RESEARCH DESIGN QUANTITATIVE 2
Email: ajtippett@liberty.edu
Part 1: Definitions
Personal Definition: Definition:
Quantitative research methods focus on collecting
numerical data and analyzing that data to find
relationships, patterns, or lack of patterns. These
results allow researchers to give data-based answers
to study questions or make educated guesses about
groups of individuals. In some cases, the analysis
may allow them to make generalizations about a
much larger group than was studied.
Definition: Quantitative research begins with an
explicitly stated hypothesis which focuses on a narrow
question. Random sampling is generally used to select
study participants, sometimes in very large groups.
Researchers generally have limited contact with the
study participants. Results are based on statistical
analysis of the data collected. Data may also be from
secondary sources, using information collected by other
reputable agencies.
Reference: Tcherni-Buzzeo & Pyrczak (2018)
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Part 2: Explore with Words
Synonym: Antonym:
Computable, Calculable Verbal, immeasurable
Words associated with: Sentence:
Numerical, measurable, correlation, variables The researchers used quantitative research methods to
examine the relationship between one-on-one instruction
and changes in student test scores.
Part 3: Purpose and Quality Indicators
Answers Research Questions about:
Quantitative research focuses on numerical patterns and data. Quantitative research allows researchers to
answer critical questions by making observations and collecting real data from a selection of participants or
particular group of people effected by the focus of the study, such as students with autism or adults with an
anxiety disorder (Ahmad et al., 2019). Numerical data allows researchers to use statistical analysis to examine
trends and patterns in the data collected to make estimates or generalizations about a larger group than can be
readily sampled. Quantitative research deals with measurable, logical information, looking for relationships
that can be expressed in graphs or statistical charts. It is designed to gather information and increase
knowledge related to a hypothetical question, producing facts through deductive and logical reasoning (Ahmad
et al., 2019).
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Characteristics:
Characteristics of quantitative research must include, according to Brown (2015), reliable measurements and
observations, valid measurements and observation which correlate with the intent of the study, documentation
and analysis sufficient for others to replicate the study, and how well the data can be used to make
generalizations about the entire population which was sampled. Other characteristics of quantitative research
include data collection, statistical analysis, and logical interpretation of results. Quantitative research can be
descriptive, correlational, quasi-experimental or experimental. This type of research is used often to add
knowledge to an already defined field of study or to confirm another researcher’s findings.
RESEARCH DESIGN QUANTITATIVE 3
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Sampling / Participants
Participants in quantitative research are individuals belonging to a very narrow, specific population being
examined within the research study. Typically, random samples are selected to give a general representation of
the entire studied population (Tcherni-Buzzeo & Pyrczak, 2018). These samples can be chosen through an
electronic method of randomization or through use of third-party data collection which eliminates researcher
bias. Often, quantitative research is experimental, requiring participants to be assigned to either the treatment or
control group, which should be randomized as well (Brown, 2015). Participants should generally not be
volunteers, as this can sometimes lead to attrition or skew the data.
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Intervention Fidelity / Independent Variable
In quantitative research, the intervention fidelity indicates the accuracy with which the interventionists
delivered the ascribed intervention to the experimental group of participants and is based on a series of
questions (Nelson et al., 2012). Did the teacher/interventionist implement the intervention, i.e. manipulate the
independent variable, as the researchers planned? The independent variable being the one aspect of an
instructional method or environmental control which is different from the control group, i.e., the only variation
from the control group. Was the quality of delivery as expected? Did the participants follow through with their
part of the intervention? And finally, did the experimental and control group differ as prescribed and expected?
This information gives researchers a better understanding of the reliability of the data collected (Nelson et al.,
2012).
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Instrumentation / Measures
In quantitative research, instrumentation selection depends on the type of research being conducted and the
research question being asked (Pentang, 2023). During the planning portion of the research study, the
independent variable and the research question dictates the instrumentation the researchers will use and what
measures are valid. Instrumentation, for example, might be a survey or even the comparison of beginning and
ending grades of students in experimental versus control groups. The instrument must measure what the
researcher has indicated as the focus of the study. For many quantitative studies, the instruments could include
tests, interviews, or surveys (Ahmad et al., 2019).
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Internal Validity
Internal validity in quantitative experimental research is the correlation between the independent and dependent
variables, and the extent to which they impact one another (Bhandari, 2023). According to Tcherni-Buzzeo and
Pyrczak (2018), internal validity indicates the extent to which researchers can be confident the experiment will
clearly indicate a cause-and-effect relationship between the independent and dependent variable. In order for
a study to be considered reliable, there must be no other explanation for the changes in the dependent variable
other than the independent variable. There are many things that can impact internal validity, including attrition
of participants, poor measures of the data, and invalid testing.
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Outcome Measures / Dependent Variable
RESEARCH DESIGN QUANTITATIVE 4
According to Tcherni-Buzzeo and Pyrczak (2018), outcome measures or dependent variables, are quantifiable
or observable changes to the dependent variable recorded during a study which measure or monitor the impacts
of the intervention or application of the independent variable. These are the responses to interventions which
show correlation between the independent and dependent variable. These changes can be compared
statistically to the same data collected from the control group without changes to the independent variable. This
type of information is generally produced by an experimental study looking to establish cause and effect
between one independent and one dependent variable (Fischer et al., 2023). Intervention fidelity plays a very
important role in the reliability of these outcomes.
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Data Collection / Analysis
Data collection and analysis in quantitative research depends on the type of study conducted, the instrument of
intervention or collection, and what types of data are expected to be analyzed. According to Ahmad et al.
(2012), data is collected via tests, questionnaires, experiments, or other instruments. Once the data is collected,
statistical analysis is performed to find patterns, relationships, or correlations between experimental and control
groups, or how the data aligns with the proposed hypothesis from the inception of the study. Data is collected
as raw numbers or general information and analyzed at the end of the study to help add to the knowledge base
regarding the given subject.
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Part 4: Strengths, Weaknesses, and Critical Issues
Strengths: Weaknesses:
There are many strengths of quantitative research,
such as consistency of statistical analysis, useful for
comparisons between groups or studies, as needed,
reliability of data and findings, and appropriate use
of statistical analysis generates reliable results
(Mohajan, 2020). Choy (2014) adds that
quantitative research offers a shorter time frame for
collection and analysis of data, as well as the
reliability and lack of subjectivity found in
numerical data. Queiros et al. (2017) point out the
advantage of experiments in a natural setting as
opposed to a laboratory, studies can be cost
effective, and reach a great number of participants
through surveys and larger scale studies. In
correlational studies, more information and different
domains can be examined without manipulation of
behavior (Queiros et al., 2017). Queiros et al. also
assert that, during simulation studies, different
what-if questions may be answered, and complex
systems can be studied. One strength many agree
upon is the data and outcomes are not affected by
the bias or subjectivity of the researchers involved
(Choy, 2014; Mahajan, 2020; Queiros et al., 2017).
In examination of the weaknesses of quantitative studies,
there are several which have been documented. Queiros
et al. (2017) compiles an extensive list, including the
challenges with controlling variables, difficulties with
replication, quantitative studies cannot capture the range
of emotions and behaviors of a participant that might be
impacting their responses or the responses of others. For
some studies, internal and external validity might be a
drawback (Choy, 2014). Choy goes on to point out that
the lack of human perceptions, potential impact from
lack of resources, and no way to measure the
experiences of the participants or interventionists
effectively are all drawbacks to quantitative research
(2014). Mohajan (2020) also indicates that some
important data may be lost in the process of aggregation,
eliminating data that might have been impactful on the
outcome in certain situations. Mohajan continues by
indicating that certain outcomes might be underreported,
such as domestic violence or other household issues
which could impact the data (2020).
RESEARCH DESIGN QUANTITATIVE 5
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Critical Issues to Identify:
When examining quantitative research, it is important to carefully examine the methods of sampling, the
selection of instrument, the construction of the experiment, and the equity and equality found within the study
participants (Queiros et al., 2017). How and who are chosen to participate in a study can have significant
impacts of the outcomes represented in the data. Tcherni-Buzzeo and Pyrczak, (2018) point out that over-
exuberant volunteers can skew the data. Poorly written survey questions can lead to invalid or inaccurate data.
A lack of equality in the sample set can also create ethical issues with the data, for example if women or certain
ethnic groups are excluded from sampling, the outcomes of the data analysis will not be consistent with the
overall population group being studied.
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What Will Help Me Remember:
Quantitative studies generally involve numerical or empirical data which can be analyzed through statistical
methods to produce new or confirmed knowledge about specific topics or people groups.
Unique to Special Education:
How does this research design meet unique needs in
special education?
What unique problems does this design have when
implementing in special education?
Within special education, there is such variety
within the different categories, quantitative studies
could be effective in studying one intervention on
one specific learning disability. Special education
studies are more credible when conducted in a
natural environment for the students, with people
they are comfortable with, and quantitative studies
allow this type of environment in which to test
(Choy, 2014).
Quantitative studies use data collected from random
participants and statistical analysis to generate results
based on very specific questions or focused on very
narrow groups of individuals. Given the enormous
variety of traits and complexity of all forms of
disabilities found in special education, generalizations
can only effectively apply to the individuals within the
study group (Queiros et al., 2017).
Prominent Researchers:
Paul Felix Lazarsfeld (first used research surveys)
Jacob Bernoulli (mathematical analysis)
Daniel Starch (used surveys to test effectiveness of his advertising methods)
Resources: (websites that might be most helpful)
http://methods.sagepub.com/ https://www.statista.com/
https://www.methodology.psu.edu/ https://www.ebscohost.com/academic/academic-search-
premier
References
Ahmad, S., Wasim, S., Irfan, S., Gogoi, S., Srivastava, A., & Farheen, Z. (2019). Qualitative v/s. quantitative
research-a summarized review.Population,1(2), 2828-2832.
RESEARCH DESIGN QUANTITATIVE 6
Bhandari, P. (2023).IInternal validity in research | Definition, threats, & examples.
Scribbr.Ihttps://www.scribbr.com/methodology/internal-validity/
Brown, J. D. (2015). Characteristics of sound quantitative research.Shiken Journal,19(2), 24-28.
Cahit, K. (2015). Internal validity: A must in research designs.Educational Research and Reviews,10(2), 111-
118.
Choy, L. T. (2014). The strengths and weaknesses of research methodology: Comparison and complimentary
between qualitative and quantitative approaches.IOSR Journal of Humanities and Social Science,19(4),
99-104.
Fischer, H. E., Boone, W. J., & Neumann, K. (2023). Quantitative research designs and approaches. In N. G.
Lederman, D. L. Zeidler, & J. S. Lederman (Eds.)IHandbook of Research on Science Education. 28-59.
Routledge.
Mohajan, H. K. (2020). Quantitative research: A successful investigation in natural and social sciences.IJournal
of Economic Development, Environment and People,I9(4), 50-
79. https://doi.org/10.26458/jedep.v9i4.679
Nelson, M. C., Cordray, D. S., Hulleman, C. S., Darrow, C. L., & Sommer, E. C. (2012). A procedure for
assessing intervention fidelity in experiments testing educational and behavioral interventions.The
Journal of Behavioral Health Services & Research,39, 374-396.
Pentang, J.T. (2023). Quantitative research instrumentation for Educators. Lecture Series on Research Process
and Publication. http://dx.doi.org/10.13140/RG.2.2.21153.28004
Queirós, A., Faria, D., & Almeida, F. (2017). Strengths and limitations of qualitative and quantitative research
methods.European Journal of Education Studies, (3)9, 369-386
Tcherni-Buzzeo, M., & Pyrczak, F. (2018). Evaluating Research in Academic Journals (7th ed.). Taylor &
