Quantitative methods emphasize objective measurements and statistical, mathematical, or numerical analysis of data. They can be collected through questionnaires and surveys. They can also be collected by manipulating pre-existing statistical data using computational techniques. Research and quantitative analysis focus on collecting numerical data and thus explaining a particular phenomenon.
Quantitative research deals with numbers, logic, and objective positions. It focuses on numerical and immutable data and detailed convergent reasoning rather than divergent reasoning. That is, the generation of a variety of ideas about a research problem spontaneously and fluidly.
Quantitative research is mainly conducted in the social sciences. It uses statistical methods to collect quantitative data from the research study. In this research method, researchers and statisticians implement mathematical frameworks and theories that belong to the quantity in question.
What is quantitative analysis?
Quantitative analysis is the process of collecting and evaluating measurable and verifiable data in order to understand the behavior and performance of a variable. According to Brians and Cols (2011), it is applicable not only to the research process, but to the field of companies. In the past, business owners and directors of companies relied heavily on their experience and instinct when making decisions. However, now in the age of data technology, quantitative analysis is now considered a better approach to making informed decisions. After a careful understanding of these numbers to predict the future of a product or service and make the corresponding changes.
An example of quantitative analysis can be a survey conducted to understand the amount of time it takes a doctor to care for a patient when they enter the hospital. A patient satisfaction survey template can be administered to ask questions about it. For example, how long it took a doctor to see a patient, how often a patient enters a hospital, and other similar questions.
Quantitative research templates are objective, elaborated and measured. The results obtained from this research method are logical, statistical and impartial. Data collection is done using a structured method and is carried out on samples representing the entire population.
Objectives of Quantitative Research
The goal in conducting quantitative research is to determine the relationship between an independent variable and a dependent or outcome variable within a population. Quantitative research designs are either descriptive or experimental. In descriptive studies, associations between variables are established. An experimental study establishes causality.
Thus, the general objective of a quantitative research study is to classify the characteristics and count them. As well as building statistical models in an attempt to explain what is observed.
Characteristics of Quantitative Research
Its main features, according to Mc Nabb (2008) are:
- Data are usually collected using structured research instruments.
- The results are based on samples that are representative of the population.
- The research study can usually be replicated or repeated, given its high reliability.
- All aspects of the study are carefully designed before data are collected.
- Data is in the form of numbers and statistics, often organized into tables, graphs, figures, or other non-textual forms.
- The researcher has a clearly defined research question to which objective answers are sought.
- It serves to generalize concepts, predict future outcomes or investigate causal relationships.
- The researcher uses tools. These can be questionnaires or computer software, to collect numerical data.
Quantitative analysis techniques
The main task of an analysis in a quantitative investigation is to present a given hypothetical situation in terms of numerical values. Quantitative analysis helps to evaluate performance, evaluate financial instruments and make predictions. It encompasses three main data measurement techniques: regression analysis, linear programming, and data mining.
Regression analysis is a common technique that is not only employed by business owners but also by statisticians and economists. It involves the use of statistical equations to predict or estimate the impact of one variable on another. For example, determine how interest rates affect consumer behavior regarding asset investment. Or establish the effect of education and work experience on employees’ annual earnings.
In the business sector, owners can use regression analysis to determine the impact of advertising expenses on business profits. By using this approach, a business owner can establish whether there is a positive or negative correlation between two variables.
Most businesses occasionally encounter a shortage of resources, such as facility space, production machinery, and labor. In such situations, company managers must find ways to allocate resources effectively. Linear programming is a quantitative method that determines how to achieve an optimal solution. It is also used to determine how a company can make optimal profits. This way you can reduce your operating costs, subject to a given set of constraints, such as labor.
Data mining is a combination of computer programming skills and statistical methods. The popularity of data mining continues to grow. It does so in parallel to the increase in the number and size of available datasets. Data mining techniques are used to evaluate very large data sets. It aims to find hidden patterns or correlations within them.
Things to consider when reporting study results using quantitative methods
In quantitative research, according to Babbie (2010), in order for the results to be understood and be of benefit to stakeholders, they must meet the following requirements:
- Explain the data collected and its statistical treatment, as well as all relevant results in relation to the research problem.
- Report unanticipated events that occurred during your data collection.
- Explain how actual analysis differs from planned analysis.
- Establish the handling of missing data and why these do not undermine the validity of the analysis.
- Explain the techniques that were used to clean up the dataset.
- Choose a minimally sufficient statistical procedure.
- Provide a justification for its use and a reference for it.
- Specify any computer programs used.
- Describe the assumptions for each procedure and the steps taken to ensure that they were not violated.
- Provide descriptive statistics, confidence intervals, and sample sizes for each variable. As well as the value of the test statistic, its direction, degrees of freedom and level of significance.
- Avoid inferring causality, particularly in non-random designs or without further experimentation.
- Use tables to provide exact values; use figures to convey global effects.
- Keep figures small in size; include graphical representations of confidence intervals whenever possible.
- Tell the reader what to look for in tables and figures.
Applications of quantitative analysis
Business owners are often forced to make decisions in conditions of uncertainty. Fortunately, quantitative research techniques allow them to make the best estimates and thus minimize the risks associated with a particular decision. Quantitative models give business owners a better understanding of information, so they can make the best possible decisions.
One area where quantitative analysis is considered an indispensable tool is in project management. Quantitative methods are used to find the best ways to allocate resources, especially if these resources are scarce. Projects are scheduled based on the availability of certain resources.
Quantitative analysis also helps people make informed product planning decisions. Suppose a company finds it difficult to estimate the size and location of a new production facility. Quantitative analysis can be used to evaluate different cost, time and location proposals. With effective product planning and scheduling, companies will be able to better meet the needs of their customers while maximizing their profits.
Every company needs a proper marketing strategy. However, setting a budget for the marketing department can be tricky, especially if you don’t set your goals. With the right quantitative method, marketers can find an easy way to set the required budget and allocate media purchases. Decisions can be based on data obtained from marketing campaigns.
A company’s accounting department also relies heavily on quantitative analysis. Accounting staff use different data and quantitative methods, such as the discounted cash flow model to estimate the value of an investment. Products can also be evaluated, based on production costs and the benefits they generate.
As you will see, these are very interesting areas for quantitative analysis and can be good ideas to develop your thesis, if you are in the area of Administration and Management. In Online-tesis.com,we can help you from the choice of the degree which can be of benefit not only to obtain your bachelor’s degree, but so that the knowledge can be applied in your professional career.
Purchasing and inventory
One of the biggest challenges companies face is being able to predict demand for a product or service. However, with quantitative techniques, companies can orient themselves on how many materials they need to buy, the level of inventory to maintain, and the costs they are likely to incur when shipping and storing finished products.
Quantitative analysis is the use of mathematical and statistical techniques to evaluate the performance of a variable. It can be used in many aspects of daily life. Before the advent of quantitative analysis, many directors of the company based their decisions on experience and instinct. Business owners can now use quantitative methods to predict trends, determine resource allocation, and manage projects.
Quantitative techniques are also used to evaluate investments. In this way, organizations can determine the best assets to invest and the best time to do so. Some of the methods of quantitative analysis include regression analysis, linear programming, and data mining.
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Babbie, E. (2010). The practice of social research. 12th ed. Belmont, CA: Wadsworth Cengage.
Brians, C. et al. (2011) Empirical political analysis: quantitative and qualitative research methods. 8th ed. Boston, MA: Longman.
McNabb, D. (2008) Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches. 2nd ed. Armonk, New York: M.E. Sharpe.
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