Though this may seem statistical we will skip numbers and focus on our essay on what we learned from the subject course, 'Quantitative Data Analysis.'
I will talk a few notes about what I learned in normality analysis, descriptive analysis, comparative analysis, correlational analysis, and regression analysis.
Normality Analysis
After some preliminaries, our professor introduced us to normality analysis. Normality analysis is a topic in Statistics where we will determine whether a particular data set is normal or not. But that is not the sole goal of this one.
In here, if it is found out that the data set is normal, then we will proceed to parametric test. Otherwise we use nonparametric tests.
We may actually proceed immediately to nonparametric tests but if we do that, we will more likely commit the Type II Error. So while there is a chance that our data is normal, we test for it's normality. If it turns out it is non normal then at least we've tested it.
Descriptive Analysis
We may already be familiar with measures of central tendency and measures of variation.
Before I only know how to computer but this time, I learned that we have different conditions where we apply or use a measure of central tendency. For nominal data, use mode. If ordinal, we use median. For interval (normal), mean. Otherwise, median. For ratio (normal), mean and if ratio (skewed), median.
In addition, we should take into account coefficient of variation (cv) for experimental and sometimes attitudinal studies. If cv is at most 30%, our data set is acceptable. If not, it is unacceptable. Either we regather data or do transformation for our data.
Comparative Analysis
In comparative analysis, we compare one or more data sets. We have what we call as independent variables and those related variables. We have to refer to standards on when and why we use particular statistical techniques. Otherwise, we will have erroneous interpretations.
Our considerations would be, independence, normality, level of measurement and direction of test
Correlational Analysis
What became very significant to me is the realization that although causation implies correlation, correlation does not necessarily imply causation. Therefore, we have to be very careful with our statements, especially with our conclusions.
Regression Analysis
As for regression analysis, we determine those factors that are predictors to a certain variable. Afterwards, we determine the equation that will give the approximate value of the variable of concern.
So those are the things I learned in our course. Something I previously lack the knowledge of.
awts, yeah. those languages scares me. hahaha.