An Empirical Study on relationship among Quality of work life and its factors
defense_mha_v16
1. Karlsruhe Service Research Institute (KSRI)
Margeret Hall | Defense | 27 July 2015
Engineering Well-being Indicators:
Personality, Social Media, Well-being
2. Margeret Hall Engineering Well-being Indicators 2
The Case for Progressive Community Management
[gallup.com, 2 Feb 2011]
[zeit.de, 21 Feb 2013]
3. Margeret Hall Engineering Well-being Indicators 3
doi: 10.3163/1536-5050.100.1.001
[bbc.com, 17 Nov 2014]
The Case for Progressive Community Management
4. Margeret Hall Engineering Well-being Indicators 4
Research Outline
1: [Kramer, 2008]
Sentiment-based tools are a logical step forward in progressive community management
Alignment of psychometrics and sentiment analyses on social media, especially Facebook, is
under-addressed1
Defining Well-being Indicators for Progressive Community Management
What are the key characteristics of digital communities’
expressions of well-being?
RQ2: Applying Well-being Measurements
Which known relationships between well-being & psychometrics
are reproduced when using text-based data from Facebook?
RQ1: Refining Well-being Measurements
6. Margeret Hall Engineering Well-being Indicators 6
Selected Attributes of Well-being
1: [Easterlin, 1974; Frey & Stutzer, 2001] 2: [Diener & Seligman, 2002]
3: [Ryan & Deci, 2001; Johns et. al., 1991] 4: [Huppert & So, 2011; Hall et. al., 2013]
Annual household income
Cardinal utility 𝑊𝐵 = 𝐻 𝑈 𝑌, 𝑡 + 𝜖
Economic Well-being1
Commonly discussed as happiness
Survey-based, single-item constructs
𝑓(𝑥1, 𝑥2, 𝑥3, 𝑥4, 𝑥5)
Subjective Well-being2
Called eudemonia, or satisfaction
Survey-based, multi-item constructs
Highly linked to personality
No literature agreement on
unified definition
Psychological Well-being3
Hybrid of Subjective and Psychological WB
Single and multi-item constructs
Measures positive emotions,
characteristics, and functioning
Human Flourishing4
U(Y,t) | Utility Estimate of Self-reported Well-being
H[U(Y,t)] | Continuous Non-differential Function Estimating
Difference Between Actual and Self-reported Well-being
ϵ | Error Term
7. Margeret Hall Engineering Well-being Indicators 7
𝐻𝐹 = 𝑝𝑒 ∗ 𝐼𝑐 ∗ 𝐼𝑓 ∗
𝑗=1
𝑛
𝑐𝑗 +
𝑘=1
𝑚
𝑓𝑘
𝐼𝑐 =
1, 𝑖𝑓 𝑃𝑐 ≥ 𝑛 − 1
0, 𝑒𝑙𝑠𝑒
𝐼𝑓 =
1, 𝑖𝑓 𝑃𝑓 ≥ 𝑚 − 1
0, 𝑒𝑙𝑠𝑒
𝑃𝑐 = 𝑐𝑗: 𝑐𝑗 > 0 , 𝑃𝑓 = 𝑓𝑘: 𝑓𝑘 > 0
pe Positive Emotion
Formal Description of Well-being
[Huppert & So, 2011; Hall et. al., 2012; Kramer, 2010; Wang et. al., 2014; Liu et. al., 2015; Johns et. al., 1991; DeNeve & Cooper, 1998]
Competence
Meaning
Engagement
Positive relationships
Emotional stability
Self-esteem
Optimism
Resilience
Vitality
Neuroticism
Extraversion
Five Factor Inventory as a proxy for
well-being in digital settings
Pc
Pj
n1
n2
n3
n4
m1
m2
m3
m4
m5
𝐻𝐹 = 𝑝𝑒 ∗ 𝐼𝑐 ∗ 𝐼𝑓 ∗
𝑗=1
𝑛
𝑐𝑗 +
𝑘=1
𝑚
𝑓𝑘
𝐼𝑐 =
1, 𝑖𝑓 𝑃𝑐 ≥ 𝑛 − 1
0, 𝑒𝑙𝑠𝑒
𝐼𝑓 =
1, 𝑖𝑓 𝑃𝑓 ≥ 𝑚 − 1
0, 𝑒𝑙𝑠𝑒
𝑃𝑐 = 𝑐𝑗: 𝑐𝑗 > 0 , 𝑃𝑓 = 𝑓𝑘: 𝑓𝑘 > 0
𝐻𝐹 = 𝑝𝑒 ∗ 𝐼𝑐 ∗ 𝐼𝑓 ∗
𝑗=1
𝑛
𝑐𝑗 +
𝑘=1
𝑚
𝑓𝑘
𝐼𝑐 =
1, 𝑖𝑓 𝑃𝑐 ≥ 𝑛 − 1
0, 𝑒𝑙𝑠𝑒
𝐼𝑓 =
1, 𝑖𝑓 𝑃𝑓 ≥ 𝑚 − 1
0, 𝑒𝑙𝑠𝑒
𝑃𝑐 = 𝑐𝑗: 𝑐𝑗 > 0 , 𝑃𝑓 = 𝑓𝑘: 𝑓𝑘 > 0
𝐻𝐹 = 𝑝𝑒 ∗ 𝐼𝑐 ∗ 𝐼𝑓 ∗
𝑗=1
𝑛
𝑐𝑗 +
𝑘=1
𝑚
𝑓𝑘
𝐼𝑐 =
1, 𝑖𝑓 𝑃𝑐 ≥ 𝑛 − 1
0, 𝑒𝑙𝑠𝑒
𝐼𝑓 =
1, 𝑖𝑓 𝑃𝑓 ≥ 𝑚 − 1
0, 𝑒𝑙𝑠𝑒
𝑃𝑐 = 𝑐𝑗: 𝑐𝑗 > 0 , 𝑃𝑓 = 𝑓𝑘: 𝑓𝑘 > 0
Human Flourishing (HF)
Positive emotion
Taking all things together, how happy would you say you are?
Competence
Most days I feel a sense of accomplishment from what I do
Meaning
I generally feel that what I do in my life is valuable and worthwhile
…
Optimism
I am always optimistic about my future
Resilience
When things go wrong in my life it generally takes me a long time to get back to normal
…
8. Margeret Hall Engineering Well-being Indicators 8
Easterlin 1974, 2010
Kahneman 2004, 2006
Diener 1984, 1994
EWB: Economic Well-being, SWB: Subjective Well-being
PWB: Psychological Well-being, HF: Human Flourishing
Measured Construct
Comparison with Related Work
SWB PWB HF
yes partially no
EWB
Community
Unobtrusive
Measurement
Individual
Waterman 1993, 2007
Deci & Ryan 2001, 2008
Huppert & So 2011
This Work
10. Margeret Hall Engineering Well-being Indicators 10
Facebook Data Extraction, Cleansing, and Analysis
[Lindner, Hall, et. al., 2015; Hall, Caton, & Weinhardt, UR]
Study Engine
Social Network
Analysis
Database
Visualisation
Interpretation
Interaction
Filtering
Social Adapter
LIWC
Developed
Hardware
Package
11. Margeret Hall Engineering Well-being Indicators 11
Predicting Psychometrics
Participant Personality
Measured by 74
items
(HF/Big 5/Self-rep)
[Yang, 2013; Hall & Caton, 2014]
Facebook Timeline
Obtained from
participant profiles
12. Margeret Hall Engineering Well-being Indicators 12
Predicting Psychometrics
Participant Personality
Measured by 74
items
(HF/Big 5/Self-rep)
Sentiment Analysis
LIWC
dictionary-based
sentiment analysis
[Yang, 2013; Hall & Caton, 2014]
Facebook Timeline
Obtained from
participant profiles
13. Margeret Hall Engineering Well-being Indicators 13
Predicting Psychometrics
Participant Personality
Measured by 74
items
(HF/Big 5/Self-rep)
Sentiment Analysis
LIWC
dictionary-based
sentiment analysis
[Yang, 2013; Hall & Caton, 2014]
Facebook Timeline
Obtained from
participant profiles
Train Test
Train sentiment scores of 90% of participants with a LINEAR model
using boosted best subsets selection for the Big Five Inventory.
14. Margeret Hall Engineering Well-being Indicators 14
Predicting Psychometrics
Participant Personality
Measured by 74
items
(HF/Big 5/Self-rep)
Sentiment Analysis
LIWC
dictionary-based
sentiment analysis
[Yang, 2013; Hall & Caton, 2014]
Facebook Timeline
Obtained from
participant profiles
Train Test
Automated Linear Modeling
Regression models
predicting Big 5
Test sentiment scores of remaining 10%
and use to predict scores of the Big Five.
Train sentiment scores of 90% of participants with a LINEAR model
using boosted best subsets selection for the Big Five Inventory.
15. Margeret Hall Engineering Well-being Indicators 15
Predicting Psychometrics
Participant Personality
Measured by 74
items
(HF/Big 5/Self-rep)
Sentiment Analysis
LIWC
dictionary-based
sentiment analysis
[Yang, 2013; Hall & Caton, 2014]
Facebook Timeline
Obtained from
participant profiles
Train Test
Automated Linear Modeling
Regression models
predicting Big 5
Test sentiment scores of remaining 10%
and use to predict scores of the Big Five.
Train sentiment scores of 90% of participants with a LINEAR model
using boosted best subsets selection for the Big Five Inventory.
Repeat 10 times (cross validate) to assess all participants
16. Margeret Hall Engineering Well-being Indicators 16
Openness
Conscientiousness
Extraversion
Agreeableness
Neuroticism
Average
Prediction Accuracy R2
77.3
64.3
69.5
71.0
68.9
70.2 78.4
Big 5 Trait Name
Personality Can be Predicted
Without Costly Surveys
[Hall & Caton, 2014; Schwartz et. al., 2013]
.79
.69
.78
.71
.76
.75
Schwartz et. al., 2013
PLoS One
Prediction Accuracy R2
.29
.29
.27
.25
.21
.26
17. Margeret Hall Engineering Well-being Indicators 17
Good predictions of personality from (short) informal text
without the need to administer costly traditional personality surveys.
Research Contribution
RQ1 Contributions and Implications
Participation does not imply comprehension
Just because you can take the data, doesn’t mean you should
Lessons Learned: Ethical Boundaries of OSN data1
Reliable findings in comparison to social
psychology literature
Aggregation level robust to noise in data
Validation of lower word count
threshold than in comparable literature
SOTA Improvement
Mitigation of common method biases of
online social network (OSN) studies
Accessing and analyzing publicaly
available OSN data is cheaper than
administering traditional surveys
Methodological Advantages
1: [Markham & Buchanan, 2010]
19. Margeret Hall Engineering Well-being Indicators 19
Digital Well-being at KIT: Scenarios
Personality
Community
Attributes
Well-beingData Extraction
and Analysis
Micro: Communal
belongingness after Excellence
Meso: Networked well-being
across the campus
Macro: KIT’S community
discourse attributes
Granularity
4 year observation
period
35,594 unique
posters
141 Facebook pages:
120 with + 50 words
2,032,323 words:
1,806,232 from
posts and 226,091
from comments
20. Margeret Hall Engineering Well-being Indicators 20
KIT is a Rich, Dense Network
KIT’s Interaction Graph
Fruchterman-Reingold
forced direction graph
Most important
(weightiest) nodes and
edges only
Location represents
network centrality
Parsed for discourse
patterns based on LIWC
analysis
[Fruchterman & Reingold, 1991; Lindner, Hall, et. al., 2015]
21. Margeret Hall Engineering Well-being Indicators 21
Pages are Characterized by Cyclic Discourse …
0
5
10
15
20
25
30
Start of Winter Semester
Mid-Winter Semester
Winter Exam Weeks
Winter Holidays
Start of Summer Semester
Mid-Summer Semester
Summer Exam Weeks
Summer Holidays
Per cent of Posts and Comments
[Lindner, Hall, et. al., 2015]
23. Margeret Hall Engineering Well-being Indicators 23
Pages’ Well-being are Mirrored by their Neighbors
– Well-being is Infectious
Node size is normalized
Location represents network centrality
Edge thickness represents interaction frequency
Similarity Patterns in Well-being
[Fruchterman & Reingold, 1991; Pennebaker et. al., 2003; Fowler & Christakis, 2008; Kramer et. al., 2014; Lindner, Hall, et. al., 2015]
1: Arbeitskreise comments
5: Innovation, Entrepreneurs,
Entwicklung comments
7: Karriere, Berufseinsteig comments
12: Uni Sports comments
Well-being Average: 4.12%
2: Fachschaften comments
3: Hochschulgruppen comments
4: Hochschulpolitik comments
8: KIT allgemein comments
10: Rund um die Bibliothek comments
11: Social comments
23: Social posts
24: Uni Sports posts
Well-being Average: 2.06%
6: Institute, Fachbereiche comments
9: Musik comments
13: Arbeitskreise posts
14: Fachschaften posts
15: Hochschulgruppen posts
16: Hochschulpolitik posts
17: Innovation, Entrepreneurs,
Entwicklung posts
18: Institute, Fachbereiche posts
19: Karriere, Berufseinstieg posts
20: KIT allgemein posts
21: Musik posts
22: Rund um die Bibliothek posts
Well-being Average: 1.52%
24. Margeret Hall Engineering Well-being Indicators 24
Topical Locality in the Aftermath of the
Second Excellence Initiative Announcement
Written topical engagement is immediate and wide-spread
Engagement stays high even one month after the decision announcement
[zeit.de, 2012; Lindner, Hall, et. al., 2015]
07– 14 June 2012
t
23 June 201215 – 22 June 2012 06 July 2012
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
Money Occupation Cause Past Future
%change
Significantly different than pre-event week at p < 0.05
Significant medium term difference at p < 0.05
25. Margeret Hall Engineering Well-being Indicators 25
Emotional Impact of the
Second Excellence Initiative Announcement
-80%
-60%
-40%
-20%
0%
20%
40%
Positive Feelings Negative Emotion Sadness Anxiety Social Inclusion
%change
Belongingness and resilience increase with the negative news
Increasing belongingness indicates increasing well-being
[Lindner, Hall, et. al., 2015]
t
Significantly different than pre-event week at p < 0.05
Significant medium term difference at p < 0.05
07– 14 June 2012 23 June 201215 – 22 June 2012 06 July 2012
26. Margeret Hall Engineering Well-being Indicators 26
Summary and Implications
This work empowers community managers and progressive institutions (like KIT) to be
proactive and (all) inclusive with respect to management practices
Progressive management by extension improves the well-being of constituents
Research Contribution: Empowerment of Progressive Managers
Well-being is networked and expressed as belongingness and
resilience, which increases with system-critical shocks
Well-being is infectious
RQ2: Applying Well-being Measurements
Extraversion and neuroticism proxy well-being and can be
predicted from Facebook data
Well-being can be unobtrusively detected
RQ1: Refining Well-being Measurements
27. Margeret Hall Engineering Well-being Indicators 27
Further Research and Future Work
Topic Modelling
Thank you very much!
Method ConsolidationParticipatory Decision Making
Multi-platform Integration
[Böcking, Hall, & Schneider, 2015][Schacht, Hall, & Chorley, 2015]
Gamification
Participation
Text
Analytics
[Bertsch, Hall, & Weinhardt, 2015]
Editor's Notes
Dear examination committee and honored guests,
It is my pleasure to have the opportunity to present selected parts of my thesis today.
My talk concentrates on the second part of my dissertation: The estimation of well-being from online social media data gathered from Facebook.
Specifically, I will focus on the alignment of psychometrics to unstructured text and investigate the unobtrusive detection of well-being in digital communities.
Everyone wants to be happy. There are few goals in life shared by so many people. However, policy-makers have been traditionally hesitant to make policy based on transient emotional states. This is because a rigorously validated, rapidly updating, cost efficient tool to estimate the well-being of institutions has been missing.
The mistake has been realized by progressively-minded community managers who are increasingly investigating sentiment-based indicators as alternative metrics to economic indicators. The integration of such social indicators to traditional metrics like economic returns is termed progressive community management.
Well-being, or the experience of living well, is a foundational, multi faceted, and prime to be investigated indicator for progressive community management.
Well-being has traditionally been estimated with surveys and focus groups; well-known criticisms and validity challenges of such approaches include:
expensive in terms of time and money
ex post
time lagged
nationally or institutionally aggregated (poor granularity)
common method biases: participant bias, social responses, observation biases
Online social media is a promising data bank for estimating community happiness as it cheap, fast, granular, and can have less bias. Simply: OSM is pervasive, instantaneous, and accessible. Size and prevalence of facebook!
Missing is a tool that validates what it means to be well as expressed on online social media.
As such, my research addresses this gap. This can be summarized as ….
In this talk, I focus on two selected research questions
First … the reproduction of known pyschometrics like personality and well-being from Facebook data. This serves to validate well-being indicies extracted from facebook.
Second … the identification and assessment of well-being in digital discourse as it pertains to online communities. Thereby we can understand the hows and why of a community that is well.
Prior to addressing the research questions, I define well-being and it‘s antecedents.
Well-being literature suggests many indicators.
Economic well-being argues that economic activity—the production of goods and services—is certainly not an end in itself but only has value in so far as it contributes to human happiness.
Calculated as ….
Error term: tends to be quite large. economic well-being cannot estimate the subjective aspects of daily life.
Economists have long left the study of happiness to other disciplines, especially psychology. From psychology, the literature diverses into two strains.
Subjective well-being is the happiness aspect. Focusing on momentary life satisfaction, it considers well-being to be the presence of positive emotions and absence of negative emotion. SWB is the most commonly researched domain but lacks descriptive attributes of what makes one well. If well-being is to be used in community management, it is imperitive to not only have happy people, but to know why they are happy in the first place.
Psychological well-being defines well-being as the feeling that comes with striving towards being a better person. Survey based, PWB uses multi-item constructs. Well-being is the satisfaction of reaching a goal, where it is expected to have negative emotions as a part of the process of becoming well.!!!!How you experience this satisfaction is tightly linked to your individual personality. Specifically extraversion and neuroticism have been found to be highly correlated with, and are antecedents of well-being. !!!!! Psychological well-being problematic as the literature is not yet in agreement which of the many metrics make up well-being. Its too broad metrics make it non-generalizable.
Human flourishing consolidated subjective and psychological well-being. Formally described, Flourishing is the experience of life going well. It is a combination of feeling good and functioning effectively.
human flourishing considers the necessary subjective aspects, is more reliable with multi-construct items, and describes a finite set of attributes which can be measured for progressive community management.
Formally described, pe is the single item “positive emotion”, Ic as the items of “positive characteristics”, and If those of “positive functioning”; where n and m are the respective item counts per group.
Author disagreement with measuring well-being online: personality serving as a WB proxy
Summary : ECN and SWB lack attributes; Ecn considers community WB, SWB considers individual WB and aggregates upwards. only ECN is unobtrusive
PWB is attribute based and considers both communities and inidivudals, but is too broad to be implemented in progressive community management
This work uses HF considering communities and individuals, implementing an unobtrusive digital approach
After introducing the concepts, we will look at research question one: can well-being be predicted by facebook posts?
ID based extractor
App written in ruby on rails
App is web-based frontend; data is stored in (JSON) flat files anonymized and analysis is done offline
Interaction filtering divides granularities like interactions and timespans of interest. Interactions and timespans are weighted to the observation period in consideration. 50 word cutoff
Sentiment analysis assisted by Linguistic Inquiry and Word Count dictionaries. LIWC is a context insenstive word counter. Created and validated with linguists&psychologists to categorize words. 72 categories over thousands of commonly used words, available in English and German plus others; largest validated dictionary-based sentiment program on the market
In order to address research question one, establishing which relationships between well-being and psychometrics are reproduced when using text-based data from Facebook, my FB app ….
FB profiles assessed with LIWC
The data is partitioned into training and testing
Training data is used to build linear model using ….
This is a appropriate because Unlike linear regression, boosting will work when there are more variables than observations.
Compared with the more common stepwise approach that economizes on computational efforts by exploring only a certain part of the model space, the all-possible-subsets approach conducts a computationally intensive search of a much larger model space by considering all possible regression models from the pool of potential predictors
Test data used to predict participants’ big five inventory
Cross validated so that all participants’ data was both trained and tested.
Why is this a good result?
Unobtrusive detection of personality, where extraversion and neuroticism are proxies of well-being
Test based predictions
Lower word threshold than similar works with similar prediction levels (now compare to schwartz paper!)
Much higher explained variance than the schwartz paper (.27)
Lower accuracy on average – suspect that it is outlier related, cook‘s distance of 1
Cook's distance measures the effect of deleting a given observation.
the five factors are not fully orthogonal to one another; that is, the five factors are not independent. Orthogonality is viewed as desirable by some researchers because it minimizes redundancy between the dimensions. This is particularly important when the goal of a study is to provide a comprehensive description of personality with as few variables as possible.
So what?
In a digital space it becomes very easy to click away your rights – ex of informed consent and AoIR guidelines.
Having looked at research question one, now we move to research question two: What are the key characteristics of digital communities’ expressions of well-being?
Looking at KIT as an online community …
Then extract personality as shown in previous use case
Granularities of analyses are normalized by taking a weighted average of words across the sentiment categories
Assess well-being
KIT is a dense rich network at the macro aggregation level.
This graph reflects direct interactions considering activity on a page such as posting, liking, tagging or sharing of and commenting on content.
Nodes are positioned according to an iterative algorithm that assumes that nodes repel each other when close, but are attracted to connected nodes. Convergence is obtained by using a "simulated annealing"-style technique with an arbitrary cooling function.
Positioning near to the center indicates that the page is well integrated into the community as a whole (homophily), whereas pages far on the outside have low interactions with other pages and audience members
Per contra, indirect relationships are generated when common third parties execute actions on both Facebook pages’ timelines or, vice versa, a third party has an activity appear on its timeline by both pages.
If we look at community discourse, we can look at when posts and comments happen. This is seen here. Cyclic semester discourse patterns
Overwhelmingly positive – approximately 75% positive even the least positive time span (mid-winter semester)
However the too-high aggregation on display leaves few informative features.
Disaggregating to only attributes of emotional speech and concentrating on university subcommunities is more revealing, as we see in these three distinct clusters.
The internal quality criterion of a cluster meets the objective function of high intra-cluster similarity when pages within a cluster are similar, and low inter-cluster similarity when pages from different clusters are dissimilar.
Again, considering the principles of homophily (explain!!!), we see that those who express high well-being as surrounded by others who express high well-being. 4% high for literature, where 2% is normal. Worth noting that this cluster is significantly different than the other two clusters.
More interesting is the split between the medium and medium-low clusters. Sensitivity of algorithm, differences between posts and comments
Clustering is (a) different from a classification, because classification assigns objects to already defined classes, whereas for clustering no a priori knowledge about the object classes and their members is provided. And a cluster analysis is (b) different from a discriminant analysis, since discriminant analysis aims to improve an already provided classification by strengthening the class demarcations, whereas the cluster analysis needs to establish the class structure first.
The two weeks representing the event and after the event comprise 1.3% of the corpus’ words.
To account for an authentic comparison baseline, the one month surrounding the excellence announcement is weighted for word count, taking the 72LIWC sentiment categories into account. . This serves as a localized baseline.
Using this baseline one can compare the relative frequencies before the event, during the event, and the medium term.
Taking the example of money, which not surprisingly rose ….
Considering the topic items that changed most significantly from all of the LIWC sentiment categories, we see ….
the week before the announcement counts 7,425 words, this amount increases by 1/3 to 11,070 words during the consecutive week and 15,072 (almost an additional 25%) two weeks after the event.
Negative emotions and anxiety increase
Positive feelings dissipate and sadness spikes with announcement
More interesting is social and inclusion. The feeling of belonging to the community of reference.
No significant change in week of announcement; significant increase in month after; signs of increased community belongingngess and ist related construct resilience.
To summarize the findings, well-being is well-positioned to become an indicator for progressive community management.
Communities that are proactive about well-being management are per definiton progressive in their management. Investments by community managers in well-being by extension improves the well-being of constitutients.
Two actionable attributes of digitally detected well-being are it‘s unobtrusive detection on Facebook as proxied by personality. as found by research question one This is done considering a relatively low threshold of words. Well-being is networked and expressed as belongingness and resilience, which increases with system-critical shocks: well-being can be increased due to its infectious nature, as discovered by research question two.
I will close with an overview of further topics studied in my thesis, as well as an outlook on future work.
The right to participate has been named by the economist Bruno Frey as the single strongest predictor of increased institutional well-being. Developing a platform for the integration of participation in progressive community management, or participation as a service, is a logical next step. Integrating gamification as well as text analysis into such a platform both triangulates the mechanism, and can serve as an incentive mechanism.
This could be enhanced by integrating platforms like linkedin, twitter, and foursquare.
Promising research is emerging focusing on topic usage that suggests combining the two methods would produce a holistic assessment of latent and manifest indicators.
![Margeret Hall Engineering Well-being Indicators 2
The Case for Progressive Community Management
[gallup.com, 2 Feb 2011]
[zeit.de, 21 Feb 2013]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)