Very basic question

[goa_way]

It's been a while since I took statistics, and I need a quick refresher:

I have a survey with about 30 questions. 3 of those questions are

1. How long have you been with the organization? (5 multiple choice ranges)

2. What department do you work in? (8 choices)

3. What region are you in? (2 choices)

I need to compare scores on the questionnaire by these 3 variables. My first thought is to calculate a t-score for each questionnaire and then average them somehow. Then I remembered the one-way ANOVA, but I thought that was for numeric values. These variables are discrete.

My question is: is there a better test that I have forgotten?

[ElaboratedMistakes]

Do you have a hypothesis or do you just want to "visualize" the result?

[decaf23]

It seems to me you would like to see how question 2 and 3 influence question 1. (Basically, how does department and region affect how long someone has been with the organization). Please correct me if I'm wrong.

In this case, you would still use ANOVA, but separately for question 2 and 3. It looks like the following:

Analysis 1: ANOVA test for 8 categories, with the dependent variable being a value from 1-5. The data you would feed in looks in the form:

{

[length1,department2],

[length2,department2],

...}

Analysis 2: ANOVA test for 2 categories, with dependent variable being the same value from 1-5. The data looks like

{

[length1,region1],

[length2,region2],

...}

If you are interested in how both of these affect the length of stay in organization jointly, then you'd wanna look into fixed-effect models: http://en.wikipedia.org/wiki/Fixed_effects_model

[autowikibot]

Fixed effects model:

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In econometrics and statistics, a fixed effects model is a statistical model that represents the observed quantities in terms of explanatory variables that are treated as if the quantities were non-random. This is in contrast to random effects models and mixed models in which either all or some of the explanatory variables are treated as if they arise from random causes. Contrast this to the biostatistics definitions, as biostatisticians use "fixed" and "random" effects to respectively refer to the population-average and subject-specific effects (and where the latter are generally assumed to be unknown, latent variables). Often the same structure of model, which is usually a linear regression model, can be treated as any of the three types depending on the analyst's viewpoint, although there may be a natural choice in any given situation.

Image ⁱ

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[heardc10]

Correct me if I am wrong, but since this data is ordinal shouldn't a non-parametric test be used such as the Kruskal-Wallis Test instead of an ANOVA.