QUANTITATIVE METHOD DESIGN

QUANTITATIVE METHOD
Aras BOZKURT
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QUANTITATIVE METHOD DESIGN
• 1. EXPERIMENTAL DESIGN
• In an experiment, you test an idea (or practice or procedure) to determine
whether it influences an outcome or dependent variable. You first decide
on an idea with which to “experiment,” assign individuals to experience it
(and have some individuals experience something different), and then
determine whether those who experienced the idea (or practice or
procedure) performed better on some outcome than those who did not
experience it.
• When Do You Use an Experiment?
• You use an experiment when you want to establish possible cause and
effect between your independent and dependent variables. This means
that you attempt to control all variables that influence the outcome
except for the independent variable. Then, when the independent
variable influences the dependent variable, we can say the independent
variable “caused” or “probably caused” the dependent variable. Because
experiments are controlled, they are the best of the quantitative designs
to use to establish probable cause and effect.

• Before you consider how to conduct an experiment, you will find it helpful
to understand in more depth several key ideas central to experimental
research. These ideas are:
• A-Random assignment
• B-Control over extraneous variables
• C-Manipulation of the treatment conditions
• D-Outcome measures
• E-Group comparisons
• G-Threats to validity

• A-Random Assignment
• As an experimental researcher, you will assign individuals to groups. The
most rigorous approach is to randomly assign individuals to the
treatments. Random assignment is the process of assigning individuals at
random to groups or to different groups in an experiment. The random
assignment of individuals to groups (or conditions within a group)
distinguishes a rigorous, “true” experiment from an adequate, but less-
than-rigorous, “quasi-experiment”
• You use random assignment so that any bias in the personal
characteristics of individuals in the experiment is distributed equally
among the groups. By randomization, you provide control for extraneous
characteristics of the participants that might infl uence the outcome (e.g.,
student ability, attention span, motivation). The experimental term for this
process is “equating” the groups. Equating the groups means that the
researcher randomly assigns individuals to groups and equally distributes
any variability of individuals between or among the groups or conditions
in the experiment
• You should not confuse random assignment with random selection. Both
are important in quantitative research, but they serve different purposes.
Quantitative researchers randomly select a sample from a population. In
this way, the sample is representative of the population and you can
generalize results obtained during the study to the population.

• B-Control Over Extraneous Variables
• In randomly assigning individuals, we say that we are controlling for
extraneous variables that might influence the relationship between the new
practice. Extraneous factors are any influences in the selection of participants,
the procedures, the statistics, or the design likely to affect the outcome and
provide an alternative explanation for our results than what we expected.
• Pretests and Posttests: To “equate” the characteristics of the groups,
experimental researchers may use a pretest. A pretest provides a measure
on some attribute or characteristic that you assess for participants in an
experiment before they receive a treatment. After the treatment, you take
another reading on the attribute or characteristic. A posttest is a measure
on some attribute or characteristic that is assessed for participants in an
experiment after a treatment.
• Covariates: Because pretests may affect aspects of the experiment, they
are often statistically con trolled for by using the procedure of covariance
rather than by simply comparing them with posttest scores. Covariates
are variables that the researcher controls for using statistics and that
relate to the dependent variable but that do not relate to the
independent variable

• B-Control Over Extraneous Variables
• Matching of Participants: Another procedure used for control in an
experiment is to match participants on one or more personal characteristics.
Matching is the process of identifying one or more personal characteristics
that influence the outcome and assigning individuals with that characteristic
equally to the experimental and control groups. Typically, experimental
researchers match on one or two of the following characteristics: gender,
pretest scores, or individual abilities.
• Homogeneous Samples: Another approach used to make the groups
comparable is to choose homogeneous samples by selecting people who vary
little in their personal characteristics.
• Blocking Variables: One such procedure is to “block” for grade level before
the experiment begins. A blocking variable is a variable the researcher
controls before the experiment starts by dividing (or “blocking”) the
participants into subgroups (or categories) and analyzing the impact of each
subgroup on the outcome. The variable (e.g., gender) can be blocked into
males and females; similarly, high school grade level can be blocked into four
categories: freshmen, sophomores, juniors, and seniors

• C-Manipulating Treatment Conditions
• Once you select participants, you randomly assign them to either a
treatment condition or the experimental group. In experimental
treatment, the researcher physically intervenes to alter the conditions
experienced by the experimental unit
• Treatment Variables
• In experiments, you need to focus on the independent variables. These
variables influence or affect the dependent variables in a quantitative
study. The two major types of independent variables were treatment and
measured variables. In experiments, treatment variables are independent
variables that the researcher manipulates to determine their effect on the
outcome, or dependent variable. Treatment variables are categorical
variables measured using categorical scales. For example, treatment
independent variables used in educational experiments might be:
– Type of instruction (small group, large group)
– Type of reading group (phonics readers, whole-language readers)

• C-Manipulating Treatment Conditions
• Conditions
• In both of these examples, we have two categories within each treatment
variable. In experiments, treatment variables need to have two or more
categories, or levels. In an experiment, levels are categories of a
treatment variable
• Intervening in the Treatment Conditions
• In an experiment, the researcher physically intervenes (or manipulates
with interventions) in one or more condition so that individuals
experience something different in the experimental conditions than in the
control conditions.

• D-Outcome Measures
• In experiments, the outcome (or response, criterion, or posttest) is the
dependent variable that is the presumed effect of the treatment variable.
It is also the effect predicted in a hypothesis in the cause-and-effect
equation
• E-Group Comparisons
• A group comparison is the process of a researcher obtaining scores for
individuals or groups on the dependent variable and comparing the
means and variance both within the group and between the groups.

• F-Threats to Validity
• Threats to validity refer to specific reasons for why we can be wrong
when we make an inference in an experiment because of covariance,
causation constructs, or whether the causal relationship holds over
variations in persons, setting, treatments, and outcomes ( Shadish, Cook,
& Campbell, 2002 ). Four types of validity they discuss are:
– Statistical conclusion validity, which refers to the appropriate use of statistics (e.g.,
violating statistical assumptions, restricted range on a variable, low power) to infer
whether the presumed independent and dependent variables covary in the experiment.
– Construct validity, which means the validity of inferences about the constructs (or
variables) in the study.
– Internal validity, which relates to the validity of inferences drawn about the cause and
effect relationship between the independent and dependent variables.
– External validity, which refers to the validity of the cause-and-effect relationship being
generalizable to other persons, settings, treatment variables, and measures.

• F-Threats to Validity
• Threats to internal validity are problems in drawing correct inferences
about whether the covariation (i.e., the variation in one variable
contributes to the variation in the other variable) between the presumed
treatment variable and the outcome reflects a causal relationship
(Shadish, Cook, & Campbell, 2002 ).
• Threats to external validity are problems that threaten our ability to draw
correct inferences from the sample data to other persons, settings,
treatment variables, and measures.

• WHAT ARE THE TYPES OF EXPERIMENTAL DESIGNS?
• Although all experiments have common characteristics, their use and
applications vary depending on the type of design used. The most
common designs you will find in educational research are:
• Between Group Designs
– True (gerçek-tam) experiments (pre- and posttest, posttest only)
– Quasi (yarı) experiments (pre- and posttest, posttest only)
– Factorial (faktöriyel) designs
• Within Group or Individual Designs
– Time series (zaman serisi) experiments (interrupted, equivalent)
– Repeated measures (tekrar ölçümlü) experiments
– Single subject (tek denekli) experiments

• Between-Group Designs
• The most frequently used
designs in education are
those where the researcher
compares two or more
groups. Illustrations
throughout this chapter
underscore the importance
of these designs.

• True Experiments
• True experiments comprise the most rigorous and strong experimental
designs because of equating the groups through random assignment. The
procedure for conducting major forms of true experiments and quasi-
experiments, viewing them in terms of activities from the beginning of the
experiment to the end. In true experiments, the researcher randomly
assigns participants to different conditions of the experimental variable.
Individuals in the experimental group receive the experimental treatment,
whereas those in the control group do not. After investigators administer
the treatment, they compile average (or mean) scores on a posttest. One
variation on this design is to obtain pretest as well as posttest measures or
observations. When experimenters collect pretest scores, they may
compare net scores (the differences between the pre- and posttests).
Alternatively, investigators may relate the pretest scores for the control
and experimental groups to see if they are statistically similar, and then
compare the two posttest group scores. In many experiments, the pretest
is a covariate and is statistically controlled by the researcher.

• Because you randomly assign individuals to the groups, most of the
threats to internal validity do not arise. Randomization or equating of the
groups minimizes the possibility of history, maturation, selection, and the
interactions between selection and other threats. Treatment threats such
as diffusion, rivalry, resentful demoralization, and compensatory
equalization are all possibilities in a between-group design because two or
more groups exist in the design. When true experiments include only a
posttest, it reduces the threats of testing, instrumentation, and regression
because you do not use a pretest. If a pretest is used, it introduces all of
these factors as possible threats to validity. Instrumentation exists as a
potential threat in most experiments, but if researchers use the same or
similar instrument for the pre- and posttest or enact standard procedures
during the study, you hold instrumentation threats to a minimum.

• Quasi-Experiments
• In education, many experimental situations occur in which researchers
need to use intact groups. This might happen because of the availability of
the participants or because the setting prohibits forming artificial groups.
Quasi-experiments include assignment, but not random assignment of
participants to groups. This is because the experimenter cannot artificially
create groups for the experiment. For example, studying a new math
program may require using existing fourth-grade classes and designating
one as the experimental group and one as the control group. Randomly
assigning students to the two groups would disrupt classroom learning.
Because educators often use intact groups (schools, colleges, or school
districts) in experiments, quasi-experimental designs are frequently used.

• Factorial Designs
• In some experimental situations, it is not enough to know the effect of a
single treatment on an outcome; several treatments may, in fact, provide
a better explanation for the outcome. Factorial designs represent a
modification of the between group design in which the researcher studies
two or more categorical, independent variables, each examined at two or
more levels (Vogt, 2005). The purpose of this design is to study the
independent and simultaneous effects of two or more independent
treatment variables on an outcome.

• Factorial Designs
• Let’s examine more closely the steps in the process of conducting a
factorial design. The researcher identifies a research question that
includes two independent variables and one dependent variable, such as
“Do rates of smoking vary under different combinations of type of
instruction and levels of depression?” To answer this question, the
experimenter identifies the levels of each factor or independent variable:
• Factor 1—types of instruction
– Level 1—a health-hazards lecture in civics class
– Level 2—a standard lecture in civics class
• Factor 2—levels of depression
– Level 1—high
– Level 2—medium
– Level 3—low
• Because you measure two levels of instruction and three levels of depression, the
design is called a two by three factorial design. It is written as “2 × 3” to indicate
the levels involved in each independent variable.

• Using a statistical software program, analysis of variance will produce
statistical results for main effects and interaction effects. Main effects are
the influence of each independent variable (e.g., type of instruction or
extent of depression) on the outcome (e.g., the dependent variable, rate
of smoking) in an experiment. Interaction effects exist when the influence
on one independent variable depends on (or co-varies with) the other
independent variable in an experiment.

• Within-Group or Individual Designs
• In any given experiment, the number of participants may be limited and it
may not be possible to involve more than one group. In these cases,
researchers study a single group using a within-group experimental
design. Also, the experimenter might examine single individuals (within-
individual design). This type of design assumes several forms: time series,
repeated measures, and single-subject designs.

• Time Series
• When an experimental researcher has access to only one group and can
study them over a period, a time series design is a good experimental
approach. A time series design consists of studying one group, over time,
with multiple pretest and posttest measures or observations made by the
researcher. This design does not require access to large numbers of
participants, and it requires only one group for the study. It is ideal for
examining change in an entire system (e.g., a school district) where it
would be difficult to find a control group or system willing to cooperate.
However, this design is labor intensive because the researcher needs to
gather multiple measures.

• Time Series
• These multiple measures are seen in two important variations of this
design. As shown in Table 10.5, the first is the interrupted time series
design. This procedure consists of studying one group, obtaining multiple
pretest measures for a period of time, administering an intervention (or
interrupting the activities), and then measuring outcomes (or posttests)
several times. Data analysis in this example consists of examining
difference scores between the pretests and posttests or posttest-only
scores and using the pretests as covariates. A variation, also seen in Table
10.5, uses an equivalent time series design, in which the investigator
alternates a treatment with a posttest measure. The data analysis then
consists of comparing posttest measures or plotting them to discern
patterns in the data over time.

• Repeated Measures
• Another experimental design that has the advantage of employing only a
single group is a repeated measures design. In a repeated measures
design, all participants in a single group participate in all experimental
treatments, with each group becoming its own control.
• The researcher compares a group’s performance under one experimental
treatment with its performance under another experimental treatment.
The experimenter decides on multiple treatments (as in factorial designs)
but administers each separately to only one group. After each
administration, the researcher obtains a measure or observation.

• Single-Subject Designs
• In your experiment, assume that you seek to learn about the behavior of
single individuals rather than groups. You also have an opportunity to
observe their behavior over time. In these situations, single-subject
experimental designs are ideal. Single-subject research (also called N of 1
research, behavior analysis, or within-subjects research) involves the
study of single individuals, their observation over a baseline period, and
the administration of an intervention. This is followed by another
observation after the intervention to determine if the treatment affects
the outcome.
– An A/B design consists of observing and measuring behavior during a trial period (A),
administering an intervention, and observing and measuring the behavior after the
intervention
– Multiple Baseline Design A frequently used single-subject design is the multiple
baseline design, as shown in Figure 10.8. In this design, each participant receives an
experimental treatment at a different time (hence, multiple baselines exist) so that
treatment diffusion will not occur among participants. Researchers choose this design
when the treatment (e.g., skill or strategy being taught) cannot be reversed and doing
so would be unethical or injurious to participants.
– An alternating treatment design is a single-subject design in which the researcher
examines the relative effects of two or more interventions and determines which
intervention is the more effective treatment on the outcome.

• 2-SURVEY RESEARCH
• Survey research designs are procedures in quantitative research in which
investigators administer a survey to a sample or to the entire population
of people to describe the attitudes, opinions, behaviors, or characteristics
of the population. survey studies describe trends in the data rather than
offer rigorous explanations.

• Cross-Sectional Survey Designs: The most popular form of survey design
used in education is a cross-sectional survey design. In a cross-sectional
survey design, the researcher collects data at one point in time.
• Longitudinal Survey Designs: An alternative to using a cross-sectional
design is to collect data over time using a longitudinal survey design. A
longitudinal survey design involves the survey procedure of collecting data
about trends with the same population, changes in a cohort group or
subpopulation, or changes in a panel group of the same individuals over
time. Thus, in longitudinal designs, the participants may be different or
the same people.

• 3-CORRELATIONAL DESIGN
• Correlational designs provide an opportunity for you to predict scores and
explain the relationship among variables. In correlational research
designs, investigators use the correlation statistical test to describe and
measure the degree of association (or relationship) between two or more
variables or sets of scores.
• An explanatory (relational) research design is a correlational design in
which the researcher is interested in the extent to which two variables (or
more) co-vary, that is, where changes in one variable are reflected in
changes in the other. Explanatory designs consist of a simple association
between two variables (e.g., sense of humor and performance in drama)
or more than two (e.g., pressure from friends or feelings of isolation that
contribute to binge drinking).
• The purpose of a prediction research design is to identify variables that
will predict an outcome or criterion. In this form of research, the
investigator identifies one or more predictor variable and a criterion (or
outcome) variable

• 4-META ANALYSIS
• In another extension of correlation research, authors integrate the
findings of many (primary source) research studies in a meta-analysis by
evaluating the results of individual studies and deriving an overall numeric
index of the magnitude of results.
• meta-analysis comprises statistical methods for contrasting and combining
results from different studies in the hope of identifying patterns among
study results, sources of disagreement among those results, or other
interesting relationships that may come to light in the context of multiple
studies. Meta-analysis can be thought of as "conducting research about
previous research." In its simplest form, meta-analysis is done by
identifying a common statistical measure that is shared between studies,
such as effect size or p-value, and calculating a weighted average of that
common measure. This weighting is usually related to the sample sizes of
the individual studies, although it can also include other factors, such as
study quality.

QUALITATIVE METHOD
Aras BOZKURT

QUALITATIVE METHOD DESIGN
• Qualitative researchers aim to gather an in-depth understanding of human
behavior and the reasons that govern such behavior. The qualitative
method investigates the why and how of decision making, not just what,
where, when. Hence, smaller but focused samples are more often used
than large samples.

• 1. Case study
• Case study research is a qualitative approach in which the investigator
explores a bounded system (a case) or multiple bounded systems (cases)
over time, through detailed, in-depth data collection involving multiple
sources of information (e.g., observations, interviews, audiovisual
material, and documents and reports), and reports a case description and
case-based themes.

• 1. Case study
• The type of analysis of these data can be a holistic analysis of the entire
case or an embedded analysis of a specific aspect of the case (Yin, 2003).
• One analytic strategy would be to identify issues within each case and
then look for common themes that transcend the cases (Yin, 2003).

• 1. Case study
• Holistic single
• Embedded single
• Holistic multible
• Embedded multible

• 2. Narrative Research
• "Narrative" might be the term assigned to any text or discourse, or, it
might be text used within the context of a mode of inquiry in qualitative
research (Chase, 2005), with a specific focus on the stories told by
individuals (Polkinghorne, 1995).
• Polkinghorne (1995) takes this approach and distinguishes between
"analysis of narratives" (p. 12), using paradigm thinking to create
descriptions of themes that hold across stories or taxonomies of types of
stories, and "narrative analysis," in which researchers collect descriptions
of events or happenings and then configure them into a story using a plot
line.

• 3. Phenomenology
• Whereas a narrative study reports the life of a single individual, a
phenomenological study describes the meaning for several individuals of
their lived experiences of a concept or a phenomenon. Phenomenologists
focus on describing what all participants have in common as they
experience a phenomenon (e.g., grief is universally experienced).
• The basic purpose of phenomenology is to reduce individual experiences
with a phenomenon to a description of the universal essence (a "grasp of
the very nature of the thing," van Manen, 1990, p. 177).

• 3. Phenomenology
• Two approaches to phenomenology are highlighted in this discussion:
hermeneutic phenomenology (van Marren, 1990) and empirical,
transcendental, or psychological phenomenology (Moustakas, 1994). Van
Marren (1990) is widely cited in the health literature (Morse & Field,
1995).
• An educator, van Marren, has written an instructive book on
hermeneutical phenomenology in which he describes research as oriented
toward lived experience (phenomenology) and interpreting the "texts" of
life (hermeneutics) (van Marren, 1990,p. 4 ).
• Moustakas's (1994) transcendental or psychological phenomenology is
focused less on the interpretations of the researcher and more on a
description of the experiences of participants.

• 3. Grounded Theory
• Although a phenomenology emphasizes the meaning of an experience for
a number of individuals, the intent of a grounded theory study is to move
beyond description and to generate or discover a theory, an abstract
analytical schema of a process (or action or interaction, Strauss & Corbin,
1998).
• Participants in the study would all have experienced the process, and the
development of the theory might help explain practice or provide a
framework for further research. A key idea is that this theory-
development does not come "off the shelf," but rather is generated or
"grounded" in data from participants who have experienced the process
(Strauss & Corbin, 1998). Thus, grounded theory is a qualitative research
design in which the inquirer generates a general explanation (a theory) of
a process, action, or interaction shaped by the views of a large number of
participants (Strauss & Corbin, 1998).

• 3. Grounded Theory
• The two popular approaches to grounded theory are the systematic
procedures of Strauss and Corbin (1990, 1998) and the constructivist
approach of Charmaz (2005, 2006).
• In the more systematic, analytic procedures of Strauss and Corbin (1990,
1998), the investigator seeks to systematically develop a theory that
explains process, action, or interaction on a topic (e.g., the process of
developing a curriculum, the therapeutic benefits of sharing psychological
test results with clients). The researcher typically conducts 20 to 30
interviews based on several visits "to the field" to collect interview data to
saturate the categories (or find information that continues to add to them
until no more can be found).
• A second variant of grounded theory is found in the constructivist writing
of Charmaz (see Charmaz, 2005, 2006). Instead of embracing the study of
a single process or core category as in the Strauss and Corbin (1998)
approach, Charmaz advocates for a social constructivist perspective that
includes emphasizing diverse local worlds, multiple realities, and the
complexities of particular worlds, views, and actions.

• 4. Ethnography
• Although a grounded theory researcher develops a theory from examining
many individuals who share in the same process, action, or interaction,
the study participants are not likely to be located in the same place or
interacting on so frequent a basis that they develop shared patterns of
behavior, beliefs, and language.
• An ethnographer is interested in examining these shared patterns, and the
unit of analysis is larger than the 20 or so individuals involved in a
grounded theory study. An ethnography focuses on an entire cultural
group. Granted, sometimes this cultural group may be small (a few
teachers, a few social workers), but typically it is large, involving many
people who interact over time (teachers in an entire school, a community
social work group). Ethnography is a qualitative design in which the
researcher describes and interprets the shared and learned patterns of
values, behaviors, beliefs, and language of a culture-sharing group (Harris,
1968).

• 4. Ethnography
• There are many forms of ethnography, such as a confessional
ethnography, life history, autoethnography, feminist ethnography,
ethnographic novels, and the visual ethnography found in photography
and video, and electronic media (Denzin, 1989a; LeCompte, Millroy, &
Preissle, 1992; Pink, 2001; VanMaanen, 1988). Two popular forms of
ethnography will be emphasized here: the realist ethnography and the
critical ethnography

• 4. Ethnography
• The realist ethnography is a traditional approach used by cultural anthropologists.
Characterized by VanMaanen (1988), it reflects a particular stance taken by the
researcher toward the individuals being studied. Realist ethnography is an
objective account of the siruation, typically written in the thirdperson point of
view and reporting objectively on the information learned from participants at a
site. In this ethnographic approach, the realist ethnographer narrates the study in
a third-person dispassionate voice and reports on what is observed or heard from
participants.
• For many researchers, ethnography today employs a "critical" approach
(Carspecken & Apple, 1992; Madison, 2005; Thomas, 1993) by including in the
research an advocacy perspective. This approach is in response to current society,
in which the systems of power, prestige, privilege, and authority serve to
marginalize individuals who are from different classes, races, and genders. The
critical ethnography is a type of ethnographic research in which the authors
advocate for the emancipation of groups marginalized in society (Thomas, 1993).
Critical researchers typically are politically minded individuals who seek, through
their research, to speak out against inequality and domination (Carspecken &
Apple, 1992).

• 5. Action research
• Action research has an applied focus. Similar to mixed methods research,
action research uses data collection based on either quantitative or
qualitative methods or both. However, it differs in that action research
addresses a specific, practical issue and seeks to obtain solutions to a
problem. Thus, action research designs are systematic procedures done by
teachers (or other individuals in an educational setting) to gather
information about, and subsequently improve, the ways their particular
educational setting operates, their teaching, and their student learning (
Mills, 2011 ).

• 5. Action research (Eylem araştırması)
• A review of the major writers in education, however, shows that the following two
basic research designs are typically discussed ( Mills, 2011 ): Practical action
research and Participatory action research
• Practical Action Research: Teachers seek to research problems in their own
classrooms so that they can improve their students’ learning and their own
professional performance. Teams composed of teachers, students, counselors, and
administrators engage in action research to address common issues such as
escalating violence in schools. In these situations, educators seek to enhance the
practice of education through the systematic study of a local problem.
• Participatory Action Research: Participatory action research (PAR) has a long
history in social inquiry involving communities, industries and corporations, and
other organizations outside of education (e.g., Kemmis & McTaggart, 2005 ).
Rather than focus on individual teachers solving immediate classroom problems or
schools addressing internal issues, PAR has a social and community orientation
and an emphasis on research that contributes to emancipation or change in our
society.
• The purpose of participatory action research is to improve the quality of people’s
organizations, communities, and family lives ( Stringer, 2007 ). Although espousing
many of the ideas of teacher and school-based practical action research, it differs
by incorporating an emancipatory aim of improving and empowering individuals
and organizations

• 6. Design based research
• Design research: to design/develop an intervention (such as programmes,
teaching-learning strategies and materials, products and systems) with
the aim to solve a complex educational problem and to advance our
knowledge about the characteristics of these interventions and the
processes to design and develop them. (p. 12)
• Generally, the main purpose that design-based research aims at achieving
is to “address complex problems in educational settings”(Sari & Lim,
2012, p. 2) in order to “build a stronger connection between educational
research and real-world problems” while supporting design and
development of prototypical products to solve complex authentic context-
specific problem”

• 6. Design based research
• In DBR design is a crucial part of the research, whereas in action research
the focus is on action and change, which can but need not involve the
design of a new learning environment. DBR also more explicitly aims for
instructional theories than does action research

• 7. Historical
• Purpose - describe and examine events of the past to understand the
present and anticipate potential future effects
• 8. Content analysis
• In the social sciences and humanities, content analysis is the analysis of
texts of various types including writing, images, recordings and cultural
artifacts. Content analysis includes both qualitative and quantitative
approaches. Content analysis is used for a variety of purposes including
attribution of texts to authors, testing of hypotheses, theory building, and
evaluation research.

• 9. Meta-synthesis
• Generally speaking, a qualitative meta-synthesis is a type of qualitative
study that uses as data the findings from other qualitative studies linked
by the same or a related topic. The sample for a meta-synthesis, then, is
made up of individual qualitative studies selected on the basis of their
relevance to a specific research question posed by the synthesist. Meta-
synthesis is not an integrated review of qualitative literature on a given
topic. Also, it is not secondary data analysis of primary data from the
selected studies; rather, it is an analysis of the findings of these studies.
That is, meta-synthesis is the synthesist’s interpretation of the
interpretations of primary data by the original authors of the constituent
studies.

MIXED METHOD DESIGN
• A mixed methods research design is a procedure for collecting, analyzing,
and “mixing” both quantitative and qualitative methods in a single study
or a series of studies to understand a research problem (Creswell & Plano
Clark, 2011). The basic assumption is that the uses of both quantitative
and qualitative methods, in combination, provide a better understanding
of the research problem and question than either method by itself.

MIXED METHOD DESIGN
• When one combines quantitative and qualitative data, “we have a very
powerful mix”.
• Triangulation: Applied to research, it meant that investigators could
improve their inquiries by collecting and converging (or integrating)
different kinds of data bearing on the same phenomenon.
• The three points to the triangle are the (1,2) two sources of the data and
the (3) phenomenon.
• philosophical worldview: pragmatism.

MIXED METHOD DESIGN
• Figure 16.2 illustrates six mixed methods designs, with the first four as the
basic designs in use today and the last two as complex designs that are
becoming increasingly popular (Creswell & Plano Clark, 2011).
• The designs are:
• the convergent parallel design
• the explanatory sequential design
• the exploratory sequential design
• the embedded design
• the transformative design
• the multiphase design

MIXED METHOD DESIGN
• 1-The Convergent (concurrent) Parallel Design
• The purpose of a convergent (or parallel or concurrent) mixed methods
design is to simultaneously collect both quantitative and qualitative data,
merge the data, and use the results to understand a research problem.

MIXED METHOD DESIGN
• 2-The Explanatory Sequential Design
• A mixed methods researcher might collect quantitative and qualitative
information sequentially in two phases, with one form of data collection
following and informing the other.
• An explanatory sequential mixed methods design (also called a two-phase
model; Creswell & Plano Clark, 2011) consists of first collecting
quantitative data and then collecting qualitative data to help explain or
elaborate on the quantitative results.

MIXED METHOD DESIGN
• 3-The Exploratory Sequential Design
• The mixed methods researcher begins with qualitative data and then
collects quantitative information. The purpose of an exploratory
sequential mixed methods design involves the procedure of first gathering
qualitative data to explore a phenomenon, and then collecting
quantitative data to explain relationships found in the qualitative data.

MIXED METHOD DESIGN
• 4-The Embedded/nested Design
• The purpose of the embedded design is to collect quantitative and
qualitative data simultaneously or sequentially, but to have one form of
data play a supportive role to the other form of data. The reason for
collecting the second form of data is that it augments or supports the
primary form of data.

MIXED METHOD DESIGN
• 5-The Transformative Design
• At a more complex level than the four previous designs, we have the
transformative mixed methods design. The intent of the transformative
mixed methods design is to use one of the four designs (convergent,
explanatory, exploratory, or embedded), but to encase the design within a
transformative framework or lens (Creswell & Plano Clark, 2011).
• This framework provides an orienting lens for the mixed methods design.
It informs the overall purpose of the study, the research questions, the
data collection, and the outcome of the study. The intent of the
framework is to address a social issue for a marginalized or
underrepresented population and engage in research that brings about
change. Thus, strength of this design is that it is value-based and
ideological ( Greene, 2007). The typical frameworks found in mixed
methods are feminist, racial, ethnic, disability, and gay or lesbian
perspectives.

MIXED METHOD DESIGN
• 5-The Transformative Design

MIXED METHOD DESIGN
• 6-Multiphase Design
• A multiphase design emerges from multiple projects conducted over time
linked together by a common purpose.
• the multiphase design is a complex design that builds on the basic
convergent, explanatory, exploratory, and embedded designs. Multiphase
mixed methods designs occur when researchers or a team of researchers
examine a problem or topic through a series of phases or separate
studies. The groups of phases or studies are considered to be a mixed
methods design and the intent of the design is to address a set of
incremental research questions that all advance one programmatic
research objective (Creswell & Plano Clark, 2011).
• The phases or studies may employ a combination of concurrent or
sequential designs and this form of design is popular in large-scale health
research and in evaluation research. The strength of this design lies in the
use of multiple projects to best understand an overall program objective.

MIXED METHOD DESIGN
• 6-Multiphase Design

QUANTITATIVE METHOD DESIGN

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