Russo ioe_nov12


Published on

  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Few contacts between philed and philsci here is a good occasion personal contact but also proximity of topics and interestsPoint of contact = relation between phil (ed) and empirical researchUse multilevel as a case to illustrate the interactions (or lack thereof)
  • Coming from PhilSciPartly true, partly not. Still a lot of abstract, toy-example based investigationWhen it is based on empresarch, phil is less precise and sharp, but perhaps more useful. A trade-off that is difficult to accept and that not always isLast para:- yes, so there must be interaction, not just phil looking into emp research. This point will come up again.
  • Two approaches:Qual more details about internal dynamics of some social system. Very limited generalisabilityQuant  big data supposedly allow more generalisabilityAlso, supposedly, quant has more rigorous methodology … so here another pandora’s box. Keep this in mind, I’m coming back in the conclusion
  • Holism: for instant sociology of DurkheimIndividualism: for imstance meth individualism in economics
  • Don’t necessarily show this slides, may be too technical.
  • School composition. Within the PRIMA project, a school composition variable was constructed on the basis of the ethnicity and education of the parents.Ethnic diversity. In addition to the school composition variable. the number of different ethnic groups was also included as a predictor variable at the level of the school. This provides an indicator of the heterogeneity of the school with regard to ethnic origin and thereby a measure of the linguistic and cultural diversity characteristic of the school.
  • Ontology: what are social entities composed of? Explanation: do social explanations need to “reduce” to facts about the actions of individuals?Causation: do “higher-level” social entities have causal powers? Inquiry: at what level should (a given style of) social inquiry focus its efforts at descriptive and explanatory investigation? Description: are there “level” requirements or constraints on social description?Generalization: are there higher-level “types” of social entities that recur in different historical and social settings?
  • Explain why I don’t agree with- Just macro properties- counterfactual element in causal question
  • Russo ioe_nov12

    1. 1. Interactions between the individual and the group:reflections from multilevel modelling in educational research Federica Russo Center Leo Apostel, VrijeUniversiteitBrussel& Centre for Reasoning, University of Kent
    2. 2. OverviewPhilosophy of education and empirical research Reverse the question: does empirical research look into philosophy?Multilevel models in educational research Definition and examples The need for an accompanying ‘substantive theory’A ‘substantive theory’ for multilevel Recent work by Little and Yilokoski Main features 2
    4. 4. Does PhilEd pay enough attention to empirical research?Phillips Hyslop-Margison&NaseemJ Phil Ed 39(4), (2005) Phl Ed Archive (2007)• PhilEd hasn’t paid enough and serious attention to • Straw man: wrong selection empirical research of critics• It is possible to study • Counterexamples: PhilEd normative processes does pay attention to empirically empirical research• Mutual benefit of PhilEd • Problem of empirical and empirical research to generalisability look into real cases 4
    5. 5. PhilSci and PhilEduPhillips (2005, p.582): The marked change in doing philosophy of science came about when it was realised that there was much to gain by taking scientific research seriously, rather than discussing an artefact of the philosophers’ imagination. […] The present essay is making a call for a parallel revolution in philosophical discussions of educational research, a revolution that entails taking examples of educational research seriously. […] Processes that humans engage in, in the real world, whether normative or cultural or psychological (or all three at once) can be studied—and probably ought to be studied—empirically, but they also need to be assessed in terms of the values (and if relevant the conception of education) that they embody. 5
    6. 6. Turn the question on its headDoes empirical research look sufficiently into philosophy of education? (Or, for the matter, into philosophy?)
    8. 8. Modelling in the social sciencesCausal relations in social contexts Marital problems ⇄ migration Maternal education → child survival Stress + physical health + …→ self-rated healthTwo approaches Qualitative: smaller and focused samples Quantitative: statistical analyses of large data 8
    9. 9. A crash courseMULTILEVEL MODELS 9
    10. 10. Why multilevel?An example of quantitative methods used in empirical research in education Going quantitative, the new panacea for evidence But … is it really panacea?It models hierarchical structures Typical of social (and education) contextsA sounding board for the question: does empirical research look into philosophy? 10
    11. 11. Multilevel modelsA special type of statistical model used in causal analysis to model hierarchical structures: Individuals / family / local population / national population Firms / regional market / national market / global market Pupils / classes / school / school systemsNo a priori reason to choose the level of analysisActually, good reasons to study the interactions between the levels 11
    12. 12. Traditional approachesHolism the system as a whole determines the behaviour of the parts in a fundamental way; the properties of a given system cannot be reduced to the mere sum of its componentsIndividualism social phenomena and behaviours have to be explained by appealing to individual decisions and actions, without invoking any factor transcending them 12
    13. 13. The ‘statistical’ counterpartsAggregate-level models explain aggregate-level outcomes through aggregate-level variablesIndividual-level models explain individual-level outcomes by individual-level explanatory variables 13
    14. 14. Types of variablesIndividual: measure individual characteristics, take values of each of the lower units in the sample. e.g. income of each individual in the sampleAggregate: summary of the characteristics of individuals composing the group e.g.: mean income of state residents 14
    15. 15. DangersAtomistic fallacy wrongly infer a relation between units at a higher level of analysis from units at a lower level of analysisEcological fallacy draw inferences about relations between individual level variables based on the group level data 15
    16. 16. Robinson: illiteracy and immigration1930 census in the US, for each of 48 states + district of ColumbiaIndividual correlation: descriptive properties of individuals Positive correlation: immigrants more illiterate than native citizensEcological correlation: descriptive properties of groups Negative correlation: correlation between being foreign-born and illiterate magnified and in the reversed directionExplanation: immigrants tend to settle down in states where native population is more literate 16
    17. 17. Courgeau: Farmers’ migration in NorwayData from the Norwegian population registry (since 1964) and from two national censuses (1970 and 1980)Aggregate model and individual model show opposite results: Aggregate: regions with more farmers are those with higher rates of migrations; Individual: in a same region migration rates are lower for farmers than for non-farmersReconciliation: multilevel model aggregate characteristics (e.g. the percentage of farmers) explain individual behaviour (e.g. migrants’ behaviour) 17
    18. 18. Types of models - summaryIndividual: explain individual-level outcomes by individual- level explanatory variables e.g.: explain the individual probability of migrating through the individual characteristics of being/not being farmerAggregate: explain aggregate-level outcomes through explanatory aggregate-level variables e.g.: explain the percentage of migrants in a region through the percentage of people in the population having a certain occupational status (e.g. being a farmer)Multilevel: make claims across the levels, from the aggregate- level to the individual-level and vice-versa e.g.: explain the individual probability to migrate for non-farmers through the percentage of farmers in the same region 18
    19. 19. Grouping in multilevelUnits grouped at different levels, a-contextual languageGrouping may be more or less randomOnce the grouping is done, differentiation: group and its member influence and are influenced by the group membership 19
    20. 20. Statistical modelling of hierarchies Yij   0 j   1 j x ij   2 z j   ijresponse variable at theindividual level explanatory variable at the individual level explanatory variable at the group leveli: index for the individualsj: index for the groupthese  vary depending on the group Errors are independent at each level and between levels 20
    21. 21. Goldstein: Multilevel in educational researchStudy school effectiveness, examination results, … All quantifiable aspects of education‘Statistical’ advantages of multilevel Efficient estimates of regression coefficient Correct standard errors, confidence intervals, significance tests for the clusters Enables measuring differences between clusters 21
    22. 22. Hierarchies in educational researchSimple hierarchy: Pupil / class / school / neighbourhood /Cross-classified structure Pupil – ethnicity // school – neighbourhood // 22
    23. 23. Goldstein et al: examination results and school differencesInner London schools Response variable: examination results Explanatory variables: standardised London reading tests, verbal reasoning category, gender, school gender (mixed, boys, girls), school religious denomination (State, Church of England, Roman Catholic, other)Results: Small effect of school gender; Roman Catholic slightly better; girls better than boys; large differences for different verbal reasoning categories.Differences between schools in examination results depend on intake achievement and curriculum subject consideredNo single dimension in which schools differ 23
    24. 24. Driessen: School composition and primary school achievementDutch primary schools Response variable: language and math proficiency Explanatory variables: parental ethnicity and education, pupils sex and age, school composition, ethnic diversityResults: Quite strong effect of school composition on language, weak on math; all children, independently of background, perform worse in schools with high ethnic diversityQuestion about distribution policy and other measures 24
    25. 25. […] despite their usefulness, models formultilevel analysis cannot be a universalpanacea. […]They are notsubstitutes for wellgrounded substantive theories […]Multilevel models are tools to be used withcare and understanding. Goldstein, Multilevel statistical models, 25
    27. 27. Modelling and explainingWhat does a multilevel model model? Relations between different levels in a hierarchical structureWhat does a multilevel model explain? How group behaviour influences individual behaviour (but not vice-versa)Statistically, multilevel achieves bothBut the ‘substantive theory’ is still wanting 27
    28. 28. What needs the ‘substantive theory’School religious denomination What social practices, norms, values are involved?School composition and ethnicity How do these influence peer relation among pupils?…What is the extra information that we need and that statistics does not give us? 28
    30. 30. Levels, beyond statisticsDan Little Levels of the social: Ontology  what social entities? Explanation  reduction? Causation  causal powers? Inquiry  what level? Description  what level requirements? Generalisation  recurrence of types? Avoid analogies with natural sciences, don’t reify social phenomena 30
    31. 31. Levels, beyond the received viewsMethodological individualism Holism• Social facts must be • Social entities and reducible to facts about structures have primacy and individuals are independent• There is no higher level • Individuals are influenced without lower level by social facts, but do not influence them• E.g.: Austrian school economics, some political • E.g.: sociologists in the scientists Durkheim tradition 31
    32. 32. Methodological localismSocial structures influence social outcomes, embodied in action of socially constructed individualsIndividuals are the bearers of social structures and causes, but individual actors are socially constructedEmphasis: Contingency of social processes Mutability of social structures over space and time Variability of human social systems (norms, social practices, urban arrangements, …)Cast doubt on generalisable theories across many populations,look for specific causal variation 32
    33. 33. Scale-based levelsPetri Ylikoski: Macro social facts are typically supra-individual Micro and macro have a part-whole relationship, but not just mereological constitution Difference in scale, not categorical A heuristic, as there is no unique micro-level, context- relativeness 33
    34. 34. Different questionsConstitutive questions Causal questions• How macro properties are constituted by smaller-scale • Origin, persistence, and entities change of macro properties• How the macro depends on • What the outcome would the micro have been, had things in the• How the macro would have causal history been been different, had the different micro been different 34
    36. 36. Levels and types of questionGive reality to levels allocation is not random, not a statistical artefactArticulate the embodied aspects of level interactions Sociology, anthropology, pedagogy, psychology, …Look for empirical origins of variations in outcome Large-scale statistical studies Small-scale qualitative studies INTEGRATIONof explanation of socially-constituted behaviours 36
    37. 37. TO SUM UP AND CONCLUDE 37
    38. 38. In this talk:PhilEd and empirical research Disputed question of the relation between PhiEd and empirical research Turn the question on its head: does empirical research look into philosophy?Empirical research Causal modelling widely used in social research, including education Sophisticated formalisms are designed to measure, model, explain social reality, including hierarchical structures Despite progress and improvements, formal methods are still in need of ‘substantive’ theoryIn search of a substantive theory Recent work by Little and Yilikoski addresses level ontology, it helps find the main features of the substantive theory 38
    39. 39. Trouble shared, trouble halved?Empirical research does not look outside statistics sufficiently A problem shared also by e.g. social epidemiologyStatistical modelling, an alleged gold standard to generate evidence A problem shared by e.g. evidence-based medicine 39
    40. 40. Remedies?Qualitative research, philosophical investigations to feed empirical researchDismantle evidence hierarchies and gold standardsBuild integrated methods Quantitative and qualitative Empirical and conceptual Multiple sources of evidence 40
    41. 41. REFERENCES 41
    42. 42. Courgeau D. 1994 Du groupeàl’individue: l’exemple des comportementsmigratoires. Population 1.Courgeau D. 2007 Multilevel synthesis. From the group to the individual. Springer.Driessen G. 2002 School composition and achievement in primary education: a large-scale multilevel approach. Studies in Educational Evaluation 28.Goldstein H. 1999 Multilevel statistical models. Wiley.Goldstein et al. 1993 A multilevel analysis of school examination results. Oxford Review of Education 19(4).Hyslop-Margison EJ and AyazNaseem M. 2007 Philosophy of education and the contested nature of empirical research: a rejoinder to D.C.Phillips. Philosophy of Education.Little D. 2006 Levels of the social. In The Philosophy of Anthropology and Sociology, Risjord and Turner (eds). Elsevier Science.Phillips D.C. 2005 The Contested Nature of Empirical Educational Research (and Why Philosophy of Education Offers Little Help). Journal of Philosophy of Education 39(4).Robinson W.S. 1950 Ecological Correlations and the Behavior of Individuals. American Sociological Review, 15(3)Russo F. Causality and causal modelling in the social sciences. Measuring variations. Springer. 2009Ylikoski P. 2012 Micro, macro, and mechanisms. In Oxford Handbook of Philosophy of Social Sciences, Kinkaid (ed) OUP 42