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interpretation of dataWhen you have all this information about your business or project saved and tracked, what do you do with it? That’s where interpretation of data comes in. It is designed to help people with limited statistical or programming skills quickly become productive in an increasingly digitized workplace.

Data analysis and interpretation is the process of assigning meaning to the collected information and determining the conclusions, significance, and implications of the findings. The steps involved in data analysis are a function of the type of information collected, however, returning to the purpose of the assessment and the assessment questions will provide a structure for the organization of the data and a focus for the analysis.

Numerical vs. Narrative – Quantitative vs. Qualititative

The analysis of numerical (quantitative) data is represented in mathematical terms. The most common statistical terms include:

Try this course for a comprehensive instruction on data analysis for excel.

The analysis of narrative (qualitative) data is conducted by organizing the data into common themes or categories. It is often more difficult to interpret narrative data since it lacks the built-in structure found in numerical data. Initially, the narrative data appears to be a collection of random, unconnected statements. The assessment purpose and questions can help direct the focus of the data organization. The following strategies may also be helpful when analyzing narrative data.

Read and organize the data from each question separately. This approach permits focusing on one question at a time (e.g., experiences with tutoring services, characteristics of tutor, student responsibility in the tutoring process). Group the comments by themes, topics, or categories. This approach allows for focusing on one area at a time (e.g., characteristics of tutor – level of preparation, knowledge of content area, availability).

Code content and characteristics of documents into various categories (e.g., training manual – policies and procedures, communication, responsibilities). This approach keeps your information organized and easily accessible when you

Code patterns from the focus of the observation (e.g., behavioral patterns – amount of time engaged/not engaged in activity, type of engagement, communication, interpersonal skills).

Data Interpretation and Analysis Techniques

The analysis of the data via statistical measures and/or narrative themes should provide answers to your assessment questions. Interpreting the analyzed data from the appropriate perspective allows for determination of the significance and implications of the assessment.

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical or structural models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.

Some Data Interpretation and Analysis Tips

Page Last Updated: May 2014

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