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A seminar on quantitave data analysis

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  • 1. A SEMINAR ON QUANTITAVE DATA ANALYSIS
  • 2. INTRODUCTION
  • 3. DEFINITION Analysis is the process of organizing and synthesizing the data so as to answer research questions and test hypothesis.  Analysis referred as a method of organizing data in such a way that research questions can be answered and hypothesis can be tested.  Analysis is the process of breaking a complex topic into smaller parts to gain better understanding of it. 
  • 4. STEPS OF QUANTITATIVE DATA ANALYSIS  1. Data preparation
  • 5. STEPS IN DATA PREPARATION Compilation:  Editing:  Coding:  Classification:  Tabulation: 
  • 6. 2. Describing the data
  • 7. Drawing the inferences of data
  • 8. Interpretation of the data:
  • 9. STRATEGIES FOR EFFECTIVE INTERPRETATIONS Interpretations must be made in light of research problem.  Critical examination of the each element of study  Careful consideration and recognition of the limitations of the research study  Interpretations must be only based on the study results.  Each part, aspect, and segment of the analyzed result must receive close attentions 
  • 10. DESCRIPTIVE STATISTICS
  • 11. Measures to condense data (frequency and percentage distribution through tabulation and graphic presentations)  Measures of central tendency  Measures of dispersion  Measures of relationship (correlation coefficient) 
  • 12. MEASURES TO CONDENSE DATA tables  charts  graphs  diagram 
  • 13. TABLES
  • 14. General principles of tabulation:      The table should be precise, understandable, and self-explanatory. Every table should have title, which is placed at the top of the table. The title must describe the content clearly and precisely. Items should be arranged alphabetically or according to size, importance, and causal relationship to facilitate comparison. Rows and columns are to be compared with one another, and should therefore have similar arrangement. The contents of the table, as a whole as well as item-wise in each column and row should be defined clearly and fully.
  • 15.      The unit of measurement must be clearly stated. Percentage can be given in parenthesis or can be worked out to one decimal figure to draw the reader attention to the fact that the figure is a percentage and not an absolute number. Totals can be placed at the bottom of the columns. Explanatory cues can be placed directly beneath the table for any explanatory footnotes. Two or three small tables are to be preferred
  • 16. PARTS OF A TABLE: Table number:  Title:  Subheads:  Caption and stubs:  Footnotes:  Source note: 
  • 17. TYPES OF THE TABLES: Frequency Distribution Table:  Contingency Tables:  Multiple-Response Tables:  Miscellaneous Tables: 
  • 18. GRAPHS AND DIAGRAMS The main uses It is one of the most systematic and concise ways in which statistical results may be presented.  They give overall view of entire data.  The tabular appearance is easy to assimilate and more appealing than the same data presented in text.  The data becomes much more easy to understand and memorize.  It facilitates comparison of data represented in different columns and rows. 
  • 19. Types of diagram and graphs: bar diagram,  pie chart,  histogram,  frequency polygon,  line graphs,  cumulative frequency curve  scattered diagrams,  pictograms,  map diagrams,. 
  • 20. Bar diagram simple  multiple  proportional bar diagram 
  • 21. Simple bar diagram
  • 22. Multiple bar diagram
  • 23. Proportion bar diagram
  • 24. Pie diagram/sector diagram:
  • 25. Histogram:
  • 26. Frequency polygon:
  • 27. Line graphs:
  • 28. Cumulative frequency curve
  • 29. Scattered or dotted diagrams:
  • 30. Pictograms or picture diagram:
  • 31. Map diagram or spot map: