REVIEW ARTICLEExploring positive pathways to care for memb.docx

REVIEW ARTICLE
Exploring positive pathways to care for members of
the UK Armed Forces receiving treatment for PTSD:
a qualitative study
Dominic Murphy1*, Elizabeth Hunt1, Olga Luzon2 and Neil
Greenberg1
1King’s Centre for Military Health Research, King’s College
London, London, UK; 2Department of
Clinical Psychology, Royal Holloway University, London, UK
Objective: To examine the factors which facilitate UK military
personnel with post-traumatic stress disorder
(PTSD) to engage in help-seeking behaviours.
Methods: The study recruited active service personnel who were
attending mental health services, employed a
qualitative design, used semi-structured interview schedules to
collect data, and explored these data using
interpretative phenomenological analysis (IPA).
Results: Five themes emerged about how participants were able
to access help; having to reach a crisis point
before accepting the need for help, overcoming feelings of
shame, the importance of having an internal locus
of control, finding a psychological explanation for their
symptoms and having strong social support.

Conclusions: This study reported that for military personnel
who accessed mental health services, there were a
number of factors that supported them to do so. In particular,
factors that combated internal stigma, such as
being supported to develop an internal locus of control,
appeared to be critical in supporting military
personnel to engage in help-seeking behaviour.
Keywords: Military health; PTSD; depression; pathways;
stigma; barriers
*Correspondence to: Dominic Murphy, KCMHR, Weston
Education Centre, Cutcombe Road, SE5 9PR
London, UK, Email: [email protected]
For the abstract or full text in other languages, please see
Supplementary files under Article Tools online
Received: 17 June 2013; Revised: 4 October 2013; Accepted: 20
November 2013; Published: 17 February 2014
S
ince 2002, the UK and US military’s have con-
ducted highly challenging operations in Afghanistan
and Iraq. These military operations have been
the focus of a number of large-scale epidemiological re-
search studies, which have investigated the psychological
health of US and UK service personnel. Studies in the

United States have observed rates of post-traumatic stress
disorder (PTSD) in deployed personnel to be between
8 and 18% (Hoge et al., 2004; Smith et al., 2008). Further,
13% of participants met criteria for alcohol problems
and 18% for symptoms of anxiety and depression, with a
very high co-morbidity rate between these disorders and
PTSD (Riddle et al., 2007; Smith et al., 2008). This
increase in the rate of PTSD following deployment has
been replicated prospectively (Vasterling et al., 2006).
However, in the UK, the effects of the conflict upon the
mental health of service personnel have been quite
different.
The most extensive UK epidemiological studies of
service personnel since 2003 have been carried out at
King’s College London. This study is based on a
randomly selected representative sample of the UK
military, and in 2006, this study reported rates of PTSD
to be 4% and symptoms of common mental health

problems (including anxiety and depression) to be 20%
(Hotopf et al., 2006); higher rates of PTSD (6%) were
found in combat troops and reserve forces. These rates
remained reasonably constant at the second wave of data
collection in 2010 (Fear et al., 2010). However, figures
released by the Ministry of Defence (MoD) demonstrate
substantially lower rates of personnel accessing services
for these problems, between 4�4.5% and 0.8�1.2%,
respectively, over the past 3 years (Defence Analytical
Services Agency, 2011). This is supported by research
that reported that only 23% of UK service personnel who
meet criteria for a mental health diagnosis are receiving
any support from mental health services (Iversen et al.,
2010). Of those who engaged in help-seeking, 77% were
getting treatment, with 56% receiving medication, 51%
psychological therapy and 3% inpatient treatment.
PSYCHOTRAUMATOLOGY
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Citation: European Journal of Psychotraumatology 2014, 5:
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A study within the UK Armed Forces followed up
service personnel who had been involved in a 6-year
longitudinal study, 3 years later (Iversen et al., 2005b).
The study observed that most ex-service personnel do

well once they leave. However, those who had a mental
health problem when they left the Armed Forces were
substantially more likely to be suffering from a mental
health problem and be unemployed 3 years after leaving
(Iversen et al., 2005b). In addition, having a mental
health problem predicted leaving the Armed Forces and
mental health status remained constant after leaving
(Iversen et al., 2005b).
As documented above, only a modest number of
military personnel experiencing mental health difficulties
are able to access treatment, and little is known about the
treatment experiences of military personnel who do
access services (Iversen et al., 2009). What we do know
is that many ex-service personnel are able to get treatment
from the NHS, which provides a range of specialist
services. Previous research has identified a number of
barriers that may explain the reluctance to access services
(Britt, Wright, & Moore, 2012; Gould et al., 2010; Iversen

et al., 2011; Kim, Thomas, Wilk, Castro, & Hoge, 2010).
These barriers broadly fit within three categories: internal
stigma (including self-stigma), external stigma (including
public stigma and mistrust in services), and access factors
(including lack of knowledge of available services).
Several trials have been conducted to improve the number
of people seeking treatment by aiming to reduce stigma.
A review of these trials concluded that there has been
little evidence of the efficacy of these interventions
(Mulligan, Fear, Jones, Wessely, & Greenberg, 2011).
The current study aims to investigate the specific
pathways to accessing mental health services for members
of the UK Armed Forces. In particular, to elucidate
factors that support individuals to access services, and
where barriers exist, how these are overcome. This is in
line with the agenda of military occupational mental
health services that have prioritised the importance of
supporting individuals to access services at the earliest

opportunity.
Methods
Setting & design
This study utilised a sample of UK service personnel who
are accessing defence mental health services. Two military
departments of community mental health (DCMHs)
located in the south east of England were selected as
they were geographically close to the investigating team;
DCMHs provide services to all military personnel. The
MoD and RHUL ethics committees granted ethical
approval for this study.
A qualitative methodology was adopted for this study
due to the exploratory nature of the research questions
under investigation. The aim of the research questions
was to understand the lived experiences of participants
during their pathways to accessing mental health services,
and interpretative phenomenological analysis (IPA) has
been argued to be the most appropriate qualitative
analytic approach to do this (Smith, Flowers, & Larkin,

2009).
Participants
A sample size of between 8 and 10 participants was
decided upon as informed by the selection of IPA (Smith
& Osborn, 2008). An ad hoc sampling strategy was used
for this study. The lead author (D. M.) met clinicians at
the DCMHs and explained the inclusion and exclusion
criteria. Clinicians were then requested to ask the clients
who met these criteria whether they wished to participate
in the study. Inclusion criteria for selection into the study
included having a diagnosis of either PTSD or depression
and currently receiving treatment. Individuals were not
selected if they were in the process of being medically
discharged from the military due to disciplinary reasons
(this exclusion criteria was requested by the MoD ethics
committee and the authors do not have access to the
reasons why service personnel were being discharged), or
if there was a clinical reason that meant it would not be

appropriate for the individual to take part in the study. In
general, these clinical reasons were if clients were new to
the service. Clinicians were concerned that the study may
be seen as an additional source of stress at a time when
clients were first engaging in treatment and could have
potentially created a barrier to their engagement in
treatment.
Materials
A semi-structured interview schedule was used. Broadly,
the aim of the interview schedule was to understand the
different pathways that participants’ took to access
services, including which factors enabled them to do so,
and how they overcame potential internal and external
barriers. The interview schedule was piloted with three
individuals who were accessing defence mental health
services. The aim of this was to ensure that the questions
were understandable and to check whether additional
questions needed to be added. Following this, the inter-
view schedule was refined taking into account feedback

from a number of pilot interviews. This included advice
about removing a number of questions and clarifying the
stems of several questions.
Participants were also asked to complete two measures
to record symptoms of mental illness. The Post Traumatic
Checklist (PCL-C) is a self-report 17-item measure of the
17 DSM-IV symptoms of PTSD (Weather & Ford, 1996).
The PCL-C has been previously validated against a
clinical interview, which recommended using a cut-off
of 50 or more (Blanchard, Jones-Alexander, Buckley, &
Dominic Murphy et al.
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Forneris, 1996). The Patient Health Questionnaire (PHQ-

9) is a self-report measure that is based directly upon the
DSM-IV criteria for depression and includes nine items.
The PHQ-9 is scored from 0 to 27, and scores give an
indication of symptom severity; scores between 15 and 19
indicates moderate to severe depression and a score of 20
or above indicates major depression (Kroenke & Spitzer,
2002). Participants were also asked a number of questions
about their demographic characteristics.
Procedure
Recruitment was carried out between March 2012 and
June 2012. The DCMH staff were approached, and the
inclusion and exclusion criteria for the study were dis-
cussed and a list of potential participants was drawn up.
After initial consent had been granted for their details to be
passed on from their treating clinician, potential partici-
pants were contacted to discuss the study, seek consent for
them to be recruited, and find a suitable date and time to
conduct the interview.
Analysis

The first stage of data analysis was to collate the demo-
graphic characteristics and data collected through the
standardised measures (PCL-C and PHQ-9). The second
stage involved analysing the qualitative data in accordance
with published guidelines for conducting IPA (Smith &
Osborn, 2008; Willig, 2008). In brief, this involved working
through a number of different stages. The first stage was to
become familiar with the first participant’s transcript. The
second stage was to make initial notations for ideas
and themes in the text. The notations remained close to
the participant’s words. The third stage was to develop
emerging themes by re-reading the initial notations and
assigning labels. The aim of these labels was to capture the
essence of what the participant had described. The fourth
stage was to search for connections between emerging
themes. The list of labels was scrutinised and emergent
themes that appeared to be connected to each other were
grouped together under super-ordinate themes. Super-

ordinate themes were broader in scope than emergent
themes and contained a number of associated sub-themes.
This process was then repeated for the next participant’s
transcript. Once analysis had been completed for each
transcript, a final master list of super-ordinate and sub-
themes was generated. During this stage, differences and
similarities between cases were noted. At this stage, themes
between transcripts were grouped together and re-labelled
where appropriate.
Results
Sample
Recruitment was carried out at two DCMHs. The sample
consisted of 8 participants, with four from each DCMH.
For the purposes of the study, participants were assigned
pseudonyms to protect their anonymity.
Data were collected on participants’ socio-demographic
characteristics to situate the sample; these are described in
Table 1. The majority of the sample were male (six out of
eight), in a relationship (7/8), had children (6/8), were

Other Ranks and not officers (5/8), were British (7/8) and
reported their ethnicity to be white (8/8). The ages of
participants ranged from early 20s to mid-50s, with the
majority or participants aged between mid-20s and mid-
30s. The lengths of service varied from 4 to 31 years, with
the mean length of service approximately 13 years. Nearly,
50% of the sample was in the Royal Navy and 50% was in
the Army.
Rates of mental health are reported in Table 2. The
results indicate that three of the participants reported
clinically significant levels of distress at the time of the
interview, as measured on both the PHQ-9 and PCL-C.
In addition, two further participants’ scores approached
the cut-offs that defined case criteria on both of the
measures. One of the inclusion criteria for the study was
that participants had a diagnosis of PTSD or major
depression. The observed variation in rates of distress
may be indicative of participants being at different stages

of treatment at the time the interviews were conducted.
Table 1. Socio-demographic characteristics of the sample
Participant Sex Age Relationship status Children Nationality
Ethnicity Service Rank (officer or in ranks) Years in military
P1 Male 42 Divorced Yes British White Army Officer 23
P2 Male 51 Married Yes British White Navy Officer 31
P3 Male 34 Married Yes British White Navy Officer 14
P4 Male 30 Married Yes British White Navy Ranks 11
P5 Female 27 Partner No British White Navy Ranks 10
P6 Female 22 Partner No British White Army Ranks 4
P7 Male 31 Married Yes British White Army Ranks 4
P8 Male 35 Married Yes New Zealand White Army Ranks 6
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Results of qualitative analysis

Five super-ordinate themes emerged from the data. Each
of these super-ordinate themes contained a number of
sub-themes; these are presented in Table 3.
Theme one: recognising something was wrong
A theme that emerged was that participants perceived it
had been difficult for them to recognise they were
experiencing mental health difficulties. This appeared to
result in participants ignoring early warning signs of
mental health difficulties and trying to carry on until it
was impossible for them to do so any longer.
Reaching a crisis point. The participants perceived
having reached a ‘‘crisis point’’ which meant they could
not ignore the mental health difficulties they were
experiencing any longer. What constituted a crisis point
differed between participants and was related to factors
in their environments.
P7: I can remember just being in such a state, I
mean, I was seriously disturbed, so there was so

many things that I felt, panic, terror, depression. I’d
be, go and find a quiet spot and just break down
and cry.
Difficulties experienced as physical symptoms. The par-
ticipants recalled that they first experienced physical
rather than psychological symptoms.
P1: So lots of things came together at that time.
My body was clearly screaming at me, I mean
there were lots, all through the years actually I had
lots and lots of not fully explained medical pro-
blems, which we now think were directly related to
PTSD.
Theme two: overcoming internal stigma
One of the super-ordinate themes that emerged from the
transcripts was related to how individuals perceived
overcoming internal stigma related to experiencing men-
tal health difficulties. Broadly, this fell into two areas:
overcoming feelings of shame about experiencing mental

health difficulties and the effect on self-esteem of being
prescribed psychiatric medication.
Shame. Participants spoke about feeling concerned that
they would experience stigma, in particular, being per-
ceived as ‘‘weak’’ by their peers. However, it appeared
that for the majority their fears were not realised, but
rather it was internal stigma they were experiencing.
Interviewer: So it sounds like you maybe had some
of those fears about stigma but they weren’t realised.
P1: But actually they didn’t, they weren’t real, they
didn’t, it’s not manifested itself. I think people are
much more aware now of it. I think the problem was
with me rather than with everybody else, it was the
anticipation of stigma, maybe that says more about
me than other people.
Table 2. PHQ-9 and PCL-C scores for sample
Participant PHQ-9 score1 Met criteria for PHQ-9 case PCL-C
score2 Met criteria for PCL-C case
P1 13 No 41 No

P2 4 No 8 No
P3 0 No 8 No
P4 23 Major depression 80 Yes
P5 4 No 28 No
P6 12 No 40 No
P7 21 Major depression 71 Yes
P8 17 Moderate to severe depression 63 Yes
1PHQ-9 scored from 0 to 27: scores 15�19 indicates moderate
to severe depression and a score of 20 or above indicates major
depression.
2PCL-C scored from 17 to 85; scores above 50 indicates
meeting criteria for post-traumatic stress reactions.
Table 3. Master list of super-ordinate and sub-themes
Super-ordinate themes Sub-themes
Recognising something
was wrong
Reaching a crisis point
Difficulties experienced as
physical symptoms
Overcoming internal stigma Shame

Stigma related to psychiatric
medication
Finding an explanation Trusted witness to difficulties
Psychological explanation
Getting a diagnosis
Not being alone Normalisation
Safe space
Sense of hope
Acceptance
Understanding
Control Autonomy
Communication
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Stigma related to psychiatric medication. Participants
highlighted the link between being offered medication
and internal stigma related from suffering with a mental
health difficulty. They discussed their ambivalence to-
wards medication. On the one hand, believing that
medication may help them, but on the other hand,
describing how taking medication meant there was
something wrong with you. Medication seemed to be
symbolic of having a mental illness that could no longer
be ignored.
P5: I kept saying, ‘‘I’m not going on medication’’
but I knew I had to, I knew I needed to in the end.
My mum, she’s always been on antidepressants and
I thought, I always said I’d never, ever wanna be like
that.
Theme three: finding an explanation
Participants highlighted the importance of being able to

find an explanation for their difficulties. By understand-
ing and accepting that their difficulties had a psycholo-
gical component, this supported participants’ to seek
help. How participants’ came to find this explanation
differed greatly.
Trusted witness to difficulties. Participants perceived the
importance of having a trusted witness to their difficulties
who could point out something was seriously wrong. This
supported participants to accept that their difficulties
were serious and that they needed to seek help.
P6: Yeah the first time round, I’ve got a very close
friend in the Paras, he’s a Liaison Officer. He
noticed that I was very down and I spoke differently,
very slowly and I just wasn’t really interested in what
he was saying and that’s not really me. I’m quite an
enthusiastic outgoing person and I changed quite a
lot the first time.
Psychological explanation. Participants described how

beneficial it was to be given a psychological explanation
for their difficulties. This may have been because it helped
them realise that their difficulties had a reason or a
function.
P2: Yeah, so I have to, like when I do anything I
have to sort of, I have to understand the mechanics
of it, so I asked the psychiatrist how does this
actually work? But if I understand the process is
find it really helpful.
Getting a diagnosis. Participants spoke about how
receiving a diagnosis was a crucial step for them in their
journey to seek help because it put a label on the
difficulties that they were experiencing.
P8: I think I was only officially told that, you know,
I think they said I had chronic PTSD and yeah it
was my nurse that told me and I don’t know and
then she told me, you know, she explained ‘‘These
symptoms that you’re having . . .’’ And obviously
there was quite a few ‘‘Is all the signs.’’

P8: I was like ‘‘Jesus it must be that.’’ Then, I don’t
know it just made me really interested, I really
wanted, cause I knew what it was then and I was like
‘‘Right I can fix myself here surely.’’
Theme four: not being alone
Another theme that emerged was related to factors that
stopped participants feeling alone supported them to
seek, or continue, treatment for the difficulties they were
experiencing.
Normalisation. Participants spoke about the positive
experience of learning that the difficulties they were
experiencing were similar to those experienced by other
people.
P4: But it’s just looking into it, because when you
look into it you realise, hang on, they’re talking
about people going through this, this, this and this,
but that’s the same as me, so you start thinking, well
I’m not the only person here.

Safe space. What appeared common across the tran-
scripts was that having a safe space allowed participants
the opportunity to take a step back and realise something
was wrong; this then provided them with the motivation
to seek help.
P4: I was sick on shore for two weeks. During that
time it gave me time to actually rest in a secure
environment because I was at home, I had my family
around me. It was a secure environment. I didn’t
have to look over my shoulder. And it gave me a lot
of thinking time. I talked things through with my
wife and thought, something’s wrong here.
Sense of hope. Hope that things could improve was a
theme that emerged in seven of the transcripts. Most of
the participants recalled that hope was connected to
feeling that treatment was available to help them over-
come their difficulties.
P1: There was part of me that was relieved, but

there’s always part of me that, nobody’s harder on
me than I am and, but there was also huge relief. It
was, I realised that finally we may be able to do
something about this.
Acceptance. Participants spoke about the fear of not
being accepted by significant people in their lives because
of their mental health difficulties. However, it seems that
often these fears were based on internal beliefs and not
realised.
P5: I don’t even know why I was worried because I
know that they wouldn’t have ever judged me but at
the time that’s how I was feeling that they were
gonna judge me.
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Understanding. Participants talked about how impor-
tant it had been for them that other people understood
the mental health difficulties they were experiencing.
Participants spoke about how this had helped them not
feel alone as they could share their experiences with
someone who understood them.
P3: If I needed to talk to somebody about it there
was always somebody that was there to talk about
it. My wife really wanted to know, she’d phone me
after every session to see how it had gone. And
there’s a lot to take away from my sessions to share
with her. And so it’s a journey we’ve been through
together.
Theme five: control
Participants perceived that their mental health difficulties
had made them feel as if they were subject to an external
locus of control. In contrast, many of the participants

spoke of how helpful it had been for them when engaging
in help-seeking behaviour to feel an internal locus of
control about their treatment options.
Autonomy. Crucial to having a sense of control was
having autonomy over their treatment plans. Tom ex-
plained how he felt supported by his line manager
because they handed him control. This may be a very
different experience compared to other aspects of military
life, where typically service personnel have less control of
their day-to-day tasks.
P3: it was a case of, well what do you want rather
than them finding me something to do, what do you
want to do? So I was lucky in that respect.
Communication. Interviewed participants were worried
about how they might be viewed by their friends or
colleagues. They had mixed views about whether it was
better to share their experiences or not.
P1 talked about how it had been a useful process for

him to share his experiences with his line manager.
P1: Yeah, and once the PTSD thing had been
diagnosed, actually I was given a printout of the
initial session. And actually what I found the best
way was actually I showed it to my boss, I said this
is medically in confidence, but I said I want, I can’t
really explain it but read this, and he read that bit,
and from then on they couldn’t do enough, it was
just.
In contrast, other participants decided that it would
not be helpful to tell their colleagues.
P2: Not many people knew about it because I just
walked out of this meeting and I went for a beer
with an air force guy, a mate, and he just said take
some time off, and that’s what I did. And of course
they didn’t know that I then went and sought help.
So there wasn’t some sort of big showdown, which
you then had to confront going back to work.

Discussion
The study explored which factors enabled serving mem-
bers of the UK Armed Forces experiencing mental health
difficulties to access care, and how they overcame
common barriers to do so. To the best of the authors’
knowledge, this approach to looking at stigma and
barriers to care has not been undertaken before with
the UK military.
We found that all of the participants spoke about
having to reach a crisis point before they sought help.
What was common between the crises was individuals
reaching the point where ‘‘something had to be done’’;
that is to say that the individual could not continue living
their life as they were. Many of the participants spoke
about a military culture that promotes the value of
‘‘cracking on despite a problem.’’ Whilst this may be
advantageous in many aspects of military life, the
participants spoke about how it led them to experience
very serious difficulties before they would accept that

they had a problem.
The majority of participants spoke about the presence
of physical symptoms prior to psychological symptoms.
It appears that participants expressed their psychological
distress through somatic symptoms. It has previously
been observed in military populations that physical
health difficulties are viewed as more acceptable than
mental health ones and that personnel are more likely
to attend appointments for the former, rather than the
latter (Rona, Jones, French, Hooper, & Wessely, 2004).
This finding is mirrored when looking between cultures
that have different explanations for mental illness, which
can lead to either the somatic or psychological expres-
sion of symptoms. For example, Chinese people have
been observed to be more likely to express symptoms
of depression somatically than north-Americans (Ryder
et al., 2008).
Overcoming feelings of shame about experiencing

mental illness was a common theme reported by partici-
pants. Many of the participants linked accessing mental
health services to their feelings of shame because this
meant they had a ‘‘problem.’’ In addition, by accessing
services it meant that their peers would also knew that
they had a ‘‘problem.’’ These two processes map on to
Corrigan’s theory of internal and external stigma (Corrigan,
2004). Participants spoke about how, over the course of
engaging with services, they were able to overcome their
internal stigma beliefs. For many, this process was related
to realising that their negative beliefs about mental illness
conflicted with the positive changes in their lives they
witnessed due to seeking help. Similarly, what seemed to
help the participants overcome their external stigma
beliefs was the realisation that their fears of rejection
from their peers were not actualised.
Three key factors that facilitated participants to engage
help-seeking behaviour emerged. The first of these was

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being supported to develop an internal locus of control
(Hiroto, 1974). Developing an internal locus of control
contrasted with how the participants described their lives
prior to seeking help; which for the majority, this period
consisted of feeling as if there was an external locus of
control. A relationship between an external locus of
control and anxiety and depression has been documen-
ted by other researchers (Vuger-Kovaèiæ, Gregurek,
Kovaèiæ, Vuger, & Kaleniæ, 2007). Furthermore, lower
levels of anxiety and depression have been observed in
individuals who report an internal, rather than an

external, locus of control (Jaswel & Dewan, 1997).
The second theme that participants reported as having
facilitated their accessing services was gaining a psycho-
logical understanding of their mental illness. This is
supported by previous literature within civilian popula-
tions that observed having a psychological understanding
predicted help-seeking behaviour (Deane, Skogstad, &
Williams, 1999). Whilst the mechanisms for this relation-
ship are unknown, from the current study it can be
hypothesised that a psychological explanation was more
culturally acceptable for members of the armed forces
than a biological explanation, which is associated with
more stigma. Indeed, many of the participants spoke
about how gaining a psychological explanation helped
allay their concerns about being ‘‘mad’’ and having
something ‘‘wrong with them.’’
Being well supported by their social networks was the
final theme described by participants as having facilitated

them to access mental health services. This finding is
supported by previous research within civilian popula-
tions that documented that individuals with mental
illness, who report better social support, were more likely
to engage in help-seeking behaviours (Briones et al.,
1990).
There are a number of limitations to this study. When
interpreting these results, it is important to acknowledge
that there may have been bias towards recruiting parti-
cipants with lower levels of psychological distress. There
was some evidence to support this in the scores reported
on the measures of psychological distress. This needs to
be interpreted carefully as there may have been a bias for
therapists to exclude potential clients if they deemed them
to be suffering from high levels of psychological distress,
or only suggest potential participants who they deemed
had shown significant improvement. Alternatively, it
could have been that only participants who had bene-

fitted from treatment were put forward, in which case
their positive experience of treatment, may have acted to
influence their recall of the factors that helped them
engage in treatment by framing this decision in a
potentially more positive light. It is regrettable that the
authors’ do not have access to information related to
stage of treatment, which may have allowed for further
exploration of this. Whilst there are good clinical reasons
for making these decisions, they could present limitations
to the findings of the current study because individuals
who have been identified as being most at risk of not
being able access services are those with higher levels of
psychological distress (Iversen et al., 2005a).
Conclusions
The results of this study suggest that there are three key
areas that support individuals to seek help. The first of
these were factors that helped individuals recognise that
they were experiencing difficulties and help them realise
that these difficulties had a psychological component.

The second were factors that helped an individual feel as
if they were no longer alone to deal with their difficulties.
For example, this included feeling accepted and sup-
ported by their social network. The final area that
supported individuals to seek help was them feeling
empowered to do so by having an internal locus of
control. In PTSD, feelings of helplessness and power-
lessness are extremely debilitating. Clinically, factors that
promote an internal locus of control are very important
for reducing these feelings. The participants spoke about
how factors that promoted an internal locus of control
helped them overcome feelings of internal stigma. It is
interesting to reflect that the factors that promoted an
internal locus of control could also have acted to reduce
the distress caused by symptoms of PTSD by helping to
tackle feelings of helplessness, isolation and powerless-
ness. Understanding the relevance of these three factors
should help military commanders to plan effective

stigma-reduction programmes.
Conflict of interest and funding
There is no conflict of interest in the present study for any
of the authors.
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Learning Outcomes
By the end of this chapter, you should be able to:
Outline the key features of descriptive,
correlational, and experimental research designs.
Explain the importance of reliability and validity in
designing research studies.
Compare and contrast the different scaling methods
for measuringvariables.
Identify the pros and cons of behavioral,
physiological, and self-report measures.
Describe the process of framing and testing
hypotheses.
In the early1950s, Canadian physician Hans Selye
introduced the term stress into both the medical

and popular
lexicons. By that time, it had been accepted
that humans have a well-evolved �ight-or-�light
response, which
prepares them either to �ight back or �lee from
danger, largely by releasing adrenaline and
mobilizing the body’s
resources more ef�iciently. While working at
McGill University, Selye began to wonder
about the health
consequences of adrenaline and designed an
experiment to test his ideasusing rats. Selye
injected rats with doses of
adrenaline over a period of several days and
then euthanized the rats in order to
examine the physical effects of the
injections. Just as he had hypothesized, rats that
were exposed to adrenaline had developed ill
effects, such as ulcers,
increased arterial plaques, and decreases in the size of
reproductive glands—all now understood to
be
2 Design, Measurement, and Testing Hypotheses
José Antonio Moreno/age fotostock/Superstock
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consequences of long-term stress exposure. But there

was just one problem. When Selye took a
second group of rats
and injected them with a placebo, they also developed
ulcers, plaques, and shrunken reproductive glands.
Fortunately, Selye was able to solve this
scienti�icmystery with a little self-re�lection.
Despite all his methodological
savvy, he turned out to be rather clumsy
when it cameto handling rats, occasionally
dropping one when he removed
it from its cage for an injection. In essence, the
experience for both groups of rats was one
that we would now call
stressful, and it is no surprise that they developed
physical ailments in response. Rather than testing
the effects of
adrenaline injections, Selye was inadvertently
testing the effects of being handled by a
clumsy scientist. It is
important to note that if Selye ran this study in
the present day, ethical guidelines would
dictate much more
stringent oversight of his procedures to protect
the welfare of the animals.
This storyillustrates two key points about the
scienti�icprocess. First, as Chapter 1
discussed,researchers should
always be attentive to apparent mistakes because
they can lead to valuable insights. Second, it is
absolutelyvital that
researchers actually measure what they thinkthey are
measuring—Selye ended up measuringthe effects
of stress
rather than just adrenaline injections. This chapter

explains what it means to do research in a
more concrete way,
beginning with a broad look at the threetypes of
research design. The goal at this stageis to
obtain a general sense
of what thesedesigns are, when they are used,
and the main differences between them.
(Chapters 3, 4, and 5 are
each dedicated to one type of research design and
will elaborate on each one.)Following the overview of
designs,
this chapter covers a set of basicprinciples that
are common to all research designs. Regardless
of the particulars of
a given design, all research studies involve
making sure measurements are accurate and
consistent and that they are
captured using the appropriate type of scale.
Finally, the chapter will discuss the general
process of hypothesis
testing, from laying out predictions to drawing
conclusions.
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2.1 Overview of Research Designs
As Chapter 1 explained, scientists can have a
wide range of goals when they begin a
research project, everything
from describing a phenomenon to changing

people’s behavior. It turnsout that these goals
will dictate different
approaches to answering a research question. That is,
researchers will approach the problem of
describing voting
patterns differently than they would approach the
problem of how to increase voterturnout. These
approaches are
called research designs, or the speci�ic methods
that are used to collect, analyze, and interpret
data. The choice of a
design is not one to be made lightly; the
way an investigator collects data trickles down
to decisions about how to
analyze the data and about the kinds of
conclusions that can be drawn from the results.
This section provides a brief
introduction to the threemain types of design—
descriptive, correlational, and experimental.
Descriptive Research
Recall from Chapter 1 that a research study
can have the basic goal of describing a
phenomenon. If a research
question centers around description, then the
research design falls under the category of
descriptive research, in
which the primary goal is to describe thoughts,
feelings, or behaviors. Descriptive research
provides a static picture
of what people are thinking, feeling, and doing at
a given moment in time,as the following
examples of research
questions illustrate:

What percentage of doctors prefer Xanax
for the treatment of anxiety? (thoughts)
What percentage of registered Republicans vote
for independent candidates? (behaviors)
What percentage of Americans blame the
president for the economic crisis? (thoughts)
What percentage of college students experience
clinical depression? (feelings)
What is the difference in crime rates between
Beverly Hills and Detroit? (behaviors)
What these�ive questions have in common is an
attempt to get a broad understanding of a
phenomenon without
trying to delve into its causes.
The crime-rateexample highlights the main advantages
and disadvantages of descriptive designs. On
the plus side,
descriptive research is a good way to achieve a
broad overview of a phenomenon and may
inspire future research. It
is also a good way to study things that are
dif�icult to translate into a controlled experimental
setting. For example,
crime rates can affect every aspect of
people’s lives, and this importance would
likely be lost in an experiment that
staged a mock crime in a laboratory.
On the downside, descriptive research provides a
static overview of a
phenomenon and cannot explore the reasons for it.
A descriptive design might tell us that
Beverly Hills residents are
half as likely as Detroit residents to be assault
victims, but it would not reveal the

underlying reasons for this
discrepancy. (If we wanted to understand why
this was true, we would use one of the other
designs.)
Descriptive research can be either qualitative or
quantitative; in fact, the largemajority of
qualitative research falls
under the category of descriptive designs.
Descriptions are quantitative when they attempt
to make comparisons or
to present a random sampling of people’s
opinions. The majority of our example questions
above would fall into this
group because they quantify opinions from samples of
households, or cities, or college students.
Good examples of
quantitative description appear in the “snapshot”
feature on the front page of USA Today. The
graphics represent
poll results from various sources; the snapshot for
May 15, 2015, reported that 90% of Americans
crave more
“variety” in their home-cooked meals (i.e.,
thoughts). View a current gallery of these
snapshot graphs here:
http://www.usatoday.com /services/snapshots/gallery/
(http://www.usatoday.com/services/snapshots/gallery/)
Descriptive designs are qualitative when they
attempt to
provide a rich description of a particular
set of
circumstances. A powerful example of this approach
can be
found in the work of the late neurologist Oliver

Sacks. Sacks
wrote several books exploring the ways that
people with
neurological damage or de�icits are able to
navigate the world
around them. In one selection from The Man Who
Mistook His
Wife for a Hat, Sacks (1998) relates the storyof
a man he calls
http://www.usatoday.com/services/snapshots/gallery/
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Johnathon Henninger/Connecticut Post/AP Images
Dr. Oliver Sacks studied how people with
neurological damage formed and retained
memories.
William Thompson. As a result of chronic
alcohol abuse,
Thompson developed Korsakov’s syndrome,a brain
disease
marked by profound memory loss. The memory loss
was so
severe that Thompson had effectively “erased”
himself and
could remember only scattered fragments of his past.
Whenever Thompson encountered people, he would

frantically try to determine who he was. He would
develop
hypotheses and test them, as in this excerpt
from one of
Sacks’s visits:
I am a grocer, and you’re my customer, right?
Well, will
that be paper or plastic? No, wait, why are you
wearing
that white coat? You must be Hymie, the kosher
butcher.
Yep. That’s it. But why are thereno bloodstains on
your
coat? (p. 112)
Sacks concluded that Thompson was “continually
creating a
world and self, to replace what was continually
being forgotten and lost” (p. 113). With
this story, Sacks helps
illuminate Thompson’s experience and fosters
readers’ gratitude for the ability to form and
retain memories.This
storyalso illustrates the trade-off in these sorts
of descriptive case studies: Despite all its
richness, we cannot
generalize these details to other cases of
brain damage; we would need to study and
describe each patient
individually.
Correlational Research
Recall from Chapter 1 that research studies can
also have the goal of trying to predict a

phenomenon. If a research
question centers around prediction, then the
research design falls under the category of
correlational research, in
which the primary goal is to understand the
relationships among various thoughts, feelings, and
behaviors.
Examples of correlational research questions include:
Are people more aggressive on hot days?
Are people more likely to smoke when they
are drinking?
Is income level associatedwith happiness?
What is the best predictor of success in
college?
Does television viewing relate to hours of
exercise?
What thesequestions have in common is the goal of
predicting one variable based on another. If
we know the
temperature, can we predict aggression? If we
know a person’s income, can we predict
her level of happiness? If we
know a student’s SAT scores, can we predict
his college GPA?
These predictive relationships can turn out in one of
threeways (Chapter 4 will provide more detail
about each): A
positive correlation means that higher values of
one variable predict higher values of the
other variable. For
instance, more money is associatedwith higher levels
of happiness, and less money is associated
with lower levels

of happiness. The key is that thesevariables move
up and down together, as the �irstrow of
Table 2.1 shows. A
negative correlation means that higher values of
one variable predict lower values of the
other variable. For
example, more television viewing is associated
with fewer hours of exercise, and fewer
hours of television is
associatedwith more hours of exercise. The key is
that one variable increases while the other
decreases, as the
second row of Table 2.1 illustrates. Finally,
worth noting is a third possibility, which
is no correlation between two
variables, meaning that we cannot predict one
variable based on another. In brief,
changes in one variable are not
associatedwith changes in the other, as seen in
the third row of Table 2.1.
Table 2.1: Three possibilities for correlational
research
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Figure 2.1: Correlation is not
causation
Outcome Description Visual

Positive
Correlation
Variables go up and down together.
For example: Taller people have bigger feet,
and shorter people have smaller feet.
Negative
Correlation
One variable goes up, and the othergoes down.
For example: As a driver’s speed goes up, the
time it takesto �inish the trip decreases.
No
Correlation
The variables have nothing to do with one
another.
For example: Shoe size and number of siblings
are completely unrelated.
Correlational designs are about testing predictions,
but we are still
unable to make causal, explanatory statements
(that comes next). A
common mantra in the �ield of psychology is
that correlation does
not equal causation. In other words, just
because variable A
predicts variable B does not mean that A causes
B. This is true for
two reasons, which we refer to as the
directionality problem and
the third variable problem. (See Figure 2.1.)

First, when we measure two variables at the
same time,we have no
way of knowing the direction of the relationship.
Take the
relationship between money and happiness: It
could be true that
money makes people happier, because they can
afford nice things
and fancy vacations. It could also be true that
happy people have
the con�idence and charm to obtain higher-
paying jobs, resulting in
more money. In a correlational study, we are
unable to distinguish
between these possibilities. Or, take the relationship
between
television viewing and obesity: It could be that
people who watch
more television get heavier, because TV makes them
snack more
and exercise less. It could also be that people
who are overweight
lack the energy to move around and end up
watching more
television as a consequence. Once again, we
cannot identify a
cause–effect relationship in a correlational study.
Second, when we measure two variables as they
naturally occur, a
third variable that actually causes both of them is
always a
possibility. For example, imagine we �inda
correlation between the
number of churches and the number of liquor

stores in a city. Do people buildmore
churches to offset the threat of
liquor stores? Do people buildmore liquor stores
to rebelagainst churches? Mostlikely, the link
involves a third
variable, population size, that causes changes in
both variables: The more people who are living in
a city, the more
churches and liquor stores they can support. As
another example, imagine a correlation
between ice cream sales and
homicide rates is discovered. Does ice cream lead
people to commit murder? Do murderers like to
buy ice cream on
the way home from the scene of the crime?
Mostlikely, the link involves a third variable,
temperature, that causes
changes in both variables: The hotter it gets outside,
the more people want ice cream, and the greater
likelihood that
disagreements will turn violent.
Experimental Research
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Finally, recall that research projects can have the
goal of attempting to explain a
phenomenon. When the research
goal involves causal explanations, then research design

falls under the category of experimental
research, in which
the primary goal is to explain thoughts, feelings,
and behaviors and to make causal statements.
Examples of
experimental research questions include:
Does smoking cause cancer?
Does drinking alcohol make people more aggressive?
Does loneliness cause alcoholism?
Does stress cause heartdisease?
Can meditation make people healthier?
Research: Making an Impact
Helping Behaviors
The 1964 murder of KittyGenovese in plainsight of
her neighbors, none of whom helped, drove
numerous
researchers to investigate why people may not
help others in need. Are individuals sel�ish
and bad, or does
a group dynamic lead to inaction? Is there
somethingwrong with our culture, or are
situations more
powerful than we think?
Among the body of research conducted in the late
1960s and 1970s was one pivotal study
that revealed
why people may not help others in emergencies.
Darley and Latané (1968) conducted an
experiment with
various individuals in different rooms who
communicated with each othervia intercom. In reality,

the
study included just one participant and a number
of confederates, one of whom pretended to
have a
seizure. Among participants who thought they were
the only otherperson listening over the intercom,
more than 80% helped, and they did so in less than 1
minute. However, among participants who thought
they were one of a group of people listening
over the intercom, less than 40% helped, and even
then only
after more than 2.5 minutes. This phenomenon—that the
more people who witness an emergency are
present, the less likely any of them is to help—
has been dubbed the “bystander effect.” One of
the main
reasons that this tendency occurs is that
responsibility for helping gets “diffused” among
all of the people
present, so that each one feels less personal
responsibility for taking action.
Darley and Latané’s research can be seen in action
and has in�luenced safety measures in today’s
society.
For example, when someone witnesses an emergency,
no longer does it suf�ice to simply yell to
the group,
“Call 911!” Because of the bystander effect,
we know that most people will believe
someone else will do it,
and the call will not be made. Instead, it is
necessary to designate a speci�ic person to
make the call. In fact,
part of modern-day CPR training involves making
individuals aware of the bystander effect

and best
practices for getting people to help and be
accountable.
Although the bystander effect may be the rule, there
are always exceptions. For example, on
September 11,
2001, the fourth hijacked airplane was overtaken by
a courageous group of passengers. Most
people on the
plane had heard about the twin tower crashes
and recognized that their plane was heading
for
Washington, D.C. Despite being amongst nearly
100 otherpeople, a few people chose to
help the intended
targets in D.C. Risking their own safety, this heroic
group chose to help to prevent others
from experiencing
death and suffering. So, although we may see events
that remind us of the reality of the
bystander effect,
we also see moments where people are willing to
help, no matter the number of people that
surround
them.
Think About It:
1. What type of research design best describes
Darley & Latane’s (1968) study?
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2. What practical applications have resulted from
research on people’s reluctanceto help in
emergencies?
What these�ive questions have in common is a
focus on understanding why somethinghappens.
Experiments move
beyond, for example, the question of whether
alcoholics are more aggressive to whether alcohol
actually causes an
increase in aggression.
Experimental designs are able to address the
shortcomings of correlational designs because the
researcher has
more control over the environment. Chapter 5 will
cover this in greatdetail, but the basicprocess
of conducting an
experiment is relatively simple: A researcher
has to control the environment as much as
possible so that all
participants in the study have the same experience.
This helps eliminate otherthird variables that might
in�luence
the results. Researchers will then manipulate, or
change, one key variable and then measure
outcomes in another
key variable. The variable manipulated by the
experimenter is called the independent
variable (IV). The outcome
variable that is measured by the experimenter is
called the dependent variable (DV). The

combination of
controlling the setting and changing one aspect of
this setting at a time allows the experimenter
to state with some
certainty that the changes caused somethingto happen.
Think of this in a little more concrete way.
Imagine that a researcherwanted to test the
hypothesis that meditation
improves health. In this case, meditation would be
the independent variable, and health would be
the dependent
variable. One way to test this hypothesis would be
to take a group of people and have half of
them meditate 20
minutes per day for several days while the other
half did somethingelse for the same amount of
time.The group
that meditates would be called the experimental
group because it provides the test of the
hypothesis. The group
that does not meditate would be called the
control group because it provides a basis of
comparison for the
experimental group.
The researcherwould want to make sure that these
groups spent the 20 minutes in similar
conditions so that the
only difference would be the presence or absence of
meditation. One way to accomplish this would
be to have all
participants sit quietly for the 20 minutes but
give the experimental group speci�ic instructions
on how to meditate.
Then, to test whether meditation led to

increased health and happiness, the researcher
would give both groups a set
of outcome measures at the end of the study—
perhaps a combination of survey measures and a
doctor’s
examination. If differences were found between
the dependent measures for the two groups, the
experimenter could
be fairly con�ident that meditation caused
them to happen. One way we can operationalize
health outcomes in this
study would be to measure blood pressure, as
higher levels of blood pressure put people
at risk for developing
cardiovascular disease. So, for example, the researcher
might �ind lower blood pressure in
the experimental
(meditation) group, which would suggest that
meditation causes blood pressure to drop.
Choosing a Research Design
The choice of a research design is guided
�irstand foremost by a researcher’s �inding
the best �it to the research
question and then adjusting it dependingon practical
and ethical concerns. At this point, a
nagging question may
come to mind: If experiments are the most
powerful type of design, why not use them
every time? Why would
anyone give up the chance to make causal
statements? One reason is that we are often
interested in variables that
cannot be manipulated, for ethical or practical
reasons, and that therefore have to be studied as

they occur naturally.
In one example, Matthias Mehl and Jamie
Pennebaker (2003) happened to start a weeklong
study of college
students’ social lives on September 10, 2001.
Following the terrorist attacks on the morning of
September 11, Mehl
and Pennebaker were able to trackchanges in
people’s social connections and use this to
understand how groups
respond to traumatic events. Of course, it would
have been unthinkable to manipulate a
terrorist attack for this
study experimentally, but sinceit occurred
naturally, the researchers were able to conduct a
correlational study of
coping.
Another reason to use descriptive and correlational
designs is that theseare useful in the early
stages of a research
program. For example, before a psychologist can
start to thinkabout the causes of binge
drinking among college
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students, it is important to understand how
common is this phenomenon. Likewise, before a
researcherdesigns a

time- and cost-intensive experiment on the effects
of meditation, it is a good idea to conduct
a correlational study to
test whether meditation even predicts health. In
fact, this latter example comes from a series
of real research studies
conducted by psychiatrist Sara Lazar and her
colleagues at Massachusetts General Hospital.
This research team �irst
discovered that experienced practitioners of
mindfulness meditation had more development in
brain areas
associatedwith control over attention and emotion. But
this study was correlational at best; perhaps
meditation
caused changes in brain structure or perhaps
people who were better at integrating
emotions were drawn to
meditation. In a follow-up study, researchers
randomly assigned people either to meditate or
to perform stretching
exercises for two months. These experimental
�indings con�irmed that mindfulness meditation
actually caused
structural changes to the brain(Hölzel et al., 2011).
This series of studies is a prime
example of how a research
program can progress from correlational to experimental
designs.
Table 2.2 summarizes the main advantages and
disadvantages of thesethreetypes of design. In
addition, the bottom
of the table includes two examples of research
topics—meditation and health, and temperature
and aggression—to

showcase the similarities and differences between
the designs.
Table 2.2: Summary of research designs
Research
Design
Descriptive Correlational Experimental
Goal
Describe characteristics of
an existing phenomenon
Predict behavior; assess
strength of relationship
between variables
Explain behavior; assess impact of
IV on DV
Advantages
Provides a complete
picture of what is
occurring at a given time
Allows testing of expected
relationships; predictions
can be made
Allows conclusions to be drawn
about causal relationships
Disadvantages
Does not assess

relationships; no
explanation for
phenomenon
Cannot draw inferences
about causal relationships
Cannot manipulate many important
variables
Example #1:
Studying
Meditation
What percentage of
college students meditate
at least once a week?
Are regular meditators
happier and healthier?
If we randomly assign people to
start meditating, do they become
happier and healthier?
Example #2:
Temperature
and Aggression
How many violent crimes
are committed in the
summer?
Are crime rates higher in
the summer than in the
winter?

If we turn up the temperature in
the laboratory, do people become
more aggressive?
Designs on the Continuum of Control
Before leaving the design overview behind, we
will consider how thesedesigns relate to one
another. The best way
to thinkabout the differences between the designs
is in terms of the amount of control a
researcherhas. That is,
experimental designs are the most powerful because
the researchercontrols everything from the
hypothesis to the
environment in which the data are collected.
Correlational designs are less powerful because the
researcher is
restricted to measuringvariables as they occur
naturally. However, with correlational designs, the
researcherdoes
maintain control over several aspects of data
collection, including the setting and the choice of
measures.
Descriptive designs are the least powerful because
researchers have dif�icultly controlling outside
in�luences on data
collection. For example, when people answer
opinion polls over the phone, they might be
sitting quietly and
pondering the questions or they might be watching
television, eating dinner, and dealing with a
fussy toddler. As a
result, researchers are more limited as to the
conclusions they can draw from these data.

Figure 2.2 shows an
overview of where research designs fall on the
continuum of control in order of increasing
control: from
descriptive, to predictive, to experimental.
Chapters 3, 4, and 5 will cover variations on
thesedesigns in more detail.
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Figure 2.2: The continuum of control framework
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2.2 Reliability and Validity
Each of the threetypes of research designs—
descriptive, correlational, and experimental—has the
same basicgoal:
to take a hypothesis about somephenomenon
and translate it into measurable and testable
terms. That is, whether
researchers use a descriptive, correlational, or
experimental design to test predictions about
income and happiness,

they still need to translate (or operationalize) the
concepts of income and happiness into
measures that will be
meaningful for the study. Unfortunately, the sad
truth is that research measurements will always be
in�luenced by
factors in addition to the conceptual variable of
interest. Answers to any set of questions about
happiness will
depend both on actual levels of happiness and
the ways people interpret the questions. The
meditation experiment
may have different effects, dependingon people’s
experience with meditation. Even describing
the percentage of
Republicans voting for independent candidates
will vary according to characteristics of a
particular candidate.
These additional sources of in�luencecan be
grouped into two categories: random and
systematic errors. Random
error involves chance �luctuations in measurements,
such as when a participant misunderstands
the question, or
shows up in a terrible mood after
walking through a blizzard to get to the study.
Although random errors can
in�luencemeasurement, they generally cancel out over
the span of an entire sample. That is,
some people may
overreact to a question while others underreact.
Heavy snowfall might put one person in a
terrible mood and make
another appreciatethe joy of winter. While both of
theseexamples would add error to a dataset,

they would cancel
each otherout in a suf�iciently largesample.
Systematic errors, in contrast, are those that
systematically increase or decrease along with
values of the measured
variable. For example, people who have more experience
with meditation may consistently showmore
improvement
in a meditation experiment than those with
less experience. Or, people who have higher
self-esteem may score
higher on a measure of happiness than those
with lower self-esteem. In this case, the
happiness scaledoes not do a
good job of homing in on the concept of
“happiness” and will end up instead assessing a
combination of happiness
and self-esteem. These types of errors can
cause more serious trouble for a researcher’s
hypothesis tests because
they interfere with the attempts to understand the
link between two variables.
In sum, the measured values of a variable re�lect
a combination of the true score, random
error, and systematic error,
as the following conceptual equation shows:
Measured Score = True Score + (Random Error
+ Systematic Error)
For example:
HappinessScore = Actual Happiness+ (Misreading
the Question + Self-Esteem)

So, if our measurements are also affected by outside
in�luences, how do we know whether our
measures are
meaningful? Occasionally, the answer to this
question is straightforward; if we ask people
to report their weight or
their income level, thesevalues can be veri�ied
using objective sources. Many research
questions within psychology,
however, involve more ambiguity. How do we know
that our happiness scaleis accurate? The problem is
that we
have no way to objectively verify happiness beyond
people’s self-reports of their own happiness.
What researchers
need, then, are ways to assess how closethey are to
measuringhappiness in a meaningful way.
This assessment
involves two related concepts: reliability, or the
consistency of a measure; and validity, or the
accuracy of a
measure. This section examines both of theseconcepts in
detail.
Reliability
The consistency of time measurement by watches,
cell phones, and clocks re�lects a high degree
of reliability. People
thinkof a watch as reliable when it keeps
trackof the time consistently—an hour should
take the same amount of
time to pass, 24 times per day. Likewise, the scaleis
reliable when it gives the same value
for weight in back-to-back

measurements—an individual’s weight should be
the same if he stepsoff the scaleand right back
on, provided he
staysawayfrom the fridge in the meantime.
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Reliability is de�ined as the extent to which
a measured variable is free from random
errors, and it is best
understood as the degree of consistency in
research measurements. As the chapter discussed
previously,
researchers’ measures are never perfect, and �ive
main sources of random error threaten reliability:
1. Transient states, or temporary �luctuations in
participants’ cognitive or mental state; for
example, some
participants may complete a study after an
exhausting midterm or after a �ight with
their signi�icant others.
2. Stable individual differences among participants;
for example, someparticipants are habitually more
motivated or happier than otherparticipants.
3. Situational factors in the administration of
the study; for example, an experiment
conducted in the early

morning may make everyone tired or grumpy.
4. Bad measures that add ambiguity or confusion to
the measurement; for example, participants may
respond
differently to a question about “the kinds of
drugs you are taking.” Some may take this to
mean illegal drugs,
whereas others interpret it as prescription or
over-the-counter drugs.
5. Mistakes in coding responses during data entry;
for example, a handwritten “7” could be
mistaken for a “4.”
(Happily, thesetypes of errors have been minimized
by the increasingrole of computersin data
collection. If
someone clicks the number “7” in an online
survey, the computer will record it as a
“7” almost every time.)
Researchers naturally want to minimize the in�luenceof
all of thesesources of error, and the text
will touch on
techniques for doing so throughout. However,
researchers are also resigned to the fact that all
measurements
contain a degree of error. The goal, then,
is to develop an estimate of how reliable
measures are. Researchers
generally estimate reliability in threeways.
1. Test–retest reliability refers to the consistency
of the measure over time—much like the
examples of a
reliable watch and a reliable scale. A fair

number of research questions in the social
and behavioral sciences
involve measuringstable qualities. For example, if
someone were to design a measure of
intelligence or
personality, both of thesecharacteristics should be
relatively stable over time.An individual score on
an
intelligence test today should be roughly the
same as the score when tested again in
�ive years. A person’s
level of extroversion today should correlate highly
with his or her level of extroversion in 20
years. The test–
retest reliability of thesemeasures is quanti�ied by
simply correlating measures at two time points.
The
higher thesecorrelations are, the higher the
reliability will be. This makes conceptual sense
as well;if
measured scores re�lect the true score more than
they re�lect random error, then this will result
in increased
stability of the measurements.
2. Inter-item reliability refers to the internal
consistency among different items on a
measure. Think back to
the last time you completeda survey. Did it seemto
ask the same questions more than once? (Chapter 4
[4.1] will discuss this technique.) The repetition is
included because a single item is more likely
to contain
measurement error than the average of several items
will—remember that small random errors
tend to

cancel out each other. Consider the following items
from Sheldon Cohen’s Perceived Stress Scale
(Cohen,
Kamarck, & Mermelstein, 1983):
In the last month, how oftenhave you felt that you
were unable to control the important things in
your life?
In the last month, how oftenhave you felt con�ident
about your ability to handle your personal
problems?
In the last month, how oftenhave you felt that things
were going your way?
In the last month, how oftenhave you felt dif�iculties
were piling up so high that you could not
overcome them?
Each of theseitems taps into the concept of feeling
“stressed out,” or overwhelmed by the demands of
life.
One standard way to evaluate a measure like this is
by computing the average correlation
between each pair
of items, a statistic referred to as Cronbach’s
alpha. The more theseitems tap into a central,
consistent
construct, the higher the value of this statistic is.
Conceptually, a higher alpha means that
variation in
responses to the different items re�lects variation in
the “true” variable being assessed by the
scaleitems.
Alpha levels range from zero to one, with higher
numbers indicating more internal consistency. As a
general
rule, researchers want this index to be above

0.70 to have any con�idence in the measure.
3. Interrater reliability refers to the consistency
among judges observing participants’ behavior.
The
previous two forms of reliability were relevant in
dealing with self-report scales; interrater
reliability is
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more applicablewhen the research involves behavioral
measures, which involve direct and systematic
recording of observable behaviors. Imagine a
researcheris studying whether alcohol consumption
makes
people behave more aggressively. One way to tackle
this hypothesis would be to have a group
of judges
observe participants after drinking and rate their levels
of aggression. In the same way that using
multiple
scaleitems helps to cancel out the small
errors of individual items, using multiple
judges cancels out the
variations in each individual’s ratings. In this case,
people could have slightly different ideasand
thresholds
for what constitutes aggression. To determine how
much thesedifferences matter, the researcher

can
evaluate the judges’ ratings by calculating the
average correlation among the ratings. The
higher the alpha
values, the more the judges agree in their ratings
of aggressive behavior. Conceptually, a higher
alpha value
means that variation in the judges’ ratings
re�lects real variation in levels of aggression.
Validity
Recall the watch and scaleexamples. Perhaps some
people set their watch 10 minutes ahead to
avoid being late. Or
perhaps certain individuals adjust their scaleby 5
pounds to boost either their motivation or
self-esteem. In these
cases, the watch and the scalemay produce
consistent measurements, but the measurements are
not accurate. It
turns out that the reliability of a measure is a
necessary but not suf�icient basis for
evaluating it. Put bluntly,
measures can be (and have to be) consistent, but
they might still be worthless.The additional piece
of the puzzle is
the validity of measures, or the extent to which
they accuratelymeasure what they are designed to
measure.
Whereas reliability is threatened more by
random error, validity is threatened more
by systematic error. If the
measured scores on the happiness scale
re�lect, say, self-esteem more than they re�lect

happiness, this would
threaten the validity of the scale. The previous
section explained that a test designed to
measure intelligence ought
to be consistent over time. And, in fact,
these tests do show very high degrees of
reliability. However, several
researchers have cast serious doubts on the validity
of intelligence testing, arguing that even scores
on an of�icial IQ
test are in�luenced by a person’s cultural
background, socioeconomic status (SES), and
experience with the process
of test-taking (for discussion of thesecritiques,
see Daniels et al., 1997; Gould, 1996).
For example, children growing
up in higher SES households tend to have more
books in the home, spend more time
interacting with one or both
parents, and attend schools that have more time and
resources available—all of which correlate with
scores on IQ
tests. Thus, because all of thesefactors could
increase scores on an intelligence test, they
amount to systematic error
in the measure of intelligence and, therefore,
threaten the validity of a measured score on
an intelligence test.
Researchers have two primary ways to discuss and
evaluate the validity, or accuracy, of measures:
construct validity
and criterion validity.
Researchers evaluate construct validity based on
how well the measures capture the underlying

conceptual ideas
(i.e., the constructs) in a study. These
constructs are equivalent to the “truescore”
discussed in the previous section.
That is, how accuratelydoes the bathroom scalemeasure
the construct of weight? How accuratelydoes an IQ
test
measure the construct of intelligence relative to
otherthings? The validity of measures can be
assessed in a couple
of ways. On the subjective end of the continuum,
researchers can evaluate construct validity by
assessing the face
validity of the measure, or the extent to which
it simply seems like a good measure of
the construct. The items from
the Perceived Stress Scale have high face validity
because the items match what we intuitively
mean by “stress” (e.g.,
“How oftenhave you felt dif�iculties were piling up
so high that you could not overcome them?”).
However, if we
were to measure an individual’s speed at eating
hot dogs and then state it was a stress
measure, the participant
might be skeptical because hot-dog eating speed
would lack face validity as a measure of
stress.
Although face validity is nice to have, it can
sometimes (ironically) reduce the validity of
the measures. Imagine
seeing the following two measures on a survey of
attitudes:
1. Do you dislike people whose skin coloris

different from yours?
2. Do you ever beat your children?
On the one hand, theseare extremely face-valid
measures of attitudes about prejudice and
corporal punishment—
the questions very much capture our intuitive ideas
about theseconcepts. On the otherhand, even
people who do
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Jupiterimages/Stockbyte/Thinkstock
Criterion validity can be used to
predict a future behavioral outcome
like management success.
support theseattitudes may be unlikely to answer
honestly because they recognize that neither attitude
is popular.
In cases like this, a measure low in face validity
might end up being the more accurate
approach. Chapter 4 will
discuss ways to strike this balance.
On the less subjective end, researchers can
evaluate construct validity by examining measures’
empirical
connections to both related and unrelated constructs.

Imagine for a moment that we are developing
a new measure
of liberal political attitudes. If we thinkabout a
person who describes herself as liberal, she is
likely to support gun
control, equal rights, and a woman’s right to
choose. And, she is less likely to be pro-
war, anti-immigration, or anti-
gay rights. Therefore, we would expect our
new liberalism measure to correlate positively with
existing measures of
attitudes toward guns, af�irmative action, and
abortion. This pattern of correlations taps into the
metric of
convergent validity, or the extent to which
our measure overlaps with conceptually similar
measures. But, we
would want to ensure that the new measure
captures somethingdistinct from otherconstructs. In
this case, we
might want to demonstrate that we have developed a
true measure of political attitudes, which does
not simply
correlate with religious beliefs. That is, we would
want to showthat liberal political views could
be independent of
religion. This hypothesized lack of correlations taps
into the metric of discriminant validity, or
the extent to which
a measure diverges from unrelated measures.
To take another example, imagine someone wanted to
develop a new measure of narcissism, usually
de�ined as an
intense desire to be likedand admired by other
people. Narcissists tend to be self-absorbed

but also very attuned to
the feedback they receive from otherpeople—especially
feedback about the extent to which people
admire them.
Narcissism somewhat resembles self-esteem but differs
enough; perhaps it is best viewed as high
and unstable self-
esteem. So, given thesefacts, a researchermight
assess the discriminant validity of the
measure by making sure it
does not overlap too closely with measures of self-
esteem or self-con�idence. This approach would
establish that the
narcissism measure stands apartfrom thesedifferent
constructs. The researchermight then assess
the convergent
validity of the measure by making sure that it
does correlate with things like sensitivity to
rejection and need for
approval. These correlations would place the
measure into a broader theoretical context
and help to establish it as a
validmeasure of the construct of narcissism.
Criterion validity involves evaluating the validity of
measures based on
the association between measures and relevant
behavioral outcomes.
The “criterion” in this case refers to a
measure that can be used to make
decisions. For example, if someone developed a
personality test to assess
an individual’s management style, the most
relevant metric of its validity
is whether it predicts a person’s actual
behavior as a manager. That is, we

might expect people scoring high on this scaleto
be able to increase the
productivity of their employees and to
maintain a comfortable work
environment. Likewise, if someone developed a
measure that predicted
the best careers for graduating seniors based
on their skills and
personalities, then criterion validity would be
assessed using people’s
actual success in these various careers.
Whereas construct validity is
more concerned with the underlying theory behind
the constructs,
criterion validity is more concerned with the
practical application of
measures. As might be expected, researchers are
more likely to use this
approach in applied settings.
That said, criterion validity is also a useful way to
supplement validation of a new questionnaire.
For example, a
questionnaire about generosity should be able to
predict people’s annual giving to charities,
and a questionnaire
about hostility ought to predict hostile
behaviors. To supplement the construct validity
of the narcissism measure, a
researchermight examine its ability to predict
the ways people respond to rejection and
approval. Based on the
de�inition of the construct, the researcher
might hypothesize that narcissists would
become hostile following

rejection and perhaps become eager to please
following approval. If these predictions were
supported, it would
mean further validation that the measure was
capturing the construct of narcissism.
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Criterion validity falls into one of two categories,
dependingon whether the researcheris interested in
the present
or the future. Predictive validity involves attempting
to predict a future behavioral outcome
based on the measure,
as in the examples of the management-style and
career-placement measures. Predictive validity is
also at work
when researchers (and colleges) try to predict
graduates’ likelihood of school success based
on SAT or GRE scores.
The goal here is to validate the construct via its
ability to predict the future.
In contrast, concurrent validity involves attempting
to link a self-report measure with a
behavioral measure
collected at the same time,as in the examples of
the generosity and hostility questionnaires. The
phrase “at the same
time” is used vaguely here;theseself-report and

behavioral measures may be separated by a
shorttime span. In fact,
concurrent validity sometimes involves trying to
predict behaviors that occurred before completion
of the scale,
such as trying to predict students’ past drinking
behaviors from an “attitudes toward alcohol” scale.
The goal in this
case is to validate the construct via its association
with similar measures.
Comparing Reliability and Validity
This section has discussed how both reliability
(consistency) and validity (accuracy) are ways to
evaluate measured
variables and to assess how well thesemeasurements
capture the underlying conceptual variable. In
establishing
estimates of both of these metrics, researchers
essentially examine a set of correlations with
their measured
variables. But while reliability involves correlating
variables with themselves (e.g., happiness scores
at week1 and
week4), validity involves correlating variables with
othervariables (e.g., happiness scalewith the number
of times a
person smiles). Figure 2.3 displays the relationships
among types of reliability and validity.
Figure 2.3: Types of reliability and validity
We learned earlier that reliability is necessary but
not suf�icient to evaluate measured variables.
That is, reliability

has to come �irstand is an essential requirement
for any variable—no one would trust a watch
that was sometimes
�ive minutes fast and othertimes ten minutes slow.
If we cannot establish that a measure is
reliable, then thereis
really no chance of establishing its construct
validity because every measurement might be
a re�lection of random
error. However, just because a measure is
consistent does not make it accurate. Someone’s
watch might consistently
be ten minutes fast; a scalemight always be
�ive pounds under the person’s actual weight.
For that matter, a test of
intelligence might result in consistent scores
but actually be capturing respondents’ cultural
background. Reliability
tells us the extent to which a measure is
free from random error. Validity takesthe second
step of telling us the
extent to which the measure is also free from
systematic error.
Finally, it is worth pointing out that establishing
validity for a new measure is hard work.
Reliability can be tested in
a single step by correlating scores from
multiple measures, multiple items, or multiple
judges within a study. But
testing the construct validity of a new measure
involves demonstrating both convergent and
discriminant validity. In
developing our narcissism scale, we would
need to show that it correlated with things
like fear of rejection

(convergent) but was reasonably different from
things like self-esteem (discriminant). The latter
criterion is
particularly dif�icult to establish because it takes
time and effort—and multiple studies—to
demonstrate that one
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scale is distinct from another. However, an easy
way exists to avoid these challenges: Use
existing measures
whenever possible. Before creating a brand-new
happiness scale, or narcissism scale, or
self-esteem scale, check the
research literature to see if one exists that has
already gone through the ordeal of being
validated.
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2.3 Scales and Types of Measurement
One of the easiest ways to decrease error variance
and thereby increase reliability and validity is to

make smart
choices when designing and selecting measures.
Throughout this book, we will discuss
guidelinesfor each type of
research design and ways to ensure that measures
are as accurate and unbiased as possible. This
section examines
somebasicrules that apply across all threetypes of
design. We �irstreview the four scales of
measurement and
discuss the proper use of each one; we then turn
our attention to threetypes of measurement
used in psychological
research studies.
Scales of Measurement
Whenever researchers perform the process of
translating conceptual variables into measurable
variables (i.e.,
operationalization; see Chapter 1, section 1.2), they
must ensure that their measurements accurately
represent the
underlying concepts. In Chapter 1, the discussion
of validity explained that this accuracy is a
critical piece of
hypothesis testing. For example, if researchers
develop a scaleto measure job satisfaction,
then they need to verify
that this is actually what the scaleis measuring.
However, measurement accuracy has an additional,
subtler dimension: We also need to be sure
that the numbers
used in our chosen measurement accuratelyre�lect
the underlying mathematical properties of the

concept. In many
cases in the natural sciences, this process is
automatically precise. When we measure the
speed of a falling object or
the temperature of a boiling object, the
underlying concepts (speed and temperature)
translate directly into scaled
measurements. In the social and behavioral
sciences, though, this process is trickier;
researchers have to decide
carefully how best to represent abstract concepts such
as happiness, aggression, and political
attitudes. As
researchers take the step of scaling variables, or
specifying the relationship between a
conceptual variable and
numbers on a quantitative measure, they have four
different scales to choose from, presented
below in order of
increasingstatistical power and �lexibility.
Nominal Scales
Nominal scales are used to label or
identify a particular group or characteristic.
For example, we can label a
person’s gender as male or female, and we can
label a person’s religion as Catholic, Protestant,
Buddhist, Jewish,
Muslim, Hindu, etc. In experimental designs,
researchers can also use nominal scales to
label the condition to which
a person has been assigned (e.g., experimental or
control groups). The assumption in using
these labels is that
members of the group have somecommon value or
characteristic, as de�ined by the label. For

example, everyone in
the Catholic group should have similar religious
beliefs, and everyone in the female group
should be of the same
gender.
Research studies commonly represent theselabels
using numeric codes in a data �ile,such as
1 to indicate females
and 2 to indicate males. However, thesenumbers
are completely arbitrary and meaningless—that is,
males do not
have more gender than females. We could just as
easily replace the 1 and the 2 with another
pair of numbers or with
a pair of letters or names. Thus, the primary
limitation of nominal scales is that the scaling
itselfis arbitrary, which
prevents us from using these values in
mathematical calculations. One helpful way to
appreciate the difference
between this scaleand the next threeis to thinkof
nominal scales as qualitative, because they
label and identify, and
to thinkof the otherscales as quantitative, because
they indicate the extent to which someone
possesses a quality or
characteristic. The next sections explore thesequantitative
scales in more detail.
Ordinal Scales
Researchers use ordinal scales to represent ranked
orders of conceptual
variables, such that higher numbers re�lect increasing
magnitude of the
underlying variable. For example, beauty contestants,

horses, and
Olympic athletes are all ranked by the order in
which they �inish—�irst,
second, third, and so on. Likewise, movies,
restaurants, and consumer
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HasseChr/iStock Editorial/Thinkstock
An ordinal scalecan place thesethree
women in �irst, second, and third, but
it cannot tell you how far apartthey
�inished in their race.
goods are oftenratedusing a system of stars
(i.e., 1 star is poor; 5 stars is
excellent) to represent their quality. In these
examples, we can draw
conclusions about the relative speed, beauty, or
deliciousness of the
rating target. Even so, the numbers used to label
theserankings do not
necessarily map directly to differences in the
conceptual variable. The
fourth-place �inisher in a race is rarely twice
as slow as the second-place
�inisher; the beauty-contest winner is not threetimes
as attractive as the
third-place �inisher; and the boost in quality

REVIEW ARTICLEExploring positive pathways to care for memb.docx

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