Educational Research - the Ethics and Aesthetics of Statistics

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162 P. Smeyers to be told. Moreover, though we may be familiar with the possible objections and take these ‘warnings’ into account, this does not really change the fact that we often unthinkingly rely on what is presented to us. Why is that the case, or in other words, what do statistics do for us that make them so irresistible? Consider for instance the following abstract which was sent around recently to the staff of the Faculty of Psychology and Educational Sciences, K.U.Leuven. The abstract was for a talk to be given by Peter Wilhelm (University of Fribourg, Switzerland) titled ‘How to Assess Synchronization in Family Members’ Daily Life?’ How family members affect each other’s feelings and behaviours is a crucial question in family and social psychology. To answer this question, ambulatory assessment techniques that capture family members’ experiences and behaviours in their usual daily life over days or weeks are especially useful. To date various diary studies have been conducted to investigate transmission of affects in couples and in parent-child dyads. However, diary studies to investigate the synchronization of current affects in entire families are rare. The aim of our research was to investigate the extent to which family members’ current moods synchronize in their usual daily life and how this synchronization can be explained. Using a computer-assisted diary approach, six times a day over the course of a week we asked 192 parents and 122 adolescent children from 96 Swiss families how they were feeling, where they were, with whom they were, and whether they had experienced conflicts with each other. To answer our research questions we propose a multilevel approach in which the similarity between family members’ average mood (trait component) and the similarity between their current mood (state component) is reflected in the covariances of the random part of the model. Using this approach we were able to show that average mood was moderately correlated among family members. As expected, family members’ current mood (state component) was more similar when they were together, than when they were apart. This indicates that family members’ mood synchronizes when they come together. By extending the analyses we could explain the amount of synchronization by shared setting factors that have an impact on mood, such as location, activity, and time of the day. However, conflicts between the family members produced the largest decreases in the covariances and therefore seem to contribute most to the synchronization of current mood in family members’ daily life. It may be fun to speculate about the practical consequences of the insights this study comes up with, i.e. the family members’ mood synchronizes when they come together especially when they have conflicts. Has it thus been established that one should test a relation to its destruction or has a more general argument been developed in favour of quality time? A positive reading may indicate that the latter is obviously the case – we need to spend time together if we desire synchronization of current affects in families. Though this seems rather obvious, this research can substantiate it. Granted, this does not go very far, but at least it provides insight into how an issue in family and social psychology is dealt with. It provides answers and takes us beyond mere speculation in regard to something we may be (really) interested in (how things are). The following abstract of a talk by Jeff Rouder (University of Missouri-Columbia) for another seminar at the same Faculty, titled ‘Using Bayesian hierarchical models to search for structure in recognition memory’, is much more straightforward when indicating what, according to its author, is at stake:
11 Statistics and the Inference to the Best Explanation: Living Without Complexity? 163 Science has conventionally progressed by identifying regularities or invariances; for instance, planets orbit the sun as ellipses. These invariances then serve as the phenomenon to be explained by theory; for instance, elliptical orbits may be explained by the Newtonian theory of mechanics. In this mode, the search for invariances is a necessary precursor to theoretical development. Though this approach has been fruitful, psychologists, who tend to focus on verbal explanations of differences idiosyncrasies across conditions and populations, have not adopted it. The search for invariances is complicated in many domains by the presence of unintended or nuisance variability. For instance, in memory research, there is variability from the selection of participants and items in addition to that from the mnemonic processes at hand. The conventional approach is to aggregate across these nuisance sources, but this aggregation may distort the structure and mask important invariances in the data. To address this problem, my colleagues and I have developed a collection of Bayesian hierarchical models for modelling nuisance variability so that structure in psychological processes may be explored. I apply these models to recognition memory, where there is a vigorous debate whether memory is supported by a single mnemonic process or by multiple distinct processes. Model analysis reveals that ROC [receiver operating characteristic] curves across people and conditions form an orderly field, much like the order in gravitational of magnetic fields. As a consequence, the structure of recognition memory seems to be one-dimensional and may be accounted for with a single parameter of mnemonic strength. A single parameter of mnemonic strength is the result of a search for invariances in this area. It is a ‘structural quality’ (a reduction of the complexity of the data here as invariance, in other cases for instance a pattern), which should now be taken up by theory and explained (or ‘given a place’). We might ask what exactly the relationship is between this result and the particular method that has been used (in this case, a collection of Bayesian hierarchical models). Furthermore, we might consider whether it is in some sense useful. Clearly this is all about reduction, about ‘seeing’ something in a simpler form. For this reason (maybe for this reason alone) it is something to be strived for. In educational research as everywhere else in science, academia or even ordinary life, we are for various reasons interested in trying to understand (some would say, ‘make sense of’) the phenomena we are surrounded by or subjected to. This understanding is supposed to give us a grip on what is happening, bring some order to the chaos. It is not in the least bit concerned with how we see ourselves, or what we aspire to achieve. Some explanations are more attractive to us than others, some are more popular in particular periods, and some focus on groups. Others centre on the individual. Crucial in this endeavour are issues about truth (A) but at the same time a lot will depend on what one is particularly interested in. (B) The latter may or may not be intertwined with one or other sort of good that is envisaged either by a person or a particular group and this opens up the sphere of manipulation (C) but also of responsibility and responsiveness. (D) Thus matters of the particular concept that is used when studying a problem come to the forefront (E) as crucial factors concerning the characterization of that phenomenon. This will have implications for where one can arrive at in terms of solutions for dealing with it. Let me begin by arguing that whether something is really explained, or whether ‘reality’ here is merely the opposite of fiction, should not necessarily invoke a correspondence theory of truth where sense data are the exclusive building blocks. Instead, as Peter Winch (1958) rightly argued, it is always about ‘what is real for
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Educational Research: The Ethics an
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Paul Smeyers · Marc Depaepe Editor
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Earlier Volumes in this Series Educ
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viii Contents 10 The Good, the Beau
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Chapter 2 The Lure of Statistics fo
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2 The Lure of Statistics for Educat
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28 M. Depaepe of the Leuven Catholi
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30 M. Depaepe Tan’s final verdict
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32 M. Depaepe of methodological bot
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34 M. Depaepe tapping other sources
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36 M. Depaepe In the context of dem
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38 M. Depaepe combination. Moreover
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40 M. Depaepe 4. See Pelleriaux (19
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42 M. Depaepe Schneider, J. (2010).
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44 J.J.H. Dekker (see Section 4.4).
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46 J.J.H. Dekker from their own par
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48 J.J.H. Dekker a cohort study tha
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50 J.J.H. Dekker child” (Van IJze
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52 J.J.H. Dekker USA resulted in ev
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54 J.J.H. Dekker of the problem”.
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56 J.J.H. Dekker Dekker. J. J. H.,
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Chapter 5 Constructing Social Unity
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212 P. Standish research none of us
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214 P. Standish of the products but
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216 About the Authors co-editor-in-
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218 About the Authors voor Wetensch
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220 Author Index Elkins, G. R., 190
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222 Author Index Silverman, J. G.,
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224 Subject Index I Inequality, 29-
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