CHAPTER 10 MIXED METHODS PROCEDURESHow would you write a mixed m

CHAPTER 10 MIXED METHODS PROCEDURES
How would you write a mixed methods procedure section for
your proposal or study? Up until this point, we have considered
collected quantitative data and qualitative data. We have not
discussed “mixing” or combining the two forms of data in a
study. We can start with the assumption that both forms of data
provide different types of information (open-ended data in the
case of qualitative and closed-ended data in the case of
quantitative). If we further assume that each type of data
collection has both limitations and strengths, we can consider
how the strengths can be combined to develop a stronger
understanding of the research problem or questions (and, as
well, overcome the limitations of each). In a sense, more insight
into a problem is to be gained from mixing or integration of the
quantitative and qualitative data. This “mixing” or integrating
of data, it can be argued, provides a stronger understanding of
the problem or question than either by itself. Mixed methods
research, therefore, is simply “mining” the databases more by
integrating them. This idea is at the core of a new methodology
called “mixed methods research.”
Conveying the nature of mixed methods research and its
essential characteristics needs to begin a good mixed methods
procedure. Start with the assumption that mixed methods is a
methodology in research and that the readers need to be
educated as to the basic intent and definition of the design, the
reasons for choosing the procedure, and the value it will lend to
a study. Then, decide on a mixed methods design to use. There
are several from which to choose; consider the different
possibilities and decide which one is best for your proposed
study. With this choice in hand, discuss the data collection, the
data analysis, and the data interpretation, discussion, and
validation procedures within the context of the design. Finally,
end with a discussion of potential ethical issues that need to be

anticipated in the study, and suggest an outline for writing the
final study. These are all standard methods procedures, and they
are framed in this chapter as they apply to mixed methods
research. Table 10.1 shows a checklist of the mixed methods
procedures addressed in this chapter.
COMPONENTS OF MIXED METHODS PROCEDURES
Mixed methods research has evolved into a set of procedures
that proposal developers and study designers can use in
planning a mixed methods study. In 2003, the Handbook of
Mixed Methods in the Social and Behavior Sciences
(Tashakkori & Teddlie, 2003) was published (and later added to
in a second edition, see Tashakkori & Teddlie, 2010), providing
a comprehensive overview of this approach. Now several
journals emphasize mixed methods research, such as the Journal
of Mixed Methods Research, Quality and Quantity, Field
Methods, and the International Journal of Multiple Research
Approaches. Additional journals actively encourage this form of
inquiry (e.g., International Journal of Social Research
Methodology, Qualitative Health Research, Annals of Family
Medicine). Numerous published research studies have
incorporated mixed methods research in the social and human
sciences in diverse fields such as occupational therapy (Lysack
& Krefting, 1994), interpersonal communication (Boneva,
Kraut, & Frohlich, 2001), AIDS prevention (Janz et al., 1996),
dementia caregiving (Weitzman & Levkoff, 2000), occupational
health (Ames, Duke, Moore, & Cunradi, 2009), mental health
(Rogers, Day, Randall, & Bentall, 2003), and in middle school
science (Houtz, 1995). New books arrive each year solely
devoted to mixed methods research (Bryman, 2006; Creswell,
2015; Creswell & Plano Clark, 2018; Greene, 2007; Morse &
Niehaus, 2009; Plano Clark & Creswell, 2008; Tashakkori &
Teddlie, 1998, 2010; Teddlie & Tashakkori, 2009).
Table 10.1 A Checklist of Questions for Designing a Mixed
Methods Procedure

___________
Is a basic definition of mixed methods research provided?
___________
Are the reasons (or justification) given for using both
quantitative and qualitative data in your study?
___________
Does the reader have a sense for the potential use of mixed
methods research?
___________
Are the criteria identified for choosing a mixed methods design?
___________
Is the mixed methods design identified?
___________
Is a visual model (a diagram) presented that illustrates the
research strategy?
___________
Are procedures of data collection and analysis mentioned as
they relate to the chosen design?
___________
Are the sampling strategies for both quantitative and qualitative

data collection mentioned for the design?
___________
Are specific data analysis procedures indicated for the design?
___________
Are the procedures for validation mentioned for the design and
for the quantitative and qualitative research?
___________
Is the narrative structure of the final study or dissertation or
thesis mentioned, and does it relate to the type of mixed
methods design being used?
Describe Mixed Methods Research
Because mixed methods research is still somewhat unknown in
the social and human sciences as a distinct research approach, it
is useful to convey a basic definition and description of the
approach in a method section of a proposal. This might include
the following:
A definition. Begin by defining mixed methods. Recall the
definition provided in Chapter 1. Elements in this definition can
now be enumerated so that a reader has a complete set of core
characteristics that describe mixed methods (see a more
expanded view of defining mixed methods research in Johnson,
Onwuegbuzie, & Turner, 2007):
It involves the collection of both qualitative (open-ended) and
quantitative (closed-ended) data in response to research
questions or hypotheses.
It includes the rigorous methods (i.e., data collection, data
analysis, and interpretation) of both quantitative and qualitative
data.

The two forms of data are integrated in the design analysis
through merging the data, explaining the data, building from
one database to another, or embedding the data within a larger
framework.
These procedures are incorporated into a distinct mixed methods
design that indicates the procedures to be used in a study.
These procedures are often informed by a philosophy (or
worldview) and a theory (see Chapter 3).
Terminology. Explain that many different terms are used for
this approach, such as integrating, synthesis, quantitative and
qualitative methods, multimethod, mixed research, or mixed
methodology but that recent writings, such as the SAGE
Handbook of Mixed Methods in the Social & Behavioral
Sciences and SAGE’s Journal of Mixed Methods Research, tend
to use the term mixed methods (Bryman, 2006; Creswell, 2015;
Tashakkori & Teddlie, 2010).
Background of methodology. Educate the reader about the
background of mixed methods by reviewing briefly the history
of this approach to research. It can be seen as a methodology
originating around the late 1980s and early 1990s in its current
form based on work from individuals in diverse fields such as
evaluation, education, management, sociology, and health
sciences. It has gone through several periods of development
and growth, and it continues to evolve, especially in procedures.
Several texts outline these developmental phases (e.g., Creswell
& Plano Clark, 2011, 2018; Teddlie & Tashakkori, 2009). This
section could also include a brief discussion about the
importance or rise of mixed methods today through indicators
such as federal funding initiatives, dissertations, and the
discipline-specific discussions about mixed methods found in
journals across the social and health sciences (see Creswell,
2010, 2011, 2015).
Reasons for choosing mixed methods research. Follow this
section with statements about the value and rationale for the
choice of mixed methods as an approach for your project. At a
general level, mixed methods is chosen because of its strength

of drawing on both qualitative and quantitative research and
minimizing the limitations of both approaches. At a practical
level, mixed methods provides a sophisticated, complex
approach to research that appeals to those on the forefront of
new research procedures. It also can be an ideal approach if the
researcher has access to both quantitative and qualitative data.
At a procedural level, it is a useful strategy to have a more
complete understanding of research problems and questions,
such as the following:
Comparing different perspectives drawn from quantitative and
qualitative data
Explaining quantitative results with a qualitative follow -up data
collection and analysis
Developing better contextualized measurement instrume nts by
first collecting and analyzing qualitative data and then
administrating the instruments to a sample
Augmenting experiments or trials by incorporating the
perspectives of individuals
Developing cases (i.e., organizations, units, or programs) or
documenting diverse cases for comparisons
Developing a more complete understanding of changes needed
for a marginalized group through the combination of qualitative
and quantitative data
Evaluating both the processes and the outcomes of a program,
an experimental intervention, or a policy decision
Indicate the type of mixed methods design that will be used in
the study and the rationale for choosing it. A detailed
discussion of the primary strategies available will be discussed
shortly. Include a figure or diagram of these procedures.
Challenges to design. Note the challenges this form of research
poses for the inquirer. These include the need for extensive data
collection, the time-intensive nature of analyzing both
qualitative and quantitative data, and the requirement for the
researcher to be familiar with both quantitative and qualitative
forms of research. The complexity of the design also calls for
clear, visual models to understand the details and the flow of

research activities in this design.
TYPES OF MIXED METHODS DESIGNS
There have been several typologies for classifying and
identifying types of mixed methods strategies that proposal
developers might use in their proposed mixed methods study.
Creswell and Plano Clark (2018) identified several
classification systems drawn from the fields of evaluation,
nursing, public health, education policy and research, and social
and behavioral research. In these classifications, authors used
diverse terms for their types of designs, and a substantial
amount of overlap of types existed in the typologies. For
purposes of clarifying the design discussion in the mixed
methods field, we will identify three core mixed methods
designs (as shown in Figures 10.1 and 10.2)—the convergent
design, the explanatory sequential design, and the exploratory
sequential design—and then briefly mention more complex
designs (i.e., the mixed methods experimental design, the mixed
methods case study design, the mixed methods participatory–
social justice design, and the mixed methods evaluation design)
in which the core designs can be embedded. Each approach will
be discussed in terms of a description of the design, the forms
of data collection and data analysis and integration,
interpretation, and validity challenges.
Convergent Mixed Methods Design
Description of the design. The convergent mixed methods
design is probably the most familiar of the core and complex
mixed methods approaches. Researchers new to mixed methods
typically first think of this approach because they feel that
mixed methods only consists of combining the quantitative and
qualitative data. In this single-phase approach, a researcher
collects both quantitative and qualitative data, analyzes them
separately, and then compares the results to see if the findings
confirm or disconfirm each other (see Figure 10.1). The key
assumption of this approach is that both qualitative and

quantitative data provide different types of information—often
detailed views of participants qualitatively and scores on
instruments quantitatively—and together they yield results that
should be the same. It builds off the historic concept of the
multimethod, multitrait idea from Campbell and Fiske (1959),
who felt that a psychological trait could best be understood by
gathering different forms of data. Although the Campbell and
Fiske conceptualization included only quantitative data, the
mixed methods researchers extended the idea to include the
collection of both quantitative and qualitative data.
Figure 10.1 Three Core Mixed Methods Designs
Data collection. The qualitative data can assume any of the
forms discussed in Chapter 9, such as interviews, observations,
documents, and records. The quantitative data can be instrument
data, observational checklists, or numeric records, such as
census data, as discussed in Chapter 8. Ideally, the key idea
with this design is to collect both forms of data using the same
or parallel variables, constructs, or concepts. In other words, if
the concept of self-esteem is being measured during quantitative
data collection, the same concept is asked during the qualitative
data collection process, such as in an open-ended interview.
Some researchers will use this design to associate certain
themes with statistical data using different forms of data for the
quantitative and qualitative data collection. For instance, Shaw
et al. (2013) compared quality improvement practices in family
medicine clinics with colorectal cancer screening rates. Another
data collection issue is the sample size for both the qualitative
and quantitative data collection process. Unquestionably, the
data for the qualitative data collection will be smaller than that
for the quantitative data collection. This is because the intent of
data collection for qualitative data is to locate and obtain
information from a small sample but to gather extensive
information from this sample; whereas, in quantitative research,
a large N is needed in order to infer meaningful statistical

results from samples to a population.
How is this inequality resolved in a convergent mixed methods
design? Sometimes mixed methods researchers will collect
information from the same number of individuals on both the
qualitative and quantitative databases. This means that the
qualitative sample will be increased, and it will limit the
amount of data collected from any one individual. Another
approach would be to weight the qualitative cases so that they
equal the N in the quantitative database. One other approach
taken by some mixed methods researchers is not to consider the
unequal sample sizes a problem. They would argue that the
intent of qualitative and quantitative research differs (one to
gain an in-depth perspective and the other, to generalize to a
population) and that each provides an adequate count. Another
issue in sampling is whether the individuals for the sample of
qualitative participants should also be individuals in the
quantitative sample. Typically, mixed methods researchers
would include the sample of qualitative participants in the
larger quantitative sample, because ultimately researchers make
a comparison between the two databases and the more they are
similar, the better the comparison.
Data analysis and integration. Data analysis in a convergent
design consists of three phases. First, analyze the qualitative
database by coding the data and collapsing the codes into broad
themes. Second, analyze the quantitative database in terms of
statistical results. Third comes the mixed methods data analysis.
This is the analysis that consists of integrating the two
databases.
This integration consists of merging the results from both the
qualitative and the quantitative findings. One challenge in this
design is how to actually merge the two databases since
bringing together a numeric quantitative database with a text

qualitative database is not intuitive. There are several ways to
merge the two databases:
The first approach is called a side-by-side comparison. These
comparisons can be seen in the discussion sections of mixed
methods studies. The researcher will first report the quantitative
statistical results and then discuss the qualitative findings (e.g.,
themes) that either confirm or disconfirm the statistical results.
Alternatively, the researcher might start with the qualitative
findings and then compare them to the quantitative results.
Mixed methods writers call this a side-by-side approach because
the researcher makes the comparison within a discussion,
presenting first one set of findings and then the other. A good
example of this can be seen in the Classen and colleagues’
(2007) study.
Researchers can also merge the two databases by changing or
transforming qualitative codes or themes into quantitative
variables and then combining the two quantitative databases—a
procedure in mixed methods research called data
transformation. The researcher takes the qualitative themes or
codes and counts them (and possibly groups them) to form
quantitative measures. Some useful procedures that mixed
methods researchers have used can be found in Onwuegbuzie
and Leech (2006). This approach is popular among researchers
trained in quantitative research who may not value or see the
worth of an independent qualitative interpretive database.
A final procedure involves merging the two forms of data in a
table or a graph. This is called a joint display of data, and it can
take many different forms. It might be a table that arrays the
themes on the horizontal axis and a categorical variable (e.g.,
different types of providers such as nurses, physician assistants,
and doctors) on the vertical axis. It might be a table with key
questions or concepts on the vertical axis and then two columns
on the horizontal axis indicating qualitative responses and
quantitative responses to the key questions or concepts (Li,

Marquart, & Zercher, 2000). The basic idea is for the researcher
to jointly display both forms of data—effectively merging
them—in a single visual and then make an interpretation of the
display (see Guetterman, Fetters, & Creswell, 2015).
Interpretation. The interpretation in the convergent approach is
typically written into a discussion section of the study. Whereas
the results section reports on the findings from the analysis of
both the quantitative and qualitative databases, the discussion
section includes a discussion comparing the results from the two
databases and notes whether there is convergence or divergence
between the two sources of information. Typically the
comparison does not yield a clean convergent or divergent
situation, and differences exist on a few concepts, themes, or
scales. When divergence occurs, steps for follow-up need to be
taken. The researcher can state divergence as a limitation in the
study without further follow-up. This approach represents a
weak solution. Alternatively, mixed methods researchers can
return to the analyses and further explore the databases, collect
additional information to resolve the differences, or discuss the
results from one of the databases as possibly limited (e.g., the
constructs were not valid quantitatively or the qualitative
themes did not match the open-ended questions). Whatever
approach the researcher takes, the key point in a convergent
design is to further discuss and probe results when divergent
findings exist.
Validity. Validity using the convergent approach should be
based on establishing both quantitative validity (e.g., construct)
and qualitative validity (e.g., triangulation) for each database.
Is there a special form of mixed methods validity that needs to
be addressed? There are certainly some potential threats to
validity in using the convergent approach, and several of these
have already been mentioned. Unequal sample sizes may
provide less of a picture on the qualitative side than the larger
N on the quantitative side. Generally we find the use of unequal
sample sizes in a convergent design study, with the researcher
acknowledging the different perspectives on size taken by

quantitative and qualitative researchers. The use of different
concepts or variables on both sides, quantitative and qualitative,
may yield incomparable and difficult-to-merge findings. Our
recommended approach is to use the same concepts for both the
quantitative and qualitative arms of the research study, but we
acknowledge that some researchers use the convergent design to
associate different qualitative and quantitative concepts. A lack
of follow-up on conclusions when the scores and themes diverge
also represents an invalid strategy of inquiry. In this discussion
we have recommended several ways to probe divergence in
more detail and would recommend the use of one or more of
these strategies in a convergent design project.
Explanatory Sequential Mixed Methods Design
Description of the design. The explanatory sequential mixed
methods approach is a design in mixed methods that appeals to
individuals with a strong quantitative background or from fields
relatively new to qualitative approaches. It involves a two-phase
data collection project in which the researcher collects
quantitative data in the first phase, analyzes the results, and
then uses the results to plan (or build on to) the second,
qualitative phase. The quantitative results typically inform the
types of participants to be purposefully selected for the
qualitative phase and the types of questions that will be asked
of the participants. The overall intent of this design is to have
the qualitative data help explain in more detail the initial
quantitative results, thus it is important to tie together or to
connect the quantitative results to the qualitative data
collection. A typical procedure might involve collecting survey
data in the first phase, analyzing the data, and then following up
with qualitative interviews to help explain confusing,
contradictory, or unusual survey responses.
Data collection. The data collection proceeds in two distinct
phases with rigorous quantitative sampling in the first phase and
with purposeful sampling in the second, qualitative phase. One
challenge in this strategy is to plan adequately what quantitative

results to follow up on and what participants to gather
qualitative data from in the second phase. The key idea is that
the qualitative data collection builds directly on the quantitative
results. The quantitative results that then are built on may be
extreme or outlier cases, significant predictors, significant
results relating variables, insignificant results, or even
demographics. For example, when using demographics, the
researcher could find in the initial quantitative phase that
individuals in different socioeconomic levels respond
differently to the dependent variables. Thus, the follow-up
qualitatively may group respondents to the quantitative phase
into different categories and conduct qualitative data collection
with individuals representing each of the categories. Another
challenge is whether the qualitative sample should be
individuals that are in the initial quantitative sample. The
answer to this question should be that they are the same
individuals, because the intent of the design is to follow up the
quantitative results and explore the results in more depth. The
idea of explaining the mechanism—how the variables interact—
in more depth through the qualitative follow-up is a key
strength of this design.
Data analysis and integration. The quantitative and the
qualitative databases are analyzed separately in this approach.
Then the researcher combines the two databases by the form of
integration called connecting the quantitative results to the
qualitative data collection. This is the point of integration in an
explanatory sequential design. Thus, the quantitative results are
then used to plan the qualitative follow-up. One important area
is that the quantitative results cannot only inform the sampling
procedure but it can also point toward the types of qualitative
questions to ask participants in the second phase. These
questions, like all good qualitative research questions, are
general and open-ended. Because analysis proceeds
independently for each phase, this design is useful for student
research and perhaps easier to accomplish (than the convergent
design) because one database explains the other and the data

collection can be spaced out over time.
Interpretation. The mixed methods researcher interprets the
follow up results in a discussion section of the study. This
interpretation follows the form of first reporting the
quantitative, first-phase results and then the qualitative, second
phase results. However, this design then employs a third form
of interpretation: how the qualitative findings help to explain
the quantitative results. A common misstep at this point by
beginning researchers is to merge the two databases. While this
approach may be helpful, the intent of the design is to have the
qualitative data help to provide more depth, more insight into
the quantitative results. Accordingly, in the interpretation
section, after the researcher presents the general quantitative
and then qualitative results, a discussion should follow that
specifies how the qualitative results help to expand or explain
the quantitative results. Because the qualitative database
questions narrows the scope of the quantitative questions, a
direct comparison of the overall results of the two databases is
not recommended.
Validity. As with all mixed methods studies, the researcher
needs to establish the validity of the scores from the
quantitative measures and to discuss the validity of the
qualitative findings. In the explanatory sequential mixed
methods approach, additional validity concerns arise. The
accuracy of the overall findings may be compromised because
the researcher does not consider and weigh all of the options for
following up on the quantitative results. We recommend that
researchers consider all options for identifying results to follow
up on before settling on one approach. Attention may focus only
on personal demographics and overlook important explanations
that need further understanding. The researcher may also
contribute to invalidated results by drawing on different
samples for each phase of the study. If explaining the
quantitative results in more depth, then it makes sense to select
the qualitative sample from individuals who participated in the
quantitative sample. This maximizes the importance of one

phase explaining the other. These are a few of the challenges
that need to be built into the planning process for a good
explanatory sequential mixed methods study.
Exploratory Sequential Mixed Methods Design
Description of the design. If we reverse the explanatory
sequential approach and start with a qualitative phase first
followed by a quantitative phase, we have an exploratory
sequential approach. A three-phase exploratory sequential
mixed methods is a design in which the researcher first begins
by exploring with qualitative data and analysis, then builds a
feature to be tested (e.g., a new survey instrument, experimental
procedures, a website, or new variables) and tests this feature in
a quantitative third phase. Like the explanatory sequential
approach, the second feature builds on the results of the initial
database. The intent of this design is to explore with a sample
first so that a later quantitative phase can be tailored to meet the
needs of the individuals being studied. Sometimes this
quantitative feature will include developing a contextually
sensitive measurement instrument and then testing it with a
sample. Other times it may involve developing new variables
not available in the literature or attuned to a specific population
being studied, or designing a website or an Internet application
shaped to the needs of the individuals being studied. This
design is popular in global health research when, for example,
investigators need to understand a community or population
before administering English-language instruments.
In this design, the researcher would first collect focus group
data, analyze the results, develop an instrument (or other
quantitative feature such as a website for testing), and then
administer it to a sample of a population. In this case, there may
not be adequate instruments to measure the concepts with the
sample the investigator wishes to study. In effect, the researcher
employs a three-phase procedure with the first phase as
exploratory, the second as instrument (or quantitative feature)
development, and the third as administering and testing the
instrument feature to a sample of a population.

Data collection. In this strategy, the data collection would occur
at two points in the design: the initial qualitative data collection
and the test of the quantitative feature in the third phase of the
project. The challenge is how to use the information from the
initial qualitative phase to build or identify the quantitative
feature in the second phase. This is the integration point in an
exploratory sequential design.
Several options exist, and we will use the approach of
developing a culturally sensitive instrument as an illustration.
The qualitative data analysis can be used to develop an
instrument with good psychometric properties (i.e., validity,
reliability). The qualitative data analysis will yield quotes,
codes, and themes (see Chapter 9). The development of an
instrument can proceed by using the quotes to write items for an
instrument, the codes to develop variables that group the items,
and themes that group the codes into scales. This is a useful
procedure for moving from qualitative data analysis to scale
development (the quantitative feature developed in the second
phase). Scale development also needs to follow good procedures
for instrument design, and the steps for this include ideas such
as item discrimination, construct validity, and reliability
estimates (see DeVellis, 2012).
Developing a good psychometric instrument that fits the sample
and population under study is not the only use of this design. A
researcher can analyze the qualitative data to develop new
variables that may not be present in the literature, to identify
the types of scales that might exist in current instruments or to
form categories of information that will be explored further in a
quantitative phase. The question arises if the sample for the
qualitative phase is the same for the quantitative phase. This
cannot be, because the qualitative sample is typically much
smaller than a quantitative sample needed to generalize from a
sample to a population. Sometimes mixed methods researchers
will use entirely different samples for the qualitative (first

phase) and quantitative components (third phase) of the study.
However, a good procedure is to draw both samples from the
same population but make sure that the individuals for both
samples are not the same. To have individuals help develop an
instrument and then to survey them in the quantitative phase
would introduce confounding factors into the study.
Data analysis and integration. In this strategy the researcher
analyzes the two databases separately and uses the findings
from the initial exploratory database to build into a feature that
can be analyzed quantitatively. So integration in this design
involves using the qualitative findings (or results) to inform the
design of a quantitative phase of the research such as the
development of a measurement instrument or new variables.
This means that the researcher needs to pay careful attention to
the qualitative data analysis steps and determine what findings
to build on. If, for example, the researcher uses grounded theory
(see Chapter 9), the theoretical model generated may provide a
model to be tested in the second, quantitative phase. A
qualitative case study can yield different cases that become the
focus of important variables in the second quantitative phase.
Interpretation. Researchers interpret the mixed methods results
in a discussion section of a study. The order of interpretation is
to first report the qualitative findings, the development or
design of the feature to be tested (e.g., the development of an
instrument, the development of new quantitative measures), and
then the quantitative test in the final phase of the study. It does
not make sense to compare the two databases, because they are
typically drawn from different samples (as noted above in the
data collection discussion) and the intent of the strategy is to
determine if the qualitative themes in the first phase can be
generalized to a larger sample.
Validity. Researchers using this strategy need to check for the
validity of the qualitative data as well as the validity of the
quantitative scores. Special validity concerns arise, however, in
using this design that need to be anticipated by the proposal or

mixed methods report developer. One concern is that the
researcher may not use appropriate steps to develop a good
psychometric instrument. Developing a good instrument is not
easy, and adequate steps need to be put in place. Another
concern is that a researcher may develop an instrument or
measures that do not take advantage of the richness of the
qualitative findings. This occurs when the qualitative data lacks
rigor or occurs simply at the theme level without the further
data analysis steps associated with using one of the qualitative
design-types, such as ethnography, grounded theory, or case
study procedures. Finally, as previously mentioned, the sample
in the qualitative phase should not be included in the
quantitative phase as this will introduce undue duplication of
responses. It is best to have the sample of qualitative
participants provide information for scale, instrument, or
variable (or website) design, but the same individuals should
not complete the follow-up instruments. This sample strategy,
therefore, differs from the sampling strategy needed for an
explanatory sequential design.
Several Complex Mixed Methods Designs
After working with these three core designs—convergent,
explanatory sequential, and exploratory sequential—that are the
foundation of good mixed methods research, we have now
branched out to incorporate more designs that typically fi t
complex projects. By complex we mean that the designs involve
more steps and procedures than are embodied in the three core
designs. These mixed methods designs are not more “advanced.”
They simply involve more steps and incorporate the core
designs into “processes” of research. We have come to this
position based on key readings in the mixed methods literature
that have surfaced in the past few years. The first step involved
isolating and thinking about the types of more complex features
that the core designs could be embedded within.
A useful typology emerged in the work of Plano Clark and

Ivankova (2016). Their book was helpful in conceptualizing the
many types of applications of complex designs. In an entire
chapter they discussed the intersection of mixed methods with
other approaches to form “advanced applications” (p. 136).
They recommended a framework for considering the
possibilities of these complex applications:
Intersecting a secondary method (mixed methods) within a
primary quantitative or qualitative research design. A research
design is a set of formal procedures for collecting, analyzing,
and interpreting data such as those found in a quantitative
experiment or qualitative case study. In this framework, a mixed
methods core design could be embedded as a secondary (or
supportive) method within a primary quantitative or qualitative
design. The typical form of this application is to embed
qualitative data collection and analysis within a quantitative
experimental or intervention design.
Intersecting mixed methods within another methodology. A
methodology is a set of procedures that guide the use of design.
These procedures exist in the research at a more practical level
than the design. In this framework, a mixed methods core design
could be added to another methodological approach. For
example, a core design could be added to a case study, an
evaluation approach, action research, social network analysis,
longitudinal research, Q methodology, phenomenology, or
grounded theory.
Intersecting mixed methods within a theoretical framework. A
theoretical framework advances an abstract and formalized set
of assumptions to guide the design and conduct of the research.
In this framework, a mixed methods core design could be
intersected with an established theory. This theoretical lens
could be drawn from perspectives such as social justice,
feminism, critical theory, participatory involvement, or other
conceptual frameworks that advance the needs and involvement
of special populations and often call for action or change.
These three types of complex designs deserve additional

attention because many researchers are conducting evaluations,
using theoretical orientations such as gender or social inequality
theories, and conducting experiments or interventions using
mixed methods. In our discussion of mixed methods, we simply
need to account for these complex applications and assess how
core designs might be embedded within them.
Another step forward in designs appeared in Nastasi and
Hitchcock (2016). Their book brought forth several ideas that
we now incorporate into our complex designs. They suggested
that distinct “processes” occur in research in which both
quantitative and qualitative data might be used in distinct steps
in the overall process. Their book focused on two ideas: the use
of mixed methods in program evaluation and its use in
experimental, intervention trials. It also relied heavily on the
authors’ mixed methods study in Sri Lanka that addressed the
mental health of youth, and they advanced the steps in their
evaluation process and embedded into these steps the use of
qualitative and quantitative data in multiple core designs. From
their work we then have some practical examples of
incorporating core designs into the complex procedures of an
evaluation and an experimental, intervention trial.
Specifically, we see embedding the core designs into larger
processes. As in Creswell and Plano Clark (2018), here we
briefly discuss four examples of complex designs and then
discuss a general model for embedding the core designs in these
processes:
Mixed methods experimental (intervention) design. The mixed
methods experimental (or intervention) design involves the
researcher collecting and analyzing both quantitative and
qualitative data and integrating the information within an
experiment or intervention trial (see Figure 10.2). This design
adds qualitative data collection into an experiment or
intervention so that the personal experiences of participants can

be included in the research. Thus the qualitative data become a
secondary source of data embedded in the experimental pre- and
posttest data collection. It requires the researcher to understand
experiments and to be able to design them in a rigorous way
(e.g., a randomized controlled trial). As shown in Figure 10.2,
researchers add the qualitative data to the experiment in
different ways: before the experiment begins, during the
experiment, or after the experiment (Sandelowsi, 1996). The
basic ideas are to embed the core exploratory sequential design
into the experiment to carry out exploration before conducting
the experiment; to embed a convergent core design during the
experiment to assess participants’ experiences with the
intervention; or to add an explanatory sequential design into the
experiment after the study to follow up on the experimental
outcomes. The points at which the qualitative data collection
and findings connect to the experiment represent the integration
in the mixed methods study. In this design it is important to be
explicit about the reasons for adding the qualitative data. We
enumerated several important reasons in Figure 10.2. These lists
are representative of the examples of mixed methods research
we have found in the literature. The qualitative data collection
can occur at a single point in time or at multiple points in time
depending on the resources available to the researcher. This
type of mixed methods use has become popular in the health
sciences.
Figure 10.2 Mixed Methods Intervention Design
Source: Adapted from Sandelowski (1996).
Case study design. The mixed methods case study design
involves the use of one or more core designs (i.e., convergent,
explanatory sequential, exploratory sequential) within the
framework of a single or multiple case study design. The intent
of this design is to develop or generate cases based on both
quantitative and qualitative results and their integration. We

have found two basic variants of this design. One is a deductive
approach where researchers establish the cases at the outset of
the study and document the differences in the cases through the
qualitative and quantitative data. A second is more of an
inductive approach where the researcher collects and analyzes
both quantitative and qualitative data and then forms cases—
often multiple cases—and then makes comparisons among the
cases. Regardless of the approach, the challenge is to identify
the cases before the study begins or to generate cases based on
the evidence collected. Another challenge is to understand case
study research (Stake, 1995; Yin, 2014) and effectively
intersect case study design with mixed methods. The type of
core design embedded within this approach can vary, but we can
find good illustrations of the design using a convergent design
(Shaw, Ohman-Strickland, & Piasecki, 2013). Within this
framework, the typical mixed methods case study design is one
where both types of data are gathered concurrently in a
convergent core design and the results are merged together to
examine a case and/or compare multiple cases. This type of
mixed methods case study design is shown in Figure 10.3. In
this hypothetical example, the researcher gathers both survey
quantitative data and qualitative interview data at roughly the
same time. Analysis of both databases produces results that can
be merged to identify specific cases. These cases portray
different profiles found in the databases, and they can be
compared in a cross-case comparison.
Participatory-social justice design. The mixed methods
participatory-social justice design is a mixed methods design in
which the researcher adds a core design within a larger
participatory and/or social justice theoretical or conceptual
framework (see Figure 10.4). The intent of this design is to give
voice to participants and collaborate with them in shaping the
research and to build evidence from both quantitative and
qualitative data. As a complex design, these frameworks span
the entire mixed methods study. The framework can be, for
example, a feminist theory or a racial theory. It might also be a

participatory theory of the involvement of stakeholders in many
aspects of the mixed methods study (Ivankova, 2015), although
it could be debated as to whether participatory action research
exists in a study as a conceptual framework or as
methodological procedures. This aside, in addition to seeing the
strong placement of this theory in the study, we can also
identify one or more of the core designs operating. Within a
feminist mixed methods study, for example, we can see both the
flow of the theory into many aspects of the project (e.g.,
informing the problem, shaping the research questions,
highlighting the outcomes) as well as an embedded core design
such as an explanatory sequential design where an initial survey
is followed by one-on-one interviews. In Figure 10.4, we see
this type of core design embedded within a participatory-social
justice framework. This is a study discussing the transition of
homeless individuals from a hospital to a shelter (Greysen,
2012). The element that makes this study participatory research
is the substantial involvement of community personnel in many
aspects of the study. What makes the project mixed methods is
the collection and analysis of both quantitative and qualitative
data. As shown in Figure 10.4, we see that multiple core designs
were embedded within the study. An exploratory sequential core
design connected identifying the research priorities and
developing a survey. Then, the data collection and analysis
portrayed a convergent design with the combination of themes
and statistical results.
Figure 10.3 Mixed Methods Case Study Design
Figure 10.4 Mixed Methods Participatory-Social Justice Design
Source: Adapted from Greysen et al. (2012); reported in
Creswell & Plano Clark (2018).
Evaluation design. The mixed methods evaluation design

consists of one or more core designs added to the steps in an
evaluation procedure typically focused on evaluating the
success of an intervention, a program, or a policy (see Figure
10.5). The intent of this design is to engage in a process of
research in which both quantitative and qualitative data and
their integration shapes one or more of the steps in the process.
This complex design illustrates a core design within another
methodology. This approach is typically used in program
evaluation where quantitative and qualitative approaches are
used over time to support the development, adaptation, and
evaluation of programs, experiments, or policies. We often find
multiple core designs occurring throughout these projects. For
example, researchers might start by conducting a qualitative
needs assessment study to understand the meaning of smoking
and health from the perspective of adolescents in this
community. Using these results, the researchers might develop
an instrument and quantitatively assess the prevalence of
different attitudes across the community. In a third phase, the
researchers might develop a program based on what they have
learned and then examine both the process and the outcomes of
this intervention program. Across these phases the researchers
would make use of exploratory (phase 1 to phase 2),
explanatory (phase 2 to phase 3), and convergent (phase 3) core
designs.
Examine Figure 10.5. This mixed methods evaluation design
was used in a study of the mental health of youth in Sri Lanka
(Nastasi & Hitchcock, 2016). In the outer circle we see the
general steps in the evaluation process. Within boxes in the
circle we also find the combination of quantitative and
qualitative research. In short, looking across these boxes within
the circle, we see that the authors have incorporated multiple
core designs at different stages in the evaluation process. The
figure also shows within the boxes the dates on which the data
were collected.
A PROCEDURE FOR EMBEDDING CORE DESIGNS INTO

COMPLEX DESIGNS
In the mixed methods evaluation design example in Figure 10.5,
we see that core designs can be embedded within a process of
evaluation. This provides important clues as to how to embed
the core designs within complex procedures such as other
designs, theories, or methodologies. It also speaks to how to
draw a diagram of the mixed methods procedures. In our
thinking, we embed the core designs into more complex
procedures using these steps:
Figure 10.5 Mixed Methods Evaluation Design
Source: Nastasi & Hitchcock (2016). Used with permission from
SAGE Publishing.
Identify the quantitative and qualitative data collection in your
study. Refer to whether the data source is closed-ended
(quantitative) or open-ended (qualitative).
Draw a diagram of the steps in the procedure. These steps
(represented by boxes) may be the phases in an experimental
design, the generation of cases, or the phases of an evaluation.
Look into the steps (boxes) and ask yourself at what steps in the
procedure you have an opportunity to collect both quantitative
and qualitative data. Such data collection, you will recall from
Chapter 1, represents a core defining characteristic of mixed
methods research.
In those boxes where you are collecting both forms of data, ask
yourself further how the databases are being connected. Are
they being merged (as in a convergent mixed methods design) or
connected (as in an explanatory sequential mixed methods
design or an exploratory sequential mixed methods design).
Discuss the procedures of using the core mixed methods
designs, paying attention to how the data are being integrated in
each step.

As is evident in our discussion, we believe in drawing diagrams
of procedures, whether of core designs or more complex
designs. Besides thinking about how to draw these diagrams,
you might consider some of the notations that have emerged in
the field of mixed methods research. Mixed methods notation
provides shorthand labels and symbols that convey important
aspects of mixed methods research, and they provide a way that
mixed methods researchers can easily communicate their
procedures (see Table 10.2). Morse (1991) first developed the
notation, and it has been added to by writers such as Tashakkori
and Teddlie (1998) and Plano Clark (2005) who suggest the
following:
QUAL and QUAN capitalization indicates an emphasis or
priority on the quantitative or qualitative data, analysis, and
interpretation in the study. In a mixed methods study, the
qualitative and quantitative data may be equally emphasized, or
one may be more emphasized than the other. Capitalization
indicates that an approach or method is emphasized. Lowercase
indicates lesser priority or emphasis on the method.
Quan and Qual stand for quantitative and qualitative,
respectively, and they use the same number of letters to indicate
equality between the forms of data.
A plus sign—+—indicates a convergent or merging integration
of data collection—with both quantitative and qualitative data
collected at same time.
An arrow—→—indicates a sequential form of data collection;
one form (e.g., qualitative data) builds or connects with the
other (e.g., quantitative data).
Parentheses—( )—indicate that one form of data collection is
embedded within another or embedded within a larger design.
Double arrows—→←—mean that the flow of activities can go
both ways.
Also in the figures we see boxes that highlight important major
components of the design—such as data collection or data
analysis.

Table 10.2 Notation Used in Mixed Methods Research
Notation
What It Indicates
Example
Citation Establishing Notation
Uppercase letters
Greater emphasis given to a method
QUAN, QUAL
Morse (1991)
Lowercase letters
Lesser emphasis given to a method
quan, qual
Morse (1991)
+
Convergent methods
QUAN + QUAL
Morse (1991)
→

Sequential methods
QUAL→quan
Morse (1991)
( )
Embed within a design or framework
QUAN(qual)
Plano Clark (2005)
→#xF0AC;
Recursive
QUAL→←QUAN
Nastasi et al. (2007)
[ ]
Study within a series
QUAL → [QUAN + qual]
Morse & Niehaus (2009)
FACTORS IMPORTANT IN CHOOSING A MIXED METHODS
DESIGN
The choice of a particular mixed methods design is based on
several factors that relate to the intent of the procedures as well
as practical considerations. We will begin with the procedural

reasons for choosing a particular mixed methods strategy. It
should be recognized that many variations exist in mixed
methods designs, and the particular approach an investigator has
in mind may not conform exactly to the approaches specified
here. However, these designs represent the common underlying
features of many designs, and, with modification, researchers
can find their own strategy. To choose a design for your project,
consider the following factors:
Choice based on outcomes expected or intent. Earlier in this
chapter, we reviewed the reasons for choosing mixed methods
research. In Table 10.3, we repeat the reasons but this time link
them to expected outcomes of a mixed methods project and the
type of mixed methods strategy. This thinking calls for the
researcher to determine the outcome anticipated at the end of
the mixed methods study and then to link it to the types. These
outcomes are, in turn, shaped by the intention behind including
and integrating both quantitative and qualitative data.
Choice based on integrating the data together. To choose a
mixed methods strategy beyond considering the outcome
anticipated, the researcher needs to consider whether mixed
methods integration of the two databases will be merged
(convergent mixed methods design), explaining (explanator y
sequential design), building (exploratory sequential design), or
embedded (the complex designs). Merging the data involves
combining the quantitative and qualitative data through the
procedures of a side-by-side comparison, data transformation,
or a joint display. Connecting the data means that the analysis
of one data set is used to lead into or build into the second data
set. In short, the data analysis of one data set informs the data
collection of the other data set. In embedding, one data set—
involving quantitative, qualitative, or combined data—is
embedded within a larger design, theory, or methodology.
Table 10.3 Choosing a Mixed Methods Project, Expected
Outcomes, Type of Design

Reasons for Choosing Mixed Methods
Expected Outcomes
Recommended Mixed Methods Design
Comparing different perspectives drawn from quantitative and
qualitative data
Merging the two databases to show how the data converge or
diverge
Convergent mixed methods design
Explaining quantitative results with qualitative data
A more in-depth understanding of the quantitative results (often
cultural relevance)
Explanatory sequential mixed methods design
Developing better measurement instruments
A test of better measures for a sample of a population
Exploratory sequential mixed methods design
Understanding experimental results by incorporating
perspectives of individuals
An understanding of participant views within the context of an
experimental intervention
Mixed methods experimental (intervention design)

Comparing one or more case studies
An understanding of the differences and similarities among
several cases
Mixed methods case study design
Developing an understanding of needed changes for a
marginalized group
A call for action
Mixed methods participatory-social justice design
Understanding the need for an impact of a program,
intervention, or policy
A formative and summative evaluation
Mixed methods evaluation design
For example, in a convergent design the two are considered to
be independent and the data collection and analysis for each
database proceed separately. In an embedded experimental
design, the qualitative data may be collected independently of
the experiment and used to support or augment the larger
design, the experiment. Alternatively, the two databases may be
connected, with one building on the other. This is a sequential
type of design (explanatory sequential design or an exploratory
sequential design), and one database does not stand in isolation
of the other database. In these sequential designs, the data
collection in the second phase cannot be conducted until the
first phase results are in. In short, the follow -up data collection
builds directly on the results of the initial data collection.
Choice based on the timing of the data collection. A related

factor is timing in mixed methods data collection—whether the
two databases are collected concurrently, at roughly the same
time, or with one following the other, sequentially. A
convergent strategy typically involves collecting data
concurrently while the explanatory sequential and the
exploratory sequential strategies means that the data will be
collected in sequence. Sometimes this criterion is difficult to
identify in published mixed methods studies, but it should go
into the thinking about choosing a mixed methods strategy. In
complex designs, the timing may vary and be included at
multiple time points in the design.
Choice based on the emphasis placed on each database. Like
timing, the emphasis placed on each database in mixed methods
research is also somewhat difficult to determine and to apply to
the question of choice. A mixed methods study can illustrate an
equal emphasis (or priority or weight) on both databases, or an
unequal emphasis. For example, a mixed methods project can
stress the qualitative phase of the research and give minimal
attention to the quantitative phase. How can we tell? We can
look at the number of pages in a study to determine emphasis,
how the study begins (e.g., with a strong quantitative theory
orientation or personal qualitative stories), the amount of depth
and sophistication given to the qualitative and quantitative data
collection and analysis, or even the background training of the
investigator. As mentioned earlier in the section on notation,
capital letters may be used in the notation for greater emphasis
(e.g., QUAN) and lowercase letters for less emphasis (e.g.,
quan). The emphasis can help determine the choice of a mixed
methods strategy. Typically if the researcher seeks to emphasize
both databases, a convergent approach is best. Alternatively, if
a stronger emphasis is sought for the quantitative approach,
then an explanatory sequential strategy is used because it began
with the quantitative component of the study. If a qualitative
approach is to be emphasized, then an exploratory sequential
strategy is chosen. These are not firm guidelines, but they may

play into the overall decision about a choice of strategy.
Choice based on type of design most suited for a field. On a
practical level, the choice of a strategy depends on the
inclination of fields toward certain mixed methods designs. For
quantitatively oriented fields, the explanatory sequential
approach seems to work well because the study begins (and
perhaps is driven) by the quantitative phase of the research. In
qualitatively oriented fields, the exploratory sequential
approach may be more appealing because it begins with an
exploration using qualitative research. However, in this
approach, an outcome may be a measurement instrument that is
tested so that the outcome, a quantitative outcome, outweighs in
importance how the study began. In some fields, the choice of
approach may be dependent on collecting data efficiently, and
this would argue for a convergent mixed methods study in
which both quantitative and qualitative data are typically
collected at roughly the same time rather than at different times
that require more visits to the research site.
Choice based on a single researcher or team. A final practical
reason for a choice of a strategy depends on whether a single
researcher (e.g., graduate student) conducts the study or a team
of researchers (e.g., funded long-term investigation). If the
investigator is a single researcher, the sequential strategies of
an explanatory sequential or exploratory sequential approach
are best because the investigation can be divided into two
manageable tasks rather than multiple data collection and
analysis procedures. The study can be projected out over a
period of time rather than collecting multiple forms of data at
the same time as in a convergent approach. When time is a
problem, we encourage students to think about a convergent
design. In this design, both forms of data are gathered at
roughly the same time, and it does not require repeated visits to
the field to gather data. Complex designs are well-suited for a
team of researchers who assist in the multiple phases of the
research and for well-funded projects that unfold over several
years.

We recommend that students find a published mixed methods
journal article that uses their design and introduce it to advisers
and faculty committees so that they have a working model to
understand the design. Since we are at the early stage of
adopting mixed methods research in many fields, a published
example of research in a field will help to create both
legitimacy for mixed methods research and the idea that it is a
feasible approach to research for graduate committees or other
audiences. If a research team is conducting the study, multiple
forms of data collection at the same time or over a long period
of time are possible, such as in an embedded or a multiphase
design. Although a single researcher can conduct a
participatory–social justice study, the labor-intensive nature of
collecting data in the field involving participants as
collaborators typically suggests more of a team approach than
the inquiry by a single investigator.
EXAMPLES OF MIXED METHODS PROCEDURES
Examples 10.1–10.4 illustrate mixed methods studies that use
both the sequential and convergent strategies and procedures.
Example 10.1A Convergent Parallel Mixed Methods Design
Classen et al. (2007) studied older driver safety in order to
develop a health promotion intervention based on modifiable
factors influencing motor vehicle crashes with older drivers
(age 65 and older). It was a good example of a convergent
mixed methods study. The central purpose of the study was
identified in the abstract:
This study provided an explicit socio-ecological view
explaining the interrelation of possible causative factors, an
integrated summary of these causative factors, and empirical
guidelines for developing public health interventions to promote
older driver safety. Using a mixed methods approach, we were
able to compare and integrate main findings from a national

crash dataset with perspectives of stakeholders. (p. 677)
This purpose statement identified the use of both quantitative
(i.e., a national crash data set) and qualitative (i.e.,
stakeholders’ perspectives) data. From one of the research
questions in the study, we learned that the authors compared the
qualitative stakeholder perspectives, needs, and goals for safe
and unsafe driving with the quantitative results of the factors
that influenced driving injuries. So the expected outcome was to
compare the findings. The method section commented on the
quantitative national data set, the statistical analysis of this data
set, and then the qualitative data set and its analysis. Although
not stated explicitly, the data were used together to form
results, not used for one database to build on another, and the
timing was to look at both databases concurrently. A diagram
illustrated the procedures involved in both collecting and
analyzing the information. A results section first reported the
quantitative results and then the qualitative results. More
emphasis was given to the quantitative results, leading to the
conclusion that this study favored the quantitative research.
However, these reports on the results from the two databases
were followed by an analysis of key findings in which the
quantitative and qualitative results were compared for
supportive and non-supportive findings. In this discussion
section, the researchers merged the two databases in a side-by-
side comparison. Looking more broadly at the topic and the
authors, we saw that the quantitative emphasis would probably
be better accepted in the field of occupational therapy than
qualitative research. Also, a scan of the authors’ biographical
sketches showed that this mixed methods study was completed
by a team of researchers drawn from individuals with
quantitative and qualitative expertise.
As suggested by this statement, the expected outcome of the
study was projected to be a detailed picture of resilience and the
personal perspectives of the survivors as learned through

qualitative data. Also, the authors intended to probe the
quantitative findings, to explain them in more detail through the
qualitative data. With this intent, the study set up as a
sequential approach, with the two databases connected and one
building on the other. Also, with this approach, the timing
illustrated the qualitative data collection followed the
quantitative results. It was difficult to discern whether this
study placed greater emphasis on the quantitative or qualitative
component of the project. The project began with a quantitative
longitudinal phase with extensive discussions of the measures
used to gather data. The authors detailed the quantitative
results. However, the qualitative findings illustrated many
themes that emerged from the interviews with the women. These
themes pointed toward new issues that helped to develop the
concept of resilience, such as the turning points in the women’s
lives, the ongoing nature of recovery, and the role of spirituality
in recovery. The study was conducted by a team of researchers
from psychology and criminal justice and supported by the
National Institutes of Health (NIH).
Example 10.2An Explanatory Sequential Mixed Methods Design
In 2007, Banyard and Williams conducted an explanatory
sequential mixed methods study examining how women recover
from childhood sexual abuse. The quantitative component of the
study consisted of structured interviews (with 136 girls in 1990
and a subset of 61 girls in 1997) looking at resilience, correlates
of resilience, over time across 7 years of early adulthood. The
qualitative aspect consisted of interviews with a subset of 21
girls about their life events, coping, recovery, and resilience.
The intent of the mixed methods study was to use the qualitative
interviews to “explore and make sense” of the quantitative
findings (p. 277). Here was the purpose statement:
Multiple methods are used to examine aspects of resilience and

recovery in the lives of female survivors of child sexual abuse
(CSA) across 7 years of early adulthood. First quantitative
changes in measures of resilience over time were examined. To
what extent did women stay the same, increase, or decrease in
functioning in a variety of sphere across 7 years during early
adulthood? Next, the role of re-traumatization as an impediment
to ongoing resilience and correlates of growth or increased
well-being over time were examined. Finally, because resilient
processes in adulthood have not been the focus of much
research and require further description, qualitative data from a
subset of participants was used to examine survivors’ own
narratives about recovery and healing to learn about key aspects
of resilience in women’s own words. (p. 278)
In this mixed methods study, the expected outcome was clearly
to develop good psychometric measures and then to use the
measures as outcomes in an experimental project. It was also to
use the qualitative data to develop hypotheses that might be
tested using the intervention in the experiment. The initial
phase qualitative data collection was connected to the
subsequent quantitative measures and their rigorous testing for
scores on validity and reliability. The entire project was timed
for the quantitative phase to follow the qualitative phase, and
the quantitative phase could be stated as the development of the
measures (and survey) and the experimental intervention study.
If we were to diagram this project, it would be qual → QUAN
→ QUAN. As this notation shows, the emphasis in the project
favored quantitative research, and the project could be seen as
pointing toward the program intervention test at the end of the
article. Recognizing that the researchers came from public
health, an organization called Partners in Health, and a
children’s hospital, the strong quantitative orientation of the
project makes sense. Overall, this mixed methods study
illustrated both the core exploratory sequential design and the
more advanced embedded experimental design with a sequential
focus. To conduct such a complex project, the study involved a

team of researchers both in the United States and in Rwanda.
Example 10.3An Exploratory Sequential Mixed Methods Design
A good example of an exploratory sequential study with an
experimental test outcome is found in Betancourt et al. (2011).
This study used mixed methods research to adapt and evaluate a
family strengthening intervention in Rwanda. The investigators
sought to examine the mental health problems facing HIV-
affected children in Rwanda. They first began with an
exploratory, qualitative first phase of interviews with children
and their caregivers. From a qualitative thematic analysis of the
data, they then performed an extensive review of the literature
to locate standardized measures that matched their qualitative
findings. They found some measures and added some new ones
to develop a survey instrument. This instrument went through
several refinements following rigorous procedures of
instrument-scale development (e.g., backward and forward
translations, a discussion of items, reliability and validity) to
develop good construct validity for the measures. These
measures (e.g., family communication, good parenting, and
others) then became the pretest and posttest assessments in an
experimental (intervention) study. For the intervention in the
study, the researchers were led to a strengths-based, family-
based prevention program that was hypothesized to be related to
the measures. The final step in the mixed methods process was
to use the validated measures within a program that featured the
prevention program. At various points in this study, the
researchers also collaborated with stakeholders to help to
develop good measures. Thus, this study illustrated a good,
complex mixed methods project with an initial qualitative
phase, an instrument development phase, and an experimental
phase. It shows how an initial exploration qualitatively can be
used to support a later quantitative testing phase. They stated
the purpose of the study as follows:

In the multi-step process used in this mental health services
research, we aimed to (1) carefully unpack locally-relevant
indicators of mental health problems and protective resources
using qualitative methods; (2) apply qualitative findings to the
adaptation of mental health measures and the development of a
locally-informed intervention; (3) validate the selected mental
health measures; and (4) apply the measures to rigorous
evaluation research on the effectiveness of the intervention
chosen through the mixed methods process. (p. 34)
Example 10.4A Social Justice Design
The final example is a feminist study using a mixed methods
social-justice explanatory sequential study by Hodgkin (2008).
This study investigated the concept of social capital for men
and women in households in a regional city in Australia. Social
capital described norms and networks that enabled people to
work collectively together to address and resolve common
problems (e.g., through social activities, the community, and
civic participation). The basic mixed methods approach was an
explanatory sequential design with an initial survey, a
quantitative phase, followed by an interview, qualitative phase.
As stated by the author, “the qualitative study elaborated on and
enhanced some of the results from the quantitative study” (p.
301). In addition, the author declared that this was a feminist
mixed methods project. This means that Hodgkin used a
feminist framework (see Chapter 3) to encase the entire mixed
methods project. She also referred to the Merten’s
transformative research paradigm (Mertens, 2007) that gave
voice to women, used a range of data collection methods, and
bridged the subjective and objective ways of knowing (see the
epistemology discussion in Chapter 3). The purpose of the study
was this:
The author will provide examples of quantitative data to
demonstrate the existence of different social capital profiles for

men and women. Stories will also be presented to provide a
picture of gender inequality and expectation. The author will
conclude by arguing that despite reluctance on the part of
feminists to embrace quantitative methods, the big picture
accompanied by the personal story can bring both depth and
texture to a study. (p. 297)
Thus, in this mixed methods study, the expected outcome for the
study was to help explain the initial survey results in more
depth with qualitative interview data. Added to this would be
the transformative perspective of seeking to provide a picture of
gender inequality and expectations. The databases were used
sequentially with the qualitative interviews following and
expanding on the quantitative surveys. While the surveys were
sent to both men and women in households (N = 1431), the
interviews included only women in the survey sample (N = 12).
The women interviewed were of different ages, they varied in
terms of their work activities (inside and outside the home),
they were mothers, and they varied in their educational level of
attainment. The timing of the data collection was in two phases
with the second-phase qualitative interviews building on the
results from the first-phase quantitative surveys. In fact, the
survey data indicated that men and women differed in terms of
their level of social participation in groups, and in community
group participation. The emphasis in this study seemed to be
equal between the quantitative and qualitative components, and
clearly the sole author of the study sought to provide a good
example of mixed methods research that used a feminist
framework.
How was this framework used? The author announced at the
beginning of the study that “the aim of this article is to
demonstrate the use of mixed methods in feminist research” (p.
296). Then the author discussed the lack of qualitative research
in the empirical studies of social capital and noted the White,

middle-class notion of community that dominated the
discussions of social capital. Further, the author talked about
lifting up the voices of those disenfranchised by gender and
engaged in a study that first pointed out gender differences in
social, community, and civic participation within a large sample
of men and women, and then focused a qualitative follow -up on
only women to understand the women’s role in more depth. The
qualitative findings then addressed themes that influence
women’s participation, such as wanting to be a “good mother,”
wanting to avoid isolation, and wanting to be a good citizen. A
summary of the qualitative findings indicates specifically how
the qualitative data helped to enhance the findings of the initial
survey results. Unlike many feminist mixed method s studies, the
conclusion did not indicate a strong call for action to change the
inequality. It only mentioned in passing that the mixed methods
study provided a powerful voice to gender inequality.
Summary
In designing the procedures for a mixed methods discussion,
begin by defining mixed methods research and its core
characteristics, briefly mentioning its historical evolution;
discuss your chosen mixed methods design; and note the
challenges in using the design. Convey a diagram of your
procedures that includes good notation to help the reader
understand the flow of activities. As you discuss your design,
convey the elements that go into it, such as the procedures used
in a convergent parallel, an explanatory sequential, or an
exploratory sequential mixed methods study. Also consider
whether you will overlay your project with a more complex
procedure that embeds the data within a larger design,
theoretical framework, or methodology. Finally, consider
factors that play into your choice of a mixed methods design.
These involve considering the intent of the design, what
outcomes you expect from the study, the integration of the
databases, the timing of them, the emphasis placed on each

database, the choice of design that matches your field, and the
conduct of the project either by yourself or a team of
researchers.
WRITING EXERCISES
Design a combined qualitative and quantitative study that
employs two phases sequentially. Discuss and provide rationales
for why the phases are ordered in the sequence you propose.
Design a combined qualitative and quantitative study that gives
emphasis to qualitative data collection and less emphasis to
quantitative data collection. Discuss the approach to be taken in
writing the introduction, the purpose statement, the research
questions, and the specific forms of data collection.
Develop a figure and specific procedures that illustrate the use
of a theoretical lens, such as a feminist perspective. Use the
procedures of either an explanatory or exploratory design for
conducting the study. Use appropriate notation in the figure.
Additional Readings
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and
conducting mixed methods research (3rd ed.) Thousand Oaks,
CA: Sage.
John Creswell and Vicki Plano Clark provide two chapters on
mixed methods research designs. Chapter 3 discusses the three
core mixed methods designs: convergent mixed methods
designs, explanatory sequential mixed methods design, and
exploratory sequential mixed methods designs. Chapter 4
advances the four examples of complex designs: mixed methods
intervention designs, mixed methods case study designs, mixed
methods participatory–social justice designs, and mixed
methods evaluation designs. The authors provide examples and
diagrams of each type of design and detail important
characteristics such as their integrative features.
Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward

a conceptual framework for mixed-method evaluation designs.
Educational Evaluation and Policy Analysis, 11(3), 255–274.
Jennifer Greene and associates undertook a study of 57 mixed
methods evaluation studies reported from 1980 to 1988. From
this analysis, they developed five different mixed methods
purposes and seven design characteristics. They found the
purposes of mixed methods studies to be based on seeking
convergence (triangulation), examining different facets of a
phenomenon (complementarity), using the methods sequentially
(development), discovering paradox and fresh perspectives
(initiation), and adding breadth and scope to a project
(expansion). They also found that the studies varied in terms of
the assumptions, strengths, and limitations of the method and
whether they addressed different phenomena or the same
phenomena; were implemented within the same or different
paradigms; were given equal or different weight in the study;
and were implemented independently, concurrently, or
sequentially. Using the purposes and the design characteristics,
the authors have recommended several mixed methods designs.
Morse, J. M. (1991). Approaches to qualitative-quantitative
methodological triangulation. Nursing Research, 40(1), 120–
123.
Janice Morse suggests that using qualitative and quantitative
methods to address the same research problem leads to issues of
weighing each method and their sequence in a study. Based on
these ideas, she then advances two forms of methodological
triangulation: (a) simultaneous, using both methods at the same
time, and (b) sequential, using the results of one method for
planning the next method. These two forms are described using
a notation of capital and lowercase letters that signify relative
weight as well as sequence. The different approaches to
triangulation are then discussed in the light of their purpose,
limitations, and approaches.

Plano Clark, V. L. & Creswell, J. W. (2008). The mixed
methods reader. Thousand Oaks, CA: Sage.
Creswell and Plano Clark have developed a comprehensive
guide to designing the steps in conducting mixed methods
research. This design theme is carried forward in specific
examples of published mixed methods studies in this reader.
Examples are provided of the convergent design, the
explanatory sequential design, and the exploratory sequential
design. Also, the book contains key articles throughout the
years that have informed the development of the field of mixed
methods.
Tashakkori, A., & Teddlie, C. (Eds.). (2010). SAGE handbook
of mixed methods in social & behavioral research (2nd ed.).
Thousand Oaks, CA: Sage.
This handbook, edited by Abbas Tashakkori and Charles
Teddlie, represents a major effort to map the field of mixed
methods research. The chapters provide an introduction to
mixed methods, illustrates methodological and analytic issues in
its use, identifies applications in the social and human sciences,
and plots future directions. For example, separate chapters
illustrate the use of mixed methods research in evaluation,
management and organization, health sciences, nursing,
psychology, sociology, and education.
https://edge.sagepub.com/creswellrd5e
Students and instructors, please visit the companion website for
videos featuring John W. Creswell, full-text SAGE journal
articles, quizzes and activities, plus additional tools for research
design.

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