The Basics of Quantitative Research
What is Quantitative Research?
Quantitative research and qualitative research are foundational in both the social sciences and natural sciences. The two research methods are not the same, in fact, they are opposite of one another. Quantitative research is the process during which numerical data is collected and analyzed. There are many reasons that quantitative research is undertaken, ranging from testing casual relationships within focus groups or testing a sample size of a larger population.
Statistical analysis helps businesses identify patterns and establish averages. Additionally, statistical analysis of numerical data allows companies to make more informed decisions and predictions. Alternatively, qualitative research is the process of non numerical data collection and analysis. Qualitative non numerical data examples commonly include audio, video, or written text.
In the business world, both quantitative and qualitative research are research methods that are commonly used. For many occasions, both quantitative and qualitative research methods are used in a complementary manner. Quantitative research can help a business with market research and awareness questions. Quantitative research may be applicable for business concerns ranging from market research focus groups to customer experience online surveys administered.
Due to the fact that quantitative research is mathematically based it is also considered statistically valid. As a result, businesses can make much more well informed predictions about the future. Future predictions that are backed by numerical data can supply a business with an invaluable competitive advantage. As a business gains more control of the market share its bottom line profitability and success will significantly grow.
Fact-: While qualitative research is based on non numerical data collection and analysis, quantitative research is based on numerical data collection and analysis.
Methods of Quantitative Research
Quantitative research can be utilized for a variety of research purposes. Methods of quantitative research include experimental, correlational, and descriptive research. Experimental and correlational research methods are commonly utilized for formal test hypotheses and future predictions. Correlational and experimental research methods may use sample size results for larger, more generalized populations.
Quantitative data collection often necessitates operational definitions with specific qualities. Operational definitions should translate into observable measures, quantifiable measures, and abstract concepts. An example of an abstract concept would be a person's mood while quantifiable and observable measure examples may include subject self ratings of specific feelings. Gaining a better understanding of quantitative research methods is a great way for business professionals to better utilize each method. Methods of quantitative research include-
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An experiment controls an independent variable in order to measure any effect on a dependent variable. Alternatively, an experiment can manipulate an independent variable for the same purpose. An example of experimental methods of quantitative research is testing if intervention combats procrastination in high school pupils.
For this experiment, high school students are split into equitable groups, with one group provided an intervention. The experimenter then compares self rating results of high school students between the groups upon the completion of the intervention.
Within the context of quantitative research, observation is the identification of a behavior occurring within its natural setting. Utilizing the same example used for experimental quantitative research methods, observation would include monitoring students in the classroom. The observers would not only be present in the classroom but would also count and record both passive and active behaviors.
Secondary data uses previously collected data for an alternative purpose. In other words, data previously gathered is recycled for another purpose outside of what it was originally intended for. An example of secondary data in quantitative research could be an analysis of climate change attitudes. Using data gathered previously from a longitudinal study, researchers could gain valuable insights into how attitudes regarding climate change have evolved over a specific period of time.
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Within the context of quantitative research, surveys ask questions to gather more information. Surveys can be incredibly versatile and adjusted to meet specific research objectives. Surveys can be administered individually or on a larger scale. Researchers commonly utilize a focus group or sample size survey in order to make larger generalizations. Surveys can be administered both in person and with the assistance of technological tools such as computers or mobile phones.
More than ever before, even in person surveys are incorporating technologically advanced tools. Online surveys are also incredibly popular quantitative research methods used due to convenience. Online surveys can be sent to focus groups or sample sizes for a wide variety of subjects, from customer experience feedback to market research initiatives. Research questions can be open ended or closed ended. Open ended questions encourage a more meaningful and full answer as opposed to closed ended questions.
Note-: Open ended questions are a great tool for sample size or focus group quantitative research discussions. Online surveys may also use open ended questions where applicable.
Quantitative Research- Data Analysis
Data gathered may necessitate processing prior to analysis. An example of quantitative research data analysis could include the translation of online survey data into words. Once collected data has been processed statistical analysis can be utilized. Ideally, statistical analysis will allow the researcher to answer any research questions applicable. An important distinction for professionals to understand is between descriptive statistics and inferential statistics. Information regarding inferential statistics and deceptive statistics includes-
One quantitative research data analysis type is descriptive statistics. Descriptive statistics supply a summary of data including measurements of viability and averages. Visual aids can be utilized for descriptive statistic purposes. For example, a graph or frequency table can be created to visually represent quantitative research descriptive statistics.
The utilization of visual aids can also help researchers to identify any outliers or trends. These trends and outliers may have been more difficult to comprehend in written language as opposed to a visual representation. An example scenario of descriptive statistics in action would occur during the study of high school aged students. If a researcher wanted to hypothesize if senior or freshmen high school students procrastinated more they may choose to use descriptive statistics.
In that scenario, descriptive statistics would prompt researchers to obtain a data summary. Two descriptive statistic techniques that would most likely be utilized would include finding both the mode and the mean. The mean is the average of the data while the mode is the most frequently occurring rating. After finding the mean and mode for both groups of high school students, plotted data could be used to reveal outliers.
Inferential is defined as characterizations or conclusions reached via reasoning and evidence. As such, inferential statistics allow researchers to utilize data gathered in order to make more informed generalizations or predictions. Inferential statistics can estimate the population parameter through sample data utilization. A hypothesis can also be tested with quantitative research inferential statistic methods.
Using the same example as for descriptive statistics, inferential statics would also be helpful for studying procrastination levels in freshman and senior high school students. A t test would allow researchers to compare the mean rating of the freshmen and seniors. The t test would reveal any significant differences between the two groups of students. As a result, the original hypothesis can be tested through inferential statistics. As seen in the above example, though descriptive statistics and inferential statistics are different, they can be used together.
Collaborate-: Similar to qualitative analysis and quantitative analysis, inferential statistics and descriptive statistics can be used together.
Advantages and Disadvantages of Quantitative Research
There are both advantages and disadvantages of quantitative research that all researchers should be aware of. Using quantitative analysis may be the best choice for some research while it may not be as ideal for other types of research. Thankfully, gaining a better understanding of when to use quantitative research is easier when understanding the disadvantages and advantages it offers. Significant advantages and disadvantages of quantitative research include-
Advantages of Quantitative Research
Hypothesis testing is a prominent advantage that quantitative research offers research professionals. The utilization of established and formalized hypothesis testing necessitates consideration of everything from data collection to research variables. Another advantage of quantitative research is that large samples can be analyzed. The procedures that quantitative research assert are both consistent and reliable.
As a result, data collected can be processed and analyzed for a large sample in an efficient way. This includes everything from older numerical data to current research project results. The ability to compare results statistically is an advantage of quantitative research methods. This research method is able to be reproduced in the future with different factors present.
For example, research methods that are quantitative will allow for a study to be reproduced with a different participant group. Additionally, a different cultural setting or time period than the original study perimeters could be utilized. Ultimately the ability to replicate or reproduce studies is supplied by standardization. Standardized data collection protocols and abstract concepts being assigned tangible definitions afford both replication and reproduction capabilities for quantitative research methods.
Disadvantages of Quantitative Research
There are different research methods available and quantitative research is not always the right choice. Understanding the disadvantages of quantitative research can help professionals decide when it is the best research method to use. One disadvantage of quantitative research is a potential lack of context. As so much quantitative research occurs in unnatural settings proper context may be severely lacking.
Common environments for quantitative research include laboratories which obviously are not the natural setting for many occurrences. As a result, a cultural and historical context may not be represented in the same ways it would be in a natural setting. Unfortunately, the lack of context can affect both data collection and the overall results of quantitative research. Even with standardized processes, structural biases can contribute to the inaccuracy of data gathered and results concluded.
Structural biases can include inappropriate sampling through a choice of research design or data collection method. Sampling bias occurs both in nonprobability and probability sampling. There are ways that sampling bias can be combatted within quantitative research through the use of careful research design and sampling procedures. A couple of sampling bias avoidance best practices include the definition of a target population and making sure online surveys are as accessible as possible.
Too often research methods do not look at the larger picture and may become preoccupied with too narrow of a focus. Measurement procedures or predetermined variables can lead to researchers overlooking relevant observations outside of their original observation scope. Another prevalent issue that the quantitative research method may be guilty of is the superficiality of complex concepts. An example of where quantitative research could be less advantageous would be in measuring the mood or emotions of human beings.
Although numerical data could be used to represent emotions or moods, human beings are very complex creatures. As such, qualitative data would likely be a more appropriate research method for human emotions as it supplies more elaborative potential. There are other research methods available like qualitative research that may be more appropriate in certain situations.
Understanding when to use and when not to use quantitative research is essential for researchers. If a researcher is having trouble figuring out which research methods to use, case studies may be helpful to consult. Locating social sciences case studies that were successful in the past can help guide researchers on whether qualitative data or quantitative data is most ideal.
Remember-: Different research methods offer distinct advantages and disadvantages. Deciding which research method is the best option may require extensive research of methods used including case studies reviewed.
Quantitative Research- Conclusion
- Any research used that is quantitative includes numerical data while research used that is qualitative uses non numerical data.
- Methods of quantitative research used include correlational, descriptive, and experimental.
- Quantitative research used correctly is a powerful tool for business professionals.
- 7 Useful Types of Quantitative Research to Know
- Gaining Insight into Quantitative Research Methods
- Quantitative Research Examples for Business Success
- Qualitative vs Quantitative Research- Differences, Methods, & More
- What is Quantitative Research? Usage, Characteristics, & Best Practice
- The Basics of Quantitative Research