Dr V K Rampal

1. Sampling Techniques, Data Collection and
tabulation in the field of Social Science
Vipan Kumar Rampal

2. Sample
It is a unit that is selected from population
Represents the whole population
Purpose to draw the inference
A sample is “a smaller (but hopefully representative) collection of units from a population
used to determine truths about that population”
Sampling Frame
Listing of population from which a sample is chose
Sampling
is the process of selecting observations (a sample) to provide an adequate description and
inferences of the population.
Purpose of Sampling
To gather data about the population in order to make an inference that can be generalized to
the population

3. Sampling Techniques
Non-probability sampling and probability sampling
Under non- probability sampling
Convenience sampling
the process of including whoever happens to be available at the time …called “accidental” or
“haphazard” sampling
Purposive sampling
the process whereby the researcher selects a sample based on experience or knowledge of the
group to be sampled …called “judgment” sampling
Quota sampling
the process whereby a researcher gathers data from individuals possessing identified
characteristics and quotas

4. Under probability sampling
(Simple random sampling, Systematic sampling, Stratified sampling and Cluster sampling)
1. Simple Random sampling
Selecting subjects so that all members of a population have an equal and independent chance
of being selected
Selection process
Identify and define the population
Determine the desired sample size
List all members of the population
Assign all members on the list a consecutive number
Select an arbitrary starting point from a table of random numbers and read the appropriate
number of digits

5. 2. Systematic sampling
Selecting every Kth subject from a list of the members of the population
Selection process
Identify and define the population
Determine the desired sample size
Obtain a list of the population
Determine what K is equal to by dividing the size of the population by the desired sample
size
Start at some random place in the population list
Take every Kth individual on the list

6. 3. Stratified random sampling
The population is divided into two or more groups called strata, according to some criterion,
such as geographic location, grade level, age, or income, and subsamples are randomly
selected from each strata
Selection process
Identify and define the population
Determine the desired sample size
Identify the variable and subgroups (i.e., strata) for which you want to guarantee appropriate
representation
Classify all members of the population as members of one of the identified subgroup
The following questions for stratified random sampling
How to form strata?
How should items be selected from each stratum?
How many items be selected from each stratum?

7. 4. Cluster sampling
The process of randomly selecting intact groups, not individuals, within the defined
population sharing similar characteristics
Clusters are locations within which an intact group of members of the population can be
found
Selection process
Identify and define the population
Determine the desired sample size
Identify and define a logical cluster
List all clusters that make up the population of clusters
Estimate the average number of population members per cluster
Determine the number of clusters needed by dividing the sample size by the estimated size of
a cluster
Randomly select the needed numbers of clusters
Include in the study all individuals in each selected cluster

8. Sample Size
According to Uma Sekaran in Research Method for Business 4th Edition, Roscoe (1975)
proposed the rules of thumb for determining sample size where sample size larger than 30 and
less than 500 are appropriate for most research, and the minimum size of sample should be
30% of the population.
The size of the sample depends on a number of factors and the researchers have to give the
statistically information before they can get an answer. For example, these information like
(confidence level, standard deviation and population size) to determine the sample size

9. Methods of data Collection
What is Data Collection
It is the process by which the researcher collects the information needed to answer the
research problem
The task of data collection begins after a research problem has been defined and research
design chalked out
There are two of data
Primary Data Primary data are those which are collected for the first time and are original in
character.
Secondary Data Secondary data are those which have already been collected by someone else
and which have through some statistical analysis
Primary Data may be collected through:
Observation
Interviews
Questionnaires
Schedules

10. Observation method is a method under which data from the field is collected with the help of
observation by the observer or by personally going to the field.
In the words “Observation may be defined as systematic viewing, coupled with consideration
of seen phenomenon
Structured Observation When the observation is characterized by a careful definition of the
units to be observed, the style of recording the observed information, standardized conditions
of observation and the selection of related data of observation.
Unstructured Observation When it takes place without the above characteristics.
Uncontrolled Observation When the observation takes place in natural condition i.e.,
uncontrolled observation. It is done to get spontaneous picture of life and persons.
Controlled Observation When observation takes place according to pre arranged plans, with
experimental procedure then it is controlled observation generally done in laboratory under
controlled condition.

11. The Interview Method of collecting data involves presentation of oral-verbal stimuli and
reply in terms of oral- verbal responses.
Interviewer asks questions (which are aimed to get information required for study) to
respondent.
Structured Interviews : In this case, a set of pre-decided questions are there.
Unstructured Interviews : In this case, we don’t follow a system of pre-determined
questions.
Focused Interviews : Attention is focused on the given experience of the respondent and its
possible effects

12. A Questionnaire is sent ( by post or by mail ) to the persons concerned with a request to
answer the questions and return the Questionnaire.
A Questionnaire consists of a number of questions printed in a definite order on a form
Open-ended questions This gives the respondents the ability to respond in their own words.
Close-ended or fixed alternative questions This allows the respondents to choose one of the
given alternatives. Types:- Dichotomous questions and Multiple Questions
Good Questionnaire has following points
Should be short and simple
Follow a sequence of questions from easy to difficult one
Technical terms should be avoided
Should provide adequate space for answers in questionnaire
Directions regarding the filling of questionnaire should be given Physical Appearance – Quality
of paper, Color
Sequence must be clear

13. Schedules
Very similar to Questionnaire method
The main difference is that a schedule is filled by the enumerator who is specially appointed
for the purpose.
Enumerator goes to the respondents, asks them the questions from the Questionnaire in the
order listed, and records the responses in the space provided.
Enumerator must be trained in administering the schedule

14. PROCESSING OF DATA
The collected data in research is processed and analyzed to come to some conclusions or to
verify the hypothesis made.
Processing of data is important as it makes further analysis of data easier and efficient.
Processing of data technically means
1.Editing of the data
2.Coding of data
3.Classification of data
4.Tabulation of data.

15. EDITING:
Data editing is a process by which collected data is examined to detect any errors or
omissions and further these are corrected as much as possible before proceeding further.
Editing is of two types:
1.Field Editing
This is a type of editing that relates to abbreviated or illegible written form of gathered data.
Such editing is more effective when done on same day or the very next day after the
interview. The investigator must not jump to conclusion while doing field editing.
2.Central Editing
Such type of editing relates to the time when all data collection process has been completed.
Here a single or common editor corrects the errors like entry in the wrong place, entry in
wrong unit etc. As a rule all the wrong answers should be dropped from the final results.

16. CODING:
Classification of responses may be done on the basis of one or more common concepts.
In coding a particular numeral or symbol is assigned to the answers in order to put the
responses in some definite categories or classes.
The classes of responses determined by the researcher should be appropriate and suitable to
the study.
Coding enables efficient and effective analysis as the responses are categorized into
meaningful classes.
Coding decisions are considered while developing or designing the questionnaire or any
other data collection tool.
Coding can be done manually or through computer.

17. CLASSIFICATION:
Classification of the data implies that the collected raw data is categorized into common group
having common feature.
Data having common characteristics are placed in a common group.
The entire data collected is categorized into various groups or classes, which convey a
meaning to the researcher.
Classification is done in two ways:
1.Classification according to attributes.
2.Classification according to the class intervals.

18. TABULATION:
The mass of data collected has to be arranged in some kind of concise and logical order.
Tabulation summarizes the raw data and displays data in form of some statistical tables.
Tabulation is an orderly arrangement of data in rows and columns.
OBJECTIVE OF TABULATION:
1.Conserves space & minimizes explanation and descriptive statements.
2.Facilitates process of comparison and summarization.
3.Facilitates detection of errors and omissions.
4.Establish the basis of various statistical computations.

19. ANALYSIS OF DATA
The important statistical measures that are used to analyze the research or the survey are:
1.Measures of central tendency(mean, median & mode)
2.Measures of dispersion(standard deviation, range, mean deviation)
3.Measures of asymmetry(skew ness)
4.Measures of relationship etc.( correlation and regression)
5.Association in case of attributes.
6.Time series Analysis

20. TESTING THE HYPOTHESIS
Several factor are considered into the determination of the appropriate statistical
technique to use when conducting a hypothesis tests. The most important are as:
1.The type of data being measured.
2.The purpose or the objective of the statistical inference.
Hypothesis can be tested by various techniques. The hypothesis testing techniques are
divided into two broad categories:
1.Parametric Tests.
2.Non- Parametric Tests.

21. PARAMETRIC TESTS:
These tests depends upon assumptions typically that the population(s) from which data are randomly sampled have
a normal distribution. Types of parametric tests are:
1.t- test
2.z- test
3.F- test
4.2- test
NON PARAMETRIC TESTS
The various types of Non Parametric Tests are:
1.Wilcox on Signed Rank Test ( for comparing two population)
2.Kolmogorov Smirnov Test( to test whether or not the sample of data is consistent with a specified distribution
function)
3.Runs Tests (in studies where measurements are made according to some well defined ordering, either in time or
space, a frequent question is whether or not the average value of the measurement is different points in the
sequence. This test provides a means of testing this.
4.Sign Test (this is single sample test that can be used instead of the single sample t- test or paired t- test.

22. INTERPRETATION:
Interpretation is the relationship amongst the collected data, with analysis. Interpretation
looks beyond the data of the research and includes researches, theory and hypothesis.
Interpretation in a way act as a tool to explain the observations of the researcher during the
research period and it acts as a guide for future researches.
WHY Interpretation?
-the researcher understands the abstract principle underlying the findings.
-Interpretation links up the findings with those of other similar studies.
-The researcher is able to make others understand the real importance of his research
findings.