Mixed Methods Approaches Research Evaluation.
Parallel mixed method data analysis: This
makes use of two separate processes the qualitative and the quantitative: in quantitative method the conventional
statistics analysis technique is used, such as frequency table; cross
tabulation, regression analysis, etc. While the qualitative method uses methods
like the content analysis, the findings of these two sets of analyses are then
compared with triangulation.
Conversion mixed methods data analysis:
This process converts qualitative data to quantitative data. Data are usually
narrative, that is, there are data from legal administration, politics, social,
humanities etc. They are usually recorded into numerical data (using the dummy
variable to “1” and “0”). Also quantitative data can be converted into
qualitative data by recreating a descriptive typology of the data.
Sequential mixed method data analysis:
This is when either qualitative or quantitative precedes one another in the
research approach. MM design used is more robust when qualitative or
quantitative is used sequentially.
Multilevel mixed method: This technique is
used at different levels of the evaluation design such as the example given in
Methodological Framework (Grounded
3.1 Grounded Theory
Two sociologists Barney G. and Anselm L Strauss in the 1960s
developed Grounded theory. They noticed other sociological theories that
dominated empirical research, with a closer view of various research methods,
they developed a theory that structures researcher away from mere analytical constructions
from pre-existing theories and categorical variables analysis to a more
substantial evident based theories from data. A grounded theory is a systematic
research methodology from the analysis of data used to explain theories. Grounded theory is a systematic qualitative
procedure used to conceptualized theories from analytical research data. It
explains a broad conceptual level, a process, an action, or an interaction
between substantial topics (Creswell 2009). It is that theory that is developed
from inductively from data from a qualitative analysis technique, it is mainly
used for qualitative research and applicable to quantitative data. In general,
theory-generating is from inductive researches, hence it is linked with
qualitative research, while theory-testing are deductive in nature, and
therefore it is linked with quantitative experiments. Since grounded theory can
also be applicable to quantitative data, it can be said that grounded theory is
developed deductively also because sometimes through developing a theory a
researcher may make use of a combination of quantitative and qualitative data
which means combining deductive and inductive elements.
After the development
of grounded theory, there has been lot of dispute about the nature and the
scope of grounded theory, Glaser and Strauss disagree about the nature and how
it ought to be practiced, this lead to other version of grounded theory such
Charmaz’s , Corbin etc. but Glaser later suggested that the original version of
grounded theory should be used (Charmaz 2003) .
The Frame of Grounded Theory and how to use it
Theory is a common word
within academic environment, it is used so often that the true meaning can be
lost. Theory can be described in three main forms in the context of research
(Reynolds, 2007). A theory can be a “conception of scientific knowledge as a
set of well-supported empirical generalizations, such as a set of laws,” or “an
trans-disciplinary interrelated set of definitions, axioms, and propositions,”
or “a set of descriptions of causal process” (Reynolds, 2007). The combination
and connection of all three parts is conceptualized as grounded theory, as the
data collected gives researcher insight about the knowledge, defines the actual
context are, and describes the interconnectivity process between different
concepts that explains the main and/or
expanding knowledge of the research. Similar to theory is synthesizing of
concepts, which holds together concepts to help describe a process (Bowen
Grounded theory assumes
a substantive constructive construct by researchers and the formulation of
theories from systematic analysis of empirical data. Grounded theory begins
with inductive data and progressively developing abstract concepts and their
relationships, which bind’s data with its theoretical analysis. This method
consists of systematic strategies to guide qualitative inquiry, particularly,
data analysis. Grounded theory promotes theory construction based on
description, individual narrative patterns, which leads to the development of
fresh ideas for applied theories (Brown et al 2002). The application of
Grounded theory is in multiple stages such as:
Data collection: there
are several methods of collecting data used in grounded theory, such as
semi-structured and unstructured interviewing, participant and none participant
observation and focus groups discussion.
Data analysis and
presentation: In Grounded theory data analysis, characters in forms of figures,
and pictorial charts represent concepts beyond the actualities by looking for
codes, then concepts and finally categories.
Coding: Is a form of
content analysis which examines and conceptualize the key issues among the
“crowd” raw data collected. The researcher becomes sensitive to the
interviewee’s words or phrases that are commonly used. The researcher notes the
words and studies the pattern in its content from their discussion or
interviews. These short phrases which are noted are termed coding.
Concepts: The analysis
of codes with related theme which are grouped together that is of higher
commonality (Allan 2003).
Memoing: Note making
about any theoretical hypotheses from coding.
Categories: This is the
grouping and regrouping of concept to find other commonality called categories
which leads to the emergence of a theory (Allan 2003).
Sorting: As soon as
data from the studies make sense or begins to add up to knowledge, the emerging
sorting is done out of the information accordingly to knowledge.
3.3 Framing Mixed
Method by Grounded theory
Grounded theory forms
the based line of qualitative research which is inductive in nature. It tends
to progress from inductive through different methods and stages. Likewise
grounded theory could also actualize its aims with the introduction of
deductive method to conceptualized its framing from none participant
observation to theoretical content. This style of research methodology could
fit in the mixed method to actualize its aim. Just like grounded theory the
mixed methods using both inductive and deductive methods in its researcher
method, the inductive method is qualitative methods and the deductive methods
is quantitative methods. Inductive promotes an unintentional observation that
is, the openness of the researcher to discovery, usually from observation and
experiences from communication with the people, culture and the environment that
will guide the conceptualization of new ideals which forms theories. The
deductive research could be applied depending on the nature of the project
begin carried. In some case the researcher need to make use of already existing
information or data which helps to randomized the sample into a frame for
Mixed method has
procedures like the grounded theories, which can go from abstract to none
abstract vice versa (Driscoll et. al 2007). But grounded theory is more
particular about the none abstract way of researching issues which tend to
portrait a none bias way of research. When using mixed methods, it always tends
to qualify the quantitative aspect by drawing logical reasoning from the
ethnography of the study which is analyzed in comparison to the quantitative
Delineate Mixed Method Framed in Grounded Theories from other Methods
A study using grounded
theory gives a series a rules and procedures which is likely to begin with a
question, or even just with the collection of qualitative data. As researchers
review the data collected; repeated ideas, concepts or elements become
apparent, and are tagged with codes, which have been extracted from the data. This
give the research work a much more robust fact from subjective statement to
objectiveness facts. The outcome can be framed within the qualitative methods
like in the social science and humanities or in quantitative frame such as the
natural and the physical science for quantifying reasoning.
Mixed methods is a more
rigorous process both in its qualitative and quantitative methods, it takes a
detailed account. It brings interplay of both the qualitative and the
quantitative perspective to research style moving from co-mixed within method
or a co-switch between methods at different levels or stages. It is a dynamic
method which helps in flexible evaluation to outcome and impact analysis
irrespective of lay down theories which reduces research bias to the nearest
minimum. It gives a new inspired thought of reasoning to fresh ideals in
research, taking away the stereotype to research methods
These categories may
become the basis for new theory. Thus, mixed method framed in grounded theory
is quite different from the traditional model of research, where the researcher
chooses an existing theoretical framework, and only then collects data to show
how the theory does or does not apply to the phenomenon under study.
Advantages and Disadvantages of Mixed Method
Mixed method (MM) are
used in the validation of the data collected using both qualitative and quantitative
methods, the variability in the data collection leads to a greater validity in
the result or outcome, it gives a broader horizon into the research project.
Mixed method (MM)
answer questions from of multiple perspective that is, from qualitative to
quantitative, brings both inductive and deductive reasoning into play. It give
room to question like what, why, how, and if. It creates flexibility within and
between its contexts.
Mixed method tries to
close the gaps or lags both in content and context of the data collected, it
ensure that the data of analysis is robust for a broader explanation or
in-depth coverage of the outcome or
Mixed methods is
dynamic in its evaluation processes, ensure to curb and manage the researcher
per-existing assumptions about a researcher area, problem or issues. It tends
to validate the researchers un-biasness from its inductive method allowing
openness to fresh ideas.
Mixed method is both
supplementary and complimentary in research work. When one method does not
provided sufficient information to the analysis or research work.
Mixed method is good
for data transformation, it can be used to transform qualitative data to
quantitative and vice versa.
The conversion of
qualitative data to quantitative data often leads to the loss of its
flexibility and depth, which are the main advantages to qualitative research.
This loss occurs because qualitative coding is multidimensional (Bazeley,
2004). Quantitative coding are fixed in a single dimension, sometimes
Mixed method design is
vulnerable to collinearity or multi-collinearity, this results from the
qualitative data begin quantitized (Roberts 2002).When researcher try to put
the data into categories to enable coding of the data collineartiy occurs. The controlling
for collinearity by the researcher reduces the validity of the Information
provided. Mixed method is expensive to conduct and it is time consuming. It is
also difficult to find a researcher with adequate experience in both QUANT AND
QUAL research methods. Another issue in mixed methods is how to interpret
conflicting results from both QUANT and QUAL analysis.
6.0 Assessing Mixed
Methods for Development Studies
MM is a very important
tool in research design for development planners and policy makers. MM gives a
deeper insight to the issues or projects by describing and analysis the facts
as related to individuals, groups, societies, and their sociological backgrounds.
It’s a good tool of policy advocacy for implementation, because MM examines
quantitative result and describes its facts in the simplest term to the policy
developers, backing up the narrative discourse with data presented in
percentages, frequency of occurrences and averages.
Most recently, MM is a
power tool for recent fields of study such as monitoring and evaluation of
programs and projects. Development itself is an evolutionary subject matter,
with dynamic issues. In the same way the development researcher and planners
tends to monitor the progress made at every given stage of the project because
of its constant changes. MM impact assessment helps researcher and development
planners to see the corresponding changes at different stage of the project
which aids clarity. Impact assessment it a vital part of the evaluation
process, it alert the planner on the extent to which the program as affected
the targeted group. These conclusions are usually drawn from the statistical
data or analysis of the data collected.
MM is a good tool for
forecasting and predicting programs and projects and can also assess the
probability of occurrences for changes base on its qualitative processes from
in-depth inductive assessment.