HLSS500 WK7 Data Analysis Tool & Qualitative Comparative Analysis Paper Responses should be a minimum of 250 words and include direct questions. You may ch

HLSS500 WK7 Data Analysis Tool & Qualitative Comparative Analysis Paper Responses should be a minimum of 250 words and include direct questions. You may challenge, support or supplement another student’s answer using the terms, concepts and theories from the required readings. Also, do not be afraid to respectfully disagree where you feel appropriate; as this should be part of your analysis process at this academic level.

Respond to Amy:
The research design being conducted in my study is a multi pronged approach of both quantitative and qualitative data. Therefore, the study will utilize mixed methods research design. Due to the fact that the research of the border security measures being taken is constantly on going, one can only gather so much statistical data during the given time periods of 2001 to present day. With this being said, hard facts from previous studies need to be utilized to further justify the hypothesis that the well-being of the United States is in high standings since the terrorist attacks in 2001.
           In order to fully support the hypothesis and thesis of the study, using a multi-phase mixed method approach allows one to analyze all factors of each focal group and its impact on the overall variables. One cannot make a credible study if one does not thoroughly examine all research and data provided from multiple approaches. Each variable will be evaluated in correlation to quantitative data and qualitative data without directly utilizing human subjects. All research will be drawn from previous works and as much numerical data as humanely possible.
           Qualitative Comparative Analysis will be used in order to examine combinations of characteristics in case studies conducted from previous researchers. With this comparative analysis, the study will take the variables of surplus of law enforcement personnel and infrastructure security, break it down, and correlate the outcome of the presence or absence of the focal groups. For instance, if there was not an increase in law enforcement personnel, would deterrence be obsolete and the crime rate be increased at the country’s borders? There are several studies and research available that can make these comparisons in an efficient and effective fashion.
           Another data analysis tool used in the study as a quantitative approach is validity and reliability. The statistical data in the study needs to specifically correlate to the thesis provided. With this being said, inter-item reliability is used to take multiple items to measure a sole ideal. The numerical data gathered from multiple sources, to include the Customs and Border Patrol HQ, all demonstrate that over the years, the apprehensions, the drug smuggling, and the immigration has dwindled greatly since the terrorist attacks in 2001. In order to support this ideal, one cannot solely take numerical data from one source and justify its worth in the study. Multiple sources need to be gathered in order to grasp the single concept that the increase of border security is justifying the United States’ well-being.  Face validity is used in this study as well so that the statistical data being used looks like what it is supposed to measure. That the numbers being provided are valid at face value. That there is no room to assume otherwise. The numbers of apprehensions, drugs, and immigration by the CBP reflects this face validity.
           One type of data collection that will be used is the Social Historical type. This type of collection is needed in order to gather elements of examination during the specific time period of September 11th 2001 to present day. The data being collected in this arena are solidified facts of the executive orders that were dropped from various Presidents in order to create a higher border security recognition and create room to correct issues at all borders. This justifies the theoretical explanations of what segments created a shift in securing the United States’ well-being and safety.
Content Analysis is conducted in this research in order to break down what integral actions were made to support the theory that deterrence played a role in the United States’ safety and border security. Content analysis is needed to decompose what the researcher needs within categories that are applicable to their study. Through this type of data collection, the study was able to be broken down six categories; importance of border security, increase of law enforcement at borders, increase of infrastructure security, impact on drug smuggling, impact on immigration, and impact on trade. Within each content, the study can be directed to reach the appropriate theory. Without these contents, the hypothesis of the study would not be whole hearteningly supported due to the lack of information being given. Running head: MIXED METHOD RESEARCH PROJECT
Mixed Method Research Project
Name
Institution Affiliation
Date
1
MIXED METHOD RESEARCH PROJECT
2
Introduction
Mixed method research comprises of both qualitative and quantitive research designs to
have merged outcomes. The two distinguishing characteristics of the method revolve around the
purpose of the design and running both qualitative and quantitative approaches either
concurrently or sequentially. In designing a mixed method research project, among the key
issues to consider from qualitative and quantitive design are language, design criteria, and
individual paradigm since they vary in mixed method (O’Cathain, 2010). For example, validity
used in a quantitative method referring to quality is unacceptable in the mixed method design
and instead inference quality is embraced. Planning is the first domain to address the quality of
mixed method research and encompasses how transparent the research is, and also designs
quality that includes transparency, suitability, and rigor. I would use concurrent design in data
collection and coding and address the issues and strategies that revolve around my research
question in my mixed method research project.
Data Collection
In data collection, I would use a triangulation design where I will collect quantitative and
qualitative data and then analyze them in a parallel way and merge to compare the results
(Bergman, 2011). I would select unequal samples to both methods with the same participants
and use smaller qualitative samples to compare with large quantitative samples. In the qualitative
approach, I would further divide the sample size to sub-samples to have a more detailed
prescription from participants and then combine the results. This will help save on cost and
labor. I would use techniques such as structured open-ended interviews, close-ended surveys,
and questionnaires. The methods of data collection would depict a clear picture to compare the
data from both approaches and have one outcome.
MIXED METHOD RESEARCH PROJECT
3
Data Coding
I would code data to derive the essence and the meaning of the research in mixed method
research project using three ways including transformation to make comparison easy, by use of
matrices to organize data from both approaches in one table, and also I would design and
implement topics and questions that can be compared. In the first strategy, using the structured
interviews and open-ended questionnaires, comparable issues would help code the two data sets.
On the strategy of using matrices, I would collect themes from the qualitative approach and those
from the quantitative method, and combine them in a single table with each in a different
column. This would help in comparing the two data sets at a close check in a single table. On the
strategy of transforming one of the data set, I would transform qualitative data to quantitive data
by using themes and frequencies and then determine the themes with high rates. After identifying
the themes and frequencies, I would conduct statistical analysis and then compare with the
quantitive data. I choose to transform qualitative data into quantitative data because it is more
comfortable.
Conclusion
Concurrent analysis of data collection and coding is time and labor consuming compared
to sequential design since it lacks phases in data analysis for mixed method research approach.
Mixed method research comprises of the concurrent and sequential designs. I would use a
parallel design in my mixed method research project to collect and code data using structured
techniques such as open-ended interviews, close-ended surveys, and questionnaires. I would use
the transformation technique, use of matrices, and also implement topics and questions that are
comparable. I would consider the language, and design criteria since they are critical areas of
4
MIXED METHOD RESEARCH PROJECT
consideration in mixed method research. I would also consider planning well in terms of
rationale transparency as well as design quality to suitability, strength, and transparency.
References
MIXED METHOD RESEARCH PROJECT
5
O’Cathain, A. (2010). Assessing the quality of mixed methods research: Toward a
comprehensive framework. Handbook of mixed methods in social and behavioral
research, 531, 555.
Bergman, M, M. (2011). 5 Methodological Issues in Conducting Mixed Methods Research
Designs In: Advances in Mixed Methods Research. Retrieved from
file:///C:/Users/user/Downloads/20190513043034chapter_5__advances_in_mixed_metho
d sresearch.pdf
5 Methodological Issues in Conducting
Mixed Methods Research Designs
In: Advances in Mixed Methods Research
Edited by: Manfred Max Bergman
Pub. Date: 2011
Access Date: May 6, 2019
Publishing Company: SAGE Publications Ltd
City: London
Print ISBN: 9781412948098
Online ISBN: 9780857024329
DOI: https://dx.doi.org/10.4135/9780857024329
Print pages: 66-83
© 2008 SAGE Publications Ltd All Rights Reserved.
This PDF has been generated from SAGE Research Methods. Please note that the pagination of the
online version will vary from the pagination of the print book.
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5 Methodological Issues in Conducting Mixed Methods Research
Designs
Introduction
A need exists in the mixed methods literature to go beyond the types of designs available to researchers
and to begin exploring issues and strategies in conducting these designs. This chapter advances potential
concerns that need to be anticipated by researchers in conducting mixed methods research designs. It begins
by locating these concerns within two broad categories of designs (i.e. concurrent and sequential designs),
identifying potential concerns researchers might anticipate in using these designs, and citing published mixed
methods studies that illustrate not only the issues but also potential strategies, expressed or implied, for
addressing them. The methodological problems we explore relate to finding contradictory evidence between
quantitative and qualitative data, the integration of data, sampling, introducing bias, participant selection,
selection of results to use, and the sequence of implementing data. Understanding these issues and exploring
alternative strategies to address them will enhance our understanding of mixed methods procedures and
encourage rigorous, thoughtful designs.
In the literature on mixed methods research, considerable attention has been directed toward organizing and
classifying types of mixed methods designs (e.g. Creswell and Plano Clark, 2007; Greene et al., 1989; Morse,
1991; Tashakkori and Teddlie, 2003). In our review of these classifications, we have located 12 configurations
developed by authors from nursing, evaluation, public health, education, and social and behavioral research
(Creswell and Plano Clark, 2007). Although the names differ for the types of designs, two characteristics
emerge that are common to many classifications: either the purpose of the design is to merge (or bring
together) the qualitative and quantitative data in a parallel or concurrent way, or to have one type of data
(quantitative or qualitative) build on or extend the other type of data (qualitative or quantitative) in a sequential
way. These two major design options seem to hold whether the research is presented as a single study, such
as found in many doctoral studies, or in a multi-phase project, such as found in the evaluation literature and
in large-scale funded projects.
Our investigations examining the application of mixed methods in many disciplines resulted in the collection
of numerous studies that fit into this concurrent or sequential schemata (e.g. in family medicine, Creswell
et al., 2004; in counseling psychology, Hanson et al., 2005; in physics education, Plano Clark, 2005; and in
family science, Plano Clark et al., in press). As we reviewed these mixed methods studies, we found few
authors discussing potential methodological issues that might limit the findings of their studies. Discussing
procedural limitations that might influence the outcomes is not new in research methodology or in quantitative
or qualitative discussions. For example, in the classic treatise on experimental and quasi-experimental
designs, Campbell and Stanley (1966) identified 16 types of designs, and discussed eight threats to internal
validity and four threats to external validity. In qualitative research, Jacob (1988) classified the traditions of
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qualitative research and discussed issues in the traditions, such as the foci of the types and their levels
of analysis. In addition, as we presented workshops and discussions about mixed methods research in the
U.S. and internationally, participants began raising questions about potential flaws or challenges that might
emerge in using different mixed methods designs. Building on these perspectives, we suggest that as mixed
methods emerges as a field of study and the types of designs become clearer to researchers, a discussion
about the methodological problems likely to arise in implementing these designs and potential strategies that
researchers might use to address them is timely.
As we wrote our recent book on mixed methods research (Creswell and Plano Clark, 2007), we began to
enumerate some of the challenges that would likely occur in different types of designs. Some methodological
problems were mentioned as we discussed each design; others we integrated into the data collection
and the data analysis discussions. Although authors have mentioned particular methodological problems,
such as sampling and contradictory findings, their work was not centered on particular mixed methods
designs (Collins et al., 2006; Erzberger and Kelle, 2003; Kemper et al., 2003; Teddlie and Yu, 2007; Trend,
1979).Thus, there was little information to help us recognize and address the issues inherent in the designs
within the mixed methods literature. We did not find any discussions in this literature that directly spoke to
these issues, aside from the general comment in Tashakkori and Teddlie (2003) that the design issues and
logistics in conducting mixed methods research were two unresolved issues.
The purpose of this chapter is to identify methodological problems and advance potential strategies for
planning and conducting mixed methods designs. Our intent is to present a pool of ideas from which
researchers can draw in their efforts to overcome methodological problems and craft rigorous studies. To this
end, we start with the issues that we specified previously (Creswell and Plano Clark, 2007), issues largely
based on conversations with U.S. and international researchers. A useful heuristic for thinking about these
methodological problems in designs occurred as we mapped the core ideas for this chapter: to organize
the issues and seek out strategies for addressing the issues using designs clustered into concurrent or
sequential designs. We therefore organized the issues into distinct themes, related them to either concurrent
or sequential designs, and looked extensively in published, empirical mixed methods articles reported in the
journal literature, for example that would illustrate the challenges as well as present potential strategies for
addressing them. As we reviewed the articles, we were also careful to identify challenges that we had not
previously anticipated.
Methodological framework
We discuss in our book four major designs, two of which can be conducted concurrently (Triangulation,
Embedded) and three that are conducted sequentially (Explanatory, Exploratory, Embedded) (see Figures 5.1
and 5.2) (Creswell and Plano Clark, 2007).
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Figure 5.1 Concurrent mixed methods designs
The Triangulation Design is a one-phase design in which quantitative and qualitative data are collected and
analyzed in parallel and then merged together to develop a more complete understanding or to compare
the different results. Although this design is the most popular mixed methods design, it is also probably the
most challenging of the four major types of designs. Researchers use a second type of concurrent design,
an Embedded Design, when they want to enhance a study based on one method by including a secondary
dataset from the other method. This design is often used when researchers need to embed a qualitative
component during an intervention in an experimental study. In this case, the qualitative data are collected
concurrent with the implementation of the intervention, and the qualitative information typically focuses on
exploring how the experimental participants experience the intervention while the quantitative arm addresses
the outcomes of the trial (see Creswell et al., 2006, for additional reasons for embedding qualitative data
within experiments).
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Figure 5.2 Sequential mixed methods designs
Turning to the sequential Explanatory, Exploratory and Embedded Designs, the quantitative and qualitative
data collection are implemented in different phases and are connected in some way. Researchers use the
Explanatory Design when they start with quantitative methods and then follow up with qualitative methods,
usually to help explain the initial quantitative results. Researchers using an Exploratory Design begin by
exploring the topic with qualitative methods and then build to a second quantitative phase where the
initial results may be tested or generalized. The sequential Embedded Design typically involves collecting
qualitative data before an intervention begins or after it is complete. When collected before the intervention,
researchers use the qualitative data to help recruit participants, to help test the treatment before the actual
experiment, or to select participants that can be best suited for the experimental and control conditions. When
collected after the intervention, the qualitative data help to explain why different outcomes resulted.
Method
These types of designs provided a framework for analyzing published mixed methods studies. We conducted
literature reviews using major databases including ERIC, PsycInfo, and PubMed. We located studies that met
our basic criterion of mixed methods research: studies in which the authors collected, analyzed, and merged
or connected both quantitative and qualitative approaches of data in a single study or in multiple studies in a
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program of inquiry (Creswell and Plano Clark, 2007). Next, we classified these studies as either concurrent
or sequential designs. To accomplish this we closely examined the articles, paying particular attention to
the methods section to determine a) if the authors of the study included both quantitative (i.e. close-ended)
and qualitative (i.e. open-ended) data; b) how they integrated both sets of data (either concurrently or
sequentially); and c) if they mentioned the type of mixed methods design they were using. Based on these
factors, we identified nearly 160 articles spanning the last 15 years. The articles were drawn from fields in
the social sciences and the health sciences. Then, we examined two sections of the studies in depth: the
methods and the results/discussion sections to find occurrences of methodological issues. We were chiefly
interested in determining if the authors mentioned specific issues or challenges in carrying out their designs
and whether the authors identified procedures (or strategies) to address the issues. We began to organize
the issues and the solutions into a table that would provide practical suggestions for conducting each of the
four mixed methods designs and provide researchers with references they could use for their procedures (see
Table 5.1). We were also open to identifying issues not in our original schema.
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TABLE 5.1 Problems and strategies for concurrent and sequential designs
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Examples of issues and strategies
Our search of the literature yielded several issues and numerous strategies either implied or expressed
by authors. We discuss these issues and strategies for both concurrent and sequential designs, providing
examples drawn from the examined studies.
Concurrent design issues and strategies
We begin with a discussion of the concurrent designs and explore how authors of published mixed methods
articles addressed contradictory findings, integrated or combined the quantitative and qualitative data, chose
participants for the samples and the size of the samples, and limited the potential bias of data collection in
their designs.
Contradictory findings
This issue may emerge during a concurrent type of design when the quantitative and qualitative results do
not agree. Disconfirming findings may indicate flaws or inconsistencies in the research design. Erzberger
and Kelle (2003) explain that discrepancies between quantitative and qualitative data may be the result of
researcher’s errors in data collection and analysis or poor application of theoretical propositions. Divergent
findings, however, can also be thought of as a means to uncovering new theories or extending existing
theories.
Disagreement between the quantitative and qualitative strands may be a minor or a major difference in the
results. These differences may be difficult to resolve and may require the collection of additional data to
resolve the differences (Creswell, 2005). This strategy, however, raises a further question about what type of
additional data will be needed, analyzed, or given priority.
Padgett’s (2004) study recounts how a team of researchers returned to their initial database for more
insights after contradictory findings emerged. This occurred during the Harlem Mammogram Study, which
was funded by the National Cancer Institute to examine factors that influenced delay in response to an
abnormal mammogram among African-American women living in New York City. The research team had
collected both structured quantitative data and open-ended interview data. After data analyses, the team
concluded that the women’s decisions to delay were not driven by factors in their quantitative model. The
researchers then turned to their qualitative data, highlighted two qualitative themes, and reexamined their
quantitative database for support for the themes. To their surprise, the quantitative data confirmed what the
participants had said. This new information, in turn, led to a further exploration of the literature, in which they
found some confirmation for the new findings. Used in this way, researchers could also view the contradiction
as a springboard for new directions of inquiry (Bryman, 1988).
To resolve contradictory findings, authors may also give priority to one form of data over the other. Chesla
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(1992) advised comparing the data from each strand by ‘weighing the evidence’ and deciding on priority by
determining which method was more …
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