CHARACTERISTICS

1. Quantitative research relies on data that can be measured and quantified. This includes variables such as numbers, quantities, and measurements. Hence, quantitative research is appropriate for research that approaches research problems that require quantification and measurement.

2. Quantitative research emphasizes objectivity, aiming to minimize bias and personal interpretation. It often involves structured data collection methods to ensure consistency and reliability.

3. Statistical techniques are a cornerstone of quantitative research. Researchers use various statistical tools to analyze data, test hypotheses, and draw conclusions with a degree of confidence.

4. Quantitative studies typically involve larger sample sizes to ensure the results are representative of the broader population being studied.

5. One of the main goals of quantitative research is to generalize findings from a sample to a larger population, increasing the study's external validity.

sTRENGTHS 

1. With proper representation on the sample size, it is easier to draw conclusions that can be generalized in the entire population.

2. Quantitative research minimizes researcher bias, leading to more objective and replicable results since it relies on the interpretation of the statistical results.

3. Statistical analysis allows for precise measurement of relationships between variables.

4. Large sample sizes and rigorous methodologies enhance the potential for findings to be applied to broader populations.

5. Quantitative research can efficiently gather data from a large number of participants, enabling a comprehensive analysis.

6. Quantitative studies are easier to replicate than qualitative research. It may provide opportunity for the comparison of the consistency of the results if same methods were adopted.

LIMITATIONS 

1. Quantitative research may not provide a deep understanding of the context and underlying reasons for observed patterns. Use of tools like questionnaire may obtain a limited or inaccurate information due to human subjectivity.

2. The focus on numerical data may oversimplify complex social or human phenomena.

3. This method may not capture the full range of human experiences, emotions, and behaviors.

4. Even though efforts are made to minimize bias, it can still influence the design, data collection, or analysis. Errors in the interpretation of the statistical result, or the statistical tool used may lead to faulty results.

5. Emphasis on generalizability hinders the in-depth understanding of the causes and factors that influence a particular case 

Step 3: Formulate Hypothesis. After identifying the variables that will be investigated for comparison, correlation, or association, a tentative educative guess (hypothesis), may be formulated. In quantitative studies, hypothesis are often written in its null form like “there is no relationship between the parent’s educational background and the career decision-making of the students.”

Step 4: Development of Data Collection Methodologies. This encompasses the selection of appropriate research design, sampling, data collection instruction, plan for data collection procedures and data analysis. Selection of the data gathering methods depend on the type of the variables included and the objectives of the researchers. Survey questionnaires are often used in descriptive type studies although checklist, and rating scales are also used. Research instruments will be subjected to validation and pilot testing to establish its reliability.

Step 5: Fieldwork (Data Collection/Experimentation). In this phase, the actual data gathering takes place. Several procedures are religiously and ethically followed to make sure that the credibility of the results will not be compromised, especially in an experimental research where human and animal rights and welfare issues are strictly observed.

Step 6: Analysis of Data. Once the data has been gathered, it will be subjected to mathematical and/or statistical analysis. Data processing can be facilitated through the use of electronic software like spreadsheet applications, SPSS (Statistical Package for the Social Sciences) and many more. There are different models and equations that may help guide the researchers in coming up with an objective interpretation of the results. Common statistics include descriptive statistics, inferential statistics, and regression analysis. Results are then interpreted based in the context of the research questions or objectives.

Step 7: Generating Conclusions and Recommendations. Conclusions are drawn directly from the interpretation of the results of the statistical analysis. Results from the data gathered will be summarized and their implications, either theoretical, practical, and social significance, will form part the recommendations of the researchers.

Step 8: Reporting of Findings. It was said that a negative finding is still a finding. Communicate the results of the study even if it did not favor your assumption. Present the methods, and the processes used in the conduct of the study. Use charts, graphs, tables, and illustrations in simplifying the delivery of the findings.

Descriptive Research. This type aims to describe one or more aspects of an one or more groups, communities, and phenomenon without manipulating the variables.

Example: A survey to determine the demographic information of the target market (customer) of a particular product, and level of political participation of community members in a particular barangay.

Correlational Research. The purpose is to discover, and then possibly  measure, relationships between two or more variables without establishing cause and effect.

Example: Investigating the relationship between exposure to social media and the political participation of HUMSS students, relationship between entrepreneurship competence and intention of students to start-up a business.

Experimental Research. Involves manipulation of an independent variable to observe its impact on the dependent variable, while controlling for extraneous (irrelevant or unrelated) factors. It established cause-effect relationships.

Example: An action research of a teacher that investigates the impact of a new teaching method on the test scores of the students. Testing the efficacy of a newly developed vaccine by randomly assigning participants to either the experimental group (receiving the vaccine) or the control group (receiving a placebo).

Causal-Comparative Research (Quasi-Experimental). The researcher investigates the cause-and-effect relationship between variables, but lacks full experimental control.

Example: Comparing the scores of sections of the students who received a new teaching method and those classes who did not.

Quantitative research plays a crucial role in advancing knowledge and decision-making across a wide range of disciplines.

1. Medicine:

Clinical Trials: Quantitative research ensures rigorous testing of medical interventions, leading to evidence-based treatments.

Epidemiology: Analyzing large datasets helps identify disease patterns, risk factors, and trends for better public health strategies.

Patient Outcomes: Quantitative analysis aids in assessing the effectiveness of medical procedures and improving patient care.

2. Education:

Learning Assessment: Quantitative research evaluates teaching methods and educational interventions to enhance student learning outcomes.

Policy Formulation: Data-driven insights guide curriculum development and educational policies for improved education quality.

3. Social Science:

Surveys and Experiments: Quantitative methods enable researchers to study human behavior, attitudes, and societal trends systematically.

Data Analysis: Quantitative research identifies correlations, patterns, and causal relationships to inform social policies and interventions.

4. Criminology and Law:

Crime Analysis: Quantitative research assists in understanding crime patterns, aiding law enforcement in effective crime prevention.

Legal Research: Data-driven analysis informs legal cases, policy-making, and criminal justice reform efforts.

5. Business:

Market Research: Quantitative data guides business decisions, from product development to identifying consumer preferences.

6. Science and Technology:

Experiments and Simulations: Quantitative research fuels scientific discoveries by enabling controlled experiments and simulations.

Data Analysis: Quantitative methods reveal insights in fields like astronomy, chemistry, and physics, aiding research breakthroughs.

7. Engineering:

Design and Optimization: Quantitative research informs engineering design, materials selection, and optimization of systems.

Performance Analysis: Quantitative methods evaluate the efficiency and reliability of engineered structures and products.

8. Information Technology:

Data Analytics: Quantitative research extracts actionable insights from vast datasets, driving improvements in IT systems and services.

Cybersecurity: Quantitative analysis helps identify vulnerabilities and assess risks in digital environments.

9. Communication:

Audience Analysis: Quantitative methods inform strategic communication planning by understanding audience preferences and behaviors.

Media Effects: Quantitative research assesses the impact of media on attitudes, behaviors, and societal trends.

TYPES OF VARIABLES

The independent variable (IV) is the antecedent while the dependent variable is the consequent. If the independent variable is an active variable then we manipulate the values of the variable to study its effect on another variable. In the above example, we alter anxiety level to see if responsiveness to pain reduction medication is enhanced. Anxiety level is the active independent variable.

·        The variable that researchers manipulate or categorize to observe its effects on other variables.

·        Often denoted as "X" in equations.

·        Example: In a study on the effect of different study techniques on exam scores, the study technique/habit is the independent variable.

Dependent variable (DV) is the variable that is affected by the independent variable. Responsiveness to pain reduction medication is the dependent variable in the above example. The dependent variable is dependent on the independent variable

·        The variable that researchers observe or measure to determine the impact of the independent variable.

·        Often denoted as "Y" in equations.

·        Example: In the same study, the exam scores would be the dependent variable.

Intervening Variable are the links between dependent and independent variables. There are certain phenomena in which, without the intervening variable, there will be no observed significant effect to the dependent variable from the independent variable. In example, a study looking into the impact of a country’s government type and its support for LGBT rights may be influenced by that country’s level of freedom of assembly.

Control Variable a variable held constant during an experiment to isolate the effects of the independent variable. Helps eliminate alternative explanations for observed results. Example: Controlling the time allocated for studying to ensure it doesn't confound the study technique effect.

1. 1. 1. Nominal scales of measure represent only qualitative differences of the attribute of interest. That is, they categorize data based on type (i.e., political party). Nominal scales create a set of labels for categories (Democrat, Republican, Independent) that are mutually exclusive and to which participants are assigned. Nominal scales are the weakest form of measurement because they assume equality among members of a particular category and differences between categories. In addition, the numerical value assigned to each category is arbitrary.

      2. The values in Ordinal Scales represent relative differences in the amount of some attribute such that the value assigned to someone or something reflects its rank among the rest of the data. An example is U.S. News & Report’s “America’s Top College.” Unlike nominal scales, ordinal scales indicate how much scores differ from one another.

      3. Interval scales also reflect quantitative differences among data, but unlike ordinal scales they assume equal differences in the amount of the attribute being measured. Temperature measured on the Fahrenheit scale is an interval measure because each incremental change in temperature (70, 71, 72, etc.) is the same.

      4. Ratio scales are the most sophisticated form of measurement. There are equal distances between values on the scale and the scale has a true zero (0) point. That said, a score of 0 indicates a true absence of the attribute in question. This enables one to create meaningful ratio, such as “10 is twice as much as 5.” Interval scales cannot make these statements because their 0 point is arbitrary.

USES OF VARIABLES IN QUANTITATIVE RESEARCH

1. 1. 1. Classifying. Variables play a principal role in descriptive research. Through variables, we describe the phenomenon or population we are studying. For example, you are interested in studying the academic performance of high school students in your school. Since outcomes can be affected by a number of factors, you could classify the student body according to demographical information – gender, age, educational level, and economic background. By classifying the data, you are providing all the necessary details that may be critical to your argument and study.

      2. Measuring. Variables allow us to measure the frequency, magnitude, and impact among others of a particular subject or concept. For instance, human development can be empirically measured. Through the conceptualization and operationalization of human development studies, organizations develop an index that measure different indicators of human development, significantly contributing to practical and theoretical applications.

      3. Explaining. Variables allow one to identify the meaning, purpose, and use of social phenomena. For example, you are studying factors that possibly trigger an increase in immigrants in a particular country. By assigning values to certain factors or phenomenon – such as the implementation of the immigration policies and socioeconomic circumstances – you can see which situations led to an increase in immigrants. By identifying particular variables and choosing appropriate indicators, you can explain a particular phenomenon.

      4. Assessing Relationships. Quantitative research tests theories through assessing the relationships between variables. Variables are critical elements of the relationship between subjects and phenomenon you are investigating. Through hypothesis testing, we can examine which variables are related to which and how these are related to one another. For example, you are specifically looking at the effect of educational investment on economic returns. You can test two variables: first, the Philippine budget allotment for state universities and colleges (educational investment) as the independent variable; second, annual family income (economic return) as the dependent variable. Through hypothesis testing, you can assess whether human development theory is applicable to the context of the Philippines.

References:

Kaur, SP. (2013). Variables in Research. https://sites.usp.br/rnp/wp-content/uploads/sites/830/2020/10/complemento-aula-1.pdf

Torneo, A.R & Clamor-Torner, H.S. (2017). Practical Research 2: An Introduction to Quantitative Research. Sibs Publishing House, Inc.: Quezon City