Competency: Illustrates the importance of quantitative research across fields.
What is Quantitative Research?
Quantitative research is defined as a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects information from existing and potential respondents using sampling methods which the result can be depicted in the form of numerical.
An example could be, a patient satisfaction survey can be administered to ask questions like how much time did a doctor take to see a patient, how often does a patient walk into a hospital, and other such questions.
Quantitative research is mostly conducted in the social sciences using the statistical methods used above to collect quantitative data from the research study. In this research method, researchers and statistician deploy mathematical frameworks and theories that pertain to the quantity under question.
Quantitative research templates are objective, elaborate, and many times, even investigational. The results achieved from this research method are logical, statistical, and unbiased. Data collection happened using a structured method and conducted on larger samples that represent the entire population.
Quantitative Research Methods: Types with Examples
There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:
1. Survey Research
Survey Research is the most fundamental tool for all quantitative research methodologies and studies. Surveys used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, etc. Every small and big organization intends to understand what their customers think about their products and ser- vices, how well are new features faring in the market and other such details.
By conducting survey research, an organization can ask multiple survey questions, collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step to- wards collecting data for any research.
This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis. A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. Traditionally, survey research was conducted face-to-face or via phone calls but with the progress made by online mediums such as email or social media, survey research has spread to online mediums as well.
There are two types of surveys, either of which can be chosen based on the time in-hand and the kind of data required:
Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which is considered for research. Throughout the survey, this one variable will stay constant.
Cross-sectional surveys are popular with retail, SMEs, healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
Using cross-sectional survey research method, multiple samples can be analyzed and compared.
Multiple variables can be evaluated using this type of survey research.
The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.
Longitudinal surveys: Longitudinal surveys are also observational surveys but, unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought-processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over five (5) years will conduct longitudinal surveys.
In cross-sectional surveys, the same variables were evaluated at a given point in time, and in longitudinal surveys, different variables can be analyzed at different intervals of time.
Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend, analyze customer satisfaction, or gain feedback on products/services.
In situations where the sequence of events is highly essential, longitudinal surveys are used.
Researchers say that when there are research subjects that need to be thoroughly inspected before concluding, they rely on longitudinal surveys.
2. Correlational Research
A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two variables and how one impacts the other and what are the changes that are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.
Researchers use this quantitative research method to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original set up. The impact of one of these variables on the other is observed along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.
Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.
Example of Correlational Research Constructs:
The relationship between stress and depression.
The equation between fame and money.
The relation between activities in a third-grade class and its students.
3. Causal-Comparative Research
This research method mainly depends on the factor of comparison. Also called the quasi-experimental research, this quantitative research method is used by researchers to conclude cause-effect equation be- tween two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural set up. As the dependent and independent variables will al- ways exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.
Causal-comparative research is not restricted to the statistical analysis of two variables but ex- tends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relation that exists between two or more variables. Statistical analysis is used to distinctly present the outcome of obtained using this quantitative research method.
Example of Causal-Comparative Research Constructs:
The impact of drugs on a teenager.
The effect of good education on a freshman.
The effect of substantial food provision in the villages of Africa.
4. Experimental Research
Also known as true experimentation, this research method is reliant on a theory. Experimental research, as the name suggests, is usually based on one or more theories. This theory has not been proven in the past and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences.
There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.
After establishing the statement, efforts are made to understand whether it is valid or invalid. This type of quantitative research method is mainly used in natural or social sciences as there are various state- ments which need to be proved right or wrong.
Traditional research methods are more effective than modern techniques.
Systematic teaching schedules help children who find it hard to cope-up with the course.
It is a boon to have responsible nursing staff for ailing parents.
Characteristics of Quantitative Research
The goal in conducting quantitative research study is to determine the relationship between one thing (an independent variable) and another (a dependent or outcome variable) within a population. Quantitative research designs are either descriptive (subjects usually measured once) or experimental (subjects measured before and after a treatment). A descriptive study establishes only associations between variables; an experimental study establishes causality.
Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning (i.e., the generation of a variety of ideas about a research problem in a spontaneous, free- flowing manner).
Its main characteristics are:
The data is usually gathered using structured research instruments.
The results are based on larger sample sizes that are representative of the population.
The research study can usually be replicated or repeated, given its high reliability.
Researcher has a clearly defined research question to which objective answers are sought.
All aspects of the study are carefully designed before data is collected.
Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
Researcher uses tools, such as questionnaires or computer software, to collect numerical data.
The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.
Things to keep in mind when reporting the results of a study using quantiative methods:
Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
Explain the techniques you used to "clean" your data set.
Choose a minimally sufficient statistical procedure; provide a rationale for its use and a reference for it. Specify any computer programs used.
Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
When using inferential statistics, provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level (report the actual p value).
Avoid inferring causality, particularly in nonrandomized designs or without further experimentation.
Use tables to provide exact values; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
Always tell the reader what to look for in tables and figures.