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					Theoretical Contributions
      The work reported here contributes to the literature in several ways. First, we have
expanded our knowledge about volunteers in general and disaster responders in particular.
Specifically, we now know what is required of a civilian who is to help disaster victims in a
distant location. This may generalize to providing aids in poverty-stricken parts of the world,
which are suffering from ongoing disasters such as war and drought. We have also begun to
gather empirical evidence on the importance of some of those skills and abilities (e.g., in medical
care and psychological counseling) to this understudied work population. This will broaden the
perspectives of psychologists interested in work performance, as well as those who work in the
area of crisis and disaster management. Second, we verified the theoretical relationships between
certain dispositional characteristics and outcome variables in an Asian setting. This was done
with a very heterogeneous Chinese sample dispersed over Sichuan Province (where the
earthquake hit), other parts of Mainland China, as well as Hong Kong, thus ensuring
generalizeability of the findings. Third, we showed that the context of volunteer work has an
important impact on the relationship between resilience and outcomes. Where the relief work
takes place away from one's hometown, a strong sense of family coherence (which is a
component of resilience) can be a liability rather than an asset. If the post hoc explanation raised
earlier for this observation is valid, clear distinction must be made not only between volunteers at
peaceful times and DRVs, but also between DRVs to serve in their hometown and those
deployed to a remote location. The same component of resilience can be beneficial or harmful,
depending on where the person is experiencing the stressors. Furthermore, we note that the five
dimensions of resilience as measured by the RSA may have distinctive or even conflicting
effects on the same outcome variable. As resilience is not a unitary construct, future research
should not overlook the specific effects that its components may have, and not to simply
aggregate the component scores into a composite score.


KSA measures. After a series of CFAs and EFAs on the items, we were satisfied that the
following KSA measures meet the minimum requirements for internal consistency:
Psychological Counseling (α=.87), Medical Care (α=.85), Mass Communication (α=.75), and
Public Administration/Management (α=.76). We ran a four-factor CFA model on these four KSA
measures and obtained satisfactory fit (χ2 = 99.90, df = 48, p < .01; CFI=.97, IFI=.97, RFI=.93,

 Psychosomatic symptoms. The Chinese version of Physical Symptoms Inventory (Spector &
Jex, 1998), which is a checklist of 18 symptoms, was used to measure physical problems
encountered during the period of volunteer work. Examples of ailments are chest pain, shortness
of breath, and stomach cramps, many of which do not have a specific physical cause. Liu,
Spector, and Shi (2007) reported a Cronbach α of .84 for their Chinese sample. In the present
study, rating was done on a 3-point scale (1=none; 2=yes, but not serious; 3=yes, rather serious)
and the Cronbach α was .80. We also conduct a PCA on these 18 items
            The Construction and Validation of a Disaster Relief Volunteer Screener:
                             Preparing for the Next Catastrophe[1]

Keywords: Volunteers, Disaster Management, Personnel Selection, Psychological Testing,


Disaster relief volunteers are ordinary citizens who provide humanitarian assistance in major
disasters. Despite the potential risk to the unsuitable volunteers themselves and the people they
seek to help, there is no systematic way to quickly screen people for this rare but critical task. In
the present investigation we derived a set of KSAO for this role. On that basis a screening
instrument was drafted. In the first study, we refined the measure. In a second study, we
constructed a model specifying that certain skills (psychological counseling, medical care, mass
communication, and public administration/management) and personality characteristics
(adaptability and resilience) measured on this instrument are predictive of the volunteers’
burnout, satisfaction, and psychosomatic symptoms. Longitudinal data collected from volunteers
in a major earthquake in China provided strong support of the model, as well as the validity of
the instrument.

Although organizational psychology has often been viewed as serving the interest of
shareholders, employers, and management, a humanist concern has always been on the mind of
many academics and practitioners. For example, the devastating human toll that disasters like
Hurricane Katrina brought has moved many organizational psychologists to volunteer. Global
problems such as poverty and climate change have stimulated discussions on how organizational
psychology can contribute. Carr and his colleagues (2008) proposed to extend the traditional
organizational psychologists’ scope of work into fighting world poverty. Williams, Carr, and
Blampied (2007) discuss good practices in responding to major disasters. Obviously, both the
demand and the potential for organizational psychology making a contribution are great. Along
this line, the purpose of this article is to report the application of personnel psychological
techniques for volunteer selection, which is an important component of many non-government
organizations’ work in fighting both long-term problems (e.g., poverty) and acute ones (e.g.,
natural disasters).
                            Volunteers and "Disaster Relief Volunteers"
Volunteering is defined as an “activity in which time is given freely to benefit another person,
group, or cause” (Wilson, 2000, p.215). Volunteers are those people who “choose to act in
recognition of a need, with an attitude of social responsibility and without concern for monetary
profit, going beyond one’s basic obligations” (Ellis & Noyes, 1990, p. 4). Very often this
determination will take them to work settings that are less comfortable than ordinary paid work
that they could have gotten with their level of experience and education. These planned, pro-
social behavior is an integral part of all human societies, the operation of which depends on both
paid workers and volunteers.
There are many types of volunteers. Some are “event volunteers” or “episodic volunteers”, who
are involved for a short term or a project; others are regular (or “long-term”) volunteers. Some
are “formal volunteers”, working with an organization; others are informal, such as those who
render regular or occasional help to their neighbors. Most volunteers are motivated by their
humanitarian values (wanting to help someone), but some participate also to increase
understanding, to gain career-related experience, to develop social network, to address personal
problems, and to seek personal psychological growth (Clary & Snyder, 1991). Regardless of
their types and motivations, these unpaid workers are indispensable, especially during and after a
disaster, which suddenly increases the demand for human resources.
Nonetheless, the number of people wanting to volunteer in humanitarian causes is small,
compared to the number of people wanting to become paid employees. This is because serving as
a volunteer does not provide one with financial reward; sometimes it can even be costly and
demanding both psychologically and physically. Furthermore, the several other functions that
Clary and Snyder (1991) identified may not be salient for some people.

While we know something about the motivation behind volunteerism (see, e.g., Allison, Okun, &
Dutridge, 2002; Snyder & Clary, 2004), we know very little about characteristics that distinguish
the effective volunteers from the less effective ones. Given the relative small supply of potential
volunteers, research conducted by social and organizational psychologists is mostly on predicting
who will enroll (e.g., Okun & Sloan, 2002), attracting this category of workers (e.g., Boezeman
& Ellemers, 2008) and retaining them (e.g., Boezeman & Ellemers, 2007). Little effort has been
devoted to volunteer selection. This is not to say, however, that everyone who volunteers is
suitable for the assignment.

[1] This article is dedicated to all those volunteers who responded to the Sichuan Earthquake on May 12, 2008, as
well as their family members and employers who showed their understanding and support. Collaborators on this
paper were involved in different phases of this project. Au led a team to conduct job analysis and refined a draft of
the instrument. Zhou took part in a latter phase, performing statistical analyses…

The Need for Selecting Volunteers
     Volunteering in a large number within a short time is rare in human history. This does not
mean, however, that it is non-existent. It can happen when major disasters occur, with the news
quickly disseminated to many people in neighboring regions, motivating and mobilizing them to
help. This was what happened soon after the earthquake in Sichuan, China, which immediately
killed xxx people and left xxx injured and/or homeless in 2008 (International Federation of Red
[Liu Dong, Could you help me find OFFICIAL statistics, with full reference? I vaguely
remember the sichuan government had a press conference on the first anniversary of the quake.
HH] While there can never be an accurate statistics of the number of people who spent days or
weeks or months on the disaster zone, it has been reported that 1,087,000 registered as volunteers
during the ten days after the earthquake (Chinese Communist Youth League, 2008).
      The sheer number of people wanting to help is a mixed blessing. On one hand, a sense of
solidarity was evident. This serves as a booster to the victims' morale. On the other, the scene of
so many volunteers at the doorstep of the disaster zone is analogous to having a crowd of the
same size applying for a job in a large company that does not have office space for everyone,
where the assembly lines have not been set up, and where company rules and policies are still
being written. Worse still, some of these “job seekers” may not be suitable for any job in the
company, although they just want to "do something". Letting these people into the premise could
result in people getting hurt, important paths being blocked, valuable resources being depleted,
and service being hindered. When exposed to gruesome scenes at the disaster zone, some
volunteers may find themselves helpless and incompetent, thus becoming “secondary victims,”
at least at a psychological level. For the benefits and well-being of the victims and other
volunteers, as well as for the efficient operations within the relief agencies, some careful
personnel selection has to be implemented.
      Because no scientific method for selecting volunteers existed, we sought in this project to
develop and validate an instrument that can screen people wanting to assist in disasters and
catastrophes, as Disaster Relief Volunteers (DRVs). We shall first describe the KSAO required
for those volunteers. This will be followed by a description of how we drafted a self-report
instrument and made preliminary effort to validate it.
Disaster Relief Volunteer KSAO
Responses to disasters and catastrophes usually occur in a few overlapping phases. The rescue
phase takes place shortly after the disaster has happened, where the situation, though risky, is not
very life-threatening for helpers. The primary goal at this phase is to save life and minimize
physical injuries. This usually involves retrieving victims from under debris, getting them to the
nearest medical centers, and providing food and water. The period when rescue can efficaciously
take place is usually as short as two or three days after the disaster, because survival rate quickly
dwindles by the hour. Therefore, the rescue phase is short unless the disaster itself is extended, as
in a civil war or famine. The rescue phase will be followed by the relief phase, when the victims
are provided with basic necessities such as water, food, medication, and shelter. During this
phase the victims gradually recover from their losses, sometimes with the relief workers' aids.
Their daily routines are gradually and partially restored. The final phase is when people work
together to rebuild a social and physical environment that is similar to or better than that before
the disaster. Infrastructures are put in place. This phase can last for years. In the first phase,
usually the emergency workers such as firefighters and the troops play the more critical roles.
They are the only people who can be mobilized at the shortest time, although sometimes
volunteers who happen to be nearby may find themselves in good positions to help. In the other
phases both volunteers and paid workers of NGOs play important roles.
DRVs are those volunteers who work under an impoverished but fast-paced condition, after a
disaster or catastrophe breaks out. (This can be a natural disaster such as an epidemic, an
earthquake, a tsunami and a famine, or a human-made disaster, such as terrorism, war, and
expulsion.) Different from support volunteers who may serve in indirect ways (as office
assistants, fund-raisers, and board members, for instance), DRVs are those who serve directly in
the front line, helping in rescue, relief, and rebuilding. The work is emotionally evocative, and
the volunteer easily distressed. Nonetheless, partly because of the relative infrequencies of major
disasters and catastrophes, this set of people who sacrificially make much contribution to our
community is understudied, and the KSAO related to their tasks unidentified. We attempted to
fill this gap.
       Although the tasks involved in these three phases (rescue, relief, and rebuilding) are not
identical, there are sufficient similarities in the work context and work content that may prescribe
a common set of KSAO. On this assumption we conducted a preliminary task analysis to identify
the KSAO, via desktop research. We searched O*NET to review related jobs[1]. We also
searched PsycInfo for competencies related to DRV (Finkelstein & Brannick, 2007; Taylor &
Pancer, 2007; Johnstone, 2007; Bonjean, Markham & Macken, 1994; Tapp & Spanier, 1973;
Karkatsoulis, Michalopoulos, & Moustakatou, 2005; Clary et al., 1998). Popular Chinese
websites calling for DRV to help with the Sichuan earthquake were consulted. In addition, we
looked for tasks and KSAO in news stories and websites related to Hurricane Katrina in USA
(2005), the Jiji Earthquake in Taiwan (1999), and the Tangshan Earthquake in China (1976).
English and Chinese websites calling for volunteers on a regular basis were also consulted.
These online sources are presented in Appendix A. The compilation of information from all
sources resulted in a set of core competencies of DRV. The KSAO outline is presented in
Appendix B.

One notable item in the KSAO is resilence. This is a protective factor against mental health
threats, which can be prevalent when a person is exposed to a disaster (Armstrong, Lund,
McWright, & Tichenor, 1995). While this construct has been conceptualized by some as a
process or outcome, in the present analysis for KSAO we regard it as an individual difference
variable. It is the ability to cope with change or misfortune (see, e.g., Wagnild & Young, 1993),
and to recover or rebound quickly from misfortune. It embodies optimism, perceived control, and
self-esteem (Chan, Lai, & Wong, 2007). Some have defined resilient individuals as people who
experience a trauma but do not develop PTSD (Hoge, Austin, & Pollak, 2006). Self-report
instruments developed to measure this construct include, for example, Baruth Protective Factors
Inventory (BPFI; Baruth & Carroll, 2002), Connor–Davidson Resilience Scale (CD-RISC;
Connor & Davidson, 2003), Resilience Scale for Adults (RSA; Friborg, Hjemdal, Rosenvinge, &
Martinussen, 2003), and Resilience Scale (RS; Wagnild & Young, 1993). As a psychological
capital, resilience has been found to be beneficial among Chinese heart patients (Chan, Lai, &
Wong, 2007), factory workers (Luthans, Avolio, Walumbwa, & Li, 2005), and health care
workers (Siu, Hui, Philips, Lin, Wong, & Shi, in press).

[1] Related jobs found on O*NET include Emergency Medical Technicians and Paramedics, Ambulance Drivers
and Attendants, Emergency Management Specialists, Lifeguards, Ski Patrol, Other Recreational Protective Service
Workers, Municipal Fire Fighters, Forest Fire Fighters, Police, Fire, and Ambulance Dispatchers, Mental Health
Counselors, Medical and Public Health Social Workers, Social and Human Service Assistants, and Mental Health
and Substance Abuse Social Workers.

                                         The Present Investigation
     An important difference between DRVs and the incumbents of paid jobs that we referred to
in our task analysis above is that the former have not been working full-time as a salaried relief
or rescue worker. Most of them have neither been in a disaster zone prior to the deployment. In
other words, a job analysis and the screening instrument developed on its basis may not fully and
adequately reflect what is required as a DRV. Obviously, an empirical assessment of the validity
of the instrument is essential.
Our aim of the present investigation, therefore, was two-fold. Besides developing the instrument
for screening of prospective volunteers, we would also validate it. To achieve this aim we had
two separate studies in mind when we planned the data collection. Specifically, we put the
people who responded to our invitation for research participation into two groups. The first
sample consists of only those respondents who either indicated that they had no intention of
volunteering in the disaster zone, or said that they did not want to participate in a follow-up
study, or did not provide us with contact information for the follow-up study, or did not respond
to our invitation to the follow-up study. Data collected on this sample would be used for scale
development and refinement (Study 1). The other sample consists of those respondents who
indicated that they had gone to the disaster zone or that they would like to go, and also responded
to our invitation to the follow-up study. Data collected on this second sample would be used for
validation of the instrument (Study 2). In the following paragraph, we shall first describe the

Participants. Because volunteers and prospective volunteers potentially came from all walks of
life, recruitment for participants had to be done through many channels. These include asking
NGOs that do relief work to forward to their contacts[1], [add note of thanks here.] asking the
researchers’ acquaintances to forward an invitation by email to prospective participants, posting
invitation and links on two university websites, sending bulk emails to students of a university
and requesting them to forward to their friends both locally and in Mainland China, inviting
bloggers who told their own earthquake relief stories, and working with several government and
business organizations to broadcast our invitation to their employees. Besides using the above
methods for active recruitment, we also recruited at our project website. We told our potential
participants upfront about the purpose of the project, and that they were welcome to participate
whether they would go to the disaster zone or not. After assuring the recipients that all
information would be kept confidential, we invited them to participate and/or to forward the
recruitment e-mail to others. At the end of the online questionnaire, we invited them to supply
email addresses of people who might respond, thus extending our reach of the referral sampling.
Data collection began on July 29, 2008 (about ten weeks after the earthquake), and ended on
February 27, 2009. A total of 1,336 individuals completed the questionnaire. For those who
indicated that they would volunteer or had already gone to the disaster zone, and gave us
permission to contact them again, we invited them in a follow-up study, to be described in more
details below. Data collection of the follow-up study began on January 12, 2009, and ended on
March 11, 2009. A total of 438 individuals participated, of whom 175 reported that they had
been to the disaster zone at least once. To summarize, the first sample consists of 898
individuals, who did not participate in the follow-up study. Their data would be used in Study 1,
for scale development. The second sample consists of 175 individuals. Their data would be used
in Study 2, for validation of the measuring instrument. Descriptive statistics of the entire sample
as well as the two sub-samples formed from it can be found in Table 1.
[1] We thank the following organizations and government units in Hong Kong for their assistance in disseminating
news and invitation to prospective participants: Agency for Volunteer Service, CLP Power Ltd., Fire Services
Department, MSI Professional Inc., Mass Transit Railway Corporation, and the Police Force. The following have for
several months used innovative means to search for disaster relief volunteers, and having found them, invited them
to take part in the study: Sally Chan, Sandy Hui, Tianyin Liu, Mo Ng, Sam Yeung, Sandra Yeung, and Xiao Zhang.
Others who assisted include Hilary Chan, Yulin Chen, Steve Hu, Bryant Hui, Josephine Sham, and Peri Asta Wong.
Desktop research to identify KSAO were conducted by Carol Chan, Kevin Chan, Kate Cheng, Tony Cheng, Koon-
yung Lam, Gloria Li, Siu-wai Yeung, Bauhinia Young, and Vivian Zhang. Our special gratitude goes to the
participants, particularly those DRVs who despite their exhaustion from working in the disaster zone allowed us to
contact them repeatedly. We also thank Dr Oddgeir Friborg for the permission to translate and modify items from
Resilience Scale for Adults, Dr Roseanna Galindo-Kuhn for the permission to translate and modify items from the
Volunteer Satisfaction Index, and the CPP Inc. for permission to translate and modify items from Maslach’s Burnout

                                        Study 1: Scale Development

Item Writing
Most items were developed based on the KSAO identified in the task analysis.
Technical abilities. A total of 28 items measured the knowledge, experience, and/or qualification
in the following areas: administration and management (3 items), building and construction (2
items), chemistry (2 items), child care (1 item), clerical work (1 item), computing (3 items),
education and training (3 items), electronic and mechanical engineering (3 items), mass
communication (4 items), medical care (3 items), psychological counseling (2 items), and
transportation (1 item). Responses were made on a 4-point scale (1=possess a degree or have
received professional/technical training or possess rich experience; 2=have attended para-
professional training or possess some experience; 3=have done some self-study or have a little
knowledge; 4=have never received any training or possess very superficial knowledge or have no
experience). Scoring was reversed so that a high score indicates a high level of ability.
      Adaptability. Adaptive performance comprises, among other things, effective performance
in different cultures, adjustment to physically uncomfortable environments, and decisive
reactions to life-threatening situations (Pulakos et al., 2000). Adaptability involves an
individual’s willingness and motivation to change or fit different task, social, and environmental
features (Ployhart & Bliese, 2006). In the interest of brevity, and to use something that is both
relevant to disaster relief and appropriate to the local culture, we developed six indigenous items
after the 55-item instrument that Ployhart and Bliese (2006) created. These items measure
adaptability to foreign and culturally different situations, as well as flexibility in handling
problems and completing tasks. A 5-point Likert format was used.

     Other knowledge and skills. We developed self-report items on verbal communication at
disaster zone (5 items), physical stamina (4 items), preparation and availability of back up
support (6 items), and knowledge about the disaster zone (9 items). These items had a 3- or 4-
point response format, with various anchors as appropriate.
     Motivation for volunteering. Six items were written to tap motivations for volunteering.
Three of them represent value expression (e.g., “Help should come from all eight points of the
compass for the one place in need.” Three other items were on the self-protective, enhancement,
and understanding functions respectively.
Resilience. To measure resilience we adopted items from the Resilience Scale for Adults (RSA;
Friborg, Hjemdal, Rosenvinge & Martinussen, 2003). This instrument comprises five
dimensions, namely Personal Competence, Social Competence, Personal Structure, Family
Coherence, and Social Support. Because of space limitation, we selected only 20 items that had
the strongest factor loadings with their respective dimensions.
Demographic information was collected in the questionnaire. There was also space for
participants to provide us with contact information (i.e., telephone number and/or email address)
should they give their consent for us to contact them again for a follow-up study.

We randomly split this sample (N=898) further into two equal-size subsamples. We used one
subsample for exploratory factor analyses, and the other to confirm structures extracted in the
exploratory analyses as well as to refine the instrument accordingly.

KSA measures. After a series of CFAs and EFAs on the items, we were satisfied that the
following KSA measures meet the minimum requirements for internal consistency:
Psychological Counseling (α=.87), Medical Care (α=.85), Mass Communication (α=.75), and
Public Administration/Management (α=.76).
Personality measures. EFA performed on the six Adaptability items revealed a one-factor
structure (α = .81). A corresponding CFA on the Adaptability scale clearly supported this factor
structure (χ2 = 16.68, df = 9, n.s.; CFI=.99, IFI=.99, RFI=.98, NNFI=.99). As for the Resilience
measure taken from Friborg et al.'s (2003) RSA , a confirmatory factor analysis recovered the
original 5-factor model (χ2 = 369.15, df = 160, p < .01; CFI=.98, IFI=.98, RFI=.95, NNFI=.97).
We subsequently computed five scores by aggregating ratings on the respective subscales. They
are intercorrelated at .32 to .51.
EFAs and reliability analyses were conducted on other measures. However, because of low
internal consistency, they would not be considered any further.

                                    Study 2: Scale Validation

The model
Having in Study 1 determined that the screening instrument could reliably measure some
theoretically meaningful predictors, we proceeded to construct a heuristic model (Figure 1) to
guide our validation work. We were interested in three outcome variables -- satisfaction about
the volunteer experience, burnout, and psychosomatic symptoms.
Galindo-Kuhn and Guzley (2001) conceptualized volunteer satisfaction as a multidimensional
construct that includes (1) the presence of educational and emotional support from the
organization, (2) participation efficacy, which is a recognition that the person's efforts would
indeed make an impact, (3) empowerment, and (4) group integration. The latter represents the
social relationships developed with other volunteers and salaried staff. Volunteer satisfaction is
indicative of the volunteer's own personal well-being. Chacon, Vecina, and Davila (2007) found
that this construct predicts intention to continue to serve, which in turn predicts duration of
service. Just as a paid worker's job satisfaction is related to such work consequences as
organizational commitment, organizational citizenship behavior, loyalty to the company, and so
forth, a volunteer's satisfaction may also be predictive of similar consequences. It was therefore
included in our theoretical model.
Job burnout typically occurs after an incumbent has been facing emotional and interpersonal
stressors in a demanding job (e.g., doctor, teacher, police officer) for an extended period of time.
It is the second key outcome variable in the model. It stands to reason that the work environment
after a major disaster (where there is a lack of water, food, medicine, electricity, communication,
and means of transportation, but plenty of groanings and fears) could only accelerate the
precipitation of the undesirable psychological state (see, e.g., Musa & Hamid, 2008). When the
burnout occurs while the DRV is still in the field, and has not yet returned home, the costs to the
relief agency, the disaster victims, as well as to other DRVs could be higher than that which
occurs after the volunteer has returned home. There is a need to understand the protective factors
against burnout experienced during a person’s tenure as a DRV.
The third outcome variable is the occurence of psychosomatic symptoms. Mental health of aid
workers has been a recent subject of discussion (Cardozo, Holtz, Kaiser, Gotway, Ghitis,
Toomey, & Salama, 2005). Compared to others, responders to traumas are at a higher risk of
developing depression, substance abuse, and anxiety disorder (Duckworth, 1986). Compared to
the general population, rescue workers at the 9/11 site have a higher chance of PTSD (Perrin et
al., 2007). In a study of 757 Red Cross workers and volunteers, McCaslin et al. (2005) found that
these individuals’ response to the 9/11 disaster (such as visiting the site and contacting survivors)
predicted negative life changes in the following year. This in turn predicted symptoms of
depression, posttraumatic stress, and anxiety. However, there is ample evidence in the stress and
coping literature that the resilient individuals are less likely to suffer the negative consequences
of being exposed to stressors (e.g., Klohen, 1996; see literature reviews such as Agaibi &
Wilson, 2005). This proposition that resilience is a protective factor against psychosomatic
symptoms can probably be generalized to volunteers who respond to disasters. For this reason
we postulated in the model psychosomatic symptoms as a possible consequence.
To summarize, we expected that people who possess the requisite KSAO are more satisfied in
their relief volunteer work, less likely to experience burnout, and less likely to suffer from
psychosomatic symptoms. We advanced six hypotheses, the first three being focused on the
criterion-related validity of KSA measures in the proposed DRV Screener, while the other three
on the criterion-related validity of the personality measures.


H1: Volunteer satisfaction could be predicted from a combination of scales measuring the KSAs
of psychological counseling, medical care, mass communication, and public
H2: Burnout could be predicted from a combination of scales measuring the KSAs of
psychological counseling, medical care, mass communication, and public
H3: Psychosomatic symptoms could be predicted from a combination of scales measuring the
KSAs of psychological counseling, medical care, mass communication, and public
H4: Volunteer satisfaction could be predicted from a combination of scales measuring the
personality characteristics psychological qualities of and resilience.
H5: Burnout could be predicted from a combination of scales measuring the personality
characteristics of adaptability and resilience.
H6: Psychosomatic symptoms could be predicted from a combination of scales measuring the
personality characteristics of adaptability and resilience.

The model presented in Figure 1 summarizes how the three outcome variables are related to the
KSAO constructs as predictors. We did not have specific predictions with regard to which
particular KSA construct or personality characteristic would be predictive of which outcome
variable. This model would be refined as we analyzed the validation data.

Supplementary hypotheses
Aside from the hypotheses developed around the nomological network for validation, we had a
few additional thoughts regarding psychological characteristics of the DRVs.
Social support and burnout. Perrin et al. (2007) found that of those who took part in the rescue
and recovery efforts after the 9/11 disaster, the unaffiliated volunteers sustained the highest
prevalence of PTSD (21.2%), as compared to those in volunteer organizations (7.2%) and police
(6.2%). We therefore hypothesized that
H7: Volunteers who went to the disaster zone alone would be more likely to experience burnout
and low satisfaction, as well as suffer more psychosomatic symptoms, than those who went with
a team.

People with high self-efficacy are more likely to volunteer (Eden & Kinnar, 1991). Michel
(2007) found that self-efficacy has a positive effect on feeling personally responsible to help
victims in a hurricane. Training is another factor underlying this sense of responsibility as well as
actual number of hours volunteered. It would be reasonable to expect self-efficacy to follow
from training and development in relevant KSAs. Furthermore, people may know themselves to
be sufficiently resilient to withstand the adverse conditions. Consequently, they are more likely
to serve as volunteers in the disaster zone. We therefore develop the following exploratory

H8: People who actually serve at the disaster zone would be different on various KSAO from
those who eventually have not gone.

Approximately six months after we launched the website for Study 1, all individuals in the
second sample were contacted again via email to determine whether they had been to the disaster
zone. If their answer was affirmative, we invited them to report to us, on an online questionnaire,
the quality of their volunteer experience as well as other indicators of volunteer success. For
those who did not respond to our email within three weeks, we sent them an email reminder and
followed up with a phone call, an internet instant message, or a text message to their mobile
Using this procedure, we were able to reach 438 individuals. Of this group, 263 indicated that
they had not gone to the disaster zone, while 175 individuals had served in the disaster zone
during the period of May 12th to December 31st. Of these DRVs, 77 were men. The DRV
sample represented a broad distribution in age, ranging from 18 to 66. About 80% were below 40
years old. Median age was 27. Mean age was 31. Their self-reported religious backgrounds were:
Christianity (N=32), Catholic (N=2), Buddhism (N=20), Islam (N=1), and none (N=120). Fifty-
eight of them went to the disaster zone more than once.

Dependent Measures
      Volunteer satisfaction. To measure satisfaction with the relief experience we adapted 23
items from the “Volunteer Satisfaction Index” (VSI; Galindo-Kuhn & Guzley, 2001). This
instrument measures volunteer’s satisfaction about four aspects: organizational support,
participation efficacy, empowerment, and group integration, on a 5-point scale (1=not satisfied;
5=satisfied). Sample items from the original instrument are: “The way in which the agency
provides me with performance feedback” (organizational support); “The progress that I have
seen in the clientele served by my organization” (participation efficacy); “The chance I have to
utilize my knowledge and skills in my volunteer work” (empowerment); and “The friendships I
have made while volunteering here” (group integration). Volunteers who did not go with a
formal organization would not be presented with items on organizational support and group
integration, which were irrelevant to their situation. These "loners" were not included in
subsequent regression analyses that predicted Volunteer Satisfaction.

     Burnout. The 22-item Maslach Burnout Inventory (MBI) were adapted to measure burnout.
Respondents were asked to report how frequent those thoughts and feelings occurred during their
time at the disaster zone. For those volunteers who had been to the disaster zone more than once,
they were asked to refer to their first visit when they responded to the items. To measure
frequency of emotional exhaustion, depersonalization, and personal accomplishment, we
modified the rating scale (1=has never happened; 5=happened several times each day).
     Psychosomatic symptoms. The Chinese version of Physical Symptoms Inventory (Spector &
Jex, 1998), which is a checklist of 18 symptoms, was used to measure physical problems
encountered during the period of volunteer work. Examples of ailments are chest pain, shortness
of breath, and stomach cramps, many of which do not have a specific physical cause. Liu,
Spector, and Shi (2007) reported a Cronbach α of .84 for their Chinese sample. In the present
study, rating was done on a 3-point scale (1=none; 2=yes, but not serious; 3=yes, rather serious).

Psychometric properties of the dependent measures
Volunteer satisfaction. The four subscales of VSI had adequate internal consistency. Cronbach
α's of Empowerment and Participation Efficacy were .72 and .86 respectively (N=175).
Cronbach α's of Group Integration and Organizational Support were .87 and .93 respectively
(N=158). The confirmatory factor analysis showed that the translated and abbreviated measure of
volunteer satisfaction has a four-factor structure that closely resembles the original scale (χ2 =
683.13, df = 224, p < .01; CFI=.94, IFI=.94, RFI=.90, NNFI=.93). As mentioned above, a small
number of people went to the disaster zone alone and therefore would not find some items in this
instrument relevant, we decided to include only those with complete data (N= 158). As these
four subscales were highly correlated with each other (ranging from .51 to .84), for the analyses
to be reported, we aggregated ratings on all items to yield a composite measure of volunteer
Burnout. A confirmatory factor analysis failed to recover the original three-factor structure of the
MBI. As Cronbach α of one of the three subscales, Depersonalization, was low (.51), we re-did a
CFA after dropping this subscale. Model fit was somewhat improved for the modified, two-
factor structure (χ2 = 304.24, df = 118, p < .01; CFI=.88, IFI=.88, RFI=.79, NNFI=.86), with
Emotional Exhaustion and Personal Accomplishment correlating at only -.01. On this basis, we
decided to use only these two subscales as indicators of burnout in the analyses to be reported.

Preliminary analyses
Descriptive statistics and correlations among the main study variables can be found in Table 2.

Regression analyses
To formally test the six validation hypotheses depicted in the model, and to determine the
relative importance of the predictors, a series of regression analyses was performed. Specifically,
we evaluated the incremental validity of each predictor over and above age and gender, on the
three outcomes measured for the trip to the disaster zone. (Education level was not used as a
control because it had no effect on the dependent variables.) H1 to H3 are pertinent to the
predictive validity of the KSAs, while H4 to H6 are pertinent to the predictive validity of the two
personality characteristics.

We entered the KSAs (i.e., Psychological Counseling, Medical Care, Mass Communication, and
Public Administration/ Management) as a block of predictors, after controlling for the two
demographic variables. Consistent with H1, this predictor block was significantly related to
Volunteer Satisfaction (∆R2 = .075, ∆F(4,148) = 3.003, p < .05). The beta coefficient for Mass
Communication was statistically significant (β = .212, p < .05).

Because only two out of three MBI subscales were sufficiently reliable, we tested H2 with two
regression analyses, separately on the two burnout subscales -- Emotional Exhaustion and
Personal Accomplishment. On Emotional Exhaustion, the KSAs (i.e., Psychological Counseling,
Medical Care, Mass Communication, and Public Administration/Management) as a block of
predictors had a very strong impact (∆R2 = .068, ∆F(4,164) = 3.083, p < .05). The beta
coefficient for Psychological Counseling was statistically significant (β = .290, p < .01). On
Personal Accomplishment, the KSAs predictor block did not bring significant improvement in
R2. H2 was partially supported.

Consistent with H3, the KSAs block significantly predicted Psychosomatic Symptoms (∆R2 =
.072, ∆F(4,164) = 3.361, p < .05). Of the several KSAs constructs, Medical Care was the
statistically significant predictor (β = .230, p < .01). In addition, age and sex were also found to
have beta values of .177 (p < .05) and -.203 (p < .01) respectively.

To test H4, we regressed Volunteer Satisfaction on Adaptability and Resilience as a block. This
predictor block was significantly related to Volunteer Satisfaction (∆R2 = .161, ∆F(2,150) =
14.379, p < .001). The beta coefficient for Resilience was statistically significant (β = .424, p <
.001). H4 was supported.
H5 was tested with two regression analyses. The predictor block of Adaptability and Resilience
was not statistically significant for predicting Emotional Exhaustion, but brought significant
improvement in R2 for predicting Personal Accomplishment (∆R2 = .11, ∆F(2,166) = 10.406, p <
.001). The beta coefficient of Adaptability was statistically significant (β = .228, p < .05). H5
received partial support.

Contrary to H6, Adaptability and Resilience did not predict Psychosomatic Symptoms.

Differences between the DRVs who went to the disaster zone alone and those who went with a
team. Of the 175 volunteers, 18 went alone, while the others were with either formal or informal
groups. Independent t-tests showed that the two types of volunteers had different experience.
Compared with those who volunteered in a team, the “loners” reported lower Personal
Accomplishment (28.22 vs. 31.69, t = -2.217, df = 173, p < .01), which implies more burnout. In
addition, they experienced more physical symptoms (23.17 vs. 21.35, t = 2.141, df = 173, p <
.01). Hypothesis 7 was therefore supported.

Differences between the DRVs and those who did not go to the disaster zone. A series of
independent t-tests showed that individuals who went to the diaster zone (N = 175) reported
significantly higher level of training and abilities in Psychological Counseling (t = 4.431, df =
436, p < .001), Medical Care (t = 2.18, df = 436, p < .05), and Mass Communication (t = 3.945,
df = 436, p < .001) than those who did not go (N = 263). However, no difference in resilience
and adaptability was found between these two groups. Thus, H8 received only partial support.

A disaster is a rather unusual work context, one which does not readily succumb to psychological
research. People who work in such work contexts are also relatively small in numbers, compared
with the populations in various industries. Yet they have much potential impacts on the lives of
people they seek to help, as well as on the lives of themselves and their colleagues. Despite that,
disaster workers are poorly understood. The selection of the best DRVs (or those who will do the
least harm to others and themselves) has never captured the attention of psychologists. To the
best of our knowledge, the work reported in this paper is the first multi-phase, longitudinal
investigation to develop an instrument for selecting DRVs and to examine the relationship
between DRVs' personal characteristics and their subsequence experience. Findings from these
two studies affirm reliability and validity of the DRV Screener. In the following sub-sections, we
shall describe what we consider to be the more notable theoretical contributions, the practical
implications, and some limitations of the investigation.

Theoretical contributions

This investigation contributes to the limited knowledge about the relationship between DRVs'
individual characteristics and their volunteering experience. Many of the hypotheses we laid out
in the theoretical model were supported, and some unexpected relationships were uncovered.
There was support for H1, which stated that prospective volunteers’ KSAs would be related to
their subsequent volunteering satisfaction. To understand which KSA constructs are most
predictive of Volunteer Satisfaction, we then inspected the specific beta value associated with
each of the four constructs, and found that Mass Communication had a significant beta of .21 (p
< .05). People skilled in delivering speeches, persuading the mass, and operating communication
channels are more likely to feel satisfied with their volunteer experience in the disaster zone.

H2 stated that KSAs would be related to burnout. Once again, examining beta values on
individual KSA allowed us to identify the most important component in this relationship.
Specifically, ability and training in Psychological Counseling (β = .29, p<.01) is a protective
factor against subsequent Emotional Exhaustion. It makes intuitive sense that volunteers who are
already trained in delivering psychological and counseling service are less likely to suffer from
this emotional aspect of burnout.
The hypothesis that KSAs would be related to Psychosomatic Symptoms (H3) was supported.
Closer examination of the results revealed that it was the ability and training in Medical Care (β
= .23, p<.01) that accounted for the most variance in the regression model. Again, this makes
intuitive sense as people who are trained in medical and health care should be the most
knowledgeable about health risks, and the most likely to stay away from unnecessary hazards in
a disaster zone.
Perrin et al. (2007) found that disaster workers engaged in tasks outside of their training were at
increased risk, as compared to those who had been trained for the tasks. Besides ensuring a
minimum level of skills, prior training provides a sense of self-efficacy. Our findings with
respect to H1 to H3 corroborate with this, and point clearly to the necessity of selecting DRVs
with the right skills and training.

There was support for H4, that personality characteristics would predict Volunteer Satisfaction.
Closer examination of the results revealed that Volunteer Satisfaction is strongly predicted by
Resilience (β = .42, p<.001). The presence of the dispositional attributes (e.g., self-efficacy, self-
esteem, etc.), family support, and external support system that promote adult resilience improves
the experienced satisfaction level under the stressful voluntary work environment.

H5 stated that personality characteristics would predict the DRVs' burnout. Consistent with this
hypothesis, we found that DRVs low on Adaptability reported lower Personal Accomplishment.
In the present Screener, Adaptability is operationalized as the readiness to adopt a contingency
plan when the original one does not work, to do whatever tasks required at the scene (even when
one may not like doing that or has not been trained to do that), and to collaborate with a diverse
range of people. It is being flexible when resources are scarce, and having more than one plan to
handle emergencies. It is not surprising that people with this characteristic would be ready and
capable of rendering help to victims in a variety of adverse conditions. Their feeling of having
contributed to the well-being of the service recipients is thus enhanced.

Although Resilience as a whole did not predict Emotional Exhaustion and Personal
Accomplishment, we performed a set of follow-up regression analyses to examine the potential
effects of the five separate resilience subscales on the two burnout measures. One resilience
factor, Personal Structure, emerged as a significant predictor of Emotional Exhaustion (β =-.194,
p<.05). Note that items measuring Personal Structure are: "I prefer to plan my actions," "Rules
and regular routines make my daily life easier," and "I keep up my daily routines even at difficult
times." We suspect that making plans and adhering to routines are more trainable than other
aspects such as Family Coherence, which are less likely to be developed within a short
timeframe. This finding may have an important practical implication, as relief organizations may
benefit from training individual volunteers or team leaders to establish personal structure. Future
research should verify if this component of resilience is indeed trainable.
Furthermore, the Social Competence subscale predicted Personal Accomplishment (β = .256, p <
.01). As Personal Accomplishment is operationalized in terms of the person's effectiveness in
interacting with service recipients and influence their lives, it is not surprising that people with
strong social skills feel being more effective and accomplished in helping the victims.

Our initial regression analysis led us to doubt H6, which stated that personality characteristics
would predict Psychosomatic Symptoms. However, an additional regression analysis revealed
that psycho-physical health was helped by one resilience factor but hurt by another.
Psychosomatic Symptoms could be predicted by low Personal Structure (β =-.22, p < .05). This
effect was cancelled out by another curious relationship: People high in Family Coherence
suffered from more Psychosomatic Symptoms (β =.20, p < .05).

This unexpected relationship between strong Family Coherence and Psychosomatic Symptoms
prompted us to revisit the items in the former subscale. Samples of those items are: “There are
strong bonds in my family,” “I enjoy being with my family,” “In my family we enjoy finding
common activities.” Under most circumstances, as when people confront stressors at their
workplace, or suffer a friend’s betrayal, loyalty among and support from family members can be
uplifting. However, a strong desire to be with family members may also induce anxiety and
homesickness when the person is alone and away from the family. This was precisely the case
for our participants, who left their families behind and labored afar. Instead of getting emotional
support from family members (which would have been available as soon as it is needed), these
people are in fact severed, at least temporarily, from their beloved. This challenge was not helped
by the broken communication infrastructure that has not yet been recovered. With news about
aftershocks and mudslides coming in everyday from all directions, the DRVs must be very
worried about what would happen to their loved ones should a fatal incident follow. Under this
circumstance, those who are psychologically very close to their families would in fact suffer
more than those whose ties with their families are not so intact. If this post hoc explanation is
correct (after confirmation by future studies), clear distinction must be made not only between
volunteers at peaceful times and DRVs, but also between DRVs to serve in their hometown and
those deployed to a remote location. There are important theoretical as well as practical
implications, for the same component of resilience can be beneficial or harmful, depending on
where the person is experiencing the stressors.

Furthermore, given that the five dimensions of resilience as measured by the RSA may have
distinctive or even conflicting effects on the same outcome variable, future research on the
resilience construct should not overlook the specific effects that its components may have, and
not to simply aggregate the component scores into a composite score. This will provide us with a
more accurate undertanding of how resilience might improve volunteer experience.
Our data suggest some self-screening might already had occurred among prospective DRVs.
Those who went to serve in the disaster zone and those who had said they would but finally did
not go are different in their KSAs. Many of those low in the required KSAs of Psychological
Counseling, Medical Care, and Mass Communication, decided not to go in person. This
indirectly protects some kind-hearted but ill-prepared people from being traumatized, and also
preserves a high skill density among those on the field. The application of the present instrument
in selection among those who come forward and register will further improve the quality of
future relief teams in terms of their members' competence skill-wise.

However, those who went were not any more adaptable and resilient than those who stayed
home. This was so despite the strong sacrificial motivation that had driven them to overcome the
initial obstacles, network with people, and be proactive enough to find their way to the disaster
zone. This finding of no difference is a cause of concern, as it implies that people did not refer to
their adaptability and resilience when making the decision to volunteer. It is likely that the
agencies that organized the relief work and dispatched volunteers did not do much psychological
screening. Perhaps the volunteers were not even aware of some of their own traits. We speculate
that when making a decision on whether to volunteer to do relief work, people tend to focus
more on their tangible abilities and skills (for which the prospective volunteers could recall
gaining qualifications and experience), than the complex psychological characteristics (for which
there may not be readily available measures). However, this lack of attention to the personality
characteristics might be problematic, given our finding that volunteers' adaptability and
resilience level had significant impact on their experienced satisfaction and personal
accomplishment. This problem can only be avoided by greater emphasis on selection.

Practical applications
      At an applied level, the present investigation has at least three contributions. First, it
provides practitioners with a list of KSAO essential for disaster relief, a task that had not been
previously analyzed. Future work in developing more sophisticated selection instruments and
performance management tools can be based on this piece of preliminary work. A second
contribution pertains to policies. For instance, our research found that DRVs who do not have
some basic training and those who go to the disaster zone alone are at risk. Governments and
NGOs should therefore consider setting policies to allow only certain types of people to
participate in disaster relief, despite a lot of people wanting to help. More importantly, we
contribute by making available a selection instrument for the measurement of at least some of
those KSAO. In so doing, we hope to reduce people's vulnerabilities to future catastrophes.
Partly because of the lack of monetary incentives, and partly because of the relatively unpleasant
work environment, people wanting to take part in voluntary work are in short supply. The
adverse effect of a mismatch and the need for finding the right match, however, are not any less.
One application of the present DRV Screener is for recruitment, in addition to selection. That is,
NGOs doing relief and rebuilding work could routinely administer this instrument to individuals
in the community, and use the assessment outcomes to follow up with those who seem to be the
most promising DRVs when a disaster occurs. With this, relief and disaster preparedness
organizations can build a register of people who not only are willing to serve but also have the
skills and psychological competence to take up the responsibility.
We are nevertheless mindful of certain methodological problems in the validation study. For
instance, a substantial number of people in the sample were psychologists and counselors, who
know about psychological testing. Therefore, they could not be regarded as “naïve subjects”. It is
unclear how their knowledge and hypotheses about psychological qualities of a DRV would
result in demand characteristics. Moreover, for at least a portion of the sample, the relief
experience was prior to or concurrent with the time the questionnaire was responded to.
Therefore, the quasi-cross-sectional nature of this research must be taken into account when
interpreting the correlational findings. Another factor that might inflate correlations is that the
outcome measures were all self-report in format. However, asking the persons themselves may
be the only feasible way to know about their inner mental state.
The DRV Screener itself is also not without methodological problems. For one thing, the
instrument was developed from a task analysis that was done on a job that did not exist, at least
not as a permanent one. As such, it may not include all the essential KSAO. In fact, even if it did,
the analyses reported in Study 1 revealed that some of the characteristics have not been reliably
measured. For example, measures of training and abilities in child care, building construction,
and computing did not have adequate reliability, and had to be dropped from subsequent
analyses. Furthermore, although the motivations behind volunteering have been found in
previous research to be related to some outcomes, there were too few items in the draft
instrument to tap the distinct motivations. (Hence, we have to remain silent on the link between
DRV effectiveness and motivations for volunteering.) Preliminary success in making predictions
with the present instrument should not lure us away from making further improvement. For
another, the instrument is in Chinese, and therefore cannot be readily applied in other societies.
Fortunately, the knowledge gained from this piece of research is potentially generalizable across
cultures. We anticipate psychologists in other countries developing similar screening instruments
for their own countries, on the basis of KSAs and personality characteristics (such as adaptability
and resilience) uncovered in the present study. At a more general level, we hope that the
experience gained in one corner of Asia will stimulate more I/O psychologists to consider how
our methodologies can be used to make the world a better place to live.

May 11, 2009

Cronbach alpha of

communication (Q42-46) .45, but .08 when Q85 added
self care .47

Hence neither of these scales will be used.

Regression on VSI:

- Controlling for demographics, we entered block 1 predictors (Psychological Service,
Medical Service, Admin and Management, and Liaison Service) and block 2 predictors
(RSA, Adaptability). All predictors are significant for Phase 1. Sample too small for
Phase 2.

Regression on Personal Accomplishment:

- Controlling for demographics, we entered block 1 predictors (Psychological Service,
Medical Service, Admin and Management, and Liaison Service) and block 2 predictors
(RSA, Adaptability). Only block 2 predictors brought significant improvement in R-
square. This is significant for Phase 1 and marginally significant for Phase 2.

Regression on Physical Symptoms:

- Age, sex, and medical service are three significant predictors for Phase 1 data. No
significant effects for Phase 2 data.

Regression analysis:

- Psychological service is a very strong predictor of impacted Emotional Exhaustion

- Resilience has a marginal but not significant effect on Emotional Exhaustion

Theoretically interesting is the observation that people who have medical and health
knowledge are less likely to suffer physical symptoms, while people who have training in
psychological service are less likely to experience emotional exhaustion.
To have an in-depth understanding of how resilience impacts volunteer experience, we
used the five RSA subscales as predictors of the dependent variables. We found

- Social Competence as the most important predictor of Personal Accomplishment.

- Social Competence and Personal Structure as the most important predictors (though
not statistically significant) of VSI. (If only Personal Structure were entered by itself, it is
significant predictor of VSI; F change 6.04, df=1/163, p<.05)

- Personal Structure by itself is the most significant predictor of physical symptoms,
although the entire block of 5 predictors isn't.

May 3, 2009

That Personal Structure (in RSA) was negatively related to Emotional Exhaustion (r=-.17,
p<.05, N = 175) may have an important practical implication. Note that items measuring
Personal Structure are: "I prefer to plan my actions," "Rules and regular routines make
my daily life easier," and "I keep up my daily routines even at difficult times." We
suspect that making plans and adhering to routines are more trainable than other
aspects such as Family Coherence, which are less likely to be developed within a short
timeframe. Therefore, relief organizations can benefit from training individual volunteers
or team leaders to establish personal structure. Future resilience research should
attempt to demonstrate that this component of resilience is indeed trainable.

A t-test shows no difference in RSA and adaptability between those who did not go and
those who did. There is also no difference between those who went once and those
who returned.

Correlational analyses showed that education level is not correlated with any
independent or dependent variables except with adaptabilility (r=-.15, p<.05, N=175)
and Social Competence of the RSA (r=-.25, p<.005, N=175). Therefore we do not have
to enter education level as a control when we do regression.
ANOVA shows no difference among Christians, Buddhists, and non-believers on any
independent and dependent variables except Emotional Exhaustion in the more recent
trip for those who went to the disaster zone more than once. ANOVA after recoding the
religion variable into a binary variable (with versus without religion) shows no difference
in any variable.

Differences between those who went to the disaster zone alone and those who went with a team.
Of the 175 volunteers, 18 went alone, while the others were with formal or informal groups.
One-way ANOVA showed that the two types of volunteers had different experience. Compared
with those who volunteered in a team, the “loners” reported lower Personal Accomplishment
(28.22 vs. 31.69, d = -.53, F=4.92, df=1, 173, p<.05), lower Empowerment (8.78 vs. 11.47, d = -
.99, F=16.01, df=1, 173, p<.001), lower Participation Efficacy (24.44 vs. 28.40, d = -.73,
F=10.17, df=1, 173, p<.005), and more physical symptoms (23.17 vs. 21.35, d = .44, F=4.59,
df=1, 173, p<.05).

Religion distribution for the 175 people wo went to the disaster zone: Christianity 32, Catholic 2,
Buddhism 20, Islam 1, No religion 120. (55 vs. 120)

Out of the 58 people who went to the disaster zone more than once, 35 are non-believers. (23 vs.

Chi-square test result: 1.32 df=1, n.s.

April 17, 2009

List of dependent variables:

Affect on the last day of service (but since later analyses showed poor reliability and
factor structure of that set of items, we decided not to proceed with the test of this

Satisfaction (b, participation efficacy; c, integration)


Psychosomatic symptoms
We would examine correlations between resilience, adaptability


- MBI (emotional exhaustion and personal accomplishment)

H1: adaptability with actual going: The more adaptable people would be more likely to
actually go

H2: adaptability with burnout: The more adaptable people would experience less

H3: The more adaptable people would have less psychosomatic symptoms -- not

H4: resilience with actual going: The more resilient people would be more likely to
actually go

H4: resilience with burnout: The more resilience people would experience less burnout

H6: The more resilient people would have less psychosomatic symptoms -- not

Analysis on the 9 sub-items under #3 (Phase 2) shows that they were not good as a
scale. Factor analysis does not help.

Latest datafile for predictive validity: drv2+1_analysis

April 8, 2009

An initial CFA on the 22-item Maslasch Burnout Inventory (MBI) could not be done; and
an EFA shows weired factor structure. We discovered that items 9 (i) and 10 (j) were
inadvertently swapped around. This was then fixed (The original master file was not
touched, though.) But in future analyses we should note that, in the master data file,
item 9 (i) is depersonalization, while item 10 (j) is personal accomplishment.
After the data set was fixed, the CFA could be run. The fit was ok. CFI=.85.


emotional exhaustion: .805, the items included exhaustion due to work and exhaution
due to people

depersonalization: .512 (if item 15 (o) is removed, .54) Therefore this one would have to
be deleted.

personal accomplishment: .834

We repeated with a 2-factor CFA (without depersonalization). CFI=.88.

We then decided to use the two factors for validation.

Cronbachs of Volunteer Satisfaction Inventory:

Empowerment = .722

Participation efficacy = .860

Group integration = .868

Organizational support = .933

Tracy also did a CFA, and confirmed the VSI structure.

March 25, 2009
Shawn got phone from participant 457, and so Tracy changed the data of departure
from July15 to July14. Two files were created:



                                                complete data from both phases, all people who
                                                went to sc once or more
                                                complete data from both phases, all people who
drv2+1_438a.sav                                 responded to Phase2, including those who did
                                                not go to sc

Investigated the six communication items, and found 2 factors using EFA. One includes
two items on language (#44 and #85). However, the reliability coefficients are low.

liaison (#10 to #13) alpha=.75

medical service (#14 to #17) alpha=.84, EFA shows one-factor solution; if child care
(#17) is dropped, alpha=.85

psychological service (#18-#19), alpha=.87

admin and mgmt (#33 to #35), alpha=.76

physical (#38, 40, 41), alpha=.31

For phase2,

alpha of anxiety symptoms=.80

March 16, 2009

438 participants responded to phase2 (263 didn’t go to Sichuan; 175 went to Sichuan)
Deleted one of ID662 (duplicate) from Phase2 data. Still waiting for ID457 to tell us when s/he
went, so that we can decide which data point to delete from Phase2 data.

Did CFA on resilience. Shawn will write up that part.

Mar 6, 2009: Tracy, Shawn, HH

Did CFA on 20 RSA items in drv-psych-484-EFA.sav, and found structure fairly similar
to the original factor structure reported by Friborg. Shawn will write it up and send to

Mar 4, 2009: Tracy, Shawn, HH (amended Mar 25 on basis of additional analysis)

We decided to use the 968 cases who did not respond to Phase2 for psychometric
analyses. Filename: drv-psych-968.sav

This file was randomly split into two, drv-psych-484-CFA.sav for exploratory analyses,
and drv-psych-484-EFA.sav for confirmatory analyses.

Initially we found the following:

1-factor solution for 4-item adaptability, cronbach at high 50;

1-factor solution for 5-item selfcare, cronbach at low 50;

6-item flexibility: cronbach is .81

the 6-item volunteerism has low reliability, but EFA shows a 4-item solution. Using that
4 items (#2, 3, 4, 6), cronbach is .67 to .69

Tracy will continue to use the EFA data set to check if the original structure of the
Resilience Scale for Adults can be recovered.

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