COLLECTING
QUANTITATIVE DATA: Sampling
and Data collection
 The process of collecting quantitative data consists of more
than simply collecting data.
 It involves the following five steps
 determining the participants to study,
 obtaining permissions needed from several individuals and
organizations,
 considering what types of information to collect from several
sources available to the quantitative research,
 locating and selecting instruments to use that will net useful
data for the study, and finally,
 administering the data collection process to collect data.
 WHAT PARTICIPANTS WILL YOU STUDY?
 These decisions require that you decide on a unit of analysis, the
group and individuals you will study, the procedure for
selecting these individuals, and assessing the numbers of people
needed for your data analysis.
 Identify Your Unit of Analysis
 Who can supply the information that you will
use to answer your quantitative research
questions or hypotheses?
 Some possibilities might be individuals, households,
organization, community, state, country etc
 Specify the Population and Sample
 you need to consider what individuals or organization or
community you will study.
 select those who are representative of the entire group.
 Representative refers to the selection of individuals from a sample of a
population such that the individuals selected are typical of the population
under study, enabling you to draw conclusions from the sample about the
population as a whole.
 A population is a group of individuals who have the same
characteristic.
 A target population(or the sampling frame) is a group of
individuals (or a group of organizations) with some common
defining characteristic that the researcher can identify and
study.
 A sample is a subgroup of the target population that the
researcher plans to study for generalizing about the target
population.
 In an ideal situation, you can select a sample of individuals who
are representative of the entire population.
Probabilistic and Non probabilistic
Sampling
 Researchers employ either probability or non probability
sampling approaches.
 several types of both approaches are available.
 Researchers decide which type of sampling to use in their
study based on such factors as
 the amount of rigor they seek for their studies,
 the characteristics of the target population, and
 the availability of participants.
probability sampling
 In probability sampling, the researcher selects individuals
from the population who are representative of that
population.
 This is the most rigorous form of sampling in quantitative
research because the investigator can claim that the sample is
representative of the population and, as such, can make
generalizations to the population.
1. Simple Random Sampling
 The most popular and rigorous form of probability sampling
from a population is simple random sampling.
 In simple random sampling, the researcher selects participants
(or units, such as households) for the sample so that any
individual has an equal probability of being selected from the
population.
 The intent of simple random sampling is to choose individuals
to be sampled who will be representative of the population.
 Any bias in the population will be equally distributed among the
people chosen.
 The typical procedure used in simple random sampling is to
assign a number to each individual (or site) in the population
and then use a random numbers table, available in many
statistics books, to select the individuals (or sites) for the
sample.
 For this procedure, you need a list of members in the target
population and a number must be assigned to each individual.
2. Systematic Sampling
 A slight variation of the simple random sampling
procedure is to use systematic sampling.
 In this procedure, you choose every nth individual or
site in the population until you reach your desired
sample size.
 This procedure is not as precise and rigorous as using the
random numbers table, but it may be more convenient
because individuals do not have to be numbered and it does
not require a random numbers table.
3. Stratified Sampling
 In stratified sampling, researchers divide (stratify) the
population on some specific characteristic (e.g., gender) and
then, using simple random sampling, sample from each
subgroup (stratum) of the population (e.g., females and males).
 It guarantees that the sample will include specific characteristics
that the researcher wants included in the sample.
 Stratification ensures that the stratum desired
(females) will be represented in the sample in
proportion to that existence in the population.
 Stratification is also used when a simple random
sampling procedure would yield fewer participants in a
specific category (e.g., females) than you need for
rigorous statistical analysis.
 The procedure for selecting a stratified random sample
consists of
dividing the population by the stratum (e.g., men and
women) and
b) sampling within each group in the stratum (e.g., women first
and then men) so that the individuals selected are
proportional to their representation in the total population.
a)
4. Multistage Cluster Sampling
 In multistage cluster sampling, the researcher chooses a sample
in two or more stages because either the researchers cannot
easily identify the population or the population is extremely
large.
 If this is the case, it can be difficult to obtain a complete list of
the members of the population. However, getting a complete
list of groups or clusters in the population might be possible
Non probability sampling
 It is not always possible to use probability sampling
 In non probability sampling researcher selects individuals
because they are available, convenient, and represent some
characteristic the investigator seeks to study.
 In some situations, you may need to involve participants who
volunteer and who agree to be studied. Further, you may not
be interested in generalizing findings to a population, but
only in describing a small group of participants in a study.
 Researchers use two popular approaches in non probability
sampling: convenience and snowball sampling approaches.
1. Convenience Sampling
 In convenience sampling the researcher selects participants
because they are willing and available to be studied.
 In this case, the researcher cannot say with confidence that the
individuals are representative of the population. However, the
sample can provide useful information for answering questions
and hypotheses.
2, Snowball Sampling
 In this case, the researcher asks participants to identify others to
become members of the sample.
Sample Size
 When selecting participants for a study, it is important to
determine the size of the sample you will need.
 A general rule of thumb is to select as large a sample as
possible from the population. The larger the sample, the less
the potential error is that the sample will be different from
the population. This difference between the sample estimate
and the true population score is called sampling error.
END!