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Making the most of your data analysis in research cvr

Making the most of your data analysis in research

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What is data analysis in research?

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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Why analyzing data in research is important?

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! 

What are the types of data in research?

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:

  • Qualitative data
    Qualitative data is the type of data that is presented by using words and descriptions. This data type is subjective which makes it difficult for researchers to analyze, especially in the case of comparison. But you can surely observe this data without any extra effort! Qualitative data is usually gathered through personal interviews, focus groups, or via open-ended questions in a survey. For instance, everything that represents someone’s experience, opinion, or taste can be considered qualitative data.
  • Quantitative data
    Quantitative data is the type of data that is expressed by using numerical figures. This type of data is easy to measure or calculate which makes it ideal for distinguishing into specific categories or groups. Quantitative data can be represented in the form of charts, graphical formats, and can work well with statistical analysis methods.  For instance, questions covering aspects like age, cost, length, rank, scores, weight, height, etc. can generate quantitative data.
  • Categorical data
    Categorical data is the form of data that is presented in groups. However, the item covered in the categorical data is restricted to its specific group and cannot be a part of more than one group. For instance, a person participating in a survey by sharing his lifestyle, marital status, smoking & drinking habits can be considered as a part of the categorical data. To effectively analyze this data, a standard method known as a chi-square test is used.
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Performing data analysis for qualitative research

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.

Start by finding patterns

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.

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What are the methods for data analysis in qualitative research?

  • Content Analysis: Being the most widely used technique for analyzing data, content analysis can help to seamlessly analyze the documented information from various forms of data including text, images, and physical items too! Based on the research questions, you can predict when to use this method for analysis.
  • Discourse Analysis: Discourse analysis is a method typically used for analyzing the interactions with respondents. However, this method always focuses on the social context which is the cause behind the specific communication between a researcher and the respondent. In fact, discourse analysis also considers the lifestyle and day-to-day environment before drawing any conclusion.
  • Narrative Analysis: Narrative analysis is a technique used for analyzing the content collected through various sources like personal interviews, surveys, as well as field observation. The stories or ideas shared by respondents usually provide a way to find answers to the concerned research questions.
  • Grounded Theory:  In case you need to explain the occurrence of a particular phenomenon, using grounded theory is the best option for analyzing quality data in that case. Grounded theory is a method used for studying data of similar cases that occur in different settings. While using this method, researchers may alter explanations or generate new ones in order to reach at some conclusion. 

 

Performing data analysis for quantitative research

Preparing data the right way

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: 

  • Data Validation: Data validation is a process that lets you determine if the data sample follows the pre-defined standards or a biased one which is further categorized into 4 stages: 
  • Fraud: To make sure a real human records every survey response.
  • Screening: To ensure every participant is selected as per the research criteria.
  • Procedure: To make sure there’s no compromise with the ethical standards while collecting data.
  • Completeness: To ensure a respondent has fully answered all the questions included in the questionnaire or survey.
  • Data Editing: Usually, a data sample for research comes with multiple errors. Sometimes respondents skip some fields or fill them incorrectly too. In the process of data editing, researchers make sure that the data sample doesn’t include such errors. By conducting all the required checks, it makes the data ready for analysis.
  • Data Coding: Among all three phases, it is the most crucial phase of research data preparation. By grouping and assigning a value to the responses of the survey participants, it helps to analyze them efficiently. For instance, if a survey of a 500 sample size gets completed, the researcher can use an age bracket to segment the respondents as per their age. Therefore, it becomes easy to analyze small fragments of data instead of dealing with massive data chunks.  

 

What are the methods for data analysis in quantitative research?

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’.

Descriptive 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

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: 

 

  • Correlation
    If the researchers do not aim to conduct experimental research where they need to understand the relationship that exists between two variables, they prefer to use correlational research methods.
  • Regression analysis
    To seamlessly understand the relationship that exists between two variables, researchers trust the highly popular regression analysis method. This method comprises a dependent variable and multiple independent variables. You are supposed to determine the impact of independent variables over the specific dependent variable.
  • Cross-tabulation
    Popularly known as contingency tables, cross-tabulation is ideal for analyzing the relationship among multiple variables. Imagine a data set that includes age and gender categories that are represented using rows and columns. With a two-dimensional cross-tabulation, the process of data analysis & research becomes seamless by easily representing the males and females of each age category.
  • Frequency tables
    It is a statistical procedure that can help to test the extent up to which two or more variables differ. A reasonable degree of variation reflects that research findings were meaningful.

 

Things to consider during research data analysis

  • Researchers need to possess the necessary skills for analyzing the data. They must have more than a basic understanding of choosing the ideal statistical method in order to gain better data insights.
  • As scientific discipline plays a key role in differentiating research and data analytics; leveraging statistical advice at the start can help to create a survey questionnaire, choose data collection methods, and select samples too.
  • The main goal of research and analysis is to gather actionable insights that are completely unbiased. Using a biased mind while collecting data or choosing an analysis method can lead to mistakes. 
  • Every form of data analysis must focus on presenting accurate and reliable data. So, it’s important to avoid statistical errors while dealing with challenges such as outliers, data altering, data mining, etc. 

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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