automatically defines several “dummy” variables (other codings are possible), making articles of type A the “reference category”. The coefficient and test for article.typeb represents the difference between articles of type A and articles of type B, the coefficient for article.typec represents ...

4

In Stata, both cases can be handled with the interval regression command intreg, which is generalization of the Tobit. It can handle point, interval, or left/right censored data (or a mixture of them all). It does assume error term normality, but the log transformation can often work if your data require and permit it. I am not sure if there are canned ...

3

If I understand you correctly you are assigning values to categories of your explanatory/independent/right-hand-side/x variable based on average values of your explained/dependent/left-hand-side/y variable. I suspect that the purpose of that excercise is to assign values to the categories of verx_s such that the linear effect of the resulting variable in ...

3

You can use dummy codes with ordinal independent variables. The effect may not be monotonic, but that's OK; in fact, it may be revealing. I am not aware of any standard methods that impose a monotonic relationship with an ordinal independent variable; there may be some. You could also probably write some function that would do it, if you are ingenious ...

3

I would combine both "no" categories into one category. Then, your data is clearly ranked. If you like, you could also see after your primarily analysis if entrepreneurs are more likely to be previous drinkers. The issue is that past drinking seems less related to your research question. If you want, you could even further segregate the categories such as: ...

3

Ordinal logistic regression is a fine choice for this situation. Your one issue is what to do with your independent variables. I would recommend against collapsing categories into just high vs. low, in general. For ordinal categories, you could represent them as nominal, via dummy codes, or represent them with a set of continuous numbers that represent ...

3

What about one of the Kendall's $\tau$s? They are a kind of rank correlation coefficient for ordinal data. Here's an example with Stata and $\tau_{b}$. A value of $−1$ implies perfect negative association, and $+1$ indicates perfect agreement. Zero indicates the absence of association. Here we see a modest, though significant, negative association between ...

3

I would use a Multinomial Generalized Linear Model for this, or rather a collection of them, one for each response. There do also exist Multivariate GLMs, but I don't know of an R package to do that and I also have never seen someone do Multivariate Multinomial GLM, but it should be possible. That said, the benefit of Multivariate GLM is usually not huge ...

3

Basically, measurement level describes certain assumption about the variables at hand. These assumptions imply additional information or structure of the data. For example, take the interval level. Just as at the ordinal level, values are ordered in a certain progression. In programming terms one could say that the interval class inherits the properties of ...

3

If you believe that your Likert item DV is tapping a continuous underlying quantity, eg. a strength of preference for or against something (which I presume you do), then your regression model should assume an ordinal DV because only then will you by trying to estimate the quantities you want: the effects of changes in IVs on the actual satisfaction level ...

3

First, a 14 point Likert scale is quite unusual. I don't think I've ever seen one. Is this the sum of two 7 point questions? Second, given that you are actually interested not in birth month, per se, but in time in school, why not use that? Beware that, by using birth month as a proxy for this, you are assuming that all the children in your study go to ...

3

You are looking for interval regression: In R you can use the survival package as explained here Or you can try with the intReg package using the intReg function

3

Here is an example using the R rms package orm function. The three variables are defined in the original question above. First we see which of 3 families yields the most parallelism. require(rms) row <- 0 for(gvar in list(pred_1, pred_2)) { row <- row + 1; col <- 0 for(fun in list(qlogis, qnorm, function(y) -log(-log(y)))) { col <- col ...

3

If you are using the ordinary parallel forms of proportion odds or CR models, the restrictions imposed on those models (equal slopes assumptions) concentrates the effects into a single parameter if the predictor is linear. There is no extra type I error. This is a form of borrowing information across $Y$ levels.

3

There are a variety of ordinal regression models (see Agresti) but they rely on certain assumptions. When those assumptions are violated, the models may become incorrect. The most common assumption is that of proportional odds. Multinomial regression does not make this assumption and can therefore model odds that are not proportional. However, an ...

2

Welcome to the site, @griseus. The simplest comparison is just a two-way contingency table. It ignores the ordinal relations between your categories, and in a sense is the most conservative test. If you have a complex sample design (stratification, clustering, unequal probabilities of selection), you need to use Rao-Scott corrections to the $\chi^2$ ...

2

What this formula computes is the the Proportion Estimation based on rankit method. X R F PE 25 1.0 .0909 .0455 28 2.0 .1818 .1364 29 3.5 .3182 .2727 29 3.5 .3182 .2727 31 5.0 .4545 .4091 32 6.0 .5455 .5000 33 8.0 .7273 .6818 33 8.0 .7273 .6818 33 8.0 .7273 .6818 35 10.0 .9091 .8636 37 11.0 1.0000 .9545 In the ...

2

Yes, you can do ordinal logistic regression with continuous independent variables. But what do you mean when you write Could I obtain in this way the D variable as percentages? You can get the D variable as percentages without doing any regression, just simple arithmetic. That's probably not what you want. You can get the predicted proportion of D in ...

2

Ordinal regression is appropriate whenever the dependent variable is ordinal: That is, when you can assume that the levels are in order, but not that the gaps between the levels are equal. If you analyze individual Likert items (each scored 1-5 or 1-7 or whatever) then this could be a very reasonable assumption. But other methods might be good too; this ...

2

Here's how you'd normally set these things out: Original table of counts: Column variable 1 2 3 4 Row 1 1 3 10 6 variable 2 2 3 10 7 3 1 6 14 12 4 0 1 9 ...

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