MIXED METHODS AND

matchmoaningΤεχνίτη Νοημοσύνη και Ρομποτική

17 Νοε 2013 (πριν από 3 χρόνια και 1 μήνα)

66 εμφανίσεις

MIXED METHODS AND

INTERDISCIPLINARY RESEARCH

QUALITATIVE DOESN’T MEAN WIMPY


H. Russell Bernard

University of Florida


University of Massachusetts
-
Boston

November 28, 2012

What this talk is about


1. How social scientists contribute to interdisciplinary
research.


It’s all about the science


Interdisciplinary
must never mean
undisciplined


2.
The mixed methods movement


Not forcing a choice between
qual

and
quant


The
qual

in the
qual
-
quant mix

About interdisciplinary research…


The first thing: be highly qualified in your discipline.


Maintain credentials in your discipline.


Publishing


This is not always easy to do




The ‘WHY’ questions


Researchers in the natural sciences bring social scientists
onto project to address the social problems that are
associated with their research.


Why
don’t government policy makers heed the advice of
scientists about how to stop pollution in the ocean?


Why do
people waste water? What can we do about it?


Why do adolescents start smoking


Why don’t people in this village use their bed nets?


Why don’t people wash
their hands after defecating
.


The most important contribution a scientist can make to
solving a problem is to be right about what causes it
.


Causal inference comes from so
-
called qualitative work.


Statistical regularities


If a boy sees his mother beaten by his father this does not
make him violent toward woman, but it increases the odds
that he will be.


Being a democracy does not prevent a nation from going
to war with other democracies, but it lowers the odds of it
happening
.


Still, no matter how strong the statistical association, we need a
mechanism to explain
how
the association comes about.

The missing link


Nomothetic knowledge


theory


requires
nonspurious
correlation, a logical time order, and a mechanism that
makes the correlation logical.


Qualitative research is the key in the
search for
mechanism
in
theory
.


Explaining contradictions


Reviewing the literature


Responding to critiques


Ethnography


Networks and HIV/AIDS


Network size for people living with AIDS is a third that of
homicide victims.


The diagnosis was so stigmatizing and traumatizing,
people pulled back toward the number who could be
trusted to know.

Kalymnian sponge divers


On Kalymnos, Greece, in 1965, young divers worked
longer under water
and came
up faster
than did older
divers

and
were
at
higher risk
for the bends.


Young
men, everyone said, have a lot of machismo

a
need to show their manhood

and so they take risks by
staying down too long and coming up too
fast

“That’s just how young men are”


Where does machismo come from?


The culture ratified but didn’t cause the behavior.


The cause was the
platika

system.


By the time they went to sea, the divers were broke and
their families had to go into debt for food and other
necessities.


The price of sponge collapsed, but the diving labor supply
collapsed faster.

Captains push the divers


Captains pressured divers
to produce
to
stay down longer and
produce more
sponges.


Result: more
accidents on the
job.


Quantitative data: correlation; time
order


Qualitative: Mechanism

What are mixed methods


Mixed methods refers to the combination of qualitative
and quantitative data at all stages of research:


Design


Data collection


Data analysis


Presentation of results


Mixed methods without
labels

The great false divide


The split in the social sciences is not just wrong, it’s
pernicious.


Learning the crude art of irony


“real knowledge building versus story telling”


“the plural of anecdote is not data”


“deep understanding and the search for meaning versus
superficial, numerical exercises”


“evidence
-
based research”

The first cut


The
first cut in research is not qualitative
-
quantitative. The
first cut is systematic
-
unsystematic.


Mixed Methods:

A safe space for empiricists


The mixed methods movement is this generation’s
attempt to deal with the
qual
-
quant wars in social science.


It’s a safe
-
space where the
qual
-
quant war is ignored.


But it requires varsity training in
methods


More on that later, too


It’s nothing short of a movement


Of >2500 references to mixed methods in the SSCI
(November 2012), all but 21 of them are since 2000.


None pre
-
date 1990.


Journal of Mixed Methods Research


Conferences on MMR


Handbook of MMR




Citations to mixed methods: 1997
-
2012







Qualitative
-
Quantitative: Data and Analysis

Qualitative
Quantitative
Analysis
(Texts)
(Ordinal/Ratio Scale)
Qualitative
Interpretive text studies.
E.g., Hermeneutics, Grounded
Theory, Phenomenology
Search for and presentation of
meaning in results of
quantitative processing
Quantitative
Turning words into numbers.
E.g., Classic Content Analysis,
Word Counts, Free Lists,
Pile Sorts, etc.
Statistical & mathematical
analysis of numeric data
Data
A
B
C
D
Qualitative
Quantitative
Analysis
(Texts)
(Ordinal/Ratio Scale)
Qualitative
Interpretive text studies.
E.g., Hermeneutics, Grounded
Theory, Phenomenology
Search for and presentation of
meaning in results of
quantitative processing
Quantitative
Turning words into numbers.
E.g., Classic Content Analysis,
Word Counts, Free Lists,
Pile Sorts, etc.
Statistical & mathematical
analysis of numeric data
Data
A
B
C
D
Galileo the Qualitative


He noticed that the moon had lighter and darker areas.
The darker ones were large and had been seen from time
immemorial.


“These I shall call the ‘large’ or ‘ancient’ spots”


The lighter spots, he said, “had never been seen by
anyone before me.”


The moon “is not smooth, uniform, and precisely
spherical” as commonly believed, but “is uneven, rough,
and full of cavities and prominences,” much like the earth.

So, what are qualitative data?


Qualitative data are NOT phenomena
.


Data are reductions of our experience
.


When we reduce our experience of people’s
behavior, thoughts, and emotions to numbers,
we produce quantitative data.

And what are quantitative data?


When we reduce our experience of people’s
behavior, thoughts, and emotions to words,
images, or sounds, we produce qualitative data.








Kinds of qualitative data


Still
images


Sounds


Moving images


Written words



Why don’t we use qualitative data more?



Most of the record of human thought and human behavior
is qualitative and it occurs naturally.


Want to know about the evolution of sexual mores in the
U.S.?


I Love Lucy
(1950s)


Two
-
and
-
a
-
half men
(today)

Enter technology


Two problems: collecting and analyzing qualitative data.


As usual technology is the game changer
.


CAQDAS


Voice recognition


Visualization methods



Kinds of text analysis


Hermeneutics


Phenomenology


Schema analysis



Grounded theory


Ethnographic decision modeling


Analytic induction (QCA)


Content analysis


All are assisted by CAQDAS

Hermeneutics


Solving puzzles in texts.


What does this text really mean?


Can we find out the meaning of a text by systematically comparing
it to others?


Can we apply analytic rules consistently in order to tease out the
meaning of a text?


Who wrote this text?


In what order were these texts written?

Constitutional law


What did the writers of each phrase in the U.S.
Constitution mean when they wrote it and how can we
interpret that meaning now?


Slavery, abortion, women's right to vote, the government's
ability to tax income, …


Criminal investigations


The Susan Smith case 1994


Susan Smith: “My children wanted me. They needed
me. And now I can't help them.”


David Smith: “They're okay. They’re going to come
home soon.”


Signals of deception:


The mixing of tense in two people’s stories about the
same event.


Like switching from “I” to “we” in the middle of reporting
events leading up to a crime.

CAQDAS the new SPSS


Text management software


SPSS brought stats to the masses.


Atlas/
ti
,
Nvivo
,
MaxQDA
, QDA Miner,
Dedoose


Coding and analyzing themes.


But again: It
takes varsity training in research methods to work with
all kinds of qualitative and quantitative data
.


This is not “mere technology”


it’s a game changer.

Systematic text analysis is used in many fields



Medicine


Education


Political science


Marketing


Organizational studies


Psychology


Anthropology


Grounded Theory


GT is a set of techniques for:


1) identifying categories and concepts (themes) that
emerge from text; and


2) linking the concepts into substantive and formal
theories to build theories to account for the facts in a
single case.


Margaret Kearney’s Study


Sample:

60 women who used crack cocaine during pregnancy.


Data:

Semi
-
structured interviews about childhood, relationships,
life context, last and previous pregnancies


Initial Coding:

Read transcript as they were produced. Looked for
social psychological themes. Asked: “What is this an example of?”



Emerging themes/categories


VALUE
: The degree to which women valued their pregnancy and baby
-
to
-
be in relation to their own priorities.


HOPE:

Expressed varying degrees of hope that their pregnancies
would end well and that they could be good mothers.


RISK:

Women were aware that cocaine use posed risks to their fetus,
but perceived that risk differently.


HARM REDUCTION:

Women tried in various ways to minimize the risk
to their fetus


STIGMA MANAGEMENT:
They used various strategies to reduce social
rejection.

[Kearney et al. 1994 ]

Facing The

Situation

Evading Harm

Salvaging

Self

Value

“That’s what makes me think I
don’t need this
baby…because I’m using. I
like drugs.”

“If I ever lost my children…to
me that would be the worst
thing that could ever happen
to me”

Hope

“I might as well smoke the
next six months if I already
have screwed him up.””

“I know if I get pregnant, I
could stop the drug.”

Risk

“I was really concerned that
he might have something
wrong with him, some
deformity.”

“It’s okay to use drugs, but in
that last month you better
stop or you ain’t gonna bring
your baby home.”

Harm

Reduction

“I been drinking a lot of pickle
juice…I’m gonna make sure
there ain’t nothing in my
system with this one.”

Stigma

Management

“The last time I went to the
doctor, they were like looking
at me funny. So I kind of knew
something was wrong and I
didn’t go back.”

“I’d lie. I’d say [that crack]
wasn’t for me, it was for
another person out of town or
something.”

After 20 Interview

After 30 Interview

After 40 Interview

Checking the validity of the model



Models are not the final product of the grounded
-
theory
approach.


Present the model to knowledgeable informants:
pregnant drug users, project staff, health/social service
professionals familiar with the population.


When this step is included, grounded theory is rigorous
and produces results that are replicable and valid … at
least for emic data.





Kearney, M. H., S. Murphy, K. Irwin, and M. Rosenbaum. 1995. Salvaging Self

A Grounded Theory of Pregnancy on Crack Cocaine.
Nursing Research 44
(4):208

213.


Content Analysis


Content analysis: procedures to make replicable
and valid inferences from text data


advertisements, films, or answers to open
-
ended
questions in surveys.


Like grounded theory, CA reduces the information
in a set of texts to a set of themes, or variables.


But classic CA is confirmatory research, and tests
explicit hypotheses.

The Pelley Case


In 1942, the U.S. Department of Justice accused
William Dudley
Pelley

of sedition.


Independent coders classified 1,240 items in
Pelley’s

publications as belonging or not
belonging to one of 14 identified Nazi
propaganda themes


Harold
Lasswell
: 96.4% of the items were
consistent with the propaganda themes.



Goldsen
, J. M. 1947. Analyzing the contents of mass communication: A step toward inter
-
group harmony.
International Journal of Opinion & Attitude Research

1:81

92.

Content analysis has evolved


CA has evolved since then:


creating a text
-
by
-
theme matrix


sampling design


checking inter
-
rater reliability


testing hypotheses about association


Physical Status

Seek

Offer

Money

Offer

Seek

Education

Offer

Seek

Occupational

Offer

Seek

Intellectual

Offer

Seek

Love

Seek

Offer

Entertainment

(non
-
sexual)

Seek

Offer

Demographic

Seek

Offer

Ethnic Info

"

"

Personality

"

"

Hypotheses

Resource

Men

Women





Hirschman’s hypothesis: men and women seek
complemetary qualities in personal ads

[

Hirschman, E. C. 1987. People as Products: Analysis of a Complex Marketing Exchange.
Journal of Marketing 51
:98

108.

]

Confirmation

Men

Women

Seek

Offer

Offer

Seek

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

Offer

"

"

"

"

Hypotheses

Resource

Men

Women

Physical Status

Seek

Offer

Money

Offer

Seek

Education

Offer

Seek

Occupational

Offer

Seek

Intellectual

Offer

Seek

Love

Seek

Offer

Entertainment

Seek

Offer

Demographic

Seek

Offer

Ethnic Info

"

"

Personality

"

"

Hirschman’s Findings

Hirschman, E. C. 1987. People as Products: Analysis of a Complex Marketing Exchange.
Journal of Marketing 51
:98

108
]

By 1998…things were changing


Internet personal ads were taking over from print, but men
continued to seek a particular kind of body in women and
women continued to offer a particular kind of body.


Men and women alike
mentioned

their financial status, but
women still were more likely to explicitly seek someone
who is financially secure.


Evidence
of a major shift … in Spain: Men of all ages
sought physical attractiveness in women.


Women under 40 sought physical attractiveness in men.




Gil
-
Burman
, C., F.
Peláez
, and S. Sánchez. 2002. Mate choice differences according to sex and age: An analysis of personal advertisements in Spanish
newspapers.
Human Nature

13:493

508.

And today …


Today, personal ads continue to inform us about
preferences in mate selection among heterosexuals,
but also among gay men, lesbians and bisexuals.


Obituaries of business leaders contain data about
men’s and women’s management practices and
about how people in different cultures memorialize
the dead.






Smith, C.

A. and S.
Stillman

2002
a
. Butch/femme in the personal advertisements of lesbians. Journal of Lesbian Studies 6:45

51.


Phua
, V. C. 2002. Sex and sexuality in men’s personal advertisements. Men and Masculinities 5:178

191.


Kirchler
, E. 1992. Adorable woman, expert man: Changing gender images of women and men in management. European Journal of Social Psyc
hol
ogy 22:363

373.


Alali
, A. O. 1993. Management of death and grief in obituary and in memoriam pages of Nigerian newspapers. Psychological Reports 7
3:8
35

842.



de
Vries
, B. and J. Rutherford 2004. Memorializing loved ones on The World Wide Web. Omega: Journal of Death and Dying 49:5

26.




Content dictionaries


To build a coding machine: assign words to categories
according to a set of rules.


Write a program that reads text and assigns words to
categories.


Phillip Stone

1960: The General Inquirer and the
Harvard Psychosocial Dictionary






Stone, P. J., D. C.
Dunphy
, M. S. Smith
,

and D. M. Ogilvie, eds. 1966. The General Inquirer: A Computer Approach
T
t
o

Content Analysis. Cambridge, MA:
M
.
I
.
T
.

Press.

Stone’s first test


66 suicide notes

33 by men who had taken their own
lives, and 33 by men who produced fake suicide notes.


The program parsed the texts and got it right 91% of the
time.


Today’s dictionary can tell if “broke” means "fractured,"
"destitute," "stopped functioning," or (when paired with
"out") "escaped."


Content dictionaries get better


Rosenberg: 71 speech samples from people with
psychological disorders (depression, paranoia) or cancer.


The human coder beat the computer in diagnosing
patients who had cancer.


The computer beat the human coder in identifying
psychological disorders
.


Today, just two decades later, every time you hear “this call
may be monitored” …







Rosenberg, Stanley D., P. P.
Schnurr
, and T. E.
Oxman

1990. Content Analysis: A Comparison of Manual and Computerized Systems.
Journal of Personality Assessment 54
(1 and 2):298

310
.



Analytic induction


Think of the difference between saying: “whenever you
see X you will see Y” and “whenever you see X, there is a
92% chance that you’ll see Y”.


The method is based on Mill’s work on logic and the
methods of agreement and difference.

Analytic induction


Ragin’s QCA method



Charles Ragin formalized the logic:


With one dichotomous variable, A, there are 2
possibilities: A and not
-
A.


With two dichotomous variables, A and B, there are 4
possibilities.





Ragin, C. C. 1987. The comparative method. Moving beyond qualitative and quantitative strategies. Berkeley: University of Cal
ifo
rnia Press.


Haworth
-
Hoeppner’s

QCA of eating disorders and
body image


30 women, 21 either anorexics or bulimics


Asked about body image and eating problems


Four Themes


(1) Constant criticism in the family

= C


(2) Coercive parental control


= R


(3) Feeling unloved by parents


= U



(4) Family discourse on weight


= D


Code transcripts for these concepts
.


Find the simplest set of features that account for
the dependent variable.



Haworth
-
Hoeppner
, S. 2000. The critical shapes of body image: The role of culture and family in the production of eating disorders. Journal o
f M
arriage and
the Family 62:212

227.




Data Matrix for Haworth
-
Hoeppner’s Study

Source: Susan Haworth
-
Hoeppner (personal communication)

Case

Critical
family
environment

Coercive
parental
control

Unloving
parent
-
child
relationship

Main dis
-
course on
weight

Suffers
from
eating
disorder

1

1

0

0

0

0

2

0

1

0

0

0

3

1

0

1

0

0

4

0

0

0

0

0

5

0

0

0

0

0

6

0

0

0

0

0

7

0

0

0

0

0

8

0

0

0

0

0

9

0

0

0

0

0

10

1

1

1

0

1

11

1

1

0

0

1

12

0

0

0

1

1

13

0

0

0

1

1

14

1

0

0

1

1

15

1

0

0

1

1

16

1

1

0

0

1

17

1

1

0

0

1

18

1

1

0

0

1

19

1

1

0

0

1

20

1

0

1

1

1

21

1

0

1

1

1

22

1

1

1

1

1

23

1

1

1

1

1

24

1

1

1

1

1

25

1

1

1

1

1

26

1

1

1

1

1

27

1

1

1

1

1

28

1

1

1

1

1

29

1

1

1

1

1

30

1

1

1

1

1


Eating disorders = CR + CD + r u D



Eating disorders
are caused by the simultaneous
presence of C AND R (Constant criticism in the family and
Coercive parental control),
AND by
the simultaneous
presence of C AND D (Constant criticism in the family and
Family discourse on weight),
AND by
the presence of D
(Family discourse on weight) in the absence of R and U
(Coercive parental control and Feeling unloved by
parents
).





Haworth
-
Hoeppner
, S. 2000. The critical shapes of body image: The role of culture and family in the production of eating disorders. Journal o
f M
arriage and
the Family 62:212

227.

Visualization methods


Make quantitative data qualitative so we
can understand them.


Relational data are very, very
complicated… pile sorts, for example.


Here’s just one:






1 1 1 1 1 1 1 1 1 1 2


1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0


-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-



1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0


2 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0


3 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0


4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0


5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1


6 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0


7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


8 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0


9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


10 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


11 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0


12 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0


13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


15 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0


16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


18 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0


19 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


20 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0



An Individual similarity matrix for
a
20
-
item pile
sort


Bernard, H. R. 2012. Social Research Methods: Qualitative and Quantitative Approaches, 2
nd

edition. Newbury Park, CA:
Sage. P. 410



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


CA FLU CLD DIA AID SCZ MLR POX HD HIV MON ART TB POL MEA PNE MUM SYP MEN DEP


----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----

----


1 CA 1.00 0.08 0.08 0.24 0.16 0.08 0.22 0.05 0.22 0.11 0.00 0.22 0.08 0.11 0.08 0.08 0.05 0.05 0.16 0.08


2 FLU 0.08 1.00 0.92 0.00 0.03 0.03 0.24 0.32 0.05 0.05 0.49 0.08 0.30 0.22 0.19 0.59 0.22 0.08 0.24 0.08


3 CLD 0.08 0.92 1.00 0.00 0.03 0.03 0.24 0.35 0.05 0.05 0.49 0.08 0.30 0.22 0.19 0.59 0.22 0.08 0.24 0.08


4 DIA 0.24 0.00 0.00 1.00 0.03 0.16 0.08 0.05 0.35 0.05 0.00 0.35 0.05 0.05 0.05 0.00 0.05 0.11 0.05 0.22


5 AID 0.16 0.03 0.03 0.03 1.00 0.00 0.16 0.03 0.22 0.84 0.14 0.05 0.16 0.08 0.08 0.11 0.05 0.43 0.14 0.00


6 SCZ 0.08 0.03 0.03 0.16 0.00 1.00 0.03 0.03 0.00 0.00 0.03 0.11 0.05 0.00 0.03 0.03 0.05 0.00 0.14 0.76


7 MLR 0.22 0.24 0.24 0.08 0.16 0.03 1.00 0.32 0.14 0.16 0.27 0.11 0.35 0.46 0.38 0.22 0.35 0.11 0.32 0.03


8 POX 0.05 0.32 0.35 0.05 0.03 0.03 0.32 1.00 0.05 0.11 0.38 0.08 0.11 0.46 0.70 0.27 0.73 0.19 0.05 0.08


9 HD 0.22 0.05 0.05 0.35 0.22 0.00 0.14 0.05 1.00 0.22 0.03 0.24 0.14 0.08 0.08 0.08 0.05 0.03 0.11 0.05


10 HIV 0.11 0.05 0.05 0.05 0.84 0.00 0.16 0.11 0.22 1.00 0.24 0.05 0.22 0.14 0.14 0.11 0.14 0.54 0.11 0.00


11 MON 0.00 0.49 0.49 0.00 0.14 0.03 0.27 0.38 0.03 0.24 1.00 0.03 0.30 0.35 0.27 0.41 0.41 0.27 0.27 0.08


12 ART 0.22 0.08 0.08 0.35 0.05 0.11 0.11 0.08 0.24 0.05 0.03 1.00 0.11 0.11 0.08 0.11 0.05 0.11 0.05 0.08


13 TB 0.08 0.30 0.30 0.05 0.16 0.05 0.35 0.11 0.14 0.22 0.30 0.11 1.00 0.22 0.24 0.54 0.19 0.11 0.41 0.05


14 POL 0.11 0.22 0.22 0.05 0.08 0.00 0.46 0.46 0.08 0.14 0.35 0.11 0.22 1.00 0.43 0.16 0.46 0.11 0.24 0.03


15 MEA 0.08 0.19 0.19 0.05 0.08 0.03 0.38 0.70 0.08 0.14 0.27 0.08 0.24 0.43 1.00 0.19 0.81 0.14 0.14 0.05


16 PNE 0.08 0.59 0.59 0.00 0.11 0.03 0.22 0.27 0.08 0.11 0.41 0.11 0.54 0.16 0.19 1.00 0.16 0.11 0.30 0.08


17 MUM 0.05 0.22 0.22 0.05 0.05 0.05 0.35 0.73 0.05 0.14 0.41 0.05 0.19 0.46 0.81 0.16 1.00 0.16 0.16 0.05


18 SYP 0.05 0.08 0.08 0.11 0.43 0.00 0.11 0.19 0.03 0.54 0.27 0.11 0.11 0.11 0.14 0.11 0.16 1.00 0.03 0.05


19 MEN 0.16 0.24 0.24 0.05 0.14 0.14 0.32 0.05 0.11 0.11 0.27 0.05 0.41 0.24 0.14 0.30 0.16 0.03 1.00 0.14


20 DEP 0.08 0.08 0.08 0.22 0.00 0.76 0.03 0.08 0.05 0.00 0.08 0.08 0.05 0.03 0.05 0.08 0.05 0.05 0.14 1.00



An aggregate similarity matrix for a 20
-
item pile
sort



Bernard, H. R. 2012. Social Research Methods: Qualitative and Quantitative Approaches, 2
nd

edition. Newbury Park, CA: Sage. P.
418


Multidimensional scaling


We can reduce complexity with factor analysis,
but this may still be too complex to understand.


MDS produces a graphic display of the relation
among any set of items.


The items might be people, or objects, or ideas,
or attitudes.


MDS turns numbers that represent relations into
a picture, which pattern
-
seeking animals like
humans can easily understand.



What does it mean to be green?


Free list produced 85 items


“wear sweaters in
the house during the winter to save energy,”
“teach kids to respect the environment”


Pile sort these little texts

what
-
goes
-
with
-
what?


and you get an 85x85 relational matrix


This is hopeless


Turn to MDS

Buy recycled prods.

Tell others not to do bad things

Support world population organizations

Save wetlands

Teach kids to preserve planet

Teach about gains from environment

Teach kids about endangered species

Join environmental groups

Show kids by example

Write congressperson

Political activities

Teach kids about recycling

“Save the Earth” t
-
shirts

Encourage recycled products

Organize drives for recyclables

Encourage others to recycle

Dolphin safe tuna

Don’t litter

Plant trees

Copper & brass

Put bins in office

Redeem cans

Pick up litter

Overpackaged foods

Restore buildings

Compost

Paper bags

Recylce toxic prods.


Recyling bins

Both sides paper


Salvation Army

No aerosol

Use own grocery bags


Reduce meat consumption


Use things longer

Plant garden


Cloth diapers

Plant shrubs


Reuse towels

Cut grass high

Use ethanol


Mulch grass clippings

Dishwasher w/ built
-
in heater

Frig. seal

Freezers on top

Dryer with moisture sensor

Insulate home

Oven door seal

Photocells

Automatic timers for house temp.

Insulate heating ducts

Dishwasher w/ airdry

Convection oven

Low
-
watt bulbs

Air off when leave

Fans

Close shades

Weatherstrip

Fluorescent bulbs

Clothes line

Furnace tune
-
up

Turn off lights

Wear sweaters

Regulate thermostat

Clean lint filter

Cool leftovers

Gas heat

Double
-
pane windows

Water
-
saving toilets


Inflate tires properly


Gas mileage on new car


Assure car runs well


Buy Electric Car


Walk or bike

Carpool

Public transport


Water lawn in morning/evening

Ride Motorcycle

Remove CFC in old refrig.

*A

*A

Lowflow shower

Full loads in dishwasher

Cold
-
water detergent

Water off while shaving

Rinse w/ cold water

Short dishwasher cycles

*B

*B

Multidimensional scaling of 85 items in two dimensions (44 informants)

Bernard, H. R., G. W. Ryan, G. W. and S. P. Borgatti, S. P.

2010. Green
cognition and behavior: A cultural domain analysis.

In
: W.
Kokot
, ed. Papers in Honor of Hartmut
LangNetworks
, Resources and Economic Action. Ethnographic Case Studies

in
Honor of Hartmut Lang, C. Greiner and W.
Kokot
, eds. Berlin, Dietrich Reimer
Verlag
. Pp. 189
-
215
.

Analysis: a dialectic from
qual

to quant to
qual




We can use cluster analysis on the same matrix
to identify groups of items.


The next slide shows the clusters superimposed
on the MDS output, using colors.
We’ve named
the chunks.


This is qualitative analysis (naming chunks) of a
picture (qualitative data) derived from a matrix
(quantitative data), derived from texts (qualitative
data).

Buy recycled prods.

Tell others not to do bad things

Support world population organizations

Save wetlands

Teach kids to preserve planet

Teach about gains from environment

Teach kids about endangered species

Join environmental groups

Show kids by example

Write congressperson

Political activities

Teach kids about recycling

“Save the Earth” t
-
shirts

Encourage recycled products

Organize drives for recyclables

Encourage others to recycle

Dolphin safe tuna

Don’t litter


Plant trees

Copper & brass

Put bins in office

Redeem cans

Pick up litter

Overpackaged
foods

Restore buildings

Compost

Paper bags


Recylce toxic prods
.


Recyling bins

Both sides paper


Salvation Army

No aerosol


Use own grocery

bags


Reduce meat consumption


Use things longer

Plant garden


Cloth diapers

Plant shrubs


Reuse towels

Cut grass high

Use ethanol


Mulch grass clippings


Dishwasher w/ built
-
in heater


Frig. seal


Freezers on top


Dryer with moisture sensor


Insulate home


Oven door seal


Photocells


Automatic timers for house temp.

Insulate heat ducts


Dishwasher w/ airdry

Convection oven


Low
-
watt bulbs


Air off when leave


Fans


Close shades


Weatherstrip


Fluorescent bulbs

Clothes line


Furnace tune
-
up


Turn off lights

Wear sweaters


Regulate thermostat

Clean lint filter

Cool leftovers

Gas heat

Double windows

Water
-
saving toilets


Inflate tires properly


Gas mileage on new car


Assure car runs well


Buy Electric Car


Walk or bike

Carpool

Public transport


Water lawn in morning/evening

Ride Motorcycle

Remove CFC in old refrig
.

*A

*A

Lowflow shower

Full loads in dishwasher

Cold
-
water detergent

Water off while shaving

Rinse w/ cold water

Short dishwasher cycles

*B

*B

Multidimensional scaling and cluster analysis of 85 items in two dimensions (N= 44)

House

Garden

Recycle

Rhetoric

Bernard, H. R., G. W. Ryan, G. W. and S. P. Borgatti, S. P. 2010. Green cognition and behavior: A cultural domain analysis.

In: W.
Kokot
, ed. Papers in Honor of Hartmut
LangNetworks
, Resources and Economic Action. Ethnographic Case Studies

in Honor of Hartmut Lang, C. Greiner and W. Kokot, eds. Berlin, Dietrich Reimer Verlag. Pp. 189-215.
Cool leftovers


Dishwasher w/ built
-
in heater

Frig. Seal


Freezers on top


Dryer with moisture sensor


Insulate home

Oven door seal

Photocells


Automatic timers for house temp.

Insulate heating ducts

Dishwasher w/ air dry

Convection oven

Low
-
watt bulbs

Air off when leave


Fans


Close shades


Weatherstrip

Fluorescent bulbs


Clothes line


Furnace tune
-
up

Turn off lights

Sweaters


Regulate thermostat


Clean lint filter


Double
-
pane windows

Water
-
saving toilets

Low
-
flow shower

Full loads in dishwasher

Cold
-
water detergent

Water off while shaving

Rinse w/ cold water

Short dishwasher cycles

Gas heat

Multidimensional scaling of 33 home
-
based items (N=44)

Heat & Light

Water

Bernard, H. R., G. W. Ryan, G. W. and S. P. Borgatti, S. P. 2010. Green cognition and behavior: A cultural domain analysis.

In: W.
Kokot
, ed. Papers in Honor of Hartmut
LangNetworks
, Resources and Economic Action. Ethnographic Case Studies

in Honor of Hartmut Lang, C. Greiner and W.
Kokot
, eds. Berlin, Dietrich Reimer
Verlag
. Pp. 189
-
215.

Eliminate

Stop Words

a

an

and

because

also

else

here

was

will

etc.

Read

All Texts

Amer. 1 Text …

Amer. 2 Text …

Amer. 3 Text …



Japan. 1 Text …

Japan. 2 Text …

Japan. 3 Text …

...

Calculate

Word Frequencies

we

655

our

788

business

180

products

172

new

185

company

170

market

113

billion

103

world

82



great

10

image

10

...

armed

1

garage

1

1 we

2 our

3 business

4 products

5 new

6 company

7 market

8 billion

9 world



94

Identify

Top 94 words

Jang and Barnett’s study of CEOs’ letters

Jang, Ha
-
Yong., and George. Barnett. 1994. Cultural Differences in
Organizational Communication: A Semantic Network Analysis.
Bulletin de
Méthodologie Sociologique 44
(Septem
-
ber):31

59.


Goal:

Compare American and Japanese business practices

Data:

CEOs’ yearly letters to stockholders from 35 firms (1992)




J

J



J

J

J



J

J

J

J



J

J

A

J
A

J

A



A
J

AA

A

A

A



A

A

J

J





A



A





A


A U.S. Company

J Japanese Company

Multidimensional
scaling of company
-
by
-
company
matrix

Jang, H
-
Y., and G. Barnett. 1994. Cultural Differences in Organizational Communication: A Semantic Network Analysis.
Bulletin de
Méthodologie Sociologique 44
(Septem
-
ber):31

59.

]

Respondents

(Companies)

Respondents

(Companies)

A

B

C

D


A


-

.9


.3


.4


B


.9
-


.4


.2


C


.3


.4


-


.8


D


.4


.2


.8


-









Visualizing complex data


There are new methods for taking all this further


visualizing very complex interactions, like those in
social networks.


We can add dimensions, color and even motion
as aids to visualizing the complex relations in
matrices.


These are all qualitative aids to understanding
numerical data.

The Freemans’ EIES data

Freeman, L. C. 2000. Visualizing social networks.
Journal of Social Structure
.
http://
www.cmu.edu/joss/content/articles/volume1/Freeman.html


These
are not your mother’s qualitative
methods




Social
scientists who can do it all


or work with teams
that, collectively, can do it
all
--

will
be in demand.


In fact, there is no shortage of jobs for social scientist.



There are exciting times ahead.