The distribution of coefficient similarity value and phenetic distance from the counting results to 32 OTU pairs based on the morphological character of the shell with
ordinal data type and 173 test characters are shown in Figure 7A.
WHY DO RESEARCHERS USE PARAMETRIC ANALYSES ON
ORDINAL DATA?
This measure has the advantage of being easily comprehensible and estimable, and this can be applied using
ordinal data. However, it suffers from the disadvantages first noticed by Sen (1976) in the unidimensional context, namely being insensitive to the depth and distribution of poverty, violating monotonicity and the transfer axiom.
McCullagh, "Regression models for
ordinal data," Journal of the Royal Statistical Society B, vol.
If such data collections prove infeasible due to the enormous effort and labor that would be needed, however, future researchers should consider height-volume estimations, similar to the data collection form used in our study, with the noted confidence in
ordinal data with ordered levels (Agresti, 2010).
To determine the accuracy of the telerehabilitation assessments, different methods were used for continuous and
ordinal data. For continuous data, differences obtained between face-to-face and telerehabilitation methods of assessment were examined using the limits of agreement statistic described by Bland and Altman [31] along with the mean absolute difference (MAD) of the assessments.
A comparison of regression models for
ordinal data in an analysis of transplanted-kidney function, Canadian Journal of Statistics 16(4): 325-335.
Internal consistency of the factors was low ([alpha] between .68 and .79) or acceptable, Cronbach's alpha for
ordinal data between .73 and .88 and omega between 75 and 88.
Even when you try to discretize simulated data from a continuous distribution to form
ordinal data such as Likert, those parametric properties don't completely go away.
If the distribution is unknown or untransformable to normality, the sample size is small (n< 30), or the data is from nominal or
ordinal data, it is best to use non-parametric tests at the cost of loss of power.
Nominal and
ordinal data would be considered categorical, and interval and ratio level are continuous.
These types of data are known as
ordinal data. That is, the scores may decrease or increase but they are not evenly distributed as is the case with interval/ratio data.