The descriptive research analysis is a straightforward analysis. It explains the “what” about a topic, by using data, statistics, and trends.
The descriptive research analysis is a type of study that companies use to understand the specific subject matter. It’s something anyone can do, but only if they understand the purpose of this analysis. It can only do so much, and if you’re not aware, it may not be helpful.
In this article, you’ll discover:
- What descriptive research analysis
is. - How companies apply it to make
strategic business decisions. - The qualities and characteristics
of this analysis. - Plus the pros and cons of using
descriptive research analysis.
Let’s begin by answering the more basic question.
What is a descriptive research analysis?
Descriptive research is understanding the "what" rather than the "why" about a particular phenomenon. The focus falls to what something is based on unbiased information.
Here's an example: A company wants to understand the purchasing habits of seniors in California. They decide to conduct descriptive research to learn what seniors' buying habits are. The "why" is irrelevant. But if the company knows what Californian seniors are buying most, they can draw conclusions based on this evidence.
Descriptive research helps companies branch into new industries, market more effectively, and develop new products or services.
Qualities of descriptive research
Descriptive research analysis relies on data analysis and asking specific people (the targets of interests) research questions. These two necessary components are broken down into four characteristics:
Cross-sectional studies: The final result of this analysis will involve using other studies to reach the final result.
Uncontrolled variables: The biggest point of descriptive research is the variables; they must not be influenced in any way. For this reason, the researcher must collect information by observing and not influence the data.
Quantitative research: Since this analysis often deals with numerical values, collecting appropriate quantifiable information is absolutely necessary. It’s with this information that companies can accurately describe demographic segments.
Additional information: Once the researcher and company have all the information necessary, it may be used in other facets for the company, such as SWOT analysis or PESTLE analysis.
How to conduct a descriptive research analysis?
You may use three main tools for descriptive research analysis: Case studies, survey research, and observational methods.
Case studies describe a hypothesis. Unfortunately, their predictions aren’t always accurate; the creators of case studies may be folly to bias.
Surveys can be polls and questionnaires where the company asks specific audience questions about a topic. The company can receive this data from the audiences’ mouth and use this information for the analysis. A good survey will combine open-ended and closed-ended questions. Companies send out surveys online, in-person, or via phone.
The Observational method is the most popular tool of choice for descriptive research. It uses both quantitative and qualitative observations.
Quantitative observation uses statistical data — no opinions, just numbers. If used in a survey, quantitative numeric values like weight, age, and volume.
Qualitative observation is all about the characteristics. The researcher will monitor the topic from afar (to not influence the environment) and note the natural characteristics of the subjects.
Descriptive research analysis examples
The results of your descriptive analysis apply to a variety of topics. Researchers will use several techniques for the analysis, depending on the objective:
Ask questions about characteristics. Researchers can draw conclusions based on the research questions they ask. New traits, patterns, and behaviors can be discovered. For instance, let’s say you want to know how often children are watching TV weekly. By uncovering this information, businesses can make strategic decisions about this topic.
Discover data trends. Data trends use statistical information, and this statistical information often reveals patterns. It’s incredibly useful for research descriptive analysis. Researchers find patterns in many subjects and topics, including genders, age groups, locations, and ethnicities. All you have to do is choose a topic and time frame, then dig in.
Highlight comparisons. It may be viable to compare information about two different groups. For example, a company may ask customers how they feel about the service lately. When the results are in, the company may compare how their customers are feelings based on income and age, and compare the differences or similarities between these groups.
Validate current knowledge. Companies use descriptive research analysis to also understand existing patterns and confirm these patterns are still valid. Using quantitative and qualitative observations allows the company or researcher to create a detailed analysis of the results.
Check the time frame. Comparing results at varying times will also showcase new results. You may see new information and patterns when doing the analysis a week from now or three months from now.
The pros and cons of descriptive research analysis
Companies use descriptive research analysis, but it has both advantages and disadvantages.
The pros:
It’s the best way to collect data without bias. Companies can collect data first-hand based on stats and unbiased information. The results apply to various other topics and departments, too.
Cost-effective and fast. Compared to other forms of analysis, collecting the necessary data for research analysis is quicker and easier.
Helpful for decision-making. It’s easier for companies to make smarter business decisions when they use this analysis. It focuses on the “what” of a topic with number-based values and statistics; the information is factual and unbiased.
The cons:
Worry. When a researcher asks questions, the person may feel uncomfortable. They may feel like they're being “monitored” and act unnaturally. In this case, the validity of the data may be compromised. Similarly, the researcher could have a bias that could seep into the data too.
Questionable samples. The samples the researcher collects could be random, which makes it more difficult to validate. In most cases, samples are small, which means it may not accurately reflect the population in which the sample is taken.
No “why”. This analysis only answers the “what”. If you want to understand the “why” or “how”, this isn’t the analysis for you.
Bottom line:
The descriptive research analysis is straightforward. It explains the “what” about a topic, by using data, statistics, and trends. It employs the use of many common characteristics companies already have access to, like case studies, surveys, and customers. It’s cheaper than other forms of analysis and if much of this information is already on hand, it’s quicker too.
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