File Name: data processing and analysis in research methodology .zip
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In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have formulated at the earlier stages of your research process.
Qualitative modes of data analysis provide ways of discerning, examining, comparing and contrasting, and interpreting meaningful patterns or themes. Meaningfulness is determined by the particular goals and objectives of the project at hand: the same data can be analyzed and synthesized from multiple angles depending on the particular research or evaluation questions being addressed. The varieties of approaches - including ethnography, narrative analysis, discourse analysis, and textual analysis - correspond to different types of data, disciplinary traditions, objectives, and philosophical orientations. However, all share several common characteristics that distinguish them from quantitative analytic approaches. In quantitative analysis, numbers and what they stand for are the material of analysis. By contrast, qualitative analysis deals in words and is guided by fewer universal rules and standardized procedures than statistical analysis. We have few agreed-on canons for qualitative data analysis, in the sense of shared ground rules for drawing conclusions and verifying their sturdiness Miles and Huberman,
Research Methodology. /Title. Data Analysis. XIII. To study the concept and method of analyzing data in research. Data processing, tabulation, graphical.
Without data processing, companies limit their access to the very data that can hone their competitive edge and deliver critical business insights. Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not to negatively affect the end product, or data output. Data processing starts with data in its raw form and converts it into a more readable format graphs, documents, etc.
Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. The results so obtained are communicated, suggesting conclusions, and supporting decision-making. Data visualization is at times used to portray the data for the ease of discovering the useful patterns in the data. The terms Data Modeling and Data Analysis mean the same. The data required for analysis is based on a question or an experiment. Based on the requirements of those directing the analysis, the data necessary as inputs to the analysis is identified e. Specific variables regarding a population e.
Data is everywhere: in spreadsheets, your sales pipeline, social media platforms, customer satisfaction surveys , customer support tickets, and more. Some of it, you may not even know how to access. There are a number of useful data analysis techniques you can use to discover insights in all types of data, and emerging data analysis trends that can help you stay ahead of your competitors. For example, more and more organizations are turning towards AI to perform data analysis for more in-depth insights. In this post, find out which data analysis methods to use and when, learn how to analyze data in a few simple steps, and discover which tools data-driven businesses are using to gain valuable insights. Data analysis is the process of cleaning, analyzing, interpreting, and visualizing data to discover valuable insights that drive smarter and more effective business decisions. Data analysis tools are used to extract useful information from business data, and help make the data analysis process easier.
There are a number of approaches used in this research method design. The purpose of this chapter is to design the methodology of the research approach through mixed types of research techniques. The research approach also supports the researcher on how to come across the research result findings. In this chapter, the general design of the research and the methods used for data collection are explained in detail. It includes three main parts. The first part gives a highlight about the dissertation design. The second part discusses about qualitative and quantitative data collection methods.
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis. A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision.
While data analysis in qualitative research can include statistical procedures, many times analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase Savenye, Robinson, The form of the analysis is determined by the specific qualitative approach taken field study, ethnography content analysis, oral history, biography, unobtrusive research and the form of the data field notes, documents, audiotape, videotape. An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings. Improper statistical analyses distort scientific findings, mislead casual readers Shepard, , and may negatively influence the public perception of research.
It is an important aspect of any type of research study. Researcher should be equally skilled in all process of research applied at varies stages.