At the end of this lesson, the student should be able to:
1. recognize the importance of data gathering;
2. distinguish primary from secondary data sources;
3. define population and sampling;
4. define census and sample;
5. identify the various data collection techniques and sources
6. describe the various instruments for data gathering;
7. cite the advantages of the use of such instruments;
8. recognize the limitations of certain research instruments;
Primary sources of data - are those that
provide information that are collected for the first
time as part of a research project.
- are tangible
materials that provide a description of a historical
event and were produced shortly after the event
Example: Newspaper stories, personal letters, public
documents, eyewitness, verbal accounts, court
decisions, and personal diaries
2. Secondary sources - are those that provide
data which have been collected previously and
reported by some individual other than the present
- borrowed knowledge from other
- refers to the processes whereby a sub-group is
picked out from a larger group and then use this subgroup as a basis for making judgments about the
- called a sample
- referred to as population
Population - is a
whether individuals, animals, objects, or events that
conform to specific criteria and to which one intend to
generalize the results of the research (McMillan, 1998; Wood
& Haber, 1998).
A census is a study that collects data from all members of
Target population is the group or set of items or
individuals from which or about which
representative information is originally desired.
Sampling population is the population from
which a sample is actually drawn.
A sample is a set of elements, or a single
element, from which data are obtained
Researchers generally use sampling because of
budget, time, and manpower constraints. Such constraints
prevent them from undertaking a complete study of the
total target population.
1. Reduced cost
2. Greater speed
3. Greater scope
4. Greater accuracy
A probability sampling method - is any method of sampling
that utilizes some form of random selection.
In order to have a random selection method, you must set up
some process or procedure that assures that the different
units in your population have equal probabilities of being
1. Simple random sampling - is a process of selecting a sample from a
set of all sampling units, giving each unit in the frame an equal chance of
being included in the sample.
Two ways of randomly selecting samples:
- lottery method
- using table of random numbers - contains columns of digits that have been
mechanically generated, usually by a computer, to assume a random order.
2. Systematic sampling - refers to the process of selecting every kth
sampling unit of the population after the first sampling unit is selected at
random from the first k sampling units
3. Stratified sampling - involves dividing the population into two or
more strata and then taking either a simple random (stratified random
sampling) or a systematic sample (stratified systematic sampling) from
4. Cluster sampling - is a method of selecting a sample of distinct
groups of clusters of smaller units called elements.
- A cluster refers to any intact group of similar
5. Multistage sampling -is a complex form of cluster sampling.
Cluster sampling is a type of sampling which involves dividing the
population into groups (or clusters).
- Using all the sample elements in all the selected
clusters may be prohibitively expensive or not necessary.
- the researcher randomly selects elements from
Two stages of Multistage sampling
First stage - Constructing the clusters
Second stage - Deciding what elements within the cluster
The technique is used frequently when a complete list of
all members of the population does not exist and is
Despite the accepted superiority of probability sampling
designs, the researcher is sometimes faced with the
problem of whether he would use nonprobability
sampling or not.
This is especially true when probability sampling
becomes expensive or when precise representatives are
Types of Nonprobability Sampling:
- is selecting sampling units that are easily
(conveniently) available to the researcher.
- It is used in exploratory research where the
researcher is interested in getting an inexpensive approximation of the truth.
- used during preliminary research efforts to get a
gross estimate of the results, without incurring the cost or time required to
select a random sample.
Judgment sampling or purposive sampling
- is selecting
units to be observed on the basis of our judgment about which one will be
useful or representative. The researcher selects the sample based on
Quota sampling - is selecting samples on the basis of
pre-specified characteristics, so that the total sample will
have the same distribution of characteristics as assumed to
exist in the population being studied. The researcher first
identifies the stratums and their proportions as they are
represented in the population.
Dimensional sampling - is a multi-dimensional
extension of quota sampling.
- In this sampling
procedure, instead of a large size, a small size is selected. It is
emphasized that all areas of interest should cover at least one case.
5. Voluntary sampling - is a special type of sampling in which
6. Snowball sampling or sometimes called networking
sampling - researcher first identifies few individuals for the sample
subjects/cases are informed about the subject matter willingly or
voluntarily participate in the study. This sampling is useful especially if
one dealing with information on sensitive or delicate issues.
and uses them as informants. On the basis of their information, the
researcher collects the name of more persons bearing similar
Useful when one wants to consider possible respondents who are not
Used when the desired sample characteristic is rare
For example, study of drug addicts in a university, or a study of socioeconomic conditions of teacher-retirees, or a study of patients with AIDS
1. Questionnaire - is often referred to as a “lazy man’s way of gaining
information”. It is also said that it is the most used and abused of data-gathering
devices. However, a carefully prepared questionnaire can yield better data.
2. Interview Method - is one of the data-gathering techniques in research. It
is defined as a face-to-face interaction between two persons. The one who asks
questions is called the interviewer and the one who supplies the information
asked for is called the interviewee or respondent.
3. Opinionnaire - is an instrument that attempts to obtain the measured
attitude or belief of an individual. The opinionnaire is usually used to infer
attitude-expressed opinion of an individual.
This may be done by: directly asking how one feels about the subject
In asking an individual directly how one feels about the subject, we may use
either semantic differential scale or the Likert scale.
4. Projective methods - involve some sort of imaginative activity on the
part of the individual in interpreting ambiguous stimuli.
Projective methods were first used by psychologists wherein tests administered
provide a comprehensive picture of an individual’s personality
structure, emotional needs, conflicts and other feelings. In these
tests, responses of the individual are not taken on face value but are based on
some pre-established psychological conceptualization. The use of
pictures, verbal techniques, and play techniques are mostly used in
5. Observation - is a process whereby the
researcher watches the research situation.
data-collecting technique is mostly used when the
respondents are unwillingly to express themselves
may be natural or contrived; disguised or
undisguised; structured or unstructured; direct or