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Methods of data collection

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Methods of data collection

  1. 1. Methods of Data Collection There are two types of data used for research work: • Primary data: Collected first-hand by the researcher. Primary data can be collected in a number of ways. • Secondary data: Already collected by someone other than the researcher. Quickly obtainable than primary data. • Common sources are Government departments, organizational records and data originally collected for other research purposes.
  2. 2. Collection of Primary Data • Questionnaires: A questionnaire is a research instrument consisting of a series of questions. • Questionnaires can be thought of as a kind of written interview. • Often a questionnaire uses both open and closed questions to collect data. • Observations: Watching behaviour of other persons as it actually happens without controlling it. Thus, recording information without asking questions.
  3. 3. • Interviews: Interview involves two groups of people, first is the interviewer (the researcher) and second is the interviewee. • Schedules: Questionnaires are sent through enumerators to collect information. • They directly meet informants with questionnaire. • It also includes methods like surveys or experiments
  4. 4. Collection of Secondary Data Secondary data is available in: • Various publications of the central, state or local governments. • Various publications by foreign governments or international bodies and their subsidiary organisations. • Technical and trade journals. • Books, magazines and newspapers • Reports and publications of various organisations connected with business and industry, bank stock exchange etc.. • Reports prepared by research scholars, universities, economists etc. in different fields. • Public records and statistics, historical documents and other sources of published information.
  5. 5. Sources of unpublished data are many and they include: • Diaries and Letters • Unpublished biographies and autobiographies • Data available with research scholars and research workers, trade associations, labour bureaus and other public/private individuals and organisations.
  6. 6. Processing and analysis of data After collection of data it has to be processed and analysed with following Process of analysis: 1. Editing: Data editing is the process of reviewing data for consistency, detection of errors and outliers (values that are extremely larger or smaller than rest of data) and correction of errors, in order to improve quality, accuracy and adequacy of data and make it suitable for the purpose for which it was collected. 2. Coding: coding is an analytical process of categorisation of data, in which both quantitative form (such as questionnaires results) or qualitative form (such as interview transcripts) are categorized to facilitate analysis. One purpose of coding is to transform the data into a form suitable for computer-aided analysis.
  7. 7. 3. Classification: Classification is a technique where we categorize data into a given number of classes. The main goal of classification is to identify the category/class to which a new data will fall under. Types of Data Classification • Content-based classification: Inspects and interprets files looking for sensitive information. • Context-based classification: Looks at application, location, or creator among other variables as indirect indicators of sensitive information.
  8. 8. 4. Tabulation • A systematic & logical presentation of data in rows and columns to facilitate comparison and statistical analysis. • In other words, the method of placing organised data into a tabular form is called as tabulation. • Objectives are to make complex data simple. • When data are arranged systematically in a table, they can be easily understood.
  9. 9. Elements/Types of Analysis • Descriptive analysis: Used to describe basic features of data in the study. • Provide simple summaries about the sample and the measures. • With simple graphical analysis form the basic virtual of any quantitative analysis. • Correlation analysis: Method of statistical evaluation used to study the strength of a relationship between two, numerically measured, continuous variables (e.g. height and weight).
  10. 10. • Multivariate analysis: Based in observation and analysis of more than one statistical outcome variable at a time. • Multiple regression analysis • Multiple discriminant analysis • Multivariate analysis of variance (or Multi-ANOVA) • Canonical analysis • Inferential analysis: Allow to draw conclusions or inferences from data. Usually this means coming to conclusions about a population on the basis of data describing a sample.
  11. 11. Hypothesis Testing Hypothesis means a mere assumption or some supposition to be proved or disapproved Characteristics of Hypothesis: • It should be clear and precise • Should be capable of being testing • It should state the relationship between variables • It should be limited by scope and be specific • It should be stated as far as possible with most simple terms so that the same is easily understandable by all concerned • It should be consisted with most known facts • It should be amenable to testing with in a reasonable time • Must explain the facts that gave rise to the need for explanation
  12. 12. Types of Hypothesis • Null hypothesis: Null hypothesis is a general statement which states that there is no relationship between two phenomenon under consideration or that there is no association between two groups. • Alternative hypothesis: An alternative hypothesis is a statement which describes that there is a relationship between two selected variables in a study. It is contrary to the null hypothesis.
  13. 13. Testing of Hypothesis Procedure of testing Hypothesis: • Formulate a null or alternative Hypothesis • Choose the level of significance of the test • Choose the location of the critical region • Choose the appropriate test statistics • Compute from sample observations for observed value of chosen statistics using relevant formula • Compare sample value of chosen statistics with theoretical (table) value that defines critical region
  14. 14. Methods of testing Hypothesis • Parametric tests or standard tests of hypothesis Relies upon the assumption that the testing data is normally distributed. If your data does not have the appropriate properties then you use a non-parametric test. The important parametric tests are: • Z – Test: Statistical calculations that can be used to compare two different population means when the variances are known and the sample size is large.
  15. 15. • T – Test: A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. • X – Test: A chi-square (χ2) statistic is a test that measures how expectations compare to actual observed data (or model results). The data used in calculating a chi-square statistic must be random, raw, mutually exclusive, drawn from independent variables, and drawn from a large enough sample. • F – Test: An F-test is any statistical test in which the test statistic has anF- distribution under null hypothesis.
  16. 16. Non-Parametric tests or distribution free test of hypothesis A non-parametric test is a hypothesis test that does not make any assumptions about the distribution of samples. a) One sample and two sample tests: • Binomial test • Chi-square test • McNemar test b) K – sample tests (K > 3): • Kruskal-Wallis test: H • Friedman test • Kendall’s coefficient of concordance: W
  17. 17. Interpretation Interpretation of data means the task of drawing conclusions and explaining their significance after a careful analysis and examination of data. Interpretation also extends beyond the data of study to inch the results of other research, theory and hypotheses.
  18. 18. Techniques of Interpretation Interpretation requires a great skill on part of the researcher. Its is an art that one learns through practice and experience. The techniques of interpretation often involves following steps: • Researcher must give reasonable explanations of the relations which have been found. • Extraneous information, if collected during the study must be considered while interpreting the final result. • It is advisable before embarking upon final interpretation to consult someone having insight into the study • Researchers must accomplish the task of interpretation only after considering all relevant factors affecting the problem.

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