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Data Analysis

What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" (Responsible Conduct in Data Management). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible data management, and maintaining "honest and accurate analysis" (Responsible Conduct in Data Management). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" (OMB Circular 110). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

  • Photographs 
  • Hand-written notes from field observation
  • Machine learning training data sets
  • Ethnographic interview transcripts
  • Sheet music
  • Scripts for plays and musicals 
  • Observations from laboratory experiments (CMU Data 101)

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

A descriptive diagram of the research data lifecycle, including the following steps: proposal planning & writing, project start up, data collection, data analysis, data sharing, and end of project.

(University of Virginia)


Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" (Creswell & Creswell, 2018, p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures (Creswell & Creswell, 2018, p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative research typically involves words and "open-ended questions and responses" (Creswell & Creswell, 2018, p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" (2018, p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" (Creswell & Creswell, 2018, p. 4). 

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