analysis and presentation of data
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The document discusses different methods of analyzing and presenting quantitative data. It describes two main types of analysis: qualitative analysis, which involves interpreting qualitative research data, and quantitative analysis, which involves presenting and interpreting numerical data through descriptive and inferential statistics. Descriptive statistics include measures of central tendency like mean, median and mode, as well as measures of variability like range and standard deviation. Inferential statistics are used to test hypotheses and generalize samples to populations. The document also discusses various methods of graphically presenting quantitative data through graphs like histograms, frequency polygons, frequency curves and Ogive curves.
analysis and presentation of data
- 1. Analysis and presentation of data Presented by, M.Uma Maheshwari, M.Sc., M.Phil.
- 3. Analysis of data Is of two types Qualitative analysis Quantitative analysis
- 4. Qualitative analysis Process of interpreting data collected during qualitative research Analysis depends on its type
- 5. Presenting and interpreting numerical data Includes descriptive analysis and inferential statistics Descriptive statistics include measures of central tendency and measures of variability Inferential statistics is to test hypotheses set and relating findings to the sample or population Quantitative analysis
- 6. Descriptive statistics Quantitative research gives masses of data, in order to give an idea of typical values in the data and their variation Two main descriptive statistics are Measures of central tendency Measures of dispersion
- 7. Measures of central tendency “A measure of central tendency is a typical value around which other figures congregate” – Simpson and Kafka Types Arithmetic mean Geometric mean mathematical averages Harmonic mean calculated averages Median Mode
- 8. Measures of dispersion “Dispersion is the measure of the variation of the items” – A.L.Bowley Types Absolute measures Range Quartile deviation Mean deviation Standard deviation Relative measures Coefficient of range Coefficient of quartile deviation Coefficient of mean deviation Coefficient of standard deviation Coefficient of range
- 9. Inferential statistics It is impossible to measure every item in the population There is an uncertainty as to how well the sample results reflects the population Two aspects of statistical inference are Estimation Hypothesis testing using statistical tests
- 11. Graphical presentation of data The statistical data represented in graph
- 12. Graphs Used to explain the relationship between different variables Geometrical image of data Drawn on graph paper Has two intersecting lines called axis- horizontal line called X-axis and vertical axis-Y axis
- 13. A suitable scale is given Independent values are in X-axis and dependent values on Y-axis A title is given The values corresponding to X and Y axis are plotted The points are joined with straight or curved lines.
- 14. Graphs Graphs of time series Graph of one variable Graph of two or more variable Range chart Band graph Graphs of frequency distribution Histogram Frequency polygon Frequency curve O gives
- 15. Graphs of time series In a line graph, the data are represented in a straight line 4 types Graph of one variable Graph of two or more variable Range chart Band graph
- 16. Graph of one variable One variable is represented Plotting time along X-axis and the value of variable on Y-axis, on a suitable scale The points are joined by straight line
- 18. Graph of two or more variable Two or more variable are taken Comparison is easier in this type of graph
- 20. Range chart Used to exhibit the minimum and maximum values of a variable Eg., range of variation in temperature on different days
- 22. Band graph The various component parts are represented Also called component part line chart or layer chart The various component parts are plotted and gaps between then are shaded with different colours
- 24. Graphs of frequency distribution Frequency distribution is represented graphically 4 types Histogram Frequency polygon Frequency curve O give
- 25. Histogram Graph with frequency as vertical rectangles It is an area diagram X-axis with class intervals Y-axis with frequencies Vertical rectangles are to the height of the frequencies Width equal to the range of the class It is two dimensional
- 26. Uses of histogram Gives clear of entire data Simplifies complex data Attractive and impressive Facilitates comparison Gives pattern distribution of variables in the population
- 28. Frequency polygon Curve consisting of straight line Lines drawn by connecting points located above the mid points of the intervals of heights corresponding to frequencies Class intervals on X-axis and frequencies on Y-axis Midpoint of various class intervals are taken The frequencies corresponding to each midpoint and all points are joined
- 29. Uses of frequency polygon Used to locate mode of frequency distribution easily It facilitates comparison of two or more frequency distribution on the same graph
- 31. Frequency curve Drawn through various points of the polygon Also called smoothed curve Smoothening of frequency polygon gives frequency curve Should begin and end at OX axis
- 33. O gives They are cumulative frequency curve When frequencies re added called cumulative frequencies Two methods ‘less than’ method ‘more than’ method
- 34. ‘less than’ method we have to plot the less than cumulative frequencies against upper class boundaries Joining the points by a smooth free hand gives ‘less than’ O gives Sloping from left to right
- 36. ‘more than’ method we have to plot the more than cumulative frequencies against upper class boundaries Joining the points by a smooth free hand gives ‘less than’ O gives Sloping from right to left