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Using quantitative methods as exploratory techniques in qualitative research projects. Richard Bell University of Melbourne

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Disclaimer There is nothing new in all this analyses carried out with standard statistical software (here, SPSS)

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The Scope of this Presentation Some preliminary remarks about qualitative & quantitative data analysis A few examples Some discussion about how to use quantitative tools in qualitative contexts

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Some preliminary remarks about qualitative & quantitative data analysis The common view of the qualitative/quantitative divide Some myths about quantitative data analysis The nature of data The purpose of data analysis A very brief history of quantitative methods for qualitative data

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The Qualitative / Quantitative divide

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Some myths about Quantitative data analysis It is all about NHST (Null Hypothesis Significance Testing) It is all about inferential statistics It is only a confirmatory procedure There is one way (the right way) to do things It measures things

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The nature of data All data can be either quantitative or qualitative Saying this piece of data can be assigned to the same class as another piece of data allows it to be treated quantitatively Numbers can always be treated as qualitative data

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The Purpose of Data Analysis Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise

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The Purpose of Data Analysis Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise John Tukey (1962) Annals of Statistics –Data analysis must progress by approximate answers, at best, since its knowledge of what the problem really is will at best be approximate

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History of quantitative methods for qualitative data Louis Guttman (1941) The quantification of a class of attributes Cyril Burt (1950) The factorial analysis of qualitative data James Lingoes (1968) The multivariate analysis of qualitative data Forrest Young (1981) Quantitative analysis of qualitative data

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Traditional Quantitative Methods for Qualitative Data Miles & Huberman (1994) –hierarchical cluster analysis Giegler & Klein (1994) –correspondence analysis Bazely (2002) –cluster analysis –correspondence analysis

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Some examples of Quantitative methods for Qualitative data Giegler & Klein analysis of personal advertisements Demographics from a market research survey Miles & Huberman school innovation table A current study of social withdrawal in early psychosis

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Giegler & Klein Examined personal advertisements in a number of German magazines eg Young man, 35 y, 176cm, slim with car, good income, looks for a lovely high-bosomed and well-developed partner for a common future.

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Correspondence Analysis Representation

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Correspondence Analysis Finds set(s) of weights for row categories and set(s) of weights for column categories so that the correlation between the sums of the weights is maximized Can produce separate maps of relationships between categories of rows or columns Can produce a joint map of categories of rows or columns

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Generalization Two aspects of Correspondence Analysis –weights –correlation Generalizes to more complex data structures –weights –correlation models multiple regression principal components

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The model Called Alternating Least Squares Procedures devised in the 1970s –Forrest Young –Yoshio Takane –Jan De Leeuw (Albert Gifi) Generally known now as optimal scaling

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Optimal Scaling a data analytic technique which assigns numerical values to observation categories in a way which maximizes the relation between the observations and the data analysis model while respecting the measurement character of the data (Young, 1981, p.358)

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Alternating least squares Find Optimal Scaling of Categories Find Relational Coefficients

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Back to Geigler & Klein data 36 rows of matrix composite rows, eg row ZFS –Z indicates magazine (6) –F indicates sex of writer (2) –S indicates image (3) self desired partner relationship

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Categorization MagazineSexConceptFitnessCompassionFigureValuesErotic ZFSelf ZFSeeking ZFRelationship ZMSelf ZMSeeking ZMRelationship10341 WNFSelf WNFSeeking WNFRelationship WNMSelf WNMSeeking WNMRelationship10100 Giegler & Klein data as a four-way table

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Not just tables of frequencies Rows are units of interest (documents, cases etc) Suppose columns are different variables and contain coding within variables eg

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Demographic variables in a market research survey

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Suppose we wished to form a composite Find weights for categories of variables to maximize correlations among them & find principal component to maximize variance of weighted sum

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Age Group Position in House Educon Level Work Status Marital Status Position in Household-.481 Education Level Work Status Marital Status Country of Birth Correlations among transformed variables

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Dimension 1 Age Group-.736 Position in Household.731 Education Level.529 Work Status.329 Marital Status.807 Country of Birth-.318 Cronbach's Alpha.659 Component Loadings of Transformed Variables

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Summary Tables An example from Miles & Huberman 12 school sites evaluated on various criteria Results summarized in a table

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Possible Research Questions Which variables predict the degree of change? Find weights for categories that maximize correlations find multiple regression coefficients

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Correlations Transformed Variables

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A current data set PhD project by Simone Pica People with psychosis featuring social withdrawal –19 young people suffering from psychosis with symptoms of social withdrawal –Unstructured interviews –Standard psychiatric measures also completed

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Aim: Linking categories evident in interviews (qualitative data) to standard quantitative measures

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Raw material Um, when I got home I thought it was probably a good thing I didnt go because um, it sort of relates to motivation as well, I wasnt really that motivated to go out and deal with people and stuff. If more of my friends were there, Id probably would have gone, if it was a party and all my friends were there I would have thought cool you know, Id have to go even if I only had a few dollars, thats cool, I can go without drinks, cigarettes, Id just want to be there you know but probably because there would have been only a couple of people I would have known there and the rest of them I wouldnt have known. I sort of thought no, I wouldnt have a good time because if I wanted to meet people, I like meeting people, but when I meet people I always have to talk about my psychosis, and whenever I have to talk about my psychosis, its like everyone is listening you know, and they all just stop what they are doing and they listen, psychosis, what is that? and then I have to explain everything about it and they are all listening type of thing, honing in type of thing.

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Classified material 3. EXPERIENCED DIFFICULTY COMMUNICATING He couldnt talk because he became jumbled, he couldnt focus on one thing he kept thinking about whether his ex-friend was going to mention the letter to other people there He stayed in small groups of people throughout the evening in order to avoid saying something inappropriate that would draw attention to him When he felt comfortable he found it easier to talk He found that the comfortable feeling didnt last, it wore off when the wall came and he found it difficult to think of things to talk about When he was with the group of people he didnt know what to talk to people about so he remained silent He didnt know what to talk about because he couldnt think of anything intelligent to say When he was with people and he didnt know what to talk about his mind was blank, he didnt think anything

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felt different stressed uncomfortable difficulty communicating concern about others views of them 1 AbsentPresentAbsent 2 Present 3 Absent Present 4 Absent Present 5 6 AbsentPresent Qualitative Data: eg Presence of categories in interview transcripts

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DSM-IIIR diagnosisFrequencyPercentCumulative Percent Schizophrenic Schizophreniform Schizoaffective Delusional Bipolar Qualitative measures: eg DSM diagnosis

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PAS ChildPAS AdolescPAS Adult Quantitative Measures: eg Premorbid Adjustment Scales

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OVERALS A tool for relating sets of variables Variant that is a common statistical model is canonical variate analysis (producing a canonical correlation between two sets of variables OVERALS –Allows for more than two sets –Allows variables to be categorical or ordinal

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Interpreting Results from Quantitative Analyses Even hypothesis testing is qualitative (accept/reject) Evaluation of model fit (variance accounted for) always subjective Most commonly the interpretation of –Factors or components, –discriminant functions, –and canonical variates always subjective & qualitative

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Note: Males are seeking erotic good figure in Z Note: Females focus on Values & Fitness with respect to Self in WN Relationship & Compassion far apart

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Return to qualitative data Examine subsets defined by groupings of variables (eg ads from males seeking relationships emphasizing Figure) for other possible connections Examine outliers (those with both compassion and relationship aspects of ads)

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Changes by age No changes by age

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Return to coded data Recode age variable into two groups Examine other codings

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Conclusions Linking qualitative and quantitative analyses is both –simpler –and more flexible than most researchers think

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Conclusions Qualitative researchers should use quantitative tools more Quantitative researchers should use qualitative data more

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