subjective well-being

1. PARADIGM SHIFT AND
MEASUREMENT ISSUES OF
SUBJECTIVE WELL-BEING.
Debdulal Dutta Roy
Psychology Research Unit
Indian Statistical Institute,
Kolkata

2. WHAT IS SWB ? , DIFFERENTIAL
PERSPECTIVES
QOL
SWLS
POS
NEG
HAPPINESS
PWB
Dr. D. Dutta Roy, ISI., Kolkata. Venue: National Seminar on
‘Wellbeing across Lifespan’ from 25 – 27 October, 2017
Legend
❏ QOL - WHO
❏ SWLS-Diener and
others (1985)
❏ Positive and Negative
Affect -watson (1988)
❏ Happiness: Peter Hills
and Argyle (2002)
❏ Psychological well being
- Ryff and Ryan
Hedonistic distance:
❖ Both QOL and SWLS are
judgment oriented..more
experiential and cognive.
❖ POS and NEG are
affective.
❖ Happiness and PWB are
from humanistic
approach.

3. Paradigm shift
Hypothesis driven
•Literature Review & Hypothesis formulation
•Sampling and structured data collection
•Guided data analysis and theory development
Psychoinformatics (Data Driven)
•No Hypothesis but research questions
•Data warehouse
•Data retrieving
•Data cleaning
•Data mining
•Pattern recognition
•Discovery of Knowledge

4. Hypothesis Driven Model

5. Reliability
Reliable SWB score = SWB score - Measurement
Error
Measurement error is broadly classified into two
- Systematic and Random.
Presence of errors affects the validation of
instrument and related theories.

6. Error margin
Factors affecting the width of the confidence
interval include the size of the sample, the
confidence level, and the variability in the
sample.
A larger sample size normally will lead to a
better estimate of the population parameter.

7. Systematic errors
Items of the instrument may suffer from fixed
errors. PANAS is commonly used instrument to
assess subjective well-being. It is assumed that
SWB has affective component. PANAS measures
the positive and negative feeling component of
SWB. Confirmatory factor analysis shows good
discriminant validity. But some items of PANAS
measure motivation not feeling (Diener, 2002).

8. Random errors
A random error, as the name suggests, is
random in nature and very difficult to predict. It
occurs because there are a very large number of
parameters beyond the control of the
experimenter that may interfere with the results
of the experiment.
For example, mood and motivation of
respondent affects scores.

9. Minimizing Measurement Error
• Pilot test the instrument, get feedback regarding extent of
easiness or difficulty from respondents and how test
environment affects the performance.
• Train the interviewers and observers about standard
instruction, double check the data whether respondent
misses any thing or not.
• Machine checking the data.
• Use multiple measures and check relationship.
• Random sampling
• Increase in sample size.
• Good rapport system.

10. Experimental design
• True experimental designs that involve experimenter manipulation of the
independent variable and experimenter control over the assignment of
participants to treatment conditions.
• It includes several Randomized group designs like one way group design,
Block design, Latin square design, Factorial design.
Study: Effect of muscular relaxation on SWB of Schizophrenia.
Pre-post or post
https://www.ncbi.nlm.nih.gov/pubmed/21402653

11. Quassi-Experimental design
• Ex-post facto research is
systematic empirical inquiry in
which the scientist does not have
direct control of independent
variables because their
manifestations have already
occurred or because they are
inherently not manipulated.
Study: Effect of Diabetes Self
Management Educational Training
Program on patient attitudes
to diabetes and their relations
with the depression were
explored.

12. Regression
• Regression is
a statistical measure to
determine the strength of the
relationship between one
dependent variable (usually
denoted by Y) and a series of
other changing variables (known
as independent variables).
• A. Simple regression
B. Multiple regression
C. Stepwise multiple regression
D. Path analysis
E. Structural equation modelling
F. Covariates

13. Path-Analysis: CFA of Positive and
Negative affective Schedule
Confirmatory factor analysis and temporal invariance of the Positive and
Negative Affect Schedule (PANAS)

14. Structural equation model
•Structural equation
models are often used to
assess unobservable
'latent' constructs.
•They often invoke a
measurement model that
defines latent
variables using one or
more observed variables,
and a structural model
that imputes relationships
between latent variables.

15. Path diagram for Mediational
Analysis
• A path diagram
that illustrates
the mediational
relationship and
indicates beta
weights is most
useful.

16. Discriminant function analysis
• Discriminant function analysis is a
statistical analysis to predict a
categorical dependent variable
(called a grouping variable) by one
or more continuous or binary
independent variables (called
predictor variables).
• The main purpose of
a discriminant function analysis is
to predict group membership based
on a linear combination of the
interval variables.
• The procedure begins with a set of
observations where both group
membership and the values of the
interval variables are known.
• Discriminant function analysis is
reversed of multivariate analysis of
variance (MANOVA). In MANOVA,
the independent variables are the
groups and the dependent variables
are the predictors.

17. The Strong Discriminating
Predictors

18. Categorical grouping

19. Correspondence map

20. Data Driven Model

21. Text Mining
• Research Question
• Data Reservoir: Select
noise free search engines
where in research answers
are available.
• Data Cleaning: Select
abstracts using inclusion
and exclusion criteria.
• Coding and Node
construction.
• Text classification using
frequency of research.
• Pattern recognition.
• Discovery of Knowledge.

22. Thank You