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The research data analysis is a process that allows researchers to reduce data into smaller fragments and interpret it for obtaining actionable insights. This process plays a crucial role in converting a large set of data into a meaningful story.
The process takes place in three stages which include — data organization, data summarization, and data analysis. While in the first stage, the data is collected and organized, the second stage involves categorizing data on the basis of patterns and themes. The third and last stage is the analysis where researchers follow either a top-down or bottom-up approach to effectively analyze the finalized data.
While data analysis can sometimes become messy, perplexing, or even time-consuming, but it indeed is a creative process that can help to streamline and organize huge chunks of data.
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There’s no doubt that researchers rely primarily on data as it helps them either tell a story or solve a problem. It begins with asking a question and the answer to that particular question is nothing but data. But, how will you gather data if there are no questions to ask? No worries, you can still explore data without using any questions and this process is known as ‘Data Mining’. It is often used to reveal some interesting data patterns that researchers find worth exploring.
Irrespective of the data type, researchers’ mission & audiences’ vision is the key to discover unique patterns that help to shape the story they need to tell. The most essential requirement at the researcher’s end is to remain open and unbiased towards unexpected expressions, patterns, and results while analyzing data. As data analysis can help you unveil some exciting and unexpected stories, so you need to enjoy this journey of exploratory research by relying on the data that you have!
Every type of data has the ability to describe things once you assign some value to it. In the case of analysis, it’s important to organize those values and present them in a specific context before putting them for use. While data can come up in different forms, it is mainly categorized into the following types:
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As qualitative data is made up of words, images, descriptions, symbols, or experiences, the data analysis of this data is slightly different from the numerical data. It is a complicated process to gain insights from such information and that’s why it is mainly used for exploratory research.
While there are numerous ways of finding patterns in a given piece of textual information, the most relied on and widely acclaimed technique for data analysis is known as the word-based method. Although, there is a manual data analysis process in the case of qualitative research. In this process, researchers focus on finding common words that are repeatedly used in the pack of available data.
Another word-based method used by most of the researchers is keyword context. In this technique, a researcher tries to dig deeper into the concept by analyzing its context on the basis of the keywords used by the participants.
Another popular text analysis method used for identifying a quality data pattern is called a scrutiny-based technique. This method makes the best use of “compare and contrast” to differentiate the similarities or differences between texts.
Metaphors can also play a crucial role in reducing the pile of data and discovering the patterns in it. This makes it easy to relate data with theory.
The first and foremost stage in research & data analysis is to prepare data and make it meaningful. The data preparation occurs in the following phases:
Once your data gets prepared for analysis, it’s time to derive actionable insights using different data analysis methods. To analyze numerical data, statistical techniques are the favorable option but these are classified into two groups namely, ‘Descriptive Statistics’ and ‘Inferential statistics’.
Being used for describing the basic features of the versatile form of data, descriptive statistics represent the data sample in a meaningful way that its pattern looks more relevant. However, it doesn’t go beyond deriving conclusions. The conclusions are usually formulated on the hypothesis that researchers have put together so far.
Using descriptive analysis while conducting quantitative market research might yield absolute numbers, but it’s difficult to illustrate the rationale behind those specific numbers. Still, it’s essential to consider the ideal method for research & data analysis that matches your survey questionnaire and the story that researchers aim to tell. As this process is used for analyzing a single variable, it is also known as ‘univariate analysis’.
Inferential statistics are mainly used for making predictions about a larger population size after analyzing the collected sample of the specific population. For instance, you can ask some 150 people at a theater if they’re enjoying the movie or not. With inferential statistics, researchers can use the collected sample to predict that around 70% of people enjoyed the movie.
It is a sophisticated analysis method that focuses on showcasing the relationship existing different variables rather than describing one variable. It is mostly used by researchers when they need to uncover the relationship between variables, which is beyond absolute numbers.
Here are some frequently used methods for data analysis in research:
With the huge amount of data generation on a daily basis, it has become imperative for businesses to focus on data analysis for staying on top in this data-driven world. Voxco offers omnichannel online survey solutions that empower brands with their data analysis and research needs and offers them a medium to gather insightful data.
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