1. KNOWLEDGE AREA MODULE 7
Research
Student: Thomas P. FitzGibbon, III
Student’s Email: tfitzgib3@ameritech.net
Student’s ID#: 0378491
Ph.D. in Applied Management and Decision Sciences
Specialization: Finance
Faculty Mentor: Dr. Mohammad Sharifzadeh
Mohammad.sharifzadeh@waldenu.edu
Faculty Assessor: Dr. Reza Hamzaee
reza.hamzaee@waldenu.edu
Walden University
February 4, 2009

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ABSTRACT
Breadth
The purpose of this KAM is to identify the theories associated with qualitative,
quantitative and mixed methods research processes in research. The Breadth Component
focuses specifically on the development of each process, related standards of
implementation and the tactics used to maintain validity and applicability to the larger
population. I will examine the theories of Earl Babbie, John W. Creswell and W.
Lawrence Neuman and how those theories assist in the definition and processes related to
the three research processes.

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ABSTRACT
Depth
The purpose of this KAM is to identify the current research and practical application of
quantitative, qualitative and mixed methods research techniques. The Depth Component
focuses specifically on a review of contemporary literature highlighting current practices
in each technique. In addition, I will discuss both the positive and negative perspectives
of each and use that information as a baseline for the Application Component of this
review.

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ABSTRACT
Application
The application section will provide an overview of the issues related to the use of
historical data in contemporary research. Within this context both qualitative and
quantitative research will be discussed as well as the advantages and disadvantages of
reusing data. Finally, the application of these strategies to contemporary research as to
the relationship between home ownership and socio-economic benefit to low-income
communities will also be reviewed and applied to the overall data sourcing strategy.

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TABLE OF CONTENTS
BREADTH........................................................................................................................6
Introduction ...........................................................................................................6
Qualitative Design .................................................................................................6
The Role of the Researcher ....................................................................................11
Data Collection Procedures....................................................................................12
Data Analysis ........................................................................................................15
Data Reliability......................................................................................................18
Grounded Theory...................................................................................................21
Quantitative Design ...............................................................................................21
Variables in Quantitative Research ........................................................................24
Threats to Validity of Data.....................................................................................25
Effective Data Analysis in Quantitative Research ..................................................30
Mixed Methods Research.......................................................................................32
Conclusion.............................................................................................................35
DEPTH .............................................................................................................................37
Annotated Bibliography.........................................................................................37
Literature Review Essay ........................................................................................53
Introduction................................................................................................53
Quantitative Analysis .................................................................................54
Research Methodology in Quantitative Research........................................54
Data Collection Methods in Quantitative Research.....................................57
Research Appraisal.....................................................................................67
Qualitative Analysis ...................................................................................67
Qualitative Research Methodology.............................................................68
Qualitative Research Approach ..................................................................68
Qualitative Data Collection ........................................................................71
Data Collection Issues in Qualitative Research...........................................74
Mixed Method Research.............................................................................76
Conclusion.................................................................................................78
APPLICATION ................................................................................................................79
Introduction ...........................................................................................................79
Historical Data in Qualitative Research..................................................................81
Uses of Historical Data in Qualitative Research.....................................................85
Application to Dissertation.....................................................................................87
Historical Data in Quantitative Research................................................................87
Uses of Historical Data in Quantitative Research ...................................................89
Application to Dissertation.....................................................................................90
Accessing Demographic Data ................................................................................91
Uses of Demographic Data ....................................................................................95
Sources of Needed Data.........................................................................................97
Potential Data Sharing Strategies ...........................................................................100
Conclusions ...........................................................................................................102
REFERENCES..................................................................................................................103

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BREADTH
AMDS 8713: RESEARCH METHODS
Introduction
Research is a primary part of the process of validating a hypothesis or an idea.
Without research, we fail to have a complete understanding of whether or not our ideas
are correct. Additionally, to provide credibility and validity to our ideas, those ideas
must be supported by data. As such, the focus of this KAM will be on the processes and
procedures related to quantitative, qualitative and mixed-model research.
The benefit of these research options is that it gives the researcher and the
audience both the objective and subjective perspectives of a surveyed population. In the
end, this provides us with an opportunity to gain insight into a greater definition of what
the data means and how that data validates the hypothesis. It is with this information that
the findings of the research are of use and applicable to a greater population. Given this,
the focus of the Breadth Section is to build a greater understanding of these research
methods based on the theories of John W. Creswell (2002), Earl R. Babbie (2006) and W.
Lawrence Neuman (2005) and how those theories support the development of an
effective research plan.
Qualitative Design
While many would consider qualitative design to focus more on subjective
analysis of information, however, that does not paint a complete picture of the purpose or
benefit of this research technique. Qualitative analysis allows the researcher to move
beyond the raw data often found in quantitative research by providing an opportunity to
provide a more holistic approach to research. This does not imply that qualitative

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research is any better or worse than quantitative. It is simply a method of gaining insight
into additional information that may not be readily available in other research methods.
Assuming that the researcher has decided on a specific topic of their research, the
next step in qualitative research is to build the process that will support the collection and
interpretation of the data supporting the effort. In his summary of the research design,
Creswell (2002) provided a detailed summary of the steps necessary to establish the
research design. Those steps focus from the criteria used for participant selection, the
setting of the research to the interpretation of the data. The summary below will provide
a detailed summary of those steps.
The initial step in the process is to determine the needs of the audience and how
the research may match those needs. Depending on the topic, there may be particular
characteristics that a participant must possess in order to be a participant. For example, if
the purpose of the research is to determine the needs of low- and moderate-income
families, the participants must meet that income level in order for the resulting data to
applicable to the research topic. In addition, the participants must possess a sufficient
level of familiarity with the research topic in order to provide a valid response to the
research questions (Creswell, 2002). Babbie (2006) takes a slightly different approach in
this effort as he considers that it is helpful for the researcher to determine particular
classes of individuals that would be a good fit for the research. By meaning, he considers
that it would be beneficial to determine whether there are particular groups that would be
the ideal candidates for the research. With that, the researcher can then take the next step
in identifying the specific audience for the research.

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Along with this, the research should also be conducted in a setting that is familiar
to the participants. By meaning, it would not be beneficial to the researcher to remove
the participants from a familiar environment and bring them to another environment less
familiar (Creswell, 2002). The risk with this effort would be that the environment could
serve as a distraction to the participants. Thus, the responses that the participants would
provide may be tainted due to the distracting environment where the research is
conducted. Additionally, Neuman (2005) considers that the location for the observation
should be considered in advance of determining the particular case study for review. The
intent with this is that the location can drive the parameters of the case study as well as
the research methods under consideration. Thus, the researcher can develop the entire
design in a much more efficient method by first identifying the location for the
observation work.
Furthermore, the researcher must also understand that they are the primary
instrument of the data collection. By meaning, while the researchers may use surveys,
role plays (Babbie, 2006) or other tactics to collect data, their actions in the research
process also have an impact on the collected results. Furthermore, the researcher will
also be the individual responsible for creating the instruments used in the data collection
(Creswell, 2002). As such, the development of the instruments will be directly impacted
by the knowledge of the researcher.
The researcher must also use multiple sources of data in their collection process.
This is typically referred to as ‘triangulation’ (Neuman, 2005). The benefit in this effort
is that it serves to validate the data by seeing if similar responses are provided by multiple
sources of data (Creswell, 2002). By meaning, the results can be assumed to be reflective

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of a larger population if more respondents provide similar responses. Thus, the
researcher can make a reasonable assumption that the responses of the participants could
be consistent with a larger population. However, this process is not limited to personal
responses from participants. Researchers can also access numerical data or existing data
that would also serve to validate the participants’ feedback in the current research.
Upon the conclusion of the data collection process, the next stage in the effort is
to develop a method of categorizing the data into useful groups. This process allows the
researcher to begin the process of organizing the data into themes that can then be used to
form the groundwork for the summarization of data (Creswell, 2002). Babbie (2006),
Creswell (2002) and Neuman (2005) define this process as coding where the researcher
can take a large set of qualitative responses and identify the major themes. This is in
contrast to a quantitative process where the data is typically summarized into useful
groups. In addition, the researcher can access data that would be considered fairly
abstract and focus on common themes identified by the participants.
Furthermore, within the categorization process, the researcher must also consider
the underlying meanings of the participant’s responses. This is a key part of the
qualitative process. By meaning, the process does not involve a simple recording of the
responses, but it should also include an attempt to understand the meaning behind the
responses (Creswell, 2002). While the research will eventually focus on the researcher’s
summary of the responses, in order to gain a fuller understanding of what those responses
mean and how they relate to the research, the data must contain the results associated
with the actual responses as well as any underlying meaning of those responses.

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Finally, the researcher must also understand that their initial research plan will
evolve over the duration of the research process (Creswell, 2002). Thus, while the
researcher will establish a process and method for attaining the information, they must
also be prepared for events taking place during the research process that may prompt a
change in tactics. However, the researcher must also consider the impact that any
changes may make on the overall research topic as well as previously collected data.
While it may be considered to be a delay in the data collection, this correction may be
necessary as it may provide greater relevance to the overall research goals.
However, there is also an underlying need throughout the process to be consistent
in the interpretation of the data (Creswell, 2002). This is especially challenging when
multiple researchers are providing an interpretation of the results. The intention with this
standardization is that it also serves to provide a consistent approach from multiple
researchers where it can serve to reduce errors related to inconsistent interpretation. This
would be in a relative contrast to quantitative research where the data could be considered
to exist in a more generally accepted format, numbers and other forms of quantifiable
data.
Furthermore, Neuman (2005) provides a slightly deeper review of the data
interpretation process. Interpretation is a stage of events that start with the researcher’s
understanding of the responses from the perspective of the subject group. Secondly, the
focus then shifts to how the researcher interprets those responses and finally, the
interpretation of those reading the research are then considered (Neuman, 2005). This
provides the researcher with the opportunity to consider how each participant in the

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research would interpret the meaning of the data as well as the application of that data to
the research questions under study.
Thus, this brings us back to the overall goal of qualitative research, to provide a
holistic perspective on the data collected (Creswell, 2002). This holistic approach takes
into account not only the information collected, but the underlying meaning and its
interpretation as well. Therefore, allowing the researcher to see the bigger picture of the
meaning of the responses rather than simply focusing on the responses themselves.
The Role of the Researcher
Beyond simply collecting and interpreting data, the researcher must also
communicate this information to their survey participants (Creswell, 2002). The intent
with this effort is that the participants not only understand the goals of the research, but
they can also understand any perspectives that the researcher may have on the topic.
Along with this, researchers also focus on the need to either build on existing knowledge
or create a new perspective for further research (Neuman, 2005). Referring back to our
example of low- and moderate-income families, the researcher could be providing new
information to existing research or building a foundation of newly discovered
information about the group under study. However, with either perspective, the
researcher must disclose their intent with the research effort under review so the
participants have an understanding of their role along with the research team.
To the contrary, there is also a risk where the researcher could be considered to be
in a position of authority for the participants (Creswell, 2002). An example of this would
be where a researcher and the participants work in the same environment, and the
researcher is assumed to be in a management position in the organization. Additionally,

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Neuman (2005) considers that there is also a risk where the subjects could consider the
authority of the researcher as a limit to the usefulness of the data. By meaning, the
subjects could assume that the researcher is already an expert on the information
discussed thus limit their feedback to the researcher in the data collection process.
Clearly, this can be detrimental to the research in that the participants would likely focus
their responses to either satisfy the request for information or avoid areas that could be
considered controversial in the workplace. On the other hand, Babbie (2006) considers
that authority should only be applicable as a starting point to further study in that those in
authority may have certain knowledge of the topic, but that they do not possess all
knowledge. However, the validity of the data and interpretation could be questioned
since the answers would be tainted by the perceived authority position of the researcher.
In conclusion, the researcher must be able to effectively communicate their
objectivity in the process to those participating in the research. They should also provide
the participants with a clear understanding of the process of their research design,
including the processes related to the Institutional Review Board in that process
(Creswell, 2002). This will provide a necessary level of assurance for the participants
providing an environment where the participants will provide honest and relevant data.
Data Collection Procedures
As discussed above, data accuracy is critical to the research process. Without
accurate data, the conclusions of the research will be questioned as to its validity and
applicability to a larger population. As such, with qualitative research there should be a
standardized process of data collection. In the event where groups of people may be
tasked with the data collection, this standardization also serves to provide the

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groundwork for consistency that is necessary where there are multiple researchers
working on the data collection process.
The first step in the process is to identify the individuals that will be participants
in the research. To clarify, by participants, we should consider those subjects who the
data will be collected from, not those that are doing the data collection. As I have
mentioned previously, the participants should have requisite knowledge of the topic so as
to provide relevant information to the researchers. Beyond that, the participants should
be from a randomly selected yet a representative group of the larger population (Neuman,
2005). The intent with this is that with a reasonably random selection process, there is
less potential for bias or the group of participants not being representative of a larger
population (Creswell, 2002).
The second step in the process is that there should be a standard set of data to be
collected in the research process (Creswell, 2002). This would also define the research
instruments to be used as well as the setting in which the research is conducted.
However, this does not imply that the research is completed at one point in time. For
example, with time studies, the researcher must not only consider the units of
measurement of the observations, but also have a stated timeframe where the data will be
collected (Neuman, 2005). Again, this is also very applicable in situations with multiple
researchers. With agreement on the data format and types, the researchers can focus their
individual efforts on collecting the specific data that is pertinent to the research topic.
The final stage of the data collection process involves the survey instruments.
Typically, qualitative research involves both interviews and observations (Creswell,
2002). Interviews can be done on an individual or group basis. The interview instrument

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would normally consist of an interview guide that provides a list of questions, both open
and closed ended, that will focus on the subject material that is pertinent to the research
topics. The intent with either option is to gain insight into the beliefs of those under
observation (Neuman, 2005). There is no standardized length or format of the questions
for an interview, however, the delivery of the interview must be consistent between
researchers.
Along with the interview guide, the researchers must also standardize their
process of observing the behavior of the participants in the process (Creswell, 2002). As
discussed earlier, while there is significant relevance to the verbal responses from the
participants, qualitative research also places significant relevance on the behavior of the
participants in the process as well. Thus, the researchers should also establish a process
of collecting the behavioral information as well. As Babbie (2006) notes, it is necessary
that the researcher not only note the subject’s behavior, but it is also important to note the
researcher’s interpretation of that behavior at the point of observation. This should be
done in parallel with the interview data so as to determine any common behaviors
between participants that may relate to specific questions in the interview process.
Finally, the qualitative researcher may also consider relevant information in the
form of documents or audio and visual materials where relevant to the research topic
(Creswell, 2002). While not specifically observed or recorded from an interview of a
participant, it may be applicable to the environment that the participant is in which may
provide insight into the environment. Again, using our example of low- and moderate-
income families as a participant group, public information in the media, may give the
researcher improved insight into the specific community participating in the research. It

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may also serve to provide a context for observed behavior and interview responses
collected in the research.
Data Analysis
Data analysis in qualitative research can be quite complicated, but with an
established process, and a consistent method of data collection, researchers can
standardize the process and get reliable data. However, within the process, qualitative
data calls for routine evaluation of the collected data as well (Creswell, 2002). As I
mentioned earlier, there maybe a need to adjust the data collection and research process
when new information is uncovered by the researcher. In this event, there may be a need
to consider new information that may not be directly related to the topic under discussion,
but may be valuable to the overall research. Additionally, the researcher should also
consider noting this additional information in their overall findings when preparing their
research summary.
Additionally, one of the challenges with qualitative research that there is a
tendency to focus on interview and survey questions that are open ended (Creswell, 2002
and Babbie, 2006). Open ended questions call for the participant to provide a more
extensive answer in comparison to closed ended questions. With that, there is a need to
develop analytical tools that will categorize the information. This process is commonly
referred to as coding.
Coding requires that the researcher make multiple reviews of the data to
determine any common themes of the responses (Creswell, 2002). The first stage of the
coding process is to initiate a general review of the information to first determine the
general themes. This process of open coding allows the researcher to understand the

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major themes of the research. From there, the researcher then follows a process referred
to as axial coding (Babbie, 2006) which then takes the major themes and applies them to
the theoretical model of the research (Creswell, 2002). Finally, the last stage of the
coding process involves taking the information and developing an understanding of
whether there are any interconnected factors between particular data points (Creswell,
2002). This information can then be used to develop the narrative of the data for the
research (Babbie, 2006).
However, within the coding process, there is also a need to standardize the codes
that the researcher will use. This code book is a common practice regardless of the
number of researchers involved in the data collection and analysis. The code book
provides an opportunity to have a consistent method of categorizing the information
(Creswell, 2002). The underlying intent in this process is that with standardized codes,
the data will be consistently reviewed and categorized. Without this standardization, the
researchers run the risk of other misinterpreting the information or potentially missing
relevant information gathered in the research.
Babbie (2006) considered the coding process to be within the need to identify
particular patterns of the collected data. By meaning, the identification of patterns serves
to provide the researcher with the ability to formulate a consistent message that the
participants wish to convey. This message can then be used in the narrative summary
resulting from the research.
In order to determine how applicable a participant’s response is to a greater
population, the first step is to determine how frequent a response occurred in the
participant group (Babbie, 2006). This will provide the researcher with an understanding

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of whether or not the response is common in the group. In the event that it could be
defined as a common response the researcher could then make a valid conclusion that the
common response would be consistent with the larger population.
The researcher must also assess the level of magnitude of particular responses
(Babbie, 2006). This is commonly used to assess the severity of a particular subject
matter from the perspective of the participant. An example of this would be the
utilization of a Likert Scale assessment built within the qualitative interview. This is in
contrary to frequency where the researcher is assessing how often something occurs.
With the assessment of magnitude, the researcher is attempting to determine the relative
impact that event has on the participant.
The next step in the process is to determine whether there is any structure to the
responses (Babbie, 2006). The intent with this effort is to assess how the particular topic
may impact the participant and their response. Using our low- and moderate-income
families example, the researcher could determine whether a particular topic may have a
mental or physical health impact. By meaning, is there a particular event where the
participant may experience depression or anxiety impacting their mental health, or high
blood pressure or other physical health problems.
Further, the researcher should also assess whether or not there are any related
processes associated with the responses (Babbie, 2006). In this context, the researcher is
attempting to determine whether there may be a sequence of events that leads the
participant to provide their response. For example, we could consider whether a job loss
may lead to financial stress which then leads to mental health issues identified in the
assessment of structure.

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Along with this, the researcher also needs to determine any applicable causes to
drive the participant’s responses (Babbie, 2006). This is similar to assessing any
underlying processes where there is a certain sequence of events that cause further events
to elicit the participant’s response to a particular question. Like the processes
assessment, the researcher can utilize this information to potentially uncover unexpected
causes that could be applicable to the greater population.
Finally, the researcher must also assess the consequences of the processes and
causes (Babbie, 2006). Simply put, consequences would define the outcome of the
processes and causes. In other words, it allows the researcher to develop an
understanding of that has or could happen to the participant group as a result of the stated
processes and causes. Again, using our low- and moderate-income example, we could
see that a consequence experienced by the community could involve foreclosure of
homes or increases in crime. Ironically, there are also many opportunities where a
consequence of a particular area of research could also be a process or cause for other
research topics as well.
The result of this information allows the researcher to develop a process of
concept mapping (Babbie, 2006). Concept mapping allows the researcher to visualize the
relationships between particular coded topics. While this may not be presented in the
final research, this mapping process provides an efficient method for the researcher to
move beyond the transcripts of interviews to see what interconnections may exist
between particular areas of research.
Data Reliability

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The last stage of the qualitative review process is to determine the level of
reliability of the collected data. In this process, the researcher is able to assess the
instruments, the surveyed population and the resulting data to determine how applicable
that information is to a larger population. Given the types of data collected, this could be
considered a more extensive process in comparison to quantitative data analysis and
reliability.
The first stage of the assessment of reliability is a reexamination of the collected
data. For qualitative analysis, this involves a thorough review of the transcripts of the
surveys and interviews to determine whether that information is consistent with the
coding process used to categorize the data (Creswell, 2002). The intent with this effort is
to identify any errors in the data collection and review process, and provide an
opportunity to make the applicable corrections to the information.
Secondly, the researcher should also review both the codes used and the
governing code book used to summarize the information (Creswell, 2002). This part of
the process is used to identify any inconsistencies in coding as well as any redundancies
that may cause confusion when the results are summarized. In the event where an
incorrect or irrelevant code is used, the researcher must take steps to correct the code as
well as correct any data where that code may have been used in the summary.
In the event where there are multiple researchers, there should also be a plan for
regular communication between the members of the team (Creswell, 2002). This is not
only beneficial for events where a correction is needed in the data collection process, but
it is also quite valuable in the reliability of the data conclusions as well. This
communication strategy serves to keep the team current with any changes or partial

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summaries that may result from the data summation process. Additionally, this is also
necessary where there may be errors in codes or coding that could negatively impact the
work of other researchers on the team.
Finally, the researcher should also consider validating their data collection and
analysis processes against the work of other researchers following similar tactics used in
their research processes (Creswell, 2002). The intent with this effort provides an
opportunity to utilize generally accepted research processes available to new works.
Additionally, this also provides an opportunity for researcher to defend their strategies to
the academic community rather than attempting to create a new process that the
researcher created for their individual purpose. In the event where a researcher is
creating a new data collection method, they should be prepared to address the reasons
why they felt there was a need to develop a new process when an existing, and
academically accepted process may be readily available.
Furthermore, we must also consider how reliable the observed data is against the
conclusions of other research available in the public domain. Neuman (2005) refers to
this as external consistency. In this effort, the researchers will begin the process of
formulating their conclusions and then compare those conclusions against existing
research. That comparison may yield irregularities in their conclusions. However, it may
also uncover new conclusions that should be rechecked by the researcher for consistency.
Along with this, both Neuman (2005) and Babbie (2006) also consider the
consistency of the interpretation of the observation. Neuman (2005) refers to this as
internal validity. In this situation, the researchers are attempting to provide a level of
consistency in their interpretation of the behavior. Furthermore, the researcher’s focus

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should be on whether the behavior is true or could be deceptive on the part of the
research subject. The intent with this is to first understand what is known about the
person under observation and then compare that knowledge against the observed
behavior of the research subject.
Grounded Theory
Regardless of the tactics or strategies used in the data collection and analysis,
qualitative researchers will develop their theories as they are collecting the data
(Neuman, 2005 and Babbie, 2006). This is referred to as Grounded Theory. The process
for this effort is to first understand the collected data and then formulate the theories
within the collected data. However, this is not a one time process. As I have mentioned
above, the collected data may result in changes to the continued research process. Thus,
the researcher should attempt to continually ground the data to ensure that the developed
theories remain consistent with the data collection process and analysis as new
information is collected from the participants.
Quantitative Design
As I have highlighted earlier, quantitative research tends to focus on more
objective criteria in comparison to qualitative research that tends to focus more on not
only verbal and non-verbal responses, but to also gain insight into the underlying
meaning of those responses. In contrast, quantitative data tends to be driven more by
numerical responses with significantly less subjective analysis of the data.
However, this does not imply that there are no common elements between the two
research methods. For example, both quantitative and qualitative research may consider
the use of surveys as an instrument to collect data. For quantitative research, the survey

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should follow a specific development plan that details the design of the instrument along
with its intended use in the research process.
With any research plan, the first step in the survey design should describe the
purpose of they survey research (Creswell, 2002). The intent in this step is to describe
the intended purpose of the research, the survey design and the population that will
receive and respond to the survey. This information will allow both the researcher and
those reviewing the research to have an understanding overall strategy and the ability to
assess how applicable that research and its results would be to a larger population.
The next step in the survey development process is to decide on and justify, the
particular survey instruments to be used. With the multitude of options available from
surveys and interviews, the researcher must develop a process to assess how the generally
accepted methods will work within the particular research strategy. Along with this, the
researcher also needs to consider the structure of the instrument and how the structure
will support efficient data collection (Creswell, 2002). The intent in this effort is to not
only design an instrument that will gather the applicable data, but to also support easy
access to the relevant data as well. Thus, the instrument should have sufficient ability to
not only measure the variables, but to link those variables to the overall research
hypothesis as well (Neuman, 2005).
Furthermore, the researcher also must identify the time frame for the data
collection. Researchers can gather data during a single point in time or over a period of
time. Again, this depends on the topic and the identified research questions (Creswell.
2002). As discussed earlier, time studies can be applied to both quantitative and
qualitative research (Neuman, 2005). Further, time studies can consider events from the

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start of the research as well as historical events that have occurred prior to the
commencement of the research (Babbie, 2006). As an example, if the researcher wished
to determine any changes in responses by the survey group, it would be more appropriate
to define the time period along with any changes to the survey instrument to access that
data. This is often used in time studies to see if the responses change over a designated
period in relation to the research topic.
Finally, the last step in the process is to determine the form of data collection. As
mentioned above, this could not only define the survey or interview, but could also take
into account other sources of data collection that could involve secondary data sources
that could serve to triangulate the results (Creswell, 2002). As with any research, using
one instrument may limit the relevance of the data, thus, quantitative researchers will also
consider other sources of information that could uncover additional information about the
population that is applicable to the research topic.
Once the survey method is designed and the instrument is ready for use, the next
step in the process is to identify the participant group in the research (Creswell, 2002).
Clearly, the intent is to identify a sample population that could be reasonably
representative of a larger population under study (Neuman, 2005). Beyond that, the
researcher should clearly should provide the demographic makeup and size of the survey
group and compare that information to the general population. Additionally, the
researcher may need to make size adjustments based on the method of data collection.
For example, if the method will be by mailed survey, the researcher should assume a
reasonable response rate which would also impact the size of the surveyed population.
However, even with an interview setting, the researcher should assume that some

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members of the survey group may not choose to participate in the interview. As such,
size adjustments may be necessary here as well.
Furthermore, if applicable to the research topic, the demographics may also
involve stratification of the data. By stratification, it may be necessary to classify the
participants in the survey group in more detailed demographic categories (Babbie, 2006).
These could include gender, age, financial status along with many other options to
identify the survey group (Creswell, 2002). This information can also provide critical
insight to the researcher when the data may indicate particular responses that may be
common within a particular stratified group, but uncommon in the overall surveyed
population.
Variables in Quantitative Research
In quantitative research, there are two different variables to consider in the data
collection and analysis process, the dependent and independent variables. The
independent variable is the variable that has an impact or effect on other variables. The
dependent variable is that is the result of the outcome of the independent variable
(Neuman, 2005). Thus, in the research process, the researcher must identify all variables
that are identified in their investigation. This will serve to understand how individual
variables may impact the results of other variables in the study.
Furthermore, the study of interrelationship between variables will allow the
researcher to understand any impacts on causality within the research topic. This
potential causality may force the researcher to consider other potential outcomes of the
research as well as different perspectives from the survey population. It may also serve
to confirm or invalidate a specific hypothesis or theory that may be the basis of the study.

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Threats to Validity of Data
As with qualitative research, there are threats to properly understanding the data.
While quantitative analysis tends to be more objective in nature that does not necessarily
imply that there is an opportunity for researchers to misinterpret the collected information
and thus invalidate their overall findings. This is especially true with experimental
design.
The first potential threat to experimental validity is caused by the effect of time
after while the experiment is in progress. This is referred to as a historical threat to
validity. In situations where an experiment is conducted over an extended period of time,
there is a risk that the experimental group could experience events that may impact the
results of the experiment. Clearly, it is not commonly appropriate to keep research
subjects in an isolated area to avoid this occurrence, but when using a control group, the
researcher could also provide an opportunity for both the control and experimental group
to experience the same event. Thus, the ability to compare the two groups would remain
valid since the external event could be assumed to impact both groups in a consistent
manner (Creswell, 2002).
However, Babbie (2006) does consider historical threats but under a different
context than Creswell. Babbie considers that historical threats are tied outside events that
may occur over the time of the experiment that could impact the responses collected over
a timed study. For example, if a researcher wished to collect feedback on home
purchases, events like the current economic crisis may taint the data received. By
meaning, there is a high likelihood that had the crisis not occurred, the responses may be
different. To address this risk, a researcher may need to consider some form of isolation

26. 26
for the group to avoid the risk that some outside event could negatively impact the
collected data.
The second threat could be related to history, but it is directly tied to changes
within the experimental group. This is referred to as maturation. Again, where
experiments are conducted over an extended period of time, there is a risk that as
members of a group mature, their responses to the experiment could change (Babbie,
2006). However, like the historical validity threat, the researcher could build their
control group with a similar demographic of the experimental group. Thus, as one group
matures, the second group should follow a similar maturation path (Creswell, 2002).
The third threat to validity considers the impact of regression on the responses
from the group. Even in situations where a experimental group may have had initial
responses that are well outside of the mean, over time, it is likely that their new responses
during the experimental process will move closer to the mean (Creswell, 2002). Thus,
the researcher may make an inappropriate conclusion of a change in response that would
not be applicable to the greater population (Babbie, 2006). Thus, it is more effective and
accurate for both the experimental and control groups to have initial responses that are
closer to the mean. With this, there is less likelihood of misinterpreting a shift in
responses of the duration of the experiment.
The fourth threat to validity is based on the selection of the experimental group.
In this particular situation, researchers must pay close attention to the randomness of the
participant selection as well as how that group relates to the larger population. Too often,
researchers will select experimental group members that may have specific characteristics
that could be considered within the population, but have other characteristics that may not

27. 27
be common. Thus, the data collected from this group would not be applicable to a larger
population and would be limited to a population that is similar to the group. To address
this, researchers should develop a method where the pool of participants is random so as
to avoid the risk of having a group that is not representative of the general population
(Creswell, 2002). Babbie (2006) also considers that along with the generalization risk
noted by Creswell, we should also take into account any regression that could occur
between an experimental group and a control group. In the event that these two groups
change over the period of the research process, there is a risk that the validity of
compared data between the groups could also be flawed.
The fifth threat involves the maintenance of the group over the duration of the
experiment. This is referred to as mortality. For example, if during the term of the
experiment, certain members leave the experimental group thus impacting the data
collected during and after the experiment (Babbie, 2006). There are several reasons why
mortality can have a significant impact on results. First, when a participant leaves the
group, the researcher no longer has access to any future data from that participant.
Secondly, with the departure of a member, there may be a risk that the remaining
experimental group would be less representative of the larger population as well
(Creswell, 2002). This could also be reasonably related to the threat of demoralization
(Babbie, 2006) where members of a group may consider either withdrawing from the
group or not actively participating in the experiment.
The sixth threat to internal validity involves the communication between members
of the experimental group and control group. This is referred to as diffusion. In this
situation, individual members of the experimental group may share information about the

28. 28
experiment that could have an adverse impact on future responses of other group
members (Babbie, 2006). Thus, tainting the future data and ending conclusions of the
research. The challenge in this situation is that if the control group is made aware of the
experiment, they may behave differently than the result if they had not known. Since the
purpose of the control group is to compare those receiving treatment to those who have
not received the treatment, any changes in behavior of the control group would limit the
ability to create a valid comparison. Thus, the researcher should take the necessary steps
to keep the groups separated so as to avoid the threat (Creswell, 2002).
The next threat to internal validity revolves around the testing methods used
during the term of the experiment. In situations where the same instrument is used
repeatedly over the experimental period, there is a risk where the participants could link
the expected outcomes of the experiment with the survey method, and then provide
responses that would best meet the needs of the outcome (Babbie, 2006). Thus, the result
is that the participants’ responses would be questioned since the responses may not be
truthful representations of actual behavior since the participants were not focused on
reporting valid information. In order to address this situation, it may be appropriate to
extend the length of time between the tests with the goal that the participants may not
remember the specific topics of the test and provide more truthful answers (Creswell,
2002).
Finally, the last major threat to internal validity involves changes to the
instrument used for data collection. In the event that the researcher changes specific
questions or portions of the test, the continued validity of the information would be
questioned. Along with that, the researcher would also lose the ability to collect data

29. 29
what would identify changes in responses over the experimental period as well. In the
end, not only losing the validity of the results, but also limiting the usefulness of the
collected data. To address this, the researcher should use the same criteria over the
testing period to maintain appropriate tracking of information and valid conclusions to
the results (Creswell, 2002).
However, Babbie (2006) provides a more extensive list of additional threats to
validity. The first of those is referred to as causal time order. In this situation, the
researchers have not provided a clear definition of the period of time for the research to
the subjects. This is especially true in situations where there is inconsistency in
determining the cause and effect of an outside stimulus since the amount of time allowed
to pass during the experiment may be inconsistent.
Secondly, is the consideration of compensation of the participants during the
research. While compensation does not necessarily imply money, there may be other
methods of compensation that one group receives that may not be provided to the other
group. In effect, this compensation could act as an additional stimulus that may alter the
generalizability of the results to a larger population and threaten the validity of the data
(Babbie, 2006).
Thirdly, there is also the threat to validity where one group is deprived of an
internal stimulus where the other group is not. This is referred to as compensatory
rivalry. In this situation, the members of one group may be aware that they are not
receiving the stimulus under evaluation. As such, they perform differently than how they
would normally behave if they were not aware of the missing stimulus (Babbie, 2006).
As an example, if an experimental group was aware that they were receiving a placebo,

30. 30
they may alter their behavior and act as if they were receiving the actual medication
under review. With that, they would not be performing as a normal placebo group nor
would they be performing as an experimental group. They would be performing in a
manner that they thought would be the performance of the experimental group. Thus,
with this threat, the ability to compare this group to other groups would be lost as their
performance would not be consistent.
Along with threats to internal validity, there are also threats to external validity.
External validity defines the potential application of the experimental results to the larger
population. For the most part, these threats deal with the interaction of the participants
and their external environment. The result of these external threats is that the researcher
is limited in their ability to generalize the resulting data and apply that data to a larger
population. In order to address this threat, the researcher may be forced to duplicate the
experiment in other environments or with additional experimental participants (Babbie,
2006).
Effective Data Analysis of Quantitative Research
At the conclusion of the research process, the next step in the overall plan is to
analyze the resulting data. Similar to the processes related to qualitative data analysis,
quantitative researchers can also follow a process of coding the results by categorizing
the information into useful groups that can then be entered in the appropriate data entry
method (Neuman, 2005). Once the codes have been established and the data is ready for
further analysis, the researcher then selects the appropriate format of the data.
From there, the researcher then needs to select the appropriate methods to
highlight the descriptive statistics that define the results of the data and the applicability

31. 31
to a larger population. Within this process, the researcher will first focus on defining
methods of central tendency of the data (Neuman, 2005). These measurements can be
classified into three primary areas, the median, mode and mean. The median is a data
point that is representative of the middle point of the results assuming that those results
have been ordered in value from lowest to highest (Babbie, 2006). The mode is the most
common data point in all of the data set. For example, this would identify the most
common response of all of the participants in the group (Babbie, 2006). Finally, the
mean is the average of all responses to a particular subject (Babbie, 2006). The goal of
this process is to determine how the responses are distributed within the group from
lowest to highest. This information is then compared and graphed reflecting all responses
to a particular topic. This graphical representation that then provide the researcher with a
visual representation where the researcher can then consider conclusions about the
distribution of the information and how that distribution would be applicable to a larger
group.
Within this distribution, the researcher must also assess the percentiles within
particular ranges of responses. These percentages should reflect the percentage of the
surveyed population and their range of responses to particular topics. Along with
percentages, and typically more common in data analysis is an additional analysis of the
data dispersion referred to as the standard deviation. The standard deviation provides an
assessment of a single response against the mean of all responses to the topic. This
information provides the researcher with an understanding of how widely or narrowly
dispersed the responses may be (Neuman, 2006).

32. 32
The next step in the process is to assess how applicable the responses of the group
are to a larger population. The processes associated with this are referred to as inferential
statistics. By meaning, this method provides the researcher with the ability to infer that
the sample population results could be applied to the population as a whole. Within this
effort, the researcher will attempt to define the statistical significance of the results of the
sample group to that of the population. While it is unlikely that the sample group will
perform in exactly the same manner as the general population, the level of significance
will determine the probability of the relationship between sample group and population
(Neuman, 2006).
The statistical significance attempts to record the probability that the sample
group results are due to random chance rather than intent. As such, the lower the level of
statistical significance, the more likely the sample group reflects the larger population.
For example, if a calculated level of significance is .01, then the probability that the
results are due to random occurrence is one percent. In other words, there would be a
ninety-nine percent probability that the sample group results are in line with the greater
population (Neuman, 2006).
In summary, quantitative analysis has a similar goal of qualitative analysis. In
that, the intent is to determine a method of applying the responses of a smaller population
to the population as a whole. While there are significant differences in the underlying
process of data collection and analysis, the goals of both methods are consistent in their
approach to understand how applicable a survey or experimental group is to the
population.
Mixed Methods Research

33. 33
In a mixed methods research model, the researcher considers the use of both
quantitative and qualitative data collection and analysis strategies. The thought behind
this effort is that a particular research topic may force a need to consider some aspects of
qualitative research and other aspects of quantitative research. Additionally, in several
cases, researchers may use a mixed methods approach to triangulate the information and
potentially validate related conclusions from both models (Creswell, 2002).
Given that there are tactics that are independent between both qualitative and
quantitative research processes, we must also understand the tactics that are necessary
when using a mixed methods approach as well. The first step in this process is to
consider the timing of the qualitative and quantitative processes. As I discussed earlier,
each process has a standard set of development steps. However, now that we are
considering using both methods within a single research plan, we also need to consider
the impact that timing has on the effort (Creswell, 2002).
Typically, qualitative research is done over a period of time where the observed
behaviors of the sample group are tracked and recorded as a part of the research process.
However, we must also understand how to integrate quantitative tactics within this effort
as well. Quantitative tactics can be implemented either at one point of time or over a
longer duration of time. Thus, there is an ability to implement a quantitative survey
during a qualitative experiment. Additionally, there would also be an opportunity to
complete both qualitative and quantitative analysis simultaneously during the duration of
an experiment as well (Creswell, 2002).
Along with this, the researcher must also be aware of the potential validity threats
that could exist. Not only are there similar threats to those that I have discusses earlier,

34. 34
but there also may be threats that could involve the usage of both tactics as well. For
example, if a researcher chooses to implement a quantitative survey, the questions on that
survey could alter the behavior in the balance of the qualitative experiment. The opposite
is true as well in that the qualitative experiment could provoke inaccurate answers to the
quantitative survey as well. In either case, the researcher must create a data collection
and analysis plan that can address these issues and provide an opportunity to eliminate or
at least minimize the validity threats.
An additional factor in the mixed methods model is determining the appropriate
weighting to the collected information (Creswell, 2002). For example, it may be
necessary to provide greater weight to a longer term qualitative experiment in comparison
to a one-time quantitative survey that is completed during the experiment. While there is
not any standardized weighting model for researchers to use, they should base their
weighting on the goals of the research. By meaning, they need to focus the weight on the
instrument and tactic that has the most relevance to the overall experiment and resulting
research findings.
Next, the researcher needs to determine and implement an appropriate strategy for
mixing the resulting information (Creswell, 2002). In this event, the researcher may use
both a qualitative and quantitative tactic to address a specific subject in the research. For
example, the researcher could use a Likert Scale to assess a participant’s impression of a
particular topic by assignment of a predetermined value to that impression. Additionally,
the researcher could also use qualitative tactics to categorize the subjective assessment of
a non-verbal response to the topic as well as an interview that would provide an
opportunity for the participant to give a verbal response to clarify that impression. This

35. 35
mixing of tactics could also serve to triangulate the data on the particular topic. Thus,
providing a greater ability to validate the information and apply that information to a
greater population. Additionally, this mixing of tactics may serve to uncover additional
meanings to the data that would not normally found by limiting the research to either
quantitative or qualitative data.
Conclusion
While there are clearly benefits to either quantitative or qualitative research, the
selected method is completely dependent on the topic of research as well as the overall
goals of the research. Qualitative research can provide an opportunity to uncover the
subjective impressions that members of a survey or experimental group could have.
Quantitative research on the other hand, tends to focus on more objective and generally
accepted findings.
However, either strategy still requires significant categorization of data either by
means of a structured coding process in the data collection or by mathematical results of
the collected data. While some theorists may consider qualitative research to take a
significantly longer period of time to collect and analyze in comparison to quantitative
research that does not imply that either method is better than the other.
As discussed earlier, there is a significant benefit to following a mixed methods
research approach. In this process, researchers have the ability to apply the best practices
of both strategies to meet their research objectives. Mixed methods provide an
opportunity to not only validate the conclusions by multiple data collection tactics, but
can also serve to provide greater insight into the meaning of the information as well. As

36. 36
such, this method could provide the most relevant information of the three methods under
review in this summary.
However, as I have stated, the researcher should not only focus on what particular
method will gain the most information for their research. They should first determine the
problems they wish to address and the questions they wish to collect data. Those topics
will assist in determining what the most appropriate research method would be in order to
meet the goals of the project. To that end, the researcher can provide an effective plan
that balances the goals to the tactics used to collect the data and formulate the findings to
meet those goals.
Finally, there should always be a conscious effort to maintain validity of the
collected data that balances the needs of the experimental group, the underlying theories
of the researcher, and the applicability of the findings to the larger population. Keeping
those factors as a priority in the process will serve to provide useful data and conclusions
that can be utilized by the outside research community.
Continuing with the Depth Section of this review, I will focus the presentation o
developing a greater understanding of qualitative and quantitative research methods by
examining contemporary usage of those standards. Additionally, I will examine the
practical best practices associated with each method by examination of their usage in
actual research.

37. 37
DEPTH
AMDS 8723: SELECTED RESEARCH METHODS
Annotated Bibliography
Meadows, K. (2003, November). So you want to do research? 4: an introduction to
quantitative methods. British Journal of Community Nursing, 8(11), 519-526.
In this review, Meadows (2003) discusses an overall introduction to the usage of
quantitative methods in research. Within the summary, Meadows discusses the typical
types of quantitative research design, the methods of data collection for these studies, the
processes related to population sampling and effective analytical strategies for
quantitative research.
Furthermore, the author also discusses areas where both qualitative and
quantitative research can work in tandem on a specific project. Specifically, he discusses
where qualitative research can assist in uncovering relevant information about a topic
where there is little information available (Meadows, 2003). With this new information,
the quantitative researcher can create a more effective research strategy that could be
more applicable to a larger population rather than attempting to implement quantitative
research which could result in data and conclusions that may not be applicable to the
subject under study.
In addition, the author also reviews the general processes related to development
of the quantitative research plan. That summary provides an overview of the general
purpose of the research, the theories that could be guiding the proposal, the specific
research questions the author is attempting to answer, the methods of data collection and
the sampling strategies the researcher will use (Meadows, 2003). While the specific

38. 38
information within the study may differ based on the subject, the author’s intent with this
summary is to provide a general roadmap for the development of the research plan.
Strickland, O., Moloney, M., Dietrich, A., Myerburg, S., Cotsonis, G., & Johnson, R.
(2003, October). Measurement issues related to data collection on the world wide
web. Advances in Nursing Science, 26(4), 246-256.
In this review, Strickland, Moloney, Myerburg, Cotsonis and Johnson (2003)
provide a summary of the processes related to data collection of survey information
through internet based resources. Within the summary, the authors discuss some of the
benefits and limitations of this method of data collection and how it can benefit efficient
research processes.
In reference to the limitations, the authors focus on the challenges involved with
providing questions that can be easily understood by a majority of the participants.
While it is clear that researchers wish to have their subjects understand what they are
attempting to answer, the risk involved is that the questions may be too simple to yield
any useful information. In addition, most internet based surveys are typically closed-
ended questions which may also limit the results. Beyond this, researchers will often
wish to gain insight into the meaning behind a particular survey response. Since internet
based surveys are designed to be anonymous, the researcher is limited in their ability to
get this information. As we will often see in internet based surveys, there are
opportunities for free-form responses, however, the ability to quantify those responses
can also be limited (Strickland, Moloney, Myerburg, Cotsonis and Johnson, 2003).
However, that does not imply that there are not any benefits to the process. As
the authors also discuss, there is a benefit to anonymity as well. The belief being that if a
survey is anonymous, the subject may be more likely to answer honestly in comparison to

39. 39
other methods of data collection. In addition, with the technological advancement of
internet based collection tools, researchers may find that they gain access to a wide range
of statistical analysis options that are included with the data collection software. The
benefit with this is that the researcher can save significant time in the data analysis
process since this would be automated (Strickland, Moloney, Myerburg, Cotsonis and
Johnson, 2003).
Barbour, R., & Barbour, M. (2003, May). Evaluating and synthesizing qualitative
research: the need to develop a distinctive approach. Journal of Evaluation in
Clinical Practice, 9(2), 179-186.
In this article, Barbour and Barbour (2003) discuss the need for developing a
standardized approach to qualitative research processes. Additionally, the general focus
of this review is the processes related to reviewing and synthesizing the collected data
into a usable format for further study. The authors also discuss the general research
processes associated with qualitative research and compare this process to the accepted
strategies within quantitative research.
Furthermore, of particular note for qualitative research is the inherent flexibility
that is built into the process (Barbour and Barbour, 2003). Unlike quantitative research,
qualitative research has the ability to evolve over the time that the data is collected and
summarized by the researcher. The benefit with this ability is that the researcher can
adjust their strategies in the event that new data is uncovered or the subjects’ responses
may define a different plan. Thus, qualitative research does not necessarily start with a
hypothesis, which is common with quantitative research. Instead, it may start with a
question or a topic that the researcher wishes to pursue for additional study.

40. 40
Therefore, without the limitations of a hypothesis, the researcher has more
freedom to explore certain avenues in their work that they did not anticipate. This option
provides both the researcher with more options to either gain an effective answer to the
research question or uncover areas that may be worthy of additional research in the
future.
Elam, G., & Fenton, K. (2003, February). Researching sensitive issues and ethnicity:
lessons from sexual health. Ethnicity & Health, 8(1), 15-25.
In this review, Elam and Fenton (2003) discuss the particular issues and strategies
involving research in sensitive areas. Along with the overall summary, the authors also
review some common examples of sensitive topics such as sexuality and sexual
relationships as well as research involving physical abuse of research subjects (Elam and
Fenton, 2003).
The primary concern identified by the authors is that researchers need to have a
clear understanding of the sensitive nature of the topic as well as effective strategies in
their data collection to mitigate any error related to the level of sensitivity. Within this
effort, researchers need to first understand the complexity and the resulting sensitivity
that subjects could encounter. From there, the next stage in the process is to develop the
survey methods and processes that balance the need to get the information, but also
reduce the errors resulting from the sensitivity of the data. Additionally, the researchers
also need to focus on developing their data collection team that has sufficient awareness
of the issues which would then allow them to collect the information in a way that
balances the data need with the personal needs of the subjects (Elam and Fenton, 2003).
Outside of the sensitivity issues related to the personal experiences of the
subjects, there is also a sensitivity issue regarding the relationship between the researcher

41. 41
and the research subjects. The authors define this as a power imbalance between the
parties (Elam and Fenton, 2003). This imbalance can significantly alter the collected data
as well as the findings of the research. In situations where there is an imbalance, the
subject may think that the researcher could influence their reputation or status in the
larger community resulting from the information the subject provides in the study.
Clearly, if the subject believes there is a risk, they are less likely to provide truthful
answers to the researcher. As such, with this and other issues on sensitivity, the authors
discuss potential strategies to reduce this risk and provide an opportunity for more
reliable data.
Cheek, J., Onslow, M., & Cream, A. (2004, September). Beyond the divide: comparing
and contrasting aspects of qualitative and quantitative research approaches.
Advances in Speech Language Pathology, 6(3), 147-152.
In this review, Cheek, Onslow and Cream (2004) provide an extensive
comparison between qualitative and quantitative research techniques. In their summary,
the authors consider qualitative research to be more based on the context of the research
question rather than the generalizability of the collected data found in quantitative
research. The difference with this effort is that qualitative research is focused on trying
to answer a specific question about a population where quantitative data is attempting to
gain information from a sample group that could be applied to a larger population.
In addition, the authors also discuss the relationship between the researcher and
the topic. For example, they consider that a qualitative researcher tends to be heavily
integrated into the topic they are studying. While they are not a subject of the research,
their persona is a part of the research process. This differs from quantitative research

42. 42
where the researcher is normally separated from the subjects regardless of particular
methodology used (Cheek, Onslow and Cream, 2004).
However, the authors do not imply that one research method is any better than the
other. In fact, the intent is to gain a greater understanding of where the common elements
of quantitative and qualitative research exist as well as the strengths and weaknesses of
each. Additionally, the authors would expect that researchers could also use this review
as a guide to determine what method is best for a particular study, but to also develop the
research plan that provides the most applicable information.
Sandelowski, M., Barroso, J., & Voils, C. (2007, February). Using qualitative
metasummary to synthesize qualitative and quantitative descriptive findings.
Research in Nursing & Health, 30(1), 99-111.
In this article, Sandelowski, Barroso and Voils (2007) discuss the processes
involved with creating a quantitative summary of qualitative data in research studies.
Along with this, the authors also provide several strategies where researchers can
categorize their collected data and then report that information using quantifiable results.
The benefit with this process is that it can result in data that is more tangible to
the research group as well as those who could use the information for other purposes. As
is often the case with qualitative research, there is a significant emphasis on data that is
not designed to be generalized. However, that lack of generalizability does not imply that
there is no ability to summarize the data using numbers instead of text summaries.
In addition, the authors also discuss how the differences in data collection
between quantitative and qualitative research can impact the researchers’ ability to
provide an information summary that could include both types of information. Thus,
with different data collection methods and summarization processes, it may be difficult to

43. 43
integrate the data together in one study. However, the authors also discuss the strategies
involved in linking the diverse data as well as utilizing both sources to answer the
research questions under study (Sandelowski, Barroso and Voils, 2007).
Duffy, J. (2005, December). Critically appraising quantitative research. Nursing &
Health Sciences, 7(4), 281-283.
In this review, Duffy (2005) provides an appraisal of quantitative research
methods. Additionally, within the review, she also discusses the different types of
quantitative studies and a critical assessment of each. Finally, the author also discusses
how the researcher can assess each process to determine which would be the best fit for
the topic under review.
Additionally, the author provides a brief summary of case studies, cohort studies,
clinical trials and systemic reviews (Duffy, 2005) and how those methods would apply to
particular research themes. However, given the medical context of the article, the author
provides a significant summary on the benefits of clinical trials on the medical related
quantifiable research.
Furthermore, in the author’s discussion of clinical trial research, the critical
component in the effort is that the research be random in nature (Duffy, 2005). Again,
the intention is to generalize the data to a larger population, thus random sampling is
required in order to effectively reduce error and bias in the data collection effort. With
this type of effort, the researchers can also compare particular groups within the study to
see if a particular experiment under review performs differently within the groups. This
is a common practice when assessing whether a particular medication is effective where
the researcher would assemble random control, experiment and placebo groups.
Assuming that the group members were randomly selected, the research could then

44. 44
validate whether or not the medication under study had any effect in the group and
potentially to the larger population.
Hart, A. (2006, September). Ten common pitfalls to avoid when conducting qualitative
research. British Journal of Midwifery, 14(9), 532-533.
In this article, Hart (2006) discusses ten potential pitfalls in quantitative research
strategies. Additionally, these particular weak areas are not designed to focus on the
processes related after the data is collected, but is designed to discuss problems through
the entire quantitative research process.
Thus, the summary starts with the lack of a clear objective in the research process
(Hart, 2006). By meaning, it is quite common for quantitative researchers to begin their
research process with no clear objective as to what they wish to determine. Thus, the
research begins without a hypothesis that the researcher wishes to validate. Clearly, this
provides a challenge to the process as without a hypothesis, the research has no direction.
Furthermore, the author discusses the lack of the right methods or tools for data
collection, the results of losing data or collecting too much data and the impacts of over
stating the conclusions of the data collected (Hart, 2006). Ironically, a researcher could
encounter only one of these issues, but that single issue alone could call into question all
of the data and conclusions in the study.
Finally, the intent with this review is that the author wishes to make the research
community aware of the problems that could occur. This is not designed to be a
guideline of the process, but it is designed to create a greater awareness by the
researchers in that these issues could happen. Along with that, the author also discusses
several strategies not only to avoid these pitfalls, but to address them should they become
evident in the research process as well.

45. 45
Coughlan, M., Cronin, P., & Ryan, F. (2007, June 14). Step-by-step guide to critiquing
research. Part 1: quantitative research. British Journal of Nursing (BJN), 16(11),
658-663.
In their review, Coughlan, Cronin and Ryan (2007) discuss the processes
associated with critiquing quantitative research. While the theme is applied to the
research processes used by nurses, the overall summary provides a foundation that could
be used by researchers in a wide range of studies. The processes identified by the authors
focuses on a range of steps from the basic critique of the research through developing a
process of understanding the results of the research under review.
What is helpful in this review is that it describes the process from the reader’s
perspective. By meaning, it is designed from the perspective of the layperson who is
reading the produced research. Thereby, the reader has the opportunity to not only
understand how the research process works, but it also provides a foundation where the
reader can understand how to assess whether or not the research has use to their particular
interest.
As with any research, the author and the reader need to have an understanding of
the general processes of research design. Thus, there should be a focus on providing a
certain level of standardization to the process that can give the general reader an effective
context to understand what they are reviewing. Thus, the summary provides detailed
information of the methods the reader can use to determine not only how valid the
information may be, but also how applicable that information is to their work.
Therefore, the Coughlan, Cronin and Ryan (2007) begin their assessment on
developing an understanding of the establishment of credibility and integrity in research.
Their intent is to assist the reader in assessing whether the research is valid and

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applicable to their work. Continuing forward, the authors also identify criteria to assess
the writing style of the research work so as to provide an assessment of whether the style
can be considered scholarly and worthy of review. Finally, the authors identify necessary
factors in the research methodology, to identify steps to determine whether the researcher
is using valid and generally accepted data collections techniques as well as how the data
supports the conclusions identified in the paper.
Ryan, F., Coughlan, M., & Cronin, P. (2007, June 28). Step-by-step guide to critiquing
research. Part 2: qualitative research. British Journal of Nursing (BJN), 16(12),
738-744.
In a complementary article to the discussion in the previous issues on critiquing
quantitative research, Ryan, Coughlin and Cronin (2007) discuss the issues related to
process in critiquing qualitative research. While there are some similarities in process
and audience, the focus of this review is to build an understanding of the steps necessary
to understand and apply qualitative research.
Since qualitative research is less data oriented than quantitative research, the
reader must first understand the differences between the two and use that understanding
as a foundation to assess the quality of the qualitative research they are reviewing. As
such, the summary first focuses on the understanding of the theory under study and the
overall purpose of the study (Ryan, Coughlan and Cronin, 2007).
Thus, by establishing the basis of the study, the reader can then focus on an
understanding of the existing research that is applicable to the topic and how that research
supports both the current research as well as the research questions under examination.
With this information, the reader can get a greater context of the value of the information
as well as an understanding of complementary topics previously considered.

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Additionally, the authors discuss the processes associated with understanding the
research methodology of the current study. This is especially helpful in situations where
qualitative processes may not be familiar to the reader. Thus, the reader not only has the
ability to understand the general processes associated with qualitative research, but they
also gain an understanding of the steps necessary to provide validity to the methodologies
used in the research.
Finally, as was the case with quantitative research, the authors conclude with a
summary of the processes associated with data collection and analysis. However, since
qualitative research goes well beyond the numerical data in quantitative research, the
authors also discuss the underlying processes associated with data collection involving
interviews and observations that tend to differ from the processes related to quantitative
data summarization. In reference to analysis, the authors not only discuss the processes
related to data summary, but also discuss how the summary is designed to discover new
theories or ideas for further research. Not necessarily to provide something that is
generalizable to a larger audience as we see in quantitative research studies.
Johnson, R., & Waterfield, J. (2004, September). Making words count: the value of
qualitative research. Physiotherapy Research International, 9(3), 121-131.
In this review, Johnson and Waterfield (2004) discuss the processes necessary to
determine the value of qualitative research. Given that theme, the authors consider that
the purpose of qualitative research should not be focused on trying to avoid or address a
wide variety of extraneous variables, but should be focused more on an understanding
that the purpose of qualitative research is not to measure individuals, but should focus on
interpreting the information gathered from individuals (Johnson and Waterfield, 2004)
within the data collection process.

48. 48
However, prior to the collection of data, the authors discuss the notion of
providing a valid sampling process. Within the sample, the demographics of the group
must be closely aligned with the goals of the research question under study. This
provides the researcher with a foundation where the collected data can be applied to the
topics they wish to study. While there is always a potential to select a sample group out
of convenience rather than intent, it is still necessary that the final sample group has the
ability to provide information that addresses the context of the research question.
Additionally, the authors also discuss the idea of triangulation in the research
process. Triangulation refers to the need to collect data from multiple sources in order to
validate the conclusions of the research. As with any research, one should not form a
conclusion based on one single source of information. The risk is that the single source
may not necessarily have information that would be reflective of similar people in a
sample group. This could also be applied to the processes of data interpretation. With
the usage of multiple reviewers in the process, there is a significantly lower risk of
misinterpretation of information from the sample group or other collected data.
Finally, in order to effectively collect and categorize data, it is also necessary to
maintain an audit trail of the collected information as well as any preliminary conclusions
or summaries of the data. The benefit with this process is to address the fact that
“qualitative data can not be replicated” (Johnson and Waterfield, p. 127) as such, it is
necessary to have a reliable tracking mechanism for the collected information in the event
that the data may need to be reviewed later in the data collection process.
Shields, L., & Twycross, A. (2008, June). Sampling in quantitative research. Paediatric
Nursing, 20(5), 37-37.

49. 49
In this review, Shields and Twycross (2008) discuss the processes related to
sampling and qualitative research. Within the discussion, the authors focus primarily on
the need that the sample group be both representative of the general demographics of the
population under study as well as of a sufficient size to reduce the possibility of error.
Additionally, the authors discuss the differences between random and convenience
samples.
A representative population is a key part of the overall process. As discussed in
some of the other articles above, the intent of qualitative research is to create a better
understanding of a specific population with the intent of developing new theories about
that population. Thus, having a representative sample allows the demographics of the
group under review to the research question the process is attempting to answer.
Size is also a significant part of the process. While researchers can not survey an
entire population, as the sample group increases in size, researchers have the ability to
learn more about the subject group. Additionally, as sample size increases, the
researchers also have the ability to collect data that may be more common within the
group, thus yield results that further define the dynamics of the group. In contrast, as size
is lower, there is also a lower potential to gain a larger variety of information about the
group. Therefore, there is a lower opportunity to access data that could tie back to the
research question under review.
Finally, the authors discuss the two primary methods of sampling, random and
convenience samples. Random samples involve identifying a group by random selection.
While it is still necessary to match the group demographics to the research question under
study, a random sample is still an option. Generally speaking, the researcher identifies a

50. 50
population that meets the needs of the research question then randomly selects members
of that general population to be participants in the sample group. This is in contrast to a
convenience sample where a larger group is identified, but the sample group is selected
based more on availability of participants rather than a random selection.
Taylor, C. (2005). Doing quantitative research in education. Nurse Researcher, 12(3), 92-
92.
In this review, Taylor (2005) discusses some recent publications related to
quantitative research in education. Along with a summarization of the need of students
conducting research to have a more standardized process of quantitative research
techniques, the author also discusses the need to not only educate researchers on the
techniques required, but to also provide guidelines on identification of the techniques that
best fit the needs.
As the author discusses, there is a wealth of publications and supporting research
that discusses the process, but what is commonly missing is the guidance that new
researchers need to identify the steps that can be the best fit for the particular project.
Thus, students have access to identifying all of the necessary options, but their ability to
apply that information to their individual project is lacking in most available publications.
Thus, the researcher understands the process of data collection, reliability,
validity, etc., but does not have an effective guide to match the process with the need.
Therefore, researcher may either spend significantly more time in developing the
applicable process due more to lack of knowledge than necessary. With that, the research
development process may become less efficient than necessary as the researchers spend
more time in trying to figure out how to research rather than focusing on the data
collection and summary necessary within the process.

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Onwuegbuzie, A., & Leech, N. (2005, December). On becoming a pragmatic researcher:
the importance of combining quantitative and qualitative research methodologies.
International Journal of Social Research Methodology, 8(5), 375-387.
In this summary, Onwuegbuzie and Leech (2005) discuss the options available in
developing a mixed model approach to research. In this effort, researchers have the
ability to combine results from both quantitative and qualitative research in their overall
research project. The benefit with this effort is that researchers can balance the
subjective data gathered in qualitative interviews and other data collection methods with
objective, often numerical information identified with quantitative research.
Additionally, the authors also discuss the conflicts between major theorists in both
qualitative and quantitative research that often disagree as to which technique is more
valid for general research. Thus, qualitative researchers tend to believe that their
supporting techniques have the ability to identify factors that can not be shown by
numbers or other objective factors. On the other hand, quantitative theorists tend to
identify the risks associated with the interpretive nature of qualitative research and use
that as a rationale to justify quantitative research as more valid as there is less error
related to interpretation of the data.
Ironically, there are common factors between qualitative and quantitative research
techniques. The first of those is that both techniques require some form of observation in
the collection process. For example, quantitative researchers may observe steps in a
process while qualitative researchers may also observe the behavior, but also takes steps
to interpret that behavior. Secondly, both techniques will commonly incorporate
triangulation in their processes. For example, quantitative researchers may access
different data points to verify a conclusion while qualitative researchers will observe

52. 52
larger populations and retrieve additional data to validate the conclusions. Additionally,
both techniques as well as general research methods require some form of data validation.
Quantitative researchers will often test and recheck data to validate that the data is
correct, while qualitative researchers use steps like audit trails and coding to validate their
information to develop a greater understanding of their sample group.
Finally, the authors discuss the need for researchers to be pragmatic in their
assessment of a particular research technique to consider. The intent is that researchers
should first focus on the goals and outcomes they wish to result from their effort. Then,
they have the ability to identify the most applicable research method. Thus, the idea is to
not let the research be driven by the technique, but let the research goals determine the
best technique.
Mehmetoglu, M. (2004, December). Quantitative or qualitative? A content analysis of
nordic research in tourism and hospitality. Scandinavian Journal of Hospitality &
Tourism, 4(3), 176-190.
In his summary of research methods in the Scandinavian tourism industry,
Mehmetoglu (2004) discusses how performance research has changed over the past
several years, but still relies mainly on quantitative research techniques. Additionally, he
discusses the justification of particular techniques and how those techniques align with
the overall goals of a particular research effort.
Furthermore, the author also provides an analysis of the applicability of
quantitative techniques as it relates to identifying particular trends in performance of
specific countries, varied tourism segments and seasonal factors that impact the financial
performance of the tourism and hospitality industries in the Scandinavian region. The
intent with his effort is to discuss the fact that the majority of quantitative studies in this

53. 53
review tend to pertain to financial or volume performance from a historical perspective.
By meaning, the rationale is to discuss how the financial performance of the industry has
changed over a specific period of time. Thus, as the author notes, given the purpose of
the research, historical financial performance, using quantitative research methods is
likely the best fit.
However, there is also a need to have a greater understanding of the behaviors
from those traveling within and to the Scandinavian region. Thus, he also discusses the
particular factors required within qualitative research. As he notes, qualitative research
can be categorized into “interviewing, observation, documentary sources and visual data”
(Mehmetoglu, p. 180). The benefit with the addition of qualitative research is that the
researchers have the ability to understand what drives an individual’s behavior to
consider visiting the region. This also provides a greater understanding of what particular
actions, namely marketing options that might drive additional visitors and thus revenue
growth to the region.
As such, he sees that both qualitative and quantitative research can truly
complement each other. As discussed above, quantitative research is best suited to
evaluate the historical performance of the industry, while qualitative research can assist
in the development of theories that would identify future opportunities to increase the
industry’s financial performance. Additionally, quantitative research could also be used
to validate the implementation of resulting theory to determine whether or not that
particular effort resulted in a financial benefit for the industry.
LITERATURE REVIEW ESSAY
Introduction

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Now that we have a general understanding of the current literature relating to
qualitative, quantitative and mixed models research, the next stage in this review will
focus on a detailed understanding of each research technique. I will focus this effort on
identifying both common and contrasting perspectives of the research options under
consideration and use that analysis as a basis for the planned research strategy for the
dissertation.
Quantitative Analysis
As I discussed in the Breadth Section of this review, quantitative analysis is
considered to be a more objective method of research. By meaning, since the collected
data tends to be driven by non-interpreted and factual data, the general assumption is that
quantitative research has less opportunity for interpretive error. Of course, this is not
meant to imply that the techniques or collected data from other research processes is of
lesser quality, quantitative research is simply a different method of study and data
collection (Mehmetoglu, 2004).
Research Methodology in Quantitative Research
Given that the goal of quantifiable research is to provide both data and
conclusions that are generalizable to a larger audience, the methodology that we follow in
this effort focuses on linking our potential hypothesis to the supporting data and
conclusions (Mehmetoglu, 2004). We can then determine whether or not that hypothesis
would apply to a larger population outside of the sample group. This is the basis of an
experimental design that would be the foundation of the methodology used in the
quantitative study (Meadows, 2003).

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As this can be considered as an experimental process, we can follow a
methodology that begins with the development of the general purpose of the effort (Hart,
2006). The purpose should outline what we are planning to accomplish and why the
research is even under consideration (Meadows, 2003). By meaning, what results do the
researchers hope to see and how do those results impact the greater population under
study. This will serve as a method of justifying the effort to a larger community. By
meaning, it should at a minimum identify the common benefit of new information and
potentially future action by other researchers.
Beyond the purpose, we also need to identify what theories we have about the
topic prior to the commencement of the data collection (Meadows, 2003). This serves
multiple purposes in the process. First, it may identify any biases or perspectives of the
researchers that could impact the techniques used or the results of the study. Secondly, it
can also serve as a general theme of the research and provide some additional focus for
the topics under consideration. In either event, any identified theories will assist the
community under study as well as consumers of the research in understanding the
perspectives of the researchers.
The next stage in the process is the development of potential research questions
(Meadows, 2003). The research questions provide a further focus on the topic as it
allows the researchers to identify the specific subjects and questions they are attempting
to answer in the research process (Hart, 2006). Additionally, these questions can also
serve to provide the audience with a general theme of what they should expect when
reviewing the collected data as well as the final conclusions of the research. Finally, the
research questions should also have a strong tie to the hypothesis of the topic. By