An ordinal categorical variable is a variable that has intrinsic monotonic relations among all its levels/stages. For example, when describing temperature using words 'hot, cold, warm, freezing', they have an order of 'hot, warm, cold, freezing'. It doesn't matter what is their effect on the dependent variable.
In your case, 'increase, sustain, decrease' has ...
In general, when you have ordinal categories, say for opinions, it is appropriate to
use the median to describe the center of the sample. Thus the median can estimate the center of the population of opinions. However, the
definitions given in the questionnaire
for your opinion categories are numerical (percentages). So you might use the mean, if you are ...
Barplots graph the counts for different values of a categorical variable, but histograms plot binned quantitative data. Your data is probably better viewed as binned quantitative data and therefore should be plotted as a histogram. This just means that the bars in your graph will be touching and the order of the bars matters because the X-axis represents the ...
It depends on the encoding of your data. Ordinal data implies some ranking, e.g. like school grades or reviews on amazon.
If you use a very simple encoding scheme (0 < 1 < 2 ... < N) then the order will be considered. However you imply distances, which not always represent reality.
Example: 'small', 'medium' and 'large' have an ordering, if you ...
Your response variable is ordinal with three levels, so try ordinal logistic regression, two models, one with each of the competing predictors, and compare them. You can for instance evaluate each of the models with cross-validation and see which is best.
Or fit one model with both predictors?
When you code your block variable as numeric and use linear model (no matter mixed or no), you silently assume that "distances" between "adjacent" blocks are the same. E.g. distance between block 1 and block 2 is the same as between block 23 and 24. And what is more distance between e.g. block 1 and block 3 is twice as big as between block 23 and 24.
Here are some simulated data for DV and IV for purposes of
dv = sample(1:7, 100, rep=T, p=c(1,2,2,3,3,4,4))
iv = sample(1:7, 150, rep=T, p=c(1,2,4,3,2,1,1))
 6 8 11 21 13 21 20 # DV
 13 27 49 20 18 11 12 # IV
Min. 1st Qu. Median Mean 3rd Qu. Max. # DV