The first real data set I ever analyzed was from my senior honors thesis as an undergraduate psychology major. I had taken both intro stats and an ANOVA class, and I applied all my new skills with gusto, analyzing every which way.
It wasn’t too many years into graduate school that I realized that these data analyses were a bit haphazard. (Okay, a LOT). And honestly, not at all well thought out.
A few decades of data analysis experience later, I realized that’s just a symptom of being an inexperienced data analyst.
But even experienced data analysts can get off track. It’s especially easy with large data sets with many variables. It’s just so tempting to try one thing, then another, and pretty soon you’ve spent weeks getting nowhere. (more…)
Before you can write a data analysis plan, you have to choose the best statistical test or model. You have to integrate a lot of information about your research question, your design, your variables, and the data itself.
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Every time you analyze data, you start with a research question and end with communicating an answer. But in between those start and end points are twelve other steps. I call this the Data Analysis Pathway. It’s a framework I put together years ago, inspired by a client who kept getting stuck in Weed #1. But I’ve honed it over the years of assisting thousands of researchers with their analysis.
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One activity in data analysis that can seem impossible is the quest to find the right analysis. I applaud the conscientiousness and integrity that underlies this quest.
The problem: in many data situations there isn’t one right analysis.
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