EDSP - Quantitative Research Design - Feb 4

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RESEARCH DESIGN QUANTITATIVE 1 Research Design: Quantitative Angela J Tippett School of Education, Liberty University Author Note Angela J Tippett I have no known conflict of interest to disclose. Correspondence concerning this article should be addressed to Angela J Tippett

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) Word Count_58/50 Word Count_61/50 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). Word Count__122/100 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 _________________________________________________________________________________________ Word Count_101/100 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. Word Count_102_ /100 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). Word Count__128 /100 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). Word Count_102_/100 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. Word Count_105 /100 Outcome Measures / Dependent Variable

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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. Word Count_116 /100 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. Word Count_109 /100 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 Word Count__175/150 Word Count__158 /150 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. Word Count___127/100 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). Internal validity in research | Definition, threats, & examples. Scribbr . https://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.) Handbook of Research on Science Education. 28-59. Routledge. Mohajan, H. K. (2020). Quantitative research: A successful investigation in natural and social sciences. Journal of Economic Development, Environment and People, 9(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 &

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RESEARCH DESIGN QUANTITATIVE 7 Francis. https://mbsdirect.vitalsource.com/books/9781351260947

EDSP - Quantitative Research Design - Feb 4

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