Graphing Likert scale responses

Alex Hoffman writes:

I am reviewing a article with a whole bunch of tables with likert scale responses. You know, the standard thing with each question on its own line, followed by 5 columns of numbers.

Is there a good way to display this data graphically? OK, there’s no one best way, but can you point your readers to a few good examples?

My reply: Some sort of small multiples. I’m thinking of lineplots. Maybe a grid of plots, each with three colored and labeled lines. For example, it might be a grid with 10 rows and 5 columns. To really know what to do, I’d have to have more sense of what’s being plotted.

Feel free to contribute your ideas in the comments.

10 thoughts on “Graphing Likert scale responses

  1. You could try to post this on stats.stackexchange (i.e. the stackoverflow for data-people). A lot of talk about Likert scales there.

  2. Take barchart where each bar is a question. Make each bar height the same (height=1). For each question/bar, split it into 5 groups based on the percentages that were recorded. Maybe something like this:

    BBBBBBBbbwwwwrrRRRR
    BBBBBbbwwwwwwrrRRRR
    BBbbbbbbbbwwrrrrrrrRRR

    B = bright blue = strongly agree
    b = baby blue = agree
    w = white = neither agree nor disagree
    r = pink = disagree
    R = red = strongly disagree

    The proportion and intensity of the blue and red will show the proportion that agree with the statement.

  3. consider using z-score for y-axis. Often the likert measures don't have much intrinsic meaning — worse, they *appear* to have more meaning intrinsically than they do (it's a mistake to assume "slightly agree" vs. "slightly disagree," e.g., is some critically important division for opinion in the world; maybe it is, but probably it isn't). Usually what the likert measure is good for is extracting meaningful effects (of a treatment, say, or of some individual characteristic) by capturing observable degrees of variance in some attitude or latent disposition that you have independent reason to think matters in the world (debates over optimal size of likert are focused on this–the tradeoff between effect size & noise as number of points on scale increases). Using the z-score transformation of the measure for tye y-axis focuses attention on *that* because readers can see how large the variance was between conditions or between different subjects relative to variance across the sample, & aren't tempted to attribute meaning to arbitrary units in the raw likert measure. Also, using z-score as y-axis avoids potentially incorrect interpretations based on where scores fall on the mean or how many likert units differences between conditions or groups span. If, e.g., the sample mean on an 11-pt measure is 6, & you have two conditions that have means of 5 & 7 & SEMs of 0.05, people will think, "gee, everybody is pretty avarge & there's really not much difference between subjects in the two conditions." Sigh. Preempting this inference inference is what motivates people to truncate their y-axis– leading others to say, "hey, don't do that! That's creating a misleading view of your effect size!" Well, *not* doing it can be misleading too if people are unable get a good sense of variance & effect sizes from looking at bars plotted over the whole scale. So use a z-score, usually with -1 & +1 as upper & lower bounds — nothing misleading about that — & plot your data (in form of bars w/ CIs or whatever) within that.

  4. I'd have to disagree with dk's suggestion. If a scales "don't have much intrinsic meaning" then taking z scores don't add in meaning. They merely express the scores in terms of the sample SD. This can differ for all sorts of reason that have nothing to do with what you are measuring (e.g., if the ratings are high or low the z scores will be bigger because of ceiling or floor effects flattening the SD).

    Furthermore if the scales are of a disagree/agree type, the most psychologically important information on the scale is probably whether they disagree or agree and this can be obscured by the z scoring.

    I do agree that the variability is important and that plotting with CIs is sensible (and z scores with CIs is probably better than raw scores without CIs). However, confounding size of effect with its variability in the sample is problematic for interpretation and decreases transparency.

  5. My preferred approach, given the Likert scales are symmetrical about a neutral point it to highlight the scores from a central neutral point. This uses a similar stacked approach to that Derek is proposing but with a single central base. I wrote a note on this, including comparison to the stacked distribution: http://www.organizationview.com/net-stacked-distr… The note includes information on how to create them in Tableau.

    Thom and dk make good points about needing to show CIs.

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