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Analysis of data

Analysis of data. Terminologies. Data: Data is the pieces of information obtained in a study . Information in raw or unorganized form (such as alphabets, numbers, or symbols ) that refer to, or represent , conditions , ideas , or objects .

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Analysis of data

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  1. Analysis of data

  2. Terminologies • Data: Data is the pieces of information obtained in a study . • Information in raw or unorganized form (such as alphabets, numbers, or symbols) that refer to, or represent, conditions, ideas, or objects. • Data entry: the process of entering data onto an input medium for computer analysis. • Data transformation: a step often undertaken before data analysis to put the data in a form that can be meaningfully analyzed (eg: recoding of values).

  3. Qualitative research: the investigation of the phenomena, typically in an in depth and holistic fashion through the collection of rich narrative materials using a flexible research design. • Qualitative data: information collected in narrative form, such as dialogue from a transcript of an unstructured interview • Qualitative analysis: The organization and interpretation of narrative data for purpose of discovering important underlying themes, categories and pattern of relationship

  4. Quantitative research: the investigation of phenomena that lends themselves to precise measurement and quantification often involving a rigorous and controlled design. • Quantitative data: information collected in numerical form. • Quantitative analysis: the manipulation of numeric data through statistical procedures for the purpose of describing phenomena or assessing the magnitude and reliability of relationships among them.

  5. Qualitizing: the process of reading and interpreting quantitative data in a qualitative manner • Quantitizing: the process of coding and analysing qualitative data quantitatively

  6. Data collection is followed by analysis and interpretation of the data • Where collected data are analyzed and interpreted in accordance with the study objectives. • Analysis and interpretation includes • Compilation • Editing • Coding • Classification • And presentation of data

  7. Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.

  8. Analysis is a process of organizing and synthesizing the data so as to answer research questions and test hypothesis. • Analysis is the process of breaking a complex topic into smaller parts to gain better understanding of it.

  9. Analysis and interpretation follows different path for • Qualitative data • Quantitative data

  10. Analysis of quantitative data

  11. Steps of quantitative data analysis • Data preperation(cleaning and organizing data for analysis • Describing the data • Drawing the inferences of data(inferential ststistics) • Interpretation of data

  12. Data preparation(cleaning and organizing data for analysis Steps • Compilation • Editing • Coding • Classification • tabulation

  13. Describing the data • (Descriptive or summary statistics) • Describes basic feature of the data and summarizes about the sample and the measures used in the study • Examples: Percentages, Means of central tendency(mean median mode) and means of dispersion(SD, Range, Mean deviation)

  14. Drawing the inferences of data • Inferential statistics • Inferences i.e finding of differences relationships and association between two or more variables • With the help of parametrc and no parametric test • Commonly used are z test, t test, chi square test, ANOVA etc…..

  15. Interpretation of data • It has to be done carefully • It is a critical thinking activity done through brainstorming to infer the condensed and statistically computed data so that research question can be answered and hypothesis can be tested. • Helps in recommendation of the study

  16. It is a subjective activity • Not guarded with scientific methods and procedures • It is liable to bias and errors

  17. Interpretation process Analyze study result -tables - Graphs -Statistical computations Careful critical examination of the study Drawing the comparative and contrast relationships

  18. DESCRIPTIVE STATISTICS

  19. WHAT IS DESCRIPTIVE STATISTICS? Descriptive Statistics is a method of organizing, summarizing, and presenting data in a convenient and informative way to draw meaningful interpretation. The actual method used depends on what information we would like to extract.

  20. classification • Measures of condensed data • Measures of central tendency • Measures of dispersion • Measures of relationships

  21. Measures to condense data Quantitative data are generally condensed and presented through tables, chats, graphs and diagrams.

  22. TABLES • Table is a tabular representation of statistical data • Tabulation is the first step • Tabulation means systematic presentation of the information contained in the data in rows and columns in accordanc ewith some common features and characteristics • Rows are horizontal and column are vertical

  23. Table1:Distribution of sample based on demographic proforma N=100

  24. General principles of tabulation • Should be precise, understandable, self explanatory • Title at the top, title should be clear and precise • Items should be arranged alphabetically according to size , importance and causal relationship • Rows and columns should be compared with one another and should have similar arrangements.

  25. Contd. • Content of the table, as a whole as well as item wise in each column and row should be defined clearly and fully • Unit of measurement should be mentioned • % can be kept in parenthesis ( ) or { } • Totals can be placed at the bottom of the column • Explanatory cues at the bottom of the table as footnotes • Two or three small tables preferred over one large table

  26. Objectives of tabulation • To summarize data systematically • To clarify data on simple • To facilitate for comparitive study • To present data in a minimum step • To give identity to data

  27. Parts of table • Table number • Title • Subheads • Caption and stubs • Body of the table • Footnotes • Source note

  28. Table 1: title(sub heads) Foot note Source note

  29. Types table • Frequency distribution table • Contingency tables • Multiple response tables • Miscellaneous tables

  30. Frequency distribution table

  31. Contingency table

  32. Multiple response tables

  33. Miscellaneous tables

  34. Graphs

  35. Simple bar diagram • Multiple bar diagram • Pie diagram • Histogram • Frequency polygon • Line graphs • Cumulative frequency curve • Scattered or dotted diagram • Pictogram • Map diagram

  36. Measures of central tendency • Mean • Median • Mode

  37. Measures of dispersion • Range • Mean deviation • Standard deviation • Quartile deviation

  38. Normal probability curve

  39. Corelation coefficient • Karl pearson’s correlation coeeficient • Spearmans correlation coefficient

  40. Inferential statistics • The sample is a set of data taken from the population to represent the population. Probability distributions, hypothesis testing, correlation testing and regression analysis all fall under the category of inferential statistics.

  41. Aspects • Type I error: • Type II error: • Level of significance: • Confidence interval • Degree of freedom • Test of significance

  42. Test of significance • Parametric test: t test, z test, ANOVA etc • Nonparametric test: chi square test, median test, McNemar test, Mann-Whitney test, Wilcoxon test, Fisher’s exact test.

  43. Computer analysis of quantitative data • Microsoft excel • SPSS- statistical package for social sciences • SAS- statistical analysis system • Minitab

  44. Qualitative data analysis

  45. ANALYSIS OF QUALITATIVE DATA • Qualitative data is “rich”, “full” and ”real” . • Is contrasted with the thin abstractions of numbers. • There is no clear and accepted single set of conventions for data analysis. • If the qualitative data is substantial, a software package can be used to manage the data. • In the analysis of qualitative data, the researcher is the tool for analysis. • The software helps with data management BUT NOT DATA ANALYSIS.

  46. WHAT ARE YOU ‘MAKING SENSE’ OF AND HOW? WHAT? • Interviews • Focus groups • Observations and field notes • Documents • Open ended questions in surveys • Audio-visual materials HOW? • Breaking down (reduction/de-contextualisation) • Building back up (interpretation/re- contextualisation)

  47. QUALITY OF THE ANALYST • Clear thinking • Process information in a meaningful and useful manner • Whatever the approach, the researcher has the responsibility of demonstrating how the conclusions were arrived at from the data.

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