Analysis of Data and Interpretation | Importance, Limitations and Methods

Analysis of Data and Interpretation

Analysis of Data and Interpretation: With the arrival of digital age data analysis and interpretation have now become most important. Lets us understand data interpretation with its meaning, importance of data analysis and interpretation in research, and also the difference between analysis and interpretation of data in research.

Meaning of Data Analysis and Interpretation

Data analysis and interpretation is that the method of assigning meaning to the data collected and determining the conclusions, significance, and implications of the findings. it’s a crucial and exciting step within the process of research. In most of the research studies, analysis follows data collection.

There are two main methods in the interpretation of data. They are quantitative data and qualitative data.

Quantitative data 

Quantitative data is statistical and is usually structured in nature meaning it’s more rigid and defined. This kind of data is measured using values and numbers, which makes it a more suitable candidate for data analysis.

Qualitative data can be created through

  • Experiments 
  • Surveys
  • Metrics
  • Tests

Qualitative data

Qualitative data is non-statistical and is usually unstructured or semi-structured in nature. This data isn’t necessarily measured using hard numbers that are used to develop graphs and charts. Instead, it’s categorized as supported properties, attributes, labels, and other identifiers.

Qualitative data can be created through

  • Symbols and Images
  • Video and audio recordings
  • Texts and documents
  • Observations and notes

Difference between Qualitative and Quantitative data 

Quantitative data will be in numeric form and this data can be measurable, countable, and expressible. Examples: Number of students in the classroom, the number of people in a city, height, etc

Qualitative data is conceptual and it is descriptive. Is data can be categorized depending on the quality and characteristics. Examples: Name, Citizenship, city of origin, etc

Importance of data analysis and interpretation in research

According to LeCompte and Schensul, analysis of data in research is a process that is used by researchers for reducing data to a story and interpreting it to derive insights. The data analysis process helps in decreasing a large chunk of data into smaller fragments, which makes sense. 

some issues with data analysis in research

There are many issues that researchers should be aware of with respect to data analysis. Some of those issues are as follows.

  • Having the necessary skills to analyze
  • Simultaneously selecting data collection methods and appropriate analysis
  • Drawing unbiased conclusion
  • Unsuitable subgroup analysis
  • Lack of clearly defined and objective outcome calculation
  • Providing honest and exact analysis
  • Data recording process
  • Split up ‘text’ when analyzing qualitative data
  • accuracy, authenticity and Validity

Read More: Data Interpretation Basics


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