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Analyzing data


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Analyzing data

  1. 1. Analyzing Data By Selliger and Shaomy
  2. 2. Data Analysis  Data analysis refers to organize and summarize all the data and hace results on the conclusion of the research and are involve a variety of techniques for analyzing data.
  3. 3. Quantitative Research  In a quantitative research the data is in a numerical form and statistics make the research more manageable and efficient.
  4. 4. Qualitative Research  The acquired data is non numerical and using qualitative procedures have a heavy burden on the research.
  5. 5. Pragmatic and non pragmatic Statics.  The pragmatic statics have a number of set assumptions but also are more powerful than non pragmatic statics. The non pragmatic statics are use for numina and ordinal data but there are less powerful in sense that is not possible to use them to establish hypothesis.
  6. 6. Analyzing Qualitative Research Data  In a qualitative research some data is collected by certain procedures for example constructed observations, open interviews and diaries. The data is usually in the form of words and written documents.
  7. 7. Analyzing Descriptive Data.  The data obtained from a descriptive research analyzed the aids of the descriptive statics. The different types of descriptive statics are central tendencies and variability.
  8. 8. Analyzing Correctional Data.  Correctional techniques are used for analyze the data from a descriptive research. Also examines existing relationships between variables. A correlation is very useful for the different purposes of a research.
  9. 9. Analyzing Multivariate research Data.  This can be analyzed through a set of techniques where the number of dependent variables ore one number of independent variables are analyze tremendously.
  10. 10. The three multivariate research data:  Multi regression.  Discriminant Analysis.  Factor Analysis.
  11. 11. Multi Regression Analysis.  This examines the relationship and the predicative power of one or more independent variables.
  12. 12. Discriminant Analysis.  This is concerned with the predication of membership in one or more categories or a dependent variable from scores on two or more independent variables.
  13. 13. Factor Analysis  Helps the researcher make large sets of data more manageable by identifying the factors that underline the data.
  14. 14. Analysis Experimental Research Data.  When two groups experimental and control are being compared. The researcher will use the T-test which is capable of comparing two groups on a given measure.  The T-test helps to determine how confident the researchers can be with the differences found between two groups.