Bivariate hypotheses

1. Likert scale data: ORDINAL. Chi Square (tables), Spearman's rho and others non-parametric analysis

2. Nominal data. NOMINAL. Chi Square (tables) and others non-parametric analysis

3. Some numeric data(e.g. study time, speaking time etc. METRIC SCALES. Pearson product-moment correlation coefficient, Spearman's rho.

4. Nominal (2) amb metric scale. t test and non-parametric analysis.

5. Nominal (>2) or ordinal, and metric scale. ANOVA and non-parametric analysis.

6. Ordinal and metric scale. Spearman's rho...

If you have data from educational testing, you could use Item Response Theory.

http://erm.uncg.edu/oaers/methodology-resources/item-response-theory/

IRT is also to applicable to Likert scales.

In addition to your concern about the measurement of the variables (nominal etc) it is often very important in educational research to consider the structure eg repeated measures of children as they learn over time; pupils nested in classes in schools; multiple tests nested with in pupils. All of these are multilevel structures or classifications and require multilevel modeling to tackle  a range of questions. Lemma is a free training course and will guide you through this material and will do so for a range of measurement types and more complex structures as when a pupil is taught by more than one teacher (multiple membership structure)

http://www.bristol.ac.uk/cmm/learning/online-course/

This represents a large scale training course in advanced statistical methods which you are very welcome to use.

Thanks everybody, specially thanks to Kelvyn to address a excellent link that was totally unknown to me. I have already registered here thanks Kelvyn again.

Saif

Thanks for the comments - to encourage others - these are the modules - you will see that we do quite a lot of single -level material as well as multilevel including discrete outcomes modelling. We provide material on the underlying concepts as well as how to do it in R, Mlwin and Stata - and there is now some availability in in SPSS

Modules

Using quantitative data in research (watch video introduction)

Introduction to quantitative data analysis (watch video introduction)

Multiple regression

Multilevel structures and classifications (watch video introduction)

Introduction to multilevel modelling

Regression models for binary responses

Multilevel models for binary responses

Multilevel modelling in practice: Research questions, data preparation and analysis

Single-level and multilevel models for ordinal responses

Single-level and multilevel models for nominal responses

Three-level multilevel models

Cross-classified multilevel models

Multiple membership multilevel models

Missing Data

Multilevel Modelling of Repeated Measures Data

Happy modelling