The way a research question is asked can make a great difference. It's only natural that a substantive research question may need a bit of "translation" when it comes to data analysis. Moreover, many people are not aware of, and are encouraged to learn of, the range of questions that are answerable using quantitative analysis (which one also might call statistical analysis, predictive modeling, data mining, or machine learning).
The following list illustrates common types of questions. Don't see your question? Email me and we can talk about ways to find answers that will be most meaningful in your context.
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 What sort of change has occurred over time?
 How valid or reliable are the indicators used for a certain purpose?
⤖ Does this accurately represent that?
 How do groups differ?
 Is one group's average, or incidence, or level of risk, higher
than the other's?
 Does one group have much more variability than the other?
 What differences might we find among geographical areas?
 How can we best characterize the relationships between or among variables?
 What relationships do we see at face value?
 How strongly is quantity A correlated with quantity B? ⤖ Does
more education tend to mean greater prosperity?
 Can we effectively predict Y if we know A, B, C, and D for a given case?
 Does the relationship between A and B change depending on the
group or region in question? ⤖ Are there "different slopes for different folks?" (statistical interactions)
 Can we go beyond mere correlation to assess cause and effect?
 ⤖ Even
though greater education tends to mean greater prosperity, which is
causing which?
 ⤖ What kind of educationprosperity connection do we see if we account for (control or adjust for) as many other relevant variables as possible?
 Can we establish webs of relationships?
 Is there a basis for taking a large set of variables/factors/characteristics and summarizing them by, or distilling them into, just a few?
 Is there a basis for clustering (people or schools or nations) to create groups in which each case has a similar profile?
Then, for most of these types of analyses, it is possible to test whether a phenomenon is statistically significant: whether
it goes beyond what chance would normally produce. This can be helpful
in establishing the reliability or definitiveness of a finding, but researchers are increasingly reluctant to place too much emphasis on statistical significance, for good reason. See my commentary
and a
position paper
of the American Statistical Association (once there, click on PDF  Free Access).
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but I will say, manageable  more meaningful." G. Burke Doctoral student in education
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'mindful' predictive models that take into account the possible
relationships between variables, especially models involving linear
and logistic regression."
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my critique of opposing expert witness's testimony
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