Steps toward Creating an
Aggregate Measure: Health Care
Orientation
Putting together a scale…
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Dara H. Sorkin, PhD
School of Medicine
Center for Health Policy Research
Annual Research Meeting
June 5, 2007
Background
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Who is responsible for your health?
Š Health locus of control (Wallston,
Wallston, & Devillis, 1978)
• Weight locus of control (Saltzer, 1982)
Š Learned
helplessness (Seligman, 1990)
(Bandura, 1997)
You have already…..
Š Created questions that you think assess
the construct of interest.
Š Translated items/questions
appropriately, as needed.
Š Collected data on the questions of
interest and included other measures
for testing construct validity.
Health Care Orientation
Self-reliant
Health Care
Orientation
Provider-dependent
Health Care
Orientation
Š Self-efficacy
• Challenges to illness management
(Glasgow, 2001)
Sample HCO Scale Questions:
SELF-RELIANT HCO
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“I prefer to have a say in
treatment decisions”
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“When it comes to my
medical care, I prefer to
depend more on myself
than on doctors”
PROVIDER
DEPENDENT HCO
z “I am very comfortable
putting myself
completely in the
doctor’s hands”
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“It is the doctor’s
responsibility to make
sure that I return for
appointments when I am
supposed to”
Sample HCO SubSub-Scales:
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Bargain with physician
PROVIDER
DEPENDENT HCO
z Efficacy of the doctor
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Patient participation
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Physician in control
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Patient locus of control
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Depend on doctor
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Patient feels helpless
SELF-RELIANT HCO
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Characterizations of HCO Groups
Construct Validity
Physician Dependent
High
High
SelfReliant
Low
Low
• Prefer personal control
• “Appoint” doctors to
manage health care
• Prefer personal control
• Prefer to self or “comanage” health care
(ACTIVE)
(ACTIVE)
• Prefer physician
control
• Deferent to physicians
• Assume neither they
nor physicians control
care
According to DeVillis (1978), “Validity is
[concentionally] inferred from the manner in
which a scale was constructed, its ability to
predict specific events, and its relationships to
measures of other constructs.”
(CHANCE)
(PASSIVE)
Reliability coefficients (in diagonals) and
correlations between HCO Scales
HCO
B
E
P
H
S
M
D
Scales
B
(.70)
E
-.17 (.45)
P
.36
-.11 (.74)
H
.02
.38
.04 (.55)
S
-.01 -.26
.19
-.18 (.04)
M
.19
.16
.36
.26
-.06 (.77)
D
-.02
.12
.07
.24
.001
.57 (.58)
B=Bargain; E=EffDrs; P=Patient participation; H=Helpless;
S=Self locus of control; M=Provider control; D=Depend on
provider
Factor Analysis
SELF- RELIANT
HCO
1
2
.638
PTPART
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Factor analysis is used to uncover the latent
structure (dimensions) of a set of variables.
Š
Š
Š
Reduces a larger number of variables to a smaller
number of factors.
Selects a subset of variables from a larger set,
based on which original variables have the
highest correlations with the principal component
factors.
Validates a scale or index by demonstrating that
its constituent items load on the same factor, and
to drop proposed scale items which cross-load on
more than one factor.
Properties of HCO Measures
PROVIDERDEPENDENT HCO
BARGAIN
Factor Analysis
.776
SELFLOC
-.375
.406
MDCONT
.705
-.485
DEPEND
.656
.256
HELPLESS
.695
EFFDRS
.609
HCO
k of
items Mean S.D.
α
Self-reliant
14
69.40
16.21
.80
Provider-dependent
12
45.04
17.60
.77
SRHCO ≈ PDHCO Pearson’s r=.11, p <.001.
-.456
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Testing the Simple
Factor Structure
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Confirmatory Factor Analysis:
Structural Equation Modeling
Confirmatory Factor Analysis (CFA)
Š
Seeks to determine if the number of factors and
the loadings of measured (indicator) variables on
them conform to what is expected on the basis of
pre-established theory.
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Key Concepts and Terms
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Example
Measured variable:
Observed variables, indicators in an SEM
design
Š Squares (in the diagram)
Latent variable:
Š Un-observable variable in the model,
construct
Š Circles (in the diagram)
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Š
z
z
z
z
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EXAMPLE: HEART FUNCTIONING
HEART
FUNCTIONING
ECHOCARDIOGRAM
EKG
LATENT
VARIABLE
Blood pressure
Pulse
Echocardiogram
EKG
Stress test
The Measurement Model
BLOOD PRESSURE
PULSE
In a nutshell, SEM:
Combines factor analysis and
regression or path analysis
Š Models can contain both
measurement and path models
Š
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The part of the model that relates indicators
to latent factors
The measurement model is the factor analytic
part of SEM
STRESS
TEST
MEASURED
VARIABLES
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Model I
Model I
EFFICACY OF
DOCTOR
PROVIDER
DEPENDENT
HCO
EFFICACY OF
DOCTOR
.23
PROVIDER
DEPENDENT
HCO
MD IN CONTROL
DEPEND ON
DOCTOR
.78
.36
.005
HELPLESS
SELF
RELIANT
HCO
PATIENT
PARTICIPATION
χ2=259.22,
p < .001 (df=15)
CFI=.59
RMSEA=.17
Model 2
PROVIDER
DEPENDENT
HCO
.83
MD IN CONTROL
.21
.12
.35
SELF
RELIANT
HCO
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.65
DEPEND ON
DOCTOR
.30
.49
.75
HELPLESS
.57
.63
.16
BARGAIN
PATIENT
PARTICIPATION
LOCUS OF
CONTROL
The Path Model
EFFICACY OF
DOCTOR
.28
DEPEND ON
DOCTOR
HELPLESS
SELF
RELIANT
HCO
BARGAIN
LOCUS OF
CONTROL
MD IN CONTROL
.71
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The part of the model that relates variables or
factors to one another
This is were we can test convergent and
discriminant validity
BARGAIN
PATIENT
PARTICIPATION
χ2=16.37,
p < .006 (df=5)
CFI=.98
RMSEA=.07
Model 3: Predicting General Ratings of Health (SF-36)
Testing validity
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In prior studies in chronic disease:
Š Self-reliant health care orientation patients
had better glycemic control, lower diastolic
blood pressure, better functional status at
baseline
EFFICACY OF
DOCTOR
.02
PROVIDER
DEPENDENT
HCO
MD IN CONTROL
DEPEND ON
DOCTOR
GENERAL
HEALTH
.23***
HELPLESS
SELF
RELIANT
HCO
BARGAIN
PATIENT
PARTICIPATION
χ2=58.92, p < .001 (df=9)
CFI=.92
RMSEA=.09
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Regression:
Predicting General Ratings of
Health (SF(SF-36)
Factor Analysis or SEM?
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Advantages of using SEM:
Reduces measurement error by having multiple
indicators per latent variable.
Š More flexible assumptions (particularly allowing
interpretation even in face of multicollinearity).
Š Ability to test models with multiple outcome
measures.
Disadvantages of using SEM:
Š Need a big sample size
Š
β
p
Self-Reliant
HCO
.26
p < .001
ProviderDependent HCO
-.02
p = .68
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Factor Analysis or SEM?
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Either can be used to create a composite
measure…
Š Factor analysis is considered to be more of
an exploratory approach.
Š SEM is considered to be more of a
confirmatory approach.
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