Data Analysis and Presentation PPTX - Dr P.Thirunagalinga Pandiyan

Dr.P.Thirunagalinga Pandiyan
College of Nursing
Madurai Medical College
Madurai

1. DATA ANALYSIS AND PRESENTATION DR.P.THIRUNAGALINGA PANDIYAN M.Sc.,(N) Ph.D (N), M.A., D.Pharm. NURSING TUTOR GRADE II COLLEGE OF NURSING GOVERNMENT MADURAI MEDICAL COLLEGE MADURAI , TAMILNADU

2. DATA ANALYSIS AND PRESENTATION Data analysis is the process of organizing and synthesizing the data, so as to answer research questions and test hypothesis

3. TYPES OF DATA

4. QUALITATIVE DATA There is no numerical value in qualitative data, so it cannot be measured It is also called as categorical data Qualitative data is subjectively observed eg . Smell, taste, colour etc It is divided into to two types 1. Nominal data 2. Ordinal data

5. QUALITATIVE DATA 1. NOMINAL DATA Nominal data is often known as labels Nominal Data is used to label variables without any order There is no intrinsic order to the variables Examples of Nominal Data Marital status (Single, Widowed, Married) Nationality (Indian, German, American) Gender (Male, Female, Others)

6. QUALITATIVE DATA 2. ORDINAL DATA The qualitative data that values are ordered. Ordinal data have natural ordering where a number is present in some kind of order by their position on the scale. Examples Economic status - (Low/ Medium/ High) Rank in position – ( First / Second/Third) Time of the day ( Morning/Noon/Night )

7. QUANTITATIVE DATA Data represented in numbers It can be measured objectively 1. DISCRETE DATA The discrete data contain the values that fall under whole numbers Examples Number of children Number of employee in a company Days in a week and month Number of players participated

8. QUANTITATIVE DATA 2. CONTINUOUS DATA : Those which have uninterrupted range of values. Can assume either integral or fractional values. Ratio scale – Any scale of measurement possessing magnitude, equal intervals and an absolute zero Example : Height, Weight b.Interval Scale – Any scale of measurement possessing magnitude, equal intervals and but not an absolute zero Example : Time

9. PROCESSING OF DATA

10. ANALYSIS OF DATA 1.DESCRIPTIVE ANALYSIS It describe the basic features of the data Provides main features of the data collection in quantitative terms like mean, median, mode, standard deviation, range 2.INFERENTIAL ANALYSIS It helps in drawing inferences like finding the differences, relationship and association between two or more variables from the data. Parametric and non parametric tests are used to drawing inferences

11. DESCRIPTIVE ANALYSIS

12. MEASURES TO CONDENSE DATA Frequency and percentage distribution through tabulation and graphic presentations

13. MEASURES TO CONDENSE DATA

14. MEASURES OF CENTRAL TENDENCY

15. MEASURES OF CENTRAL TENDENCY MEAN The mean is the sum of the scores divided by the number of scores being summed. MEDIAN Median is the middle most value when the data is arranged in ascending order of magnitude MODE It is the value which has the highest frequency. That means mode is the most frequency occurring value in the data. It is denoted by Z

16. MEASURES OF DISPERSION

17. MEASURES OF DISPERSION RANGE - It is the difference between highest and lowest value in the data R= H- L QUARTILE DEVIATION In this method the series is divided into four equal quarters. They are represented as Q1,Q2,Q3 . The difference between third quartile Q3 and first Quartile Q1 is the quartile deviation MEAN DEVIATION Mean deviation is an average mean of the deviations of values from the central value STANDARD DEVIATION It is the positive square root of mean of the squared deviations of values from the arithmetic mean

18. MEASURES OF RELATIONSHIP CORRELATION Relationship between two variable is called correlation. Changes in the one variable are associated with changes in another variable Measuring the Degree of relationship between two variables are called as correlation coefficient Correlation coefficient will vary from -1 to +1 A correlation of -1.0 shows a perfect negative correlation, while a correlation of 1.0 shows a perfect positive correlation. A correlation of 0.0 shows no linear relationship between the movement of the two variables.

19. MEASURES OF RELATIONSHIP

20. MEASURES OF RELATIONSHIP SCATTER DIAGRAM It is the form of graphic presentation of degree and direction of correlation between two variables

21. MEASURES OF RELATIONSHIP KARL PEARSON CORRELATION COEFFICIENT It is used to measure the degree of linear relationship between two variables. It is also called Product Moment Correlation Coefficient It is denoted by r Formula is

22. MEASURES OF RELATIONSHIP SPEARMAN RANK CORRELATION COEFFICIENT It is a method of finding the correlation between two variables by taking their ranks This method of finding correlation is specially useful in dealing with qualitative data It is denoted by p (rho ) Formula is

23. INFERENTIAL ANALYSIS The process of acquiring an unknown information from the what is known is called inferential analysis

24. PRESENTATION OF DATA PRINCIPLES OF PRESENTATION Data should be presented in simple form Arose interest in reader Should be concise but without losing important details Facilitate further statistical analysis Define the problem and should suggest its solution

25. PRESENTATION OF DATA

26. DIFFERENCE BETWEEN GRAPH AND DIAGRAM GRAPH Construct in graph paper Represents mathematical relationships between variables More appropriate to represent frequency distribution and time series Less attractive DIAGRAM Construct in plain paper Diagram does not represent mathematical relationship Not at all used to represent frequency distribution More attractiv e

27. GRAPHICAL PRESENTATION OF DATA LINE FREQUNCY GRAPH Simplest form of graphical presentation Variables is plotted at X axis and on Y axis the frequencies Straight line at each observations indicates frequency

28. GRAPHICAL PRESENTATION OF DATA HISTOGRAM It is used for grouped frequency distribution Variables are indicated in x axis frequency in y axis Frequency of each group form rectangle. Such diagram is called histogram The height of each rectangle is proportional to the frequency

29. GRAPHICAL PRESENTATION OF DATA FREQUENCY POLYGON It the curve obtained by the joining the mid points of the top of the rectangles in a histogram by straight line Histogram is the bar graph while the frequency polygon is the line graph

30. GRAPHICAL PRESENTATION OF DATA CUMULATIVE FREQUENCY CURVE This graph represents the data of a frequency distribution. It is otherwise called as ogive Ordinary frequency is converted into cumulative frequency table The CF is plotted The points corresponding to the cumulative frequency at each upper limit of the classes are joined by free hard curve.

31. GRAPHICAL PRESENTATION OF DATA SCATTER DIAGRAM It is a graphic presentation of correlation between two variables It is also called as dot diagram or correlation diagram

32. III. DIAGRAMATIC PRESENTATION OF DATA BAR DIAGRAM It is useful for displaying nominal and ordinal data Length of the bars drawn vertical or horizontal indicates the frequency of a character Types 1. Simple bar diagram 2. Multiple bar diagram

33. III. DIAGRAMATIC PRESENTATION OF DATA PIE DIAGRAM OR SECTOR DIAGARAM It is useful in representing qualitative data like Rh groups or gender groups The size of the each angle is calculated by formula Class frequency / Total observations x 360

34. III. DIAGRAMATIC PRESENTATION OF DATA PICTOGRAM OR PICTURE DIAGRAM This method is used to impress the frequency of the occurrence of events to common man such as attack, death, no of operated, admission and discharge

35. III. DIAGRAMATIC PRESENTATION OF DATA MAP DIAGRAM These maps are prepared to show the geographical representation of frequencies of the characters Numerical facts are shown in the form of maps. It is also called as cartogram Different values are represented by different colour

36. TAKE HOME MESSAGES Data analysis is the process of organizing and synthesizing the data Data processing and Data Analysis are two important steps in analytic phase Descriptive analysis is just describe the characters of the data Inferential analysis is method of drawing conclusion Data presentation methods are tabular, graphical and diagrammatic methods

37. THANK YOU ALL