Data Train Starter Track: About the meaningfulness of data
Data are not, as etymology suggests, „the given“, but they are generated, constructed,made, and sometimes „set up“ in the bad understanding of the wording. There is, of course, a (hopefully) large part to it that originates from the subject or the phenomenon under scrutinity, and that’s what you’re after: the true score if you embrace this concept. There also is, however, a substantial share that is not merely due to random „error“, but that is added sytematically, as being added by you, as a result of decisisions you make in the process of obtainingand processing data. So your data may be technically clean, but, epistemologically, things are far more complicated. It is common to consider data as answers, but we know that the question determines, to a degree, the answer. As a consequence, when it comes to „meaning“, it is essential to reflect on the nature of the questions and see to what drives the ones proposing them, p.ex. in terms of a paradigm. This is why, when you want to become a scientist, they teach you how to ask good questions, and here, „good“ does not pertain to sensibility in a content – related, intellectual manner but to the way these questions are set up: on sound theoretical grounds, abiding by the rules of logic, targeting precise hypotheses, including all relevant parameters (and relevance is a major issue). So much for theory, and off you go, further down the rocky road of practical research, deciding on hundreds of options as regards erm, design logic, reliable and valid measurement, sampling, coding, preprocessing of data, choice of analytical models and implementation tools, and finally, interpretation of results with back – reference to question and theory. Some of your decisions are accounted for in your study protocol, and some are not. There may be some that you are not even aware of, and a psychologist will tell you that there’s sort of a purpose to unawareness. This session is titled „the meaningfulness of data. We need to discuss how meaning relates to data, or results of data analysis. For a start, let’s assume that there is no meaning IN the data, but that meaning happens to data, it is attached to it. In fact, YOU attach it, and therefore you must assume liability for it in both the scientific and the legal sense.
- Working definitions: data, meaning, and models.
- Central thesis: meaning is not in facts, but in human reasoning.
- Rolling it up from behind: statistics.
- Measurement: how to translate meaning in phenomena into numbers.
- Modeling: how to reduce meaning into models for information.
- Norming: how to scale and compare meaning using data
- Caveat: no data how to retrieve meaning from essentially nothing.
- Caveat: outliers how to decide on who’s hot and who’s not in meaning.
- Caveat: graphics how to put meaning in the eye of the beholder (or not)
4. Meaning and liability
Since, as a psychologist and statistician, I cannot claim expertise in your respective field of work, I will not, and cannot, tell you how to “do it right”. But the patterns behind „doing it wrong“ are quite universal: a moody remark that is warranted by 25 years of statistical consulting. My aim is to create awareness, make the implicite explicit, and foster a critical mindset when it comes to relating data and meaning in your specific discipline. You are welcome to bring along your own doubts and questions, or stories on big misunderstandings of data.
Speaker : Hans-Christian Waldmann, Professor of Theoretical Psychology & Psychometrics, Department of Psychology, University of Bremen
Please register here.