Quantitative Tools for Qualitative Data - ccsracuk：定性数据ccsracuk定量工具

1、Quantitative Tools for Qualitative DataRichard BellUniversity of MelbourneFor copies of this presentation130 slides(about 500kb in a zipped file)email: What kind of Qualitative Data can be Analysed?Not raw continuous text dataDiscrete text units that are replicatedAny kind of coding that has been ma

2、deWhat does the data have to look likeIt must be able to be represented by a tablenot necessarily a two-way tablefor exampleGiegler & Klein coding of personal advertisementsa four-way table: magazine, sex, concept, categoryCategorizationMagazineSexConceptFitnessCompassionFigureValuesEroticZFSelf4499

3、5011101ZFSeeking411291185ZFRelationship601253ZMSelf67976718207ZMSeeking80911937ZMRelationship10341WNFSelf8141718107WNFSeeking19143859WNFRelationship200030WNMSelf974342WNMSeeking1126319WNMRelationship10100Giegler & Klein data as a four-way tableHere, data is stored as a table, the first 4 columns def

4、ine the cells, the last column gives the frequency in the cell. To analyse this data at a case levelUse the SPSS WEIGHT BY function ieWEIGHT BY FREQ.Kinds of tablesRows are participants, columns are categoriesRows are categories, columns are participantsRows are one set of categories, columns are an

5、other set of categoriesData in cells of tableIndicator to indicate present/absence of relationship between rows and columnsFrequencies or counts of indicatorsValues of categoriesIndicator of present/absence of relationship between rows and columnsData from Huber (1997)Site A B C D E F G H ISIN: Stud

6、ents learn as self-regulated individualsSGR: Students learn in autonomous groupsTCO: Teacher is in controlTIN: Teacher dominates, but allows some individual autonomyTGR: Teacher dominates, but allows some small group autonomyTOP: Teacher dominates, but is open to students initiativesProportions of A

7、ctivities by Site (Frequencies)Values of categoriesGetting data into statistical packages such as SPSSTransfer data directly from qualitative packages such as NvivoUse SPSS text-import wizard (best with precoded data, ie numbers)Enter data by handTransfer data from qualitative packagesNeed to be abl

8、e to export tables. Should only be done for tables where rows (or columns) are units of analysis (ie documents or respondents)should be saved as a text file (ie has the extension .txt as in table1.txt)Transfer data from printed tableType into SPSSTransfer table from Word documentWord document to Exc

9、el spreadsheetExcel spreadsheet to SPSS spreadsheetThe Table in Word1. Remove the heading row1. Remove the headingsMove subheadings into a columnInsert a new column into the tableCopy subheading into empty column cells that subheading applied toShorten text and insert headings in columns that willbe

10、come SPSS variable names (ie 9 characters no spacesSelect table and copy to clipboardOpen ExcelPaste from clipboardSave spreadsheetOpen SPSSUnder the File pull down to Open new DataChange the file type to Excel files .xlsAnd open the saved Excel spreadsheetIf you have names as column headings in the

11、 first row of the Excel spreadsheetSPSS can read them as its variable namesSPSS opens the file(the variable view)The data view Notice there are dud lines in this file-they need to be edited outThe file fixed upNow we need to change oura) repeated phrases (variable type)b) symbols (variables p1 to p8

12、)into numbersDo this thru Automatic Recodeunder the Transform tabNeed to create a numeric variableinto which values of alphanumericvariable are transformed(alphanumeric values saved as labels)Transferring Cross-Category tables into SPSSwhere Rows are one set of categories, columns are another set of

13、 categoriesThree types of table:Cells of the table contain frequenciesCells of the table contain other dataCells of the table contain binary indicator (yes/no, true/false, present/absent etc)Transferring Frequency Tables: 1If only two dimensions to table (rows are categories of one variable, columns

14、 are categories of another)can feed table straight in as tableeasy but wont have labelled outputfeed table in cell by cell (as for more complex tables)more complex but allows for labelled output and other possibilitiesFeeding table in as tableOnly have cells of table as dataCan only run one procedur

15、e (correspondence analysis) via syntax.Feeding table in cell by cellHave to use syntax (data list function)data list free / block slice row column frequency.begin data.1 1 1 2871 1 2 1431 2 1 941 2 2 23end data.Data list FREE / EMS PMS GENDER MARSTAT FREQ.Weight by freq.Begin data.1 1 1 1 171 1 1 2

16、41 1 2 1 281 1 2 2 111 2 1 1 361 2 1 2 41 2 2 1 171 2 2 2 42 1 1 1 542 1 1 2 252 1 2 1 602 1 2 2 422 2 1 1 2142 2 1 2 3222 2 2 1 682 2 2 2 130end data.Var labels EMS, Extramarital Sex/ PMS, Premarital Sex / GENDER, Gender / MARSTAT,Marital Status.Value labels EMS, PMS, 1 Yes 2 No / GENDER, 1 Women 2

17、 Men /MARSTAT, 1 Divorced 2 Still Married.Traditional Quantitative Methods for Qualitative DataMiles & Huberman (1994)hierarchical cluster analysisGiegler & Klein (1994)correspondence analysisBazely (2002)cluster analysiscorrespondence analysisCluster AnalysisFigure 9.11 (p.203) from Graham Gibbs (2

18、002)Qualitative data Analysis: Explorations with Nvivoas an SPSS data fileCluster Analysis: Solution I Dendrogram using Average Linkage (Between Groups): Chi-square measure Rescaled Distance Cluster Combine C A S E 0 5 10 15 20 25 Label Num +-+-+-+-+-+ Worklink 10 Youth Training 11 Adult training 1

19、Redundancy Counselli 6 Start Up Business un 7 Training Access Poin 8 Workers Coops 9 Business Access Sche 3 Careers & Education 4 BCETA 2 Careers Information 5 Cluster Analysis: Solution IIDendrogram using Average Linkage (Between Groups): Anderbergs D Measure Rescaled Distance Cluster Combine C A S

20、 E 0 5 10 15 20 25 Label Num +-+-+-+-+-+ Careers & Education 4 Training Access Poin 8 BCETA 2 Start Up Business un 7 Careers Information 5 Adult training 1 Worklink 10 Youth Training 11 Workers Coops 9 Redundancy Counselli 6 Business Access Sche 3 Cluster Analysis: Solution IIIDendrogram using Singl

21、e Linkage Rescaled Distance Cluster Combine C A S E 0 5 10 15 20 25 Label Num +-+-+-+-+-+ Careers & Education 4 Training Access Poin 8 Worklink 10 Youth Training 11 Workers Coops 9 Business Access Sche 3 BCETA 2 Start Up Business un 7 Careers Information 5 Adult training 1 Redundancy Counselli 6 Clu

22、ster AnalysisVaries according with coefficient chosen as measure of association between rows (or columns)Varies according to method of clusteringUse with extreme cautionOther Quantitative MethodsFind weights for categories of variable that maximize relationships between variablescorrespondence analy

23、sisfinds weights for categories of row and categories of columnalso traditional least-squares procedureseg regression, principal components & othersCorrespondence AnalysisSimilar to principal componentsOriginally derived for tables of frequenciesfor statistics to apply need one respondent per cell,

24、but can be used with multiple responses across cellsbut can be used with indicator dataCan produce separate maps of relationships between categories of rows or columnsCan produce a joint map of categories of rows or columnsGiegler & KleinExamined personal advertisementsin a number of German magazine

25、s 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. CI IM AP HEC FB CLC SEX BA HIP IV SBE SB FO HT NAT 30Y 45Y 60Y OLD PO1001 2 2 1 0 1 3 0 1 0 1 2 0 1 1 0 1 0 0 0 21002 2 1 0 0 1 0 0 0 0 1 1 1 0 1 1 0 0 1 0 1Data:O

26、ne row per adEach column contains number of instancesfor each coding categoryie Each ad will appear a number of times in the cell of any table total frequency of table is the number ofcodings not the number of adsCut-down version of Giegler & Klein exampleCategoryMagazineZWNWAZTIPEXPH&WHigh SES23439

27、27142927Fitness2396858445544Compassion2172443193513Sex499525471127Figure1523257498546Image434125303690Values58651614645Erotic434227303313268374Friend303104132182197224Family515197291282344353Travel260149111989013030yo2085713511614328345yo1322058525411660yo37101132931Old3610885689744Hedonist165124187

28、146156127Wowser701099134113160Social141324529148Single56132391218Separated541526221681Correspondence AnalysisIn SPSS one of the data reduction options (like factor analysis) as Correspondence Analysis can be run as syntax or point-and-clickalso a syntax-only option called ANACOR which is more limite

29、d but can analyse a table directly when the only data in the SPSS spreadsheet is the table frequencies.data list free / A B C D E F G H J.begin data.0.11 0.07 0.09 0.13 0.15 0.16 0.15 0.12 0.17 0.21 0.19 0.09 0.13 0.2 0.13 0.16 end data.do repeat xs = A to J.compute xs = xs * 100.end repeat.ANACOR T

30、ABLE = ALL (6,9).ANACOR syntax example: Huberman proportions table shown earlierIndicates data values separated by spacesIdentifies columnsChanges data values from proportions to percentagesSimplest ANACOR syntax (just identifies numbers of rows & columns)Correspondence AnalysisThe point-and-click w

31、ayDimensionSingular ValueInertiaChi SquareSig.Proportion of InertiaConfidence Singular Value Accounted forCumulativeStandard DeviationCorrelation 21.299.089 .576.576.008.1612.198.039 .252.828.009 3.141.020 .129.956 4.062.004 .024.981 5.055.003 .0191.000 Total .1551948.580.000(a)1.0001.000 Five possi

32、ble dimensionsSingular value square root of eigenvalueInertia eigenvalues (variance)Chi-square could be partitioned between dimensions(only valid if cells in table are independent)a.b.c.d.How many dimensions?Fit of SolutionScore in DimensionInertiaContributionOf Points to Inertia of DimensionOf Dime

33、nsion to Inertia of Point Mass 121212TotalZ.264-.779.380.056.537.193.863.136.999WN.106-.349-.939.030.043.473.130.625.755WAZ.143.137-.276.007.009.055.123.331.454TIP.138.391-.166.011.071.019.559.067.625EXP.149.213-.218.010.023.035H&W.199.691.472.042.318.224.676.208.884Active Total1.000 .15

34、51.0001.000 Details for MagazinesLocation in spatial representationDifferent ways ofdescribing fit ofeach magazineContentMassScore in Dimension1Contribution 12 Of Point to Inertia of DimensionOf Dimension to Inertia of Point 212TotalHigh SES.029-1.463.670.022.211.067.873.121.995Fitness.040-.939.125.

35、011.119.003.985.011.996Compassion.028-1.407.626.019.185.055.859.113.971Sex.029.830.438.007.066.028.801.147.949Figure.034-.418.085.004.020.001.487.013.501Image.021.470.012.004.016.000.360.000.360Values.016-.456-.639.010.011.042Erotic.153.109-.172.002.006.023.262.437.699.091.040.061.001.00

36、0.002.034.050.084Family.158-.004-.063.001.000.003.001.109.110Travel.067-.367-.278.005.030.026.493.188.68130yo.075.384.384.006.037.056.548.363.91145yo.034.079.582.002.001.059.026.944.97060yo.010.130.350.002.001.006.030.145.175Old.035.181-1.417.015.004.354.023.955.978Hedonist.072.118-.578.006.003.122.

37、048.756.804Wowser.047.814.160.012.103.006.767.020.786Social.0211.482.970.021.204.926Single.010-.676.275.002.016.004.742.081.823Separated.017.379.717.004.008.044.192.455.647Active Total1.000 .1551.0001.000 Similar Fit information for ad categorizationsMore Complex versionsSometimes known

38、as Multiple Correspondence AnalysisHOMALS HOMogeneity analysis by Alternating Least SquaresFor exampleThe complete data structure of Giegler & KleinCategorizationMagazineSexConceptFitnessCompassionFigureValuesEroticZFSelf44995011101ZFSeeking411291185ZFRelationship601253ZMSelf67976718207ZMSeeking8091

39、1937ZMRelationship10341WNFSelf8141718107WNFSeeking19143859WNFRelationship200030WNMSelf974342WNMSeeking1126319WNMRelationship10100Giegler & Klein data as a four-way tableSome other questionsHow well could we predict magazine usage from the other factors?Could use multinomial regression if cells indep

40、endent (and sample size very large)categorical regression if just want to look at effectsA new issue:The kind of transformation to be chosen Kinds of tranformationsDepends on what we want to assumeNot inherent in the dataBasic KindsNominal - Categorical (unordered categories)Ordinal (Assumes data ar

41、e ordered)Numeric -Interval (Assumes data on a scale with equal intervals)Recent advanceSpline (smoothes ordinal & nominal transformations)Model SummaryMultiple RR SquareAdjusted R Square.338.115.113Dependent Variable: MAGAZINEPredictors: SEX CONCEPT CATEGORYStandardized Coefficients BetaStd. Error

42、df F-ratio ProbSEX-.195.0082535.707.000CONCEPT-.034.008316.377.000CATEGORY.273.008201052.267.000Another example: How do characteristics distinguish among groups?Famous example (Not real)GROUPInteraction IntensityInteraction FrequencyFeeling of BelongingPhysical ProximityRelationship FormalityCrowdsl

43、ightslightnonecloseformalAudiencelownonrecurringslightcloseformalPublicslightslightslightdistantno relationshipMobhighnonrecurringhighcloseinformalFamilyhighfrequenthighcloseinformalRelativesmoderateinfrequentvariabledistantformalCommunitylowinfrequentvariablecloseformalSummary of a qualitative anal

44、ysis of the characteristics of groups as postulated by Gutman from Bell & Sirjamaki (1962) Category QuantificationsHere the data were all treated as nominalDimensions were quantification valuesDifferent quantifications for different dimensionsOnly possible for nominal dataOther (ordinal, numeric) mu

45、st have same quantification on each dimension. Nominal can also be similarly restricted.For example: Using regressionMake the group the dependent variable Other nominal variables cannot be multiple-nominal because regression coefficients are unidimensionalUse other variables to predict groupArtifici

46、al example few cases relatively many variables will give perfect prediction Can still compare prediction & evaluate categoriesPredictors of Group Standardized Coefficients BetaInteraction Intensity-1.084Interaction Frequency.689Feeling of Belonging1.219Physical Proximity-.209Relationship Formality.0

47、60Principal Components: DemographicsAge Group treat as ordinalEducation Level treat as ordinalMarital Status nominal Work Status nominal allow different quantications for different dimensions Combining Qualitative & Quantitative DataThe availability of numeric and other transformationsmakes the comb

48、ining of quantitative & qualitative datasimpleCombining Qualitative & Quantitative DataUse Categorical Regression setting measurement levels appropriatelyUse Categorical Principal Components setting measurement levels appropriatelySave transformed variables and use ordinary regression or factor anal

49、ysis for better options (eg hierarchical regression or factor rotation)Combining Qualitative & Quantitative DataPreserve independence of sets of dataGeneralized (more than two sets) non-linear canonical variate analysisOVERALSOVERALSA tool for relating sets of variablesVariant that is a common stati

50、stical model is canonical variate analysis (producing a canonical correlation between two sets of variablesOVERALS Allows for more than two setsAllows variables to be numeric, categorical or ordinalA current data setPhD project by Simone PicaPeople with psychosis featuring social withdrawal19 young

51、people suffering from psychosis with symptoms of social withdrawalUnstructured interviewsStandard psychiatric measures also completedDataInterviews transcribed, categories formed from content, coding madeDiagnosis (DSM III-R)Scores on quantitative measuresPremorbid Adjustment Scale (PAS)Symptoms of

52、Negative Schizophrenia (SANS)Raw materialUm, 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,

53、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

54、 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 listenin

55、g 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.Classified material3. EXPERIENCED DIFFICULTY COMMUNICATINGHe couldnt talk because he bec

56、ame jumbled, he couldnt focus on one thing he kept thinking about whether his ex-friend was going to mention the letter to other people thereHe stayed in small groups of people throughout the evening in order to avoid saying something inappropriate that would draw attention to himWhen he felt comfor

57、table he found it easier to talkHe 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 aboutWhen he was with the group of people he didnt know what to talk to people about so he remained silentHe didnt know what to talk abou