Data Analysis – In its most common sense, methodology is the study of research methods. However, the term can also refer to the methods themselves or to the philosophical discussion of associated background assumptions. A method is a structured procedure for bringing about a certain goal, like acquiring knowledge or verifying knowledge claims. This normally involves various steps, like choosing a sample, collecting data from this sample, and interpreting the data. The study of methods concerns a detailed description and analysis of these processes. It includes evaluative aspects by comparing different methods.
In this way, their benefits and drawbacks are evaluated, as well as the research goals for which they may be used. These descriptions and evaluations are predicated on philosophical background assumptions; examples include how to conceptualize the phenomena under study and what constitutes evidence in favor of or against them. In its broadest sense, methodology encompasses the discussion of these more abstract issues.
Definition of Data Analysis:
Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. According to Shamoo and Resnik (2003) various analytic procedures “provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data”..
Or
“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”.
[Ref.www.wikipedia.com]
Data Interpretation:
Data interpretation is the process of assigning meaning to the collected information and determining the conclusions, significance, and implications of the findings.
Or
Data interpretation is the process of making sense of numerical data that has been collected, analyzed and presented.
Considerations/Issues in Data Analysis:
There are a number of issues that researchers should be cognizant of with respect to data analysis. These include:
- Having the necessary skills to analyze
- Concurrently selecting data collection methods and appropriate analysis
- Drawing unbiased inference
- Inappropriate subgroup analysis
- Following acceptable norms for disciplines Determining statistical significance.
- Lack of clearly defined and objective outcome measurements
- Providing honest and accurate analysis
- Manner of presenting data
- Environmental/contextual issues
- Data recording method
- Partitioning ‘text’ when analyzing qualitative data
- Training of staff conducting analyses
- Reliability and Validity
- Extent of analysis
(Ref-Dr. Md. Zahid Hossain Sharif/1st)
Central Tendency
If we arrange a number of observations, there is generally a tendency of observations to cluster around a central value. This tendency is known as central tendency.
Or
[Ref: Dr. Md. ZHS 1″/44]
In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution. It may also be called a center or location of the distribution.
Or
In case of large number of observations there is generally a tendency of the observations to cluster around a central value. This is known as central tendency.
[Ref-Rashid, Khabir, Hyder’s/5116]
Read more: