Research in psychology
RESEARCH IN PSYCHOLOGY
What counts as ‘‘good’’
quantitative research and what can we say about when to use quantitative and/or
qualitative methods?
1. How interpretation enters into inquiry
To set the stage for
discussing the scope of ‘‘good’’ quantitative research, I will briefly
reconsider the role played by interpretation in the process of inquiry. In my
position paper, I argued that when we try to understand psychological
phenomena, we have to take as bedrock the practices in which people are
engaged. These practices are concretely meaningful in a way that cannot be
explained by other, supposedly more basic, terms. I also pointed out that this
idea is very closely linked to the view that psychologists themselves are
participants in the world of practices. Inquiry in psychology is itself
practical activity. As I discussed elsewhere at some length (Westerman, 2004),
practices of inquiry in our field are based in part on the ways in which we
learn about things in everyday life (e.g., a teacher trying to discover the
best way to teach 7-year-olds how to read) and also on the practices in which
we participate in our lives in general (e.g., all the practices in the given
culture in which reading plays a role). Two points follow from this view of interpretation
that will provide the basis for my responses to issues raised in the
commentaries. The first point is that research in psychology is irreducibly
interpretive. It cannot be a transparent process of learning what human
behavior is ‘‘really like’’ in a final sense-the kind of understanding an
uninvolved subject might garner from a removed point of view. On this point,
all three commentaries at least appear to agree with me. Stiles directly
asserts his agreement with this idea, and Dawson et al. and also Stam argue
against the notion that research can provide us with a ‘‘view from nowhere.’’
The second point that
follows from what I have said so far concerns what it means to say that
research is interpretive. Most often, calls for an interpretive approach to
research-for example, by proponents of qualitative methods-emphasize the
subjective appreciation of meanings. We see this in the fact that almost all
qualitative studies are based on interviews aimed at learning about
participants’ subjective experiences. But this approach also appears when we go
beyond interview-based research and consider efforts that emphasize the
investigators’ ‘‘views’’ of the phenomenon of interest, for example, themes
they identify in their research.
In contrast to this focus
on how we think about or experience things, my understanding of
‘‘interpretation’’ emphasizes how research irreducibly refers to how we do
things as participants always already engaged in practical activities. As I
discussed in my position paper, my approach centers on the role played by
prereflective understanding, or a familiarity with things that is prior to any
efforts aimed at thematized knowledge. In our everyday example of figuring out
how to teach a class to read, the teacher’s ‘‘investigation’’ takes place
against the background of his or her sense of what counts as progress (e.g.,
reading with some indications of comprehension, unless this is a class in
reading Hebrew aimed largely at preparing students to sound out words in order
to recite prayers in the synagogue). This background is not primarily a matter
of how the teacher thinks about things. One way to put it is that the relevant
background is what comes prior to what the teacher thinks about.1 This point
holds for psychological research as well. The process of inquiry is always
embedded in our ways of life. Research is indexical in the sense that every
aspect of what we do as investigators, including what we take as important
problems to explore and what we learn from our inquiries, always refers beyond
itself to our prior involvement in the world of practical activities. Although
it is not clear to me what Stam meant when he said that both Yanchar and I used
the term ‘‘interpretation’’ in two different ways, for me, the use of the term
that refers to investigators’ prior familiarity with practices-which may be
what Stam (2006) refers to as a ‘‘rather ordinary’’ use of the term-is the
crucial one.
I should note that
although Stiles and I agree on many points, my guiding perspective is quite different
from his experiential correspondence theory of truth. Stiles focused on what
seems to be a subjectivist matching notion: ‘‘A statement is true for you to
the extent that your experience of the statement corresponds to your experience
of the event (object, state of affairs) that it describes.’’ He talked about
good research as inquiry that effectively shares experiences. As I see it,
these ideas depart markedly from a view of research as practical activity,
although Stiles (footnote 2) also said he agreed with this view. For me, the
key criterion of truth is pragmatic (i.e., what works, but taking this in a
broad sense that includes whether something we believe we have learned
contributes-not necessarily in any simple, direct way at all-to our ways of
life) and research, ultimately, is not learning the way things (including my
experience of things) are, but an activity that is part of doing things.
2. If not ‘‘real’’ measures, then what?
Stam argued that my view
of quantitative research is problematic because such research should be based
on ‘‘real’’ measures, that is, assessments that ‘‘refer back to some concrete
feature of the world,’’ whereas what I call measurement amounts to nothing more
than simply ‘‘assigning numbers to things.’’ As I noted at the outset, Dawson
et al. similarly advocated the value of adhering to the classical definition of
measurement, although they expressed much more optimism than Stam about the
possibility of developing such ‘‘strong’’ measures.
I believe that it is not
possible to develop measures that meet the criteria for ‘‘real’’ measures and
that we should not aim to develop such measures. These claims follow directly
from the first point above about interpretation. All research is interpretive,
and this certainly includes the key research process of measurement. While it
may or may not be the case that the classical notion of measurement can and
should apply in some natural sciences, it does not apply in research in the
human sciences. But, we can employ measurement procedures and, therefore, make
use of quantification in our investigations- so long as we understand what we
are doing in a novel way, which could be called a different ‘‘theory of
measurement.’’
Here, the second point
about interpretation comes into play. We can make use of measurement so long as
we recognize that our measures are indexical, that is, interpretive in the
sense that they always refer beyond themselves to our prior familiarity with
practices. Such measures can be of very different kinds, which, very roughly
speaking, mark out a continuum ranging from the very concrete to the obviously
meaning-laden. Measures of decibel levels lie quite far to the ‘‘concrete’’ end
of this continuum, the coding category ‘‘yells’’ moves away from that end, and
global ratings of ‘‘behaves in a hostile manner’’ lie well to the
‘‘meaning-laden’’ end. Note, however, that because all measures are indexical,
all points along this continuum are ultimately both concrete and meaningful.
They are all examples of phenomena of interest that, in varying ways,
concretely specify the phenomena but at the same time reflect the fact that the
concrete specifications are never exhaustive. A measure on the concrete end of
this continuum based on decibel levels might be the dependent variable in an
experimental paradigm within which high-decibel verbalizations are examples of
angry behavior. At the other end of the continuum, global ratings will be based
on a manual that uses concrete examples to define the phenomenon of interest.
Given this ‘‘theory of
measurement,’’ I think that it is misleading to say-as Stam and Dawson et al.
claimed-that I call for ‘‘weak’’ measures rather than ‘‘strong’’ ones. As I see
it, I am offering a different framework that incorporates many measures that
might well be called ‘‘strong’’ measures (those near the ‘‘concrete’’ end of
the continuum), although they do not conform to the classical definition of
measurement. Stam argued that my position would lead to confusion in the field.
He characterized it as calling for an ‘‘arbitrary’’ process of assigning
numbers to events, asked us to ‘‘imagine a world where we each developed our
own measures of length or temperature,’’ and cited the multitude of personality
measures that exist as an example of how things have, in fact, already gotten
out of hand. I do not find these arguments convincing. For one thing, any
concern about diversity of viewpoints surely holds at least as clearly for
qualitative research, which Stam supports. Moreover, while I agree with Stam
that cooperation in the field is desirable, I do not believe my position works
against it. My second point about interpretation is relevant here. I am not
advocating inquiry that is interpretive in the sense that it is based on however
an investigator happens to think about things. As I suggested in my position
paper, practices of inquiry are relative to investigators’ prereflective
understanding, but they are not arbitrary. Interpretive inquiry does not lead
to a problematic free-for-all by any means. Only certain ways of proceeding
will prove to be useful for people who are participants in a shared world of
practical activities (see Sugarman & Martin, 2005). Furthermore,
investigators can make their procedures public and cooperate in using one set
of measures when that seems useful in a given situation, even if they can never
fully explicate the procedures because they always refer beyond themselves to
the background of the shared world. It is true that there is likely to be a
diversity of approaches to any given issue, but this is desirable. Diversity in
measures and other research procedures often is a function of differences in
research goals (see Westerman, 2004, p. 137). Fundamentally, diversity in
approaches is both good and necessary because investigators in psychology
address issues that do not have final, determinate answers.
3. How is interpretive quantitative research helpful?
Even if employing
interpretive quantitative measures does not have the downside of leading to a
confusing free-for-all, we can still ask, along with Stam, whether there is
something to be gained by using numbers in our investigations. As I pointed out
in my position paper, I agree with researchers who embrace positivism about
some of the useful features of quantitative measures and quantitative research
procedures in general (e.g., they enhance our ability to investigate group
differences without being unduly influenced by dramatic instances of a
phenomenon). I want to mark out an additional basis for appreciating what
quantitative methods have to offer.
In my position paper, I
argued that quantitative research procedures can make a special contribution
because they require us to concretely specify our ideas about psychological
phenomena. I endorsed such measurement procedures as relational coding, which
could be called ‘‘soft’’ measurement, but I also discussed how what could be
considered ‘‘strong’’ measures and related quantitative procedures (e.g.,
coding discrete behaviors, conducting experiments) also offer useful ways to
concretely specify phenomena of interest-although I argued for
reconceptualizing these methods as interpretive procedures and recognizing that
they do not exhaustively specify the constructs and processes under
investigation. Now, I want to extend my analysis of the ways such ‘‘apparently
strong’’ measures and procedures can be extremely helpful. To begin with,
‘‘apparently strong’’ procedures can be highly informative about particular
situations that are of interest in connection with particular applied problems.
For example, consider
Wood’s (e.g., Wood & Middleton, 1975) paradigm for examining how mothers
scaffold their children’s attempts to learn how to build a block puzzle, which
I referred to in my position paper. That paradigm includes a clearly delineated
procedure for identifying the specificity of parental bids at guiding a child.
Although the goal is to explore a relational process (i.e., do mothers home in
and out contingently as a function of the child’s moment-to-moment success),
the specificity measure does not rely on relational coding. Instead, each bid
is coded based on its own properties. Investigating parent-child interaction in
this specific situation has been shown to have applied utility. In a study I
conducted (Westerman, 1990), assessments of maternal behavior in the context of
Wood’s paradigm discriminated between mother-preschooler dyads with and without
compliance problems. In an experimental study, Strand (2002) found that
teaching mothers to home in and out when they show their children how to build
Wood’s puzzle leads to greater child compliance in a separate context.
‘‘Apparently strong’’
quantitative methods also can lead to the discovery that specific, concrete
forms play a role in many situations, not just the original measurement
context. For example, Strand (2002) found that the specificity scale was useful
when applied to a task other than Wood’s block puzzle. Similarly, we might find
that measures which were initially employed in particular structured observation
contexts, say a measure of verbal aggression based on decibel levels or, more
likely, a measure of activation in a certain part of the brain, identify
specific concrete forms that play a particular role quite generally.
Merleau-Ponty (1962) used the term ‘‘sediment’’ to refer to concrete forms of
this sort. Sediment often plays a part in psychological phenomena, and
‘‘apparently strong’’ quantitative procedures can be very helpful because they
enable us to learn about these aspects of practical activity.
Two qualifications are
in order, however. First, even when one aspect of a phenomenon of interest
typically takes a specific concrete form, we need to recognize that it is part
of a larger, meaningful process. For example, even if Wood’s specificity scale
worked in all contexts-which is extremely unlikely-it would be crucial to
appreciate the role that the specificity of maternal directives plays as part
of doing something, that is, teaching a child. It is not specificity per se,
but the modulation of maternal efforts as a function of the child’s success at
what he or she is doing that is crucial. The second qualification is that there
are always limits to the ways in which specific concrete contents function in a
particular manner. It is useful to discover that a certain area of the brain
typically functions in a particular way as part of what a person is doing, but
another area might play this role under particular circumstances, perhaps due
to brain plasticity. ‘‘Apparently strong’’ quantitative studies can be helpful
here, too, because they are useful for marking out the relevant limits.
Such research has other
benefits that can be considered the flipside of the advantages I have mentioned
so far. Studies employing ‘‘apparently strong’’ quantitative procedures can
help us understand psychological phenomena in terms of richly generative
principles, because quantitative measures such as discrete behavior codes
provide concrete examples of meaningful constructs and quantitative procedures
like experiments constitute concrete examples of meaningful processes. For
example, research employing Wood’ paradigm suggests the general principles that
‘‘homing in and out’’ is a crucial feature of parenting and that this process
refers to modulating the specificity of parental bids. ‘‘Apparently strong’’
quantitative methods are well suited for investigating these claims. We might
well find, for example, that Wood’s specificity scale itself is relevant only
in a few contexts, but that some other concrete characterization of modulating
specificity is on the mark quite generally. Alternatively, we might find that,
however understood, modulation of specificity has limited relevance, but that
‘‘homing in and out’’ captures an important process if we define it in concrete
ways that share in common contingently providing more or less help.
‘‘Apparently strong’’ quantitative research is very useful for learning about
general principles because these principles are concretely meaningful; they are
not abstract ideas. Even if we somehow knew beforehand that a given principle
was true (which, of course, is never the case), we would not know what it
actually means because there is no transparent mapping from the principle to
concrete events. ‘‘Apparently strong’’ quantitative research procedures would
help us greatly in this hypothetical situation, and they help us all the more
in real research situations in which we simultaneously must learn the
principles and what they mean concretely.
4. It’s ‘‘good’’ quantitative research and it’s interpretive
Do their examples show
that Stam was off the mark when he argued that, although such research is
highly desirable, it is something that we see rarely if at all in the field? Do
the examples demonstrate that my approach fails to incorporate an important
range of research efforts?
In fact, I believe that
this research offers us excellent examples of ‘‘good’’ quantitative research. I
disagree with Dawson et al.’s characterizations of their own research, however.
As I see it, the research in question is a fascinating example of one of the
situations I described earlier: the case in which initial findings in a
particular domain or a few domains suggest a general principle. In particular,
in this situation, the general principle is the developmental sequence of
hierarchical integration. There is a real risk here (given our philosophical
tradition) of imagining that this sequence is a fully abstract, reified
structure that ‘‘lies behind’’ concrete phenomena and failing to recognize the
ways in which interpretation enters into the research.
The studies by Fischer,
Dawson, and their colleagues employ measures that are extremely useful, but not
‘‘strong’’ in the positivist sense marked out by classical notions of
measurement or Stam’s idea about measures that ‘‘refer back to some concrete
feature of the world.’’ Consider examples from Dawson’s (2006) LecticalTM
Assessment System. In that system, a child’s understanding is said to be at the
level of single representations if the child offers a statement like ‘‘Camping
is fun’’ in an assessment interview. By contrast, the child’s understanding
would be at the higher level of representational mappings if he or she employed
an expression describing a ‘‘linear’’ relationship, such as ‘‘If you don’t do
what your father tells you to do, he will be really mad at you.’’ But
determining the level of such responses is by no means a transparent process.
For one thing, there is no one-to-one relationship between developmental level
and form of speech. A child might say, ‘‘If I go camping, I have fun’’ and
still be at the level of single representations, if the statement really boils
down to ‘‘Camping is fun’’ because the child cannot actually coordinate
relevant single representations in a mapping relationship. Dawson (2006)
herself noted that meaning is ‘‘central’’ to the scoring procedure and gave an
example concerning the interview question, ‘‘Could you have a good life without
having had a good education?’’ In this example, a rater found it difficult to
score a response that included the word ‘‘richer’’ because it was not clear
whether this word referred to having more money or having a life with
broader/deeper significance.
Dawson (2006) claimed
that the ‘‘developmental ruler’’ provides a way to ‘‘look through’’ content in
assessments of structure, but in my view, the brief remarks I have just offered
point to a crucial sense in which hierarchical integration is a concretely
meaningful idea. When we apply the developmental ruler to a new domain, we have
to discover not only whether the developmental sequence holds in that domain
but also what counts as single representations, representational mappings, and
so forth in this context. The ‘‘ruler’’ provides us with valuable ideas about
how to think about complexity, but in itself it is empty. To use it,
investigators have to proceed with the crucial steps of designing an assessment
procedure and preparing scoring manuals for each domain. These steps reflect
the investigators’ rich appreciation of the concretely meaningful practices in
the domain, including what kinds of connections can obtain within this range of
phenomena. This rich appreciation is largely prereflective understanding.
Hence, the procedures and scoring manuals for each domain play a truly central
role that is not ‘‘given’’ by the general principles. Furthermore, they do not
offer exhaustive concrete specifications of the phenomena of interest. Raters
have to draw on their own prior familiarity with the way things work.
Some further comments
are in order concerning the fact that most or all of the studies under
consideration were based on assessing individuals’ developmental level in a
structured interview or by means of some other similar structured procedure.
These assessments provide measures that have considerable precision. Moreover,
they unquestionably tap important skills. Nevertheless, we should recognize
that such investigations differ from other possible studies that would examine
what an individual does when he or she is engaged in ongoing activities. For
example, consider ‘‘leadership,’’ one of the research areas discussed by Dawson
et al. Dawson (2006) described a carefully developed system for assessing a
subject’s level of understanding this concept from responses during an
assessment interview. But instead of proceeding this way, an investigator could
examine what a subject does upon encountering a particular item while going
through his or her ‘‘inbox’’ or when a subordinate asks the subject a specific
question in a naturally occurring situation. Thinking about such in situ
examples makes it clear that we could not possibly map what a person might do
in such situations onto the developmental sequence without drawing on rich
prior familiarity with the relevant practices. Therefore, these examples
underscore the role played by interpretation. Looking at this matter the other
way, the in situ examples help us to see how much actually is involved when we
do use the structured assessment procedures that these investigators have so
successfully developed. A great wealth of interpretive appreciation of the
phenomena is concretized in those measurement procedures.
The in situ examples
also raise a new issue: is the 13-level sequence relevant for some or all
naturally occurring situations, or is its relevance limited to the kind of
skills that can be assessed in the particular ways typically employed in the
research in question, which could be called skills at understanding of a more
reflective sort? I am not asserting that the complexity sequence would not hold
for a broad range of skills involving in situ behavior. I only wish to point
out that the sequence might be limited in these ways. The work is interpretive.
It is based on procedures that provide concrete examples of certain meaningful
phenomena. Therefore, we can ask whether the assessments made in these
investigations actually serve as concrete examples of clearly in situ psychological
phenomena and, more generally, we can ask what is the range of phenomena that
are successfully tapped by the structured assessments. None of this is to argue
against the value of this research. It is possible for raters to draw upon
their prereflective understanding and employ the carefully developed manuals
and the developmental model to assess complexity levels. Furthermore, it is of
great interest that research efforts along these levels have demonstrated that
the developmental sequence holds in many different areas when skills are
assessed using the kinds of procedures that have been employed. In sum, I
believe that the research by Dawson, Fischer, and their colleagues represents
examples of excellent, ‘‘apparently strong’’ quantitative research.
5. Caveats concerning possible pitfalls
In my position paper, I
made several specific suggestions about how researchers should change the ways
they use quantitative methods. For example, I argued for using relational codes
instead of always coding discrete behaviors. It should now be clear that my
comments along those lines were misleading if they suggested that I believe
certain quantitative methods (e.g., discrete behavior codes) are always
problematic-or, if they suggested that I wanted to rule out quite generally
what others might call ‘‘strong’’ quantitative methods. According to my
approach, ‘‘good’’ quantitative research includes many examples of what others
consider ‘‘strong’’ methods in addition to many examples of ‘‘soft’’ methods.
Notwithstanding possible
confusion, at this juncture, I also want to state that this does not amount to
wholesale approval of all quantitative research. I agree with Stam that there
are real dangers in what he calls ‘‘Pythagoreanism.’’ Quantitative methods
frequently are employed in a problematic manner. In my opinion, this occurs
when they are used in such a way that they cannot serve their interpretive
function. For example, measures of decibel levels are likely to fail at
assessing angry behavior if the vocalizations in question do not occur in a
structured situation in which loudness serves as a concrete example of such
behavior (this is circular, and that is the point). In general, quantitative
methods are unhelpful in a particular case insofar as they are actually used in
a way that conforms to traditional positivist conceptualizations about ‘‘real’’
measures and the like. Hence, I hope that my position paper and this rejoinder
serve to mark out a view about how to employ quantitative methods and how not
to employ them, rather than a position about which quantitative methods we
should use.
Some brief comments are
in order about techniques for statistical analysis. In large measure, I agree
with the cautionary remarks Stiles offered in his commentary about ‘‘high end
statistics.’’ How researchers use these techniques very often reflects what I
view as a misguided understanding of quantitative research. As Stiles noted,
these sophisticated techniques are often applied to decontextualized variables.
One of the examples I mentioned in my position paper is relevant here. I argued
that investigators studying parent-child interaction from a social learning
theory vantage point attempt to explain interactions by breaking them down into
isolable, elemental behaviors (e.g., prosocial child behavior, parental praise)
instead of taking as their starting point what parent and child are doing
together. These researchers then try to put together an account of the
exchanges by statistically examining sequential dependencies between these
putative building block behaviors. In my opinion, there is a great deal that
cannot be recovered about the interactions when we proceed in this way, no
matter how sophisticated we may get at looking at dependencies across multiple
lags. At the same time, I also agree with Stiles when he urges us not to throw
out inferential statistics because of its historical association with misguided
notions about methodology. Notwithstanding Stam’s interesting points about
longstanding problems that remain unresolved in the logic of hypothesis
testing, I think these techniques can be useful. But I do wonder whether
something could be gained by reexamining the assumptions of the statistical
procedures we employ and considering whether some other analytic techniques are
called for in light of the approach to quantitative research I have offered.
I would like to
underscore one point from my position paper that is highly relevant when it
comes to pitfalls associated with quantitative research. I believe that theory
plays an extremely important role in whether quantitative researchers proceed
in ways that are truly useful. In particular, I believe that researchers are
likely to conduct ‘‘good’’ quantitative studies if they are guided by theories
that are based on the idea that people are always already involved in practical
activities in the world. In my position paper, I briefly discussed my
participatory approach, which is an attempt to mark out a general framework for
theories of this sort (also see Westerman & Steen, in press). I also gave
the example of scaffolding research and pointed out that investigators in that
area-in contrast to social learning theory researchers-often use relational
codes rather than discrete behavior coding. In this rejoinder, I noted that
when Wood did code discrete behaviors in his investigations of scaffolding
(e.g., Wood & Middleton, 1975), he did so in a way (his specificity scale)
that still made it possible to examine what parent and child were doing (i.e.,
the parent was attempting to teach the child how to build the puzzle) instead
of breaking down what they were doing into isolable behaviors (e.g., prosocial
behaviors, praise). He even examined sequential dependencies in a simple
statistical way to study maternal homing in and out (also see Westerman, 1990),
which suggests that statistical analyses, too, are likely to be useful so long
as a study is based on helpful theory.
6. Concluding remarks
I have attempted to
offer a reconceptualization of quantitative procedures that is much more
focused on how we should employ these procedures than on endorsing some of
these methods over others. This reconceptualization also puts the distinction
between quantitative and qualitative research in a new light. There are
differences between the two kinds of research-for example, quantitative research
directs more attention to concretely specifying phenomena-but the contrast is
less fundamental than most researchers think. From my vantage point, both types
of research are aimed at learning about concretely meaningful practices and
both are pursued by investigators who are themselves participants in the world
of practices.
In their commentary,
Dawson et al. suggested that my view is a transitional one because, while it
attempts to integrate quantitative and qualitative methods, it comes down on the
side of interpretation, privileges qualitative research over quantitative, and
excludes positivist approaches. They claimed that their problem-focused
methodological pluralism represents a fully integrative model because it
includes both positivist and what they call post-positivist approaches. In my
opinion, it is the other way around. I believe that in order to integrate the
two types of research we need to incorporate all useful examples of both types
of work in a new overarching framework that differs from the notions that
typically have served to guide each kind of inquiry in the past. As I see it,
Dawson et al.’s position is a transitional attempt at integration because it
does not go beyond calling for blending the two approaches and their guiding viewpoints.
Remarks Yanchar offered in his position paper about mixed-model approaches very
effectively present the problems with this strategy for integration (also see
Yanchar & Williams, in press). By contrast, I believe that my approach
offers the requisite appropriately inclusive overarching framework, which
itself is derived from a hermeneutic perspective based on practices. In
particular, in this rejoinder, I have tried to show that my approach does not
exclude what others would call ‘‘strong’’ quantitative procedures. In addition,
my approach does not subordinate this type of quantitative research to ‘‘soft’’
quantitative research, nor does it lead to subordinating quantitative research
to qualitative. I believe that all of these research endeavors represent ways
of understanding concretely meaningful phenomena while they differ in the
degree to which they focus on concretely specifying those phenomena versus
characterizing them in meaning-laden terms. All, however, are interpretive.
I will conclude with
some comments on a related issue: what can we say about when to use
quantitative and/or qualitative approaches? All three commentaries include the
idea that choice of methods should depend on the research problem at hand. I
agree with this viewpoint. In fact, I believe it is another example of the
limits of inquiry, a notion that is central to my perspective. General
considerations can only provide what might be called an ‘‘outer envelope’’ for
thinking about how to proceed in any given research situation. This outer
envelope tells us that we need to find some interpretive method for
investigating the phenomenon of interest, that the phenomenon is concretely
meaningful in nature, and that the challenge is to find a method or set of
methods that is appropriate for this particular problem given where the
possible methods fall along a continuum that ranges from the concrete to the
meaning laden-although all points along this continuum have concrete and
meaningful aspects. Beyond this, however, we must decide just how to explore
the particular research problem at hand as investigators who ultimately pursue
our investigations-as Dawson et al. said-in medias res.
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