There is a tendency for researchers to take continuous variables and recode them into ordinal or categorical variables. For example, researchers may ask participants to answer if they are 20-30 years old, 31-40 years old, 41-50 years old, 51-60 years old, or 60+ years old. Or, they may set an arbitrary "cut-off" of values above or below a certain value (People who are 55 years and older versus everyone younger than 55 years).

Researchers lose valuable precision and accuracy in measurement when continuous variables are demoted to ordinal or categorical levels. It is ALWAYS better to take an actual numerical value with a "true zero" and analyze it using parametric statistics. If there is a theoretical, conceptual, or empirical basis for pairing down continuous measures into lower levels of measurement, then and only then should it be done. If you were a researcher and wanted to know the most precise and accurate measure possible of my age, which of the following is the best way to ask?

1. How many years old are you?  (continuous)

2. How old are you? (circle one)  20-30    31-40    41-50    51-60   60+  (ordinal)

3. Are you above or below the age of 55?  (categorical)

The continuous method will give you a stronger measure of age, which can then be broken down into separate ordinal or categorical levels, AT YOUR DISCRETION. So, always measure at the continuous level if at all possible.

With this being said, PLEASE realize that while we can go from continuous to ordinal and continuous levels of measurement, it is IMPOSSIBLE to change categorical and ordinal variable into a continuous level of measurement.

Let's use a basic example:

Gender - 0 = male and 1 = female

Is there any way to convert this into a continuous variable? No.

Here is another example:

How old are you? (circle one)  20-30    31-40    41-50    51-60   60+

Can you convert this into a continuous variable? No, again.

In conclusion, ALWAYS try to measure your variables at a continuous level, if at all possible or feasible. They can be broken down into ordinal and categorical variables as needed. Also, REALIZE that once you have decided to measure something at a categorical or ordinal level, it cannot be converted to continuous.

Here is an interesting trick I picked up along the way when it comes to ordinal outcomes and some unvalidated measures. If you run skewness and kurtosis statistics on the ordinal variable and its distribution meets the assumption of normality (skewness and kurtosis statistics are less than an absolute value of 2.0), then you can "upgrade" the variable to a continuous level of measurement and analyze it using more powerful parametric statistics.  

This type of thinking is the reason that the SAT, ACT, GRE, MCAT, LSAT, and validated psychological instruments are perceived at a continuous level. The scores yielded from these instruments, by definition, are not continuous because a "true zero" does not exist. Scores from these tests are often norm- or criterion-referenced to the population so that they can be interpreted in the correct context. Therefore, with the subjectivity and measurement error associated with classical test theory and item response theory, the scores are actually ordinal.

With that being said, if the survey instrument or ordinal outcome is used in the empirical literature often and it meets the assumption of normality as per skewness and kurtosis statistics, treat the ordinal variable as a continuous variable and run analyses using parametric statistics (t-tests, ANOVA, regression) versus non-parametric statistics (Chi-square, Mann-Whitney U, Kruskal-Wallis, McNemar's, Wicoxon, Friedman's ANOVA, logistic regression).