Quantitative research methods have a few designs to choose from, mostly rooted in the postpositivist worldview. The experimental design, quasi-experimental design and single subject experimental design (Bloomfield & Fisher, 2019; Creswell & Creswell, 2018). Single- subject or applied behavioral analysis consists of administering an experimental treatment to a person or small group of people over an extended period of time. Of the quasi experimental designs, subcategories; causal-comparative design and correlational design. Causal-comparative research allows for the investigator to compare two or more groups in terms of a treatment that has already happened. For correlational design, the researcher is looking to examine the relationship between variables or set of scores (Bloomfield & Fisher, 2019; Creswell & Creswell, 2018).
Generally, these kinds of designs fall into two categories, Survey Research and Experimental Research. Survey research uses a quantitative (numerical) description of trends, attitudes, opinions of a population by examining a sample of that population through questionnaires or structured interviews for data collection (Fowler, 2008; Fowler, 2014; Bloomfield & Fisher, 2019; Creswell & Creswell, 2018). These studies can be cross-sectional and longitudinal. Ultimately, the goal is to analyze the data and have the finding be generalizable to the entire population.
Experimental research uses the scientific method to determine if a specific treatment influences an outcome. This design requires random assignment of treatment conditions, and the quasi-experimental and single subject version of this uses nonrandomized assignment of treatment (Bloomfield & Fisher, 2019).
Survey research methods are widely used and follow a standard format. Examining survey research in scholarly journals would be a great way to familiarize yourself with the format and determine how to do it and, more importantly, if this method is right for your research.
How to prepare to do survey research? Creswell and Creswell (2018) as well as Fowler (2014), have provided basic framework for the rationale of survey research that you consider as you make the decision abou what kind of methods you will employ to conduct your inquiry.
As you determine what instrument you will use, a survey you create or that has been used and created by someone else, you should consider the following (Fowler, 2008; Creswell & Creswell, 2018; Bloomfield & Fisher, 2019):
There are three components to experimental design which also follows a standard form: participants and design, procedure and measurement. There are a few considerations that Bouma et al. (2012), Bloomfield and Fisher (2019), Creswell and Creswell (2018) suggest you determine early on in your design.
Just like with survey research, it is important to discuss how you are collecting your data, through what instrument or instruments are used, what scales are used, what their reliability and validity are based on past uses (Bouma et al., 2012; Creswell & Creswell, 2018; Bloomfield and Fisher, 2019). Ultimately, some quantitative experimental models may use data sets that have already been collected like the National Center for Educational Statistics (NCES). In that case, you will be able to discuss the validity and reliability easily as it is well-established. However, if you are collecting your own data, you must discuss in detail what materials are used in the manipulation of variables. For example, you might want to pilot test the experiment so you have a detailed knowledge of the procedure (Bouma et al., 2012; Creswell & Creswell, 2018).
Also, often in experimental design, you don’t want the participants to know which variables are being manipulated or which group they are being assigned to. In order to be sure you are in line with IRB regulations (See IRB section), you want to draft a letter that will be used to explain the procedures and the study’s purpose to the participants (Creswell & Creswell, 2018). If there is any deception used in the study, be sure to check the IRB guidelines to ensure that you have all procedures and documents approved by Kean University’s IRB.
Measurement and Data Analysis for Quantitative Methods
It is important to reiterate that there are several kinds of ways to collect data for a quantitative study. The data is always numerical, as opposed to qualitative data, which is largely narrative. The most common data collection methods for quantitative research are:
The data is collected across populations, using a large sample size and then is analyzed using statistical analysis. Then, the results would be generalizable across populations. However, before you collect the data, you need to determine what exactly you are proposing to measure as you choose your variables and you. There are several kinds of statistical measurements in quantitative research. Each has its own purpose and objective. Ultimately, you need to decide if you are going to describe, explain, predict, or control your numerical data.
Quantitative data collection typically means there are a lot of data. Once the data is gathered, it may seem to be messy and disorganized at first. Your job as the researcher is to organize and then make the significance of the data clear. You do this by cleaning your data through “measurements” or scales and then running statistical analysis tests through your statistical analysis software program.
There are several purposes to statistical analysis in a quantitative study, such as (Kumar, 2015):
It is important to know that in order to properly analyze your numerical data, you will need access to statistical analysis software such as SPSS. The OCIS Help Desk website provides information on how to access SPSS under the Remote Learning (Students) section.
Once you have collected your numerical data, you can run a series of statistical tests on your data, depending on your research questions.
There are four kinds of statistical measurements that you will be able to choose from in order to determine the best statistical tests to be utilized to explore your research inquiry. These measurements are also referred to as scales, and have very particular sets of statistical analysis tools that go along with each kind of scale (Bryman & Cramer, 2009).
Nominal measurements are labels (names, hence nominal) of specific categories within mutually exclusive populations or treatment groups. These labels delineate non-numerical data such as gender, city of birth, race, ethnicity, or marital status (Bryman & Cramer, 2009; Ong & Puteh, 2017).
Ordinal measurements detail the order in which data is organized and ranked. These measures or scales deal with the greater than( >)compared to those less than (<) within a data set. Again, these are organized (named/ categorized) and ranked (ordinal), such as class rank, ability level (beginner, intermediate, expert), or Likert scale answers (strongly agree, agree, undecided, disagree, strongly disagree) (Bryman & Cramer, 2009; Ong & Puteh, 2017).
Interval measurements take data and order them (nominal), rank them (ordinal) and then evenly distribute them in equal intervals. There is also a zero point which is established. deal with equal units where a zero point is established. Interval measurements can be used for height, weight where there would be an absence of one of those variables (Bryman & Cramer, 2009; Ong & Puteh, 2017).
Ratio measurements allow for data to be measured by equal units (interval) and an absolute zero point is established. Here, in ratio measurements, the absolute zero value signifies the absence of the variable. For example, 0 lbs means the absence of weight. Height, weight, temperature are all examples of variables that can be measured through ratio scale (Bryman & Cramer, 2009; Ong & Puteh, 2017).
Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian Rehabilitation Nurses Association, 22(2), 27-30. https://doi-org.kean.idm.oclc.org/10.33235/jarna.22.2.27-30
Bouma, G. D., Ling, R., & Wilkinson, L. (2012). The research process (2nd Canadian ed.). Oxford University Press.
Bryman, A., & Cramer, D. (2009). Quantitative data analysis with SPSS 14, 15 & 16: A guide for social scientists. Routledge/Taylor & Francis Group.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.
Fowler, F. J., Jr. (2008). Survey research methods (4th ed.). Sage.
Fowler, F. J., Jr. (2014). The problem with survey research. Contemporary Sociology, 43(5): 660-662.
Kraemer, H. C., & Blasey, C. (2015). How many subjects?: Statistical power analysis in research. Sage.
Kumar, S. (2015). IRS introduction to research in special and inclusive education. [PowerPoint slides 4, 5, 37, 38, 39,43]. Informační systém Masarykovy univerzity. https://is.muni.cz/el/1441/podzim2015/SP_IRS/
Ong, M. H. A., & Puteh, F. (2017). Quantitative data analysis: Choosing between SPSS, PLS, and AMOS in social science research. International Interdisciplinary Journal of Scientific Research, 3(1), 14-25.
Sharma, G. (2017). Pros and cons of different sampling techniques. International Journal of Applied Research, 3(7), 749-752.