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What is Quantitative Techniques and Statistics?

Quantitative techniques use mathematics and statistics to analyze data in order to describe or forecast outcomes. For example, a marketing researcher might use quantitative techniques to determine which new product is the most desired by conducting an experiment or survey. Quantitative methods are often used because they are standardized so that the results can be compared across other studies. Quantitative techniques tend to involve larger samples than qualitative techniques. For example, social scientists ask larger numbers of people to participate in an experiment, thereby creating a more robust study.

Each type of quantitative technique has a specific range of values with which the overall outcome is quantified. Since each technique has its own range, quantifying outcomes is as important as knowing what to measure and how to measure it. Quantitative techniques can be uni-dimensional or multidimensional.

Statistical techniques are based on mathematics and statistics and are used to organize, analyze, summarize and interpret data. These techniques help us establish facts or draw inferences with evidence in a format that can be presented to others.

So what does that means? We can apply statistics to data. In other words, we can make guesses and estimates based on the numbers we see in the world.

According to Lazarsfeld; “statistics allows us to use complex phenomena and measurements” (Alderfer, 1960). Statistics is used to facilitate quantitative analyses. It helps us focus on the important aspects of a particular situation and dismiss less important details.

Types of Quantitative Techniques and Statistics include:

1. Theory testing – is looking for ways to prove or disprove when a theory is understood.

Quantitative measures are needed to test the idea. For example

• Mann Whitney U test – Used to determine if there is a significant difference in the two groups that are measured. It tests to determine whether two samples are from the same population.
• Coefficient of Determination (R2) – this method is used in data analysis and regression analysis.
1. Operationalization- Operationalization is the process of turning quantitative data from abstract ideas into concrete and meaningful statements.
2. Quantitative research designs – are ways of studying quantitative research designs are either experimental or survey.
3. Data Analysis – Includes the following techniques: (a) descriptive statistics, (b) measures of association, (c) nonparametric tests, and (d) multiple regression analysis.
4. Sampling – Sampling is the process of collecting data from a population to study.
5. Post Data Analysis – After Data Analysis, we can look at it, evaluate it and have a better understanding of it. Then we can make better decisions to improve our lifestyle.

### Uses of Quantitative Techniques and Statistics

A manager may use quantitative techniques to help members of the organization improve quality, efficiency, and productivity within their jobs. This type of data may be collected by using surveys or focus groups.

1. A political party may use quantitative or statistical data to help develop a platform for a national convention or campaign for public office.
2. A scientist may use quantitative techniques to conduct clinical research. This type of data may be collected by observing or measuring the response of a patient to or the condition of a thing, such as an environmental condition or physical object.
3. Public health officials may use quantitative techniques to determine whether various areas of metropolitan cities are experiencing an increase in preventable diseases, such as AIDS and other sexually transmitted diseases.
4. A marketing researcher may conduct a marketing survey for a specific product within the organization and using data about its consumers. Surveys are very useful in determining customers’ preferences, tendencies, and habits.

### Importance of Quantitative Techniques and Statistics

We benefit in so many ways by using quantitative techniques and statistics.

1. First, data helps us make decisions based on facts. When you have data then you can use it to compare or contrast various situations or conditions. You can make judgments on the basis of data without being biased in your judgment.
2. We need to use quantitative techniques and statistics not only for research but also for applied research such as industrial, business, and marketing research.
3. We can use quantitative techniques and statistics in education to help students understand fundamental research.
4. We can use quantitative techniques and statistics in management to improve our performance of our business.
5. Fifth, we can use quantitative techniques and statistics to teach quantitative subjects such as statistics, probability, correlation, regression analysis and so on.
6. We can use qualitative techniques and statistics in qualitative research methods. They include content analysis and thematic analysis where one looks at the context of documents rather than the words themselves, and ethnography where one looks closely at individual human’s social life or culture.
7. Finally, we can use the results of our data analysis to make recommendations to management that they might take into consideration and improve their operations.

#### Units/courses studied under Quantitative Techniques and Statistics

Units

• Computers
• Microsoft Office: Word, Excel, PowerPoint and others
• Statistics: Time Series (e.g., Smoothing)
• Regression and Correlation
• Correlation Coefficient
• Regression Analysis (Principle Components Analysis)
• Interpreting Regression Results (e.g., CV/R2; R-Squared)
• Inferential Statistics for Research & Biostatistics (Data Analysis in Research)
• Statistical Thinking and Analysis
• Reliability of Data Collection Methods
• Manipulation and analysis of collected data
• Measurement and Analysis of Variables (e.g., Continuous vs. Discrete)
• Independent-samples t -test
• Analysis of variance, Student’s t test (e.g., ANOVA; Factorial Design)

Course studies

1. Quality Management
2. Marketing Evaluation
3. Data Mining
4. Operations Research
5. Financial Management and Analysis
6. Statistical Decision Making and Regression Analysis in Business
7. Operation Research: Quantitative Techniques
8. Production Planning & Control

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