BIODIVERSITY and HEALTHY POPULATIONSECS111 P DR. SE.docx

BIODIVERSITY and HEALTHY
POPULATIONS
ECS111 P
DR. SEALEY
SPRING 2019
FINAL FOOTPRINT: THIS IS 15% OF YOUR GRADE.
Pick your parameters – something you can
document and measure.
Start doing some research on that parameter,
create a bibliography in end note.
EMAIL Dr. SEALEY or MAKE AN APPOINTMENT
if you have questions, want help, need
references or just want to be sure you are on the
right track
Start now, you may have to start over if the first
attempts don’t work out.

EQUILIBRIUM THEORY
Ecosystems are stable environments in
which species interact constantly in well
balanced predator-prey and competitive
relationships.
“Balance of nature” idea derived from this
theory.
BIODIVERSITY IS LINKED TO POPULATIONS
DYNAMICS OF NATURAL
POPULATIONS
• Population growth curves
• Biotic potential versus environmental
resistance
• Density dependence and critical number

POPULATION = BIRTHS – DEATHS -
EMMIGRATION + IMMIGRATION
POPULATION
GROWTH
Exponential vs. Logistical Growth
LIFE HISTORY OR
REPRODUCTIVE STRATEGIES
Many offspring with
low parental care
Few offspring with
high parental careJ-shaped growth curve
S-shaped growth curve
WHICH SPECIES WILL SURVIVE
IN A RAPIDLY CHANGING
ENVIRONMENT?

Panda
Narrow distribution
Small population
Low genetic variation
Large size
Small # of offspring
Limited migration
Mouse
Wide distribution
Large population
High genetic variation
Small size
Large # of offspring
High migration
POPULATION DYNAMICS
Environmental resistance: combination of
biotic and abiotic factors that may limit

population increase.
Biotic potential: combination of biotic and
abiotic factors that enhance population
increase.
YOU TUBE BREAK:
WHAT DETERMINES BIODIVERISTY?
HEALTHY POPULATIONS OF MANY
SPECIES!!!!
HOW CLOSE ARE WE TO
RESURRECTING EXTINCT SPECIES?
HTTPS://WWW.YOUTUBE.COM/WATCH?V
=QA1_MDIDGYK
https://www.youtube.com/watch?v=qA1_mdiDgyk
ESSAY # 9 : ARE WE READY FOR DE-
EXTINCTION?
DE-EXTINCTION AND RECOVERY OF SPECIES:

Should scientists pursue species recovery of extinct
species?
Give a specific example of one species you believe
should or should not be brought back from
extinction.
Write a persuasive essay to discuss the ecological,
economic and ethical ramifications of re-creating the
genome of an extinct species, re-creating
populations, and then releasing these animals into
the wild.
Mammal Population Losses and the Extinction Crisis
Gerardo Ceballos and Paul R. Ehrlich
The disappearance of populations is a prelude to species
extinction. No geographically explicit estimates have been
made of current population losses of major indicator taxa.
Here we compare historic and present distributions of 173
declining mammal species from six continents. These

species have collectively lost over 50% of their historic
range area, mostly where human activities are intensive.
This implies a serious loss of ecosystem services and
goods. It also signals a substantial threat to species
diversity
SCIENCE IS DOCUMENTING THE LOSS OF
SPECIES
Percent of species
that have
disappeared
Number of species
lost
Historic number of
species with
populations in these
areas

Why did these
species go
extinct and
others survive?
PEOPLE MOVE IN AND WILDLIFE MOVES OUT:
How can wildlife and society co-exist?
Key deer (Odocoileus virginianus clavium), the smallest of all
white-tailed
deer, is a subspecies of the Virginia white-tailed deer. These
deer inhabit Big
Pine Key and various surrounding keys. They are not found
anywhere else in
the world.
Mass extinction events in the Geological record
CLIMATE HEATING AND COOLING
SEA LEVEL RISE AND CHANGES IN CURRENTS
ACID RAIN

DISEASE AND EPIDEMICS
INVASIVE SPECIES
SPECIES NATURALLY
GO EXTINCT
Mass extinction event between Mesozoic and Cenozoic
CLIMATE HEATING
AND COOLING
SEA LEVEL RISE AND CHANGES
IN CURRENTS
ACID RAIN
DISEASE AND
EPIDEMICS
INVASIVE SPECIES

Increased human population
Destruction/Fragmentation of Habitat
Pollution
Climate Change/Global Warming
Extinctions caused by humans are generally considered to
be a recent phenomena …HOWEVER
Major fauna changes occurred in Australia and North
America shortly after the arrival of humans 17,000 years ago..
TOP HUMAN CAUSES
OF EXTINCTION
Local extinction of species can occur with a substantial delay
following habitat loss or degradation.
Accumulating evidence suggests that such extinction debts
pose a significant but often unrecognized challenge for
biodiversity conservation across a wide range of taxa and

ecosystems.
Species with long generation times and populations near
their extinction threshold are most likely to have an
extinction debt.
EXTINCTION DEBT
Conceptual model of extinction debt. (a) At equilibrium, species
richness in a habitat patch is high. After an environmental
perturbation, habitat is lost but this does not necessarily cause
immediate extinction of all species to reach a new equilibrium.
The
difference between the number of species remaining after
habitat loss and a new theoretical equilibrium represents a
possible
extinction debt. Relaxation time is the time elapsed since the
habitat was lost until the new equilibrium is attained. Short-
lived
species (orange dots) are likely to show faster relaxation to a
new equilibrium than long-lived species (blue dots). The
photographs on the right-hand side are examples of a long-lived
perennial herb (Hippocrepis comosa) and a short-lived butterfly
species (Polyommatus coridon).

Extinction debt means there is a delayed response after a major
disturbance
when species richness may appear high, but many species occur
in non-
viable or fragmented populations.
Validity and reliability in quantitative studies
Roberta Heale,1 Alison Twycross2
Evidence-based practice includes, in part, implementa-
tion of the findings of well-conducted quality research
studies. So being able to critique quantitative research is
an important skill for nurses. Consideration must be
given not only to the results of the study but also the
rigour of the research. Rigour refers to the extent to
which the researchers worked to enhance the quality of
the studies. In quantitative research, this is achieved
through measurement of the validity and reliability.1
Validity
Validity is defined as the extent to which a concept is
accurately measured in a quantitative study. For
example, a survey designed to explore depression but
which actually measures anxiety would not be consid-
ered valid. The second measure of quality in a quantita-
tive study is reliability, or the accuracy of an instrument.
In other words, the extent to which a research instru-
ment consistently has the same results if it is used in the

same situation on repeated occasions. A simple example
of validity and reliability is an alarm clock that rings at
7:00 each morning, but is set for 6:30. It is very reliable
(it consistently rings the same time each day), but is not
valid (it is not ringing at the desired time). It’s important
to consider validity and reliability of the data collection
tools (instruments) when either conducting or critiquing
research. There are three major types of validity. These
are described in table 1.
The first category is content validity. This category
looks at whether the instrument adequately covers all
the content that it should with respect to the variable. In
other words, does the instrument cover the entire
domain related to the variable, or construct it was
designed to measure? In an undergraduate nursing
course with instruction about public health, an examin-
ation with content validity would cover all the content
in the course with greater emphasis on the topics that
had received greater coverage or more depth. A subset of
content validity is face validity, where experts are asked
their opinion about whether an instrument measures the
concept intended.
Construct validity refers to whether you can draw
inferences about test scores related to the concept being
studied. For example, if a person has a high score on a
survey that measures anxiety, does this person truly
have a high degree of anxiety? In another example, a
test of knowledge of medications that requires dosage
calculations may instead be testing maths knowledge.
There are three types of evidence that can be used to
demonstrate a research instrument has construct
validity:

1 Homogeneity—meaning that the instrument mea-
sures one construct.
2 Convergence—this occurs when the instrument mea-
sures concepts similar to that of other instruments.
Although if there are no similar instruments avail-
able this will not be possible to do.
3 Theory evidence—this is evident when behaviour is
similar to theoretical propositions of the construct
measured in the instrument. For example, when an
instrument measures anxiety, one would expect to
see that participants who score high on the instru-
ment for anxiety also demonstrate symptoms of
anxiety in their day-to-day lives.2
The final measure of validity is criterion validity. A cri-
terion is any other instrument that measures the same
variable. Correlations can be conducted to determine the
extent to which the different instruments measure the
same variable. Criterion validity is measured in three
ways:
1 Convergent validity—shows that an instrument is
highly correlated with instruments measuring similar
variables.
2 Divergent validity—shows that an instrument is
poorly correlated to instruments that measure differ-
ent variables. In this case, for example, there should
be a low correlation between an instrument that mea-
sures motivation and one that measures self-efficacy.
3 Predictive validity—means that the instrument
should have high correlations with future criterions.2

For example, a score of high self-efficacy related to
performing a task should predict the likelihood a
participant completing the task.
Reliability
Reliability relates to the consistency of a measure. A par-
ticipant completing an instrument meant to measure
motivation should have approximately the same
responses each time the test is completed. Although it is
not possible to give an exact calculation of reliability,
an estimate of reliability can be achieved through differ-
ent measures. The three attributes of reliability are out-
lined in table 2. How each attribute is tested for is
described below.
Homogeneity (internal consistency) is assessed using
item-to-total correlation, split-half reliability, Kuder-
Richardson coefficient and Cronbach’s α. In split-half
reliability, the results of a test, or instrument, are
Table 1 Types of validity
Type of
validity Description
Content
validity
The extent to which a research instrument
accurately measures all aspects of a
construct
Construct
validity

(or tool) measures the intended construct
Criterion
validity
is related to other instruments that
measure the same variables
Editor’s choice
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free content
10.1136/eb-2015-102129
1School of Nursing, Laurentian
University, Sudbury, Ontario,
Canada
2Faculty of Health and Social
Care, London South Bank
University, London, UK
Correspondence to:
Dr Roberta Heale,
School of Nursing, Laurentian
University, Ramsey Lake Road,
Sudbury, Ontario, Canada
P3E2C6;
[email protected]
66 Evid Based Nurs July 2015 | volume 18 | number 3 |
Research made simple
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divided in half. Correlations are calculated comparing
both halves. Strong correlations indicate high reliability,
while weak correlations indicate the instrument may not
be reliable. The Kuder-Richardson test is a more compli-
cated version of the split-half test. In this process the
average of all possible split half combinations is deter-

mined and a correlation between 0–1 is generated. This
test is more accurate than the split-half test, but can
only be completed on questions with two answers (eg,
yes or no, 0 or 1).3
Cronbach’s α is the most commonly used test to
determine the internal consistency of an instrument. In
this test, the average of all correlations in every combin-
ation of split-halves is determined. Instruments with
questions that have more than two responses can be
used in this test. The Cronbach’s α result is a number
between 0 and 1. An acceptable reliability score is one
that is 0.7 and higher.1 3
Stability is tested using test–retest and parallel or
alternate-form reliability testing. Test–retest reliability is
assessed when an instrument is given to the same
participants more than once under similar circumstances.
A statistical comparison is made between participant’s
test scores for each of the times they have completed it.
This provides an indication of the reliability of the instru-
ment. Parallel-form reliability (or alternate-form reliabil-
ity) is similar to test–retest reliability except that a
different form of the original instrument is given to parti-
cipants in subsequent tests. The domain, or concepts
being tested are the same in both versions of the instru-
ment but the wording of items is different.2 For an instru-
ment to demonstrate stability there should be a high
correlation between the scores each time a participant
completes the test. Generally speaking, a correlation coef-
ficient of less than 0.3 signifies a weak correlation, 0.3–
0.5 is moderate and greater than 0.5 is strong.4
Equivalence is assessed through inter-rater reliability.
This test includes a process for qualitatively determining

the level of agreement between two or more observers.
A good example of the process used in assessing inter-
rater reliability is the scores of judges for a skating com-
petition. The level of consistency across all judges in the
scores given to skating participants is the measure of
inter-rater reliability. An example in research is when
researchers are asked to give a score for the relevancy of
each item on an instrument. Consistency in their scores
relates to the level of inter-rater reliability of the
instrument.
Determining how rigorously the issues of reliability
and validity have been addressed in a study is an essen-
tial component in the critique of research as well as
influencing the decision about whether to implement of
the study findings into nursing practice. In quantitative
studies, rigour is determined through an evaluation of
the validity and reliability of the tools or instruments
utilised in the study. A good quality research study will
provide evidence of how all these factors have been
addressed. This will help you to assess the validity and
reliability of the research and help you decide whether
or not you should apply the findings in your area of
clinical practice.
Twitter Follow Roberta Heale at @robertaheale and
Alison Twycross at @alitwy
Competing interests None declared.
References
1. Lobiondo-Wood G, Haber J. Nursing research in Canada.
Methods, critical appraisal, and utilization. 3rd Canadian edn.
Toronto: Elsevier, 2013.

2. Korb K. Conducting Educational Research. Validity of
Instruments. 2012. http://korbedpsych.com/R09eValidity.html
3. Shuttleworth M. Internal Consistency Reliability. 2015.
https://
explorable.com/internal-consistency-reliability
4. Laerd Statistics. Determining the correlation coefficient.
2013.
https://statistics.laerd.com/premium/pc/pearson-correlation-in-
spss-8.php
Table 2 Attributes of reliability
Attributes Description
Homogeneity (or
internal consistency)
The extent to which all the items
on a scale measure one
construct
Stability The consistency of results using
an instrument with repeated
testing
Equivalence Consistency among responses of
multiple users of an instrument,
or among alternate forms of an
instrument
Evid Based Nurs July 2015 | volume 18 | number 3 | 67
Research made simple

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http://twitter.com/alitwy
http://korbedpsych.com/R09eValidity.html
http://korbedpsych.com/R09eValidity.html
https://explorable.com/internal-consistency-reliability
spss-8.php
spss-8.php

spss-8.php
http://ebn.bmj.com/Validity and reliability in quantitative
studiesValidityReliabilityReferences
Qualitative Health Research
2015, Vol. 25(9) 1212 –1222
© The Author(s) 2015
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1049732315588501
qhr.sagepub.com
General: Keynote
In the 1980s, Guba and Lincoln transformed the nature of
qualitative inquiry by developing criteria to ensure rigor
(which they termed trustworthiness) during qualitative
inquiry, to evaluate the credibility, transferability, depend-
ability, and the trustworthiness of the completed product
(Guba, 1981; Guba & Lincoln, 1985, 1989). These crite-
ria included strategies that, in various forms, have been
used extensively for ensuring rigor for four decades, yet,
with few exceptions (Krefting, 1991; O’Neill, 1995;
Tuckett, 2005), have not been questioned. They have
been used as the criteria to ensure rigor during the con-
duct of the research, as checklists for the evaluation of the
research, and as indicators of the worthiness of the
research (Morse, in press-a, in press-b). All textbooks on
qualitative methods contain a description of these strate-
gies to be used in inquiry; funding agencies use them to
provide an assurance of quality, and journal reviewers use
them for the evaluation of the reliability and the validity
of the completed research.

However, despite these strategies being universally
perceived as essential in qualitative inquiry, their use has
not been carefully examined. We have neither critiqued
their use, nor determined how they actually affect research
design and the quality of inquiry. We have not even deter-
mined which strategies are appropriate for use in each
type of inquiry, or for each type of data collection strategy.
We have not determined how they actually ensure the
quality of inquiry and how they change the final product
of our research.
What are these strategies? Guba and Lincoln (1989,
pp. 301–329) identified the overall goal of trustworthi-
ness, consisting of credibility, transferability, dependabil-
ity, and confirmability, to be respectively equivalent to
quantitative criteria of internal validity, external validity,
reliability, and objectivity. These criteria have remained
the same over time (see, for instance, Creswell, 2012),
but the strategies for attaining each have changed over
time. The strategies proposed by Guba and Lincoln
(1989) were as follows:
Credibility (i.e., internal validity): Prolonged engage-
ment, persistent observation, triangulation, peer
debriefing, negative case analysis, referential ade-
quacy, and member checks (process and terminal).
Transferability (external validity, or generalizability):
Thick description is essential for “someone interested”
588501QHRXXX10.1177/1049732315588501Qualitative Health
ResearchMorse
research-article2015
1University of Utah, Salt Lake City, Utah, USA

Corresponding Author:
Janice M. Morse, University of Utah, 2151 E 900 S, Salt Lake
City, UT
84108, USA.
Email: [email protected]
Critical Analysis of Strategies for
Determining Rigor in Qualitative Inquiry
Janice M. Morse1
Abstract
Criteria for determining the trustworthiness of qualitative
research were introduced by Guba and Lincoln in the
1980s when they replaced terminology for achieving rigor,
reliability, validity, and generalizability with dependability,
credibility, and transferability. Strategies for achieving
trustworthiness were also introduced. This landmark
contribution to qualitative research remains in use today, with
only minor modifications in format. Despite the
significance of this contribution over the past four decades, the
strategies recommended to achieve trustworthiness
have not been critically examined. Recommendations for where,
why, and how to use these strategies have not been
developed, and how well they achieve their intended goal has
not been examined. We do not know, for example, what
impact these strategies have on the completed research. In this
article, I critique these strategies. I recommend that
qualitative researchers return to the terminology of social
sciences, using rigor, reliability, validity, and generalizability.
I then make recommendations for the appropriate use of the
strategies recommended to achieve rigor: prolonged
engagement, persistent observation, and thick, rich description;
inter-rater reliability, negative case analysis; peer
review or debriefing; clarifying researcher bias; member
checking; external audits; and triangulation.

Keywords
qualitative; trustworthiness; rigor; reliability; validity;
generalizability; dependability; credibility; transferability
mailto:[email protected]
http://crossmark.crossref.org/dialog/?doi=10.1177%2F10497323
15588501&domain=pdf&date_stamp=2015-07-16
Morse 1213
(p. 316) to transfer the original findings to another
context, or individuals.
Dependability (i.e., reliability): Attainable through
credibility, the use of “overlapping methods” (triangu-
lation), “stepwise replication” (splitting data and
duplicating the analysis), and use of an “inquiry audit”
(p. 317) or audit trail.
Confirmability (Objectivity): Using strategies of trian-
gulation and the audit trail.
The final step, addressing all of the above criteria, is
the reflexive journal.
However, how these strategies are operationalized
has developed over time, and it is not clear whether
Guba and Lincoln expected all to be practiced for every
project. Creswell (2012) noted that “qualitative
researchers should engage in at least two of them in any
given study” (p. 253), but he did not tell us which two,
or why, when, and how to use each strategy. Furthermore,
we do not know whether and how the use of these strat-
egies, as presently operationalized during inquiry,
affects the process of inquiry. Do they enhance the pro-
cesses of conceptualization for the investigators?

Inhibit inquiry? Do they work—making the inquiry
more rigorous—or do they make no difference to the
developing research? Do they actually make our
research valid, reliable and trustworthy? These ques-
tions suggest it is time to reopen the discussion on the
determination of trustworthiness in qualitative inquiry:
We must critique the use of recommended strategies
and reexamine our discipline.
Components of Rigor
While Guba and Lincoln’s (1989) goal was trustworthi-
ness, elsewhere (Morse, in press-b), I have suggested that
it is time we return to the terminology of mainstream
social science, using rigor (rather than trustworthiness),
and replacing dependability, credibility, and transferabil-
ity with the more generally used reliability, validity, and
generalizability.
Validity (or internal validity) is usually defined as, the
“degree to which inferences made in a study are accurate
and well-founded” (Polit & Beck, 2012, p. 745). Miller
(2008b) further defined it as “the ‘goodness’ or ‘sound-
ness’ of a study” (p. 909). In qualitative inquiry, this is
usually “operationalized” by how well the research repre-
sents the actual phenomenon. Is the resemblance accu-
rate? Does the description of the essence actually match
the essence of the phenomenon? Ask: Can the description
be recognized by others who have had the experience, or
appreciated by those who have not had the experience? Is
the description detailed? Decontextualized? Abstracted?
Expressed logically?
Reliability is “broadly described as the dependability,
consistency, and/or repeatability of a project’s data col-
lection, interpretation, and/or analysis” (Miller, 2008a,

p. 745). Basically, it is the ability to obtain the same
results if the study were to be repeated.
Generalizability (or external validity) is “extending
the research results, conclusions, or other accounts that
are based on the study of particular individuals, setting,
times or institutions, to other individuals, setting, times
or institutions than those directly studied” (Maxwell &
Chmiel, 2014; Polit & Beck, 2012). In qualitative
inquiry, the application of the findings to another situa-
tion or population is achieved through decontextualiza-
tion and abstraction of emerging concepts and theory.
Although Guba and Lincoln (1989) suggested that this
be the prerogative of a third party, I consider such rec-
ommendations also to be the prerogative of the original
investigator.
In this article, I will argue that in qualitative inquiry,
validity and reliability are often intertwined, with reli-
ability attainment inherently integrated as processes of
verification in the attainment of validity, hence, agreeing
with Guba (1981) that validity may be supported inter-
nally by reliability. In addition, I will discuss that some
strategies are particular to certain types of interviews or
sampling strategies (selection and segmentation), and
that some strategies, as presently practiced, may even
invalidate the research. Finally, I will make a case for the
increasing use of generalizability in qualitative research.
Achieving Rigor
Both criteria of reliability and validity are intended to
make qualitative research rigorous (formerly referred to
as trustworthy). Through particular representation,
abstraction, and theory development, validity enables
qualitative theories to be generalizable and useful when

recontextualized and applied to other settings. Reliability
makes replication possible, although qualitative research-
ers themselves recognize induction is difficult (or even
impossible) to maintain with replication (Morse, 2008a),
so the process of replication itself destroys induction.
Therefore, replication of a project is unnecessary and
undesirable in qualitative inquiry.
Nevertheless, rigor as a concept is an important goal,
and rigor is the concern of external evaluators who ulti-
mately determine the worth of qualitative research.
Evaluators’ concerns primarily arise from the intimate
relationship between the researchers and their data: The
unstructured processes of obtaining data within verbal
interaction or observation, the interpretative nature of
analysis, and the subjective nature of data itself are per-
ceived as threats to validity. Furthermore, qualitative
inquiry does not demand the checks and balances of
1214 Qualitative Health Research 25(9)
journalism (Morse, in press-a), is perceived as threatened
by the imagination and creativity of interpretation and the
threat of fiction, and is not intended to follow the strin-
gent demands of randomization.1 These features provide
an opportunity for those who do not appreciate the inter-
nal methodological mechanisms, the checks, and bal-
ances of qualitative analytic processes. They demand
demonstrable checks and balances as “proof” of rigorous
inquiry. Thus, the strategies for demonstrating reliability
and validity identified by Guba and Lincoln (1985) have
become standards for attaining rigor in qualitative inquiry.
We do not have reliability coefficients or other statistical
tests of quantitative inquiry to offer as evidence. Presently,

in qualitative inquiry, provided some of the strategies are
named in the final report by the investigator as having
been attended to in the process of inquiry, our research is
considered “trustworthy.” However, the appropriateness
of these strategies has never been evaluated, nor do we
know how they facilitate or hinder the actual practice of
qualitative inquiry. Therefore, we will now explore the
appropriateness of the recommended strategies for ensur-
ing rigor.
Exploring Strategies to Determine
Validity
The strategies for ensuring validity are prolonged engage-
ment, persistent observation, and thick, rich description;
negative case analysis; peer review or debriefing; clarify-
ing researcher bias; member checking; external audits;
and triangulation. Each of these will be discussed below.
Prolonged Engagement, Persistent
Observation, and Thick, Rich Description
These three strategies are highly interdependent:
Prolonged engagement and persistent observation are
both necessary for producing thick, rich data. The
assumption underlying these criteria is that spending
more time on data collection in a particular setting pro-
vides time for trust to be established with participants.
With increased trust (and intimacy), you will get better,
richer data. More will be revealed, and therefore, data
will be more valid. Thus, studies that use observation,
and require contact with participants, must follow this
practice, called “getting in” by ethnographers, or the first
stage of fieldwork (Agar, 1996). However, with unstruc-
tured interviews, provided the participant has had time to
assess the interviewer before starting the interview, in the

process of relating one’s experience, the participant inter-
nally focuses and provides rich data (Corbin & Morse,
2003). Time to get to know the interviewer is now consid-
ered necessary for unstructured interview research.
Nevertheless, obtaining thick and rich data is more
than simply obtaining good data from one participant.
Thick and rich data refer to the entire data set, so data
quality is also associated with the number of interviews
and/or participants. To have a thick and rich data set, the
researcher must attend to sample size and appropriateness
(i.e., selecting an appropriate sample) of the data.
What about sample size? Recently, there has been an
extraordinary debate in qualitative research, with
researchers trying to calculate sample size á priori (see,
for instance, Guest, Bunce, & Johnson, 2006). Yet, we
know that the size of the sample depends on the nature of
the phenomenon, its concrete versus subjective nature,
the amount of complexity and scope of the phenomenon,
and of course, how much is already known about the
topic. Into this mix, consider the quality of the interview
(the interviewer’s techniques and the quality of the par-
ticipant’s report), the nature of the research topic, the type
of interview, and the researcher’s analytic skill. The short
answer is that trying to predetermine sample size is a
futile task. But, we do know that if the sample is too
small, data are not saturated, the results obtained are
superficial and obvious, and cherry picking (Morse,
2010) occurs in the data. Data will be difficult to analyze,
because the categories will not be evident, and theorizing
will be difficult if not impossible. There may be limited
variation, with the findings limited in scope, and the
researcher will be uncertain about the findings. In the
worst case, if the sample is small and perhaps also using
semi-structured interviews, with these restricted data, the

researcher will have nothing of interest to write up. The
results will be predictable and easily predetermined.
Thus, the lack of an adequate sample is a validity issue,2
because the research lacks variation and depth, and can
neither provide detailed understanding nor adequately
represent the phenomenon.
What about sampling appropriateness? When com-
mencing qualitative data collection, the researcher may
know little about the topic. Data collection must begin
somewhere, and at the beginning, data collection may be
hit and miss. The researcher may use a convenience sam-
ple, interviewing everyone who volunteers, or use a quota
sample, interviewing a certain number of people with
particular positions of interest. Nevertheless, once the
researcher’s analysis of the interviews begins to provide
some understanding, and categories or themes begin to
develop, sampling strategy changes. As patterns and con-
jectures emerge, the sampling approach changes from a
convenience sample to theoretical sampling. That is,
rather than interviewing those who are available or who
volunteer, the researcher seeks information from those
most likely to know the information required to verify or
to move understanding forward. Data collected may
come from their own experiences or may be shadowed
Morse 1215
data (Morse, 2001b) that comes from beyond their own
experiences. Shadowed data is information about what
they know more generally about the topic and the behav-
ior of others. Thus, data collected are always about more
than the person’s own experiences; it is also about the
experiences of those they know. In this way, qualitative

studies always include information far beyond the “num-
bers of participants” that are listed in the demographic
tables (Morse, 2008b).
Negative Case Analysis
Negative cases often provide the key to understanding the
norm, or the most commonly occurring cases. They are
not ignored, or as outliers in quantitative research, dis-
carded. Rather, they are analyzed as carefully as the more
frequently occurring cases, and as with all data, addi-
tional cases are sought and compared. Thus, data from
negative cases must also be saturated.
What do I mean by “the key to understanding the
norm”? Comparison of the negative cases with the more
commonly occurring cases will reveal important differ-
ences, and it is the developing understanding of these dif-
ferences that is often critical to understanding the process
as a whole. Therefore, this is a critical analytic strategy
for the development of validity.
Peer Review or Debriefing
Peer review or debriefing is intended to prevent bias and
aid conceptual development of the study. The opportunity
to present one’s findings is a strategy that enables concep-
tualization of the theory, in particular for new investiga-
tors. It assists new researchers to synthesize and to see
patterns in their data—sometimes by the questions asked
by their peers, and sometimes even by listening to their
own voice. As such, it assists with the development of
internal validity.
However, I am not certain about “peer review” as
something that facilitates validity, nor as a strategy for

“debriefing.” Elsewhere, I have written that validity is
not something that is awarded by committee consensus
(Morse, 1998), but is something that must be analytically
earned. There is documentation of problems occurring
when key informants and editors have demanded that
their theoretical ideas be added to ethnographies (“Who’s
the anthropologist here?” see Nussbaum, 1998). As it is
the researcher who is the closest to these data, responsible
for the analysis, and the most familiar with the research
agenda and relevant theories, the researcher must be
responsible for the research outcome. It is recommended
that the researcher listens to alternative points of view,
but takes final responsibility for the results, and its impli-
cations and applications.
Development of a Coding System
Coding systems must be used cautiously with research
using unstructured interview research. With unstructured
interview research, it is not possible to develop a detailed
priori coding system at the beginning of the study, as you
are conducting this study because you know little about
the phenomenon, and therefore, guessing what these
codes might be may destroy validity. However, develop-
ing a coding system after the interviews have commenced
will also not be possible, because the interviews change
as the study progresses. Interviews using the same codes
as used for interviews early in the study will not be suit-
able for coding more specific interviews conducted later
in the study.3
However, it is desirable to establish a coding system
for research using semi-structured interviews. These
interviews are used when the researcher has more infor-
mation about the topic, and the question stem format
keeps the interviews relatively standardized. Obtaining

the coding system to ensure that the meaning of the anal-
ysis is the same between coders enhances validity and
certainty of the findings.
Clarifying Researcher Bias
There are two sources of researcher bias, and the investi-
gator must be aware of each. First, there is the “pink ele-
phant” bias (Morse & Mitcham, 2002; Spiers, in press).
This is the tendency for the researcher to see what is
anticipated. However, research may also be value-laden;
if we expect a situation to have particular characteristics
(e.g., anti-feminist) or use a value-laden theory, these fea-
tures may be unfairly emphasized in data during our anal-
ysis. Investigators are always advised to enter a setting
with a “neutral stance” (Popper, 1963/1965). The
responses to this problem are difficult, as research, by
definition, is always topic/question focused (Morse &
Mitcham, 2002). Furthermore, if the researcher uses step-
wise verification during data gathering, data will correct
themselves during the processes of collection and analy-
sis and strategies of verification will provide a system of
checks and balances (Meadows & Morse, 2001; Morse,
Barrett, Mayan, Olson, & Spiers, 2002).
Another source of bias is inherent in qualitative meth-
ods. Because we do not use a random sample, and because
our samples are small and our data cumbersome, we
select relatively small and excellent examples of what we
are studying. Such sampling patterns are essential for the
understanding of the concepts of interest. For example, in
my examination of comforting, I elected to study comfort
where patients were most in need of comfort—those in
agony—so I decided to explore comfort in the trauma
center (Morse, in press-c). This enabled me to develop an

understanding of comfort, so that when I explored com-
fort in a less extreme situation, I could distinguish the
concept from the surrounding conceptual “noise.” This
principle of sampling is essential for validity, but the
researcher must remember that the initial investigations
are with the best (or most pure) examples of the behavior,
and the investigator risks invalidity when conducting the
research in a more “average” situation, where the “sheep”
may not be as easily separated from the “goats.”
The third type of bias occurs unconsciously in the
research design. The questions may be biased; a compar-
ative sample designed with non-equivalent samples, or
non-equivalent experiences or interventions. For instance,
a researcher may compare perceived quality of care
offered by male and female nurses (a gender-biased ques-
tion), yet disregard the gender of the patient in the
research design. In these cases, the onus is on the
researcher to be strongly vigilant about such comparisons
and conclusions, maintaining an inductive perspective
and verifying all data during processes of data gathering.
Member Checking
Member checking refers to giving the transcribed inter-
view (or the completed analysis) back to the participant to
obtain additional information or to correct data. It is not
clear why one should provide the participant with such an
opportunity to change his or her mind; it is not required in
other types of research. Member checking of the analysis,
however, is not practical. If the participant does not agree
with the analysis, this must place the researcher in an

awkward position. Recall that the analysis is usually a
synthesis of all of the interviews, and the text will have
been abstracted; therefore, it is highly unlikely that a par-
ticipant would recognize their own story in the combined
text. Of greater concern, the text may have been analyzed
interpretively. Thus, if the participant does not think the
analysis is right, what does the investigator do? Change
the text to whatever the participant thinks it should be?
Discard that part of the analysis? What does the researcher
do if the participant suggests changes? The researcher’s
background in theory and research methods must outrank
the participant as a judge of the analysis. Therefore,
member checking as a strategy is not recommended.
External Audits
External audits are best used as a system of auditing, and
should, therefore, be conducted when the researcher’s
findings are suspect. Perhaps a granting agency suspects
that the results are not as balanced as they should be, that
the results are “too good” to be true, or that some type of
undesirable investigator bias is present. External auditors
would, therefore, request to see the researcher’s data,
explore the processes of conceptualization, and reexam-
ine the investigator’s conclusions.
The findings of a qualitative researcher are rarely chal-
lenged. One high-profile case was Margaret Mead’s
(1928) work in Samoa, which was challenged by Freeman
in 1983. This led to one of the most controversial debates
in the literature (see, for instance, Ember, 1985; Feinberg,
1988; Grant, 1995; Hill, 1992; Shankman, 2009)—a
debate centered on validity of ethnographic fieldwork.
Triangulation

Triangulation has a number of levels—investigator,
data, theory, or methods. For establishing validity, it
usually refers to the use of two or more sets of data or
methods to answer one question. This process increases
scope or depth of the study, because different sets of
data or different qualitative methods may each elicit dif-
ferent data, different participants, or perspective, and it
may beg the question, “Which data set or methods is the
correct one?”
However, with the difficulty of collecting qualitative
data or conducting a qualitative method, a larger ques-
tion arises: “Why conduct the same study twice?” “Does
one not have adequate trust in the first method, such as to
go to the bother of duplicating the study using a second
method?” Qualitative studies take much time and effort,
so going to the additional work of duplicating the study
in a way that gives little gain (i.e., determining validity)
appears pointless. Finally, researchers do not usually
publish both studies in the same journal, which would
allow the reader to judge whether both studies are the
same. If, however, the second study was conducted to
gain information that the first method did not provide,
then a second study—a multiple-method design—may
be worthwhile.
An interesting attempt at using the same data with dif-
ferent methods was conducted using the same data with
five different qualitative methods (Wertz et al., 2011).
The different lens of each method (phenomenology,
grounded theory, discourse analysis, narrative inquiry,
and intuitive inquiry), and a chapter comparing the results
of each analysis, provides an important example of the
value of a multiple-method project and the different
understandings gained from each perspective and method.

This variation endorses the significance of the research
basing their study on an overt theoretical foundation or
context. It provides the reader with some theoretical con-
text with which to judge the validity of the results.
Summary
The strategies for attaining validity are summarized in Table 1.
Of importance, member checking is not recommended, and
Morse 1217
all of the other strategies have caveats, limiting their
application.
Exploring Strategies to Ensure
Reliability
In qualitative inquiry the major strategies for determining
reliability occur primarily during coding. Inter-rater
reliability is the comparison of the results of a first coder
and a second coder. Reliable coding is demonstrated by
duplication, which is ensured by explicit coding deci-
sions, communicated by clear definitions in a codebook,
so that a trained second coder may make the same coding
decisions as the first coder. Reliable studies are stable and
not subject to random variation. Therefore, the major
strategies to ensure reliability are the development of a
coding system and inter-coder agreement. Other strate-
gies for reliability include member checks and peer
review debriefing, triangulation, and external audits.
Development of a Coding System

The development of a coding system for use with semi-
structured interviews is the listing of all possible responses
(or codes) to all items (see, McIntosh & Morse, in press).
When preparing semi-structured interviews, questions are
pre-selected within a delimited domain, and the format of
the interviews contains the stems of these fixed questions.
Table 1. Summary of Recommendations for Establishing
Validity in Qualitative Inquiry.
Strategy Validity Comments and Caveats
Prolonged engagement Necessary for observational research to
reduce “observer effects”
Not necessary for interview research
Observation Prolonged observation reduces observer
effect
Spot observation sampling will reduce
“observer effects”
Thick description Unstructured interviews Attend to indices of
saturation
An adequate and appropriate sample
necessary
For unstructured interviews: A large
sample is necessary to obtain adequacy
Description enhanced in semi-structured
interview research if probes are used

Triangulation Methodological triangulation may enhance
validity as multiple-method research
It is used to expand understanding
Development of a coding system
and inter-rater reliability
Use only for semi-structured research Will invalidate research
that uses
unstructured open-ended and guided
interview
Researcher bias 1. Unconscious bias: may be evident in the
question and design
2. May be used beneficially to maximize
phenomena
1. May warp findings
2. May validate findings
Negative case analysis Necessary for research using
unstructured interviews
With semi-structured interviews, attend to
questions that have not been answered
Peer review debriefing May be used to assist the researcher
conceptualize
If researching using unstructured
interviews

Member checking Not used
External audits Not routinely used Problematic—too late to fix
identified
concerns
This set of questions is asked of all participants, in the
same order. Participants are free to answer each question
as they wish, but answers are restricted by the question’s
stem, and become patterned relatively quickly, although
the investigator may ask follow-up probes to obtain addi-
tional information. These follow-up questions may or may
not be included in the coding scheme.
The coding of these responses for semi-structured
interviews is limited to rather short statements about a
very restricted topic. The structure and order of the inter-
views and the questions do not change. The responses to
these questions are then summarized into a clearly delim-
ited set of responses that are numbered and well described.
Thus, a second coder may recognize the descriptions and
code using the same numbers (codes) for the same item,
and make the same coding decisions as the first coder,
using any one piece of text.
This standardization enables the responses to be sys-
tematically coded—and also numerically coded—item-by-
item. Inter-rater reliability is the obtaining of a second
coder to recode data—to see whether that person obtains
the same results as the first coder. The second coder usu-
ally randomly selects sections of data to recode; rarely is
the entire data set double coded (Morse, 1997). Inter-coder

reliability statistics may be used to numerically determine
the accuracy of such a coding system, thereby demonstrat-
ing the stability of the coding and the results.
With unstructured interviews, however, this system
cannot be used, and it actually invalidates the research.
Unstructured interviews are narrative interviews, in
which, in response to a general question, the participant
“tells his story” without interruption. Not only is the
interview unstructured, but also research design in eth-
nography and grounded theory demand that the researcher
is learning about the phenomenon as the study progresses.
Subsequently, unstructured interviews change as the
researcher becomes more knowledgeable about the topic,
so that one interview does not cover exactly the same
material as the next interview. Later interviews may be
more detailed about some particular aspect; they may be
used to confirm data from other interviews, or even to test
conjectures. Therefore, coding decisions are not made
from patterned replication (as with semi-structured inter-
views), but interpretively, within the context of all of the
previous interviews. Pieces of the interview are interde-
pendent, one portion enlightening the researcher’s under-
standing about other portions. The first coder codes
according to what data signify. Later, coding decisions
are made in the context of the knowledge or information
gained from all of the interviews. This is the essence of
interpretive coding.
Thus, small pieces of data may have important signifi-
cance to a researcher who is coding and intimately famil-
iar with the total data set, but the significance of such
cues and clues will be missed by the second coder,
because the second coder has not listened intently to the
entire interview, or set of interviews. The second coder is

making the coding decision entirely on the piece of text
before him (rather than the interview or data set as a
whole), and does not, and cannot, code as interpretively
as the first coder. Keeping the coding reliable occurs at
the expense of interpretive insight. Using inter-rater
reliability with unstructured interviews keeps the coding
system descriptive and inhibits interpretive coding. The
use of a second coder will keep the analysis superficial,
trivial, obvious, insignificant, uninteresting, and trite. In
other words, while the use of multiple coders enhances
demonstrated reliability for semi-structured research, it
invalidates research that uses unstructured interviews.
What is “demonstrated reliability”? Demonstrated
reliability occurs when a study uses thick, rich and satu-
rated data, data that are verified in each step of data col-
lection, and has data that represent the phenomenon
(either concrete or experiential and abstract) to such a
degree that others may instantly recognize it (Morse, in
press-a). Demographic tables in qualitative studies should
list the reasons for selecting participants in the study,
rather than reporting irrelevant demographic characteris-
tics (Morse, 2008b), thereby providing information about
the sampling rationale.
Member Checks4
If your analysis was detailed, rich, and descriptive, any
reliability checks and balances will have been conducted
in the course of verification during concurrent data col-
lection and analysis. Member checking is more than
returning an interview to participants to check that the
transcription is correct. Member checking may be con-
ducted during the process of data collection to check data
between participants: “Other people tell me [thus and
so]. Is this how it is for you?” Notice that this member

checking is not done with the original participants, but
with others. Such replication determines normative pat-
terns of behavior, hence achieves reliability.
Does the researcher understand/interpret the partici-
pant correctly? In the text, as a reliability issue, if you do
not understand what is going on, your analysis is unsta-
ble, and the results cannot be repeated, therefore, it is
impossible to get reliable results.
Thick Description
When data are plentiful, data naturally overlap. Of course,
examples are not exactly the same, but key issues bear
resemblances. This overlap enables the researcher to see
replication; hence, thick description contributes to inter-
nal reliability.
Morse 1219
Triangulation
Triangulation as a strategy for reliability is very closely
associated with using triangulation as a strategy for valid-
ity. For processes of data triangulation to provide reliable
results, analysis of data must provide similar results. This
may be done by splitting the data into two sets and having
two investigators analyze each set (of course, making cer-
tain that the data in each pile is adequate), or asking two
investigators to independently analyze the same data—
which, of course, may be cumbersome.
If the researcher uses two methods, the researcher
again examines the results of the two methods, asking,

“Do both methods give the same results?” If the same
results are obtained with each analysis, theoretically, the
results are reliable. But, of course, you recognize that two
different methods use two different perspectives and will
not give the same results. Furthermore, if there are two
researchers, their skill in interpreting the data is a large
factor in the nature and quality of the findings, even when
both are using the same research question, literature, and
data.5 Therefore, obtaining the same results indepen-
dently, simply to demonstrate reliability, is rather a lost
cause and not recommended.
External Audits
External audits may reveal internal linkages as the theory
develops, but it is not a routine strategy, and it is of lim-
ited use as a tool to achieve validity, not reliability.
Summary
Reliability is a well-developed concept for semi-structured
interviews that have a relatively rigid structure and guidelines
for use. Determining reliability for research using unstruc-
tured interviews is managed internally as data accrues, and
the similarity between data from different participants is
observed. Other strategies, summarized in Table 2, are not
practical or well suited to determine reliability.
Recommendations
What does this all mean for determining the rigor of quali-
tative inquiry? First, it means that rigor, comprising both
validity and reliability, is achieved primarily by research-
ers in the process of data collection and analysis. Most of
the strategies are researcher centered (see Table 3): pro-

longed engagement, thick description, development of the
coding system and inter-rater reliability, the correction of
researcher bias, negative case analysis, and peer review/
briefing. Member checking is conducted with participants
during the analytic process, and external audits are con-
ducted by “auditors” following the completion of the proj-
ect, and are not intended for routine use by investigators.
From this critique, it is clear that many of the strate-
gies do not achieve their intended goal of contributing to
rigorous research, and must be used judiciously. Member
checking is of little value to determine validity and does
not achieve reliability. Some strategies may be used only
for unstructured interviews (i.e., thick description, nega-
tive case analysis, researcher bias, and perhaps peer
review), and some only used for semi-structured research
(i.e., use of a coding system and inter-rater reliability).
Some strategies may only be used with particular meth-
ods, such as prolonged engagement with observational
research, or methodological triangulation with mixed-
method designs. The indiscriminate use of strategies with
any type of qualitative research is clearly harmful to the
goal of conducting rigorous research.
Table 2. Summary of Recommendations for Establishing
Reliability in Qualitative Inquiry.
Strategy Reliability Comments and Caveats
Only for semi-structured interview research Coding system and
codebook are
essential

Member checks Does the researcher understand/interpret
the participant correctly?
In text: As a reliability issue, if you do
not understand what is going on,
your analysis is unstable, and cannot be
repeated to get the same results.
Thick description Provides opportunity for seeing replication/
duplication
Interviews overlap, and therefore, verify
the data set internally.
Peer review debriefing Not usually a reliability issue, except for
team research
External audits Do not ensure reliability
Not routinely used
Problematic—too late to fix identified
concerns
An additional problem occurs with external evalua-
tion of rigor following the completion of the research
(Sparkes & Smith, 2009). The “audit” is not practical for
use by journal or grant reviewers or readers, perhaps
interested in implementing or teaching the research. The
Guba and Lincoln criteria are only useful if the reviewers
note that the strategies have been done during the course
of the research and reported in the article—they are not

evident in the quality of the completed research. As a
result, new evaluation criteria are emerging that appears
to be more useful for post-completion rating, such as the
criteria proposed by Tracy (2010); Cohen and Crabtree
(2008). These criteria often are less specific than the
Guba and Lincoln criteria (for instance, focusing on
coherence, contribution, and significance of the research
rather than specific strategies). The problem with such
criteria is that they largely remain subjective, and do not,
for instance, assist with the “quality” questions that are
demanded by those who, for instance, are conducting
meta synthesis.
There is still much work to do in this area of rigor. We
do not know the extent to which these strategies are used
incorrectly, or non-specifically, and the price to qualita-
tive inquiry, in research time, research costs, and in the
diminishing quality of our research. However, rather than
focusing on such problems, I recommend that we develop,
refine, and test analytic processes and strategies that fit
qualitative inquiry while also remaining consistent with
concepts used by the larger social science community.
Only then will we be able to describe our methods in a
way that other social sciences will comprehend and
respect our research.
Author’s Note
This article was presented as a keynote address at the 4th
Global
Congress for Qualitative Health Research as “Reformulating
Reliability and Validity in Qualitative Inquiry,” Mérida,
Mexico, March 18, 2015.
Declaration of Conflicting Interests

The author declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author received no financial support for the research,
authorship, and/or publication of this article.
Notes
1. In qualitative inquiry, because of sample size and the
comparatively cumbersome modes of data collection and
analysis, the practice of randomization would decrease
Table 3. Summary of Recommendations for Strategies for
Establishing Rigor in Qualitative Inquiry.
Strategy Validity Reliability
Prolonged engagement Yes: for research using observation No
Thick description Yes: for research using unstructured
interviews More opportunity for replication of data
Triangulation Yes: for mixed-method research No
Yes: only for semi-structured interview research Yes: essential
for semi-structure
interview research and multiple coders
Researcher bias May be evident in research question and design

(groups not equivalent etc.)
Not a reliability concern
Data will correct themselves if researcher is
responsive to the principles of induction
Negative case analysis Yes: for research using unstructured
interviews Not used as a reliability measure
With semi-structured interviews, attend to
missing responses
Peer review/debriefing Yes: may assist with conceptualization
Not used
Member checking Not used Not used
External audits Not routinely used Not routinely used
Morse 1221
validity, as the chances of gaining understanding, so essen-
tial in theoretical sampling, would be lost. When quantita-
tive researchers consider theoretical sampling as a source
of bias, they are removing the practice from the checks and
balances of the process of data collection and analysis as a
whole (Morse, 2006).
2. This is another issue that differentiates qualitative inquiry
from quantitative, where in qualitative inquiry validity
issues are primarily applied to measurement (M. A. Carey,

personal communication, May 4, 2015).
3. This point, that some of these strategies for ensuring trust-
worthiness may be used for some strategies or methods,
but not for others, has not been noted by other researchers.
For instance, Creswell (2012) states that multiple coders
are “needed for highly interpretive texts” (p. 253). This is
incorrect.
4. Peer review debriefing described by Guba refers only to
validity. It is not clear why Creswell (2012) included it as
a strategy for determining reliability.
5. Wertz et al. (2011) attempted to demonstrate the differ-
ences of five qualitative methods, using the same quali-
tative data, and then comparing their findings with the
participant’s analysis. Unfortunately, the demonstration
was not, as you can imagine, a “success.” “We six authors,
leave you, as reader, to draw your own conclusions . . .”
(p. 367). This is also another example of the “Blind men
and the elephant” parable, and different conclusions being
reached by examining different parts of the whole.
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Author Biographies
Janice M. Morse, PhD, is a professor and Barnes Presidential
Chair, University of Utah, and professor emeritus, University of
Alberta, Canada.

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