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Using quantitative methods as exploratory techniques in qualitative research projects. Richard Bell...
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Transcript of Using quantitative methods as exploratory techniques in qualitative research projects. Richard Bell...
Using quantitative methods as exploratory techniques in qualitative
research projects.
Richard Bell
University of Melbourne
Disclaimer
• There is nothing new in all this
• analyses carried out with standard statistical software (here, SPSS)
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
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
Quantitative Qualitative Objective Subjective
Tests theory Develops theory focus is concise and narrow focus is complex and broad Reduction, control, precision Discovery, description, understanding, shared
Measurable Interpretive Report statistical analysis. Report rich narrative, individual interpretation.
Basic element of analysis is numbers Basic element of analysis is words/ideas. Researcher is separate Researcher is part of process
Context free Context dependent Hypotheses Research questions
Reasoning is logistic & deductive Reasoning is dialectic & inductive Establishes relationships, causation Describes meaning, discovery
Uses instruments Uses communication and observation Designs: descriptive, correlational, quasi-
experimental, experimental Designs: phenomenological, grounded theory, ethnographic, historical, philosophical, case
study. Sample size: determined by statistical power required
Sample size is not a concern; seeks "information rich" sample
The Qualitative / Quantitative divide
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
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
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”
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’
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
‘Traditional’ Quantitative Methods for Qualitative Data
• Miles & Huberman (1994)– hierarchical cluster analysis
• Giegler & Klein (1994)– correspondence analysis
• Bazely (2002)– cluster analysis– correspondence analysis
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
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.
Dimension 1
2.52.01.51.0.50.0-.5-1.0
Dimension 2
2.0
1.5
1.0
.5
0.0
-.5
-1.0
-1.5
-2.0
MARGIN
Column
Row
Separate
Single
Social
Wowser
HedonistOld
60yo
45yo
30yo
Nationality
TravelFamily
Friendly
Erotic
Values
Image
Figure
Sex
Compassion
Fitness
High SES
H&WMR
H&WMP
H&WMS
H&WFR
H&WFPH&WFS
WNMR
WNMP
WNMS WNFR
WNFP
WNFSEXPMR
EXPMP
EXPMS
EXPFR
EXPFP
EXPFS
WAZMR
WAZMPWAZMS
WAZFR
WAZFP
WAZFS
TIPMR
TIPMPTIPMS
TIPFRTIPFPTIPFS ZMR
ZMP
ZMS
ZFR
ZFPZFS
Correspondence Analysis Representation
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
Generalization
• Two aspects of Correspondence Analysis– weights– correlation
• Generalizes to more complex data structures– weights– correlation models
• multiple regression
• principal components
The model
• Called ‘Alternating Least Squares’
• Procedures devised in the 1970’s – Forrest Young– Yoshio Takane– Jan De Leeuw (‘Albert Gifi’)
• Generally known now as ‘optimal scaling’
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)
Alternating least squares
Find Optimal Scaling of Categories
Find Relational Coefficients
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
Categorization
Magazine Sex Concept Fitness Compassion Figure Values Erotic
Z F Self 44 99 50 11 101
Z F Seeking 41 12 9 11 85
Z F Relationship 6 0 12 5 3
Z M Self 67 97 67 18 207
Z M Seeking 80 9 11 9 37
Z M Relationship 1 0 3 4 1
WN F Self 8 14 17 18 107
WN F Seeking 19 1 4 38 59
WN F Relationship 20 0 0 3 0
WN M Self 9 7 4 3 42
WN M Seeking 11 2 6 3 19
WN M Relationship 1 0 1 0 0
Giegler & Klein data as a four-way table
Multiple Correspondence Analysis
[HOMALS]
Dimension 1
2.01.51.0.50.0-.5-1.0-1.5
Dim
en
sio
n 2
.8
.6
.4
.2
0.0
-.2
-.4
-.6-.8
Category
Concept
Sex
Magazine
Erotic
Values
Figure
Compassion
Fitness.
Relationship
Seeking
Self
M
F WN
Z
Not just tables of frequencies
• Rows are units of interest (documents, cases etc)
• Suppose columns are different variables and contain coding within variables
• eg
Demographic variables in a market research survey
Age
Group
Position in Household
Education Level
Work
Status
Marital Status
Country of
Birth 18-19yrs Male head Primary yr6 Full-time 1st marriage Australia 20-24yrs Female
head Secondary yr8
Part-time 2nd marriage New Zealand
25-29yrs Other female
Secondary yr9
Don't work
living together UK
30-34yrs Other male Secondary yr10
Unemployed
Divorced-Separated
Italy-Malta
35-39yrs
Secondary yr11
Other Widowed Greece-Cyprus
40-44yrs
Secondary yr12
Single Other
Europe 45-49yrs Trade Qual Middle East 50-54yrs Tech-CAE Asia 55-59yrs Uni-I Other 60-64yrs Uni-II Bach deg PG deg
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
Transformation: Marital Status
Optimal Scaling Level: Nominal.
Variable Principal Normalization.
Categories
Single
Widowed
Divorced-Separated
living together
2nd marriage
1st marriage
Qu
an
tific
atio
ns
2
1
0
-1
-2
-3
Transformation: Work Status
Optimal Scaling Level: Nominal.
Variable Principal Normalization.
Categories
OtherUnemployedDon't workPart-timeFull-time
Qu
an
tific
atio
ns
2.0
1.5
1.0
.5
0.0
-.5
-1.0
-1.5
-2.0
Transformation: Age Group
Optimal Scaling Level: Ordinal.
Variable Principal Normalization.
Categories
60-64yrs
55-59yrs
50-54yrs
45-49yrs
40-44yrs
35-39yrs
30-34yrs
25-29yrs
20-24yrs
18-19yrs
.
Qu
an
tific
atio
ns
2
1
0
-1
-2
-3
-4
-5
Age Group
Position in House
Educon Level
Work Status
Marital Status
Position in Household -.481
Education Level -.167 .178
Work Status -.004 .163 .234
Marital Status -.515 .469 .297 .158
Country of Birth .119 -.029 -.232 -.086 -.158
Correlations among transformed variables
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 Loadingsof Transformed Variables
Object scores dimension 1
3.002.50
2.001.50
1.00.50
0.00-.50
-1.00-1.50
-2.00
Distribution of
Demographic Aggregate120
100
80
60
40
20
0
Std. Dev = 1.00
Mean = -.00
N = 536.00
202330N =
House owned or rented
Rent houseOwn house
Ob
ject
sco
res
dim
en
sio
n 1
4
3
2
1
0
-1
-2
-3
Component Loadings
Variable Principal Normalization.
Dimension 1
1.0.50.0-.5-1.0
Dim
en
sio
n 2
.8
.6
.4
.2
0.0
-.2
-.4
-.6
-.8
Country of Birth
Marital Status
Work Status
Education Level
Position in Househol
Age Group
Summary Tables
• An example from Miles & Huberman
• 12 school sites evaluated on various criteria
• Results summarized in a table
N/AN/AX-OProville
(X)X-OX-ODunHollow
XXXBurton
(X)(X)XXAstoria
(X)(X)XXLido
(X)(X)(X)(X)XXClaston
(X)(X)X(X)XPerryParkdale
N/AXXX(X)XCarson
N/AXXXXTindale
(X)XXXXXBanestown
XN/A(X)XXXXPlummet
XXXXXXXMasepa
Basic constructs, attitudes
Transfer
Self-efficacy
UnderstandingsRelationshipsRepertoireDaily Routines
SITE
No changeNot
ApplicableNot
ApplicableNo changeNo changeNo changechange-
revertProville
Change-oneNo changeNo changeNo changeNo changeChange-revert
change-revert
DunHollow
No changeNo changeNo changeNo changeNo changeChange-several
no change
Burton
No changeNo changeNo changeChange-severalChange-oneChange-several
change-several
Astoria
Change-oneNo changeNo changeChange-oneNo changeChange-several
change-several
Lido
Change-oneNo changeChange-one
Change-oneChange-oneChange-several
change-several
Claston
Change-oneNot Applicable
Change-one
Change-oneChange-several
Change-one
change-several
PerryParkdale
No changeNot Applicable
Change-several
Change-severalChange-several
Change-one
change-several
Carson
No changeChange-several
Change-several
Change-severalNo changeChange-several
change-several
Tindale
No changeChange-one
Change-several
Change-severalChange-several
Change-several
change-several
Banestown
Change-several
Not Applicable
Change-one
Change-severalChange-several
Change-several
change-several
Plummet
Change-several
Change-several
Change-several
Change-severalChange-several
Change-several
change-several
Masepa
Basic constructs, attitudes
TransferSelf-efficacy
UnderstandingsRelationshipsRepertoireDaily Routines
SITE
Possible Research Questions
• Which variables predict the degree of change?
• Find weights for categories that maximize correlations
• find multiple regression coefficients
Source of Innovation
Daily Routines
Repert-oire
Relation-ships
Under-standings
Self-efficacy Transfer
Basic constructs attitudes
Source of Innovation
1.000 .219 .560 .072 .066 .245 -.212 .078
Daily Routines .219 1.000 .606 .529 .875 -.097 .374 .278 Repertoire .560 .606 1.000 .261 .652 .255 .155 .134 Relationships .072 .529 .261 1.000 .597 -.264 .524 .377 Understandings .066 .875 .652 .597 1.000 .062 .551 .139 Self-efficacy .245 -.097 .255 -.264 .062 1.000 -.250 -.204 Transfer -.212 .374 .155 .524 .551 -.250 1.000 .091 Basic constructs, attitudes .078 .278 .134 .377 .139 -.204 .091 1.000
Correlations Transformed Variables
Standardized Coefficients Correlations
Beta Std. Error Zero-Order Part Importance Source of Innovation .367 . .079 .218 .029 Daily Routines -.337 . -.656 -.116 .221 Repertoire -.478 . -.439 -.234 .210 Relationships -.497 . -.822 -.304 .409 Understandings .440 . -.727 .109 -.320 Self-efficacy -.066 . .255 -.049 -.017 Transfer -.452 . -.754 -.313 .340 Basic constructs, attitudes
-.242 . -.525 -.201 .127
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
Aim:
• Linking categories evident in interviews (qualitative data) to standard quantitative measures
Raw materialUm, when I got home I thought it was probably a good thing I didn’t
go because um, it sort of relates to motivation as well, I wasn’t really that motivated to go out and deal with people and stuff. If more of my friends were there, I’d probably would have gone, if it was a party and all my friends were there I would have thought cool you know, I’d have to go even if I only had a few dollars, that’s cool, I can go without drinks, cigarettes, I’d 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 wouldn’t have known. I sort of thought no, I wouldn’t 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.
Classified material• 3. EXPERIENCED DIFFICULTY COMMUNICATING• He couldn’t talk because he became jumbled, he couldn’t 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 didn’t 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 didn’t know what to talk to people about so he remained silent
• He didn’t know what to talk about because he couldn’t think of anything intelligent to say
• When he was with people and he didn’t know what to talk about his mind was blank, he didn’t think anything
felt differentstressed
uncomfortabledifficulty
communicatingconcern about others
views of them
1 Absent Present Absent Absent
2 Absent Present Present Present
3 Absent Absent Absent Present
4 Present Absent Absent Present
5 Present Present Present Present
6 Present Absent Present Present
Qualitative Data: eg Presence of categories in interview transcripts
DSM-IIIR diagnosis Frequency Percent Cumulative Percent
Schizophrenic 11 55.0 57.9
Schizophreniform 3 15.0 73.7
Schizoaffective 2 10.0 84.2
Delusional 2 10.0 94.7
Bipolar 1 5.0 100.0
Qualitative measures: eg DSM diagnosis
PAS Child PAS Adolesc PAS Adult
14 6 9
21 6 8
35 8 11
46 5 4
54 5 5
64 6 7
74 4 5
Quantitative Measures: eg Premorbid Adjustment Scales
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
DIM1
1.0.8.6.4.20.0-.2-.4
DIM
21.0
.8
.6
.4
.2
0.0
-.2
-.4
-.6
-.8
SET
Diagnosis
Interview
SANS
PAS
DSM-IIIR Dimension 2
DSM-IIIR Dimension 1
self boring
want to be alone
shy/inferior
stigma judged reject
.concern others views
.stressed incommunica
Attention
Anhedonia
Avolition
Alogia
Affect Adult
Adolesc
Child
Dimension 1 Transformation Plot for DSM-IIIR
DSM-IIIR
BipolarDelusionalSzoaffectiveSzphreniformSzphrenic
Ca
teg
ory
Qu
an
tific
atio
ns
for
DS
M-I
IIR
1
0
-1
-2
-3
Dimension 2 Transformation Plot for DSM-IIIR
DSM-IIIR
BipolarDelusionalSzoaffectiveSzphreniformSzphrenic
Ca
teg
ory
Qu
an
tific
atio
ns
for
DS
M-I
IIR
2
1
0
-1
-2
-3
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
Multiple Correspondence Analysis
[HOMALS]
Dimension 1
2.01.51.0.50.0-.5-1.0-1.5
Dim
en
sio
n 2
.8
.6
.4
.2
0.0
-.2
-.4
-.6-.8
Category
Concept
Sex
Magazine
Erotic
Values
Figure
Compassion
Fitness.
Relationship
Seeking
Self
M
F WN
Z
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
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)
Transformation: Age Group
Optimal Scaling Level: Ordinal.
Variable Principal Normalization.
Categories
60-64yrs
55-59yrs
50-54yrs
45-49yrs
40-44yrs
35-39yrs
30-34yrs
25-29yrs
20-24yrs
18-19yrs
.
Qu
an
tific
atio
ns
2
1
0
-1
-2
-3
-4
-5
Changes by age
No changes by age
Return to coded data
• Recode age variable into two groups
• Examine other codings
Conclusions
• Linking qualitative and quantitative analyses is both– simpler– and more flexible
• than most researchers think
Conclusions
• Qualitative researchers should use quantitative tools more
• Quantitative researchers should use qualitative data more