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Data analysis means to analyze a set of data which was collected for further interpretation. Data analysis can be done by differentiating between the statisticsdata, as whether its qualitative data or quantitative data. Thus, a qualitative data analysis usually concerned with that data which a researcher gets from one to one individual interview. This qualitative data analysis includes, the methods of notes and observations,
Newspaper clippings, personal journal, Surveys or Questionnaires or Interview tapes and transcripts etc. Thus, a qualitative analysis of data uses the data to provide the answers which are reliable based on the analysis but whereas Quantitative analysis of data uses the data to give the answers which can be expressed numerically.
The main steps of qualitative data analysis includes, organizing the data collected (thorough surveys etc.), then organizing those ideas and information, ensuring the reliability and validity of that finding and then explaining the findings by giving an overview as the final step. Whereas, the main steps for quantitative analysis of data includes, organizing the data collected and then performing the required calculations by interpreting the information from applying the numerical formulas learned and at the end explaining the limitations.
A quantitative data have certain general characteristics which may differ from each other in the following ways:
They show a tendency to concentrate on certain values, usually somewhere in the center of the distribution, that is why we use measures of central tendency or averages to perform the required calculations during data analysis steps. Measures of Central tendency includes, mean = the sum of total number of observations / the total number of observations, median which generally divides the data into two equal parts, thus it’s a positional average, mode which is the value occurring most frequently in a set of observations, geometric mean defined as the nth root of the product of a set of n observations and harmonic mean is the reciprocal of the arithmetic mean of the reciprocals of a number of observations, none of which is zero.
The quantitative data always vary about a measure of central tendency and these measures of deviation are called as measures of variation or dispersion. Measures of variation or dispersion include range, quartile deviation, mean deviation, standard deviation, coefficient of variation etc.
The data which often in the form of frequency distribution fall into symmetrical or asymmetrical patterns, where the measures of the direction and degree of asymmetry are called as measures of skewness.
Lastly, the frequency curves of the frequency distribution has a certain flatness or peak headed, where the measures of this flatness or peak headed of the frequency curves are called measures of kurtosis.
All these measures of quantitative data analysis help in analyzing the data in a more accurate way by keeping them very accurate and reliable. This is how we can analyze any of the data given to us and can interpret all the things basis the data provided.
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