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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.
Collection of Primary Data
• Questionnaires: A questionnaire is a research
instrument consisting of a series
• Questionnaires can be thought of as a kind of
• 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
• 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
• It also includes methods like surveys or
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
• Public records and statistics, historical documents and other sources of
Sources of unpublished data are many and they include: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• Unpublished biographies and autobiographies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.
Processing and analysis of data
After collection of data it has to be processed and analysed with following Process
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.
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
• Context-based classification: Looks at application, location, or creator among
other variables as indirect indicators of sensitive information.
• 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.
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
• 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).
• 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.
Hypothesis means a mere assumption or some supposition to be proved or
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
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.
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
• Compare sample value of chosen statistics with theoretical (table) value that
defines critical region
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.
• 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
• 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.
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
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.
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
• 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.