Quantitative Research
Quantitative research is dependent on the creation of hypothesis followed by accurate analysis of the statistics in order to understand and explain the research findings. It focuses more on the quantity of things and their statistical patterns. Quantitative research method has proven to be
beneficial in the following ways:
• Quantitative data provides a macro view with all the required details and
comparatively larger samples.
• Larger sample sizes enable the conclusion to be generalized.
• Evaluation of the multiple data sets can be done at once and that too at a faster pace and accurately.
• This method is called to be appropriate when there is a need of systematic and standardized comparisons.
• The manual implementations of ideas can be automated completely which can save time.
Weaknesses of Quantitative Data
• Quantitative Method reveals what and to what extent but often fails to answer more on why and how.
• This type of research requires the model performance to be monitored on constant basis in order to ensure its compliance with the original hypotheses.
• The impression of homogeneity in a sample may turn out to be fake in this method.
• This method involves limited number of Quants supply and also involves complex disciplines which are hard to master.
There are four main types of quantitative research designs namely; descriptive, correlational, casual-comparative/quasi-experimental and experimental research.
Descriptive Research
Descriptive research seeks to describe the current status of an identified variable. These research projects are designed to provide systematic information about a phenomenon. The researcher does not usually begin with a hypothesis, but is likely to develop one after collecting data. The analysis and synthesis of the data provide the test of the hypothesis. Systematic collection of information requires careful selection of the units studied and careful measurement of each variable.
Examples of descriptive research:
1. A description of the tobacco use habits of teenagers.
2. A description of the kinds of physical activities that typically occur in nursing
homes, and how frequently each occurs.
Correlational Research
Correlational research attempts to determine the extent of a relationship between two or more variables using statistical data. In this type of design, relationships between and among a number of facts are sought and interpreted. This type of research will
recognize trends and patterns in data, but it does not go so far in its analysis to prove causes for these observed patterns. Cause and effect is not the basis of this type of observational research. The data, relationships, and distributions of variables are studied only. Variables are not manipulated; they are only identified and are studied as they occur in a natural setting.
Examples of correlation research:
1. The covariance of smoking and lung disease
2. The relationship between diet and anxiety.
Causal-Comparative/Quasi-Experimental
Causal-comparative quasi-experimental research attempts to establish cause and effect relationships among the variables. These types of design are very similar to true experiments, but with some key differences. An independent variable is identified but not manipulated by the experimenter, and effects of the independent variable on the dependent variable are measured. The researcher does not randomly assign groups and must use ones that are naturally formed or pre-existing groups. Identified control groups exposed to the treatment variable are studied and compared to groups who are not. When analyses and conclusions are made, determining causes must be done .
Examples of causal-comparative/quasi-experimental research
1. The effect of online classes to student's performance.
2. The effect of feeding program on students' attendance.
Experimental research
Experimental research, often called true experimentation, uses the scientific method to establish the cause-effect relationship among a group of variables that make up a study. The true experiment is often thought of as a laboratory study, but this is not
always the case; a laboratory setting has nothing to do with it. A true experiment is any study where an effort is made to identify and impose control over all other variables except one. An independent variable is manipulated to determine the effects on the dependent variables.Subjects are randomly assigned experimental treatments rather than identified in naturally occurring groups.
Examples of experimental research:
1. The effect of a new treatment plan on breast cancer.
2. The effect of a systematic preparation and support system on children who were scheduled for surgery on the amount of psychological upset and cooperation.
