Research Methods

Research design refers to the specific type of study that you will conduct. Research design is normally consistent with one’s philosophical worldview and the methodological approach the researcher chooses. In this case, you are using a quantitative methodology. As we have discussed in this course, quantitative research designs can be experimental and non-experimental. You will be using a non-experimental design that can include descriptive statistics, correlational or causal-comparative research methods.

Research methods refer to specific procedures selected based on the chosen design. This is where you will provide detail on how you collected and analyzed your data. For quantitative methodologies, research methods can be quite detailed and require that attention be paid to recruitment, sampling, sampling frame, sample size, surveys, pilot tests, observations, data collection, data analysis, statistical procedures, data interpretation, coding, validity, reliability, generalizability, reporting, etc.

For this assignment, you will develop the research design for the Sun Coast project, utilizing this

template

(SeeAttached) to complete your assignment.

Your Unit III research design submission should include the below elements.

• Research Methodology: Describe and justify the choice of research methodology and why it was most suitable to solve the problems. Be sure to compare and contrast this choice with the design that was not selected.
• Research Design: Explain whether the research design is exploratory, causal, or descriptive. Provide the rationale for the choice.
• Research Methods: Review the research questions and hypotheses you developed in Unit II, and then decide on the most appropriate research methods to test your hypotheses. They might include a combination of experimentation, descriptive statistics, correlation, and casual-comparative methods. Be sure to specify which method will be used to test which research question and hypotheses, and explain why that method was most appropriate. 
• Data Collection Methods: Specify how the data were most likely collected to test the hypotheses. Data collection methods include, but are not limited to, survey, observation, and records analysis. Be sure to specify which data collection method was used to collect the data needed for each research question and hypothesis. Please note that one data collection method could capture the data for several research questions and hypotheses.
• Sampling Design: Briefly describe the type of sampling design that was most likely used for the data that were collected. Choices include, but are not limited to, random sample, convenience sample, etc. Explain your rationale for your sampling design selection(s).
• Data Analysis Procedures: Specify which statistical procedures will be used to test each of your hypotheses from among correlation, regression,  t test, and ANOVA. Explain why each procedure was the most appropriate choice.

The title and reference pages do not count toward the page requirement for this assignment. This assignment should be no less than two pages in length, follow APA-style formatting and guidelines, and use references and citations as necessary.

1

4

Insert Title Here

Insert Your Name Here

Insert University Here

Course Name Here

Instructor Name Here

Date

Research Methodology, Design, and Methods

After providing a brief introduction to this section, students should detail the research design they have selected and why it is an appropriate research approach for addressing the business problems. Use the following subheadings to include all required information. Important Note: Students should refer to the information presented in the Unit III Study Guide and the Unit III Syllabus instructions to complete this section of the project. Delete this before you begin.

Research Methodology

Explain the research methodology chosen for this research project and provide rationale for why it is appropriate given the problems.

Research Design

Students should explain whether the research design is exploratory, causal, or descriptive. Provide rationale for the choice.

Research Methods

Students should describe the research methods used for this research study based on the research methodology, research design, and research questions, and provide a rationale as to why they were chosen. They might include a combination of experimentation, descriptive statistics, correlation, and causal-comparative methods.

Data Collection Methods

Students should specify how the data were most likely collected to test the hypotheses. Data collection methods include, but are not limited to, survey, observation, and records analysis.

Sampling Design

Students should briefly describe the type of sampling design that was most likely used for the data that were collected. Choices include, but are not limited to, random sample, convenience sample, etc. Explain your rationale for your sampling design selection(s).

Data Analysis Procedures

Students should specify the statistical procedures used to test each set of hypotheses from among correlation, regression, t test, and ANOVA. They should explain why each procedure was the most appropriate choice.

Example:

Correlation is the preferred procedure to use to test the RQ1 hypotheses since the interest is whether a relationship exists between an independent variable (IV) and dependent variable (DV). Correlation will indicate if there is a relationship between height (IV) and weight (DV), the strength of the relationship, and the direction of the relationship.

References

Include references here using hanging indentations like the example below. Remember to delete this example.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage.

MBA 5652, Research Methods 1

Course Learning Outcomes for Unit III

Upon completion of this unit, students should be able to:

3. Compare and contrast the differences between qualitative and quantitative research methodologies.
3.1 Explain why a quantitative study is superior to a qualitative study for the Sun Coast Consulting

project.

4. Evaluate different types of research methods.
4.1 Develop the research design for the Sun Coast Consulting project.
4.2 Evaluate appropriate research methods to answer business problems.

Course/Unit
Learning Outcomes

Learning Activity

3.1

Unit Lesson
Chapter 8
Chapter 9
Article: “The Place of Quantitative Methods in a Management Curriculum”
Article: “Quantitative Versus Qualitative Research Methods—Two Approaches
to Organisation Studies”
Unit III Scholarly Activity

4.1
Unit Lesson
Video:

Research Design

Unit III Scholarly Activity

4.2
Unit Lesson
Unit III Scholarly Activity

Required Unit Resources

Chapter 8: Quantitative Methods

Chapter 9: Qualitative Methods, pp. 179–188

In order to access the following resources, click the links below:

Bagchi, A. (2005). The place of quantitative methods in a management curriculum. Decision, 32(2), 107–111.

Retrieved from
https://libraryresources.columbiasouthern.edu/login?url=http://search.ebscohost.com/login.aspx?direc
t=true&db=bth&AN=19511433&site=ehost-live&scope=site

RanYwayZ. (2016, September 20). Research design [Video file]. Retrieved from

https://www.youtube.com/watch?v=WY9j_t570LY

A transcript of this video is available.

Lee, J. S. K. (1992). Quantitative versus qualitative research methods: Two approaches to organisation

studies. Asia Pacific Journal of Management, 9(1), 87-94. Retrieved from
https://libraryresources.columbiasouthern.edu/login?url=http://search.ebscohost.com/login.aspx?direc
t=true&db=bth&AN=16852430&site=ehost-live&scope=site

UNIT III STUDY GUIDE

Research Design

https://libraryresources.columbiasouthern.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=19511433&site=ehost-live&scope=site

https://online.columbiasouthern.edu/bbcswebdav/xid-117740624_1

https://libraryresources.columbiasouthern.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=16852430&site=ehost-live&scope=site

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MBA 5652, Research Methods 2

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Unit Lesson

Research Methodology, Design, and Methods

A lot of ground has already been covered in the course, including understanding why business research is
conducted. At its core, business research is conducted to improve performance and reduce risk by helping to
make decisions when faced with management problems. The process begins with a management problem,
which is clarified with a statement of the problems and the objectives of the research. Research questions
and hypotheses are then formulated to form questions and testable predictions about the relationship
between variables. Statistical tests are then used to determine if the null hypothesis should be accepted or
rejected based on whether the test results are statistically significant or not. These results inform business
decision-making.

The next several units will focus on the additional steps in the research process. This includes how to design
research, which is the topic of Unit III. Research design is the blueprint of investigation to obtain answers to
research questions. It is the framework of what the researcher will do (Cooper & Schindler, 2011).

Research Methodology

Research methodology refers to general categorical approaches to research (e.g., quantitative, qualitative,
mixed methods). As discussed in Unit I, these categories, or approaches, are rooted in different philosophical
traditions. For example, a quantitative methodology is rooted in a positivist tradition while a qualitative
methodology is rooted in a constructivist tradition. Mixed methods, as its name suggests, is rooted in both
positivist and constructivist research traditions. A researcher’s approach to investigation is typically aligned
with his or her philosophical worldview. This can be visualized as a top-down hierarchy where constructivists
prefer a qualitative methodology to solve problems, while positivists prefer a quantitative methodology.

The choice of research methodology, qualitative or quantitative, flows down to a choice among various
research designs, research methods, data collection methods, sampling designs, and data analysis designs.
Many of the designs and methods are familiar to business students. The table below summarizes some of
these design decisions for quantitative and qualitative research methodologies.

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Methodological Approaches to Research (partial list)

Methodology Research
Design

Research
Methods

Data
Collection
Methods

Sampling
Design

Data
Analysis
Procedures

Quantitative

Causal
(experimental)

– True
experimentation

– Quasi-
experimentation

– Laboratory
experiment

– Field
experiment

– Target
population

– Sample size

– Sample type
(random,
stratified, quota,
convenience)

– Pearson’s r

-Simple
regression

-Multiple
regression

– Independent
samples t test

– Paired
samples t test

– One-way
ANOVA

– Factorial
ANOVA

– MANOVA

Quantitative

Descriptive
(non-
experimental)

– Descriptive stats

– Correlational

– Causal-
comparative

– Survey
(telephone,
mail,
electronic)

– Observation
(participant,
non-
participant)

– Document
analysis

– Target
population

– Sample size

– Sample type
(random,
stratified, quota,
convenience)
– Pearson’s r

-Simple
regression

-Multiple
regression

– Independent
samples t test

– Paired
samples t test

– One-way
ANOVA

– Factorial
ANOVA

– MANOVA

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Qualitative

Exploratory
(non-
experimental)

– Case study

– Ethnography

– Grounded
Theory

– Phenomenology

– Heuristics

– Interview
(structured,
unstructured)

– Document
analysis

– Focus group

– Observation
(participant,
non-
participant)

n/a – Coding

– Content
analysis

– Word count

– Concepts
and themes

Research Design

Research design refers to the specific type of study that will be conducted. One’s choice of research
methodology, qualitative or quantitative, dictates the type of research design. Research design must be
carefully planned before collecting any data. Planning is important so that time and money is not spent only to
find out that the incorrect variables were analyzed.

There are three primary design types: exploratory, descriptive, and causal. Exploratory designs are
customarily qualitative and subjective in nature versus causal and descriptive designs. Descriptive studies are
representative of highly structured research designs intended to use statistical analysis to test formal
hypotheses. As mentioned previously, these types of studies look at the relationships between variables. The
final type of design, causal, attempts to understand if one variable causes an effect on another. These types
of designs are extraordinarily controlled experiments and not typically conducted in general business research
(Cooper & Schindler, 2011). It should also be noted that causal studies are the only type of research study for
which a causation can be identified. Another way to think of research designs would be as either experimental
or non-experimental. Qualitative research designs are always non-experimental. Quantitative research
designs can be either experimental or non-experimental.

Research Methods

Research methods refer to specific procedures that will be used based on the chosen methodology and
design. Quantitative causal design methods include true experiments and quasi-experiments. Descriptive
quantitative design methods include causal-comparative, descriptive statistics, and correlation. Examples of
exploratory design research methods include case study, ethnography, phenomenology, grounded theory,
and heuristics. Mixed method designs use a combination of quantitative and qualitative methods. An example
would be a design that is first exploratory using qualitative methods (e.g., case studies) to uncover issues that
would then be explained using quantitative methods (e.g., correlation).

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This course focuses primarily on quantitative research designs and methods to facilitate business decision-
making for the remainder of the term. As was mentioned in Unit I, quantitative research uses the scientific
method. Researchers use empiricism to attempt to describe, explain, and make predictions by collecting
factual information about hypothesized relationships that can be used to decide if a particular understanding
of a problem and its possible solution are correct. The strength of quantitative research is the ability to
measure phenomena.

Design and Method Challenges

For quantitative research designs, methods are quite detailed and require that attention be paid to participant
recruitment, sampling, sampling frame, sample size, survey instrument construction, pilot tests, data
collection, data analysis, statistical procedures, data interpretation, coding, validity, reliability, generalizability,
reporting, etc. The Journal of Management Studies rejects 70% of article submissions because of research
method errors as most quantitative rejections were because of sampling errors (“Research Methods,” 2006).
This excerpt highlights the fact that there are many interrelated parts to a quantitative research project, with
each part integral to validity, reliability, and generalizability of results.

Business phenomena are often far more complex than they appear on the surface. Even the independent
variables under study may be influenced by the dependent variables the study is trying to predict. Lee (1992)
also pointed out a quantitative limitation in measuring variables excludes the gestalt of human behavior. While
quantitative results may be statistically reliable and valid, they likely will not encapsulate the entire
phenomena of behavioral influences.

Further complicating the challenge of inter-correlated variables is what Bulmer (2001) lamented was an
absence of consensus among social scientists on standards of measurement and operationalization of
variables. Business research, which is closely related to social science in many respects, often has no
standard measurements, such as height, length, mass, monetary value, etc. Social scientists, as do market
researchers and human research professionals, often measure preferences, attitudes, beliefs, perceptions,
and feelings based on constructs and concepts. This is a reminder that a necessary starting point for any

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research project is a review of the literature to see how others have defined the concepts and constructs
under consideration. If an existing instrument that measures the construct is available, it is important to
confirm its validity. If designing a new survey, or altering an existing one, pre-testing and field testing are
required to confirm validity and reliability. This helps prevent Type I and Type II errors related to one’s
hypotheses. Incorrectly measuring just one independent variable may bias other coefficients (Echambadi,
Campbell, & Agarwal, 2006).

Design Choice

Quantitative research design methods can be broadly placed into two main categories of experimental or non-
experimental. The challenge is to select the most appropriate methods (e.g., experimental, correlational,
causal-comparative, descriptive stats) for the research problem.

The purest form of scientific research is the experimental design. This is the only design option in which
causation may be determined. However, experimental design is especially challenging in quantitative
research. Particularly in business research, it is difficult to replicate in a controlled experiment many of the
variables in the process. For example, while certain aspects of selling (e.g., volume of telephone cold calls)
could be controlled in a laboratory experiment, other sales processes (e.g., face-to-face prospect meetings)
make controlling variables impossible. To highlight this point, consider a laboratory experiment to measure a
salesperson’s adaptive selling ability to different prospect personality types. In this example, adaptive selling
ability is the dependent variable and personality type is the independent variable. Researchers can control the
independent variable by using scripted prospect actors to measure the salesperson’s adaptability to each
script. In live selling situations, salesperson adaptability still can be measured, but researchers cannot control
the prospect personality type. For research that cannot be conducted in a controlled experiment, non-
experimental designs are appropriate.

Unlike experimental designs, non-experimental designs are challenged, or limited, in that they cannot
establish causality. These design methods include correlational, causal-comparative, and descriptive
statistics. Correlational methods, perhaps the most widely recognized design among practitioners, attempt to
reveal the existence and intensity of shared variation between independent and dependent variables (Rumrill,
2004). Causal-comparative studies attempt to determine the existence of statistically significant differences
between groups. While the name implies testing for causality, it is a bit misleading. It is not possible to
establish causality in comparative studies since the independent variables cannot be controlled (Rumrill,
2004). An example was provided in the sales scenario above where it was not possible to control for sales
prospects’ personality types (independent variable) that are encountered in live selling situations. Therefore, it
is not possible to determine if a salesperson’s adaptability to personality causes a successful or unsuccessful
sales call. However, with a causal-comparative design, it would be possible to determine if significant
differences exist between annual sales revenue between groups of high-adaptability sales people and low-
adaptability salespeople.

An additional challenge in non-experimental designs is in selecting the correct types of variables.
Correlational designs typically require continuous independent and dependent variables. Causal-comparative
designs usually have a nominal independent grouping variable and theoretical dependent variable (Rumrill,
2004).

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Sampling and Data Collection

Sampling is basically selecting individuals from a population and then conclusions can be created for the
population as a whole (Cooper & Schindler, 2011). This is referred to as generalizing to the population.
Random samples are required to make generalizations about the population. In business research, one of the
main reasons to use a sample is because it simply would not be feasible from a financial or time perspective
to question or survey every single person in the population (Cooper & Schindler, 2011). If we could reach
each person in the population, this would be a census. Good sampling is critical to business research and can
make or break the outcome of the project. Sampling methods are either probability or nonprobability, and

different types of samples
include random, systematic,
stratified random, proportionate
versus disproportionate, cluster,
double, sequential, multiphase,
convenience, judgment, quoted,
snowball, etc. (Cooper &
Schindler, 2011). Researchers
will also find that there is no
standard sample size that is
used in all business research.
Some sample size
requirements are large, and
some sample requirements are
small. There is no general rule
of thumb when it comes to
sample size. The best advice
when the time comes to
conduct business research and
statistical analysis is to consult
research and statistics
textbooks and reputable
websites to seek agreement on
the preferred sample size for
the statistical procedure.

There are many common data
collection challenges that may
be mitigated by using best
practices in survey research
methods. Depending on the

research design and statistical procedure to be used, sampling error and sample size must be considered.

Sampling error, including random variability and bias, is a primary area of concern. Sampling error is the
difference between the sample average and the population average (Renckly, 2002). This error occurs purely
by chance since the sample will not exactly represent the population of interest. For example, if the population
contains an equal number of men and women, it is unlikely that a sample would be comprised of exactly 50%
women and 50% men. The degree of error will be determined by the amount of variance between the sample
and population of interest (Fowler, 2009). This is relevant to research designs requiring random samples
intended to make predictions about the population. Random variability occurs when the sample is non-
representative of the population through purely random reasons (Fowler, 2009).

The sample itself is drawn from the sampling frame, which is the pool of participants who have a chance of
selection. Another challenge is in determining how the sample will be drawn from the sampling frame. For
some but not all statistical procedures, it is necessary to draw a purely random sample. The simple random
sample is the prototype for a purely random sample where each participant has a known non-zero chance of
being selected for participation. Random sampling produces the smallest sampling error because the random
sample most closely represents the population of interest and, therefore, has the smallest variance than
would be observed with other sampling methods (Fowler, 2009; Renckly, 2002).

Sampling techniques
(lamnee, n.d.)

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Common Types of Sampling

Random sample: A random sample is a probability sample. Samples are drawn from the population of
interest, and each participant has an equal chance of being selected for the sample. For example, a city
planner in a large metropolitan area with a population of 500,000 adult residents would like to survey a
sample of this population to determine attitudes about adding a city park. It would be cost prohibitive to survey
all 500,000 adult residents, so she decides to use a sample size of 250 participants. She has a database
containing the names of all 500,000 adult residents and uses a program to randomly select 250 participants,
who will each receive a survey. All adult city residents had an equal chance of being randomly selected, so
this would be the purest form of sampling.

Stratified sample: A stratified sample is a probability sample. The population of interest is first divided into
discrete groups, or strata. These groups are often determined demographically by age, education, income,
geography, and other characteristics. A random sample is then drawn from each discrete group. For example,
the city planner first divides the resident database by annual income ranges of less than $25,000 per resident,
$25,000 to $50,000, $50,001 to $75,000, $75,001 to $100,000, greater than $100,000. She then uses a
program to select a random sample from each income range based upon its representation in the population.
If the greater than $100,000 population comprises 20% of the residents, she would randomly select 50
residents from this stratum to contribute to the total sample size, which will eventually be 250 participants.

Quota sample: A quota sample is a non-probability sample where the researcher sets an arbitrary
percentage, such as 20%, for each stratum. For example, the city planner may stand on a street corner and
survey people until she has 50 surveys (20% of 250) from each of the five income groups. These 250 people
will very likely not accurately represent the population of interest, all adult residents in the community, since it
is very unlikely each income group is exactly 20% of the population, which is the reason this is a non-
probability sample.

Convenience sample: A convenience sample is a non-probability sample. It uses any participant or data that
is conveniently available. For example, the city planner may stand on the street corner and survey the first
250 adults she meets to collect data about their attitudes for the newly proposed park. These 250 people will
very likely not accurately represent the population of interest (all adult residents in the community).

Either a table of random numbers or a computer program, like Excel, can be used to randomly select
numbered participants (Fowler, 2009). While the simple random sample is pure and straightforward in
concept, it is difficult to apply in many research scenarios. Complicating the process of drawing samples may
include a large number of participants in the sampling frame, unnumbered lists, patterned lists, or no lists at
all. Depending on the uniqueness of the sampling frame, alternatives to a simple random sampling design are
available. They include systematic sampling, stratified sampling, probabilities of selection, and multistage
sampling, such as cluster sampling and area probability sampling (Fowler, 2009). It is important to remember
that if the sample was not randomly drawn, it is not possible to generalize results to the population (Renckly,
2002). To confirm that the sample is random rather than non-random, two conditions are necessary. Each
participant must have an equal non-zero chance of being selected for the sample and participant selections
must be independent of one another (Renckly, 2002).

Bias is a second type of sampling error that may occur in a number of ways. Bias, unlike random variability, is
not random, but systematic (Fowler, 2009). For example, the sample participants answer systematically to a
survey that differs from how the population would respond (Fowler, 2009). The challenge of bias can be
reduced by selecting a sampling frame that is inclusive and representative of all participants in the population,
using a random process for selecting sample participants and reducing non-response or incomplete
responses from participants (Fowler, 2009). If bias occurs when sample participants are systematically
included or excluded in the sample, the sample would not be a fair representation of the population of interest.
Bias is especially concerning in research designs that require random sampling and may arise within the
study if answers are not obtained from all participants selected for the study (Fowler, 2009). Non-responses
can affect the study in two ways. It can bias results if a certain common segment of participants fail to
respond, and their information is excluded. Results may be biased if the survey is not distributed evenly
across the sample population (Renckly, 2002). For example, an electronically delivered survey could
introduce bias if there were some participants who were not computer literate. The bias would be

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compounded if the non-respondents have similar characteristics that are different from those participants who
answered the questions (Fowler, 2009).

Sample size is the final challenge to discuss related to data collection. Like many decisions relating to
research design, there is often disagreement about how large a sample should be for a study (Fowler, 2009).
Bartlett, Kotrlik, and Higgins (2001) add that lack of consensus and understanding of appropriate sample size
affects the quality and accuracy of research results. Fowler (2009) warns readers to avoid conventional
thinking regarding sample size since it is mostly inaccurate.

Data Analysis Design

The hypotheses will dictate the data analysis design. As mentioned in Unit II, quantitative hypotheses
generally predict relationships between variables or differences between variables or groups. There are many
statistical procedures that can be used to test hypotheses. This course will consider four commonly used
procedures.

Correlation: Correlation is used to test a null hypothesis stating no relationship exists between variables. The
correlation may show no relationship, a positive relationship, or a negative relationship. This concept makes
sense intuitively as there are associations between variables all around in daily life. These associations and
relationships can be positive or negative. Please note that, in correlation, the terms positive or negative are
not used in the context of making a value judgment. A positive or negative relationship in statistical terms
means the direction of the relationship. An example of a positive relationship between variables is when the
consumer confidence index increases, there is an increase in the stock market indices. This is a positive
relationship being that both variables move in the same direction. An example of a negative relationship
between variables is when the temperature decreases, our energy consumption (and monthly expenditure) for
heating increases. This is a negative relationship because the variables move in opposite directions. As one
variable decreases, the other increases (Field, 2005).

An important distinction needs to be made between correlation and causation. Even if a statistical test (e.g.,
Pearson’s r) indicates a relationship between variables, it must never be stated that one variable causes the
change in the other variable. For example, there is a positive correlation between ice cream consumption and
sunburn. It would be absurd to say that ice cream consumption causes sunburn, but the relationship between
variables does exist due to extraneous variables like hot and sunny weather. This extreme example makes
the point that correlation does not mean causation. Causation can only be statistically shown via experimental
research designs, which have tight controls to manipulate independent variables.

Regression: Although correlation can detect relationships between variables, it lacks predictive power.
Relationships between variables are more useful if they can be used to make predictions. Another statistical
procedure that has predictive power is regression analysis. Regression analysis is used to test a null
hypothesis stating there is no statistically significant prediction of Y by X. If we know the relationship, or
association, between the variables X and Y, through simple regression analysis, we can make a prediction of
how a change in X will relate to a change in Y. For example, if job satisfaction is related to productivity, a
manufacturer could use regression analysis make a prediction about what the firm could expect in increased
production if they can improve job satisfaction. Remember, this is not to state how a change in X causes a
change in Y. Regression only predicts a change based on the relationship between variables. Regression can
be very powerful, especially when multiple X variables are included in the analysis to make a prediction about
a change in a single Y variable. This is called multiple regression (Field, 2005).

The t test: The t test is used to test a null hypothesis stating there is no statistically significant difference
between two means. Means can be compared from two different groups. For example, a researcher may
want to know if Group A (restoration technicians) did better on the annual safety test than Group B
(remediation specialists). Means can also be compared for the same group over time. For example, did Group
C’s average safety test scores increase after safety training?

ANOVA: Analysis of variance (ANOVA) is similar to the t test but is used to test a null hypothesis that no
statistically significant differences exist among means for three or more groups. For example, a researcher
may want to determine if there are statistically significant differences in average annual safety scores among

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Group A (restoration technicians), Group B (remediation specialists), Group C (mid-level engineers), and
Group D (environmental scientists).

In Closing

Although there are many things to consider when conducting research, there are an overwhelming number of
great resources that will help guide researchers as they formulate their research designs. No one commits
everything to memory, and that is why it is important to refer to sources when assembling a research study.
The next three units will cover the data analysis.

References

Bartlett, J. E., II, Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research: Determining appropriate

sample size in survey research. Information Technology, Learning, and Performance Journal, 19(1),
43–50. Retrieved from https://search-proquest-
com.libraryresources.columbiasouthern.edu/docview/219816871?accountid=33337

Bulmer, M. (2001). Social measurement: What stands in its way? Social Research, 68(2), 455–480.

Cooper, D. R., & Schindler, P. S. (2011). Business research methods (11th ed.). New York, NY: McGraw-

Hill/Irwin.

Echambadi, R., Campbell, B., & Agarwal, R. (2006). Encouraging best practice in quantitative management

research: An incomplete list of opportunities. Journal of Management Studies, 43(8), 1801–1820.

Field, A. (2005). Discovering stats using SPSS (2nd ed.). London, England: Sage.

Fowler, F. J. (2009). Survey research methods (4th ed.). Thousand Oaks, CA: Sage.

lamnee. (n.d.) Classification of sampling methods in qualitative research (ID 84524908) [Image]. Retrieved

from www.dreamstime.com

Lee, J. S. K. (1992). Quantitative versus qualitative research methods: Two approaches to organisation

studies. Asia Pacific Journal of Management, 9(1), 87–94. Retrieved from https://search-proquest-
com.libraryresources.columbiasouthern.edu/docview/228433051?accountid=

Renckly, T. R. (Ed.). (2002). Air university sampling and surveying handbook: Guidelines for planning,

organizing, and conducting surveys. Maxwell Air Force Base, AL: Air University.

Research methods in management research. (2006). Journal of Management Studies, 43(8), 1799–1800.

Rumrill, P. D., Jr. (2004). Non-manipulation quantitative designs. Work, 22(3), 255–260.

Suggested Unit Resources

The video below provides further insight about qualitative and quantitative research methodologies. By
watching this video you will see the moderator explain the differences between qualitative and quantitative
research designs using practical and entertaining examples to elucidate dissimilarity in various research
methods.

ChrisFlipp. (2014, January 15). Qualitative vs. quantitative [Video file]. Retrieved from

https://www.youtube.com/watch?v=2X-QSU6-hPU

A transcript of this video is available.

https://www.youtube.com/watch?v=2X-QSU6-hPU

https://online.columbiasouthern.edu/bbcswebdav/xid-117740623_1